Bio


Dr. Sylvia K. Plevritis is Professor of Biomedical Data Science, and of Radiology at
Stanford University, and Chair of Biomedical Data Science. She leads a systems
biology cancer research program that bridges genomics, biocomputation, imaging and
population sciences to decipher properties of cancer progression to guide advances in
early detection and treatment response. Dr. Plevritis received her Ph.D. in Electrical
Engineering and M.S. in Health Services Research, both from Stanford University, with
a focus on cancer imaging physics and modeling cancer outcomes, respectively. She
has had a primary authorship role on over 100 scientific cancer-related articles. She is a
fellow of the American Institute for Medical and Biological Engineering (AIMBE) and
Distinguished Investigator in the Academy of Radiology Research. She received
the 2016 Inaugural Award for Basic Scientist of the Year in Stanford Radiology. Dr.
Plevritis has served on numerous NIH study sections, chaired scientific programs for
the several professional societies including the American Association for Cancer
Research (AACR) and presented keynote lectures across multiple scales of
computational cancer biology. Sylvia Plevritis is the Program Director of the Stanford
Center in Cancer Systems Biology (CCSB), Program Director of the Stanford Cancer
Systems Biology Scholars Program (CSBS), and co-Division Chief of Integrative
Biomedical Imaging Informatics at Stanford (IBIIS). In addition, she has been a Principal
Investigator with the NCI Cancer Intervention Surveillance Network (CISNET) for over
fifteen years. She serves on NCI Board of Scientific Advisors, Leadership Council of
the Stanford Cancer Institute and the Leadership Council of the Stanford Bio-X
Program.

Academic Appointments


Administrative Appointments


  • Chair, Department of Biomedical Data Science (2019 - Present)
  • Director, Biomedical Informatics Training Program (2019 - Present)
  • co-Lead, Stanford Cancer Institute: Cancer Biology Program (2018 - Present)

Professional Education


  • M.S., Stanford University, Health Services Research (1996)
  • PhD, Stanford University, Electrical Engineering (1992)
  • B.E., The Cooper Union, Electrical Engineering (1985)

Current Research and Scholarly Interests


My research program focuses on computational modeling of cancer biology and cancer outcomes. My laboratory develops stochastic models of the natural history of cancer based on clinical research data. We estimate population-level outcomes under differing screening and treatment interventions. We also analyze genomic and proteomic cancer data in order to identify molecular networks that are perturbed in cancer initiation and progression and relate these perturbations to patient outcomes.

2021-22 Courses


Stanford Advisees


Graduate and Fellowship Programs


All Publications


  • Evaluation of Alternative Diagnostic Follow-up Intervals for Lung Reporting and Data System Criteria on the Effectiveness of Lung Cancer Screening. Journal of the American College of Radiology : JACR Bastani, M., Toumazis, I., Hedou', J., Leung, A., Plevritis, S. K. 2021

    Abstract

    PURPOSE: The ACR developed the Lung CT Screening Reporting and Data System (Lung-RADS) to standardize the diagnostic follow-up of suspicious screening findings. A retrospective analysis showed that Lung-RADS would have reduced the false-positive rate in the National Lung Screening Trial, but the optimal timing of follow-up examinations has not been established. In this study, we assess the effectiveness of alternative diagnostic follow-up intervals on lung cancer screening.METHODS: We used the Lung Cancer Outcome Simulator to estimate population-level outcomes of alternative diagnostic follow-up intervals for Lung-RADS categories 3 and 4A. The Lung Cancer Outcome Simulator is a microsimulation model developed within the Cancer Intervention and Surveillance Modeling Network Consortium to evaluate outcomes of national screening guidelines. Here, among the evaluated outcomes are percentage of mortality reduction, screens performed, lung cancer deaths averted, screen-detected cases, and average number of screens and follow-ups per death averted.RESULTS: The recommended 3-month follow-up interval for Lung-RADS category 4A is optimal. However, for Lung-RADS category 3, a 5-month, instead of the recommended 6-month, follow-up interval yielded higher mortality reduction (0.08% for men versus 0.05% for women), and higher number of deaths averted (36 versus 27), higher number of screen-detected cases (13 versus 7), and lower number of combined low-dose CTs and diagnostic follow-ups per death avoided (8 versus 5), per one million general population. Sensitivity analysis of nodule progression threshold verifies higher mortality reduction with 1-month earlier follow-up for Lung-RADS3.CONCLUSIONS: One month earlier diagnostic follow-ups for individuals with Lung-RADS category 3 nodules may result in higher mortality reduction and warrants further investigation.

    View details for DOI 10.1016/j.jacr.2021.08.001

    View details for PubMedID 34419477

  • Reflecting on 20 years of breast cancer modeling in CISNET: Recommendations for future cancer systems modeling efforts. PLoS computational biology Trentham-Dietz, A., Alagoz, O., Chapman, C., Huang, X., Jayasekera, J., van Ravesteyn, N. T., Lee, S. J., Schechter, C. B., Yeh, J. M., Plevritis, S. K., Mandelblatt, J. S., Breast Working Group of the Cancer Intervention and Surveillance Modeling Network (CISNET) 2021; 17 (6): e1009020

    Abstract

    Since 2000, the National Cancer Institute's Cancer Intervention and Surveillance Modeling Network (CISNET) modeling teams have developed and applied microsimulation and statistical models of breast cancer. Here, we illustrate the use of collaborative breast cancer multilevel systems modeling in CISNET to demonstrate the flexibility of systems modeling to address important clinical and policy-relevant questions. Challenges and opportunities of future systems modeling are also summarized. The 6 CISNET breast cancer models embody the key features of systems modeling by incorporating numerous data sources and reflecting tumor, person, and health system factors that change over time and interact to affect the burden of breast cancer. Multidisciplinary modeling teams have explored alternative representations of breast cancer to reveal insights into breast cancer natural history, including the role of overdiagnosis and race differences in tumor characteristics. The models have been used to compare strategies for improving the balance of benefits and harms of breast cancer screening based on personal risk factors, including age, breast density, polygenic risk, and history of Down syndrome or a history of childhood cancer. The models have also provided evidence to support the delivery of care by simulating outcomes following clinical decisions about breast cancer treatment and estimating the relative impact of screening and treatment on the United States population. The insights provided by the CISNET breast cancer multilevel modeling efforts have informed policy and clinical guidelines. The 20 years of CISNET modeling experience has highlighted opportunities and challenges to expanding the impact of systems modeling. Moving forward, CISNET research will continue to use systems modeling to address cancer control issues, including modeling structural inequities affecting racial disparities in the burden of breast cancer. Future work will also leverage the lessons from team science, expand resource sharing, and foster the careers of early stage modeling scientists to ensure the sustainability of these efforts.

    View details for DOI 10.1371/journal.pcbi.1009020

    View details for PubMedID 34138842

  • Evaluation of the Benefits and Harms of Lung Cancer Screening With Low-Dose Computed Tomography: Modeling Study for the US Preventive Services Task Force. JAMA Meza, R., Jeon, J., Toumazis, I., Ten Haaf, K., Cao, P., Bastani, M., Han, S. S., Blom, E. F., Jonas, D. E., Feuer, E. J., Plevritis, S. K., de Koning, H. J., Kong, C. Y. 2021; 325 (10): 988–97

    Abstract

    Importance: The US Preventive Services Task Force (USPSTF) is updating its 2013 lung cancer screening guidelines, which recommend annual screening for adults aged 55 through 80 years who have a smoking history of at least 30 pack-years and currently smoke or have quit within the past 15 years.Objective: To inform the USPSTF guidelines by estimating the benefits and harms associated with various low-dose computed tomography (LDCT) screening strategies.Design, Setting, and Participants: Comparative simulation modeling with 4 lung cancer natural history models for individuals from the 1950 and 1960 US birth cohorts who were followed up from aged 45 through 90 years.Exposures: Screening with varying starting ages, stopping ages, and screening frequency. Eligibility criteria based on age, cumulative pack-years, and years since quitting smoking (risk factor-based) or on age and individual lung cancer risk estimation using risk prediction models with varying eligibility thresholds (risk model-based). A total of 1092 LDCT screening strategies were modeled. Full uptake and adherence were assumed for all scenarios.Main Outcomes and Measures: Estimated lung cancer deaths averted and life-years gained (benefits) compared with no screening. Estimated lifetime number of LDCT screenings, false-positive results, biopsies, overdiagnosed cases, and radiation-related lung cancer deaths (harms).Results: Efficient screening programs estimated to yield the most benefits for a given number of screenings were identified. Most of the efficient risk factor-based strategies started screening at aged 50 or 55 years and stopped at aged 80 years. The 2013 USPSTF-recommended criteria were not among the efficient strategies for the 1960 US birth cohort. Annual strategies with a minimum criterion of 20 pack-years of smoking were efficient and, compared with the 2013 USPSTF-recommended criteria, were estimated to increase screening eligibility (20.6%-23.6% vs 14.1% of the population ever eligible), lung cancer deaths averted (469-558 per 100 000 vs 381 per 100 000), and life-years gained (6018-7596 per 100 000 vs 4882 per 100 000). However, these strategies were estimated to result in more false-positive test results (1.9-2.5 per person screened vs 1.9 per person screened with the USPSTF strategy), overdiagnosed lung cancer cases (83-94 per 100 000 vs 69 per 100 000), and radiation-related lung cancer deaths (29.0-42.5 per 100 000 vs 20.6 per 100 000). Risk model-based vs risk factor-based strategies were estimated to be associated with more benefits and fewer radiation-related deaths but more overdiagnosed cases.Conclusions and Relevance: Microsimulation modeling studies suggested that LDCT screening for lung cancer compared with no screening may increase lung cancer deaths averted and life-years gained when optimally targeted and implemented. Screening individuals at aged 50 or 55 years through aged 80 years with 20 pack-years or more of smoking exposure was estimated to result in more benefits than the 2013 USPSTF-recommended criteria and less disparity in screening eligibility by sex and race/ethnicity.

    View details for DOI 10.1001/jama.2021.1077

    View details for PubMedID 33687469

  • A risk-based framework for assessing real-time lung cancer screening eligibility that incorporates life expectancy and past screening findings. Cancer Toumazis, I., Alagoz, O., Leung, A., Plevritis, S. K. 2021

    Abstract

    Current lung cancer risk-based screening approaches use a single risk-threshold, disregard life-expectancy, and ignore past screening findings. We address these limitations with a comprehensive analytical framework, the individualized lung cancer screening decision (ENGAGE) tool that aims to optimize lung cancer screening for US ever-smokers under dynamic risk assessment by incorporating life expectancy and past screening findings over time.ENGAGE employs a partially observable Markov decision process framework that integrates published risk prediction and disease progression models, to dynamically assess the trade-off between the expected health benefits and harms associated with screening. ENGAGE evaluates lung cancer risk annually and provides real-time screening eligibility that maximizes the expected quality-adjusted life-years (QALYs) of ever-smokers. We compare ENGAGE against the 2013 U.S. Preventive Services Task Force (USPSTF) lung cancer screening guideline and single-threshold risk-based screening paradigms.Compared with the 2013 USPSTF guidelines, ENGAGE expands screening coverage among ever-smokers (ENGAGE: 78%, USPSTF: 61%), while reducing the number of screening examinations per person (ENGAGE:10.43, USPSTF:12.07, P < .001), yields higher effectiveness in terms of increased lung cancer-specific mortality reduction (ENGAGE: 19%, USPSTF: 15%, P < .001) and improves screening efficiency (ENGAGE: 696, USPSTF: 819 screens per death avoided, P < .001). When compared against a single-threshold risk-based screening strategy, ENGAGE increases QALY requiring 30% fewer screens per death avoided (ENGAGE: 696, single-threshold: 889, P < .001), and reduces false positives by 40%.ENGAGE provides a comprehensive framework for dynamic risk-based assessment of lung cancer screening eligibility by incorporating life expectancy and past screening findings that can serve to guide future policies on the effectiveness and efficiency of screening.A novel decision-analytical screening framework was developed for lung cancer, the individualized lung cancer screening decision (ENGAGE) tool to provide personalized screening schedules for ever-smokers. ENGAGE captures the dynamic nature of lung cancer risk and incorporates life expectancy into the screening decision-making process. ENGAGE integrates past screening findings and changes in smoking behavior of individuals and provides informed screening decisions that outperform existing screening guidelines and single-threshold risk-based screening approaches. A personalized lung cancer screening program facilitated by a tool such as ENGAGE could enhance the efficiency of lung cancer screening.

    View details for DOI 10.1002/cncr.33835

    View details for PubMedID 34383299

  • Risk-Based lung cancer screening: A systematic review. Lung cancer (Amsterdam, Netherlands) Toumazis, I., Bastani, M., Han, S. S., Plevritis, S. K. 2020; 147: 154–86

    Abstract

    Lung cancer remains the leading cause of cancer related deaths worldwide. Lung cancer screening using low-dose computed tomography (LDCT) has been shown to reduce lung cancer specific mortality. In 2013, the United States Preventive Services Task Force (USPSTF) recommended annual lung cancer screening with LDCT for smokers aged between 55 years to 80 years, with at least 30 pack-years of smoking exposure that currently smoke or who have quit smoking within 15 years. Risk-based lung cancer screening is an alternative approach that defines screening eligibility based on the personal risk of individuals. Selection of individuals for lung cancer screening based on their personal lung cancer risk has been shown to improve the sensitivity and specificity associated with the eligibility criteria of the screening program as compared to the 2013 USPSTF criteria. Numerous risk prediction models have been developed to estimate the lung cancer risk of individuals incorporating sociodemographic, smoking, and clinical risk factors associated with lung cancer, including age, smoking history, sex, race/ethnicity, personal and family history of cancer, and history of emphysema and chronic obstructive pulmonary disease (COPD), among others. Some risk prediction models include biomarker information, such as germline mutations or protein-based biomarkers as independent risk predictors, in addition to clinical, smoking, and sociodemographic risk factors. While, the majority of lung cancer risk prediction models are suitable for selecting high-risk individuals for lung cancer screening, some risk models have been developed to predict the probability of malignancy of screen-detected solidary pulmonary nodules or to optimize the screening frequency of eligible individuals by incorporating past screening findings. In this systematic review, we provide an overview of existing risk prediction models and their applications to lung cancer screening. We discuss potential strengths and limitations of lung cancer screening using risk prediction models and future research directions.

    View details for DOI 10.1016/j.lungcan.2020.07.007

    View details for PubMedID 32721652

  • Disparities of national lung cancer screening guidelines in the U.S. population. Journal of the National Cancer Institute Han, S. S., Chow, E., Ten Haaf, K., Toumazis, I., Cao, P., Bastani, M., Tammemagi, M., Jeon, J., Feuer, E., Meza, R., Plevritis, S. 2020

    Abstract

    BACKGROUND: Current U.S. Preventive Services Task Force (USPSTF) lung cancer screening guidelines are based on smoking history and age (55-80 y). These guidelines may miss those at higher risk, even at lower exposures of smoking or younger ages, due to other risk factors such as race, family history or comorbidity. In this study, we characterized the demographic and clinical profiles of those selected by risk-based screening criteria but missed by USPSTF guidelines in younger (50-54 y) and older (71-80 y) age groups.METHODS: We used data from the National Health Interview Survey, the CISNET Smoking History Generator, and results of logistic prediction models to simulate life-time lung cancer risk-factor data for 100,000 individuals in the 1950-1960 birth cohorts. We calculated age-specific 6-year lung cancer risk for each individual from ages 50-90 y using the PLCOm2012 model, and evaluated age-specific screening eligibility by USPSTF guidelines and by risk-based criteria (varying thresholds between 1.3%-2.5%).RESULTS: In the 1950 birth cohort, 5.4% would have been ineligible for screening by USPSTF criteria in their younger ages, but eligible based on risk-based criteria. Similarly, 10.4% of the cohort would be ineligible for screening by USPSTF in older ages. Notably, high proportions of Blacks were ineligible for screening by USPSTF criteria at younger (15.6%) and older (14.2%) ages, which were statistically significantly greater than those of Whites (4.8% and 10.8%, respectively, P<0.001). Similar results were observed with other risk thresholds and for the 1960 cohort.CONCLUSIONS: Further consideration is needed to incorporate comprehensive risk factors, including race/ethnicity, into lung cancer screening to reduce potential racial disparities.

    View details for DOI 10.1093/jnci/djaa013

    View details for PubMedID 32040195

  • A human lung tumor microenvironment interactome identifies clinically relevant cell-type cross-talk. Genome biology Gentles, A. J., Hui, A. B., Feng, W. n., Azizi, A. n., Nair, R. V., Bouchard, G. n., Knowles, D. A., Yu, A. n., Jeong, Y. n., Bejnood, A. n., Forgó, E. n., Varma, S. n., Xu, Y. n., Kuong, A. n., Nair, V. S., West, R. n., van de Rijn, M. n., Hoang, C. D., Diehn, M. n., Plevritis, S. K. 2020; 21 (1): 107

    Abstract

    Tumors comprise a complex microenvironment of interacting malignant and stromal cell types. Much of our understanding of the tumor microenvironment comes from in vitro studies isolating the interactions between malignant cells and a single stromal cell type, often along a single pathway.To develop a deeper understanding of the interactions between cells within human lung tumors, we perform RNA-seq profiling of flow-sorted malignant cells, endothelial cells, immune cells, fibroblasts, and bulk cells from freshly resected human primary non-small-cell lung tumors. We map the cell-specific differential expression of prognostically associated secreted factors and cell surface genes, and computationally reconstruct cross-talk between these cell types to generate a novel resource called the Lung Tumor Microenvironment Interactome (LTMI). Using this resource, we identify and validate a prognostically unfavorable influence of Gremlin-1 production by fibroblasts on proliferation of malignant lung adenocarcinoma cells. We also find a prognostically favorable association between infiltration of mast cells and less aggressive tumor cell behavior.These results illustrate the utility of the LTMI as a resource for generating hypotheses concerning tumor-microenvironment interactions that may have prognostic and therapeutic relevance.

    View details for DOI 10.1186/s13059-020-02019-x

    View details for PubMedID 32381040

  • Multi-omic single-cell snapshots reveal multiple independent trajectories to drug tolerance in a melanoma cell line. Nature communications Su, Y. n., Ko, M. E., Cheng, H. n., Zhu, R. n., Xue, M. n., Wang, J. n., Lee, J. W., Frankiw, L. n., Xu, A. n., Wong, S. n., Robert, L. n., Takata, K. n., Yuan, D. n., Lu, Y. n., Huang, S. n., Ribas, A. n., Levine, R. n., Nolan, G. P., Wei, W. n., Plevritis, S. K., Li, G. n., Baltimore, D. n., Heath, J. R. 2020; 11 (1): 2345

    Abstract

    The determination of individual cell trajectories through a high-dimensional cell-state space is an outstanding challenge for understanding biological changes ranging from cellular differentiation to epigenetic responses of diseased cells upon drugging. We integrate experiments and theory to determine the trajectories that single BRAFV600E mutant melanoma cancer cells take between drug-naive and drug-tolerant states. Although single-cell omics tools can yield snapshots of the cell-state landscape, the determination of individual cell trajectories through that space can be confounded by stochastic cell-state switching. We assayed for a panel of signaling, phenotypic, and metabolic regulators at points across 5 days of drug treatment to uncover a cell-state landscape with two paths connecting drug-naive and drug-tolerant states. The trajectory a given cell takes depends upon the drug-naive level of a lineage-restricted transcription factor. Each trajectory exhibits unique druggable susceptibilities, thus updating the paradigm of adaptive resistance development in an isogenic cell population.

    View details for DOI 10.1038/s41467-020-15956-9

    View details for PubMedID 32393797

  • Cost-Effectiveness Analysis of Lung Cancer Screening in the United States. Annals of internal medicine Criss, S. D., de Koning, H. J., Plevritis, S. K., Meza, R. n., Kong, C. Y. 2020; 172 (10): 706–7

    View details for DOI 10.7326/L20-0072

    View details for PubMedID 32422089

  • TRAIL-induced variation of cell signaling states provides nonheritable resistance to apoptosis. Life science alliance Baskar, R., Fienberg, H. G., Khair, Z., Favaro, P., Kimmey, S., Green, D. R., Nolan, G. P., Plevritis, S., Bendall, S. C. 2019; 2 (6)

    Abstract

    TNFalpha-related apoptosis-inducing ligand (TRAIL), specifically initiates programmed cell death, but often fails to eradicate all cells, making it an ineffective therapy for cancer. This fractional killing is linked to cellular variation that bulk assays cannot capture. Here, we quantify the diversity in cellular signaling responses to TRAIL, linking it to apoptotic frequency across numerous cell systems with single-cell mass cytometry (CyTOF). Although all cells respond to TRAIL, a variable fraction persists without apoptotic progression. This cell-specific behavior is nonheritable where both the TRAIL-induced signaling responses and frequency of apoptotic resistance remain unaffected by prior exposure. The diversity of signaling states upon exposure is correlated to TRAIL resistance. Concomitantly, constricting the variation in signaling response with kinase inhibitors proportionally decreases TRAIL resistance. Simultaneously, TRAIL-induced de novo translation in resistant cells, when blocked by cycloheximide, abrogated all TRAIL resistance. This work highlights how cell signaling diversity, and subsequent translation response, relates to nonheritable fractional escape from TRAIL-induced apoptosis. This refined view of TRAIL resistance provides new avenues to study death ligands in general.

    View details for DOI 10.26508/lsa.201900554

    View details for PubMedID 31704709

  • Precision Medicine in Pancreatic Disease-Knowledge Gaps and Research Opportunities: Summary of a National Institute of Diabetes and Digestive and Kidney Diseases Workshop. Pancreas Lowe, M. E., Andersen, D. K., Caprioli, R. M., Choudhary, J., Cruz-Monserrate, Z., Dasyam, A. K., Forsmark, C. E., Gorelick, F. S., Gray, J. W., Haupt, M., Kelly, K. A., Olive, K. P., Plevritis, S. K., Rappaport, N., Roth, H. R., Steen, H., Swamidass, S. J., Tirkes, T., Uc, A., Veselkov, K., Whitcomb, D. C., Habtezion, A. 2019; 48 (10): 1250–58

    Abstract

    A workshop on research gaps and opportunities for Precision Medicine in Pancreatic Disease was sponsored by the National Institute of Diabetes and Digestive Kidney Diseases on July 24, 2019, in Pittsburgh. The workshop included an overview lecture on precision medicine in cancer and 4 sessions: (1) general considerations for the application of bioinformatics and artificial intelligence; (2) omics, the combination of risk factors and biomarkers; (3) precision imaging; and (4) gaps, barriers, and needs to move from precision to personalized medicine for pancreatic disease. Current precision medicine approaches and tools were reviewed, and participants identified knowledge gaps and research needs that hinder bringing precision medicine to pancreatic diseases. Most critical were (a) multicenter efforts to collect large-scale patient data sets from multiple data streams in the context of environmental and social factors; (b) new information systems that can collect, annotate, and quantify data to inform disease mechanisms; (c) novel prospective clinical trial designs to test and improve therapies; and (d) a framework for measuring and assessing the value of proposed approaches to the health care system. With these advances, precision medicine can identify patients early in the course of their pancreatic disease and prevent progression to chronic or fatal illness.

    View details for DOI 10.1097/MPA.0000000000001412

    View details for PubMedID 31688587

  • Cost-Effectiveness Analysis of Lung Cancer Screening Accounting for the Effect of Indeterminate Findings JNCI CANCER SPECTRUM Toumazis, I., Tsai, E. B., Erdogan, S., Han, S. S., Wan, W., Leung, A., Plevritis, S. K. 2019; 3 (3)
  • Lymph node colonization promotes distant tumor metastasis through the induction of systemic immune tolerance Reticker-Flynn, N. E., Martins, M. M., Basto, P. A., Zhang, W., Bejnood, A., Gentles, A. J., Sunwoo, J. B., Plevritis, S. K., Engleman, E. G. AMER ASSOC CANCER RESEARCH. 2019
  • Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen NATURE COMMUNICATIONS Menden, M. P., Wang, D., Mason, M. J., Szalai, B., Bulusu, K. C., Guan, Y., Yu, T., Kang, J., Jeon, M., Wolfinger, R., Nguyen, T., Zaslavskiy, M., Jang, I., Ghazoui, Z., Ahsen, M., Vogel, R., Neto, E., Norman, T., Tang, E. Y., Garnett, M. J., Di Veroli, G. Y., Fawell, S., Stolovitzky, G., Guinney, J., Dry, J. R., Saez-Rodriguez, J., Abante, J., Abecassis, B., Aben, N., Aghamirzaie, D., Aittokallio, T., Akhtari, F. S., Al-lazikani, B., Alam, T., Allam, A., Allen, C., de Almeida, M., Altarawy, D., Alves, V., Amadoz, A., Anchang, B., Antolin, A. A., Ash, J. R., Romeo Aznar, V., Ba-alawi, W., Bagheri, M., Bajic, V., Ball, G., Ballester, P. J., Baptista, D., Bare, C., Bateson, M., Bender, A., Bertrand, D., Wijayawardena, B., Boroevich, K. A., Bosdriesz, E., Bougouffa, S., Bounova, G., Brouwer, T., Bryant, B., Calaza, M., Calderone, A., Calza, S., Capuzzi, S., Carbonell-Caballero, J., Carlin, D., Carter, H., Castagnoli, L., Celebi, R., Cesareni, G., Chang, H., Chen, G., Chen, H., Chen, H., Cheng, L., Chernomoretz, A., Chicco, D., Cho, K., Cho, S., Choi, D., Choi, J., Choi, K., Choi, M., De Cock, M., Coker, E., Cortes-Ciriano, I., Cserzo, M., Cubuk, C., Curtis, C., Van Daele, D., Dang, C. C., Dijkstra, T., Dopazo, J., Draghici, S., Drosou, A., Dumontier, M., Ehrhart, F., Eid, F., ElHefnawi, M., Elmarakeby, H., van Engelen, B., Engin, H., de Esch, I., Evelo, C., Falcao, A. O., Farag, S., Fernandez-Lozano, C., Fisch, K., Flobak, A., Fornari, C., Foroushani, A. K., Fotso, D., Fourches, D., Friend, S., Frigessi, A., Gao, F., Gao, X., Gerold, J. M., Gestraud, P., Ghosh, S., Gillberg, J., Godoy-Lorite, A., Godynyuk, L., Godzik, A., Goldenberg, A., Gomez-Cabrero, D., Gonen, M., de Graaf, C., Gray, H., Grechkin, M., Guimera, R., Guney, E., Haibe-Kains, B., Han, Y., Hase, T., He, D., He, L., Heath, L. S., Hellton, K. H., Helmer-Citterich, M., Hidalgo, M. R., Hidru, D., Hill, S. M., Hochreiter, S., Hong, S., Hovig, E., Hsueh, Y., Hu, Z., Huang, J. K., Huang, R., Hunyady, L., Hwang, J., Hwang, T., Hwang, W., Hwang, Y., Isayev, O., Walk, O., Jack, J., Jahandideh, S., Ji, J., Jo, Y., Kamola, P. J., Kanev, G. K., Karacosta, L., Karimi, M., Kaski, S., Kazanov, M., Khamis, A. M., Khan, S., Kiani, N. A., Kim, A., Kim, J., Kim, J., Kim, K., Kim, K., Kim, S., Kim, Y., Kim, Y., Kirk, P. W., Kitano, H., Klambauer, G., Knowles, D., Ko, M., Kohn-Luque, A., Kooistra, A. J., Kuenemann, M. A., Kuiper, M., Kurz, C., Kwon, M., van Laarhoven, T., Laegreid, A., Lederer, S., Lee, H., Lee, J., Lee, Y., Leppaho, E., Lewis, R., Li, J., Li, L., Liley, J., Lim, W., Lin, C., Liu, Y., Lopez, Y., Low, J., Lysenko, A., Machado, D., Madhukar, N., De Maeyer, D., Malpartida, A., Mamitsuka, H., Marabita, F., Marchal, K., Marttinen, P., Mason, D., Mazaheri, A., Mehmood, A., Mehreen, A., Michaut, M., Miller, R. A., Mitsopoulos, C., Modos, D., Van Moerbeke, M., Moo, K., Motsinger-Reif, A., Movva, R., Muraru, S., Muratov, E., Mushthofa, M., Nagarajan, N., Nakken, S., Nath, A., Neuvial, P., Newton, R., Ning, Z., De Niz, C., Oliva, B., Olsen, C., Palmeri, A., Panesar, B., Papadopoulos, S., Park, J., Park, S., Park, S., Pawitan, Y., Peluso, D., Pendyala, S., Peng, J., Perfetto, L., Pirro, S., Plevritis, S., Politi, R., Poon, H., Porta, E., Prellner, I., Preuer, K., Angel Pujana, M., Ramnarine, R., Reid, J. E., Reyal, F., Richardson, S., Ricketts, C., Rieswijk, L., Rocha, M., Rodriguez-Gonzalvez, C., Roell, K., Rotroff, D., de Ruiter, J. R., Rukawa, P., Sadacca, B., Safikhani, Z., Safitri, F., Sales-Pardo, M., Sauer, S., Schlichting, M., Seoane, J. A., Serra, J., Shang, M., Sharma, A., Sharma, H., Shen, Y., Shiga, M., Shin, M., Shkedy, Z., Shopsowitz, K., Sinai, S., Skola, D., Smirnov, P., Soerensen, I., Soerensen, P., Song, J., Song, S., Soufan, O., Spitzmueller, A., Steipe, B., Suphavilai, C., Tamayo, S., Tamborero, D., Tang, J., Tanoli, Z., Tarres-Deulofeu, M., Tegner, J., Thommesen, L., Tonekaboni, S., Tran, H., De Troyer, E., Truong, A., Tsunoda, T., Turu, G., Tzeng, G., Verbeke, L., Videla, S., Vis, D., Voronkov, A., Votis, K., Wang, A., Wang, H., Wang, P., Wang, S., Wang, W., Wang, X., Wang, X., Wennerberg, K., Wernisch, L., Wessels, L., van Westen, G. P., Westerman, B. A., White, S., Willighagen, E., Wurdinger, T., Xie, L., Xie, S., Xu, H., Yadav, B., Yau, C., Yeerna, H., Yin, J., Yu, M., Yu, M., Yun, S., Zakharov, A., Zamichos, A., Zanin, M., Zeng, L., Zenil, H., Zhang, F., Zhang, P., Zhang, W., Zhao, H., Zhao, L., Zheng, W., Zoufir, A., Zucknick, M., AstraZeneca-Sanger Drug Combinatio 2019; 10: 2674

    Abstract

    The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal patient-specific treatments. Here we report AstraZeneca's large drug combination dataset, consisting of 11,576 experiments from 910 combinations across 85 molecularly characterized cancer cell lines, and results of a DREAM Challenge to evaluate computational strategies for predicting synergistic drug pairs and biomarkers. 160 teams participated to provide a comprehensive methodological development and benchmarking. Winning methods incorporate prior knowledge of drug-target interactions. Synergy is predicted with an accuracy matching biological replicates for >60% of combinations. However, 20% of drug combinations are poorly predicted by all methods. Genomic rationale for synergy predictions are identified, including ADAM17 inhibitor antagonism when combined with PIK3CB/D inhibition contrasting to synergy when combined with other PI3K-pathway inhibitors in PIK3CA mutant cells.

    View details for DOI 10.1038/s41467-019-09799-2

    View details for Web of Science ID 000471758500010

    View details for PubMedID 31209238

    View details for PubMedCentralID PMC6572829

  • Sparse discriminative latent characteristics for predicting cancer drug sensitivity from genomic features. PLoS computational biology Knowles, D. A., Bouchard, G., Plevritis, S. 2019; 15 (5): e1006743

    Abstract

    Drug screening studies typically involve assaying the sensitivity of a range of cancer cell lines across an array of anti-cancer therapeutics. Alongside these sensitivity measurements high dimensional molecular characterizations of the cell lines are typically available, including gene expression, copy number variation and genomic mutations. We propose a sparse multitask regression model which learns discriminative latent characteristics that predict drug sensitivity and are associated with specific molecular features. We use ideas from Bayesian nonparametrics to automatically infer the appropriate number of these latent characteristics. The resulting analysis couples high predictive performance with interpretability since each latent characteristic involves a typically small set of drugs, cell lines and genomic features. Our model uncovers a number of drug-gene sensitivity associations missed by single gene analyses. We functionally validate one such novel association: that increased expression of the cell-cycle regulator C/EBPdelta decreases sensitivity to the histone deacetylase (HDAC) inhibitor panobinostat.

    View details for DOI 10.1371/journal.pcbi.1006743

    View details for PubMedID 31136571

  • Cost-Effectiveness Analysis of Lung Cancer Screening in the United States: A Comparative Modeling Study. Annals of internal medicine Criss, S. D., Cao, P. n., Bastani, M. n., Ten Haaf, K. n., Chen, Y. n., Sheehan, D. F., Blom, E. F., Toumazis, I. n., Jeon, J. n., de Koning, H. J., Plevritis, S. K., Meza, R. n., Kong, C. Y. 2019

    Abstract

    Recommendations vary regarding the maximum age at which to stop lung cancer screening: 80 years according to the U.S. Preventive Services Task Force (USPSTF), 77 years according to the Centers for Medicare & Medicaid Services (CMS), and 74 years according to the National Lung Screening Trial (NLST).To compare the cost-effectiveness of different stopping ages for lung cancer screening.By using shared inputs for smoking behavior, costs, and quality of life, 4 independently developed microsimulation models evaluated the health and cost outcomes of annual lung cancer screening with low-dose computed tomography (LDCT).The NLST; Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial; SEER (Surveillance, Epidemiology, and End Results) program; Nurses' Health Study and Health Professionals Follow-up Study; and U.S. Smoking History Generator.Current, former, and never-smokers aged 45 years from the 1960 U.S. birth cohort.45 years.Health care sector.Annual LDCT according to NLST, CMS, and USPSTF criteria.Incremental cost-effectiveness ratios (ICERs) with a willingness-to-pay threshold of $100 000 per quality-adjusted life-year (QALY).The 4 models showed that the NLST, CMS, and USPSTF screening strategies were cost-effective, with ICERs averaging $49 200, $68 600, and $96 700 per QALY, respectively. Increasing the age at which to stop screening resulted in a greater reduction in mortality but also led to higher costs and overdiagnosis rates.Probabilistic sensitivity analysis showed that the NLST and CMS strategies had higher probabilities of being cost-effective (98% and 77%, respectively) than the USPSTF strategy (52%).Scenarios assumed 100% screening adherence, and models extrapolated beyond clinical trial data.All 3 sets of lung cancer screening criteria represent cost-effective programs. Despite underlying uncertainty, the NLST and CMS screening strategies have high probabilities of being cost-effective.CISNET (Cancer Intervention and Surveillance Modeling Network) Lung Group, National Cancer Institute.

    View details for DOI 10.7326/M19-0322

    View details for PubMedID 31683314

  • A comparative modeling analysis of risk-based lung cancer screening strategies. Journal of the National Cancer Institute Ten Haaf, K. n., Bastani, M. n., Cao, P. n., Jeon, J. n., Toumazis, I. n., Han, S. S., Plevritis, S. K., Blom, E. F., Kong, C. Y., Tammemägi, M. C., Feuer, E. J., Meza, R. n., de Koning, H. J. 2019

    Abstract

    Risk-prediction models have been proposed to select individuals for lung cancer screening. However, their long-term effects are uncertain. This study evaluates long-term benefits and harms of risk-based screening compared to current United States Preventive Services Task Force (USPSTF) recommendations.Four independent natural-history models performed a comparative modeling study evaluating long-term benefits and harms of selecting individuals for lung cancer screening through risk-prediction models. 363 risk-based screening strategies varying by screening starting and stopping age, risk-prediction model used for eligibility (Bach, PLCOm2012, LCDRAT), and risk-threshold were evaluated for a 1950 U.S. birth-cohort. Among the evaluated outcomes were percentage of individuals ever screened, screens required, lung cancer deaths averted, life-years gained and overdiagnosis.Risk-based screening strategies requiring similar screens among individuals aged 55-80 as the USPSTF-criteria (corresponding risk-thresholds: Bach: 2.8%, PLCOm2012: 1.7%, LCDRAT: 1.7%) averted considerably more lung cancer deaths (Bach: 693, PLCOm2012: 698, LCDRAT: 696, USPSTF: 613). However, life-years gained were only modestly higher (Bach: 8,660, PLCOm2012: 8,862, LCDRAT, 8,631,USPSTF: 8,590) and risk-based strategies had more overdiagnosis (Bach: 149, PLCOm2012: 147, LCDRAT: 150, USPSTF: 115). Sensitivity analyses suggests excluding individuals with limited life-expectancies (<5 years) from screening retains the life-years gained by risk-based screening, while reducing overdiagnosis by > 65.3%.Risk-based lung cancer screening strategies prevent considerably more lung cancer deaths than current recommendations. However, they yield modest additional life-years and increased overdiagnosis due to predominantly selecting older individuals. Efficient implementation of risk-based lung cancer screening requires careful consideration of life-expectancy for determining optimal individual stopping ages.

    View details for DOI 10.1093/jnci/djz164

    View details for PubMedID 31566216

  • Mapping lung cancer epithelial-mesenchymal transition states and trajectories with single-cell resolution. Nature communications Karacosta, L. G., Anchang, B. n., Ignatiadis, N. n., Kimmey, S. C., Benson, J. A., Shrager, J. B., Tibshirani, R. n., Bendall, S. C., Plevritis, S. K. 2019; 10 (1): 5587

    Abstract

    Elucidating the spectrum of epithelial-mesenchymal transition (EMT) and mesenchymal-epithelial transition (MET) states in clinical samples promises insights on cancer progression and drug resistance. Using mass cytometry time-course analysis, we resolve lung cancer EMT states through TGFβ-treatment and identify, through TGFβ-withdrawal, a distinct MET state. We demonstrate significant differences between EMT and MET trajectories using a computational tool (TRACER) for reconstructing trajectories between cell states. In addition, we construct a lung cancer reference map of EMT and MET states referred to as the EMT-MET PHENOtypic STAte MaP (PHENOSTAMP). Using a neural net algorithm, we project clinical samples onto the EMT-MET PHENOSTAMP to characterize their phenotypic profile with single-cell resolution in terms of our in vitro EMT-MET analysis. In summary, we provide a framework to phenotypically characterize clinical samples in the context of in vitro EMT-MET findings which could help assess clinical relevance of EMT in cancer in future studies.

    View details for DOI 10.1038/s41467-019-13441-6

    View details for PubMedID 31811131

  • Cost-Effectiveness Analysis of Lung Cancer Screening Accounting for the Effect of Indeterminate Findings. JNCI cancer spectrum Toumazis, I. n., Tsai, E. B., Erdogan, S. A., Han, S. S., Wan, W. n., Leung, A. n., Plevritis, S. K. 2019; 3 (3): pkz035

    Abstract

    Numerous health policy organizations recommend lung cancer screening, but no consensus exists on the optimal policy. Moreover, the impact of the Lung CT screening reporting and data system guidelines to manage small pulmonary nodules of unknown significance (a.k.a. indeterminate nodules) on the cost-effectiveness of lung cancer screening is not well established.We assess the cost-effectiveness of 199 screening strategies that vary in terms of age and smoking eligibility criteria, using a microsimulation model. We simulate lung cancer-related events throughout the lifetime of US-representative current and former smokers. We conduct sensitivity analyses to test key model inputs and assumptions.The cost-effectiveness efficiency frontier consists of both annual and biennial screening strategies. Current guidelines are not on the frontier. Assuming 4% disutility associated with indeterminate findings, biennial screening for smokers aged 50-70 years with at least 40 pack-years and less than 10 years since smoking cessation is the cost-effective strategy using $100 000 willingness-to-pay threshold yielding the highest health benefit. Among all health utilities, the cost-effectiveness of screening is most sensitive to changes in the disutility of indeterminate findings. As the disutility of indeterminate findings decreases, screening eligibility criteria become less stringent and eventually annual screening for smokers aged 50-70 years with at least 30 pack-years and less than 10 years since smoking cessation is the cost-effective strategy yielding the highest health benefit.The disutility associated with indeterminate findings impacts the cost-effectiveness of lung cancer screening. Efforts to quantify and better understand the impact of indeterminate findings on the effectiveness and cost-effectiveness of lung cancer screening are warranted.

    View details for DOI 10.1093/jncics/pkz035

    View details for PubMedID 31942534

    View details for PubMedCentralID PMC6947892

  • A radiogenomic dataset of non-small cell lung cancer SCIENTIFIC DATA Bakr, S., Gevaert, O., Echegaray, S., Ayers, K., Zhou, M., Shafiq, M., Zheng, H., Benson, J., Zhang, W., Leung, A. C., Kadoch, M., Hoang, C. D., Shrager, J., Quon, A., Rubin, D. L., Plevritis, S. K., Napel, S. 2018; 5
  • A radiogenomic dataset of non-small cell lung cancer. Scientific data Bakr, S., Gevaert, O., Echegaray, S., Ayers, K., Zhou, M., Shafiq, M., Zheng, H., Benson, J. A., Zhang, W., Leung, A. N., Kadoch, M., D Hoang, C., Shrager, J., Quon, A., Rubin, D. L., Plevritis, S. K., Napel, S. 2018; 5: 180202

    Abstract

    Medical image biomarkers of cancer promise improvements in patient care through advances in precision medicine. Compared to genomic biomarkers, image biomarkers provide the advantages of being non-invasive, and characterizing a heterogeneous tumor in its entirety, as opposed to limited tissue available via biopsy. We developed a unique radiogenomic dataset from a Non-Small Cell Lung Cancer (NSCLC) cohort of 211 subjects. The dataset comprises Computed Tomography (CT), Positron Emission Tomography (PET)/CT images, semantic annotations of the tumors as observed on the medical images using a controlled vocabulary, and segmentation maps of tumors in the CT scans. Imaging data are also paired with results of gene mutation analyses, gene expression microarrays and RNA sequencing data from samples of surgically excised tumor tissue, and clinical data, including survival outcomes. This dataset was created to facilitate the discovery of the underlying relationship between tumor molecular and medical image features, as well as the development and evaluation of prognostic medical image biomarkers.

    View details for PubMedID 30325352

  • Caution Needed for Analyzing the Risks of Second Cancers JOURNAL OF THORACIC ONCOLOGY Han, S. S., Plevritis, S. K., Wakelee, H. A. 2018; 13 (9): E172–E173
  • Caution Needed for Analyzing the Risks of Second Cancers. Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer Han, S. S., Plevritis, S. K., Wakelee, H. A. 2018; 13 (9): e172–e173

    View details for PubMedID 30166015

  • Identifying dynamic EMT states and constructing a proteomic EMT landscape of lung cancer using single cell multidimensional analysis Karacosta, L. G., Anchang, B., Kimmey, S., van de Rijn, M., Shrager, J. B., Bendall, S. C., Plevritis, S. K. AMER ASSOC CANCER RESEARCH. 2018
  • GFPT2-Expressing Cancer-Associated Fibroblasts Mediate Metabolic Reprogramming in Human Lung Adenocarcinoma CANCER RESEARCH Zhang, W., Bouchard, G., Yu, A., Shafiq, M., Jamali, M., Shrager, J. B., Ayers, K., Bakr, S., Gentles, A. J., Diehn, M., Quon, A., West, R. B., Nair, V., van de Rijn, M., Napel, S., Plevritis, S. K. 2018; 78 (13): 3445–57
  • Individualized drug combination based on single-cell drug perturbations Anchang, B., Davis, K., Fienberg, H., Bendall, S., Karacosta, L., Nolan, G., Plevritis, S. K. AMER ASSOC CANCER RESEARCH. 2018
  • Development of plasma cell-free DNA (cfDNA) assays for early cancer detection: first insights from the Circulating Cell-Free Genome Atlas Study (CCGA) Aravanis, A. A., Oxnard, G. R., Maddala, T., Hubbell, E., Venn, O., Jamshidi, A., Shen, L., Amini, H., Beausang, J. A., Betts, C., Civello, D., Davydov, K., Fazullina, S., Filippova, D., Gnerre, S., Gross, S., Hou, C., Jiang, R., Jung, B., Kurtzman, K., Melton, C., Nautiyal, S., Newman, J., Newman, J., Nicolaou, C., Rava, R., Sakarya, O., Satya, R., Shojaee, S., Steffen, K., Valouev, A., Xu, H., Yue, J., Zhang, N., Baselga, J., Lapham, R., Davis, D. G., Smith, D., Richards, D., Seiden, M. V., Swanton, C., Yeatman, T. J., Tibshirani, R., Curtis, C., Plevritis, S. K., Williams, R., Klein, E., Hartman, A., Liu, M. C. AMER ASSOC CANCER RESEARCH. 2018
  • Studying tumor metabolic reprogramming through integration of metabolomics and transcriptomics Zhang, W., Plevritis, S. AMER ASSOC CANCER RESEARCH. 2018
  • Genome-wide sequencing for early stage lung cancer detection from plasma cell-free DNA (cfDNA): The Circulating Cancer Genome Atlas (CCGA) study. Oxnard, G. R., Maddala, T., Hubbell, E., Aravanis, A., Zhang, N., Venn, O., Valouev, A., Shen, L., Patel, S., Jamshidi, A., Jagadeesh, K., Gross, S., Filippova, D., Beausang, J. F., Liu, M. C., Richards, D. A., Plevritis, S., Williams, R., Hartman, A., Swanton, C. AMER SOC CLINICAL ONCOLOGY. 2018
  • Contributions of Screening and Treatment to Mortality From Breast Cancer Reply JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION Plevritis, S. K., Berry, D. A., Mandelblatt, J. S. 2018; 319 (22): 2336

    View details for PubMedID 29896623

  • GFPT2-expressing cancer-associated fibroblasts mediate metabolic reprogramming in human lung adenocarcinoma. Cancer research Zhang, W., Bouchard, G., Yu, A., Shafiq, M., Jamali, M., Shrager, J. B., Ayers, K., Bakr, S., Gentles, A. J., Diehn, M., Quon, A., West, R. B., Nair, V., van de Rijn, M., Napel, S., Plevritis, S. K. 2018

    Abstract

    Metabolic reprogramming of the tumor microenvironment is recognized as a cancer hallmark. To identify new molecular processes associated with tumor metabolism, we analyzed the transcriptome of bulk and flow-sorted human primary non-small cell lung cancer (NSCLC) together with 18FDG-positron emission tomography scans, which provide a clinical measure of glucose uptake. Tumors with higher glucose uptake were functionally enriched for molecular processes associated with invasion in adenocarcinoma (AD) and cell growth in squamous cell carcinoma (SCC). Next, we identified genes correlated to glucose uptake that were predominately overexpressed in a single cell-type comprising the tumor microenvironment. For SCC, most of these genes were expressed by malignant cells, whereas in AD they were predominately expressed by stromal cells, particularly cancer-associated fibroblasts (CAFs). Among these AD genes correlated to glucose uptake, we focused on Glutamine-Fructose-6-Phosphate Transaminase 2 (GFPT2), which codes for the Glutamine-Fructose-6-Phosphate Aminotransferase 2 (GFAT2), a rate-limiting enzyme of the hexosamine biosynthesis pathway (HBP), which is responsible for glycosylation. GFPT2 was predictive of glucose uptake independent of GLUT1, the primary glucose transporter, and was prognostically significant at both gene and protein level. We confirmed that normal fibroblasts transformed to CAF-like cells, following TGF-beta treatment, upregulated HBP genes, including GFPT2, with less change in genes driving glycolysis, pentose phosphate pathway and TCA cycle. Our work provides new evidence of histology-specific tumor-stromal properties associated with glucose uptake in NSCLC and identifies GFPT2 as a critical regulator of tumor metabolic reprogramming in AD.

    View details for PubMedID 29760045

  • Comparing CISNET Breast Cancer Models Using the Maximum Clinical Incidence Reduction Methodology MEDICAL DECISION MAKING van den Broek, J. J., van Ravesteyn, N. T., Mandelblatt, J. S., Cevik, M., Schechter, C. B., Lee, S. J., Huang, H., Li, Y., Munoz, D. F., Plevritis, S. K., de Koning, H. J., Stout, N. K., van Ballegooijen, M. 2018; 38: 112S–125S

    Abstract

    Collaborative modeling has been used to estimate the impact of potential cancer screening strategies worldwide. A necessary step in the interpretation of collaborative cancer screening model results is to understand how model structure and model assumptions influence cancer incidence and mortality predictions. In this study, we examined the relative contributions of the pre-clinical duration of breast cancer, the sensitivity of screening, and the improvement in prognosis associated with treatment of screen-detected cases to the breast cancer incidence and mortality predictions of 5 Cancer Intervention and Surveillance Modeling Network (CISNET) models.To tease out the impact of model structure and assumptions on model predictions, the Maximum Clinical Incidence Reduction (MCLIR) method compares changes in the number of breast cancers diagnosed due to clinical symptoms and cancer mortality between 4 simplified scenarios: 1) no-screening; 2) one-time perfect screening exam, which detects all existing cancers and perfect treatment (i.e., cure) of all screen-detected cancers; 3) one-time digital mammogram and perfect treatment of all screen-detected cancers; and 4) one-time digital mammogram and current guideline-concordant treatment of all screen-detected cancers.The 5 models predicted a large range in maximum clinical incidence (19% to 71%) and in breast cancer mortality reduction (33% to 67%) from a one-time perfect screening test and perfect treatment. In this perfect scenario, the models with assumptions of tumor inception before it is first detectable by mammography predicted substantially higher incidence and mortality reductions than models with assumptions of tumor onset at the start of a cancer's screen-detectable phase. The range across models in breast cancer clinical incidence (11% to 24%) and mortality reduction (8% to 18%) from a one-time digital mammogram at age 62 y with observed sensitivity and current guideline-concordant treatment was considerably smaller than achievable under perfect conditions.The timing of tumor inception and its effect on the length of the pre-clinical phase of breast cancer had a substantial impact on the grouping of models based on their predictions for clinical incidence and breast cancer mortality reduction. This key finding about the timing of tumor inception will be included in future CISNET breast analyses to enhance model transparency. The MCLIR approach should aid in the interpretation of variations in model results and could be adopted in other disease screening settings to enhance model transparency.

    View details for PubMedID 29554471

  • Estimating Breast Cancer Survival by Molecular Subtype in the Absence of Screening and Adjuvant Treatment MEDICAL DECISION MAKING Munoz, D. F., Plevritis, S. K. 2018; 38: 32S–43S

    Abstract

    As molecular subtyping of breast cancer influences clinical management, the evaluation of screening and adjuvant treatment interventions at the population level needs to account for molecular subtyping. Performing such analyses are challenging because molecular subtype-specific, long-term outcomes are not readily accessible; these markers were not historically recorded in tumor registries. We present a modeling approach to estimate historical survival outcomes by estrogen receptor (ER) and human epidermal growth factor receptor 2 (HER2) status.Our approach leverages a simulation model of breast cancer outcomes and integrates data from two sources: the Surveillance Epidemiology and End Results (SEER) databases and the Breast Cancer Surveillance Consortium (BCSC). We not only produce ER- and HER2-specific estimates of breast cancer survival in the absence of screening and adjuvant treatment but we also estimate mean tumor volume doubling time (TVDT) and mean mammographic detection threshold by ER/HER2-status.In general, we found that tumors with ER-negative and HER2-positive status are associated with more aggressive growth, have lower TVDTs, are harder to detect by mammography, and have worse survival outcomes in the absence of screening and adjuvant treatment. Our estimates have been used as inputs into model-based analyses that evaluate the effects of screening and adjuvant treatment interventions on population outcomes by ER and HER2 status developed by the Cancer Intervention and Surveillance Modeling Network (CISNET) Breast Cancer Working Group. In addition, our estimates enable a re-assessment of historical trends in breast cancer incidence and mortality in terms of contemporary molecular tumor characteristics.Our approach can be generalized beyond breast cancer and to more complex molecular profiles.

    View details for PubMedID 29554464

  • A Molecular Subtype-Specific Stochastic Simulation Model of US Breast Cancer Incidence, Survival, and Mortality Trends from 1975 to 2010 MEDICAL DECISION MAKING Munoz, D. F., Xu, C., Plevritis, S. K. 2018; 38: 89S–98S

    Abstract

    We present a Monte Carlo simulation model that reproduces US invasive breast cancer incidence and mortality trends from 1975 to 2010 as a function of screening and adjuvant treatment. This model was developed for multiple purposes, including to quantify the impact of screening and adjuvant therapy on past and current trends, predict future trends, and evaluate potential outcomes under hypothetical screening and treatment interventions. The model first generates the life histories of individual breast cancer patients by determining the patient's age, tumor size, estrogen receptor (ER) status, human epidermal growth factor 2 (HER2) status, SEER (Surveillance, Epidemiology, and End Results) historic stage, detection mode at time of detection, preclinical tumor course, and death age and cause of death (breast cancer v. other causes). The model incorporates common inputs used by the Cancer Intervention and Surveillance Modeling Network (CISNET), including the dissemination patterns for screening mammography, breast cancer survival in the absence of adjuvant therapy, dissemination and efficacy of treatment by ER and HER2 status, and death from causes other than breast cancer. In this article, predicted mortality outcomes are compared assuming proportional v. nonproportional hazards effects of treatment on breast cancer survival. We found that the proportional hazards treatment effects are sufficient for ER-negative disease. However, for ER-positive disease, the treatment effects appear to be higher during the early years following diagnosis and then diminish over time. Using nonproportional hazards effects for ER-positive cases, the predicted breast cancer mortality rates closely match the SEER mortality trends from 1975 to 2010, particularly after 1995. Our work indicates that population-level simulation modeling may have a broader role in assessing the time dependence of treatment effects.

    View details for PubMedID 29554473

  • Re: Think before you leap INTERNATIONAL JOURNAL OF CANCER Han, S. S., ten Haaf, K., Hazelton, W. D., Jeon, J., Meza, R., Kong, C., Feuer, E. J., de Koning, H. J., Plevritis, S. K. 2018; 142 (7): 1507–9

    View details for PubMedID 29194597

  • Comparing CISNET Breast Cancer Incidence and Mortality Predictions to Observed Clinical Trial Results of Mammography Screening from Ages 40 to 49 MEDICAL DECISION MAKING van den Broek, J. J., van Ravesteyn, N. T., Mandelblatt, J. S., Huang, H., Ergun, M., Burnside, E. S., Xu, C., Li, Y., Alagoz, O., Lee, S. J., Stout, N. K., Song, J., Trentham-Dietz, A., Plevritis, S. K., Moss, S. M., de Koning, H. J. 2018; 38: 140S–150S

    Abstract

    The UK Age trial compared annual mammography screening of women ages 40 to 49 years with no screening and found a statistically significant breast cancer mortality reduction at the 10-year follow-up but not at the 17-year follow-up. The objective of this study was to compare the observed Age trial results with the Cancer Intervention and Surveillance Modeling Network (CISNET) breast cancer model predicted results.Five established CISNET breast cancer models used data on population demographics, screening attendance, and mammography performance from the Age trial together with extant natural history parameters to project breast cancer incidence and mortality in the control and intervention arm of the trial.The models closely reproduced the effect of annual screening from ages 40 to 49 years on breast cancer incidence. Restricted to breast cancer deaths originating from cancers diagnosed during the intervention phase, the models estimated an average 15% (range across models, 13% to 17%) breast cancer mortality reduction at the 10-year follow-up compared with 25% (95% CI, 3% to 42%) observed in the trial. At the 17-year follow-up, the models predicted 13% (range, 10% to 17%) reduction in breast cancer mortality compared with the non-significant 12% (95% CI, -4% to 26%) in the trial.The models underestimated the effect of screening on breast cancer mortality at the 10-year follow-up. Overall, the models captured the observed long-term effect of screening from age 40 to 49 years on breast cancer incidence and mortality in the UK Age trial, suggesting that the model structures, input parameters, and assumptions about breast cancer natural history are reasonable for estimating the impact of screening on mortality in this age group.

    View details for PubMedID 29554468

  • Introduction to the Cancer Intervention and Surveillance Modeling Network (CISNET) Breast Cancer Models MEDICAL DECISION MAKING Alagoz, O., Berry, D. A., de Koning, H. J., Feuer, E. J., Lee, S. J., Plevritis, S. K., Schechter, C. B., Stout, N. K., Trentham-Dietz, A., Mandelblatt, J. S., CISNET Breast Canc Working Grp 2018; 38: 3S–8S

    Abstract

    The Cancer Intervention and Surveillance Modeling Network (CISNET) Breast Cancer Working Group is a consortium of National Cancer Institute-sponsored investigators who use statistical and simulation modeling to evaluate the impact of cancer control interventions on long-term population-level breast cancer outcomes such as incidence and mortality and to determine the impact of different breast cancer control strategies. The CISNET breast cancer models have been continuously funded since 2000. The models have gone through several updates since their inception to reflect advances in the understanding of the molecular basis of breast cancer, changes in the prevalence of common risk factors, and improvements in therapy and early detection technology. This article provides an overview and history of the CISNET breast cancer models, provides an overview of the major changes in the model inputs over time, and presents examples for how CISNET breast cancer models have been used for policy evaluation.

    View details for PubMedID 29554472

  • Distinguishing Between CISNET Model Results Versus CISNET Models CANCER Berry, D. A., de Koning, H. J., Lee, S. J., Mandelblatt, J. S., Plevritis, S. K., Schechter, C. B., Stout, N. K., Trentham-Dietz, A., CISNET Breast Working Grp Princ 2018; 124 (5): 1083–84

    View details for PubMedID 29278430

  • Association of Screening and Treatment With Breast Cancer Mortality by Molecular Subtype in US Women, 2000-2012. JAMA Plevritis, S. K., Munoz, D. n., Kurian, A. W., Stout, N. K., Alagoz, O. n., Near, A. M., Lee, S. J., van den Broek, J. J., Huang, X. n., Schechter, C. B., Sprague, B. L., Song, J. n., de Koning, H. J., Trentham-Dietz, A. n., van Ravesteyn, N. T., Gangnon, R. n., Chandler, Y. n., Li, Y. n., Xu, C. n., Ergun, M. A., Huang, H. n., Berry, D. A., Mandelblatt, J. S. 2018; 319 (2): 154–64

    Abstract

    Given recent advances in screening mammography and adjuvant therapy (treatment), quantifying their separate and combined effects on US breast cancer mortality reductions by molecular subtype could guide future decisions to reduce disease burden.To evaluate the contributions associated with screening and treatment to breast cancer mortality reductions by molecular subtype based on estrogen-receptor (ER) and human epidermal growth factor receptor 2 (ERBB2, formerly HER2 or HER2/neu).Six Cancer Intervention and Surveillance Network (CISNET) models simulated US breast cancer mortality from 2000 to 2012 using national data on plain-film and digital mammography patterns and performance, dissemination and efficacy of ER/ERBB2-specific treatment, and competing mortality. Multiple US birth cohorts were simulated.Screening mammography and treatment.The models compared age-adjusted, overall, and ER/ERBB2-specific breast cancer mortality rates from 2000 to 2012 for women aged 30 to 79 years relative to the estimated mortality rate in the absence of screening and treatment (baseline rate); mortality reductions were apportioned to screening and treatment.In 2000, the estimated reduction in overall breast cancer mortality rate was 37% (model range, 27%-42%) relative to the estimated baseline rate in 2000 of 64 deaths (model range, 56-73) per 100 000 women: 44% (model range, 35%-60%) of this reduction was associated with screening and 56% (model range, 40%-65%) with treatment. In 2012, the estimated reduction in overall breast cancer mortality rate was 49% (model range, 39%-58%) relative to the estimated baseline rate in 2012 of 63 deaths (model range, 54-73) per 100 000 women: 37% (model range, 26%-51%) of this reduction was associated with screening and 63% (model range, 49%-74%) with treatment. Of the 63% associated with treatment, 31% (model range, 22%-37%) was associated with chemotherapy, 27% (model range, 18%-36%) with hormone therapy, and 4% (model range, 1%-6%) with trastuzumab. The estimated relative contributions associated with screening vs treatment varied by molecular subtype: for ER+/ERBB2-, 36% (model range, 24%-50%) vs 64% (model range, 50%-76%); for ER+/ERBB2+, 31% (model range, 23%-41%) vs 69% (model range, 59%-77%); for ER-/ERBB2+, 40% (model range, 34%-47%) vs 60% (model range, 53%-66%); and for ER-/ERBB2-, 48% (model range, 38%-57%) vs 52% (model range, 44%-62%).In this simulation modeling study that projected trends in breast cancer mortality rates among US women, decreases in overall breast cancer mortality from 2000 to 2012 were associated with advances in screening and in adjuvant therapy, although the associations varied by breast cancer molecular subtype.

    View details for PubMedID 29318276

  • Change in survival in metastatic breast cancer with treatment advances: meta-analysis and systematic review JNCI Cancer Spectrum Caswell-Jin, J. L., Plevritis, S. K., Tian, L., Cadham, C. J., Xu, C., Stout, N. K., Sledge, G. W., Mandelblatt, J. S., Kurian, A. W. 2018; 2 (4)

    View details for DOI 10.1093/jncics/pky062

  • Change in Survival in Metastatic Breast Cancer with Treatment Advances: Meta-Analysis and Systematic Review. JNCI cancer spectrum Caswell-Jin, J. L., Plevritis, S. K., Tian, L. n., Cadham, C. J., Xu, C. n., Stout, N. K., Sledge, G. W., Mandelblatt, J. S., Kurian, A. W. 2018; 2 (4): pky062

    Abstract

    Metastatic breast cancer (MBC) treatment has changed substantially over time, but we do not know whether survival post-metastasis has improved at the population level.We searched for studies of MBC patients that reported survival after metastasis in at least two time periods between 1970 and the present. We used meta-regression models to test for survival improvement over time in four disease groups: recurrent, recurrent estrogen (ER)-positive, recurrent ER-negative, and de novo stage IV. We performed sensitivity analyses based on bias in some studies that could lead earlier cohorts to include more aggressive cancers.There were 15 studies of recurrent MBC (N = 18 678 patients; 3073 ER-positive and 1239 ER-negative); meta-regression showed no survival improvement among patients recurring between 1980 and 1990, but median survival increased from 21 (95% confidence interval [CI] = 18 to 25) months to 38 (95% CI = 31 to 47) months from 1990 to 2010. For ER-positive MBC patients, median survival increased during 1990-2010 from 32 (95% CI = 23 to 43) to 57 (95% CI = 37 to 87) months, and for ER-negative MBC patients from 14 (95% CI = 11 to 19) to 33 (95% CI = 21 to 51) months. Among eight studies (N = 35 831) of de novo stage IV MBC, median survival increased during 1990-2010 from 20 (95% CI = 16 to 24) to 31 (95% CI = 24 to 39) months. Results did not change in sensitivity analyses.By bridging studies over time, we demonstrated improvements in survival for recurrent and de novo stage IV MBC overall and across ER-defined subtypes since 1990. These results can inform patient-doctor discussions about MBC prognosis and therapy.

    View details for PubMedID 30627694

  • DRUG-NEM: Optimizing drug combinations using single-cell perturbation response to account for intratumoral heterogeneity. Proceedings of the National Academy of Sciences of the United States of America Anchang, B. n., Davis, K. L., Fienberg, H. G., Williamson, B. D., Bendall, S. C., Karacosta, L. G., Tibshirani, R. n., Nolan, G. P., Plevritis, S. K. 2018; 115 (18): E4294–E4303

    Abstract

    An individual malignant tumor is composed of a heterogeneous collection of single cells with distinct molecular and phenotypic features, a phenomenon termed intratumoral heterogeneity. Intratumoral heterogeneity poses challenges for cancer treatment, motivating the need for combination therapies. Single-cell technologies are now available to guide effective drug combinations by accounting for intratumoral heterogeneity through the analysis of the signaling perturbations of an individual tumor sample screened by a drug panel. In particular, Mass Cytometry Time-of-Flight (CyTOF) is a high-throughput single-cell technology that enables the simultaneous measurements of multiple ([Formula: see text]40) intracellular and surface markers at the level of single cells for hundreds of thousands of cells in a sample. We developed a computational framework, entitled Drug Nested Effects Models (DRUG-NEM), to analyze CyTOF single-drug perturbation data for the purpose of individualizing drug combinations. DRUG-NEM optimizes drug combinations by choosing the minimum number of drugs that produce the maximal desired intracellular effects based on nested effects modeling. We demonstrate the performance of DRUG-NEM using single-cell drug perturbation data from tumor cell lines and primary leukemia samples.

    View details for PubMedID 29654148

  • Common Model Inputs Used in CISNET Collaborative Breast Cancer Modeling. Medical decision making : an international journal of the Society for Medical Decision Making Mandelblatt, J. S., Near, A. M., Miglioretti, D. L., Munoz, D. n., Sprague, B. L., Trentham-Dietz, A. n., Gangnon, R. n., Kurian, A. W., Weedon-Fekjaer, H. n., Cronin, K. A., Plevritis, S. K. 2018; 38 (1_suppl): 9S–23S

    Abstract

    Since their inception in 2000, the Cancer Intervention and Surveillance Network (CISNET) breast cancer models have collaborated to use a nationally representative core of common input parameters to represent key components of breast cancer control in each model. Employment of common inputs permits greater ability to compare model output than when each model begins with different input parameters. The use of common inputs also enhances inferences about the results, and provides a range of reasonable results based on variations in model structure, assumptions, and methods of use of the input values. The common input data are updated for each analysis to ensure that they reflect the most current practice and knowledge about breast cancer. The common core of parameters includes population rates of births and deaths; age- and cohort-specific temporal rates of breast cancer incidence in the absence of screening and treatment; effects of risk factors on incidence trends; dissemination of plain film and digital mammography; screening test performance characteristics; stage or size distribution of screen-, interval-, and clinically- detected tumors by age; the joint distribution of ER/HER2 by age and stage; survival in the absence of screening and treatment by stage and molecular subtype; age-, stage-, and molecular subtype-specific therapy; dissemination and effectiveness of therapies over time; and competing non-breast cancer mortality.In this paper, we summarize the methods and results for the common input values presently used in the CISNET breast cancer models, note assumptions made because of unobservable phenomena and/or unavailable data, and highlight plans for the development of future parameters.These data are intended to enhance the transparency of the breast CISNET models.

    View details for PubMedID 29554466

  • Non-Small Cell Lung Cancer Radiogenomics Map Identifies Relationships between Molecular and Imaging Phenotypes with Prognostic Implications. Radiology Zhou, M. n., Leung, A. n., Echegaray, S. n., Gentles, A. n., Shrager, J. B., Jensen, K. C., Berry, G. J., Plevritis, S. K., Rubin, D. L., Napel, S. n., Gevaert, O. n. 2018; 286 (1): 307–15

    Abstract

    Purpose To create a radiogenomic map linking computed tomographic (CT) image features and gene expression profiles generated by RNA sequencing for patients with non-small cell lung cancer (NSCLC). Materials and Methods A cohort of 113 patients with NSCLC diagnosed between April 2008 and September 2014 who had preoperative CT data and tumor tissue available was studied. For each tumor, a thoracic radiologist recorded 87 semantic image features, selected to reflect radiologic characteristics of nodule shape, margin, texture, tumor environment, and overall lung characteristics. Next, total RNA was extracted from the tissue and analyzed with RNA sequencing technology. Ten highly coexpressed gene clusters, termed metagenes, were identified, validated in publicly available gene-expression cohorts, and correlated with prognosis. Next, a radiogenomics map was built that linked semantic image features to metagenes by using the t statistic and the Spearman correlation metric with multiple testing correction. Results RNA sequencing analysis resulted in 10 metagenes that capture a variety of molecular pathways, including the epidermal growth factor (EGF) pathway. A radiogenomic map was created with 32 statistically significant correlations between semantic image features and metagenes. For example, nodule attenuation and margins are associated with the late cell-cycle genes, and a metagene that represents the EGF pathway was significantly correlated with the presence of ground-glass opacity and irregular nodules or nodules with poorly defined margins. Conclusion Radiogenomic analysis of NSCLC showed multiple associations between semantic image features and metagenes that represented canonical molecular pathways, and it can result in noninvasive identification of molecular properties of NSCLC. Online supplemental material is available for this article.

    View details for PubMedID 28727543

  • Risk Stratification for Second Primary Lung Cancer JOURNAL OF CLINICAL ONCOLOGY Han, S. S., Rivera, G. A., Tammemagi, M. C., Plevritis, S. K., Gomez, S. L., Cheng, I., Wakelee, H. A. 2017; 35 (25): 2893-+

    Abstract

    Purpose This study estimated the 10-year risk of developing second primary lung cancer (SPLC) among survivors of initial primary lung cancer (IPLC) and evaluated the clinical utility of the risk prediction model for selecting eligibility criteria for screening. Methods SEER data were used to identify a population-based cohort of 20,032 participants diagnosed with IPLC between 1988 and 2003 and who survived ≥ 5 years after the initial diagnosis. We used a proportional subdistribution hazards model to estimate the 10-year risk of developing SPLC among survivors of lung cancer LC in the presence of competing risks. Considered predictors included age, sex, race, treatment, histology, stage, and extent of disease. We examined the risk-stratification ability of the prediction model and performed decision curve analysis to evaluate the clinical utility of the model by calculating its net benefit in varied risk thresholds for screening. Results Although the median 10-year risk of SPLC among survivors of LC was 8.36%, the estimated risk varied substantially (range, 0.56% to 14.3%) when stratified by age, histology, and extent of IPLC in the final prediction model. The stratification by deciles of estimated risk showed that the observed incidence of SPLC was significantly higher in the tenth-decile group (12.5%) versus the first-decile group (2.9%; P < 10-10). The decision curve analysis yielded a range of risk thresholds (1% to 11.5%) at which the clinical net benefit of the risk model was larger than those in hypothetical all-screening or no-screening scenarios. Conclusion The risk stratification approach in SPLC can be potentially useful for identifying survivors of LC to be screened by computed tomography. More comprehensive environmental and genetic data may help enhance the predictability and stratification ability of the risk model for SPLC.

    View details for PubMedID 28644772

  • Evaluating the impact of varied compliance to lung cancer screening recommendations using a microsimulation model CANCER CAUSES & CONTROL Han, S. S., Erdogan, S., Toumazis, I., Leung, A., Plevritis, S. K. 2017; 28 (9): 947–58

    Abstract

    The US preventive services task force (USPSTF) recently recommended that individuals aged 55-80 with heavy smoking history be annually screened by low-dose computed tomography (LDCT), thereby extending the stopping age from 74 to 80 compared to the national lung screening trial (NLST) entry criterion. This decision was made partly with model-based analyses from cancer intervention and surveillance modeling network (CISNET), which assumed perfect compliance to screening.As part of CISNET, we developed a microsimulation model for lung cancer (LC) screening and calibrated and validated it using data from NLST and the prostate, lung, colorectal, and ovarian cancer screening trial (PLCO), respectively. We evaluated population-level outcomes of the lifetime screening program recommended by the USPSTF by varying screening compliance levels.Validation using PLCO shows that our model reproduces observed PLCO outcomes, predicting 884 LC cases [Expected(E)/Observed(O) = 0.99; CI 0.92-1.06] and 563 LC deaths (E/O = 0.94 CI 0.87-1.03) in the screening arm that has an average compliance rate of 87.9% over four annual screening rounds. We predict that perfect compliance to the USPSTF recommendation saves 501 LC deaths per 100,000 persons in the 1950 U.S. birth cohort; however, assuming that compliance behaviors extrapolated and varied from PLCO reduces the number of LC deaths avoided to 258, 230, and 175 as the average compliance rate over 26 annual screening rounds changes from 100 to 46, 39, and 29%, respectively.The implementation of the USPSTF recommendation is expected to contribute to a reduction in LC deaths, but the magnitude of the reduction will likely be heavily influenced by screening compliance.

    View details for PubMedID 28702814

    View details for PubMedCentralID PMC5880208

  • Prediction of EGFR and KRAS mutation in non-small cell lung cancer using quantitative 18F FDG-PET/CT metrics. Oncotarget Minamimoto, R., Jamali, M., Gevaert, O., Echegaray, S., Khuong, A., Hoang, C. D., Shrager, J. B., Plevritis, S. K., Rubin, D. L., Leung, A. N., Napel, S., Quon, A. 2017

    Abstract

    This study investigated the relationship between epidermal growth factor receptor (EGFR) and Kirsten rat sarcoma viral oncogene homolog (KRAS) mutations in non-small-cell lung cancer (NSCLC) and quantitative FDG-PET/CT parameters including tumor heterogeneity. 131 patients with NSCLC underwent staging FDG-PET/CT followed by tumor resection and histopathological analysis that included testing for the EGFR and KRAS gene mutations. Patient and lesion characteristics, including smoking habits and FDG uptake parameters, were correlated to each gene mutation. Never-smoker (P < 0.001) or low pack-year smoking history (p = 0.002) and female gender (p = 0.047) were predictive factors for the presence of the EGFR mutations. Being a current or former smoker was a predictive factor for the KRAS mutations (p = 0.018). The maximum standardized uptake value (SUVmax) of FDG uptake in lung lesions was a predictive factor of the EGFR mutations (p = 0.029), while metabolic tumor volume and total lesion glycolysis were not predictive. Amongst several tumor heterogeneity metrics included in our analysis, inverse coefficient of variation (1/COV) was a predictive factor (p < 0.02) of EGFR mutations status, independent of metabolic tumor diameter. Multivariate analysis showed that being a never-smoker was the most significant factor (p < 0.001) for the EGFR mutations in lung cancer overall. The tumor heterogeneity metric 1/COV and SUVmax were both predictive for the EGFR mutations in NSCLC in a univariate analysis. Overall, smoking status was the most significant factor for the presence of the EGFR and KRAS mutations in lung cancer.

    View details for DOI 10.18632/oncotarget.17782

    View details for PubMedID 28538213

  • Risk prediction models for selection of lung cancer screening candidates: A retrospective validation study PLOS MEDICINE ten Haaf, K., Jeon, J., Tammemagi, M. C., Han, S. S., Kong, C. Y., Plevritis, S. K., Feuer, E. J., de Koning, H. J., Steyerberg, E. W., Meza, R. 2017; 14 (4)

    Abstract

    Selection of candidates for lung cancer screening based on individual risk has been proposed as an alternative to criteria based on age and cumulative smoking exposure (pack-years). Nine previously established risk models were assessed for their ability to identify those most likely to develop or die from lung cancer. All models considered age and various aspects of smoking exposure (smoking status, smoking duration, cigarettes per day, pack-years smoked, time since smoking cessation) as risk predictors. In addition, some models considered factors such as gender, race, ethnicity, education, body mass index, chronic obstructive pulmonary disease, emphysema, personal history of cancer, personal history of pneumonia, and family history of lung cancer.Retrospective analyses were performed on 53,452 National Lung Screening Trial (NLST) participants (1,925 lung cancer cases and 884 lung cancer deaths) and 80,672 Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial (PLCO) ever-smoking participants (1,463 lung cancer cases and 915 lung cancer deaths). Six-year lung cancer incidence and mortality risk predictions were assessed for (1) calibration (graphically) by comparing the agreement between the predicted and the observed risks, (2) discrimination (area under the receiver operating characteristic curve [AUC]) between individuals with and without lung cancer (death), and (3) clinical usefulness (net benefit in decision curve analysis) by identifying risk thresholds at which applying risk-based eligibility would improve lung cancer screening efficacy. To further assess performance, risk model sensitivities and specificities in the PLCO were compared to those based on the NLST eligibility criteria. Calibration was satisfactory, but discrimination ranged widely (AUCs from 0.61 to 0.81). The models outperformed the NLST eligibility criteria over a substantial range of risk thresholds in decision curve analysis, with a higher sensitivity for all models and a slightly higher specificity for some models. The PLCOm2012, Bach, and Two-Stage Clonal Expansion incidence models had the best overall performance, with AUCs >0.68 in the NLST and >0.77 in the PLCO. These three models had the highest sensitivity and specificity for predicting 6-y lung cancer incidence in the PLCO chest radiography arm, with sensitivities >79.8% and specificities >62.3%. In contrast, the NLST eligibility criteria yielded a sensitivity of 71.4% and a specificity of 62.2%. Limitations of this study include the lack of identification of optimal risk thresholds, as this requires additional information on the long-term benefits (e.g., life-years gained and mortality reduction) and harms (e.g., overdiagnosis) of risk-based screening strategies using these models. In addition, information on some predictor variables included in the risk prediction models was not available.Selection of individuals for lung cancer screening using individual risk is superior to selection criteria based on age and pack-years alone. The benefits, harms, and feasibility of implementing lung cancer screening policies based on risk prediction models should be assessed and compared with those of current recommendations.

    View details for DOI 10.1371/journal.pmed.1002277

    View details for PubMedID 28376113

  • Predictive radiogenomics modeling of EGFR mutation status in lung cancer SCIENTIFIC REPORTS Gevaert, O., Echegaray, S., Khuong, A., Hoang, C. D., Shrager, J. B., Jensen, K. C., Berry, G. J., Guo, H. H., Lau, C., Plevritis, S. K., Rubin, D. L., Napel, S., Leung, A. N. 2017; 7

    Abstract

    Molecular analysis of the mutation status for EGFR and KRAS are now routine in the management of non-small cell lung cancer. Radiogenomics, the linking of medical images with the genomic properties of human tumors, provides exciting opportunities for non-invasive diagnostics and prognostics. We investigated whether EGFR and KRAS mutation status can be predicted using imaging data. To accomplish this, we studied 186 cases of NSCLC with preoperative thin-slice CT scans. A thoracic radiologist annotated 89 semantic image features of each patient's tumor. Next, we built a decision tree to predict the presence of EGFR and KRAS mutations. We found a statistically significant model for predicting EGFR but not for KRAS mutations. The test set area under the ROC curve for predicting EGFR mutation status was 0.89. The final decision tree used four variables: emphysema, airway abnormality, the percentage of ground glass component and the type of tumor margin. The presence of either of the first two features predicts a wild type status for EGFR while the presence of any ground glass component indicates EGFR mutations. These results show the potential of quantitative imaging to predict molecular properties in a non-invasive manner, as CT imaging is more readily available than biopsies.

    View details for DOI 10.1038/srep41674

    View details for PubMedID 28139704

  • The impact of overdiagnosis on the selection of efficient lung cancer screening strategies. International journal of cancer Han, S. S., ten Haaf, K., Hazelton, W. D., Munshi, V. N., Jeon, J., Erdogan, S. A., Johanson, C., McMahon, P. M., Meza, R., Kong, C. Y., Feuer, E. J., de Koning, H. J., Plevritis, S. K. 2017

    Abstract

    The U.S. Preventive Services Task Force (USPSTF) recently updated their national lung screening guidelines and recommended low-dose computed tomography (LDCT) for lung cancer (LC) screening through age 80. However, the risk of overdiagnosis among older populations is a concern. Using four comparative models from the Cancer Intervention and Surveillance Modeling Network, we evaluate the overdiagnosis of the screening program recommended by USPSTF in the U.S. 1950 birth cohort. We estimate the number of LC deaths averted by screening (D) per overdiagnosed case (O), yielding the ratio D/O, to quantify the trade-off between the harms and benefits of LDCT. We analyze 576 hypothetical screening strategies that vary by age, smoking, and screening frequency and evaluate efficient screening strategies that maximize the D/O ratio and other metrics including D and life-years gained (LYG) per overdiagnosed case. The estimated D/O ratio for the USPSTF screening program is 2.85 (model range: 1.5-4.5) in the 1950 birth cohort, implying LDCT can prevent ∼3 LC deaths per overdiagnosed case. This D/O ratio increases by 22% when the program stops screening at an earlier age 75 instead of 80. Efficiency frontier analysis shows that while the most efficient screening strategies that maximize the mortality reduction (D) irrespective of overdiagnosis screen through age 80, screening strategies that stop at age 75 versus 80 produce greater efficiency in increasing life-years gained per overdiagnosed case. Given the risk of overdiagnosis with LC screening, the stopping age of screening merits further consideration when balancing benefits and harms.

    View details for DOI 10.1002/ijc.30602

    View details for PubMedID 28073150

  • Intestinal Enteroendocrine Lineage Cells Possess Homeostatic and Injury-Inducible Stem Cell Activity Cell Stem Cell Yan, K., Gevaert, O., Zheng, G., Anchang, B., Probert, C., et al 2017; 21 (1): 78 - 90.e6

    Abstract

    Several cell populations have been reported to possess intestinal stem cell (ISC) activity during homeostasis and injury-induced regeneration. Here, we explored inter-relationships between putative mouse ISC populations by comparative RNA-sequencing (RNA-seq). The transcriptomes of multiple cycling ISC populations closely resembled Lgr5+ISCs, the most well-defined ISC pool, but Bmi1-GFP+cells were distinct and enriched for enteroendocrine (EE) markers, including Prox1. Prox1-GFP+cells exhibited sustained clonogenic growth in vitro, and lineage-tracing of Prox1+cells revealed long-lived clones during homeostasis and after radiation-induced injury in vivo. Single-cell mRNA-seq revealed two subsets of Prox1-GFP+cells, one of which resembled mature EE cells while the other displayed low-level EE gene expression but co-expressed tuft cell markers, Lgr5 and Ascl2, reminiscent of label-retaining secretory progenitors. Our data suggest that the EE lineage, including mature EE cells, comprises a reservoir of homeostatic and injury-inducible ISCs, extending our understanding of cellular plasticity and stemness.

    View details for DOI 10.1016/j.stem.2017.06.014

    View details for PubMedCentralID PMC5642297

  • Intestinal Enteroendocrine Lineage Cells Possess Homeostatic and Injury-Inducible Stem Cell Activity. Cell stem cell Yan, K. S., Gevaert, O. n., Zheng, G. X., Anchang, B. n., Probert, C. S., Larkin, K. A., Davies, P. S., Cheng, Z. F., Kaddis, J. S., Han, A. n., Roelf, K. n., Calderon, R. I., Cynn, E. n., Hu, X. n., Mandleywala, K. n., Wilhelmy, J. n., Grimes, S. M., Corney, D. C., Boutet, S. C., Terry, J. M., Belgrader, P. n., Ziraldo, S. B., Mikkelsen, T. S., Wang, F. n., von Furstenberg, R. J., Smith, N. R., Chandrakesan, P. n., May, R. n., Chrissy, M. A., Jain, R. n., Cartwright, C. A., Niland, J. C., Hong, Y. K., Carrington, J. n., Breault, D. T., Epstein, J. n., Houchen, C. W., Lynch, J. P., Martin, M. G., Plevritis, S. K., Curtis, C. n., Ji, H. P., Li, L. n., Henning, S. J., Wong, M. H., Kuo, C. J. 2017; 21 (1): 78–90.e6

    Abstract

    Several cell populations have been reported to possess intestinal stem cell (ISC) activity during homeostasis and injury-induced regeneration. Here, we explored inter-relationships between putative mouse ISC populations by comparative RNA-sequencing (RNA-seq). The transcriptomes of multiple cycling ISC populations closely resembled Lgr5+ISCs, the most well-defined ISC pool, but Bmi1-GFP+cells were distinct and enriched for enteroendocrine (EE) markers, including Prox1. Prox1-GFP+cells exhibited sustained clonogenic growth in vitro, and lineage-tracing of Prox1+cells revealed long-lived clones during homeostasis and after radiation-induced injury in vivo. Single-cell mRNA-seq revealed two subsets of Prox1-GFP+cells, one of which resembled mature EE cells while the other displayed low-level EE gene expression but co-expressed tuft cell markers, Lgr5 and Ascl2, reminiscent of label-retaining secretory progenitors. Our data suggest that the EE lineage, including mature EE cells, comprises a reservoir of homeostatic and injury-inducible ISCs, extending our understanding of cellular plasticity and stemness.

    View details for PubMedID 28686870

  • Prediction of EGFR and KRAS mutation in non-small cell lung cancer using quantitative 18F FDG-PET/CT metrics. Oncotarget Minamimoto, R. n., Jamali, M. n., Gevaert, O. n., Echegaray, S. n., Khuong, A. n., Hoang, C. D., Shrager, J. B., Plevritis, S. K., Rubin, D. L., Leung, A. N., Napel, S. n., Quon, A. n. 2017; 8 (32): 52792–801

    Abstract

    This study investigated the relationship between epidermal growth factor receptor (EGFR) and Kirsten rat sarcoma viral oncogene homolog (KRAS) mutations in non-small-cell lung cancer (NSCLC) and quantitative FDG-PET/CT parameters including tumor heterogeneity. 131 patients with NSCLC underwent staging FDG-PET/CT followed by tumor resection and histopathological analysis that included testing for the EGFR and KRAS gene mutations. Patient and lesion characteristics, including smoking habits and FDG uptake parameters, were correlated to each gene mutation. Never-smoker (P < 0.001) or low pack-year smoking history (p = 0.002) and female gender (p = 0.047) were predictive factors for the presence of the EGFR mutations. Being a current or former smoker was a predictive factor for the KRAS mutations (p = 0.018). The maximum standardized uptake value (SUVmax) of FDG uptake in lung lesions was a predictive factor of the EGFR mutations (p = 0.029), while metabolic tumor volume and total lesion glycolysis were not predictive. Amongst several tumor heterogeneity metrics included in our analysis, inverse coefficient of variation (1/COV) was a predictive factor (p < 0.02) of EGFR mutations status, independent of metabolic tumor diameter. Multivariate analysis showed that being a never-smoker was the most significant factor (p < 0.001) for the EGFR mutations in lung cancer overall. The tumor heterogeneity metric 1/COV and SUVmax were both predictive for the EGFR mutations in NSCLC in a univariate analysis. Overall, smoking status was the most significant factor for the presence of the EGFR and KRAS mutations in lung cancer.

    View details for PubMedID 28881771

  • PS01.77: Risk-Stratification for Second Primary Lung Cancer: Topic: Medical Oncology. Journal of thoracic oncology Han, S., Rivera, G. A., Cheng, I., Gomez, S., Plevritis, S. K., Wakelee, H. 2016; 11 (11S): S319-S320

    View details for DOI 10.1016/j.jtho.2016.09.112

    View details for PubMedID 27969544

  • Visualization and cellular hierarchy inference of single-cell data using SPADE. Nature protocols Anchang, B., Hart, T. D., Bendall, S. C., Qiu, P., Bjornson, Z., Linderman, M., Nolan, G. P., Plevritis, S. K. 2016; 11 (7): 1264-1279

    Abstract

    High-throughput single-cell technologies provide an unprecedented view into cellular heterogeneity, yet they pose new challenges in data analysis and interpretation. In this protocol, we describe the use of Spanning-tree Progression Analysis of Density-normalized Events (SPADE), a density-based algorithm for visualizing single-cell data and enabling cellular hierarchy inference among subpopulations of similar cells. It was initially developed for flow and mass cytometry single-cell data. We describe SPADE's implementation and application using an open-source R package that runs on Mac OS X, Linux and Windows systems. A typical SPADE analysis on a 2.27-GHz processor laptop takes ∼5 min. We demonstrate the applicability of SPADE to single-cell RNA-seq data. We compare SPADE with recently developed single-cell visualization approaches based on the t-distribution stochastic neighborhood embedding (t-SNE) algorithm. We contrast the implementation and outputs of these methods for normal and malignant hematopoietic cells analyzed by mass cytometry and provide recommendations for appropriate use. Finally, we provide an integrative strategy that combines the strengths of t-SNE and SPADE to infer cellular hierarchy from high-dimensional single-cell data.

    View details for DOI 10.1038/nprot.2016.066

    View details for PubMedID 27310265

  • Collaborative Modeling of the Benefits and Harms Associated With Different US Breast Cancer Screening Strategies ANNALS OF INTERNAL MEDICINE Mandelblatt, J. S., Stout, N. K., Schechter, C. B., van den Broek, J. J., Miglioretti, D. L., Krapcho, M., Trentham-Dietz, A., Munoz, D., Lee, S. J., Berry, D. A., van Ravesteyn, N. T., Alagoz, O., Kerlikowske, K., Tosteson, A. N., Near, A. M., Hoeffken, A., Chang, Y., Heijnsdijk, E. A., Chisholm, G., Huang, X., Huang, H., Ergun, M. A., Gangnon, R., Sprague, B. L., Plevritis, S., Feuer, E., de Koning, H. J., Cronin, K. A. 2016; 164 (4): 215-?

    Abstract

    Controversy persists about optimal mammography screening strategies.To evaluate screening outcomes, taking into account advances in mammography and treatment of breast cancer.Collaboration of 6 simulation models using national data on incidence, digital mammography performance, treatment effects, and other-cause mortality.United States.Average-risk U.S. female population and subgroups with varying risk, breast density, or comorbidity.Eight strategies differing by age at which screening starts (40, 45, or 50 years) and screening interval (annual, biennial, and hybrid [annual for women in their 40s and biennial thereafter]). All strategies assumed 100% adherence and stopped at age 74 years.Benefits (breast cancer-specific mortality reduction, breast cancer deaths averted, life-years, and quality-adjusted life-years); number of mammograms used; harms (false-positive results, benign biopsies, and overdiagnosis); and ratios of harms (or use) and benefits (efficiency) per 1000 screens.Biennial strategies were consistently the most efficient for average-risk women. Biennial screening from age 50 to 74 years avoided a median of 7 breast cancer deaths versus no screening; annual screening from age 40 to 74 years avoided an additional 3 deaths, but yielded 1988 more false-positive results and 11 more overdiagnoses per 1000 women screened. Annual screening from age 50 to 74 years was inefficient (similar benefits, but more harms than other strategies). For groups with a 2- to 4-fold increased risk, annual screening from age 40 years had similar harms and benefits as screening average-risk women biennially from 50 to 74 years. For groups with moderate or severe comorbidity, screening could stop at age 66 to 68 years.Other imaging technologies, polygenic risk, and nonadherence were not considered.Biennial screening for breast cancer is efficient for average-risk populations. Decisions about starting ages and intervals will depend on population characteristics and the decision makers' weight given to the harms and benefits of screening.National Institutes of Health.

    View details for DOI 10.7326/M15-1536

    View details for Web of Science ID 000370135300012

    View details for PubMedID 26756606

  • Integrating Tumor and Stromal Gene Expression Signatures With Clinical Indices for Survival Stratification of Early-Stage Non-Small Cell Lung Cancer. Journal of the National Cancer Institute Gentles, A. J., Bratman, S. V., Lee, L. J., Harris, J. P., Feng, W., Nair, R. V., Shultz, D. B., Nair, V. S., Hoang, C. D., West, R. B., Plevritis, S. K., Alizadeh, A. A., Diehn, M. 2015; 107 (10)

    Abstract

    Accurate survival stratification in early-stage non-small cell lung cancer (NSCLC) could inform the use of adjuvant therapy. We developed a clinically implementable mortality risk score incorporating distinct tumor microenvironmental gene expression signatures and clinical variables.Gene expression profiles from 1106 nonsquamous NSCLCs were used for generation and internal validation of a nine-gene molecular prognostic index (MPI). A quantitative polymerase chain reaction (qPCR) assay was developed and validated on an independent cohort of formalin-fixed paraffin-embedded (FFPE) tissues (n = 98). A prognostic score using clinical variables was generated using Surveillance, Epidemiology, and End Results data and combined with the MPI. All statistical tests for survival were two-sided.The MPI stratified stage I patients into prognostic categories in three microarray and one FFPE qPCR validation cohorts (HR = 2.99, 95% CI = 1.55 to 5.76, P < .001 in stage IA patients of the largest microarray validation cohort; HR = 3.95, 95% CI = 1.24 to 12.64, P = .01 in stage IA of the qPCR cohort). Prognostic genes were expressed in distinct tumor cell subpopulations, and genes implicated in proliferation and stem cells portended poor outcomes, while genes involved in normal lung differentiation and immune infiltration were associated with superior survival. Integrating the MPI with clinical variables conferred greatest prognostic power (HR = 3.43, 95% CI = 2.18 to 5.39, P < .001 in stage I patients of the largest microarray cohort; HR = 3.99, 95% CI = 1.67 to 9.56, P < .001 in stage I patients of the qPCR cohort). Finally, the MPI was prognostic irrespective of somatic alterations in EGFR, KRAS, TP53, and ALK.The MPI incorporates genes expressed in the tumor and its microenvironment and can be implemented clinically using qPCR assays on FFPE tissues. A composite model integrating the MPI with clinical variables provides the most accurate risk stratification.

    View details for DOI 10.1093/jnci/djv211

    View details for PubMedID 26286589

  • Integrating Tumor and Stromal Gene Expression Signatures With Clinical Indices for Survival Stratification of Early-Stage Non-Small Cell Lung Cancer. Journal of the National Cancer Institute Gentles, A. J., Bratman, S. V., Lee, L. J., Harris, J. P., Feng, W., Nair, R. V., Shultz, D. B., Nair, V. S., Hoang, C. D., West, R. B., Plevritis, S. K., Alizadeh, A. A., Diehn, M. 2015; 107 (10)

    Abstract

    Accurate survival stratification in early-stage non-small cell lung cancer (NSCLC) could inform the use of adjuvant therapy. We developed a clinically implementable mortality risk score incorporating distinct tumor microenvironmental gene expression signatures and clinical variables.Gene expression profiles from 1106 nonsquamous NSCLCs were used for generation and internal validation of a nine-gene molecular prognostic index (MPI). A quantitative polymerase chain reaction (qPCR) assay was developed and validated on an independent cohort of formalin-fixed paraffin-embedded (FFPE) tissues (n = 98). A prognostic score using clinical variables was generated using Surveillance, Epidemiology, and End Results data and combined with the MPI. All statistical tests for survival were two-sided.The MPI stratified stage I patients into prognostic categories in three microarray and one FFPE qPCR validation cohorts (HR = 2.99, 95% CI = 1.55 to 5.76, P < .001 in stage IA patients of the largest microarray validation cohort; HR = 3.95, 95% CI = 1.24 to 12.64, P = .01 in stage IA of the qPCR cohort). Prognostic genes were expressed in distinct tumor cell subpopulations, and genes implicated in proliferation and stem cells portended poor outcomes, while genes involved in normal lung differentiation and immune infiltration were associated with superior survival. Integrating the MPI with clinical variables conferred greatest prognostic power (HR = 3.43, 95% CI = 2.18 to 5.39, P < .001 in stage I patients of the largest microarray cohort; HR = 3.99, 95% CI = 1.67 to 9.56, P < .001 in stage I patients of the qPCR cohort). Finally, the MPI was prognostic irrespective of somatic alterations in EGFR, KRAS, TP53, and ALK.The MPI incorporates genes expressed in the tumor and its microenvironment and can be implemented clinically using qPCR assays on FFPE tissues. A composite model integrating the MPI with clinical variables provides the most accurate risk stratification.

    View details for DOI 10.1093/jnci/djv211

    View details for PubMedID 26286589

  • ARF: Connecting senescence and innate immunity for clearance AGING-US Kearney, A. Y., Anchang, B., Plevritis, S., Felsher, D. W. 2015; 7 (9): 613-615

    Abstract

    We have found evidence suggesting that ARF and p53 are essential for tumor regression upon MYC inactivation through distinct mechanisms ARF through p53-independent affect, is required to for MYC to regulate the expression of genes that are required for both the induction of cellular senescence as well as recruitment of innate immune activation. Our observations have possible implications for mechanisms of therapeutic resistance to targeted oncogene inactivation.

    View details for PubMedID 26412380

  • The prognostic landscape of genes and infiltrating immune cells across human cancers NATURE MEDICINE Gentles, A. J., Newman, A. M., Liu, C. L., Bratman, S. V., Feng, W., Kim, D., Nair, V. S., Xu, Y., Khuong, A., Hoang, C. D., Diehn, M., West, R. B., Plevritis, S. K., Alizadeh, A. A. 2015; 21 (8): 938-945

    Abstract

    Molecular profiles of tumors and tumor-associated cells hold great promise as biomarkers of clinical outcomes. However, existing data sets are fragmented and difficult to analyze systematically. Here we present a pan-cancer resource and meta-analysis of expression signatures from ∼18,000 human tumors with overall survival outcomes across 39 malignancies. By using this resource, we identified a forkhead box MI (FOXM1) regulatory network as a major predictor of adverse outcomes, and we found that expression of favorably prognostic genes, including KLRB1 (encoding CD161), largely reflect tumor-associated leukocytes. By applying CIBERSORT, a computational approach for inferring leukocyte representation in bulk tumor transcriptomes, we identified complex associations between 22 distinct leukocyte subsets and cancer survival. For example, tumor-associated neutrophil and plasma cell signatures emerged as significant but opposite predictors of survival for diverse solid tumors, including breast and lung adenocarcinomas. This resource and associated analytical tools (http://precog.stanford.edu) may help delineate prognostic genes and leukocyte subsets within and across cancers, shed light on the impact of tumor heterogeneity on cancer outcomes, and facilitate the discovery of biomarkers and therapeutic targets.

    View details for DOI 10.1038/nm.3909

    View details for PubMedID 26193342

  • Mutations in early follicular lymphoma progenitors are associated with suppressed antigen presentation. Proceedings of the National Academy of Sciences of the United States of America Green, M. R., Kihira, S., Liu, C. L., Nair, R. V., Salari, R., Gentles, A. J., Irish, J., Stehr, H., Vicente-Dueñas, C., Romero-Camarero, I., Sanchez-Garcia, I., Plevritis, S. K., Arber, D. A., Batzoglou, S., Levy, R., Alizadeh, A. A. 2015; 112 (10): E1116-25

    Abstract

    Follicular lymphoma (FL) is incurable with conventional therapies and has a clinical course typified by multiple relapses after therapy. These tumors are genetically characterized by B-cell leukemia/lymphoma 2 (BCL2) translocation and mutation of genes involved in chromatin modification. By analyzing purified tumor cells, we identified additional novel recurrently mutated genes and confirmed mutations of one or more chromatin modifier genes within 96% of FL tumors and two or more in 76% of tumors. We defined the hierarchy of somatic mutations arising during tumor evolution by analyzing the phylogenetic relationship of somatic mutations across the coding genomes of 59 sequentially acquired biopsies from 22 patients. Among all somatically mutated genes, CREBBP mutations were most significantly enriched within the earliest inferable progenitor. These mutations were associated with a signature of decreased antigen presentation characterized by reduced transcript and protein abundance of MHC class II on tumor B cells, in line with the role of CREBBP in promoting class II transactivator (CIITA)-dependent transcriptional activation of these genes. CREBBP mutant B cells stimulated less proliferation of T cells in vitro compared with wild-type B cells from the same tumor. Transcriptional signatures of tumor-infiltrating T cells were indicative of reduced proliferation, and this corresponded to decreased frequencies of tumor-infiltrating CD4 helper T cells and CD8 memory cytotoxic T cells. These observations therefore implicate CREBBP mutation as an early event in FL evolution that contributes to immune evasion via decreased antigen presentation.

    View details for DOI 10.1073/pnas.1501199112

    View details for PubMedID 25713363

  • Mutations in early follicular lymphoma progenitors are associated with suppressed antigen presentation. Proceedings of the National Academy of Sciences of the United States of America Green, M. R., Kihira, S., Liu, C. L., Nair, R. V., Salari, R., Gentles, A. J., Irish, J., Stehr, H., Vicente-Dueñas, C., Romero-Camarero, I., Sanchez-Garcia, I., Plevritis, S. K., Arber, D. A., Batzoglou, S., Levy, R., Alizadeh, A. A. 2015; 112 (10): E1116-25

    Abstract

    Follicular lymphoma (FL) is incurable with conventional therapies and has a clinical course typified by multiple relapses after therapy. These tumors are genetically characterized by B-cell leukemia/lymphoma 2 (BCL2) translocation and mutation of genes involved in chromatin modification. By analyzing purified tumor cells, we identified additional novel recurrently mutated genes and confirmed mutations of one or more chromatin modifier genes within 96% of FL tumors and two or more in 76% of tumors. We defined the hierarchy of somatic mutations arising during tumor evolution by analyzing the phylogenetic relationship of somatic mutations across the coding genomes of 59 sequentially acquired biopsies from 22 patients. Among all somatically mutated genes, CREBBP mutations were most significantly enriched within the earliest inferable progenitor. These mutations were associated with a signature of decreased antigen presentation characterized by reduced transcript and protein abundance of MHC class II on tumor B cells, in line with the role of CREBBP in promoting class II transactivator (CIITA)-dependent transcriptional activation of these genes. CREBBP mutant B cells stimulated less proliferation of T cells in vitro compared with wild-type B cells from the same tumor. Transcriptional signatures of tumor-infiltrating T cells were indicative of reduced proliferation, and this corresponded to decreased frequencies of tumor-infiltrating CD4 helper T cells and CD8 memory cytotoxic T cells. These observations therefore implicate CREBBP mutation as an early event in FL evolution that contributes to immune evasion via decreased antigen presentation.

    View details for DOI 10.1073/pnas.1501199112

    View details for PubMedID 25713363

    View details for PubMedCentralID PMC4364211

  • p19ARF is a critical mediator of both cellular senescence and an innate immune response associated with MYC inactivation in mouse model of acute leukemia ONCOTARGET Yetil, A., Anchang, B., Gouw, A. M., Adam, S. J., Zabuawala, T., Parameswaran, R., van Riggelen, J., Plevritis, S., Felsher, D. W. 2015; 6 (6): 3563-3577

    Abstract

    MYC-induced T-ALL exhibit oncogene addiction. Addiction to MYC is a consequence of both cell-autonomous mechanisms, such as proliferative arrest, cellular senescence, and apoptosis, as well as non-cell autonomous mechanisms, such as shutdown of angiogenesis, and recruitment of immune effectors. Here, we show, using transgenic mouse models of MYC-induced T-ALL, that the loss of either p19ARF or p53 abrogates the ability of MYC inactivation to induce sustained tumor regression. Loss of p53 or p19ARF, influenced the ability of MYC inactivation to elicit the shutdown of angiogenesis; however the loss of p19ARF, but not p53, impeded cellular senescence, as measured by SA-beta-galactosidase staining, increased expression of p16INK4A, and specific histone modifications. Moreover, comparative gene expression analysis suggested that a multitude of genes involved in the innate immune response were expressed in p19ARF wild-type, but not null, tumors upon MYC inactivation. Indeed, the loss of p19ARF, but not p53, impeded the in situ recruitment of macrophages to the tumor microenvironment. Finally, p19ARF null-associated gene signature prognosticated relapse-free survival in human patients with ALL. Therefore, p19ARF appears to be important to regulating cellular senescence and innate immune response that may contribute to the therapeutic response of ALL.

    View details for PubMedID 25784651

  • Molecular subtyping for clinically defined breast cancer subgroups BREAST CANCER RESEARCH Zhao, X., Rodland, E. A., Tibshirani, R., Plevritis, S. 2015; 17

    Abstract

    Breast cancer is commonly classified into intrinsic molecular subtypes. Standard gene centering is routinely done prior to molecular subtyping, but it can produce inaccurate classifications when the distribution of clinicopathological characteristics in the study cohort differs from that of the training cohort used to derive the classifier.We propose a subgroup-specific gene-centering method to perform molecular subtyping on a study cohort that has a skewed distribution of clinicopathological characteristics relative to the training cohort. On such a study cohort, we center each gene on a specified percentile, where the percentile is determined from a subgroup of the training cohort with clinicopathological characteristics similar to the study cohort. We demonstrate our method using the PAM50 classifier and its associated University of North Carolina (UNC) training cohort. We considered study cohorts with skewed clinicopathological characteristics, including subgroups composed of a single prototypic subtype of the UNC-PAM50 training cohort (n = 139), an external estrogen receptor (ER)-positive cohort (n = 48) and an external triple-negative cohort (n = 77).Subgroup-specific gene centering improved prediction performance with the accuracies between 77% and 100%, compared to accuracies between 17% and 33% from standard gene centering, when applied to the prototypic tumor subsets of the PAM50 training cohort. It reduced classification error rates on the ER-positive (11% versus 28%; P = 0.0389), the ER-negative (5% versus 41%; P < 0.0001) and the triple-negative (11% versus 56%; P = 0.1336) subgroups of the PAM50 training cohort. In addition, it produced higher accuracy for subtyping study cohorts composed of varying proportions of ER-positive versus ER-negative cases. Finally, it increased the percentage of assigned luminal subtypes on the external ER-positive cohort and basal-like subtype on the external triple-negative cohort.Gene centering is often necessary to accurately apply a molecular subtype classifier. Compared with standard gene centering, our proposed subgroup-specific gene centering produced more accurate molecular subtype assignments in a study cohort with skewed clinicopathological characteristics relative to the training cohort.

    View details for DOI 10.1186/s13058-015-0520-4

    View details for Web of Science ID 000351829500001

    View details for PubMedID 25849221

    View details for PubMedCentralID PMC4365540

  • Pancancer analysis of DNA methylation-driven genes using MethylMix. Genome biology Gevaert, O., Tibshirani, R., Plevritis, S. K. 2015; 16: 17-?

    Abstract

    Aberrant DNA methylation is an important mechanism that contributes to oncogenesis. Yet, few algorithms exist that exploit this vast dataset to identify hypo- and hypermethylated genes in cancer. We developed a novel computational algorithm called MethylMix to identify differentially methylated genes that are also predictive of transcription. We apply MethylMix to 12 individual cancer sites, and additionally combine all cancer sites in a pancancer analysis. We discover pancancer hypo- and hypermethylated genes and identify novel methylation-driven subgroups with clinical implications. MethylMix analysis on combined cancer sites reveals 10 pancancer clusters reflecting new similarities across malignantly transformed tissues.

    View details for DOI 10.1186/s13059-014-0579-8

    View details for PubMedID 25631659

    View details for PubMedCentralID PMC4365533

  • Multi-target drug combinations from single drug responses measured at the level of single cells using Mixture Nested Effects Models (MNEMs) applied to cancer. Special Conference on Computational and Systems Biology of Cancer Anchang, B., Fienberg, H., et al 2015
  • Effects of Screening and Systemic Adjuvant Therapy on ER-Specific US Breast Cancer Mortality JNCI-JOURNAL OF THE NATIONAL CANCER INSTITUTE Munoz, D., Near, A. M., van Ravesteyn, N. T., Lee, S. J., Schechter, C. B., Alagoz, O., Berry, D. A., Burnside, E. S., Chang, Y., Chisholm, G., de Koning, H. J., Ergun, M. A., Heijnsdijk, E. A., Huang, H., Stout, N. K., Sprague, B. L., Trentham-Dietz, A., Mandelblatt, J. S., Plevritis, S. K. 2014; 106 (11)
  • Effects of screening and systemic adjuvant therapy on ER-specific US breast cancer mortality. Journal of the National Cancer Institute Munoz, D., Near, A. M., van Ravesteyn, N. T., Lee, S. J., Schechter, C. B., Alagoz, O., Berry, D. A., Burnside, E. S., Chang, Y., Chisholm, G., de Koning, H. J., Ali Ergun, M., Heijnsdijk, E. A., Huang, H., Stout, N. K., Sprague, B. L., Trentham-Dietz, A., Mandelblatt, J. S., Plevritis, S. K. 2014; 106 (11)

    Abstract

    Molecular characterization of breast cancer allows subtype-directed interventions. Estrogen receptor (ER) is the longest-established molecular marker.We used six established population models with ER-specific input parameters on age-specific incidence, disease natural history, mammography characteristics, and treatment effects to quantify the impact of screening and adjuvant therapy on age-adjusted US breast cancer mortality by ER status from 1975 to 2000. Outcomes included stage-shifts and absolute and relative reductions in mortality; sensitivity analyses evaluated the impact of varying screening frequency or accuracy.In the year 2000, actual screening and adjuvant treatment reduced breast cancer mortality by a median of 17 per 100000 women (model range = 13-21) and 5 per 100000 women (model range = 3-6) for ER-positive and ER-negative cases, respectively, relative to no screening and no adjuvant treatment. For ER-positive cases, adjuvant treatment made a higher relative contribution to breast cancer mortality reduction than screening, whereas for ER-negative cases the relative contributions were similar for screening and adjuvant treatment. ER-negative cases were less likely to be screen-detected than ER-positive cases (35.1% vs 51.2%), but when screen-detected yielded a greater survival gain (five-year breast cancer survival = 35.6% vs 30.7%). Screening biennially would have captured a lower proportion of mortality reduction than annual screening for ER-negative vs ER-positive cases (model range = 80.2%-87.8% vs 85.7%-96.5%).As advances in risk assessment facilitate identification of women with increased risk of ER-negative breast cancer, additional mortality reductions could be realized through more frequent targeted screening, provided these benefits are balanced against screening harms.

    View details for DOI 10.1093/jnci/dju289

    View details for PubMedID 25255803

  • Raising the Bar for the US Preventive Services Task Force ANNALS OF INTERNAL MEDICINE de Koning, H. J., Meza, R., Plevritis, S. K. 2014; 161 (7): 532–33

    View details for DOI 10.7326/L14-5019-3

    View details for Web of Science ID 000343896700018

    View details for PubMedID 25285549

  • Glioblastoma Multiforme: Exploratory Radiogenomic Analysis by Using Quantitative Image Features RADIOLOGY Gevaert, O., Mitchell, L. A., Achrol, A. S., Xu, J., Echegaray, S., Steinberg, G. K., Cheshier, S. H., Napel, S., Zaharchuk, G., Plevritis, S. K. 2014; 273 (1): 168-174

    Abstract

    To derive quantitative image features from magnetic resonance (MR) images that characterize the radiographic phenotype of glioblastoma multiforme (GBM) lesions and to create radiogenomic maps associating these features with various molecular data.Clinical, molecular, and MR imaging data for GBMs in 55 patients were obtained from the Cancer Genome Atlas and the Cancer Imaging Archive after local ethics committee and institutional review board approval. Regions of interest (ROIs) corresponding to enhancing necrotic portions of tumor and peritumoral edema were drawn, and quantitative image features were derived from these ROIs. Robust quantitative image features were defined on the basis of an intraclass correlation coefficient of 0.6 for a digital algorithmic modification and a test-retest analysis. The robust features were visualized by using hierarchic clustering and were correlated with survival by using Cox proportional hazards modeling. Next, these robust image features were correlated with manual radiologist annotations from the Visually Accessible Rembrandt Images (VASARI) feature set and GBM molecular subgroups by using nonparametric statistical tests. A bioinformatic algorithm was used to create gene expression modules, defined as a set of coexpressed genes together with a multivariate model of cancer driver genes predictive of the module's expression pattern. Modules were correlated with robust image features by using the Spearman correlation test to create radiogenomic maps and to link robust image features with molecular pathways.Eighteen image features passed the robustness analysis and were further analyzed for the three types of ROIs, for a total of 54 image features. Three enhancement features were significantly correlated with survival, 77 significant correlations were found between robust quantitative features and the VASARI feature set, and seven image features were correlated with molecular subgroups (P < .05 for all). A radiogenomics map was created to link image features with gene expression modules and allowed linkage of 56% (30 of 54) of the image features with biologic processes.Radiogenomic approaches in GBM have the potential to predict clinical and molecular characteristics of tumors noninvasively. Online supplemental material is available for this article.

    View details for DOI 10.1148/radiol.14131731

    View details for Web of Science ID 000344232100019

    View details for PubMedCentralID PMC4263772

  • Glioblastoma multiforme: exploratory radiogenomic analysis by using quantitative image features. Radiology Gevaert, O., Mitchell, L. A., Achrol, A. S., Xu, J., Echegaray, S., Steinberg, G. K., Cheshier, S. H., Napel, S., Zaharchuk, G., Plevritis, S. K. 2014; 273 (1): 168-174

    Abstract

    To derive quantitative image features from magnetic resonance (MR) images that characterize the radiographic phenotype of glioblastoma multiforme (GBM) lesions and to create radiogenomic maps associating these features with various molecular data.Clinical, molecular, and MR imaging data for GBMs in 55 patients were obtained from the Cancer Genome Atlas and the Cancer Imaging Archive after local ethics committee and institutional review board approval. Regions of interest (ROIs) corresponding to enhancing necrotic portions of tumor and peritumoral edema were drawn, and quantitative image features were derived from these ROIs. Robust quantitative image features were defined on the basis of an intraclass correlation coefficient of 0.6 for a digital algorithmic modification and a test-retest analysis. The robust features were visualized by using hierarchic clustering and were correlated with survival by using Cox proportional hazards modeling. Next, these robust image features were correlated with manual radiologist annotations from the Visually Accessible Rembrandt Images (VASARI) feature set and GBM molecular subgroups by using nonparametric statistical tests. A bioinformatic algorithm was used to create gene expression modules, defined as a set of coexpressed genes together with a multivariate model of cancer driver genes predictive of the module's expression pattern. Modules were correlated with robust image features by using the Spearman correlation test to create radiogenomic maps and to link robust image features with molecular pathways.Eighteen image features passed the robustness analysis and were further analyzed for the three types of ROIs, for a total of 54 image features. Three enhancement features were significantly correlated with survival, 77 significant correlations were found between robust quantitative features and the VASARI feature set, and seven image features were correlated with molecular subgroups (P < .05 for all). A radiogenomics map was created to link image features with gene expression modules and allowed linkage of 56% (30 of 54) of the image features with biologic processes.Radiogenomic approaches in GBM have the potential to predict clinical and molecular characteristics of tumors noninvasively. Online supplemental material is available for this article.

    View details for DOI 10.1148/radiol.14131731

    View details for PubMedID 24827998

  • Oncogenic transformation of diverse gastrointestinal tissues in primary organoid culture NATURE MEDICINE Li, X., Nadauld, L., Ootani, A., Corney, D. C., Pai, R. K., Gevaert, O., Cantrell, M. A., Rack, P. G., Neal, J. T., Chan, C. W., Yeung, T., Gong, X., Yuan, J., Wilhelmy, J., Robine, S., Attardi, L. D., Plevritis, S. K., Hung, K. E., Chen, C., Ji, H. P., Kuo, C. J. 2014; 20 (7): 769-777

    Abstract

    The application of primary organoid cultures containing epithelial and mesenchymal elements to cancer modeling holds promise for combining the accurate multilineage differentiation and physiology of in vivo systems with the facile in vitro manipulation of transformed cell lines. Here we used a single air-liquid interface culture method without modification to engineer oncogenic mutations into primary epithelial and mesenchymal organoids from mouse colon, stomach and pancreas. Pancreatic and gastric organoids exhibited dysplasia as a result of expression of Kras carrying the G12D mutation (Kras(G12D)), p53 loss or both and readily generated adenocarcinoma after in vivo transplantation. In contrast, primary colon organoids required combinatorial Apc, p53, Kras(G12D) and Smad4 mutations for progressive transformation to invasive adenocarcinoma-like histology in vitro and tumorigenicity in vivo, recapitulating multi-hit models of colorectal cancer (CRC), as compared to the more promiscuous transformation of small intestinal organoids. Colon organoid culture functionally validated the microRNA miR-483 as a dominant driver oncogene at the IGF2 (insulin-like growth factor-2) 11p15.5 CRC amplicon, inducing dysplasia in vitro and tumorigenicity in vivo. These studies demonstrate the general utility of a highly tractable primary organoid system for cancer modeling and driver oncogene validation in diverse gastrointestinal tissues.

    View details for DOI 10.1038/nm.3585

    View details for Web of Science ID 000338689500021

  • CCAST: A Model-Based Gating Strategy to Isolate Homogeneous Subpopulations in a Heterogeneous Population of Single Cells PLOS COMPUTATIONAL BIOLOGY Anchang, B., Do, M. T., Zhao, X., Plevritis, S. K. 2014; 10 (7)

    Abstract

    A model-based gating strategy is developed for sorting cells and analyzing populations of single cells. The strategy, named CCAST, for Clustering, Classification and Sorting Tree, identifies a gating strategy for isolating homogeneous subpopulations from a heterogeneous population of single cells using a data-derived decision tree representation that can be applied to cell sorting. Because CCAST does not rely on expert knowledge, it removes human bias and variability when determining the gating strategy. It combines any clustering algorithm with silhouette measures to identify underlying homogeneous subpopulations, then applies recursive partitioning techniques to generate a decision tree that defines the gating strategy. CCAST produces an optimal strategy for cell sorting by automating the selection of gating markers, the corresponding gating thresholds and gating sequence; all of these parameters are typically manually defined. Even though CCAST is optimized for cell sorting, it can be applied for the identification and analysis of homogeneous subpopulations among heterogeneous single cell data. We apply CCAST on single cell data from both breast cancer cell lines and normal human bone marrow. On the SUM159 breast cancer cell line data, CCAST indicates at least five distinct cell states based on two surface markers (CD24 and EPCAM) and provides a gating sorting strategy that produces more homogeneous subpopulations than previously reported. When applied to normal bone marrow data, CCAST reveals an efficient strategy for gating T-cells without prior knowledge of the major T-cell subtypes and the markers that best define them. On the normal bone marrow data, CCAST also reveals two major mature B-cell subtypes, namely CD123+ and CD123- cells, which were not revealed by manual gating but show distinct intracellular signaling responses. More generally, the CCAST framework could be used on other biological and non-biological high dimensional data types that are mixtures of unknown homogeneous subpopulations.

    View details for DOI 10.1371/journal.pcbi.1003664

    View details for PubMedID 25078380

  • Comparing Benefits from Many Possible Computed Tomography Lung Cancer Screening Programs: Extrapolating from the National Lung Screening Trial Using Comparative Modeling PLOS ONE McMahon, P. M., Meza, R., Plevritis, S. K., Black, W. C., Tammemagi, C. M., Erdogan, A., ten Haaf, K., Hazelton, W., Holford, T. R., Jeon, J., Clarke, L., Kong, C. Y., Choi, S. E., Munshi, V. N., Han, S. S., van Rosmalen, J., Pinsky, P. F., Moolgavkar, S., de Koning, H. J., Feuer, E. J. 2014; 9 (6)

    Abstract

    The National Lung Screening Trial (NLST) demonstrated that in current and former smokers aged 55 to 74 years, with at least 30 pack-years of cigarette smoking history and who had quit smoking no more than 15 years ago, 3 annual computed tomography (CT) screens reduced lung cancer-specific mortality by 20% relative to 3 annual chest X-ray screens. We compared the benefits achievable with 576 lung cancer screening programs that varied CT screen number and frequency, ages of screening, and eligibility based on smoking.We used five independent microsimulation models with lung cancer natural history parameters previously calibrated to the NLST to simulate life histories of the US cohort born in 1950 under all 576 programs. 'Efficient' (within model) programs prevented the greatest number of lung cancer deaths, compared to no screening, for a given number of CT screens. Among 120 'consensus efficient' (identified as efficient across models) programs, the average starting age was 55 years, the stopping age was 80 or 85 years, the average minimum pack-years was 27, and the maximum years since quitting was 20. Among consensus efficient programs, 11% to 40% of the cohort was screened, and 153 to 846 lung cancer deaths were averted per 100,000 people. In all models, annual screening based on age and smoking eligibility in NLST was not efficient; continuing screening to age 80 or 85 years was more efficient.Consensus results from five models identified a set of efficient screening programs that include annual CT lung cancer screening using criteria like NLST eligibility but extended to older ages. Guidelines for screening should also consider harms of screening and individual patient characteristics.

    View details for DOI 10.1371/journal.pone.0099978

    View details for Web of Science ID 000338506400012

    View details for PubMedCentralID PMC4076275

  • Comparative Analysis of 5 Lung Cancer Natural History and Screening Models That Reproduce Outcomes of the NLST and PLCO Trials CANCER Meza, R., ten Haaf, K., Kong, C. Y., Erdogan, A., Black, W. C., Tammemagi, M. C., Choi, S. E., Jeon, J., Han, S. S., Munshi, V., van Rosmalen, J., Pinsky, P., McMahon, P. M., de Koning, H. J., Feuer, E. J., Hazelton, W. D., Plevritis, S. K. 2014; 120 (11): 1713-1724

    Abstract

    The National Lung Screening Trial (NLST) demonstrated that low-dose computed tomography screening is an effective way of reducing lung cancer (LC) mortality. However, optimal screening strategies have not been determined to date and it is uncertain whether lighter smokers than those examined in the NLST may also benefit from screening. To address these questions, it is necessary to first develop LC natural history models that can reproduce NLST outcomes and simulate screening programs at the population level.Five independent LC screening models were developed using common inputs and calibration targets derived from the NLST and the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial (PLCO). Imputation of missing information regarding smoking, histology, and stage of disease for a small percentage of individuals and diagnosed LCs in both trials was performed. Models were calibrated to LC incidence, mortality, or both outcomes simultaneously.Initially, all models were calibrated to the NLST and validated against PLCO. Models were found to validate well against individuals in PLCO who would have been eligible for the NLST. However, all models required further calibration to PLCO to adequately capture LC outcomes in PLCO never-smokers and light smokers. Final versions of all models produced incidence and mortality outcomes in the presence and absence of screening that were consistent with both trials.The authors developed 5 distinct LC screening simulation models based on the evidence in the NLST and PLCO. The results of their analyses demonstrated that the NLST and PLCO have produced consistent results. The resulting models can be important tools to generate additional evidence to determine the effectiveness of lung cancer screening strategies using low-dose computed tomography. Cancer 2014. © 2014 American Cancer Society.

    View details for DOI 10.1002/cncr.28623

    View details for Web of Science ID 000336619700021

  • Benefits and Harms of Computed Tomography Lung Cancer Screening Strategies: A Comparative Modeling Study for the US Preventive Services Task Force ANNALS OF INTERNAL MEDICINE de Koning, H. J., Meza, R., Plevritis, S. K., ten Haaf, K., Munshi, V. N., Jeon, J., Erdogan, S. A., Kong, C. Y., Han, S. S., van Rosmalen, J., Choi, S. E., Pinsky, P. F., de Gonzalez, A. B., Berg, C. D., Black, W. C., Tammemaegi, M. C., Hazelton, W. D., Feuer, E. J., McMahon, P. M. 2014; 160 (5): 311-?

    Abstract

    The optimum screening policy for lung cancer is unknown.To identify efficient computed tomography (CT) screening scenarios in which relatively more lung cancer deaths are averted for fewer CT screening examinations.Comparative modeling study using 5 independent models.The National Lung Screening Trial; the Prostate, Lung, Colorectal, and Ovarian Cancer Screening trial; the Surveillance, Epidemiology, and End Results program; and the U.S. Smoking History Generator.U.S. cohort born in 1950.Cohort followed from ages 45 to 90 years.Societal.576 scenarios with varying eligibility criteria (age, pack-years of smoking, years since quitting) and screening intervals.Benefits included lung cancer deaths averted or life-years gained. Harms included CT examinations, false-positive results (including those obtained from biopsy/surgery), overdiagnosed cases, and radiation-related deaths.The most advantageous strategy was annual screening from ages 55 through 80 years for ever-smokers with a smoking history of at least 30 pack-years and ex-smokers with less than 15 years since quitting. It would lead to 50% (model ranges, 45% to 54%) of cases of cancer being detected at an early stage (stage I/II), 575 screening examinations per lung cancer death averted, a 14% (range, 8.2% to 23.5%) reduction in lung cancer mortality, 497 lung cancer deaths averted, and 5250 life-years gained per the 100,000-member cohort. Harms would include 67,550 false-positive test results, 910 biopsies or surgeries for benign lesions, and 190 overdiagnosed cases of cancer (3.7% of all cases of lung cancer [model ranges, 1.4% to 8.3%]).The number of cancer deaths averted for the scenario varied across models between 177 and 862; the number of overdiagnosed cases of cancer varied between 72 and 426.Scenarios assumed 100% screening adherence. Data derived from trials with short duration were extrapolated to lifetime follow-up.Annual CT screening for lung cancer has a favorable benefit-harm ratio for individuals aged 55 through 80 years with 30 or more pack-years' exposure to smoking.National Cancer Institute.

    View details for Web of Science ID 000332793900003

    View details for PubMedCentralID PMC4116741

  • NF-?B protein expression associates with (18)F-FDG PET tumor uptake in non-small cell lung cancer: A radiogenomics validation study to understand tumor metabolism. Lung cancer Nair, V. S., Gevaert, O., Davidzon, G., Plevritis, S. K., West, R. 2014; 83 (2): 189-196

    Abstract

    We previously demonstrated that NF-κB may be associated with (18)F-FDG PET uptake and patient prognosis using radiogenomics in patients with non-small cell lung cancer (NSCLC). To validate these results, we assessed NF-κB protein expression in an extended cohort of NSCLC patients.We examined NF-κBp65 by immunohistochemistry (IHC) using a Tissue Microarray. Staining intensity was assessed by qualitative ordinal scoring and compared to tumor FDG uptake (SUVmax and SUVmean), lactate dehydrogenase A (LDHA) expression (as a positive control) and outcome using ANOVA, Kaplan Meier (KM), and Cox-proportional hazards (CPH) analysis.365 tumors from 355 patients with long-term follow-up were analyzed. The average age for patients was 67±11 years, 46% were male and 67% were ever smokers. Stage I and II patients comprised 83% of the cohort and the majority had adenocarcinoma (73%). From 88 FDG PET scans available, average SUVmax and SUVmean were 8.3±6.6, and 3.7±2.4 respectively. Increasing NF-κBp65 expression, but not LDHA expression, was associated with higher SUVmax and SUVmean (p=0.03 and 0.02 respectively). Both NF-κBp65 and positive FDG uptake were significantly associated with more advanced stage, tumor histology and invasion. Higher NF-κBp65 expression was associated with death by KM analysis (p=0.06) while LDHA was strongly associated with recurrence (p=0.04). Increased levels of combined NF-κBp65 and LDHA expression were synergistic and associated with both recurrence (p=0.04) and death (p=0.03).NF-κB IHC was a modest biomarker of prognosis that associated with tumor glucose metabolism on FDG PET when compared to existing molecular correlates like LDHA, which was synergistic with NF-κB for outcome. These findings recapitulate radiogenomics profiles previously reported by our group and provide a methodology for studying tumor biology using computational approaches.

    View details for DOI 10.1016/j.lungcan.2013.11.001

    View details for PubMedID 24355259

  • Bridging Population and Tissue Scale Tumor Dynamics: A New Paradigm for Understanding Differences in Tumor Growth and Metastatic Disease CANCER RESEARCH Gallaher, J., Babu, A., Plevritis, S., Anderson, A. A. 2014; 74 (2): 426–35

    Abstract

    To provide a better understanding of the relationship between primary tumor growth rates and metastatic burden, we present a method that bridges tumor growth dynamics at the population level, extracted from the SEER database, to those at the tissue level. Specifically, with this method, we are able to relate estimates of tumor growth rates and metastatic burden derived from a population-level model to estimates of the primary tumor vascular response and the circulating tumor cell (CTC) fraction derived from a tissue-level model. Variation in the population-level model parameters produces differences in cancer-specific survival and cure fraction. Variation in the tissue-level model parameters produces different primary tumor dynamics that subsequently lead to different growth dynamics of the CTCs. Our method to bridge the population and tissue scales was applied to lung and breast cancer separately, and the results were compared. The population model suggests that lung tumors grow faster and shed a significant number of lethal metastatic cells at small sizes, whereas breast tumors grow slower and do not significantly shed lethal metastatic cells until becoming larger. Although the tissue-level model does not explicitly model the metastatic population, we are able to disengage the direct dependency of the metastatic burden on primary tumor growth by introducing the CTC population as an intermediary and assuming dependency. We calibrate the tissue-level model to produce results consistent with the population model while also revealing a more dynamic relationship between the primary tumor and the CTCs. This leads to exponential tumor growth in lung and power law tumor growth in breast. We conclude that the vascular response of the primary tumor is a major player in the dynamics of both the primary tumor and the CTCs, and is significantly different in breast and lung cancer.

    View details for PubMedID 24408919

    View details for PubMedCentralID PMC3913019

  • Oncogenic transformation of diverse gastrointestinal tissues in primary organoid culture. Nature medicine Li, X. n., Nadauld, L. n., Ootani, A. n., Corney, D. C., Pai, R. K., Gevaert, O. n., Cantrell, M. A., Rack, P. G., Neal, J. T., Chan, C. W., Yeung, T. n., Gong, X. n., Yuan, J. n., Wilhelmy, J. n., Robine, S. n., Attardi, L. D., Plevritis, S. K., Hung, K. E., Chen, C. Z., Ji, H. P., Kuo, C. J. 2014

    Abstract

    The application of primary organoid cultures containing epithelial and mesenchymal elements to cancer modeling holds promise for combining the accurate multilineage differentiation and physiology of in vivo systems with the facile in vitro manipulation of transformed cell lines. Here we used a single air-liquid interface culture method without modification to engineer oncogenic mutations into primary epithelial and mesenchymal organoids from mouse colon, stomach and pancreas. Pancreatic and gastric organoids exhibited dysplasia as a result of expression of Kras carrying the G12D mutation (Kras(G12D)), p53 loss or both and readily generated adenocarcinoma after in vivo transplantation. In contrast, primary colon organoids required combinatorial Apc, p53, Kras(G12D) and Smad4 mutations for progressive transformation to invasive adenocarcinoma-like histology in vitro and tumorigenicity in vivo, recapitulating multi-hit models of colorectal cancer (CRC), as compared to the more promiscuous transformation of small intestinal organoids. Colon organoid culture functionally validated the microRNA miR-483 as a dominant driver oncogene at the IGF2 (insulin-like growth factor-2) 11p15.5 CRC amplicon, inducing dysplasia in vitro and tumorigenicity in vivo. These studies demonstrate the general utility of a highly tractable primary organoid system for cancer modeling and driver oncogene validation in diverse gastrointestinal tissues.

    View details for PubMedID 24859528

  • Comparing benefits from many possible computed tomography lung cancer screening programs: extrapolating from the National Lung Screening Trial using comparative modeling. PloS one McMahon, P. M., Meza, R., Plevritis, S. K., Black, W. C., Tammemagi, C. M., Erdogan, A., ten Haaf, K., Hazelton, W., Holford, T. R., Jeon, J., Clarke, L., Kong, C. Y., Choi, S. E., Munshi, V. N., Han, S. S., van Rosmalen, J., Pinsky, P. F., Moolgavkar, S., de Koning, H. J., Feuer, E. J. 2014; 9 (6)

    Abstract

    The National Lung Screening Trial (NLST) demonstrated that in current and former smokers aged 55 to 74 years, with at least 30 pack-years of cigarette smoking history and who had quit smoking no more than 15 years ago, 3 annual computed tomography (CT) screens reduced lung cancer-specific mortality by 20% relative to 3 annual chest X-ray screens. We compared the benefits achievable with 576 lung cancer screening programs that varied CT screen number and frequency, ages of screening, and eligibility based on smoking.We used five independent microsimulation models with lung cancer natural history parameters previously calibrated to the NLST to simulate life histories of the US cohort born in 1950 under all 576 programs. 'Efficient' (within model) programs prevented the greatest number of lung cancer deaths, compared to no screening, for a given number of CT screens. Among 120 'consensus efficient' (identified as efficient across models) programs, the average starting age was 55 years, the stopping age was 80 or 85 years, the average minimum pack-years was 27, and the maximum years since quitting was 20. Among consensus efficient programs, 11% to 40% of the cohort was screened, and 153 to 846 lung cancer deaths were averted per 100,000 people. In all models, annual screening based on age and smoking eligibility in NLST was not efficient; continuing screening to age 80 or 85 years was more efficient.Consensus results from five models identified a set of efficient screening programs that include annual CT lung cancer screening using criteria like NLST eligibility but extended to older ages. Guidelines for screening should also consider harms of screening and individual patient characteristics.

    View details for DOI 10.1371/journal.pone.0099978

    View details for PubMedID 24979231

  • CREATING A RADIOGENOMICS MAP OF MULTI-OMICS AND QUANTITATIVE IMAGE FEATURES IN GLIOBLASTOMA MULTIFORME Gevaert, O., Mitchell, L., Achrol, A., Xu, J., Steinberg, G., Cheshier, S., Napel, S., Zaharchuk, G., Plevritis, S. OXFORD UNIV PRESS INC. 2013: 140–41
  • GLIOMETH: A NOVEL DNA METHYLATION SIGNATURE PREDICTS OVERALL SURVIVAL IN GLIOBLASTOMA MULTIFORME Gevaert, O., Achrol, A., Chang, S., Harsh, G., Steinberg, G., Cheshier, S., Plevritis, S. OXFORD UNIV PRESS INC. 2013: 141
  • CANCER STEM CELL TRANSCRIPTIONAL SUBTYPING OF GLIOBLASTOMA MULTIFORME CORRELATES WITH CLINICALLY RELEVANT MOLECULAR AND IMAGING PHENOTYPES Gevaert, O., Achrol, A., Gholamin, S., Mitra, S., Westbroek, E., Loya, J., Mitchell, L., Chang, S., Steinberg, G., Plevritis, S., Cheshier, S. OXFORD UNIV PRESS INC. 2013: 140
  • Ly6d marks the earliest stage of B-cell specification and identifies the branchpoint between B-cell and T-cell development. Genes & development Inlay, M. A., Bhattacharya, D., Sahoo, D., Serwold, T., Seita, J., Karsunky, H., Plevritis, S. K., Dill, D. L., Weissman, I. L. 2013; 27 (18): 2063-?

    View details for PubMedID 24065771

  • Improvements in observed and relative survival in follicular grade 1-2 lymphoma during 4 decades: the Stanford University experience. Blood Tan, D., Horning, S. J., Hoppe, R. T., Levy, R., Rosenberg, S. A., Sigal, B. M., Warnke, R. A., Natkunam, Y., Han, S. S., Yuen, A., Plevritis, S. K., Advani, R. H. 2013; 122 (6): 981-987

    Abstract

    Recent studies report an improvement in overall survival (OS) of patients with follicular lymphoma (FL). Previously untreated patients with grade 1-2 FL referred from 1960-2003 and treated at Stanford were identified. Four eras were considered: era 1, pre-anthracycline (1960-1975, n=180); era 2, anthracycline (1976-1986, n=426), era 3, aggressive chemotherapy/purine analogs (1987-1996, n=471) and era 4, rituximab (1997-2003, n=257). Clinical characteristics, patterns of care and survival outcomes were assessed. Observed OS was compared with the expected OS calculated from Berkeley Mortality Database life tables derived from population matched by gender and age at time of diagnosis. The median OS was 13.6 years. Age, gender and stage did not differ across the eras. Although primary treatment varied, event free survival after the first treatment did not differ between eras (p=0.17). Median OS improved from approximately 11 years in eras 1 and 2 to 18.4 years in era 3 and has not yet been reached for era 4 (p<0.001) with no suggestion of a plateau in any era. These improvements in OS exceeded improvements in survival in the general population during the same time period. Several factors, including better supportive care and effective therapies for relapsed disease, are likely responsible for this improvement.

    View details for DOI 10.1182/blood-2013-03-491514

    View details for PubMedID 23777769

  • Identification of ovarian cancer driver genes by using module network integration of multi-omics data INTERFACE FOCUS Gevaert, O., Villalobos, V., Sikic, B. I., Plevritis, S. K. 2013; 3 (4)
  • Identification of ovarian cancer driver genes by using module network integration of multi-omics data. Interface focus Gevaert, O., Villalobos, V., Sikic, B. I., Plevritis, S. K. 2013; 3 (4): 20130013-?

    Abstract

    The increasing availability of multi-omics cancer datasets has created a new opportunity for data integration that promises a more comprehensive understanding of cancer. The challenge is to develop mathematical methods that allow the integration and extraction of knowledge from large datasets such as The Cancer Genome Atlas (TCGA). This has led to the development of a variety of omics profiles that are highly correlated with each other; however, it remains unknown which profile is the most meaningful and how to efficiently integrate different omics profiles. We developed AMARETTO, an algorithm to identify cancer drivers by integrating a variety of omics data from cancer and normal tissue. AMARETTO first models the effects of genomic/epigenomic data on disease-specific gene expression. AMARETTO's second step involves constructing a module network to connect the cancer drivers with their downstream targets. We observed that more gene expression variation can be explained when using disease-specific gene expression data. We applied AMARETTO to the ovarian cancer TCGA data and identified several cancer driver genes of interest, including novel genes in addition to known drivers of cancer. Finally, we showed that certain modules are predictive of good versus poor outcome, and the associated drivers were related to DNA repair pathways.

    View details for DOI 10.1098/rsfs.2013.0013

    View details for PubMedID 24511378

  • Feasibility evaluation of an online tool to guide decisions for BRCA1/2 mutation carriers FAMILIAL CANCER Schackmann, E. A., Munoz, D. F., Mills, M. A., Plevritis, S. K., Kurian, A. W. 2013; 12 (1): 65-73

    Abstract

    Women with BRCA1 or BRCA2 (BRCA1/2) mutations face difficult decisions about managing their high risks of breast and ovarian cancer. We developed an online tool to guide decisions about cancer risk reduction (available at: http://brcatool.stanford.edu ), and recruited patients and clinicians to test its feasibility. We developed questionnaires for women with BRCA1/2 mutations and clinicians involved in their care, incorporating the System Usability Scale (SUS) and the Center for Healthcare Evaluation Provider Satisfaction Questionnaire (CHCE-PSQ). We enrolled BRCA1/2 mutation carriers who were seen by local physicians or participating in a national advocacy organization, and we enrolled clinicians practicing at Stanford University and in the surrounding community. Forty BRCA1/2 mutation carriers and 16 clinicians participated. Both groups found the tool easy to use, with SUS scores of 82.5-85 on a scale of 1-100; we did not observe differences according to patient age or gene mutation. General satisfaction was high, with a mean score of 4.28 (standard deviation (SD) 0.96) for patients, and 4.38 (SD 0.89) for clinicians, on a scale of 1-5. Most patients (77.5 %) were comfortable using the tool at home. Both patients and clinicians agreed that the decision tool could improve patient-doctor encounters (mean scores 4.50 and 4.69, on a 1-5 scale). Patients and health care providers rated the decision tool highly on measures of usability and clinical relevance. These results will guide a larger study of the tool's impact on clinical decisions.

    View details for DOI 10.1007/s10689-012-9577-8

    View details for PubMedID 23086584

  • Hierarchy in somatic mutations arising during genomic evolution and progression of follicular lymphoma. Blood Green, M. R., Gentles, A. J., Nair, R. V., Irish, J. M., Kihira, S., Liu, C. L., Kela, I., Hopmans, E. S., Myklebust, J. H., Ji, H., Plevritis, S. K., Levy, R., Alizadeh, A. A. 2013; 121 (9): 1604-1611

    Abstract

    Follicular lymphoma (FL) is currently incurable using conventional chemotherapy or immunotherapy regimes, compelling new strategies. Advances in high-throughput sequencing technologies that can reveal oncogenic pathways have stimulated interest in tailoring therapies toward actionable somatic mutations. However, for mutation-directed therapies to be most effective, the mutations must be uniformly present in evolved tumor cells as well as in the self-renewing tumor-cell precursors. Here, we show striking intratumoral clonal diversity within FL tumors in the representation of mutations in the majority of genes as revealed by whole exome sequencing of subpopulations. This diversity captures a clonal hierarchy, resolved using immunoglobulin somatic mutations and IGH-BCL2 translocations as a frame of reference and by comparing diagnosis and relapse tumor pairs, allowing us to distinguish early versus late genetic eventsduring lymphomagenesis. We provide evidence that IGH-BCL2 translocations and CREBBP mutations are early events, whereas MLL2 and TNFRSF14 mutations probably represent late events during disease evolution. These observations provide insight into which of the genetic lesions represent suitable candidates for targeted therapies.

    View details for DOI 10.1182/blood-2012-09-457283

    View details for PubMedID 23297126

  • TreeVis: A MATLAB-based tool for tree visualization COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE Qiu, P., Plevritis, S. K. 2013; 109 (1): 74-76

    Abstract

    Network-based analyses of high-dimensional biological data often produce results in the form of tree structures. Generating easily interpretable layouts to visualize these tree structures is a non-trivial task. We present a new visualization algorithm to generate two-dimensional layouts for complex tree structures. Implementations in both MATLAB and R are provided.

    View details for DOI 10.1016/j.cmpb.2012.08.008

    View details for Web of Science ID 000312473300007

    View details for PubMedID 23036855

    View details for PubMedCentralID PMC3508366

  • Identifying master regulators of cancer and their downstream targets by integrating genomic and epigenomic features. Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing Gevaert, O., Plevritis, S. 2013: 123-134

    Abstract

    Vast amounts of molecular data characterizing the genome, epigenome and transcriptome are becoming available for a variety of cancers. The current challenge is to integrate these diverse layers of molecular biology information to create a more comprehensive view of key biological processes underlying cancer. We developed a biocomputational algorithm that integrates copy number, DNA methylation, and gene expression data to study master regulators of cancer and identify their targets. Our algorithm starts by generating a list of candidate driver genes based on the rationale that genes that are driven by multiple genomic events in a subset of samples are unlikely to be randomly deregulated. We then select the master regulators from the candidate driver and identify their targets by inferring the underlying regulatory network of gene expression. We applied our biocomputational algorithm to identify master regulators and their targets in glioblastoma multiforme (GBM) and serous ovarian cancer. Our results suggest that the expression of candidate drivers is more likely to be influenced by copy number variations than DNA methylation. Next, we selected the master regulators and identified their downstream targets using module networks analysis. As a proof-of-concept, we show that the GBM and ovarian cancer module networks recapitulate known processes in these cancers. In addition, we identify master regulators that have not been previously reported and suggest their likely role. In summary, focusing on genes whose expression can be explained by their genomic and epigenomic aberrations is a promising strategy to identify master regulators of cancer.

    View details for PubMedID 23424118

  • Cross-Species Functional Analysis of Cancer-Associated Fibroblasts Identifies a Critical Role for CLCF1 and IL-6 in Non-Small Cell Lung Cancer In Vivo CANCER RESEARCH Vicent, S., Sayles, L. C., Vaka, D., Khatri, P., Gevaert, O., Chen, R., Zheng, Y., Gillespie, A. K., Clarke, N., Xu, Y., Shrager, J., Hoang, C. D., Plevritis, S., Butte, A. J., Sweet-Cordero, E. A. 2012; 72 (22): 5744-5756

    Abstract

    Cancer-associated fibroblasts (CAF) have been reported to support tumor progression by a variety of mechanisms. However, their role in the progression of non-small cell lung cancer (NSCLC) remains poorly defined. In addition, the extent to which specific proteins secreted by CAFs contribute directly to tumor growth is unclear. To study the role of CAFs in NSCLCs, a cross-species functional characterization of mouse and human lung CAFs was conducted. CAFs supported the growth of lung cancer cells in vivo by secretion of soluble factors that directly stimulate the growth of tumor cells. Gene expression analysis comparing normal mouse lung fibroblasts and mouse lung CAFs identified multiple genes that correlate with the CAF phenotype. A gene signature of secreted genes upregulated in CAFs was an independent marker of poor survival in patients with NSCLC. This secreted gene signature was upregulated in normal lung fibroblasts after long-term exposure to tumor cells, showing that lung fibroblasts are "educated" by tumor cells to acquire a CAF-like phenotype. Functional studies identified important roles for CLCF1-CNTFR and interleukin (IL)-6-IL-6R signaling in promoting growth of NSCLCs. This study identifies novel soluble factors contributing to the CAF protumorigenic phenotype in NSCLCs and suggests new avenues for the development of therapeutic strategies.

    View details for DOI 10.1158/0008-5472.CAN-12-1097

    View details for PubMedID 22962265

  • CytoSPADE: high-performance analysis and visualization of high-dimensional cytometry data BIOINFORMATICS Linderman, M. D., Bjornson, Z., Simonds, E. F., Qiu, P., Bruggner, R. V., Sheode, K., Meng, T. H., Plevritis, S. K., Nolan, G. P. 2012; 28 (18): 2400-2401

    Abstract

    MOTIVATION: Recent advances in flow cytometry enable simultaneous single-cell measurement of 30+ surface and intracellular proteins. CytoSPADE is a high-performance implementation of an interface for the Spanning-tree Progression Analysis of Density-normalized Events algorithm for tree-based analysis and visualization of this high-dimensional cytometry data. AVAILABILITY: Source code and binaries are freely available at http://cytospade.org and via Bioconductor version 2.10 onwards for Linux, OSX and Windows. CytoSPADE is implemented in R, C++ and Java. CONTACT: michael.linderman@mssm.edu SUPPLEMENTARY INFORMATION: Additional documentation available at http://cytospade.org.

    View details for DOI 10.1093/bioinformatics/bts425

    View details for PubMedID 22782546

  • Prognostic PET F-18-FDG Uptake Imaging Features Are Associated with Major Oncogenomic Alterations in Patients with Resected Non-Small Cell Lung Cancer CANCER RESEARCH Nair, V. S., Gevaert, O., Davidzon, G., Napel, S., Graves, E. E., Hoang, C. D., Shrager, J. B., Quon, A., Rubin, D. L., Plevritis, S. K. 2012; 72 (15): 3725-3734

    Abstract

    Although 2[18F]fluoro-2-deoxy-d-glucose (FDG) uptake during positron emission tomography (PET) predicts post-surgical outcome in patients with non-small cell lung cancer (NSCLC), the biologic basis for this observation is not fully understood. Here, we analyzed 25 tumors from patients with NSCLCs to identify tumor PET-FDG uptake features associated with gene expression signatures and survival. Fourteen quantitative PET imaging features describing FDG uptake were correlated with gene expression for single genes and coexpressed gene clusters (metagenes). For each FDG uptake feature, an associated metagene signature was derived, and a prognostic model was identified in an external cohort and then tested in a validation cohort of patients with NSCLC. Four of eight single genes associated with FDG uptake (LY6E, RNF149, MCM6, and FAP) were also associated with survival. The most prognostic metagene signature was associated with a multivariate FDG uptake feature [maximum standard uptake value (SUV(max)), SUV(variance), and SUV(PCA2)], each highly associated with survival in the external [HR, 5.87; confidence interval (CI), 2.49-13.8] and validation (HR, 6.12; CI, 1.08-34.8) cohorts, respectively. Cell-cycle, proliferation, death, and self-recognition pathways were altered in this radiogenomic profile. Together, our findings suggest that leveraging tumor genomics with an expanded collection of PET-FDG imaging features may enhance our understanding of FDG uptake as an imaging biomarker beyond its association with glycolysis.

    View details for DOI 10.1158/0008-5472.CAN-11-3943

    View details for PubMedID 22710433

  • Non-Small Cell Lung Cancer: Identifying Prognostic Imaging Biomarkers by Leveraging Public Gene Expression Microarray Data-Methods and Preliminary Results RADIOLOGY Gevaert, O., Xu, J., Hoang, C. D., Leung, A. N., Xu, Y., Quon, A., Rubin, D. L., Napel, S., Plevritis, S. K. 2012; 264 (2): 387-396

    Abstract

    To identify prognostic imaging biomarkers in non-small cell lung cancer (NSCLC) by means of a radiogenomics strategy that integrates gene expression and medical images in patients for whom survival outcomes are not available by leveraging survival data in public gene expression data sets.A radiogenomics strategy for associating image features with clusters of coexpressed genes (metagenes) was defined. First, a radiogenomics correlation map is created for a pairwise association between image features and metagenes. Next, predictive models of metagenes are built in terms of image features by using sparse linear regression. Similarly, predictive models of image features are built in terms of metagenes. Finally, the prognostic significance of the predicted image features are evaluated in a public gene expression data set with survival outcomes. This radiogenomics strategy was applied to a cohort of 26 patients with NSCLC for whom gene expression and 180 image features from computed tomography (CT) and positron emission tomography (PET)/CT were available.There were 243 statistically significant pairwise correlations between image features and metagenes of NSCLC. Metagenes were predicted in terms of image features with an accuracy of 59%-83%. One hundred fourteen of 180 CT image features and the PET standardized uptake value were predicted in terms of metagenes with an accuracy of 65%-86%. When the predicted image features were mapped to a public gene expression data set with survival outcomes, tumor size, edge shape, and sharpness ranked highest for prognostic significance.This radiogenomics strategy for identifying imaging biomarkers may enable a more rapid evaluation of novel imaging modalities, thereby accelerating their translation to personalized medicine.

    View details for DOI 10.1148/radiol.12111607

    View details for PubMedID 22723499

  • A Simulation Model to Predict the Impact of Prophylactic Surgery and Screening on the Life Expectancy of BRCA1 and BRCA2 Mutation Carriers CANCER EPIDEMIOLOGY BIOMARKERS & PREVENTION Sigal, B. M., Munoz, D. F., Kurian, A. W., Plevritis, S. K. 2012; 21 (7): 1066-1077

    Abstract

    Women with inherited mutations in the BRCA1 or BRCA2 (BRCA1/2) genes are recommended to undergo a number of intensive cancer risk-reducing strategies, including prophylactic mastectomy, prophylactic oophorectomy, and screening. We estimate the impact of different risk-reducing options at various ages on life expectancy.We apply our previously developed Monte Carlo simulation model of screening and prophylactic surgery in BRCA1/2 mutation carriers. Here, we present the mathematical formulation to compute age-specific breast cancer incidence in the absence of prophylactic oophorectomy, which is an input to the simulation model, and provide sensitivity analysis on related model parameters.The greatest gains in life expectancy result from conducting prophylactic mastectomy and prophylactic oophorectomy immediately after BRCA1/2 mutation testing; these gains vary with age at testing, from 6.8 to 10.3 years for BRCA1 and 3.4 to 4.4 years for BRCA2 mutation carriers. Life expectancy gains from delaying prophylactic surgery by 5 to 10 years range from 1 to 9.9 years for BRCA1 and 0.5 to 4.2 years for BRCA2 mutation carriers. Adding annual breast screening provides gains of 2.0 to 9.9 years for BRCA1 and 1.5 to 4.3 years for BRCA2. Results were most sensitive to variations in our assumptions about the magnitude and duration of breast cancer risk reduction due to prophylactic oophorectomy.Life expectancy gains depend on the type of BRCA mutation and age at interventions. Sensitivity analysis identifies the degree of breast cancer risk reduction due to prophylactic oophorectomy as a key determinant of life expectancy gain.Further study of the impact of prophylactic oophorectomy on breast cancer risk in BRCA1/2 mutation carriers is warranted.

    View details for DOI 10.1158/1055-9965.EPI-12-0149

    View details for PubMedID 22556274

  • Quantitative Proteomic Profiling Identifies Protein Correlates to EGFR Kinase Inhibition MOLECULAR CANCER THERAPEUTICS Kani, K., Faca, V. M., Hughes, L. D., Zhang, W., Fang, Q., Shahbaba, B., Luethy, R., Erde, J., Schmidt, J., Pitteri, S. J., Zhang, Q., Katz, J. E., Gross, M. E., Plevritis, S. K., McIntosh, M. W., Jain, A., Hanash, S., Agus, D. B., Mallick, P. 2012; 11 (5): 1071-1081

    Abstract

    Clinical oncology is hampered by lack of tools to accurately assess a patient's response to pathway-targeted therapies. Serum and tumor cell surface proteins whose abundance, or change in abundance in response to therapy, differentiates patients responding to a therapy from patients not responding to a therapy could be usefully incorporated into tools for monitoring response. Here, we posit and then verify that proteomic discovery in in vitro tissue culture models can identify proteins with concordant in vivo behavior and further, can be a valuable approach for identifying tumor-derived serum proteins. In this study, we use stable isotope labeling of amino acids in culture (SILAC) with proteomic technologies to quantitatively analyze the gefitinib-related protein changes in a model system for sensitivity to EGF receptor (EGFR)-targeted tyrosine kinase inhibitors. We identified 3,707 intracellular proteins, 1,276 cell surface proteins, and 879 shed proteins. More than 75% of the proteins identified had quantitative information, and a subset consisting of 400 proteins showed a statistically significant change in abundance following gefitinib treatment. We validated the change in expression profile in vitro and screened our panel of response markers in an in vivo isogenic resistant model and showed that these were markers of gefitinib response and not simply markers of phospho-EGFR downregulation. In doing so, we also were able to identify which proteins might be useful as markers for monitoring response and which proteins might be useful as markers for a priori prediction of response.

    View details for DOI 10.1158/1535-7163.MCT-11-0852

    View details for Web of Science ID 000307984800003

    View details for PubMedID 22411897

  • Multiscale perspective of combination therapy for human cancer Plevritis, S. K. AMER ASSOC CANCER RESEARCH. 2012
  • Radiogenomic analysis indicates MR images are potentially predictive of EGFR mutation status in glioblastoma multiforme Gevaert, O., Mitchell, L., Xu, J., Yu, C., Rubin, D., Zaharchuk, G., Napel, S., Plevritis, S. AMER ASSOC CANCER RESEARCH. 2012
  • Online Tool to Guide Decisions for BRCA1/2 Mutation Carriers JOURNAL OF CLINICAL ONCOLOGY Kurian, A. W., Munoz, D. F., Rust, P., Schackmann, E. A., Smith, M., Clarke, L., Mills, M. A., Plevritis, S. K. 2012; 30 (5): 497-506

    Abstract

    Women with BRCA1 or BRCA2 (BRCA1/2) mutations must choose between prophylactic surgeries and screening to manage their high risks of breast and ovarian cancer, comparing options in terms of cancer incidence, survival, and quality of life. A clinical decision tool could guide these complex choices.We built a Monte Carlo model for BRCA1/2 mutation carriers, simulating breast screening with annual mammography plus magnetic resonance imaging (MRI) from ages 25 to 69 years and prophylactic mastectomy (PM) and/or prophylactic oophorectomy (PO) at various ages. Modeled outcomes were cancer incidence, tumor features that shape treatment recommendations, overall survival, and cause-specific mortality. We adapted the model into an online tool to support shared decision making.We compared strategies on cancer incidence and survival to age 70 years; for example, PO plus PM at age 25 years optimizes both outcomes (incidence, 4% to 11%; survival, 80% to 83%), whereas PO at age 40 years plus MRI screening offers less effective prevention, yet similar survival (incidence, 36% to 57%; survival, 74% to 80%). To characterize patients' treatment and survivorship experiences, we reported the tumor features and treatments associated with risk-reducing interventions; for example, in most BRCA2 mutation carriers (81%), MRI screening diagnoses stage I, hormone receptor-positive breast cancers, which may not require chemotherapy.Cancer risk-reducing options for BRCA1/2 mutation carriers vary in their impact on cancer incidence, recommended treatments, quality of life, and survival. To guide decisions informed by multiple health outcomes, we provide an online tool for joint use by patients with their physicians (http://brcatool.stanford.edu).

    View details for DOI 10.1200/JCO.2011.38.6060

    View details for PubMedID 22231042

  • Comparing the benefits of screening for breast cancer and lung cancer using a novel natural history model CANCER CAUSES & CONTROL Lin, R. S., Plevritis, S. K. 2012; 23 (1): 175-185

    Abstract

    To estimate the impact of early detection of cancer, knowledge of how quickly primary tumors grow and at what size they shed lethal metastases is critical. We developed a natural history model of cancer to estimate the probability of disease-specific cure as a function of tumor size, the tumor volume doubling time (TVDT), and disease-specific mortality reduction achievable by screening. The model was applied to non-small-cell lung carcinoma (NSCLC) and invasive ductal carcinoma (IDC), separately. Model parameter estimates were based on Surveillance Epidemiology and End Results (SEER) cancer registry datasets and validated on screening trials. Compared to IDC, NSCLC is estimated to have a lower probability of disease-specific cure at the same detected tumor size, shed lethal metastases at smaller sizes (median: 19 mm for IDC versus 8 mm for NSCLC), have a TVDT that is almost half as long (median: 252 days for IDC versus 134 days for NSCLC). Consequently, NSCLC is associated with a lower mortality reduction from screening at the same screen detection threshold and screening interval. In summary, using a similar natural history model of cancer, we quantify the disease-specific curability attributable to screening for breast cancer, and separately lung cancer, in terms of the TVDT and onset of lethal metastases.

    View details for DOI 10.1007/s10552-011-9866-9

    View details for PubMedID 22116537

  • Reconstructing Directed Signed Gene Regulatory Network From Microarray Data IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING Qiu, P., Plevritis, S. K. 2011; 58 (12): 3518-3521

    Abstract

    Great efforts have been made to develop both algorithms that reconstruct gene regulatory networks and systems that simulate gene networks and expression data, for the purpose of benchmarking network reconstruction algorithms. An interesting observation is that although many simulation systems chose to use Hill kinetics to generate data, none of the reconstruction algorithms were developed based on the Hill kinetics. One possible explanation is that, in Hill kinetics, activation and inhibition interactions take different mathematical forms, which brings additional combinatorial complexity into the reconstruction problem. We propose a new model that qualitatively behaves similar to the Hill kinetics, but has the same mathematical form for both activation and inhibition. We developed an algorithm to reconstruct gene networks based on this new model. Simulation results suggested a novel biological hypothesis that in gene knockout experiments, repressing protein synthesis to a certain extent may lead to better expression data and higher network reconstruction accuracy.

    View details for DOI 10.1109/TBME.2011.2163188

    View details for Web of Science ID 000297341500021

    View details for PubMedID 21803675

  • Lymphomas that recur after MYC suppression continue to exhibit oncogene addiction PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA Choi, P. S., van Riggelen, J., Gentles, A. J., Bachireddy, P., Rakhra, K., Adam, S. J., Plevritis, S. K., Felsher, D. W. 2011; 108 (42): 17432-17437

    Abstract

    The suppression of oncogenic levels of MYC is sufficient to induce sustained tumor regression associated with proliferative arrest, differentiation, cellular senescence, and/or apoptosis, a phenomenon known as oncogene addiction. However, after prolonged inactivation of MYC in a conditional transgenic mouse model of Eμ-tTA/tetO-MYC T-cell acute lymphoblastic leukemia, some of the tumors recur, recapitulating what is frequently observed in human tumors in response to targeted therapies. Here we report that these recurring lymphomas express either transgenic or endogenous Myc, albeit in many cases at levels below those in the original tumor, suggesting that tumors continue to be addicted to MYC. Many of the recurring lymphomas (76%) harbored mutations in the tetracycline transactivator, resulting in expression of the MYC transgene even in the presence of doxycycline. Some of the remaining recurring tumors expressed high levels of endogenous Myc, which was associated with a genomic rearrangement of the endogenous Myc locus or activation of Notch1. By gene expression profiling, we confirmed that the primary and recurring tumors have highly similar transcriptomes. Importantly, shRNA-mediated suppression of the high levels of MYC in recurring tumors elicited both suppression of proliferation and increased apoptosis, confirming that these tumors remain oncogene addicted. These results suggest that tumors induced by MYC remain addicted to overexpression of this oncogene.

    View details for DOI 10.1073/pnas.1107303108

    View details for PubMedID 21969595

  • Modeling the impact of population screening on breast cancer mortality in the United States BREAST Mandelblatt, J. S., Cronin, K. A., Berry, D. A., Chang, Y., de Koning, H. J., Lee, S. J., Plevritis, S. K., Schechter, C. B., Stout, N. K., van Ravesteyn, N. T., Zelen, M., Feuer, E. J. 2011; 20: S75-S81

    Abstract

    Optimal US screening strategies remain controversial. We use six simulation models to evaluate screening outcomes under varying strategies.The models incorporate common data on incidence, mammography characteristics, and treatment effects. We evaluate varying initiation and cessation ages applied annually or biennially and calculate mammograms, mortality reduction (vs. no screening), false-positives, unnecessary biopsies and over-diagnosis.The lifetime risk of breast cancer death starting at age 40 is 3% and is reduced by screening. Screening biennially maintains 81% (range 67% to 99%) of annual screening benefits with fewer false-positives. Biennial screening from 50-74 reduces the probability of breast cancer death from 3% to 2.3%. Screening annually from 40 to 84 only lowers mortality an additional one-half of one percent to 1.8% but requires substantially more mammograms and yields more false-positives and over-diagnosed cases.Decisions about screening strategy depend on preferences for benefits vs. potential harms and resource considerations.

    View details for Web of Science ID 000311077400013

    View details for PubMedID 22015298

    View details for PubMedCentralID PMC3457919

  • Extracting a cellular hierarchy from high-dimensional cytometry data with SPADE NATURE BIOTECHNOLOGY Qiu, P., Simonds, E. F., Bendall, S. C., Gibbs, K. D., Bruggner, R. V., Linderman, M. D., Sachs, K., Nolan, G. P., Plevritis, S. K. 2011; 29 (10): 886-U181

    Abstract

    The ability to analyze multiple single-cell parameters is critical for understanding cellular heterogeneity. Despite recent advances in measurement technology, methods for analyzing high-dimensional single-cell data are often subjective, labor intensive and require prior knowledge of the biological system. To objectively uncover cellular heterogeneity from single-cell measurements, we present a versatile computational approach, spanning-tree progression analysis of density-normalized events (SPADE). We applied SPADE to flow cytometry data of mouse bone marrow and to mass cytometry data of human bone marrow. In both cases, SPADE organized cells in a hierarchy of related phenotypes that partially recapitulated well-described patterns of hematopoiesis. We demonstrate that SPADE is robust to measurement noise and to the choice of cellular markers. SPADE facilitates the analysis of cellular heterogeneity, the identification of cell types and comparison of functional markers in response to perturbations.

    View details for DOI 10.1038/nbt.1991

    View details for PubMedID 21964415

  • Prediction of survival in diffuse large B-cell lymphoma based on the expression of 2 genes reflecting tumor and microenvironment BLOOD Alizadeh, A. A., Gentles, A. J., Alencar, A. J., Liu, C. L., Kohrt, H. E., Houot, R., Goldstein, M. J., Zhao, S., Natkunam, Y., Advani, R. H., Gascoyne, R. D., Briones, J., Tibshirani, R. J., Myklebust, J. H., Plevritis, S. K., Lossos, I. S., Levy, R. 2011; 118 (5): 1350-1358

    Abstract

    Several gene-expression signatures predict survival in diffuse large B-cell lymphoma (DLBCL), but the lack of practical methods for genome-scale analysis has limited translation to clinical practice. We built and validated a simple model using one gene expressed by tumor cells and another expressed by host immune cells, assessing added prognostic value to the clinical International Prognostic Index (IPI). LIM domain only 2 (LMO2) was validated as an independent predictor of survival and the "germinal center B cell-like" subtype. Expression of tumor necrosis factor receptor superfamily member 9 (TNFRSF9) from the DLBCL microenvironment was the best gene in bivariate combination with LMO2. Study of TNFRSF9 tissue expression in 95 patients with DLBCL showed expression limited to infiltrating T cells. A model integrating these 2 genes was independent of "cell-of-origin" classification, "stromal signatures," IPI, and added to the predictive power of the IPI. A composite score integrating these genes with IPI performed well in 3 independent cohorts of 545 DLBCL patients, as well as in a simple assay of routine formalin-fixed specimens from a new validation cohort of 147 patients with DLBCL. We conclude that the measurement of a single gene expressed by tumor cells (LMO2) and a single gene expressed by the immune microenvironment (TNFRSF9) powerfully predicts overall survival in patients with DLBCL.

    View details for DOI 10.1182/blood-2011-03-345272

    View details for PubMedID 21670469

  • Single-Cell Mass Cytometry of Differential Immune and Drug Responses Across a Human Hematopoietic Continuum SCIENCE Bendall, S. C., Simonds, E. F., Qiu, P., Amir, E. D., Krutzik, P. O., Finck, R., Bruggner, R. V., Melamed, R., Trejo, A., Ornatsky, O. I., Balderas, R. S., Plevritis, S. K., Sachs, K., Pe'er, D., Tanner, S. D., Nolan, G. P. 2011; 332 (6030): 687-696

    Abstract

    Flow cytometry is an essential tool for dissecting the functional complexity of hematopoiesis. We used single-cell "mass cytometry" to examine healthy human bone marrow, measuring 34 parameters simultaneously in single cells (binding of 31 antibodies, viability, DNA content, and relative cell size). The signaling behavior of cell subsets spanning a defined hematopoietic hierarchy was monitored with 18 simultaneous markers of functional signaling states perturbed by a set of ex vivo stimuli and inhibitors. The data set allowed for an algorithmically driven assembly of related cell types defined by surface antigen expression, providing a superimposable map of cell signaling responses in combination with drug inhibition. Visualized in this manner, the analysis revealed previously unappreciated instances of both precise signaling responses that were bounded within conventionally defined cell subsets and more continuous phosphorylation responses that crossed cell population boundaries in unexpected manners yet tracked closely with cellular phenotype. Collectively, such single-cell analyses provide system-wide views of immune signaling in healthy human hematopoiesis, against which drug action and disease can be compared for mechanistic studies and pharmacologic intervention.

    View details for DOI 10.1126/science.1198704

    View details for PubMedID 21551058

  • Integrating medical images and transcriptomic data in non-small cell lung cancer Gevaert, O., Xu, J., Hoang, C., Leung, A., Quon, A., Rubin, D., Napel, S., Plevritis, S. AMER ASSOC CANCER RESEARCH. 2011
  • Clinically relevant interactions between micro-RNAs and genes in malignant mesothelioma characterized by an integrated analysis Hoang, C. D., Gentles, A., Xu, Y., Plevritis, S. K., Merritt, R., Whyte, R., Shrager, J., Kratzke, R. A. AMER ASSOC CANCER RESEARCH. 2011
  • Discovering Biological Progression Underlying Microarray Samples PLOS COMPUTATIONAL BIOLOGY Qiu, P., Gentles, A. J., Plevritis, S. K. 2011; 7 (4)

    Abstract

    In biological systems that undergo processes such as differentiation, a clear concept of progression exists. We present a novel computational approach, called Sample Progression Discovery (SPD), to discover patterns of biological progression underlying microarray gene expression data. SPD assumes that individual samples of a microarray dataset are related by an unknown biological process (i.e., differentiation, development, cell cycle, disease progression), and that each sample represents one unknown point along the progression of that process. SPD aims to organize the samples in a manner that reveals the underlying progression and to simultaneously identify subsets of genes that are responsible for that progression. We demonstrate the performance of SPD on a variety of microarray datasets that were generated by sampling a biological process at different points along its progression, without providing SPD any information of the underlying process. When applied to a cell cycle time series microarray dataset, SPD was not provided any prior knowledge of samples' time order or of which genes are cell-cycle regulated, yet SPD recovered the correct time order and identified many genes that have been associated with the cell cycle. When applied to B-cell differentiation data, SPD recovered the correct order of stages of normal B-cell differentiation and the linkage between preB-ALL tumor cells with their cell origin preB. When applied to mouse embryonic stem cell differentiation data, SPD uncovered a landscape of ESC differentiation into various lineages and genes that represent both generic and lineage specific processes. When applied to a prostate cancer microarray dataset, SPD identified gene modules that reflect a progression consistent with disease stages. SPD may be best viewed as a novel tool for synthesizing biological hypotheses because it provides a likely biological progression underlying a microarray dataset and, perhaps more importantly, the candidate genes that regulate that progression.

    View details for DOI 10.1371/journal.pcbi.1001123

    View details for PubMedID 21533210

  • Bayesian gene set analysis for identifying significant biological pathways JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES C-APPLIED STATISTICS Shahbaba, B., Tibshirani, R., Shachaf, C. M., Plevritis, S. K. 2011; 60: 541-557

    Abstract

    We propose a hierarchical Bayesian model for analyzing gene expression data to identify pathways differentiating between two biological states (e.g., cancer vs. non-cancer and mutant vs. normal). Finding significant pathways can improve our understanding of biological processes. When the biological process of interest is related to a specific disease, eliciting a better understanding of the underlying pathways can lead to designing a more effective treatment. We apply our method to data obtained by interrogating the mutational status of p53 in 50 cancer cell lines (33 mutated and 17 normal). We identify several significant pathways with strong biological connections. We show that our approach provides a natural framework for incorporating prior biological information, and it has the best overall performance in terms of correctly identifying significant pathways compared to several alternative methods.

    View details for DOI 10.1111/j.1467-9876.2011.00765.x

    View details for Web of Science ID 000293235800004

    View details for PubMedCentralID PMC3156489

  • Association of a Leukemic Stem Cell Gene Expression Signature With Clinical Outcomes in Acute Myeloid Leukemia JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION Gentles, A. J., Plevritis, S. K., Majeti, R., Alizadeh, A. A. 2010; 304 (24): 2706-2715

    Abstract

    In many cancers, specific subpopulations of cells appear to be uniquely capable of initiating and maintaining tumors. The strongest support for this cancer stem cell model comes from transplantation assays in immunodeficient mice, which indicate that human acute myeloid leukemia (AML) is driven by self-renewing leukemic stem cells (LSCs). This model has significant implications for the development of novel therapies, but its clinical relevance has yet to be determined.To identify an LSC gene expression signature and test its association with clinical outcomes in AML.Retrospective study of global gene expression (microarray) profiles of LSC-enriched subpopulations from primary AML and normal patient samples, which were obtained at a US medical center between April 2005 and July 2007, and validation data sets of global transcriptional profiles of AML tumors from 4 independent cohorts (n = 1047).Identification of genes discriminating LSC-enriched populations from other subpopulations in AML tumors; and association of LSC-specific genes with overall, event-free, and relapse-free survival and with therapeutic response.Expression levels of 52 genes distinguished LSC-enriched populations from other subpopulations in cell-sorted AML samples. An LSC score summarizing expression of these genes in bulk primary AML tumor samples was associated with clinical outcomes in the 4 independent patient cohorts. High LSC scores were associated with worse overall, event-free, and relapse-free survival among patients with either normal karyotypes or chromosomal abnormalities. For the largest cohort of patients with normal karyotypes (n = 163), the LSC score was significantly associated with overall survival as a continuous variable (hazard ratio [HR], 1.15; 95% confidence interval [CI], 1.08-1.22; log-likelihood P <.001). The absolute risk of death by 3 years was 57% (95% CI, 43%-67%) for the low LSC score group compared with 78% (95% CI, 66%-86%) for the high LSC score group (HR, 1.9 [95% CI, 1.3-2.7]; log-rank P = .002). In another cohort with available data on event-free survival for 70 patients with normal karyotypes, the risk of an event by 3 years was 48% (95% CI, 27%-63%) in the low LSC score group vs 81% (95% CI, 60%-91%) in the high LSC score group (HR, 2.4 [95% CI, 1.3-4.5]; log-rank P = .006). In multivariate Cox regression including age, mutations in FLT3 and NPM1, and cytogenetic abnormalities, the HRs for LSC score in the 3 cohorts with data on all variables were 1.07 (95% CI, 1.01-1.13; P = .02), 1.10 (95% CI, 1.03-1.17; P = .005), and 1.17 (95% CI, 1.05-1.30; P = .005).High expression of an LSC gene signature is independently associated with adverse outcomes in patients with AML.

    View details for PubMedID 21177505

  • A Simulation Model Investigating the Impact of Tumor Volume Doubling Time and Mammographic Tumor Detectability on Screening Outcomes in Women Aged 40-49 Years JOURNAL OF THE NATIONAL CANCER INSTITUTE Bailey, S. L., Sigal, B. M., Plevritis, S. K. 2010; 102 (16): 1263-1271

    Abstract

    Compared with women aged 50-69 years, the lower sensitivity of mammographic screening in women aged 40-49 years is largely attributed to the lower mammographic tumor detectability and faster tumor growth in the younger women.We used a Monte Carlo simulation model of breast cancer screening by age to estimate the median tumor size detectable on a mammogram and the mean tumor volume doubling time. The estimates were calculated by calibrating the predicted breast cancer incidence rates to the actual rates from the Surveillance, Epidemiology, and End Results (SEER) database and the predicted distributions of screen-detected tumor sizes to the actual distributions obtained from the Breast Cancer Surveillance Consortium (BCSC). The calibrated parameters were used to estimate the relative impact of lower mammographic tumor detectability vs faster tumor volume doubling time on the poorer screening outcomes in younger women compared with older women. Mammography screening outcomes included sensitivity, mean tumor size at detection, lifetime gained, and breast cancer mortality. In addition, the relationship between screening sensitivity and breast cancer mortality was investigated as a function of tumor volume doubling time, mammographic tumor detectability, and screening interval.Lowered mammographic tumor detectability accounted for 79% and faster tumor volume doubling time accounted for 21% of the poorer sensitivity of mammography screening in younger women compared with older women. The relative contributions were similar when the impact of screening was evaluated in terms of mean tumor size at detection, lifetime gained, and breast cancer mortality. Screening sensitivity and breast cancer mortality reduction attributable to screening were almost linearly related when comparing annual or biennial screening with no screening. However, when comparing annual with biennial screening, the greatest reduction in breast cancer mortality attributable to screening did not correspond to the greatest gain in screening sensitivity and was more strongly affected by the mammographic tumor detectability than tumor volume doubling time.The age-specific differences in mammographic tumor detection contribute more than age-specific differences in tumor growth rates to the lowered performance of mammography screening in younger women.

    View details for DOI 10.1093/jnci/djq271

    View details for PubMedID 20664027

  • Incidental Extracardiac Findings at Coronary CT: Clinical and Economic Impact AMERICAN JOURNAL OF ROENTGENOLOGY Lee, C. I., Tsai, E. B., Sigal, B. M., Plevritis, S. K., Garber, A. M., Rubin, G. D. 2010; 194 (6): 1531-1538

    Abstract

    The purpose of this study was to evaluate the prevalence of incidental extracardiac findings on coronary CT, to determine the associated downstream resource utilization, and to estimate additional costs per patient related to the associated diagnostic workup.This retrospective study examined incidental extracardiac findings in 151 consecutive adults (69.5% men and 30.5% women; mean age, 54 years) undergoing coronary CT during a 7-year period. Incidental findings were recorded, and medical records were reviewed for downstream diagnostic examinations for a follow-up period of 1 year (minimum) to 7 years (maximum). Costs of further workup were estimated using 2009 Medicare average reimbursement figures.There were 102 incidental extracardiac findings in 43% (65/151) of patients. Fifty-two percent (53/102) of findings were potentially clinically significant, and 81% (43/53) of these findings were newly discovered. The radiology reports made specific follow-up recommendations for 36% (19/53) of new significant findings. Only 4% (6/151) of patients actually underwent follow-up imaging or intervention for incidental findings. One patient was found to have a malignancy that was subsequently treated. The average direct costs of additional diagnostic workup were $17.42 per patient screened (95% CI, $2.84-$32.00) and $438.39 per patient with imaging follow-up (95% CI, $301.47-$575.31).Coronary CT frequently reveals potentially significant incidental extracardiac abnormalities, yet radiologists recommend further evaluation in only one-third of cases. An even smaller fraction of cases receive further workup. The failure to follow-up abnormal incidental findings may result in missed opportunities to detect early disease, but also limits the short-term attributable costs.

    View details for DOI 10.2214/AJR.09.3587

    View details for PubMedID 20489093

  • MiDReG: A method of mining developmentally regulated genes using Boolean implications PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA Sahoo, D., Seita, J., Bhattacharya, D., Inlay, M. A., Weissman, I. L., Plevritis, S. K., Dill, D. L. 2010; 107 (13): 5732-5737

    Abstract

    We present a method termed mining developmentally regulated genes (MiDReG) to predict genes whose expression is either activated or repressed as precursor cells differentiate. MiDReG does not require gene expression data from intermediate stages of development. MiDReG is based on the gene expression patterns between the initial and terminal stages of the differentiation pathway, coupled with "if-then" rules (Boolean implications) mined from large-scale microarray databases. MiDReG uses two gene expression-based seed conditions that mark the initial and the terminal stages of a given differentiation pathway and combines the statistically inferred Boolean implications from these seed conditions to identify the relevant genes. The method was validated by applying it to B-cell development. The algorithm predicted 62 genes that are expressed after the KIT+ progenitor cell stage and remain expressed through CD19+ and AICDA+ germinal center B cells. qRT-PCR of 14 of these genes on sorted B-cell progenitors confirmed that the expression of 10 genes is indeed stably established during B-cell differentiation. Review of the published literature of knockout mice revealed that of the predicted genes, 63.4% have defects in B-cell differentiation and function and 22% have a role in the B cell according to other experiments, and the remaining 14.6% are not characterized. Therefore, our method identified novel gene candidates for future examination of their role in B-cell development. These data demonstrate the power of MiDReG in predicting functionally important intermediate genes in a given developmental pathway that is defined by a mutually exclusive gene expression pattern.

    View details for DOI 10.1073/pnas.0913635107

    View details for PubMedID 20231483

  • Reducing the Computational Complexity of Information Theoretic Approaches for Reconstructing Gene Regulatory Networks JOURNAL OF COMPUTATIONAL BIOLOGY Qiu, P., Gentles, A. J., Plevritis, S. K. 2010; 17 (2): 169-176

    Abstract

    Information theoretic approaches are increasingly being used for reconstructing regulatory networks from microarray data. These approaches start by computing the pairwise mutual information (MI) between all gene pairs. The resulting MI matrix is then manipulated to identify regulatory relationships. A barrier to these approaches is the time-consuming step of computing the MI matrix. We present a method to reduce this computation time. We apply spectral analysis to re-order the genes, so that genes that share regulatory relationships are more likely to be placed close to each other. Then, using a "sliding window" approach with appropriate window size and step size, we compute the MI for the genes within the sliding window, and the remainder is assumed to be zero. Using both simulated data and microarray data, we demonstrate that our method does not incur performance loss in regions of high-precision and low-recall, while the computational time is significantly lowered. The proposed method can be used with any method that relies on the mutual information to reconstruct networks.

    View details for DOI 10.1089/cmb.2009.0052

    View details for PubMedID 20078227

  • Survival Analysis of Cancer Risk Reduction Strategies for BRCA1/2 Mutation Carriers JOURNAL OF CLINICAL ONCOLOGY Kurian, A. W., Sigal, B. M., Plevritis, S. K. 2010; 28 (2): 222-231

    Abstract

    Women with BRCA1/2 mutations inherit high risks of breast and ovarian cancer; options to reduce cancer mortality include prophylactic surgery or breast screening, but their efficacy has never been empirically compared. We used decision analysis to simulate risk-reducing strategies in BRCA1/2 mutation carriers and to compare resulting survival probability and causes of death.We developed a Monte Carlo model of breast screening with annual mammography plus magnetic resonance imaging (MRI) from ages 25 to 69 years, prophylactic mastectomy (PM) at various ages, and/or prophylactic oophorectomy (PO) at ages 40 or 50 years in 25-year-old BRCA1/2 mutation carriers.With no intervention, survival probability by age 70 is 53% for BRCA1 and 71% for BRCA2 mutation carriers. The most effective single intervention for BRCA1 mutation carriers is PO at age 40, yielding a 15% absolute survival gain; for BRCA2 mutation carriers, the most effective single intervention is PM, yielding a 7% survival gain if performed at age 40 years. The combination of PM and PO at age 40 improves survival more than any single intervention, yielding 24% survival gain for BRCA1 and 11% for BRCA2 mutation carriers. PM at age 25 instead of age 40 offers minimal incremental benefit (1% to 2%); substituting screening for PM yields a similarly minimal decrement in survival (2% to 3%).Although PM at age 25 plus PO at age 40 years maximizes survival probability, substituting mammography plus MRI screening for PM seems to offer comparable survival. These results may guide women with BRCA1/2 mutations in their choices between prophylactic surgery and breast screening.

    View details for DOI 10.1200/JCO.2009.22.7991

    View details for PubMedID 19996031

  • Effects of Mammography Screening Under Different Screening Schedules: Model Estimates of Potential Benefits and Harms ANNALS OF INTERNAL MEDICINE Mandelblatt, J. S., Cronin, K. A., Bailey, S., Berry, D. A., de Koning, H. J., Draisma, G., Huang, H., Lee, S. J., Munsell, M., Plevritis, S. K., Ravdin, P., Schechter, C. B., Sigal, B., Stoto, M. A., Stout, N. K., van Ravesteyn, N. T., Venier, J., Zelen, M., Feuer, E. J. 2009; 151 (10): 738-W247

    Abstract

    Despite trials of mammography and widespread use, optimal screening policy is controversial.To evaluate U.S. breast cancer screening strategies.6 models using common data elements.National data on age-specific incidence, competing mortality, mammography characteristics, and treatment effects.A contemporary population cohort.Lifetime.Societal.20 screening strategies with varying initiation and cessation ages applied annually or biennially.Number of mammograms, reduction in deaths from breast cancer or life-years gained (vs. no screening), false-positive results, unnecessary biopsies, and overdiagnosis.The 6 models produced consistent rankings of screening strategies. Screening biennially maintained an average of 81% (range across strategies and models, 67% to 99%) of the benefit of annual screening with almost half the number of false-positive results. Screening biennially from ages 50 to 69 years achieved a median 16.5% (range, 15% to 23%) reduction in breast cancer deaths versus no screening. Initiating biennial screening at age 40 years (vs. 50 years) reduced mortality by an additional 3% (range, 1% to 6%), consumed more resources, and yielded more false-positive results. Biennial screening after age 69 years yielded some additional mortality reduction in all models, but overdiagnosis increased most substantially at older ages.Varying test sensitivity or treatment patterns did not change conclusions.Results do not include morbidity from false-positive results, patient knowledge of earlier diagnosis, or unnecessary treatment.Biennial screening achieves most of the benefit of annual screening with less harm. Decisions about the best strategy depend on program and individual objectives and the weight placed on benefits, harms, and resource considerations. Primary Funding Source: National Cancer Institute.

    View details for Web of Science ID 000272145100007

    View details for PubMedID 19920274

    View details for PubMedCentralID PMC3515682

  • Modeling the transition of lung cancer from early to advanced stage CANCER CAUSES & CONTROL Pashkevich, M. A., Sigal, B. M., Plevritis, S. K. 2009; 20 (9): 1559-1569

    Abstract

    We present a stochastic parametric model of the natural history of lung cancer that predicts the primary tumor volume at the moment the disease transits from early to advanced stage. Our model also produces estimates for the probability of symptomatic detection as a function of tumor volume and clinical stage. We estimate model parameters by likelihood maximization using data from the Mayo Lung Project (MLP), which was a clinical trial that evaluated screening for lung cancer in the 1970s. Mayo Lung Project cancer cases reported in Stage III or greater, according to the 1979 AJCC staging for lung cancer, were considered advanced stage. Our estimator distinguishes between the cases detected because of clinical symptoms and cases detected by screening. For nonsmall cell lung cancer cases detected in MLP, we estimate that the median primary tumor diameter at the onset of advanced stage disease was 4.1 cm. In addition, we estimate that the rate of patients symptomatically detected with their disease increases as their primary tumor increases in size, and for patients with a primary tumor of a given size, the rate of symptomatic detection is 12.8 times greater among patients with advanced stage disease compared to patients with early stage disease.

    View details for DOI 10.1007/s10552-009-9401-4

    View details for PubMedID 19629730

  • Ly6d marks the earliest stage of B-cell specification and identifies the branchpoint between B-cell and T-cell development GENES & DEVELOPMENT Inlay, M. A., Bhattacharya, D., Sahoo, D., Serwold, T., Seita, J., Karsunky, H., Plevritis, S. K., Dill, D. L., Weissman, I. L. 2009; 23 (20): 2376-2381

    Abstract

    Common lymphoid progenitors (CLPs) clonally produce both B- and T-cell lineages, but have little myeloid potential in vivo. However, some studies claim that the upstream lymphoid-primed multipotent progenitor (LMPP) is the thymic seeding population, and suggest that CLPs are primarily B-cell-restricted. To identify surface proteins that distinguish functional CLPs from B-cell progenitors, we used a new computational method of Mining Developmentally Regulated Genes (MiDReG). We identified Ly6d, which divides CLPs into two distinct populations: one that retains full in vivo lymphoid potential and produces more thymocytes at early timepoints than LMPP, and another that behaves essentially as a B-cell progenitor.

    View details for DOI 10.1101/gad.1836009

    View details for PubMedID 19833765

  • Simultaneous Class Discovery and Classification of Microarray Data Using Spectral Analysis JOURNAL OF COMPUTATIONAL BIOLOGY Qiu, P., Plevritis, S. K. 2009; 16 (7): 935-944

    Abstract

    Classification methods are commonly divided into two categories: unsupervised and supervised. Unsupervised methods have the ability to discover new classes by grouping data into clusters or tree structures without using the class labels, but they carry the risk of producing noninterpretable results. On the other hand, supervised methods always find decision rules that discriminate samples with different class labels. However, the class label information plays such an important role that it confines supervised methods by defining the possible classes. Consequently, supervised methods do not have the ability to discover new classes. To overcome the limitations of unsupervised and supervised methods, we propose a new method, which utilizes the class labels to a less important role so as to perform class discovery and classification simultaneously. The proposed method is called SPACC (SPectral Analysis for Class discovery and Classification). In SPACC, the training samples are nodes of an undirected weighted network. Using spectral analysis, SPACC iteratively partitions the network into a top-down binary tree. Each partitioning step is unsupervised, and the class labels are only used to define the stopping criterion. When the partitioning ends, the training samples have been divided into several subsets, each corresponding to one class label. Because multiple subsets can correspond to the same class label, SPACC may identify biologically meaningful subclasses, and minimize the impact of outliers and mislabeled data. We demonstrate the effectiveness of SPACC for class discovery and classification on microarray data of lymphomas and leukemias. SPACC software is available at http://icbp.stanford.edu/software/SPACC/.

    View details for DOI 10.1089/cmb.2008.0227

    View details for PubMedID 19580522

  • A stem cell-like signature predicts histological transformation and influences survival in follicular lymphoma patients Gentles, A., Alizadeh, A., Lee, S., Shahbaba, B., Shachaf, C., Levy, R., Koller, D., Plevritis, S. AMER ASSOC CANCER RESEARCH. 2009
  • Fast calculation of pairwise mutual information for gene regulatory network reconstruction COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE Qiu, P., Gentles, A. J., Plevritis, S. K. 2009; 94 (2): 177-180

    Abstract

    We present a new software implementation to more efficiently compute the mutual information for all pairs of genes from gene expression microarrays. Computation of the mutual information is a necessary first step in various information theoretic approaches for reconstructing gene regulatory networks from microarray data. When the mutual information is estimated by kernel methods, computing the pairwise mutual information is quite time-consuming. Our implementation significantly reduces the computation time. For an example data set of 336 samples consisting of normal and malignant B-cells, with 9563 genes measured per sample, the current available software for ARACNE requires 142 hours to compute the mutual information for all gene pairs, whereas our algorithm requires 1.6 hours. The increased efficiency of our algorithm improves the feasibility of applying mutual information based approaches for reconstructing large regulatory networks.

    View details for DOI 10.1016/j.cmpb.2008.11.003

    View details for PubMedID 19167129

  • Identification of the branchopoint between B and T cell development through MiDReG Inlay, M. A., Bhattacharya, D., Sahoo, D., Seita, J., Karsunky, H., Serwold, T., Plevritis, S., Dill, D., Weissman, I. L. AMER ASSOC IMMUNOLOGISTS. 2009
  • A Bayesian nonparametric method for model evaluation: application to genetic studies JOURNAL OF NONPARAMETRIC STATISTICS Shahbaba, B., Gentles, A. J., Beyene, J., Plevritis, S. K., Greenwood, C. M. 2009; 21 (3): 379-396
  • Characterization of Patient Specific Signaling via Augmentation of Bayesian Networks with Disease and Patient State Nodes Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society Sachs, K., Gentles, A. J., Youland, R., Itani, S., Irish, J., Nolan, G. P., Plevritis, S. K. IEEE. 2009: 6624–6627

    Abstract

    Characterization of patient-specific disease features at a molecular level is an important emerging field. Patients may be characterized by differences in the level and activity of relevant biomolecules in diseased cells. When high throughput, high dimensional data is available, it becomes possible to characterize differences not only in the level of the biomolecules, but also in the molecular interactions among them. We propose here a novel approach to characterize patient specific signaling, which augments high throughput single cell data with state nodes corresponding to patient and disease states, and learns a Bayesian network based on this data. Features distinguishing individual patients emerge as downstream nodes in the network. We illustrate this approach with a six phospho-protein, 30,000 cell-per-patient dataset characterizing three comparably diagnosed follicular lymphoma, and show that our approach elucidates signaling differences among them.

    View details for Web of Science ID 000280543605113

    View details for PubMedID 19963681

    View details for PubMedCentralID PMC3124088

  • Genomic and proteomic analysis reveals a threshold level of MYC required for tumor maintenance CANCER RESEARCH Shachaf, C. M., Gentles, A. J., Elchuri, S., Sahoo, D., Soen, Y., Sharpe, O., Perez, O. D., Chang, M., Mitchel, D., Robinson, W. H., Dill, D., Nolan, G. P., Plevritis, S. K., Felsher, D. W. 2008; 68 (13): 5132-5142

    Abstract

    MYC overexpression has been implicated in the pathogenesis of most types of human cancers. MYC is likely to contribute to tumorigenesis by its effects on global gene expression. Previously, we have shown that the loss of MYC overexpression is sufficient to reverse tumorigenesis. Here, we show that there is a precise threshold level of MYC expression required for maintaining the tumor phenotype, whereupon there is a switch from a gene expression program of proliferation to a state of proliferative arrest and apoptosis. Oligonucleotide microarray analysis and quantitative PCR were used to identify changes in expression in 3,921 genes, of which 2,348 were down-regulated and 1,573 were up-regulated. Critical changes in gene expression occurred at or near the MYC threshold, including genes implicated in the regulation of the G(1)-S and G(2)-M cell cycle checkpoints and death receptor/apoptosis signaling. Using two-dimensional protein analysis followed by mass spectrometry, phospho-flow fluorescence-activated cell sorting, and antibody arrays, we also identified changes at the protein level that contributed to MYC-dependent tumor regression. Proteins involved in mRNA translation decreased below threshold levels of MYC. Thus, at the MYC threshold, there is a loss of its ability to maintain tumorigenesis, with associated shifts in gene and protein expression that reestablish cell cycle checkpoints, halt protein translation, and promote apoptosis.

    View details for DOI 10.1158/0008-5472.CAN-07-6192

    View details for PubMedID 18593912

  • Boolean implication networks derived from large scale, whole genome microarray datasets GENOME BIOLOGY Sahoo, D., Dill, D. L., Gentles, A. J., Tibshirani, R., Plevritis, S. K. 2008; 9 (10)

    Abstract

    We describe a method for extracting Boolean implications (if-then relationships) in very large amounts of gene expression microarray data. A meta-analysis of data from thousands of microarrays for humans, mice, and fruit flies finds millions of implication relationships between genes that would be missed by other methods. These relationships capture gender differences, tissue differences, development, and differentiation. New relationships are discovered that are preserved across all three species.

    View details for PubMedID 18973690

  • Extracting binary signals from microarray time-course data NUCLEIC ACIDS RESEARCH Sahoo, D., Dill, D. L., Tibshirani, R., Plevritis, S. K. 2007; 35 (11): 3705-3712

    Abstract

    This article presents a new method for analyzing microarray time courses by identifying genes that undergo abrupt transitions in expression level, and the time at which the transitions occur. The algorithm matches the sequence of expression levels for each gene against temporal patterns having one or two transitions between two expression levels. The algorithm reports a P-value for the matching pattern of each gene, and a global false discovery rate can also be computed. After matching, genes can be sorted by the direction and time of transitions. Genes can be partitioned into sets based on the direction and time of change for further analysis, such as comparison with Gene Ontology annotations or binding site motifs. The method is evaluated on simulated and actual time-course data. On microarray data for budding yeast, it is shown that the groups of genes that change in similar ways and at similar times have significant and relevant Gene Ontology annotations.

    View details for DOI 10.1093/nar/gkm284

    View details for PubMedID 17517782

  • Ductal pattern enhancement on magnetic resonance imaging of the breast due to ductal lavage BREAST JOURNAL Ghanouni, P., Kurian, A. W., Margolis, D., Hartman, A., Mills, M. A., Plevritis, S. K., Ford, J. M., Daniel, B. L. 2007; 13 (3): 281-286

    Abstract

    Our purpose is to describe the appearance of breast ductal enhancement found on magnetic resonance imaging (MRI) after breast ductal lavage (DL). We describe a novel etiology of enhancement in a ductal pattern on postcontrast MRI of the breast. Knowledge of the potential for breast MRI enhancement subsequent to DL, which can mimic the appearance of a pathologic lesion, is critical to the care of patients who undergo breast MRI and DL or other intraductal cannulation procedures.

    View details for PubMedID 17461903

  • A natural history model of stage progression applied to breast cancer STATISTICS IN MEDICINE Plevritis, S. K., Salzman, P., Sigal, B. M., Glynn, P. W. 2007; 26 (3): 581-595

    Abstract

    Invasive breast cancer is commonly staged as local, regional or distant disease. We present a stochastic model of the natural history of invasive breast cancer that quantifies (1) the relative rate that the disease transitions from the local, regional to distant stages, (2) the tumour volume at the stage transitions and (3) the impact of symptom-prompted detection on the tumour size and stage of invasive breast cancer in a population not screened by mammography. By symptom-prompted detection, we refer to tumour detection that results when symptoms appear that prompt the patient to seek clinical care. The model assumes exponential tumour growth and volume-dependent hazard functions for the times to symptomatic detection and stage transitions. Maximum likelihood parameter estimates are obtained based on SEER data on the tumour size and stage of invasive breast cancer from patients who were symptomatically detected in the absence of screening mammography. Our results indicate that the rate of symptom-prompted detection is similar to the rate of transition from the local to regional stage and an order of magnitude larger than the rate of transition from the regional to distant stage. We demonstrate that, in the even absence of screening mammography, symptom-prompted detection has a large effect on reducing the occurrence of distant staged disease at initial diagnosis.

    View details for DOI 10.1002/sim.2550

    View details for PubMedID 16598706

  • Cost-effectiveness of screening BRCA1/2 mutation carriers with breast magnetic resonance imaging JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION Plevritis, S. K., Kurian, A. W., Sigal, B. M., Daniel, B. L., Ikeda, D. M., Stockdale, F. E., Garber, A. M. 2006; 295 (20): 2374-2384

    Abstract

    Women with inherited BRCA1/2 mutations are at high risk for breast cancer, which mammography often misses. Screening with contrast-enhanced breast magnetic resonance imaging (MRI) detects cancer earlier but increases costs and results in more false-positive scans.To evaluate the cost-effectiveness of screening BRCA1/2 mutation carriers with mammography plus breast MRI compared with mammography alone.A computer model that simulates the life histories of individual BRCA1/2 mutation carriers, incorporating the effects of mammographic and MRI screening was used. The accuracy of mammography and breast MRI was estimated from published data in high-risk women. Breast cancer survival in the absence of screening was based on the Surveillance, Epidemiology and End Results database of breast cancer patients diagnosed in the prescreening period (1975-1981), adjusted for the current use of adjuvant therapy. Utilization rates and costs of diagnostic and treatment interventions were based on a combination of published literature and Medicare payments for 2005.The survival benefit, incremental costs, and cost-effectiveness of MRI screening strategies, which varied by ages of starting and stopping MRI screening, were computed separately for BRCA1 and BRCA2 mutation carriers.Screening strategies that incorporate annual MRI as well as annual mammography have a cost per quality-adjusted life-year (QALY) gained ranging from less than 45,000 dollars to more than 700,000 dollars, depending on the ages selected for MRI screening and the specific BRCA mutation. Relative to screening with mammography alone, the cost per QALY gained by adding MRI from ages 35 to 54 years is 55,420 dollars for BRCA1 mutation carriers, 130,695 dollars for BRCA2 mutation carriers, and 98,454 dollars for BRCA2 mutation carriers who have mammographically dense breasts.Breast MRI screening is more cost-effective for BRCA1 than BRCA2 mutation carriers. The cost-effectiveness of adding MRI to mammography varies greatly by age.

    View details for PubMedID 16720823

  • A comparative review of CISNET breast models used to analyze U.S. breast cancer incidence and mortality trends. Journal of the National Cancer Institute. Monographs Clarke, L. D., Plevritis, S. K., Boer, R., Cronin, K. A., Feuer, E. J. 2006: 96-105

    Abstract

    The CISNET Breast Cancer program is a National Cancer Institute-sponsored collaboration composed of seven research groups that have modeled the impact of screening and adjuvant treatment on trends in breast cancer incidence and mortality over the period 1975-2000 (base case). This collaboration created a unique opportunity to make direct comparison of results from different models of population-based cancer screening produced in response to the same question. Comparing results in all but the most cursory way necessitates comparison of the models themselves. Previous chapters have discussed the models individual in detail. This chapter will aid the reader in understanding key areas of difference between the models. A focused analysis of differences and similarities between the models is presented with special attention paid to areas deemed most likely to contribute substantially to the results of the target analysis.

    View details for PubMedID 17032899

  • Impact of adjuvant therapy and mammography on U.S. mortality from 1975 to 2000: comparison of mortality results from the cisnet breast cancer base case analysis. Journal of the National Cancer Institute. Monographs Cronin, K. A., Feuer, E. J., Clarke, L. D., Plevritis, S. K. 2006: 112-121

    Abstract

    The CISNET breast cancer program is a consortium of seven research groups modeling the impact of various cancer interventions on the national trends of breast cancer incidence and mortality. Each of the modeling groups participated in a CISNET breast cancer base case analysis with the objective of assessing the impact of mammography and adjuvant therapy on breast cancer mortality between 1975 and 2000. The comparative modeling approach used to address this question allowed for a unique view into the process of modeling. Results shown here expand on those recently reported in the New England Journal of Medicine (Berry et al., N Engl J Med 2005;353:1784-92) by presenting mortality impact in several different ways to facilitate comparisons between models. Comparisons of each group's results in the context of modeling assumptions made during the process gave insight into how specific model assumptions may have affected the results. The median estimate for the percent decline in breast cancer mortality due to mammography was 15% (range of 8%-23%), and the median estimate for the percent decline in mortality due to adjuvant treatment was 19% (range of 12%-21%). A detailed discussion of the differences in modeling approaches and how those differences may have influenced the mortality results concludes the chapter.

    View details for PubMedID 17032901

  • A stochastic simulation model of U.S. breast cancer mortality trends from 1975 to 2000. Journal of the National Cancer Institute. Monographs Plevritis, S. K., Sigal, B. M., Salzman, P., Rosenberg, J., Glynn, P. 2006: 86-95

    Abstract

    We present a simulation model that predicts U.S. breast cancer mortality trends from 1975 to 2000 and quantifies the impact of screening mammography and adjuvant therapy on these trends. This model was developed within the Cancer Intervention and Surveillance Network (CISNET) consortium.A Monte Carlo simulation is developed to generate the life history of individual breast cancer patients by using CISNET base case inputs that describe the secular trend in breast cancer risk, dissemination patterns for screening mammography and adjuvant treatment, and death from causes other than breast cancer. The model generates the patient's age, tumor size and stage at detection, mode of detection, age at death, and cause of death (breast cancer versus other) based in part on assumptions on the natural history of breast cancer. Outcomes from multiple birth cohorts are summarized in terms of breast cancer mortality rates by calendar year.Predicted breast cancer mortality rates follow the general shape of U.S. breast cancer mortality rates from 1975 to 1995 but level off after 1995 as opposed to following an observed decline. Sensitivity analysis revealed that the impact adjuvant treatment may be underestimated given the lack of data on temporal variation in treatment efficacy.We developed a simulation model that uses CISNET base case inputs and closely, but not exactly, reproduces U.S. breast cancer mortality rates. Screening mammography and adjuvant therapy are shown to have both contributed to a decline in U.S. breast cancer mortality.

    View details for PubMedID 17032898

  • Effect of screening and adjuvant therapy on mortality from breast cancer NEW ENGLAND JOURNAL OF MEDICINE Berry, D. A., Cronin, K. A., Plevritis, S. K., Fryback, D. G., Clarke, L., Zelen, M., Mandelblatt, J. S., Yakovlev, A. Y., Habbema, J. D., Feuer, E. J. 2005; 353 (17): 1784-1792

    Abstract

    We used modeling techniques to assess the relative and absolute contributions of screening mammography and adjuvant treatment to the reduction in breast-cancer mortality in the United States from 1975 to 2000.A consortium of investigators developed seven independent statistical models of breast-cancer incidence and mortality. All seven groups used the same sources to obtain data on the use of screening mammography, adjuvant treatment, and benefits of treatment with respect to the rate of death from breast cancer.The proportion of the total reduction in the rate of death from breast cancer attributed to screening varied in the seven models from 28 to 65 percent (median, 46 percent), with adjuvant treatment contributing the rest. The variability across models in the absolute contribution of screening was larger than it was for treatment, reflecting the greater uncertainty associated with estimating the benefit of screening.Seven statistical models showed that both screening mammography and treatment have helped reduce the rate of death from breast cancer in the United States.

    View details for Web of Science ID 000232813000006

    View details for PubMedID 16251534

  • Decision analysis and simulation modeling for evaluating diagnostic tests on the basis of patient outcomes. AMERICAN JOURNAL OF ROENTGENOLOGY Plevritis, S. K. 2005; 185 (3): 581-590

    View details for PubMedID 16120903

  • The effect of age, race, tumor size, tumor grade, and disease stage on invasive ductal breast cancer survival in the USSEER database BREAST CANCER RESEARCH AND TREATMENT Rosenberg, J., Chia, Y. L., Plevritis, S. 2005; 89 (1): 47-54

    Abstract

    To examine the effect of patient and tumor characteristics on breast cancer survival as recorded in the U.S. National Cancer Institute's Surveillance, Epidemiology, and End Results (SEER) database from 1973 to 1998.A sample of 72,367 female cases from 1973 to 1998 aged 21-90 years with invasive ductal breast cancer were examined with Cox proportional hazards regression to determine the effect of age at diagnosis, race, tumor size, tumor grade, disease stage, and year of diagnosis on disease-specific survival.Larger tumor size and higher tumor grade were found to have large negative effects on survival. Blacks had a 47 % greater risk of death than whites. Year of diagnosis had a positive effect, with a 15 % reduction in risk for each decade in the time period under study. The effects of patient age and disease stage violated the proportional hazards assumption, with distant disease having much poorer short-term survival than one would expect from a proportional hazards model, and younger age groups matching or even falling below the survival rate of the oldest group over time.Tumor size, grade, race, and year of diagnosis all have significant constant effects on disease-specific survival in breast cancer, while the effects of age at diagnosis and disease stage have significant effects that vary over time.

    View details for PubMedID 15666196

  • Breast magnetic resonance image screening and ductal lavage in women at high genetic risk for breast carcinoma CANCER Hartman, A. R., Daniel, B. L., Kurian, A. W., Mills, M. A., Nowels, K. W., Dirbas, F. M., Kingham, K. E., Chun, N. M., Herfkens, R. J., Ford, J. M., Plevritis, S. K. 2004; 100 (3): 479-489

    Abstract

    Intensive screening is an alternative to prophylactic mastectomy in women at high risk for developing breast carcinoma. The current article reports preliminary results from a screening protocol using high-quality magnetic resonance imaging (MRI), ductal lavage (DL), clinical breast examination, and mammography to identify early malignancy and high-risk lesions in women at increased genetic risk of breast carcinoma.Women with inherited BRCA1 or BRCA2 mutations or women with a >10% risk of developing breast carcinoma at 10 years, as estimated by the Claus model, were eligible. Patients were accrued from September 2001 to May 2003. Enrolled patients underwent biannual clinical breast examinations and annual mammography, breast MRI, and DL.Forty-one women underwent an initial screen. Fifteen of 41 enrolled women (36.6%) either had undergone previous bilateral oophorectomy and/or were on tamoxifen at the time of the initial screen. One patient who was a BRCA1 carrier had high-grade ductal carcinoma in situ (DCIS) that was screen detected by MRI but that was missed on mammography. High-risk lesions that were screen detected by MRI in three women included radial scars and atypical lobular hyperplasia. DL detected seven women with cellular atypia, including one woman who had a normal MRI and mammogram.Breast MRI identified high-grade DCIS and high-risk lesions that were missed by mammography. DL detected cytologic atypia in a high-risk cohort. A larger screening trial is needed to determine which subgroups of high-risk women will benefit and whether the identification of malignant and high-risk lesions at an early stage will impact breast carcinoma incidence and mortality.

    View details for DOI 10.1002/cncr.11926

    View details for PubMedID 14745863

  • Diversity of model approaches for breast cancer screening: a review of model assumptions by The Cancer Intervention and Surveillance Network (CISNET) Breast Cancer Groups STATISTICAL METHODS IN MEDICAL RESEARCH Boer, R., Plevritis, S., Clarke, L. 2004; 13 (6): 525-538

    Abstract

    The National Cancer Institute-sponsored Cancer Intervention and Surveillance Network program on breast cancer is composed of seven research groups working largely independently to model the impact of screening and adjuvant therapy on breast cancer mortality trends in the US from 1975 to 2000. Each of the groups has chosen a different modeling methodology without purposeful attempt to be in contrast with each other. The seven groups have met biannually since November 2000 to discuss their methodology and results. This article investigates the differences in methodology. To facilitate this comparison, each of the groups submitted a description of their model into a uniformly structured web based 'model profiler'. Six of the seven models simulate a preclinical natural history that cannot be observed directly with parameters estimated from published evidence concerning screening and therapy effects. The remaining model regards published evidence on intervention effects as prior information and updates that with information from the US population in a Bayesian type analysis. In general, the differences between the models appear to be small, particularly among the models driven by natural history assumptions. However, we demonstrate that such apparently small differences can have a large impact on surveillance of population trends. We describe a systematic approach to evaluating differences in model assumptions and results, as well as differences in modeling culture underlying the differences in model structure and parameters.

    View details for DOI 10.1191/0962280204sm381ra

    View details for Web of Science ID 000225102100007

    View details for PubMedID 15587437

  • Simulation-based parameter estimation for complex models: a breast cancer natural history modelling illustration STATISTICAL METHODS IN MEDICAL RESEARCH Chia, Y. L., Salzman, P., Plevritis, S. K., Glynn, P. W. 2004; 13 (6): 507-524

    Abstract

    Simulation-based parameter estimation offers a powerful means of estimating parameters in complex stochastic models. We illustrate the application of these ideas in the setting of a natural history model for breast cancer. Our model assumes that the tumor growth process follows a geometric Brownian motion; parameters are estimated from the SEER registry. Our discussion focuses on the use of simulation for computing the maximum likelihood estimator for this class of models. The analysis shows that simulation provides a straightforward means of computing such estimators for models of substantial complexity.

    View details for DOI 10.1191/0962280204sm380ra

    View details for PubMedID 15587436

  • SPECTRAL EXTRAPOLATION OF SPATIALLY BOUNDED IMAGES IEEE TRANSACTIONS ON MEDICAL IMAGING Plevritis, S. K., Macovski, A. 1995; 14 (3): 487-497

    Abstract

    A spectral extrapolation algorithm for spatially bounded images is presented. An image is said to be spatially bounded when it is confined to a closed region and is surrounded by a background of zeros. With prior knowledge of the spatial domain zeros, the extrapolation algorithm extends the image's spectrum beyond a known interval of low-frequency components. The result, which is referred to as the finite support solution, has space variant resolution; features near the edge of the support region are better resolved than those in the center. The resolution of the finite support solution is discussed as a function of the number of known spatial zeros and known spectral components. A regularized version of the finite support solution is included for handling the case where the known spectral components are noisy. For both the noiseless and noisy cases, the resolution of the finite support solution is measured in terms of its impulse response characteristics, and compared to the resolution of the zerofilled and Nyquist solutions. The finite support solution is superior to the zerofilled solution for both the noisy and noiseless data cases. When compared to the Nyquist solution, the finite support solution may be preferred in the noisy data case. Examples using medical image data are provided.

    View details for Web of Science ID A1995RU69200009

    View details for PubMedID 18215853