Stanford Advisors


All Publications


  • Development and Validation of a Risk Prediction Tool for Second Primary Lung Cancer. Journal of the National Cancer Institute Choi, E., Sanyal, N., Ding, V. Y., Gardner, R. M., Aredo, J. V., Lee, J., Wu, J. T., Hickey, T. P., Barrett, B., Riley, T. L., Wilkens, L. R., Leung, A. N., Le Marchand, L., Tammemagi, M. C., Hung, R. J., Amos, C. I., Freedman, N. D., Cheng, I., Wakelee, H. A., Han, S. S. 2021

    Abstract

    BACKGROUND: With advancing therapeutics, lung cancer (LC) survivors are rapidly increasing in number. While mounting evidence suggests LC survivors have high risk of second primary lung cancer (SPLC), there is no validated prediction tool available for clinical use to identify high-risk LC survivors for SPLC.METHODS: Using data from 6,325 ever-smokers in the Multiethnic Cohort (MEC) diagnosed with initial primary lung cancer (IPLC) in 1993-2017, we developed a prediction model for 10-year SPLC risk after IPLC diagnosis using cause-specific Cox regression. We evaluated the model's clinical utility using decision curve analysis and externally validated it using two population-based data, PLCO and NLST, that included 2,963 and 2,844 IPLC (101 and 93 SPLC cases), respectively.RESULTS: Over 14,063 person-years, 145 (2.3%) developed SPLC in MEC. Our prediction model demonstrated a high predictive accuracy (Brier score = 2.9, 95% confidence interval [CI] = 2.4-3.3) and discrimination (AUC = 81.9%, 95% CI=78.2%-85.5%) based on bootstrap validation in MEC. Stratification by the estimated risk quartiles showed that the observed SPLC incidence was statistically significantly higher in the 4th versus 1st quartile (9.5% versus 0.2%; P<.001). Decision curve analysis indicated that in a wide range of 10-year risk thresholds from 1% to 20%, the model yielded a larger net-benefit versus hypothetical all-screening or no-screening scenarios. External validation using PLCO and NLST showed an AUC of 78.8% (95% CI=74.6%-82.9%) and 72.7% (95% CI=67.7%-77.7%), respectively.CONCLUSIONS: We developed and validated a SPLC prediction model based on large population-based cohorts. The proposed prediction tool can help identify high-risk LC patients for SPLC and can be incorporated into clinical decision-making for SPLC surveillance and screening.

    View details for DOI 10.1093/jnci/djab138

    View details for PubMedID 34255071

  • Patterns in cancer management changes for patients with COVID-19 in northern California. Glover, M., Wu, J., Kwon, D. H., Zhang, S., Henry, S., Wood, D., Rubin, D., Borno, H., Small, E., Schapira, L., Koshkin, V. S., Shah, S. LIPPINCOTT WILLIAMS & WILKINS. 2021
  • Impact of COVID-19 on breast cancer care at a Bay Area academic center Wu, J., Bobo, S., Henry, S., Mills, M., Kurian, A., Dirbas, F. AMER ASSOC CANCER RESEARCH. 2021
  • Impact of Low-Dose CT Screening for Primary Lung Cancer on Subsequent Risk of Brain Metastasis. Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer Su, C. C., Wu, J. T., Neal, J. W., Popat, R. A., Kurian, A. W., Backhus, L. M., Nagpal, S., Leung, A. N., Wakelee, H. A., Han, S. S. 2021

    Abstract

    Brain metastasis (BM) is one of the most common metastases from primary lung cancer (PLC). Recently, the National Lung Screening Trial (NLST) demonstrated the efficacy of low-dose computed tomography (LDCT) screening on LC mortality reduction. However, it remains unknown if early detection of PLC through LDCT may be potentially beneficial in reducing the risk of subsequent metastases. Our study aimed to investigate the impact of LDCT screening for PLC on the risk of developing BM after PLC diagnosis.We used NLST data to identify 1,502 participants who were diagnosed with PLC in 2002-2009 and have follow-up data for BM. Cause-specific competing risk regression was applied to evaluate an association between BM risk and the mode of PLC detection-i.e., LDCT screen-detected versus non-LDCT screen-detected. Subgroup analyses were conducted in early-stage PLC patients and those who underwent surgery for PLC.Of 1502 participants, 41.4% had PLC detected through LDCT-screening versus 58.6% detected through other methods, e.g., chest X-Ray or incidental detection. Patients whose PLC was detected with LDCT-screening had a significantly lower 3-year incidence of BM (6.5%) versus those without (11.9%), with a cause-specific hazard ratio (HR) of 0.53 (p=0.001), adjusting for PLC stage, histology, diagnosis age and smoking status. This significant reduction in BM risk among PLCs detected through LDCT-screening persisted in subgroups of early-stage PLC participants (HR 0.47, p=0.002) and those who underwent surgery (HR 0.37, p=0.001).Early detection of PLC using LDCT-screening is associated with lower risk of BM after PLC diagnosis based on a large population-based study.

    View details for DOI 10.1016/j.jtho.2021.05.010

    View details for PubMedID 34091050

  • Utilization of COVID-19 treatments and clinical outcomes among patients with cancer: A COVID-19 and Cancer Consortium (CCC19) cohort study. Cancer discovery Rivera, D. R., Peters, S., Panagiotou, O. A., Shah, D. P., Kuderer, N. M., Hsu, C., Rubinstein, S. M., Lee, B. J., Choueiri, T. K., de Lima Lopes, G., Grivas, P., Painter, C. A., Rini, B. I., Thompson, M. A., Arcobello, J., Bakouny, Z., Doroshow, D. B., Egan, P. C., Farmakiotis, D., Fecher, L. A., Friese, C. R., Galsky, M. D., Goel, S., Gupta, S., Halfdanarson, T. R., Halmos, B., Hawley, J. E., Khaki, A. R., Lemmon, C. A., Mishra, S., Olszewski, A. J., Pennell, N. A., Puc, M. M., Revankar, S. G., Schapira, L., Schmidt, A., Schwartz, G. K., Shah, S. A., Wu, J. T., Xie, Z., Yeh, A. C., Zhu, H., Shyr, Y., Lyman, G. H., Warner, J. L. 2020

    Abstract

    Among 2,186 US adults with invasive cancer and laboratory-confirmed SARS-CoV-2 infection, we examined the association of COVID-19 treatments with 30-day all-cause mortality, and factors associated with treatment. Logistic regression with multiple adjustments (e.g., comorbidities, cancer status, baseline COVID-19 severity) was performed. Hydroxychloroquine with any other drug was associated with increased mortality versus treatment with any COVID-19 treatment other than hydroxychloroquine or untreated controls; this association was not present with hydroxychloroquine alone. Remdesivir had numerically reduced mortality versus untreated controls that did not reach statistical significance. Baseline COVID-19 severity was strongly associated with receipt of any treatment. Black patients were approximately half as likely to receive remdesivir as white patients. While observational studies can be limited by potential unmeasured confounding, our findings add to the emerging understanding of patterns of care for patients with cancer and COVID-19 and support evaluation of emerging treatments through prospective controlled trials inclusive of this population.

    View details for DOI 10.1158/2159-8290.CD-20-0941

    View details for PubMedID 32699031

  • Opportunities and Challenges for Analyzing Cancer Data at the Inter- and Intra-Institutional Levels JCO PRECISION ONCOLOGY Wu, J., Bryan, J., Rubinstein, S. M., Wang, L., Lenoue-Newton, M., Zuhour, R., Levy, M., Micheel, C., Xu, Y., Bhavnani, S. K., Mackey, L., Warner, J. L. 2020; 4: 743–56

    Abstract

    Our goal was to identify the opportunities and challenges in analyzing data from the American Association of Cancer Research Project Genomics Evidence Neoplasia Information Exchange (GENIE), a multi-institutional database derived from clinically driven genomic testing, at both the inter- and the intra-institutional level. Inter-institutionally, we identified genotypic differences between primary and metastatic tumors across the 3 most represented cancers in GENIE. Intra-institutionally, we analyzed the clinical characteristics of the Vanderbilt-Ingram Cancer Center (VICC) subset of GENIE to inform the interpretation of GENIE as a whole.We performed overall cohort matching on the basis of age, ethnicity, and sex of 13,208 patients stratified by cancer type (breast, colon, or lung) and sample site (primary or metastatic). We then determined whether detected variants, at the gene level, were associated with primary or metastatic tumors. We extracted clinical data for the VICC subset from VICC's clinical data warehouse. Treatment exposures were mapped to a 13-class schema derived from the HemOnc ontology.Across 756 genes, there were significant differences in all cancer types. In breast cancer, ESR1 variants were over-represented in metastatic samples (odds ratio, 5.91; q < 10-6). TP53 mutations were over-represented in metastatic samples across all cancers. VICC had a significantly different cancer type distribution than that of GENIE but patients were well matched with respect to age, sex, and sample type. Treatment data from VICC was used for a bipartite network analysis, demonstrating clusters with a mix of histologies and others being more histology specific.This article demonstrates the feasibility of deriving meaningful insights from GENIE at the inter- and intra-institutional level and illuminates the opportunities and challenges of the data GENIE contains. The results should help guide future development of GENIE, with the goal of fully realizing its potential for accelerating precision medicine.

    View details for DOI 10.1200/PO.19.00394

    View details for Web of Science ID 000615673800001

    View details for PubMedID 32923903

    View details for PubMedCentralID PMC7446524

  • Clinical impact of COVID-19 on patients with cancer (CCC19): a cohort study. Lancet (London, England) Kuderer, N. M., Choueiri, T. K., Shah, D. P., Shyr, Y. n., Rubinstein, S. M., Rivera, D. R., Shete, S. n., Hsu, C. Y., Desai, A. n., de Lima Lopes, G. n., Grivas, P. n., Painter, C. A., Peters, S. n., Thompson, M. A., Bakouny, Z. n., Batist, G. n., Bekaii-Saab, T. n., Bilen, M. A., Bouganim, N. n., Larroya, M. B., Castellano, D. n., Del Prete, S. A., Doroshow, D. B., Egan, P. C., Elkrief, A. n., Farmakiotis, D. n., Flora, D. n., Galsky, M. D., Glover, M. J., Griffiths, E. A., Gulati, A. P., Gupta, S. n., Hafez, N. n., Halfdanarson, T. R., Hawley, J. E., Hsu, E. n., Kasi, A. n., Khaki, A. R., Lemmon, C. A., Lewis, C. n., Logan, B. n., Masters, T. n., McKay, R. R., Mesa, R. A., Morgans, A. K., Mulcahy, M. F., Panagiotou, O. A., Peddi, P. n., Pennell, N. A., Reynolds, K. n., Rosen, L. R., Rosovsky, R. n., Salazar, M. n., Schmidt, A. n., Shah, S. A., Shaya, J. A., Steinharter, J. n., Stockerl-Goldstein, K. E., Subbiah, S. n., Vinh, D. C., Wehbe, F. H., Weissmann, L. B., Wu, J. T., Wulff-Burchfield, E. n., Xie, Z. n., Yeh, A. n., Yu, P. P., Zhou, A. Y., Zubiri, L. n., Mishra, S. n., Lyman, G. H., Rini, B. I., Warner, J. L. 2020

    Abstract

    Data on patients with COVID-19 who have cancer are lacking. Here we characterise the outcomes of a cohort of patients with cancer and COVID-19 and identify potential prognostic factors for mortality and severe illness.In this cohort study, we collected de-identified data on patients with active or previous malignancy, aged 18 years and older, with confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection from the USA, Canada, and Spain from the COVID-19 and Cancer Consortium (CCC19) database for whom baseline data were added between March 17 and April 16, 2020. We collected data on baseline clinical conditions, medications, cancer diagnosis and treatment, and COVID-19 disease course. The primary endpoint was all-cause mortality within 30 days of diagnosis of COVID-19. We assessed the association between the outcome and potential prognostic variables using logistic regression analyses, partially adjusted for age, sex, smoking status, and obesity. This study is registered with ClinicalTrials.gov, NCT04354701, and is ongoing.Of 1035 records entered into the CCC19 database during the study period, 928 patients met inclusion criteria for our analysis. Median age was 66 years (IQR 57-76), 279 (30%) were aged 75 years or older, and 468 (50%) patients were male. The most prevalent malignancies were breast (191 [21%]) and prostate (152 [16%]). 366 (39%) patients were on active anticancer treatment, and 396 (43%) had active (measurable) cancer. At analysis (May 7, 2020), 121 (13%) patients had died. In logistic regression analysis, independent factors associated with increased 30-day mortality, after partial adjustment, were: increased age (per 10 years; partially adjusted odds ratio 1·84, 95% CI 1·53-2·21), male sex (1·63, 1·07-2·48), smoking status (former smoker vs never smoked: 1·60, 1·03-2·47), number of comorbidities (two vs none: 4·50, 1·33-15·28), Eastern Cooperative Oncology Group performance status of 2 or higher (status of 2 vs 0 or 1: 3·89, 2·11-7·18), active cancer (progressing vs remission: 5·20, 2·77-9·77), and receipt of azithromycin plus hydroxychloroquine (vs treatment with neither: 2·93, 1·79-4·79; confounding by indication cannot be excluded). Compared with residence in the US-Northeast, residence in Canada (0·24, 0·07-0·84) or the US-Midwest (0·50, 0·28-0·90) were associated with decreased 30-day all-cause mortality. Race and ethnicity, obesity status, cancer type, type of anticancer therapy, and recent surgery were not associated with mortality.Among patients with cancer and COVID-19, 30-day all-cause mortality was high and associated with general risk factors and risk factors unique to patients with cancer. Longer follow-up is needed to better understand the effect of COVID-19 on outcomes in patients with cancer, including the ability to continue specific cancer treatments.American Cancer Society, National Institutes of Health, and Hope Foundation for Cancer Research.

    View details for DOI 10.1016/S0140-6736(20)31187-9

    View details for PubMedID 32473681

  • Changes in Cancer Management due to COVID-19 Illness in Patients with Cancer in Northern California. JCO oncology practice Wu, J. T., Kwon, D. H., Glover, M. J., Henry, S. n., Wood, D. n., Rubin, D. L., Koshkin, V. S., Schapira, L. n., Shah, S. A. 2020: OP2000790

    Abstract

    The response to the COVID-19 pandemic has affected the management of patients with cancer. In this pooled retrospective analysis, we describe changes in management patterns for patients with cancer diagnosed with COVID-19 in two academic institutions in the San Francisco Bay Area.Adult and pediatric patients diagnosed with COVID-19 with a current or historical diagnosis of malignancy were identified from the electronic medical record at the University of California, San Francisco, and Stanford University. The proportion of patients undergoing active cancer management whose care was affected was quantified and analyzed for significant differences with regard to management type, treatment intent, and the time of COVID-19 diagnosis. The duration and characteristics of such changes were compared across subgroups.A total of 131 patients were included, of whom 55 were undergoing active cancer management. Of these, 35 of 55 (64%) had significant changes in management that consisted primarily of delays. An additional three patients not undergoing active cancer management experienced a delay in management after being diagnosed with COVID-19. The decision to change management was correlated with the time of COVID-19 diagnosis, with more delays identified in patients treated with palliative intent earlier in the course of the pandemic (March/April 2020) compared with later (May/June 2020) (OR, 4.2; 95% CI, 1.03 to 17.3; P = .0497). This difference was not seen among patients treated with curative intent during the same timeframe.We found significant changes in the management of cancer patients with COVID-19 treated with curative and palliative intent that evolved over time. Future studies are needed to determine the impact of changes in management and treatment on cancer outcomes for patients with cancer and COVID-19.

    View details for DOI 10.1200/OP.20.00790

    View details for PubMedID 33332170