Clinical Focus

  • Anatomic and Clinical Pathology
  • Gastrointestinal and Hepatic Pathology
  • Pancreatobiliary Pathology
  • Artificial Intelligence
  • Digital and Computational Pathology

Academic Appointments

Honors & Awards

  • Biodesign Faculty Fellowship, Stanford Byers Center for Biodesign (2019)
  • Cancer Innovation Award, Stanford Cancer Institute (2019)
  • Pathology in Precision Health Research Award, Stanford University Department of Pathology (2017)
  • Best Abstract Award Runner-up, Rodger C. Haggitt GI Pathology Society (2013)
  • Joseph J. and Ernesta G. Mira Scholarship, Washington University, School of Medicine (2009)
  • George F. Gill Scholarship, Washington University, School of Medicine (2009)
  • E.A. Marquard Memorial Student Scholarship, Washington University, School of Medicine (2008)
  • Dr. Larry T. Chiang Scholarship, Washington University, School of Medicine (2006)
  • President’s Scholar Award, Stanford University (2001)

Boards, Advisory Committees, Professional Organizations

  • NCCN Clinical Practice Guidelines in Oncology, Ampullary Adenocarcinoma Panel Member, National Comprehensive Cancer Network (NCCN) (2021 - Present)
  • Associate Director (Pathology), Center for Artificial Intelligence in Medical Imaging (AIMI), Stanford University (2020 - Present)
  • ECOG-ACRIN Laboratory Science & Pathology Review Committee Member, Eastern Cooperative Oncology Group-American College of Radiology Imaging Network (ECOG-ACRIN) (2020 - Present)
  • NCCN Clinical Practice Guidelines in Oncology, Pancreatic Adenocarcinoma Panel Member, National Comprehensive Cancer Network (NCCN) (2020 - Present)
  • Abstract Review Committee Member, Gastrointestinal Pathology, United States and Canadian Academy of Pathology (USCAP) (2017 - Present)
  • Member, Executive Committee, Center for Artificial Intelligence in Medical Imaging (AIMI), Stanford University (2017 - Present)
  • Member, College of American Pathologists (2012 - Present)
  • Member, United States and Canadian Academy of Pathology (USCAP) (2011 - Present)

Professional Education

  • Fellowship, Brigham and Women's Hospital, Harvard Medical School, Gastrointestinal and Hepatopancreatobiliary Pathology (2015)
  • Board Certification: American Board of Pathology, Anatomic and Clinical Pathology (2014)
  • Residency, Brigham and Women's Hospital, Harvard Medical School, Anatomic and Clinical Pathology (2014)
  • MD, Washington University in St. Louis, Doctor of Medicine (2010)
  • BS, Stanford University, Biological Sciences (2005)

Current Research and Scholarly Interests

Gastrointestinal and pancreatobiliary pathology, with major emphasis on GI and pancreatic neoplasia, inflammatory bowel disease, biodesign innovation, and the application of machine learning to digital pathology.

Stanford Advisees

All Publications

  • Inflamed immune phenotype predicts favorable clinical outcomes of immune checkpoint inhibitor therapy across multiple cancer types. Journal for immunotherapy of cancer Shen, J., Choi, Y. L., Lee, T., Kim, H., Chae, Y. K., Dulken, B. W., Bogdan, S., Huang, M., Fisher, G. A., Park, S., Lee, S. H., Hwang, J. E., Chung, J. H., Kim, L., Song, H., Pereira, S., Shin, S., Lim, Y., Ahn, C. H., Kim, S., Oum, C., Kim, S., Park, G., Song, S., Jung, W., Kim, S., Bang, Y. J., Mok, T. S., Ali, S. M., Ock, C. Y. 2024; 12 (2)


    The inflamed immune phenotype (IIP), defined by enrichment of tumor-infiltrating lymphocytes (TILs) within intratumoral areas, is a promising tumor-agnostic biomarker of response to immune checkpoint inhibitor (ICI) therapy. However, it is challenging to define the IIP in an objective and reproducible manner during manual histopathologic examination. Here, we investigate artificial intelligence (AI)-based immune phenotypes capable of predicting ICI clinical outcomes in multiple solid tumor types.Lunit SCOPE IO is a deep learning model which determines the immune phenotype of the tumor microenvironment based on TIL analysis. We evaluated the correlation between the IIP and ICI treatment outcomes in terms of objective response rates (ORR), progression-free survival (PFS), and overall survival (OS) in a cohort of 1,806 ICI-treated patients representing over 27 solid tumor types retrospectively collected from multiple institutions.We observed an overall IIP prevalence of 35.2% and significantly more favorable ORRs (26.3% vs 15.8%), PFS (median 5.3 vs 3.1 months, HR 0.68, 95% CI 0.61 to 0.76), and OS (median 25.3 vs 13.6 months, HR 0.66, 95% CI 0.57 to 0.75) after ICI therapy in IIP compared with non-IIP patients, respectively (p<0.001 for all comparisons). On subgroup analysis, the IIP was generally prognostic of favorable PFS across major patient subgroups, with the exception of the microsatellite unstable/mismatch repair deficient subgroup.The AI-based IIP may represent a practical, affordable, clinically actionable, and tumor-agnostic biomarker prognostic of ICI therapy response across diverse tumor types.

    View details for DOI 10.1136/jitc-2023-008339

    View details for PubMedID 38355279

    View details for PubMedCentralID PMC10868175

  • Computational pathology in 2030: a Delphi study forecasting the role of AI in pathology within the next decade. EBioMedicine Berbís, M. A., McClintock, D. S., Bychkov, A., Van der Laak, J., Pantanowitz, L., Lennerz, J. K., Cheng, J. Y., Delahunt, B., Egevad, L., Eloy, C., Farris, A. B., Fraggetta, F., García Del Moral, R., Hartman, D. J., Herrmann, M. D., Hollemans, E., Iczkowski, K. A., Karsan, A., Kriegsmann, M., Salama, M. E., Sinard, J. H., Tuthill, J. M., Williams, B., Casado-Sánchez, C., Sánchez-Turrión, V., Luna, A., Aneiros-Fernández, J., Shen, J. 2023; 88: 104427


    Artificial intelligence (AI) is rapidly fuelling a fundamental transformation in the practice of pathology. However, clinical integration remains challenging, with no AI algorithms to date in routine adoption within typical anatomic pathology (AP) laboratories. This survey gathered current expert perspectives and expectations regarding the role of AI in AP from those with first-hand computational pathology and AI experience.Perspectives were solicited using the Delphi method from 24 subject matter experts between December 2020 and February 2021 regarding the anticipated role of AI in pathology by the year 2030. The study consisted of three consecutive rounds: 1) an open-ended, free response questionnaire generating a list of survey items; 2) a Likert-scale survey scored by experts and analysed for consensus; and 3) a repeat survey of items not reaching consensus to obtain further expert consensus.Consensus opinions were reached on 141 of 180 survey items (78.3%). Experts agreed that AI would be routinely and impactfully used within AP laboratory and pathologist clinical workflows by 2030. High consensus was reached on 100 items across nine categories encompassing the impact of AI on (1) pathology key performance indicators (KPIs) and (2) the pathology workforce and specific tasks performed by (3) pathologists and (4) AP lab technicians, as well as (5) specific AI applications and their likelihood of routine use by 2030, (6) AI's role in integrated diagnostics, (7) pathology tasks likely to be fully automated using AI, and (8) regulatory/legal and (9) ethical aspects of AI integration in pathology.This systematic consensus study details the expected short-to-mid-term impact of AI on pathology practice. These findings provide timely and relevant information regarding future care delivery in pathology and raise key practical, ethical, and legal challenges that must be addressed prior to AI's successful clinical implementation.No specific funding was provided for this study.

    View details for DOI 10.1016/j.ebiom.2022.104427

    View details for PubMedID 36603288

    View details for PubMedCentralID PMC9823157

  • Learning domain-agnostic visual representation for computational pathology using medically-irrelevant style transfer augmentation IEEE Transactions on Medical Imaging Yamashita, R., Long, J., Banda, S., Shen*, J., Rubin*, D. L., (*equal contribution) 2021: 3945-3954


    Suboptimal generalization of machine learning models on unseen data is a key challenge which hampers the clinical applicability of such models to medical imaging. Although various methods such as domain adaptation and domain generalization have evolved to combat this challenge, learning robust and generalizable representations is core to medical image understanding, and continues to be a problem. Here, we propose STRAP (Style TRansfer Augmentation for histoPathology), a form of data augmentation based on random style transfer from non-medical style sources such as artistic paintings, for learning domain-agnostic visual representations in computational pathology. Style transfer replaces the low-level texture content of an image with the uninformative style of randomly selected style source image, while preserving the original high-level semantic content. This improves robustness to domain shift and can be used as a simple yet powerful tool for learning domain-agnostic representations. We demonstrate that STRAP leads to state-of-the-art performance, particularly in the presence of domain shifts, on two particular classification tasks in computational pathology. Our code is available at

    View details for DOI 10.1109/TMI.2021.3101985

  • Deep learning for the prediction of microsatellite instability in colorectal cancer: a diagnostic study The Lancet Oncology Yamashita, R., Long, J., Longacre, T., Peng, L., Berrry, G. J., Martin, B., Higgins, J. P., Rubin, D. L., Shen, J. 2021; 22 (1): 132-141
  • Deep learning predicts postsurgical recurrence of hepatocellular carcinoma from digital histopathologic images. Scientific reports Yamashita, R. n., Long, J. n., Saleem, A. n., Rubin, D. L., Shen, J. n. 2021; 11 (1): 2047


    Recurrence risk stratification of patients undergoing primary surgical resection for hepatocellular carcinoma (HCC) is an area of active investigation, and several staging systems have been proposed to optimize treatment strategies. However, as many as 70% of patients still experience tumor recurrence at 5 years post-surgery. We developed and validated a deep learning-based system (HCC-SurvNet) that provides risk scores for disease recurrence after primary resection, directly from hematoxylin and eosin-stained digital whole-slide images of formalin-fixed, paraffin embedded liver resections. Our model achieved concordance indices of 0.724 and 0.683 on the internal and external test cohorts, respectively, exceeding the performance of the standard Tumor-Node-Metastasis classification system. The model's risk score stratified patients into low- and high-risk subgroups with statistically significant differences in their survival distributions, and was an independent risk factor for post-surgical recurrence in both test cohorts. Our results suggest that deep learning-based models can provide recurrence risk scores which may augment current patient stratification methods and help refine the clinical management of patients undergoing primary surgical resection for HCC.

    View details for DOI 10.1038/s41598-021-81506-y

    View details for PubMedID 33479370

    View details for PubMedCentralID PMC7820423

  • Deep learning assistance for the histopathologic diagnosis of Helicobacter pylori Intelligence-Based Medicine Zhou, S., Marklund, H., Blaha, O., Desai, M., Martin, B., Bingham, D., Berry, G. J., Gomulia, E., Ng, A. Y., Shen, J. 2020
  • Impact of a deep learning assistant on the histopathologic classification of liver cancer. NPJ digital medicine Kiani, A. n., Uyumazturk, B. n., Rajpurkar, P. n., Wang, A. n., Gao, R. n., Jones, E. n., Yu, Y. n., Langlotz, C. P., Ball, R. L., Montine, T. J., Martin, B. A., Berry, G. J., Ozawa, M. G., Hazard, F. K., Brown, R. A., Chen, S. B., Wood, M. n., Allard, L. S., Ylagan, L. n., Ng, A. Y., Shen, J. n. 2020; 3: 23


    Artificial intelligence (AI) algorithms continue to rival human performance on a variety of clinical tasks, while their actual impact on human diagnosticians, when incorporated into clinical workflows, remains relatively unexplored. In this study, we developed a deep learning-based assistant to help pathologists differentiate between two subtypes of primary liver cancer, hepatocellular carcinoma and cholangiocarcinoma, on hematoxylin and eosin-stained whole-slide images (WSI), and evaluated its effect on the diagnostic performance of 11 pathologists with varying levels of expertise. Our model achieved accuracies of 0.885 on a validation set of 26 WSI, and 0.842 on an independent test set of 80 WSI. Although use of the assistant did not change the mean accuracy of the 11 pathologists (p = 0.184, OR = 1.281), it significantly improved the accuracy (p = 0.045, OR = 1.499) of a subset of nine pathologists who fell within well-defined experience levels (GI subspecialists, non-GI subspecialists, and trainees). In the assisted state, model accuracy significantly impacted the diagnostic decisions of all 11 pathologists. As expected, when the model's prediction was correct, assistance significantly improved accuracy (p = 0.000, OR = 4.289), whereas when the model's prediction was incorrect, assistance significantly decreased accuracy (p = 0.000, OR = 0.253), with both effects holding across all pathologist experience levels and case difficulty levels. Our results highlight the challenges of translating AI models into the clinical setting, and emphasize the importance of taking into account potential unintended negative consequences of model assistance when designing and testing medical AI-assistance tools.

    View details for DOI 10.1038/s41746-020-0232-8

    View details for PubMedID 32140566

    View details for PubMedCentralID PMC7044422

  • Changes in the dielectric spectra of murine colon during neoplastic progression Biomedical Physics & Engineering Express Sabuncu*, A. C., Shen*, J., Zaki, M., Beskok, A., (*equal contribution) 2018; 4 (3): 035003

    View details for DOI 10.1088/2057-1976/aaad81

  • Prediction of immunochemotherapy response for diffuse large B-cell lymphoma using artificial intelligence digital pathology. The journal of pathology. Clinical research Lee, J. H., Song, G. Y., Lee, J., Kang, S. R., Moon, K. M., Choi, Y. D., Shen, J., Noh, M. G., Yang, D. H. 2024; 10 (3): e12370


    Diffuse large B-cell lymphoma (DLBCL) is a heterogeneous and prevalent subtype of aggressive non-Hodgkin lymphoma that poses diagnostic and prognostic challenges, particularly in predicting drug responsiveness. In this study, we used digital pathology and deep learning to predict responses to immunochemotherapy in patients with DLBCL. We retrospectively collected 251 slide images from 216 DLBCL patients treated with rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP), with their immunochemotherapy response labels. The digital pathology images were processed using contrastive learning for feature extraction. A multi-modal prediction model was developed by integrating clinical data and pathology image features. Knowledge distillation was employed to mitigate overfitting on gigapixel histopathology images to create a model that predicts responses based solely on pathology images. Based on the importance derived from the attention mechanism of the model, we extracted histological features that were considered key textures associated with drug responsiveness. The multi-modal prediction model achieved an impressive area under the ROC curve of 0.856, demonstrating significant associations with clinical variables such as Ann Arbor stage, International Prognostic Index, and bulky disease. Survival analyses indicated their effectiveness in predicting relapse-free survival. External validation using TCGA datasets supported the model's ability to predict survival differences. Additionally, pathology-based predictions show promise as independent prognostic indicators. Histopathological analysis identified centroblastic and immunoblastic features to be associated with treatment response, aligning with previous morphological classifications and highlighting the objectivity and reproducibility of artificial intelligence-based diagnosis. This study introduces a novel approach that combines digital pathology and clinical data to predict the response to immunochemotherapy in patients with DLBCL. This model shows great promise as a diagnostic and prognostic tool for clinical management of DLBCL. Further research and genomic data integration hold the potential to enhance its impact on clinical practice, ultimately improving patient outcomes.

    View details for DOI 10.1002/2056-4538.12370

    View details for PubMedID 38584594

    View details for PubMedCentralID PMC10999948

  • Spatially Segregated Macrophage Populations Predict Distinct Outcomes In Colon Cancer. Cancer discovery Matusiak, M., Hickey, J. W., van IJzendoorn, D. G., Lu, G., Kidzinski, L., Zhu, S., Colburg, D. R., Luca, B., Phillips, D. J., Brubaker, S. W., Charville, G. W., Shen, J., Loh, K. M., Okwan-Duodu, D. K., Nolan, G. P., Newman, A. M., West, R. B., van de Rijn, M. 2024


    Tumor-associated macrophages are transcriptionally heterogeneous, but the spatial distribution and cell interactions that shape macrophage tissue roles remain poorly characterized. Here, we spatially resolve five distinct human macrophage populations in normal and malignant human breast and colon tissue and reveal their cellular associations. This spatial map reveals that distinct macrophage populations reside in spatially segregated micro-environmental niches with conserved cellular compositions that are repeated across healthy and diseased tissue. We show that IL4I1+ macrophages phagocytose dying cells in areas with high cell turnover and predict good outcome in colon cancer. In contrast, SPP1+ macrophages are enriched in hypoxic and necrotic tumor regions and portend worse outcome in colon cancer. A subset of FOLR2+ macrophages is embedded in plasma cell niches. NLRP3+ macrophages co-localize with neutrophils and activate an inflammasome in tumors. Our findings indicate that a limited number of unique human macrophage niches function as fundamental building blocks in tissue.

    View details for DOI 10.1158/2159-8290.CD-23-1300

    View details for PubMedID 38552005

  • Generation of synthetic whole-slide image tiles of tumours from RNA-sequencing data via cascaded diffusion models. Nature biomedical engineering Carrillo-Perez, F., Pizurica, M., Zheng, Y., Nandi, T. N., Madduri, R., Shen, J., Gevaert, O. 2024


    Training machine-learning models with synthetically generated data can alleviate the problem of data scarcity when acquiring diverse and sufficiently large datasets is costly and challenging. Here we show that cascaded diffusion models can be used to synthesize realistic whole-slide image tiles from latent representations of RNA-sequencing data from human tumours. Alterations in gene expression affected the composition of cell types in the generated synthetic image tiles, which accurately preserved the distribution of cell types and maintained the cell fraction observed in bulk RNA-sequencing data, as we show for lung adenocarcinoma, kidney renal papillary cell carcinoma, cervical squamous cell carcinoma, colon adenocarcinoma and glioblastoma. Machine-learning models pretrained with the generated synthetic data performed better than models trained from scratch. Synthetic data may accelerate the development of machine-learning models in scarce-data settings and allow for the imputation of missing data modalities.

    View details for DOI 10.1038/s41551-024-01193-8

    View details for PubMedID 38514775

  • National Comprehensive Cancer Network, NCCN Clinical Practice Guidelines in Oncology (NCCN Guidelines), Pancreatic Adenocarcinoma. Version 1.2024 National Comprehensive Cancer Network Tempero, M. A., Malafa, M. P., Benson III, A. B., Cardin, D. B., Chiorean, E. G., Christensen, J. A., Chung, V., Czito, B., Chiaro, M. D., Dillhoff, M., Donahue, T. R., Dotan, E., Fountzilas, C., Glazer, E. S., Hardacre, J., Hawkins, W. G., Klute, K., Ko, A. H., Kunstman, J. W., LoConte, N., Lowy, A. M., Masood, A., Moravek, C., Nakakura, E. K., Narang, A. K. 2024
  • Deep learning-based phenotyping reclassifies combined hepatocellular-cholangiocarcinoma. Nature communications Calderaro, J., Ghaffari Laleh, N., Zeng, Q., Maille, P., Favre, L., Pujals, A., Klein, C., Bazille, C., Heij, L. R., Uguen, A., Luedde, T., Di Tommaso, L., Beaufrère, A., Chatain, A., Gastineau, D., Nguyen, C. T., Nguyen-Canh, H., Thi, K. N., Gnemmi, V., Graham, R. P., Charlotte, F., Wendum, D., Vij, M., Allende, D. S., Aucejo, F., Diaz, A., Rivière, B., Herrero, A., Evert, K., Calvisi, D. F., Augustin, J., Leow, W. Q., Leung, H. H., Boleslawski, E., Rela, M., François, A., Cha, A. W., Forner, A., Reig, M., Allaire, M., Scatton, O., Chatelain, D., Boulagnon-Rombi, C., Sturm, N., Menahem, B., Frouin, E., Tougeron, D., Tournigand, C., Kempf, E., Kim, H., Ningarhari, M., Michalak-Provost, S., Gopal, P., Brustia, R., Vibert, E., Schulze, K., Rüther, D. F., Weidemann, S. A., Rhaiem, R., Pawlotsky, J. M., Zhang, X., Luciani, A., Mulé, S., Laurent, A., Amaddeo, G., Regnault, H., De Martin, E., Sempoux, C., Navale, P., Westerhoff, M., Lo, R. C., Bednarsch, J., Gouw, A., Guettier, C., Lequoy, M., Harada, K., Sripongpun, P., Wetwittayaklang, P., Loménie, N., Tantipisit, J., Kaewdech, A., Shen, J., Paradis, V., Caruso, S., Kather, J. N. 2023; 14 (1): 8290


    Primary liver cancer arises either from hepatocytic or biliary lineage cells, giving rise to hepatocellular carcinoma (HCC) or intrahepatic cholangiocarcinoma (ICCA). Combined hepatocellular- cholangiocarcinomas (cHCC-CCA) exhibit equivocal or mixed features of both, causing diagnostic uncertainty and difficulty in determining proper management. Here, we perform a comprehensive deep learning-based phenotyping of multiple cohorts of patients. We show that deep learning can reproduce the diagnosis of HCC vs. CCA with a high performance. We analyze a series of 405 cHCC-CCA patients and demonstrate that the model can reclassify the tumors as HCC or ICCA, and that the predictions are consistent with clinical outcomes, genetic alterations and in situ spatial gene expression profiling. This type of approach could improve treatment decisions and ultimately clinical outcome for patients with rare and biphenotypic cancers such as cHCC-CCA.

    View details for DOI 10.1038/s41467-023-43749-3

    View details for PubMedID 38092727

    View details for PubMedCentralID 8317046

  • Predicting lymph node metastasis from primary tumor histology and clinicopathologic factors in colorectal cancer using deep learning. Communications medicine Krogue, J. D., Azizi, S., Tan, F., Flament-Auvigne, I., Brown, T., Plass, M., Reihs, R., Müller, H., Zatloukal, K., Richeson, P., Corrado, G. S., Peng, L. H., Mermel, C. H., Liu, Y., Chen, P. C., Gombar, S., Montine, T., Shen, J., Steiner, D. F., Wulczyn, E. 2023; 3 (1): 59


    Presence of lymph node metastasis (LNM) influences prognosis and clinical decision-making in colorectal cancer. However, detection of LNM is variable and depends on a number of external factors. Deep learning has shown success in computational pathology, but has struggled to boost performance when combined with known predictors.Machine-learned features are created by clustering deep learning embeddings of small patches of tumor in colorectal cancer via k-means, and then selecting the top clusters that add predictive value to a logistic regression model when combined with known baseline clinicopathological variables. We then analyze performance of logistic regression models trained with and without these machine-learned features in combination with the baseline variables.The machine-learned extracted features provide independent signal for the presence of LNM (AUROC: 0.638, 95% CI: [0.590, 0.683]). Furthermore, the machine-learned features add predictive value to the set of 6 clinicopathologic variables in an external validation set (likelihood ratio test, p < 0.00032; AUROC: 0.740, 95% CI: [0.701, 0.780]). A model incorporating these features can also further risk-stratify patients with and without identified metastasis (p < 0.001 for both stage II and stage III).This work demonstrates an effective approach to combine deep learning with established clinicopathologic factors in order to identify independently informative features associated with LNM. Further work building on these specific results may have important impact in prognostication and therapeutic decision making for LNM. Additionally, this general computational approach may prove useful in other contexts.

    View details for DOI 10.1038/s43856-023-00282-0

    View details for PubMedID 37095223

    View details for PubMedCentralID 5069274

  • The polyclonal path to malignant transformation in familial adenomatous polyposis Schenck, R. O., Khan, A., Horning, A., Esplin, E. D., Monte, E., Wu, S., Hanson, C., Bararpour, N., Neves, S., Jiang, L., Contrepois, K., Lee, H., Guha, T. K., Hu, Z., Laquindanum, R., Mills, M. A., Chaib, H., Chiu, R., Jian, R., Chan, J., Ellenberger, M., Becker, W. R., Bahmani, B., Michael, B., Shen, J., Lancaster, S., Ladabaum, U., Kundaje, A., Longacre, T. A., Greenleaf, W. J., Ford, J. M., Snyder, M. P., Curtis, C. AMER ASSOC CANCER RESEARCH. 2023
  • Development and Validation of a Machine Learning Model for Detection and Classification of Tertiary Lymphoid Structures in Gastrointestinal Cancers. JAMA network open Li, Z., Jiang, Y., Li, B., Han, Z., Shen, J., Xia, Y., Li, R. 2023; 6 (1): e2252553


    Tertiary lymphoid structures (TLSs) are associated with a favorable prognosis and improved response to cancer immunotherapy. The current approach for evaluation of TLSs is limited by interobserver variability and high complexity and cost of specialized imaging techniques.To develop a machine learning model for automated and quantitative evaluation of TLSs based on routine histopathology images.In this multicenter, international diagnostic/prognostic study, an interpretable machine learning model was developed and validated for automated detection, enumeration, and classification of TLSs in hematoxylin-eosin-stained images. A quantitative scoring system for TLSs was proposed, and its association with survival was investigated in patients with 1 of 6 types of gastrointestinal cancers. Data analysis was performed between June 2021 and March 2022.The diagnostic accuracy for classification of TLSs into 3 maturation states and the association of TLS score with survival were investigated.A total of 1924 patients with gastrointestinal cancer from 7 independent cohorts (median [IQR] age ranging from 57 [49-64] years to 68 [58-77] years; proportion by sex ranging from 214 of 409 patients who were male [52.3%] to 134 of 155 patients who were male [86.5%]). The machine learning model achieved high accuracies for detecting and classifying TLSs into 3 states (TLS1: 97.7%; 95% CI, 96.4%-99.0%; TLS2: 96.3%; 95% CI, 94.6%-98.0%; TLS3: 95.7%; 95% CI, 93.9%-97.5%). TLSs were detected in 62 of 155 esophageal cancers (40.0%) and up to 267 of 353 gastric cancers (75.6%). Across 6 cancer types, patients were stratified into 3 risk groups (higher and lower TLS score and no TLS) and survival outcomes compared between groups: higher vs lower TLS score (hazard ratio [HR]; 0.27; 95% CI, 0.18-0.41; P < .001) and lower TLS score vs no TLSs (HR, 0.65; 95% CI, 0.56-0.76; P < .001). TLS score remained an independent prognostic factor associated with survival after adjusting for clinicopathologic variables and tumor-infiltrating lymphocytes (eg, for colon cancer: HR, 0.11; 95% CI, 0.02-0.47; P = .003).In this study, an interpretable machine learning model was developed that may allow automated and accurate detection of TLSs on routine tissue slide. This model is complementary to the cancer staging system for risk stratification in gastrointestinal cancers.

    View details for DOI 10.1001/jamanetworkopen.2022.52553

    View details for PubMedID 36692877

  • Relationship between tumor microenvironment (TME)-based histomic TGFβ signature (TGFBs), stromal fibroblast recruitment, and exclusion of immune cells as immunotherapy resistance mechanisms Journal of Clinical Oncology Park, G., Park, J., Shen, J., Choi, Y., Lee, T., Kim, H., Chae, Y., Lee, S., Park, S., Chung, J., Oum, C., Ma, M., Seo, M., Ock, C. 2023; 41 (16)
  • PlexusNet: A Neural Network Architectural Concept for Medical Image Classification Computers in Biology and Medicine Eminaga, O., Abbas, M., Shen, J., Laurie, M., Brooks, J. D., Liao, J., Rubin, D. L. 2023
  • RNA-to-image multi-cancer synthesis using cascaded diffusion models bioRxiv Carrillo-Perez, F., Pizurica, M., Zheng, Y., Shen, J., Gevaert, O. 2023
  • Subcutaneous Sweet Syndrome Successfully Treated With Ustekinumab in a Patient With Ulcerative Colitis. ACG case reports journal Hu, K. A., Shen, J., Rieger, K., Wei, M. T., Gubatan, J. 2022; 9 (11): e00881


    Ustekinumab, an inhibitor of the interleukin-12/23 pathway, received Food and Drug Administration (FDA) approval in 2019 for induction and maintenance therapy in patients with moderate-to-severe ulcerative colitis (UC). Data regarding the efficacy of ustekinumab in the treatment of extraintestinal manifestations of UC are unclear. Sweet syndrome, an acute febrile neutrophilic dermatosis, is a cutaneous manifestation of inflammatory bowel disease that parallels disease activity. In this study, we present the first case of subcutaneous Sweet syndrome with sterile osteomyelitis in a patient with UC successfully treated with ustekinumab.

    View details for DOI 10.14309/crj.0000000000000881

    View details for PubMedID 36447766

    View details for PubMedCentralID PMC9699508

  • Nestin as a diagnostic and prognostic marker for combined hepatocellular-cholangiocarcinoma. Journal of hepatology Calderaro, J., Di Tommaso, L., Maille, P., Beaufrere, A., Nguyen, C. T., Heij, L., Gnemmi, V., Graham, R. P., Charlotte, F., Chartier, S., Wendum, D., Vij, M., Allende, D., Diaz, A., Fuster, C., Riviere, B., Herrero, A., Augustin, J., Evert, K., Calvisi, D. F., Leow, W. Q., Wai Leung, H. H., Bednarsch, J., Boleslawski, E., Rela, M., Wing-Hung Chan, A., Forner, A., Reig, M., Pujals, A., Favre, L., Allaire, M., Scatton, O., Uguen, A., Trepo, E., Sanchez, L. O., Chatelain, D., Remmelink, M., Boulagnon-Rombi, C., Bazille, C., Sturm, N., Menahem, B., Frouin, E., Tougeron, D., Tournigand, C., Kempf, E., Kim, H., Ningarhari, M., Michalak-Provost, S., Kather, J. N., Gouw, A. S., Gopal, P., Brustia, R., Vibert, E., Schulze, K., Ruther, D. F., Weidemann, S. A., Rhaiem, R., Nault, J., Laurent, A., Amaddeo, G., Regnault, H., de Martin, E., Sempoux, C., Navale, P., Shinde, J., Bacchuwar, K., Westerhoff, M., Cheuk-Lam Lo, R., Sebbagh, M., Guettier, C., Lequoy, M., Komuta, M., Ziol, M., Paradis, V., Shen, J., Caruso, S. 2022


    BACKGROUND AND AIMS: Combined Hepatocellular-Cholangiocarcinoma (cHCC-CCA) is a rare primary liver cancer (PLC) associated with a poor prognosis. Given the challenges in its identification and its clinical implications, biomarkers are critically needed. We aimed to investigate the diagnostic and prognostic value of the immunohistochemical expression of Nestin, a progenitor cell marker, in a large multicentric series of PLC.METHODS: We collected 461 cHCC-CCA samples from 32 different clinical centers. Control cases included 368 hepatocellular carcinomas (HCC) and 221 intrahepatic cholangiocarcinomas (ICCA). Nestin immunohistochemistry was performed on whole tumor sections. Diagnostic and prognostic performances of Nestin expression were determined using receiver operating characteristic curves and cox regression modeling.RESULTS: Nestin was able to distinguish cHCC-CCA from HCC with AUC of 0.85 and 0.86 on surgical and biopsy samples, respectively. Performance was lower for the distinction of cHCC-CCA from ICCA (AUC of 0.59 and 0.60). Nestin, however, showed a high prognostic value, allowing identification of the subset of cHCC-CCA ("Nestin High", >30% neoplastic cells with positive staining) associated with the worst clinical outcome (shorter disease-free and overall survival) after surgical resection and liver transplantation, as well as when assessment was performed on biopsies.CONCLUSION: We show in different clinical settings that Nestin has a diagnostic value and that it is a useful biomarker to identify the subset of cHCC-CCA associated with the worst clinical outcome. Nestin immunohistochemistry may be used to refine risk stratification and improve treatment allocation for patients with this highly aggressive malignancy.LAY SUMMARY: Combined Hepatocellular-Cholangiocarcinoma (cHCC-CCA) is a rare primary liver cancer (PLC) that lacks robust tissue biomarkers. We show in different clinical settings that Nestin immunohistochemical staining has a diagnostic value and is a useful biomarker to identify the subset of cHCC-CCA associated with the worst clinical outcome. Nestin immunohistochemistry may be used to refine risk stratification and improve treatment allocation for patients with this highly aggressive malignancy.

    View details for DOI 10.1016/j.jhep.2022.07.019

    View details for PubMedID 35987274

  • Rapid Deployment of Whole Slide Imaging for Primary Diagnosis in Surgical Pathology at Stanford Medicine. Archives of pathology & laboratory medicine Rojansky, R., Jhun, I., Dussaq, A. M., Chirieleison, S. M., Nirschl, J. J., Born, D., Fralick, J., Hetherington, W., Kerr, A. M., Lavezo, J., Lawrence, D. B., Lummus, S., Macasaet, R., Montine, T. J., Ryan, E., Shen, J., Shoemaker, J., Tan, B., Vogel, H., Waraich, P. S., Yang, E., Young, A., Folkins, A. 2022


    Stanford Pathology began stepwise subspecialty implementation of whole slide imaging (WSI) in 2018 soon after the first US Food and Drug Administration approval. In 2020, during the COVID-19 pandemic, the Centers for Medicare & Medicaid Services waived the requirement for pathologists to perform diagnostic tests in Clinical Laboratory Improvement Amendments (CLIA)-licensed facilities. This encouraged rapid implementation of WSI across all surgical pathology subspecialties.To present our experience with validation and implementation of WSI at a large academic medical center encompassing a caseload of more than 50 000 cases per year.Validation was performed independently for 3 subspecialty services with a diagnostic concordance threshold above 95%. Analysis of user experience, staffing, infrastructure, and information technology was performed after department-wide expansion.Diagnostic concordance was achieved in 96% of neuropathology cases, 100% of gynecologic pathology cases, and 98% of immunohistochemistry cases. After full implementation, 8 high-capacity scanners were operational, with whole slide images generated on greater than 2000 slides per weekday, accounting for approximately 80% of histologic slides at Stanford Medicine. Multiple modifications in workflow and information technology were needed to improve performance. Within months of full implementation, most attending pathologists and trainees had adopted WSI for primary diagnosis.WSI across all surgical subspecialities is achievable at scale at an academic medical center; however, adoption required flexibility to adjust workflows and develop tailored solutions. WSI at scale supported the health and safety of medical staff while facilitating high-quality patient care and education during COVID-19 restrictions.

    View details for DOI 10.5858/arpa.2021-0438-OA

    View details for PubMedID 35802938

  • The inflamed immune phenotype (IIP): A clinically actionable artificial intelligence (AI)-based biomarker predictive of immune checkpoint inhibitor (ICI) outcomes across > 16 primary tumor types Shen, J., Choi, Y., Lee, T., Kim, H., Chae, Y., Dulken, B., Bogdan, S., Huang, M., Fisher, G. A., Park, S., Lee, S., Hwang, J., Chung, J., Kim, L., Shin, S., Lim, Y., Song, H., Pereira, S., Ock, C. LIPPINCOTT WILLIAMS & WILKINS. 2022
  • AI-enabled in silico immunohistochemical characterization for Alzheimer's disease. Cell reports methods He, B., Bukhari, S., Fox, E., Abid, A., Shen, J., Kawas, C., Corrada, M., Montine, T., Zou, J. 2022; 2 (4): 100191


    We develop a deep learning approach, in silico immunohistochemistry (IHC), which takes routinely collected histochemical-stained samples as input and computationally generates virtual IHC slide images. We apply in silico IHC to Alzheimer's disease samples, where several hallmark changes are conventionally identified using IHC staining across many regions of the brain. In silico IHC computationally identifies neurofibrillary tangles, beta-amyloid plaques, and neuritic plaques at a high spatial resolution directly from the histochemical images, with areas under the receiver operating characteristic curve of between 0.88 and 0.92. In silico IHC learns to identify subtle cellular morphologies associated with these lesions and can generate in silico IHC slides that capture key features of the actual IHC.

    View details for DOI 10.1016/j.crmeth.2022.100191

    View details for PubMedID 35497493

  • MITI minimum information guidelines for highly multiplexed tissue images. Nature methods Schapiro, D., Yapp, C., Sokolov, A., Reynolds, S. M., Chen, Y., Sudar, D., Xie, Y., Muhlich, J., Arias-Camison, R., Arena, S., Taylor, A. J., Nikolov, M., Tyler, M., Lin, J., Burlingame, E. A., Human Tumor Atlas Network, Chang, Y. H., Farhi, S. L., Thorsson, V., Venkatamohan, N., Drewes, J. L., Pe'er, D., Gutman, D. A., Herrmann, M. D., Gehlenborg, N., Bankhead, P., Roland, J. T., Herndon, J. M., Snyder, M. P., Angelo, M., Nolan, G., Swedlow, J. R., Schultz, N., Merrick, D. T., Mazzili, S. A., Cerami, E., Rodig, S. J., Santagata, S., Sorger, P. K., Abravanel, D. L., Achilefu, S., Ademuyiwa, F. O., Adey, A. C., Aft, R., Ahn, K. J., Alikarami, F., Alon, S., Ashenberg, O., Baker, E., Baker, G. J., Bandyopadhyay, S., Bayguinov, P., Beane, J., Becker, W., Bernt, K., Betts, C. B., Bletz, J., Blosser, T., Boire, A., Boland, G. M., Boyden, E. S., Bucher, E., Bueno, R., Cai, Q., Cambuli, F., Campbell, J., Cao, S., Caravan, W., Chaligne, R., Chan, J. M., Chasnoff, S., Chatterjee, D., Chen, A. A., Chen, C., Chen, C., Chen, B., Chen, F., Chen, S., Chheda, M. G., Chin, K., Cho, H., Chun, J., Cisneros, L., Coffey, R. J., Cohen, O., Colditz, G. A., Cole, K. A., Collins, N., Cotter, D., Coussens, L. M., Coy, S., Creason, A. L., Cui, Y., Zhou, D. C., Curtis, C., Davies, S. R., Bruijn, I., Delorey, T. M., Demir, E., Denardo, D., Diep, D., Ding, L., DiPersio, J., Dubinett, S. M., Eberlein, T. J., Eddy, J. A., Esplin, E. D., Factor, R. E., Fatahalian, K., Feiler, H. S., Fernandez, J., Fields, A., Fields, R. C., Fitzpatrick, J. A., Ford, J. M., Franklin, J., Fulton, B., Gaglia, G., Galdieri, L., Ganesh, K., Gao, J., Gaudio, B. L., Getz, G., Gibbs, D. L., Gillanders, W. E., Goecks, J., Goodwin, D., Gray, J. W., Greenleaf, W., Grimm, L. J., Gu, Q., Guerriero, J. L., Guha, T., Guimaraes, A. R., Gutierrez, B., Hacohen, N., Hanson, C. R., Harris, C. R., Hawkins, W. G., Heiser, C. N., Hoffer, J., Hollmann, T. J., Hsieh, J. J., Huang, J., Hunger, S. P., Hwang, E., Iacobuzio-Donahue, C., Iglesia, M. D., Islam, M., Izar, B., Jacobson, C. A., Janes, S., Jayasinghe, R. G., Jeudi, T., Johnson, B. E., Johnson, B. E., Ju, T., Kadara, H., Karnoub, E., Karpova, A., Khan, A., Kibbe, W., Kim, A. H., King, L. M., Kozlowski, E., Krishnamoorthy, P., Krueger, R., Kundaje, A., Ladabaum, U., Laquindanum, R., Lau, C., Lau, K. S., LeBoeuf, N. R., Lee, H., Lenburg, M., Leshchiner, I., Levy, R., Li, Y., Lian, C. G., Liang, W., Lim, K., Lin, Y., Liu, D., Liu, Q., Liu, R., Lo, J., Lo, P., Longabaugh, W. J., Longacre, T., Luckett, K., Ma, C., Maher, C., Maier, A., Makowski, D., Maley, C., Maliga, Z., Manoj, P., Maris, J. M., Markham, N., Marks, J. R., Martinez, D., Mashl, J., Masilionis, I., Massague, J., Mazurowski, M. A., McKinley, E. T., McMichael, J., Meyerson, M., Mills, G. B., Mitri, Z. I., Moorman, A., Mudd, J., Murphy, G. F., Deen, N. N., Navin, N. E., Nawy, T., Ness, R. M., Nevins, S., Nirmal, A. J., Novikov, E., Oh, S. T., Oldridge, D. A., Owzar, K., Pant, S. M., Park, W., Patti, G. J., Paul, K., Pelletier, R., Persson, D., Petty, C., Pfister, H., Polyak, K., Puram, S. V., Qiu, Q., Villalonga, A. Q., Ramirez, M. A., Rashid, R., Reeb, A. N., Reid, M. E., Remsik, J., Riesterer, J. L., Risom, T., Ritch, C. C., Rolong, A., Rudin, C. M., Ryser, M. D., Sato, K., Sears, C. L., Semenov, Y. R., Shen, J., Shoghi, K. I., Shrubsole, M. J., Shyr, Y., Sibley, A. B., Simmons, A. J., Sinha, A., Sivagnanam, S., Song, S., Southar-Smith, A., Spira, A. E., Cyr, J. S., Stefankiewicz, S., Storrs, E. P., Stover, E. H., Strand, S. H., Straub, C., Street, C., Su, T., Surrey, L. F., Suver, C., Tan, K., Terekhanova, N. V., Ternes, L., Thadi, A., Thomas, G., Tibshirani, R., Umeda, S., Uzun, Y., Vallius, T., Van Allen, E. R., Vandekar, S., Vega, P. N., Veis, D. J., Vennam, S., Verma, A., Vigneau, S., Wagle, N., Wahl, R., Walle, T., Wang, L., Warchol, S., Washington, M. K., Watson, C., Weimer, A. K., Wendl, M. C., West, R. B., White, S., Windon, A. L., Wu, H., Wu, C., Wu, Y., Wyczalkowski, M. A., Xu, J., Yao, L., Yu, W., Zhang, K., Zhu, X. 2022; 19 (3): 262-267

    View details for DOI 10.1038/s41592-022-01415-4

    View details for PubMedID 35277708

  • Ampullary Adenocarcinoma, Version 1.2022 NCCN Clinical Practice Guidelines in Oncology (NCCN Guidelines®) Tempero, M. A., Malafa, M. P., Behrman, S. W., Chiorean, G. E., Al-Hawary, M., Benson, A. B., Cardin, D. B., Chung, V., Czito, B., Del Chiaro, M., Dillhoff, M., Donahue, T. R., Dotan, E., Ferrone, C. R., Fountzilas, C., Hardacre, J., Hawkins, W. G., Kim, E. J., Klute, K., Ko, A. H., Kunstman, J. W., LoConte, N., Lowy, A. M., Moravek, C., Nakakura, E. K., et al 2022
  • Synthetic whole-slide image tile generation with gene expression profiles infused deep generative models bioRxiv Carrillo-Perez, F., Pizurica, M., Ozawa, M. G., Vogel, H., West, R. B., Kong, C. S., Herrera, L. J., Shen, J., Gevaert, O. 2022
  • Deep Learning-Based Sparse Whole-Slide Image Analysis for the Diagnosis of Gastric Intestinal Metaplasia arXiv Braatz, J., Rajpurkar, P., Zhang, S., Ng, A. Y., Shen, J. 2022
  • A spatial map of human macrophage niches links tissue location with function in colon and breast cancer bioRxiv Matusiak, M., Hickey, J., Luca, B., Lu, G., Kidziński, L., Szu, S., Colburg, D. R., Phillips, D., Charville, G., Shen, J., Nolan, G. P., Newman, A., West, R. B., van de Rijn, M. 2022
  • Global loss of fine-scale chromatin architecture and rebalancing of gene expression during early colorectal cancer development bioRxiv Zhu, Y., Lee, H., Weimer, A. K., Horning, A., Nevins, S. A., Esplin, E. D., Paul, K., Krieger, G., Shipony, Z., Mills, M., Laquindanum, R., Ladabaum, U., Chiu, R., Longacre, T., Shen, J., Jaimovich, A., Lipson, D., Kundaje, A., Greenleaf, W. J., Curtis, C., Ford, J. M., Snyder, M. P. 2022
  • Stomach Mills and Sternberg’s Diagnostic Surgical Pathology, 7th Edition Longacre, T., Shen, J. Wolters Kluwer. 2022; 7: 1576-1624
  • Single-cell analyses define a continuum of cell state and composition changes in the malignant transformation of polyps to colorectal cancer Nature Genetics Becker, W. R., Nevins, S. A., Chen, D. C., Chiu, R., Horning, A., Laquindanum, R., Mills, M., Chaib, H., Ladabaum, U., Longacre, T., Shen, J., Esplin, E. D., Kundaje, A., Ford, J. M., Curtis, C., Snyder, M. P. 2022; 54: 985-995
  • Distinct cell states define the developmental trajectories of mucinous appendiceal neoplasms towards pseudomyxoma metastases bioRxiv Ayala, C., Sathe, A., Bai, X., Grimes, S. M., Shen, J., Poultsides, G. A., Lee, B., Ji, H. P. 2022
  • Pancreatic Adenocarcinoma, Version 2.2021 JOURNAL OF THE NATIONAL COMPREHENSIVE CANCER NETWORK Tempero, M. A., Malafa, M. P., Al-Hawary, M., Behrman, S. W., Benson, A. B., Cardin, D. B., Chiorean, E., Chung, V., Czito, B., Del Chiaro, M., Dillhoff, M., Donahue, T. R., Dotan, E., Ferrone, C. R., Fountzilas, C., Hardacre, J., Hawkins, W. G., Klute, K., Ko, A. H., Kunstman, J. W., LoConte, N., Lowy, A. M., Moravek, C., Nakakura, E. K., Narang, A. K., Obando, J., Polanco, P. M., Reddy, S., Reyngold, M., Scaife, C., Shen, J., Vollmer, C., Wolff, R. A., Wolpin, B. M., Lynn, B., George, G. 2021; 19 (4): 439-457


    Pancreatic cancer is the fourth leading cause of cancer-related death among men and women in the United States. A major challenge in treatment remains patients' advanced disease at diagnosis. The NCCN Guidelines for Pancreatic Adenocarcinoma provides recommendations for the diagnosis, evaluation, treatment, and follow-up for patients with pancreatic cancer. Although survival rates remain relatively unchanged, newer modalities of treatment, including targeted therapies, provide hope for improving patient outcomes. Sections of the manuscript have been updated to be concordant with the most recent update to the guidelines. This manuscript focuses on the available systemic therapy approaches, specifically the treatment options for locally advanced and metastatic disease.

    View details for DOI 10.6004/jnccn.2021.0017

    View details for Web of Science ID 000655302000009

    View details for PubMedID 33845462

  • Artificial intelligence-powered spatial analysis of tumor-infiltrating lymphocytes predicts survival after immune checkpoint inhibitor therapy across multiple cancer types. Journal of Clinical Oncology Shen, J., Lee, T., Hwang, J., Choi, Y., Lee, S., Kim, H., Chung, J., Bogdan, S., Huang, M., Raclin, T., Fisher, G. A., Pereira, S., Park, S., Ma, M., Yoo, D., Shin, S., Paeng, K., Ock, C., Mok, T., Bang, Y. 2021; 39
  • Single-cell analyses reveal a continuum of cell state and composition changes in the malignant transformation of polyps to colorectal cancer bioRxiv Becker, W. R., Nevins, S. A., Chen, D. C., Chiu, R., Horning, A., Laquindanum, R., Mills, M., Chaib, H., Ladabaum, U., Longacre, T., Shen, J., Esplin, E. D., Kundaje, A., Ford, J. M., Curtis, C., Snyder, M. P., Greenleaf, W. J. 2021
  • Pepsinogens and Gastrin Demonstrate Low Discrimination for Gastric Precancerous Lesions in a Multi-Ethnic United States Cohort. Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association Huang, R. n., Park, S. n., Shen, J. n., Longacre, T. n., Ji, H. n., Hwang, J. H. 2021

    View details for DOI 10.1016/j.cgh.2021.01.009

    View details for PubMedID 33434656

  • Development and Use of Natural Language Processing for Identification of Distant Cancer Recurrence and Sites of Distant Recurrence Using Unstructured Electronic Health Record Data. JCO clinical cancer informatics Karimi, Y. H., Blayney, D. W., Kurian, A. W., Shen, J. n., Yamashita, R. n., Rubin, D. n., Banerjee, I. n. 2021; 5: 469–78


    Large-scale analysis of real-world evidence is often limited to structured data fields that do not contain reliable information on recurrence status and disease sites. In this report, we describe a natural language processing (NLP) framework that uses data from free-text, unstructured reports to classify recurrence status and sites of recurrence for patients with breast and hepatocellular carcinomas (HCC).Using two cohorts of breast cancer and HCC cases, we validated the ability of a previously developed NLP model to distinguish between no recurrence, local recurrence, and distant recurrence, based on clinician notes, radiology reports, and pathology reports compared with manual curation. A second NLP model was trained and validated to identify sites of recurrence. We compared the ability of each NLP model to identify the presence, timing, and site of recurrence, when compared against manual chart review and International Classification of Diseases coding.A total of 1,273 patients were included in the development and validation of the two models. The NLP model for recurrence detects distant recurrence with an area under the curve of 0.98 (95% CI, 0.96 to 0.99) and 0.95 (95% CI, 0.88 to 0.98) in breast and HCC cohorts, respectively. The mean accuracy of the NLP model for detecting any site of distant recurrence was 0.9 for breast cancer and 0.83 for HCC. The NLP model for recurrence identified a larger proportion of patients with distant recurrence in a breast cancer database (11.1%) compared with International Classification of Diseases coding (2.31%).We developed two NLP models to identify distant cancer recurrence, timing of recurrence, and sites of recurrence based on unstructured electronic health record data. These models can be used to perform large-scale retrospective studies in oncology.

    View details for DOI 10.1200/CCI.20.00165

    View details for PubMedID 33929889

  • Learning domain-agnostic visual representation for computational pathology using medically-irrelevant style transfer augmentation Arxiv Yamashita, R., Long, J., Banda, S., Shen, J., Rubin, D. L. 2021
  • Multi-omic Analysis of Familial Adenomatous Polyposis Reveals Molecular Pathways and Polyclonal Spreading Associated with Early Tumorigenesis 19 May 2021, PREPRINT (Version 1) available at Research Square Horning, A. M., Esplin, E. D., Wu, S., Hanson, C., Bararpour, N., Nevins, S. A., Jiang, L., Contrepois, K., Lee, H., Guha, T. K., Hu, Z., Laquindanum, R., Mills, M. A., Chaib, H., Chiu, R., Jian, R., Chan, J., Ellenberger, M., Becker, W. R., Bahmani, B., Khan, A., Michael, B., Esplin, D., Shen, J., Lancaster, S., et al 2021
  • In the Thick of It: The Many Faces of Collagenous Gastritis. Digestive diseases and sciences Singh, S., Jing, E., Shen, J. 2020

    View details for DOI 10.1007/s10620-019-06003-9

    View details for PubMedID 31919637

  • Enteric Glia Play a Critical Role in Promoting the Development of Colorectal Cancer Frontiers in Oncology Yuan, R., Bhattacharya, N., Kenkel, J. A., Shen, J., DiMaio, M. A., Bagchi, S., Prestwood, T. R., Habtezion, A., Engleman, E. G. 2020; 10: 595892


    Enteric glia are a distinct population of peripheral glial cells in the enteric nervous system that regulate intestinal homeostasis, epithelial barrier integrity, and gut defense. Given these unique attributes, we investigated the impact of enteric glia depletion on tumor development in azoxymethane/dextran sodium sulfate (AOM/DSS)-treated mice, a classical model of colorectal cancer (CRC). Depleting GFAP+ enteric glia resulted in a profoundly reduced tumor burden in AOM/DSS mice and additionally reduced adenomas in the ApcMin/+ mouse model of familial adenomatous polyposis, suggesting a tumor-promoting role for these cells at an early premalignant stage. This was confirmed in further studies of AOM/DSS mice, as enteric glia depletion did not affect the properties of established malignant tumors but did result in a marked reduction in the development of precancerous dysplastic lesions. Surprisingly, the protective effect of enteric glia depletion was not dependent on modulation of anti-tumor immunity or intestinal inflammation. These findings reveal that GFAP+ enteric glia play a critical pro-tumorigenic role during early CRC development and identify these cells as a potential target for CRC prevention.

    View details for DOI 10.3389/fonc.2020.595892

    View details for PubMedCentralID PMC7691584

  • A case-control study of risk factors for advanced gastric intestinal metaplasia in a multiethnic United States population (The Stanford GAPS Study) Cancer Epidemiology, Biomarkers & Prevention Huang, R. J., Park, S., Chitre, T., Shen, J., Longacre, T., Hwang, J. 2020
  • Hispanic/Latino gastric adenocarcinoma patients have distinct molecular profiles including a high rate of germline CDH1 mutations Cancer Res, 2020 Apr 8. pii: canres.2918.2019 [Epub ahead of print] Wang, S. C., Yeu, Y., Hammer, S. T., Xiao, S., Zhu, M., Hong, C., Yoon, L. Y., Nassour, I., Shen, J., Agarwal, D., Reznik, . I., Mansour, J. C., Yopp, A. C., Zhu, H., Hwang, T., Porembka, M. R. 2020
  • Deep learning predicts post-surgical recurrence of hepatocellular carcinoma from digital whole-slide images MedRxiv Yamashita, R., Long, J., Saleem, A., Rubin, D. L., Shen, J. 2020
  • Deep learning-based Helicobacter pylori detection: A diagnostic pathology study MedRxiv Zhou, S., Marklund, H., Blaha, O., Desai, M., Martin, B., Bingham, D., Berry, G. J., Gomulia, E., Ng, A. Y., Shen, J. 2020
  • Plexus Convolutional Neural Network (PlexusNet): A novel neural network architecture for histologic image analysis ArXiv Eminaga, O., Abbas, M., Kunder, C., , Loening, A. M., Shen, J., Brooks, J. D., Langlotz, C. P., Rubin, D. L. 2019
  • Deep Learning for the Digital Pathologic Diagnosis of Cholangiocarcinoma and Hepatocellular Carcinoma: Evaluating the Impact of a Web-based Diagnostic Assistant 2019 Conference on Neural Information Processing Systems (NeurIPS), Machine Learning for Health (ML4H) Uyumazturk, B., Kiani, A., Rajpurkar, P., Wang, A., Ball, R. L., Gao, R., Yu, Y., Jones, E., Langlotz, C. P., Martin, B., Berry, G. J., Ozawa, M. G., Hazard, F. K., Brown, R. A., Chen, S. B., Wood, M., Allard, L. S., Ylagan, L., Ng, A. Y., Shen, J. 2019
  • SWI/SNF component ARID1A restrains pancreatic neoplasia formation. Gut Wang, S. C., Nassour, I., Xiao, S., Zhang, S., Luo, X., Lee, J., Li, L., Sun, X., Nguyen, L. H., Chuang, J., Peng, L., Daigle, S., Shen, J., Zhu, H. 2018


    OBJECTIVE: ARID1A is commonly mutated in pancreatic ductal adenocarcinoma (PDAC), but the functional effects of ARID1A mutations in the pancreas are unclear. Understanding the molecular mechanisms that drive PDAC formation may lead to novel therapies.DESIGN: Concurrent conditional Arid1a deletion and Kras activation mutations were modelled in mice. Small-interfering RNA (siRNA) and CRISPR/Cas9 were used to abrogate ARID1A in human pancreatic ductal epithelial cells.RESULTS: We found that pancreas-specific Arid1a loss in mice was sufficient to induce inflammation, pancreatic intraepithelial neoplasia (PanIN) and mucinous cysts. Concurrent Kras activation accelerated the development of cysts that resembled intraductal papillary mucinous neoplasm. Lineage-specific Arid1a deletion confirmed compartment-specific tumour-suppressive effects. Duct-specific Arid1a loss promoted dilated ducts with occasional cyst and PDAC formation. Heterozygous acinar-specific Arid1a loss resulted in accelerated PanIN and PDAC formation with worse survival. RNA-seq showed that Arid1a loss induced gene networks associated with Myc activity and protein translation. ARID1A knockdown in human pancreatic ductal epithelial cells induced increased MYC expression and protein synthesis that was abrogated with MYC knockdown. ChIP-seq against H3K27ac demonstrated an increase in activated enhancers/promoters.CONCLUSIONS: Arid1a suppresses pancreatic neoplasia in a compartment-specific manner. In duct cells, this process appears to be associated with MYC-facilitated protein synthesis.

    View details for PubMedID 30315093

  • Continuity of transcriptomes among colorectal cancer subtypes based on meta-analysis GENOME BIOLOGY Ma, S., Ogino, S., Parsana, P., Nishihara, R., Qian, Z., Shen, J., Mima, K., Masugi, Y., Cao, Y., Nowak, J. A., Shima, K., Hoshida, Y., Giovannucci, E. L., Gala, M. K., Chan, A. T., Fuchs, C. S., Parmigiani, G., Huttenhower, C., Waldron, L. 2018; 19: 142


    Previous approaches to defining subtypes of colorectal carcinoma (CRC) and other cancers based on transcriptomes have assumed the existence of discrete subtypes. We analyze gene expression patterns of colorectal tumors from a large number of patients to test this assumption and propose an approach to identify potentially a continuum of subtypes that are present across independent studies and cohorts.We examine the assumption of discrete CRC subtypes by integrating 18 published gene expression datasets and > 3700 patients, and contrary to previous reports, find no evidence to support the existence of discrete transcriptional subtypes. Using a meta-analysis approach to identify co-expression patterns present in multiple datasets, we identify and define robust, continuously varying subtype scores to represent CRC transcriptomes. The subtype scores are consistent with established subtypes (including microsatellite instability and previously proposed discrete transcriptome subtypes), but better represent overall transcriptional activity than do discrete subtypes. The scores are also better predictors of tumor location, stage, grade, and times of disease-free survival than discrete subtypes. Gene set enrichment analysis reveals that the subtype scores characterize T-cell function, inflammation response, and cyclin-dependent kinase regulation of DNA replication.We find no evidence to support discrete subtypes of the CRC transcriptome and instead propose two validated scores to better characterize a continuity of CRC transcriptomes.

    View details for DOI 10.1186/s13059-018-1511-4

    View details for Web of Science ID 000445752300001

    View details for PubMedID 30253799

    View details for PubMedCentralID PMC6154428

  • Clinicopathological characteristics of invasive gastric Helicobacter pylori HUMAN PATHOLOGY Dudley, J., Wieczorek, T., Selig, M., Cheung, H., Shen, J., Odze, R., Deshpande, V., Zukerberg, L. 2017; 61: 19-25
  • Using NSG recipient mice improves engraftment of gastric cancer patient derived xenografts. Wang, S. C., Zhu, M., Nassour, I., Shen, J., Mansour, J. C., Agarwal, D., Zhu, H., Porembka, M. R. AMER SOC CLINICAL ONCOLOGY. 2017
  • Using NSG recipient mice improves engraftment of gastric cancer patient derived xenografts Journal of Clinical Oncology Wang, S. C., Zhu, M., Nassour, I., Shen, J., Mansour, J. C., Agarwal, D., Zhu, H., Porembka, M. M. 2017; 35: 70-70
  • Pathology and Molecular Pathology of Colorectal Cancer Pathology and Epidemiology of Cancer: Molecular Underpinnings Poulin, E. J., Shen, J., Gierut, J. J., Haigis, K. M. Springer International. 2017; 1: 409–446
  • Clinical, pathologic, and outcome study of hyperplastic and sessile serrated polyps in inflammatory bowel disease HUMAN PATHOLOGY Shen, J., Gibson, J. A., Schulte, S., Khurana, H., Farraye, F. A., Levine, J., Burakoff, R., Cerda, S., Qazi, T., Hamilton, M., Srivastava, A., Odze, R. D. 2015; 46 (10): 1548-1556


    There is evidence that some cancers in patients with inflammatory bowel disease (IBD) develop via the serrated pathway of carcinogenesis. This study examined the clinicopathological features and outcome of 115 IBD patients (65 with ulcerative colitis, 50 with Crohn disease), all with at least 1 serrated polyp at endoscopy or colon resection, including the presence of synchronous and metachronous conventional neoplastic lesions (dysplasia or adenocarcinoma), over an average follow-up period of 56.4 months. Conventional neoplasia was categorized as flat dysplasia (low or high grade), sporadic adenoma, adenoma-like dysplasia-associated lesion or mass, or adenocarcinoma. Overall, 97% of patients had at least 1 hyperplastic polyp (HP), 6% had a sessile serrated adenoma/polyp, and none had a traditional serrated adenoma. Eight patients (7%) had a synchronous conventional neoplastic lesion; only 1 had flat dysplasia (1%) and 2 had adenocarcinoma (2%). Thirteen patients developed a metachronous conventional neoplastic lesion, with 8 developing their conventional neoplasm within an area of previous or concurrent colitis; only 1 patient developed flat dysplasia (1%), and none developed adenocarcinoma. A higher proportion of patients with both an HP and a synchronous conventional neoplastic lesion at index developed a metachronous conventional neoplastic lesion, compared with those with an index HP only (25% versus 7%). These results suggest that IBD patients (both ulcerative colitis and Crohn disease patients) with HP have a very low risk of developing a conventional neoplastic lesion (flat dysplasia or adenocarcinoma) that would warrant surgical resection.

    View details for DOI 10.1016/j.humpath.2015.16.019

    View details for Web of Science ID 000362061400017

    View details for PubMedID 26297256

  • Clinicopathologic Significance of Macrocystic Change in Esophageal Adenocarcinoma Setia, N., Agoston, A., Shen, J., Tippawong, M., Bueno, R., Zheng, Y., Odze, R. D., Srivastava, A. NATURE PUBLISHING GROUP. 2014: 203A
  • Hermansky-pudlak syndrome complicated by pulmonary fibrosis: radiologic-pathologic correlation and review of pulmonary complications. Journal of clinical imaging science Kelil, T., Shen, J., O'Neill, A. C., Howard, S. A. 2014; 4: 59-?


    Hermansky-Pudlak syndrome (HPS) is a rare autosomal recessive disorder characterized by oculocutaneous hypopigmentation, platelet dysfunction, and in many cases, life-threatening pulmonary fibrosis. We report the clinical course, imaging, and postmortem findings of a 38-year-old female with HPS-related progressive pulmonary fibrosis, highlighting the role of imaging in assessment of disease severity and prognosis.

    View details for DOI 10.4103/2156-7514.143437

    View details for PubMedID 25379352

    View details for PubMedCentralID PMC4220421

  • Prevalence and clinicopathologic significance of micropapillary differentiation in esophageal adenocarcinomas. Modern Pathology Shen, J., Tippayawang, M., Agoston, A., Bueno, R., Zheng, Y., Odze, R. D., Srivastava, A. 2014; 27
  • Clinicopathologic significance of macrocystic change in esophageal adenocarcinoma Modern Pathology Setia, N., Agoston, A., Shen, J., Tippawong, M., Bueno, R., Zheng, Y., Odze, R. D., Srivastava, A. 2014; 27
  • Clinical, Pathologic, and Biologic Features Associated with BRAF Mutations in Non-Small Cell Lung Cancer CLINICAL CANCER RESEARCH Cardarella, S., Ogino, A., Nishino, M., Butaney, M., Shen, J., Lydon, C., Yeap, B. Y., Sholl, L. M., Johnson, B. E., Jaenne, P. A. 2013; 19 (16): 4532-4540


    BRAF mutations are found in a subset of non-small cell lung cancers (NSCLC). We examined the clinical characteristics and treatment outcomes of patients with NSCLC harboring BRAF mutations.Using DNA sequencing, we successfully screened 883 patients with NSCLC for BRAF mutations between July 1, 2009 and July 16, 2012. Baseline characteristics and treatment outcomes were compared between patients with and without BRAF mutations. Wild-type controls consisted of patients with NSCLC without a somatic alteration in BRAF, KRAS, EGFR, and ALK. In vitro studies assessed the biologic properties of selected non-V600E BRAF mutations identified from patients with NSCLC.Of 883 tumors screened, 36 (4%) harbored BRAF mutations (V600E, 18; non-V600E, 18) and 257 were wild-type for BRAF, EGFR, KRAS, and ALK negative. Twenty-nine of 36 patients with BRAF mutations were smokers. There were no distinguishing clinical features between BRAF-mutant and wild-type patients. Patients with advanced NSCLC with BRAF mutations and wild-type tumors showed similar response rates and progression-free survival (PFS) to platinum-based combination chemotherapy and no difference in overall survival. Within the BRAF cohort, patients with V600E-mutated tumors had a shorter PFS to platinum-based chemotherapy compared with those with non-V600E mutations, although this did not reach statistical significance (4.1 vs. 8.9 months; P = 0.297). We identified five BRAF mutations not previously reported in NSCLC; two of five were associated with increased BRAF kinase activity.BRAF mutations occur in 4% of NSCLCs and half are non-V600E. Prospective trials are ongoing to validate BRAF as a therapeutic target in NSCLC.

    View details for DOI 10.1158/1078-0432.CCR-13-0657

    View details for Web of Science ID 000323147700025

    View details for PubMedID 23833300

    View details for PubMedCentralID PMC3762878

  • Microsatellite Instability and BRAF Mutation Testing in Colorectal Cancer Prognostication JNCI-JOURNAL OF THE NATIONAL CANCER INSTITUTE Lochhead, P., Kuchiba, A., Imamura, Y., Liao, X., Yamauchi, M., Nishihara, R., Qian, Z. R., Morikawa, T., Shen, J., Meyerhardt, J. A., Fuchs, C. S., Ogino, S. 2013; 105 (15): 1151-1156


    BRAF mutation in colorectal cancer is associated with microsatellite instability (MSI) through its relationship with high-level CpG island methylator phenotype (CIMP) and MLH1 promoter methylation. MSI and BRAF mutation analyses are routinely used for familial cancer risk assessment. To clarify clinical outcome associations of combined MSI/BRAF subgroups, we investigated survival in 1253 rectal and colon cancer patients within the Nurses' Health Study and Health Professionals Follow-up Study with available data on clinical and other molecular features, including CIMP, LINE-1 hypomethylation, and KRAS and PIK3CA mutations. Compared with the majority subtype of microsatellite stable (MSS)/BRAF-wild-type, MSS/BRAF-mutant, MSI-high/BRAF-mutant, and MSI-high/BRAF-wild-type subtypes showed multivariable colorectal cancer-specific mortality hazard ratios of 1.60 (95% confidence interval [CI] =1.12 to 2.28; P = .009), 0.48 (95% CI = 0.27 to 0.87; P = .02), and 0.25 (95% CI = 0.12 to 0.52; P < .001), respectively. No evidence existed for a differential prognostic role of BRAF mutation by MSI status (P(interaction) > .50). Combined BRAF/MSI status in colorectal cancer is a tumor molecular biomarker for prognosic risk stratification.

    View details for DOI 10.1093/jnci/djt173

    View details for Web of Science ID 000322976000013

    View details for PubMedID 23878352

    View details for PubMedCentralID PMC3735463

  • Neurocandidiasis: a case report and consideration of the causes of restricted diffusion. Journal of radiology case reports Lin, D. J., Sacks, A., Shen, J., Lee, T. C. 2013; 7 (5): 1-5


    Diffusion weighted magnetic resonance imaging has risen to the forefront of imaging for acute stroke. However, the differential diagnosis of restricted diffusion is wide and includes ischemia, metabolic derangements, infections, and highly-cellular masses. We present a case of central nervous system (CNS) candidiasis presenting radiographically as bilateral punctate areas of restricted magnetic resonance (MR) diffusion in the basal ganglia. This case illustrates the value of carefully considering the causes of restricted diffusion in the brain, notably to be broader than acute stroke and to include invasive fungal infections.

    View details for DOI 10.3941/jrcr.v7i5.1319

    View details for PubMedID 23705051

    View details for PubMedCentralID PMC3661417

  • Isolated Ileitis May Be a Manifestation of Crohn's Disease, But Only in Symptomatic Patients: A Multi-Institution Study of 131 Cases Modern Pathology Shen, J., Najarian, R. M., Dessauvagie, B., Deshpande, V., Kumarasinghe, M. P., Hamilton, M., Shahid, M., Lauwers, G., Odze, R. D., Srivastava, A. 2013; 26
  • HER2 Mutated Lung Adenocarcinoma Is a Distinct Molecular and Clinicopathologic Entity Shen, J., Taneja, K., Zhang, W., Dillon, D. A., Gandhi, L., Sholl, L. M. NATURE PUBLISHING GROUP. 2012: 490A
  • Prognostic Role of Combined MSI and BRAF Mutation Status in Colorectal Cancer: Toward Routine Clinical Use Shen, J., Morikawa, T., Fuchs, C. S., Ogino, S. NATURE PUBLISHING GROUP. 2012: 179A
  • HER2 Mutated Lung Adenocarcinoma Is a Distinct Molecular and Clinicopathologic Entity Modern Pathology Shen, J., Taneja, K., Zhang, W., Dillon, D. A., Gandhi, L., Sholl, L. M. 2012
  • Prognostic Role of Combined MSI and BRAF Mutation Status in Colorectal Cancer: Toward Routine Clinical Use Modern Pathology Shen, J., Morikawa, T., Kuchiba, A., Fuchs, C. S., Ogino, S. 2012
  • The persistent problem of new-onset postoperative atrial fibrillation: A single-institution experience over two decades JOURNAL OF THORACIC AND CARDIOVASCULAR SURGERY Shen, J., Lall, S., Zheng, V., Buckley, P., Damiano, R. J., Schuessler, R. B. 2011; 141 (2): 559-570


    Postoperative atrial fibrillation is the most common complication after cardiac surgery. A variety of postoperative atrial fibrillation risk factors have been reported, but study results have been inconsistent or contradictory, particularly in patients with preexisting atrial fibrillation. The incidence of postoperative atrial fibrillation was evaluated in a group of 10,390 patients undergoing cardiac surgery among a comprehensive range of risk factors to identify reliable predictors of postoperative atrial fibrillation.This 20-year retrospective study examined the relationship between postoperative atrial fibrillation and demographic factors, preoperative health conditions and medications, operative procedures, and postoperative complications. Multivariate logistic regression models were used to evaluate potential predictors of postoperative atrial fibrillation.Increasing age, mitral valve surgery (odds ratio=1.91), left ventricular aneurysm repair (odds ratio=1.57), aortic valve surgery (odds ratio=1.52), race (Caucasian) (odds ratio=1.51), use of cardioplegia (odds ratio=1.36), use of an intraaortic balloon pump (odds ratio=1.28), previous congestive heart failure (odds ratio=1.28), and hypertension (odds ratio=1.15) were significantly associated with postoperative atrial fibrillation. The non-linear relationship between age and postoperative atrial fibrillation revealed the acceleration of postoperative atrial fibrillation risk in patients aged 55 years or more. In patients undergoing coronary artery bypass grafting, increasing age and previous congestive heart failure were the only factors associated with a higher risk of postoperative atrial fibrillation. There was no trend in incidence of postoperative atrial fibrillation over time. No protective factors against postoperative atrial fibrillation were detected, including commonly prescribed categories of medications.The persistence of the problem of postoperative atrial fibrillation and the modest predictability using common risk factors suggest that limited progress has been made in understanding its cause and treatment.

    View details for DOI 10.1016/j.jtcvs.2010.03.011

    View details for Web of Science ID 000286222800042

    View details for PubMedID 20434173

    View details for PubMedCentralID PMC2917532

  • Acute Glomerulitis with Neutrophils May Underscore the Development of Glomerular Basement Membrane Multi-Lamination in Transplant Glomerulopathy Gaut, J. P., Shen, J., DeGuire, M., Klein, C., Liapis, H. NATURE PUBLISHING GROUP. 2011: 344A
  • Acute glomerulitis with neutrophils may underscore the development of glomerular basement membrane multi-lamination in transplant glomerulopathy Modern Pathology Gaut, J. P., Shen, J., DeGuire, M., Klein, C., Liapis, H. 2011
  • Surgery for Lone Atrial Fibrillation: Present State-of-the-Art. Innovations (Philadelphia, Pa.) Shen, J., Bailey, M., Damiano, R. J. 2009; 4 (5): 248-255


    For two decades, the cut-and-sew Cox-Maze III procedure was the gold standard for the surgical treatment of atrial fibrillation (AF), and proved to be effective at curing lone AF and preventing its most dreaded complication, stroke. However, this procedure was not widely adopted due to its complexity and technical difficulty. Over the last 5-10 years, the introduction of new ablation technology has led to the development of the Cox-Maze IV procedure, as well as, more limited lesion sets, with the ultimate goal of performing a minimally-invasive lesion set on the beating heart, without the need for cardiopulmonary bypass. This review summarizes the current state of the art and future directions in the surgical treatment of lone atrial fibrillation. The hope is that as we learn more about the mechanisms of AF and develop preoperative diagnostic technologies capable of precisely locating the areas responsible for AF, it will become possible to tailor specific lesion sets and ablation modalities to individual patients, making the surgical treatment of lone AF available to a larger population of patients.

    View details for PubMedID 20473355

    View details for PubMedCentralID PMC2868583

  • The surgical treatment of atrial fibrillation HEART RHYTHM Shen, J., Bailey, M. S., Damiano, R. J. 2009; 6 (8): S45-S50


    For two decades, the cut-and-sew Cox-maze III procedure was the gold standard for the surgical treatment of atrial fibrillation (AF) and has proven to be effective at eliminating AF. The incidence of late stroke was also very low. However, this procedure was not widely adopted owing to its complexity and technical difficulty. Over the last 5-10 years, the introduction of new ablation technology has led to the development of the Cox-maze IV procedure as well as more limited lesion sets, with the ultimate goal of performing a minimally invasive lesion set on the beating heart without the need for cardiopulmonary bypass. This review summarizes the current state of the art and future directions in the stand-alone surgical treatment of AF. The hope is that as more is learned about the mechanisms of AF and with better preoperative diagnostic technologies capable of precisely locating the areas responsible for AF, it will become possible to tailor specific lesion sets and ablation modalities to individual patients, making the surgical treatment of AF available to a larger population of patients.

    View details for DOI 10.1016/j.hrthm.2009.05.019

    View details for Web of Science ID 000268867800009

    View details for PubMedID 19631907

    View details for PubMedCentralID PMC2760330

  • A temporal switch from Notch to Wnt signaling in muscle stem cells is necessary for normal adult myogenesis CELL STEM CELL Brack, A. S., Conboy, I. M., Conboy, M. J., Shen, J., Rando, T. A. 2008; 2 (1): 50-59


    The temporal switch from progenitor cell proliferation to differentiation is essential for effective adult tissue repair. We previously reported the critical role of Notch signaling in the proliferative expansion of myogenic progenitors in mammalian postnatal myogenesis. We now show that the onset of differentiation is due to a transition from Notch signaling to Wnt signaling in myogenic progenitors and is associated with an increased expression of Wnt in the tissue and an increased responsiveness of progenitors to Wnt. Crosstalk between these two pathways occurs via GSK3beta, which is maintained in an active form by Notch but is inhibited by Wnt in the canonical Wnt signaling cascade. These results demonstrate that the temporal balance between Notch and Wnt signaling orchestrates the precise progression of muscle precursor cells along the myogenic lineage pathway, through stages of proliferative expansion and then differentiation, during postnatal myogenesis.

    View details for DOI 10.1016/j.stem.2007.10.006

    View details for Web of Science ID 000252606400011

    View details for PubMedID 18371421