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262 results on '"Mermel, Craig H."'

1. Predicting lymph node metastasis from primary tumor histology and clinicopathologic factors in colorectal cancer using deep learning

Author

Krogue, Justin D., Azizi, Shekoofeh, Tan, Fraser, Flament-Auvigne, Isabelle, Brown, Trissia, Plass, Markus, Reihs, Robert, Müller, Heimo, Zatloukal, Kurt, Richeson, Pema, Corrado, Greg S., Peng, Lily H., Mermel, Craig H., Liu, Yun, Chen, Po-Hsuan Cameron, Gombar, Saurabh, Montine, Thomas, Shen, Jeanne, Steiner, David F., and Wulczyn, Ellery

Published
2023
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2. Health equity assessment of machine learning performance (HEAL): a framework and dermatology AI model case study

Author

Schaekermann, Mike, Spitz, Terry, Pyles, Malcolm, Cole-Lewis, Heather, Wulczyn, Ellery, Pfohl, Stephen R., Martin, Donald, Jr., Jaroensri, Ronnachai, Keeling, Geoff, Liu, Yuan, Farquhar, Stephanie, Xue, Qinghan, Lester, Jenna, Hughes, Cían, Strachan, Patricia, Tan, Fraser, Bui, Peggy, Mermel, Craig H., Peng, Lily H., Matias, Yossi, Corrado, Greg S., Webster, Dale R., Virmani, Sunny, Semturs, Christopher, Liu, Yun, Horn, Ivor, and Cameron Chen, Po-Hsuan

Published
2024
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3. Artificial intelligence for diagnosis and Gleason grading of prostate cancer: the PANDA challenge

Author

Bulten, Wouter, Kartasalo, Kimmo, Chen, Po-Hsuan Cameron, Ström, Peter, Pinckaers, Hans, Nagpal, Kunal, Cai, Yuannan, Steiner, David F, van Boven, Hester, Vink, Robert, Hulsbergen-van de Kaa, Christina, van der Laak, Jeroen, Amin, Mahul B, Evans, Andrew J, van der Kwast, Theodorus, Allan, Robert, Humphrey, Peter A, Grönberg, Henrik, Samaratunga, Hemamali, Delahunt, Brett, Tsuzuki, Toyonori, Häkkinen, Tomi, Egevad, Lars, Demkin, Maggie, Dane, Sohier, Tan, Fraser, Valkonen, Masi, Corrado, Greg S, Peng, Lily, Mermel, Craig H, Ruusuvuori, Pekka, Litjens, Geert, and Eklund, Martin

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Oncology and Carcinogenesis, Cancer, Urologic Diseases, Prostate Cancer, Algorithms, Biopsy, Cohort Studies, Humans, Male, Neoplasm Grading, Prostatic Neoplasms, Reproducibility of Results, PANDA challenge consortium, Medical and Health Sciences, Immunology, Biomedical and clinical sciences, Health sciences
Abstract

Artificial intelligence (AI) has shown promise for diagnosing prostate cancer in biopsies. However, results have been limited to individual studies, lacking validation in multinational settings. Competitions have been shown to be accelerators for medical imaging innovations, but their impact is hindered by lack of reproducibility and independent validation. With this in mind, we organized the PANDA challenge-the largest histopathology competition to date, joined by 1,290 developers-to catalyze development of reproducible AI algorithms for Gleason grading using 10,616 digitized prostate biopsies. We validated that a diverse set of submitted algorithms reached pathologist-level performance on independent cross-continental cohorts, fully blinded to the algorithm developers. On United States and European external validation sets, the algorithms achieved agreements of 0.862 (quadratically weighted κ, 95% confidence interval (CI), 0.840-0.884) and 0.868 (95% CI, 0.835-0.900) with expert uropathologists. Successful generalization across different patient populations, laboratories and reference standards, achieved by a variety of algorithmic approaches, warrants evaluating AI-based Gleason grading in prospective clinical trials.

Published
2022

4. Predicting Prostate Cancer-Specific Mortality with A.I.-based Gleason Grading

Author

Wulczyn, Ellery, Nagpal, Kunal, Symonds, Matthew, Moran, Melissa, Plass, Markus, Reihs, Robert, Nader, Farah, Tan, Fraser, Cai, Yuannan, Brown, Trissia, Flament-Auvigne, Isabelle, Amin, Mahul B., Stumpe, Martin C., Muller, Heimo, Regitnig, Peter, Holzinger, Andreas, Corrado, Greg S., Peng, Lily H., Chen, Po-Hsuan Cameron, Steiner, David F., Zatloukal, Kurt, Liu, Yun, and Mermel, Craig H.

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Gleason grading of prostate cancer is an important prognostic factor but suffers from poor reproducibility, particularly among non-subspecialist pathologists. Although artificial intelligence (A.I.) tools have demonstrated Gleason grading on-par with expert pathologists, it remains an open question whether A.I. grading translates to better prognostication. In this study, we developed a system to predict prostate-cancer specific mortality via A.I.-based Gleason grading and subsequently evaluated its ability to risk-stratify patients on an independent retrospective cohort of 2,807 prostatectomy cases from a single European center with 5-25 years of follow-up (median: 13, interquartile range 9-17). The A.I.'s risk scores produced a C-index of 0.84 (95%CI 0.80-0.87) for prostate cancer-specific mortality. Upon discretizing these risk scores into risk groups analogous to pathologist Grade Groups (GG), the A.I. had a C-index of 0.82 (95%CI 0.78-0.85). On the subset of cases with a GG in the original pathology report (n=1,517), the A.I.'s C-indices were 0.87 and 0.85 for continuous and discrete grading, respectively, compared to 0.79 (95%CI 0.71-0.86) for GG obtained from the reports. These represent improvements of 0.08 (95%CI 0.01-0.15) and 0.07 (95%CI 0.00-0.14) respectively. Our results suggest that A.I.-based Gleason grading can lead to effective risk-stratification and warrants further evaluation for improving disease management.

Published
2020
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5. Interpretable Survival Prediction for Colorectal Cancer using Deep Learning

Author

Wulczyn, Ellery, Steiner, David F., Moran, Melissa, Plass, Markus, Reihs, Robert, Tan, Fraser, Flament-Auvigne, Isabelle, Brown, Trissia, Regitnig, Peter, Chen, Po-Hsuan Cameron, Hegde, Narayan, Sadhwani, Apaar, MacDonald, Robert, Ayalew, Benny, Corrado, Greg S., Peng, Lily H., Tse, Daniel, Müller, Heimo, Xu, Zhaoyang, Liu, Yun, Stumpe, Martin C., Zatloukal, Kurt, and Mermel, Craig H.

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

Deriving interpretable prognostic features from deep-learning-based prognostic histopathology models remains a challenge. In this study, we developed a deep learning system (DLS) for predicting disease specific survival for stage II and III colorectal cancer using 3,652 cases (27,300 slides). When evaluated on two validation datasets containing 1,239 cases (9,340 slides) and 738 cases (7,140 slides) respectively, the DLS achieved a 5-year disease-specific survival AUC of 0.70 (95%CI 0.66-0.73) and 0.69 (95%CI 0.64-0.72), and added significant predictive value to a set of 9 clinicopathologic features. To interpret the DLS, we explored the ability of different human-interpretable features to explain the variance in DLS scores. We observed that clinicopathologic features such as T-category, N-category, and grade explained a small fraction of the variance in DLS scores (R2=18% in both validation sets). Next, we generated human-interpretable histologic features by clustering embeddings from a deep-learning based image-similarity model and showed that they explain the majority of the variance (R2 of 73% to 80%). Furthermore, the clustering-derived feature most strongly associated with high DLS scores was also highly prognostic in isolation. With a distinct visual appearance (poorly differentiated tumor cell clusters adjacent to adipose tissue), this feature was identified by annotators with 87.0-95.5% accuracy. Our approach can be used to explain predictions from a prognostic deep learning model and uncover potentially-novel prognostic features that can be reliably identified by people for future validation studies.

Published
2020
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6. Deep learning-based survival prediction for multiple cancer types using histopathology images

Author

Wulczyn, Ellery, Steiner, David F., Xu, Zhaoyang, Sadhwani, Apaar, Wang, Hongwu, Flament, Isabelle, Mermel, Craig H., Chen, Po-Hsuan Cameron, Liu, Yun, and Stumpe, Martin C.

Subjects
Quantitative Biology - Quantitative Methods, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

Prognostic information at diagnosis has important implications for cancer treatment and monitoring. Although cancer staging, histopathological assessment, molecular features, and clinical variables can provide useful prognostic insights, improving risk stratification remains an active research area. We developed a deep learning system (DLS) to predict disease specific survival across 10 cancer types from The Cancer Genome Atlas (TCGA). We used a weakly-supervised approach without pixel-level annotations, and tested three different survival loss functions. The DLS was developed using 9,086 slides from 3,664 cases and evaluated using 3,009 slides from 1,216 cases. In multivariable Cox regression analysis of the combined cohort including all 10 cancers, the DLS was significantly associated with disease specific survival (hazard ratio of 1.58, 95% CI 1.28-1.70, p<0.0001) after adjusting for cancer type, stage, age, and sex. In a per-cancer adjusted subanalysis, the DLS remained a significant predictor of survival in 5 of 10 cancer types. Compared to a baseline model including stage, age, and sex, the c-index of the model demonstrated an absolute 3.7% improvement (95% CI 1.0-6.5) in the combined cohort. Additionally, our models stratified patients within individual cancer stages, particularly stage II (p=0.025) and stage III (p<0.001). By developing and evaluating prognostic models across multiple cancer types, this work represents one of the most comprehensive studies exploring the direct prediction of clinical outcomes using deep learning and histopathology images. Our analysis demonstrates the potential for this approach to provide prognostic information in multiple cancer types, and even within specific pathologic stages. However, given the relatively small number of clinical events, we observed wide confidence intervals, suggesting that future work will benefit from larger datasets.

Published
2019
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7. Similar Image Search for Histopathology: SMILY

Author

Hegde, Narayan, Hipp, Jason D., Liu, Yun, Buck, Michael E., Reif, Emily, Smilkov, Daniel, Terry, Michael, Cai, Carrie J., Amin, Mahul B., Mermel, Craig H., Nelson, Phil Q., Peng, Lily H., Corrado, Greg S., and Stumpe, Martin C.

Subjects
Computer Science - Computer Vision and Pattern Recognition, Quantitative Biology - Quantitative Methods
Abstract

The increasing availability of large institutional and public histopathology image datasets is enabling the searching of these datasets for diagnosis, research, and education. Though these datasets typically have associated metadata such as diagnosis or clinical notes, even carefully curated datasets rarely contain annotations of the location of regions of interest on each image. Because pathology images are extremely large (up to 100,000 pixels in each dimension), further laborious visual search of each image may be needed to find the feature of interest. In this paper, we introduce a deep learning based reverse image search tool for histopathology images: Similar Medical Images Like Yours (SMILY). We assessed SMILY's ability to retrieve search results in two ways: using pathologist-provided annotations, and via prospective studies where pathologists evaluated the quality of SMILY search results. As a negative control in the second evaluation, pathologists were blinded to whether search results were retrieved by SMILY or randomly. In both types of assessments, SMILY was able to retrieve search results with similar histologic features, organ site, and prostate cancer Gleason grade compared with the original query. SMILY may be a useful general-purpose tool in the pathologist's arsenal, to improve the efficiency of searching large archives of histopathology images, without the need to develop and implement specific tools for each application., Comment: 23 Pages with 6 figures and 3 tables. The file also has 6 pages of supplemental material. Improved figure resolution, edited metadata

Published
2019
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8. Whole-Slide Image Focus Quality: Automatic Assessment and Impact on AI Cancer Detection

Author

Kohlberger, Timo, Liu, Yun, Moran, Melissa, Po-Hsuan, Chen, Brown, Trissia, Mermel, Craig H., Hipp, Jason D., and Stumpe, Martin C.

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Digital pathology enables remote access or consults and powerful image analysis algorithms. However, the slide digitization process can create artifacts such as out-of-focus (OOF). OOF is often only detected upon careful review, potentially causing rescanning and workflow delays. Although scan-time operator screening for whole-slide OOF is feasible, manual screening for OOF affecting only parts of a slide is impractical. We developed a convolutional neural network (ConvFocus) to exhaustively localize and quantify the severity of OOF regions on digitized slides. ConvFocus was developed using our refined semi-synthetic OOF data generation process, and evaluated using real whole-slide images spanning 3 different tissue types and 3 different stain types that were digitized by two different scanners. ConvFocus's predictions were compared with pathologist-annotated focus quality grades across 514 distinct regions representing 37,700 35x35 $\mu$m image patches, and 21 digitized "z-stack" whole-slide images that contain known OOF patterns. When compared to pathologist-graded focus quality, ConvFocus achieved Spearman rank coefficients of 0.81 and 0.94 on two scanners, and reproduced the expected OOF patterns from z-stack scanning. We also evaluated the impact of OOF on the accuracy of a state-of-the-art metastatic breast cancer detector and saw a consistent decrease in performance with increasing OOF. Comprehensive whole-slide OOF categorization could enable rescans prior to pathologist review, potentially reducing the impact of digitization focus issues on the clinical workflow. We show that the algorithm trained on our semi-synthetic OOF data generalizes well to real OOF regions across tissue types, stains, and scanners. Finally, quantitative OOF maps can flag regions that might otherwise be misclassified by image analysis algorithms, preventing OOF-induced errors.

Published
2019
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9. Development and Validation of a Deep Learning Algorithm for Improving Gleason Scoring of Prostate Cancer

Author

Nagpal, Kunal, Foote, Davis, Liu, Yun, Po-Hsuan, Chen, Wulczyn, Ellery, Tan, Fraser, Olson, Niels, Smith, Jenny L., Mohtashamian, Arash, Wren, James H., Corrado, Greg S., MacDonald, Robert, Peng, Lily H., Amin, Mahul B., Evans, Andrew J., Sangoi, Ankur R., Mermel, Craig H., Hipp, Jason D., and Stumpe, Martin C.

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

For prostate cancer patients, the Gleason score is one of the most important prognostic factors, potentially determining treatment independent of the stage. However, Gleason scoring is based on subjective microscopic examination of tumor morphology and suffers from poor reproducibility. Here we present a deep learning system (DLS) for Gleason scoring whole-slide images of prostatectomies. Our system was developed using 112 million pathologist-annotated image patches from 1,226 slides, and evaluated on an independent validation dataset of 331 slides, where the reference standard was established by genitourinary specialist pathologists. On the validation dataset, the mean accuracy among 29 general pathologists was 0.61. The DLS achieved a significantly higher diagnostic accuracy of 0.70 (p=0.002) and trended towards better patient risk stratification in correlations to clinical follow-up data. Our approach could improve the accuracy of Gleason scoring and subsequent therapy decisions, particularly where specialist expertise is unavailable. The DLS also goes beyond the current Gleason system to more finely characterize and quantitate tumor morphology, providing opportunities for refinement of the Gleason system itself.

Published
2018
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10. Deep learning models for histologic grading of breast cancer and association with disease prognosis

Author

Jaroensri, Ronnachai, Wulczyn, Ellery, Hegde, Narayan, Brown, Trissia, Flament-Auvigne, Isabelle, Tan, Fraser, Cai, Yuannan, Nagpal, Kunal, Rakha, Emad A., Dabbs, David J., Olson, Niels, Wren, James H., Thompson, Elaine E., Seetao, Erik, Robinson, Carrie, Miao, Melissa, Beckers, Fabien, Corrado, Greg S., Peng, Lily H., Mermel, Craig H., Liu, Yun, Steiner, David F., and Chen, Po-Hsuan Cameron

Published
2022
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13. Interpretable survival prediction for colorectal cancer using deep learning

Author

Wulczyn, Ellery, Steiner, David F., Moran, Melissa, Plass, Markus, Reihs, Robert, Tan, Fraser, Flament-Auvigne, Isabelle, Brown, Trissia, Regitnig, Peter, Chen, Po-Hsuan Cameron, Hegde, Narayan, Sadhwani, Apaar, MacDonald, Robert, Ayalew, Benny, Corrado, Greg S., Peng, Lily H., Tse, Daniel, Müller, Heimo, Xu, Zhaoyang, Liu, Yun, Stumpe, Martin C., Zatloukal, Kurt, and Mermel, Craig H.

Published
2021
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14. Predicting prostate cancer specific-mortality with artificial intelligence-based Gleason grading

Author

Wulczyn, Ellery, Nagpal, Kunal, Symonds, Matthew, Moran, Melissa, Plass, Markus, Reihs, Robert, Nader, Farah, Tan, Fraser, Cai, Yuannan, Brown, Trissia, Flament-Auvigne, Isabelle, Amin, Mahul B., Stumpe, Martin C., Müller, Heimo, Regitnig, Peter, Holzinger, Andreas, Corrado, Greg S., Peng, Lily H., Chen, Po-Hsuan Cameron, Steiner, David F., Zatloukal, Kurt, Liu, Yun, and Mermel, Craig H.

Published
2021
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15. Comparative analysis of machine learning approaches to classify tumor mutation burden in lung adenocarcinoma using histopathology images

Author

Sadhwani, Apaar, Chang, Huang-Wei, Behrooz, Ali, Brown, Trissia, Auvigne-Flament, Isabelle, Patel, Hardik, Findlater, Robert, Velez, Vanessa, Tan, Fraser, Tekiela, Kamilla, Wulczyn, Ellery, Yi, Eunhee S., Mermel, Craig H., Hanks, Debra, Chen, Po-Hsuan Cameron, Kulig, Kimary, Batenchuk, Cory, Steiner, David F., and Cimermancic, Peter

Published
2021
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17. Subtype-specific genomic alterations define new targets for soft-tissue sarcoma therapy

Author

Barretina, Jordi, Taylor, Barry S, Banerji, Shantanu, Ramos, Alexis H, Lagos-Quintana, Mariana, DeCarolis, Penelope L, Shah, Kinjal, Socci, Nicholas D, Weir, Barbara A, Ho, Alan, Chiang, Derek Y, Reva, Boris, Mermel, Craig H, Getz, Gad, Antipin, Yevgenyi, Beroukhim, Rameen, Major, John E, Hatton, Charles, Nicoletti, Richard, Hanna, Megan, Sharpe, Ted, Fennell, Tim J, Cibulskis, Kristian, Onofrio, Robert C, Saito, Tsuyoshi, Shukla, Neerav, Lau, Christopher, Nelander, Sven, Silver, Serena J, Sougnez, Carrie, Viale, Agnes, Winckler, Wendy, Maki, Robert G, Garraway, Levi A, Lash, Alex, Greulich, Heidi, Root, David E, Sellers, William R, Schwartz, Gary K, Antonescu, Cristina R, Lander, Eric S, Varmus, Harold E, Ladanyi, Marc, Sander, Chris, Meyerson, Matthew, and Singer, Samuel

Subjects
Pediatric, Biotechnology, Cancer, Human Genome, Genetics, Adult, Aged, Female, Genes, Tumor Suppressor, Genome, Histiocytoma, Malignant Fibrous, Humans, Liposarcoma, Male, Middle Aged, Mutation, Sarcoma, Biological Sciences, Medical and Health Sciences, Developmental Biology
Abstract

Soft-tissue sarcomas, which result in approximately 10,700 diagnoses and 3,800 deaths per year in the United States, show remarkable histologic diversity, with more than 50 recognized subtypes. However, knowledge of their genomic alterations is limited. We describe an integrative analysis of DNA sequence, copy number and mRNA expression in 207 samples encompassing seven major subtypes. Frequently mutated genes included TP53 (17% of pleomorphic liposarcomas), NF1 (10.5% of myxofibrosarcomas and 8% of pleomorphic liposarcomas) and PIK3CA (18% of myxoid/round-cell liposarcomas, or MRCs). PIK3CA mutations in MRCs were associated with Akt activation and poor clinical outcomes. In myxofibrosarcomas and pleomorphic liposarcomas, we found both point mutations and genomic deletions affecting the tumor suppressor NF1. Finally, we found that short hairpin RNA (shRNA)-based knockdown of several genes amplified in dedifferentiated liposarcoma, including CDK4 and YEATS4, decreased cell proliferation. Our study yields a detailed map of molecular alterations across diverse sarcoma subtypes and suggests potential subtype-specific targets for therapy.

Published
2010

18. Data from Systematic Interrogation of 3q26 Identifies TLOC1 and SKIL as Cancer Drivers

Author

Hagerstrand, Daniel, primary, Tong, Alexander, primary, Schumacher, Steven E., primary, Ilic, Nina, primary, Shen, Rhine R., primary, Cheung, Hiu Wing, primary, Vazquez, Francisca, primary, Shrestha, Yashaswi, primary, Kim, So Young, primary, Giacomelli, Andrew O., primary, Rosenbluh, Joseph, primary, Schinzel, Anna C., primary, Spardy, Nicole A., primary, Barbie, David A., primary, Mermel, Craig H., primary, Weir, Barbara A., primary, Garraway, Levi A., primary, Tamayo, Pablo, primary, Mesirov, Jill P., primary, Beroukhim, Rameen, primary, and Hahn, William C., primary

Published
2023
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19. Supplementary Table S3 from Systematic Interrogation of 3q26 Identifies TLOC1 and SKIL as Cancer Drivers

Author

Hagerstrand, Daniel, primary, Tong, Alexander, primary, Schumacher, Steven E., primary, Ilic, Nina, primary, Shen, Rhine R., primary, Cheung, Hiu Wing, primary, Vazquez, Francisca, primary, Shrestha, Yashaswi, primary, Kim, So Young, primary, Giacomelli, Andrew O., primary, Rosenbluh, Joseph, primary, Schinzel, Anna C., primary, Spardy, Nicole A., primary, Barbie, David A., primary, Mermel, Craig H., primary, Weir, Barbara A., primary, Garraway, Levi A., primary, Tamayo, Pablo, primary, Mesirov, Jill P., primary, Beroukhim, Rameen, primary, and Hahn, William C., primary

Published
2023
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20. Supplementary Figure 1 from Systematic Interrogation of 3q26 Identifies TLOC1 and SKIL as Cancer Drivers

Author

Hagerstrand, Daniel, primary, Tong, Alexander, primary, Schumacher, Steven E., primary, Ilic, Nina, primary, Shen, Rhine R., primary, Cheung, Hiu Wing, primary, Vazquez, Francisca, primary, Shrestha, Yashaswi, primary, Kim, So Young, primary, Giacomelli, Andrew O., primary, Rosenbluh, Joseph, primary, Schinzel, Anna C., primary, Spardy, Nicole A., primary, Barbie, David A., primary, Mermel, Craig H., primary, Weir, Barbara A., primary, Garraway, Levi A., primary, Tamayo, Pablo, primary, Mesirov, Jill P., primary, Beroukhim, Rameen, primary, and Hahn, William C., primary

Published
2023
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21. Data from Clinical Acquired Resistance to RAF Inhibitor Combinations in BRAF-Mutant Colorectal Cancer through MAPK Pathway Alterations

Author

Ahronian, Leanne G., primary, Sennott, Erin M., primary, Van Allen, Eliezer M., primary, Wagle, Nikhil, primary, Kwak, Eunice L., primary, Faris, Jason E., primary, Godfrey, Jason T., primary, Nishimura, Koki, primary, Lynch, Kerry D., primary, Mermel, Craig H., primary, Lockerman, Elizabeth L., primary, Kalsy, Anuj, primary, Gurski, Joseph M., primary, Bahl, Samira, primary, Anderka, Kristin, primary, Green, Lisa M., primary, Lennon, Niall J., primary, Huynh, Tiffany G., primary, Mino-Kenudson, Mari, primary, Getz, Gad, primary, Dias-Santagata, Dora, primary, Iafrate, A. John, primary, Engelman, Jeffrey A., primary, Garraway, Levi A., primary, and Corcoran, Ryan B., primary

Published
2023
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22. Supplementary Figure S5 from Clinical Acquired Resistance to RAF Inhibitor Combinations in BRAF-Mutant Colorectal Cancer through MAPK Pathway Alterations

Author

Ahronian, Leanne G., primary, Sennott, Erin M., primary, Van Allen, Eliezer M., primary, Wagle, Nikhil, primary, Kwak, Eunice L., primary, Faris, Jason E., primary, Godfrey, Jason T., primary, Nishimura, Koki, primary, Lynch, Kerry D., primary, Mermel, Craig H., primary, Lockerman, Elizabeth L., primary, Kalsy, Anuj, primary, Gurski, Joseph M., primary, Bahl, Samira, primary, Anderka, Kristin, primary, Green, Lisa M., primary, Lennon, Niall J., primary, Huynh, Tiffany G., primary, Mino-Kenudson, Mari, primary, Getz, Gad, primary, Dias-Santagata, Dora, primary, Iafrate, A. John, primary, Engelman, Jeffrey A., primary, Garraway, Levi A., primary, and Corcoran, Ryan B., primary

Published
2023
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23. Supplementary Table S1 from Clinical Acquired Resistance to RAF Inhibitor Combinations in BRAF-Mutant Colorectal Cancer through MAPK Pathway Alterations

Author

Ahronian, Leanne G., primary, Sennott, Erin M., primary, Van Allen, Eliezer M., primary, Wagle, Nikhil, primary, Kwak, Eunice L., primary, Faris, Jason E., primary, Godfrey, Jason T., primary, Nishimura, Koki, primary, Lynch, Kerry D., primary, Mermel, Craig H., primary, Lockerman, Elizabeth L., primary, Kalsy, Anuj, primary, Gurski, Joseph M., primary, Bahl, Samira, primary, Anderka, Kristin, primary, Green, Lisa M., primary, Lennon, Niall J., primary, Huynh, Tiffany G., primary, Mino-Kenudson, Mari, primary, Getz, Gad, primary, Dias-Santagata, Dora, primary, Iafrate, A. John, primary, Engelman, Jeffrey A., primary, Garraway, Levi A., primary, and Corcoran, Ryan B., primary

Published
2023
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24. Supplementary Figure 3 from Systematic Interrogation of 3q26 Identifies TLOC1 and SKIL as Cancer Drivers

Author

Hagerstrand, Daniel, primary, Tong, Alexander, primary, Schumacher, Steven E., primary, Ilic, Nina, primary, Shen, Rhine R., primary, Cheung, Hiu Wing, primary, Vazquez, Francisca, primary, Shrestha, Yashaswi, primary, Kim, So Young, primary, Giacomelli, Andrew O., primary, Rosenbluh, Joseph, primary, Schinzel, Anna C., primary, Spardy, Nicole A., primary, Barbie, David A., primary, Mermel, Craig H., primary, Weir, Barbara A., primary, Garraway, Levi A., primary, Tamayo, Pablo, primary, Mesirov, Jill P., primary, Beroukhim, Rameen, primary, and Hahn, William C., primary

Published
2023
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25. Supplementary Legends from Systematic Interrogation of 3q26 Identifies TLOC1 and SKIL as Cancer Drivers

Author

Hagerstrand, Daniel, primary, Tong, Alexander, primary, Schumacher, Steven E., primary, Ilic, Nina, primary, Shen, Rhine R., primary, Cheung, Hiu Wing, primary, Vazquez, Francisca, primary, Shrestha, Yashaswi, primary, Kim, So Young, primary, Giacomelli, Andrew O., primary, Rosenbluh, Joseph, primary, Schinzel, Anna C., primary, Spardy, Nicole A., primary, Barbie, David A., primary, Mermel, Craig H., primary, Weir, Barbara A., primary, Garraway, Levi A., primary, Tamayo, Pablo, primary, Mesirov, Jill P., primary, Beroukhim, Rameen, primary, and Hahn, William C., primary

Published
2023
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26. Supplementary Figure 2 from Systematic Interrogation of 3q26 Identifies TLOC1 and SKIL as Cancer Drivers

Author

Hagerstrand, Daniel, primary, Tong, Alexander, primary, Schumacher, Steven E., primary, Ilic, Nina, primary, Shen, Rhine R., primary, Cheung, Hiu Wing, primary, Vazquez, Francisca, primary, Shrestha, Yashaswi, primary, Kim, So Young, primary, Giacomelli, Andrew O., primary, Rosenbluh, Joseph, primary, Schinzel, Anna C., primary, Spardy, Nicole A., primary, Barbie, David A., primary, Mermel, Craig H., primary, Weir, Barbara A., primary, Garraway, Levi A., primary, Tamayo, Pablo, primary, Mesirov, Jill P., primary, Beroukhim, Rameen, primary, and Hahn, William C., primary

Published
2023
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29. Systematic investigation of genetic vulnerabilities across cancer cell lines reveals lineage-specific dependencies in ovarian cancer

Author

Cheung, Hiu Wing, Cowley, Glenn S., Weir, Barbara A., Boehm, Jesse S., Rusin, Scott, Scott, Justine A., East, Alexandra, Ali, Levi D., Lizotte, Patrick H., Wong, Terence C., Jiang, Guozhi, Hsiao, Jessica, Mermel, Craig H., Getz, Gad, Barretina, Jordi, Gopal, Shuba, Tamayo, Pablo, Gould, Joshua, Tsherniak, Aviad, Stransky, Nicolas, Luo, Biao, Ren, Yin, Drapkin, Ronny, Bhatia, Sangeeta N., Mesirov, Jill P., Garraway, Levi A., Meyerson, Matthew, Lander, Eric S., Root, David E., and Hahn, William C.

Published
2011

31. Discovery and saturation analysis of cancer genes across 21 tumour types

Author

Lawrence, Michael S., Stojanov, Petar, Mermel, Craig H., Robinson, James T., Garraway, Levi A., Golub, Todd R., Meyerson, Matthew, Gabriel, Stacey B., Lander, Eric S., and Getz, Gad

Subjects
Oncology, Experimental, Tumors -- Physiological aspects -- Genetic aspects, Cancer -- Genetic aspects -- Research, Environmental issues, Science and technology, Zoology and wildlife conservation
Abstract

Although a few cancer genes are mutated in a high proportion of tumours of a given type (>20%), most are mutated at intermediate frequencies (2-20%). To explore the feasibility of creating a comprehensive catalogue of cancer genes, we analysed somatic point mutations in exome sequences from 4,742 human cancers and their matched normal-tissue samples across 21 cancer types. We found that large-scale genomic analysis can identify nearly all known cancer genes in these tumour types. Our analysis also identified 33 genes that were not previously known to be significantly mutated in cancer, including genes related to proliferation, apoptosis, genome stability, chromatin regulation, immune evasion, RNA processing and protein homeostasis. Down-sampling analysis indicates that larger sample sizes will reveal many more genes mutated at clinically important frequencies. We estimate that near-saturation may be achieved with 600-5,000 samples per tumour type, depending on background mutation frequency. The results may help to guide the next stage of cancer genomics., Comprehensive knowledge of the genes underlying human cancers is a critical foundation for cancer diagnostics, therapeutics, clinical-trial design and selection of rational combination therapies. It is now possible to use [...]

Published
2014
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33. Age-Related Clonal Hematopoiesis Associated with Adverse Outcomes

Author

Jaiswal, Siddhartha, Fontanillas, Pierre, Flannick, Jason, Manning, Alisa, Grauman, Peter V., Mar, Brenton G., Lindsley, Coleman R., Mermel, Craig H., Burtt, Noel, Chavez, Alejandro, Higgins, John M., Moltchanov, Vladislav, Kuo, Frank C., Kluk, Michael J., Henderson, Brian, Kinnunen, Leena, Koistinen, Heikki A., Ladenvall, Claes, Getz, Gad, Correa, Adolfo, Banahan, Benjamin F., Gabriel, Stacey, Kathiresan, Sekar, Stringham, Heather M., McCarthy, Mark I., Boehnke, Michael, Tuomilehto, Jaakko, Haiman, Christopher, Groop, Leif, Atzmon, Gil, Wilson, James G., Neuberg, Donna, Altshuler, David, and Ebert, Benjamin L.

Published
2014
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34. Prognostically relevant gene signatures of high-grade serous ovarian carcinoma

Author

Verhaak, Roel G.W., Tamayo, Pablo, Yang, Ji-Yeon, Hubbard, Diana, Zhang, Hailei, Creighton, Chad J., Fereday, Sian, Lawrence, Michael, Carter, Scott L., Mermel, Craig H., Kostic, Aleksandar D., Etemadmoghadam, Dariush, Saksena, Gordon, Cibulskis, Kristian, Duraisamy, Sekhar, Levanon, Keren, Sougnez, Carrie, Tsherniak, Aviad, Gomez, Sebastian, Onofrio, Robert, Gabriel, Stacey, Chin, Lynda, Zhang, Nianxiang, Spellman, Paul T., Zhang, Yiqun, Akbani, Rehan, Hoadley, Katherine A., Kahn, Ari, Kobel, Martin, Huntsman, David, Soslow, Robert A., Defazio, Anna, Birrer, Michael J., Gray, Joe W., Weinstein, John N., Bowtell, David D., Drapkin, Ronny, Mesirov, Jill P., Getz, Gad, Levine, Douglas A., and Meyerson, Matthew

Subjects
Gene expression -- Research, Ovarian cancer -- Genetic aspects -- Risk factors -- Research -- Prognosis, Health care industry
Abstract

Because of the high risk of recurrence in high-grade serous ovarian carcinoma (HGS-OvCa), the development of outcome predictors could be valuable for patient stratification. Using the catalog of The Cancer [...]

Published
2013
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35. Determining breast cancer biomarker status and associated morphological features using deep learning

Author

Gamble, Paul, primary, Jaroensri, Ronnachai, additional, Wang, Hongwu, additional, Tan, Fraser, additional, Moran, Melissa, additional, Brown, Trissia, additional, Flament-Auvigne, Isabelle, additional, Rakha, Emad A., additional, Toss, Michael, additional, Dabbs, David J., additional, Regitnig, Peter, additional, Olson, Niels, additional, Wren, James H., additional, Robinson, Carrie, additional, Corrado, Greg S., additional, Peng, Lily H., additional, Liu, Yun, additional, Mermel, Craig H., additional, Steiner, David F., additional, and Chen, Po-Hsuan Cameron, additional

Published
2021
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36. The histone methyltransferase SETDB1 is recurrently amplified in melanoma and accelerates its onset

Author

Ceol, Craig J., Houvras, Yariv, Jane-Valbuena, Judit, Bilodeau, Steve, Orlando, David A., Battisti, Valentine, Fritsch, Lauriane, Lin, William M., Hollmann, Travis J., Ferre, Fabrizio, Bourque, Caitlin, Burke, Christopher J., Turner, Laura, Uong, Audrey, Johnson, Laura A., Beroukhim, Rameen, Mermel, Craig H., Loda, Massimo, Ait-Si-Ali, Slimane, Garraway, Levi A., Young, Richard A., and Zon, Leonard I.

Subjects
Gene expression -- Physiological aspects -- Research -- Genetic aspects, Methyltransferases -- Physiological aspects -- Genetic aspects -- Research, Melanoma -- Genetic aspects -- Risk factors -- Development and progression -- Research, Environmental issues, Science and technology, Zoology and wildlife conservation
Abstract

The most common mutation in human melanoma, BRAF(V600E), activates the serine/threonine kinase BRAF and causes excessive activity in the mitogen-activated protein kinase pathway (1,2). BRAF(V600E) mutations are also present in benign melanocytic naevi (3), highlighting the importance of additional genetic alterations in the genesis of malignant tumours. Such changes include recurrent copy number variations that result in the amplification of oncogenes (4,5). For certain amplifications, the large number of genes in the interval has precluded an understanding of the cooperating oncogenic events. Here we have used a zebrafish melanoma model to test genes in a recurrently amplified region of chromosome 1 for the ability to cooperate with BRAF(V600E) and accelerate melanoma. SETDB1, an enzyme that methylates histone H3 on lysine 9 (H3K9), was found to accelerate melanoma formation significantly in zebrafish. Chromatin immunoprecipitation coupled with massively parallel DNA sequencing and gene expression analyses uncovered genes, including HOX genes, that are transcriptionally dysregulated in response to increased levels of SETDB1. Our studies establish SETDB1 as an oncogene in melanoma and underscore the role of chromatin factors in regulating tumorigenesis., To identify genes that promote melanoma, we focused on genomic regions that are subject to copy number amplification in human tumour samples. In a study of 101 cell lines and [...]

Published
2011
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37. Evaluation of the Use of Combined Artificial Intelligence and Pathologist Assessment to Review and Grade Prostate Biopsies

Author

Steiner, David F., primary, Nagpal, Kunal, additional, Sayres, Rory, additional, Foote, Davis J., additional, Wedin, Benjamin D., additional, Pearce, Adam, additional, Cai, Carrie J., additional, Winter, Samantha R., additional, Symonds, Matthew, additional, Yatziv, Liron, additional, Kapishnikov, Andrei, additional, Brown, Trissia, additional, Flament-Auvigne, Isabelle, additional, Tan, Fraser, additional, Stumpe, Martin C., additional, Jiang, Pan-Pan, additional, Liu, Yun, additional, Chen, Po-Hsuan Cameron, additional, Corrado, Greg S., additional, Terry, Michael, additional, and Mermel, Craig H., additional

Published
2020
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38. Mutational heterogeneity in cancer and the search for new cancer-associated genes

Author

Lawrence, Michael S., Stojanov, Petar, Polak, Paz, Kryukov, Gregory V., Cibulskis, Kristian, Sivachenko, Andrey, Carter, Scott L., Stewart, Chip, Mermel, Craig H., Roberts, Steven A., Kiezun, Adam, Hammerman, Peter S., McKenna, Aaron, Drier, Yotam, Zou, Lihua, Ramos, Alex H., Pugh, Trevor J., Stransky, Nicolas, Helman, Elena, Kim, Jaegil, Sougnez, Carrie, Ambrogio, Lauren, Nickerson, Elizabeth, Shefler, Erica, Cortés, Maria L., Auclair, Daniel, Saksena, Gordon, Voet, Douglas, Noble, Michael, DiCara, Daniel, Lin, Pei, Lichtenstein, Lee, Heiman, David I., Fennell, Timothy, Imielinski, Marcin, Hernandez, Bryan, Hodis, Eran, Baca, Sylvan, Dulak, Austin M., Lohr, Jens, Landau, Dan-Avi, Wu, Catherine J., Melendez-Zajgla, Jorge, Hidalgo-Miranda, Alfredo, Koren, Amnon, McCarroll, Steven A., Mora, Jaume, Lee, Ryan S., Crompton, Brian, Onofrio, Robert, Parkin, Melissa, Winckler, Wendy, Ardlie, Kristin, Gabriel, Stacey B., Roberts, Charles W. M., Biegel, Jaclyn A., Stegmaier, Kimberly, Bass, Adam J., Garraway, Levi A., Meyerson, Matthew, Golub, Todd R., Gordenin, Dmitry A., Sunyaev, Shamil, Lander, Eric S., and Getz, Gad

Published
2013
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40. Development and Validation of a Deep Learning Algorithm for Gleason Grading of Prostate Cancer From Biopsy Specimens

Author

Nagpal, Kunal, primary, Foote, Davis, additional, Tan, Fraser, additional, Liu, Yun, additional, Chen, Po-Hsuan Cameron, additional, Steiner, David F., additional, Manoj, Naren, additional, Olson, Niels, additional, Smith, Jenny L., additional, Mohtashamian, Arash, additional, Peterson, Brandon, additional, Amin, Mahul B., additional, Evans, Andrew J., additional, Sweet, Joan W., additional, Cheung, Carol, additional, van der Kwast, Theodorus, additional, Sangoi, Ankur R., additional, Zhou, Ming, additional, Allan, Robert, additional, Humphrey, Peter A., additional, Hipp, Jason D., additional, Gadepalli, Krishna, additional, Corrado, Greg S., additional, Peng, Lily H., additional, Stumpe, Martin C., additional, and Mermel, Craig H., additional

Published
2020
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41. Abstract 2096: A deep learning system to predict disease-specific survival in stage II and stage III colorectal cancer

Author

Wulczyn, Ellery, primary, Steiner, David F., additional, Moran, Melissa, additional, Plass, Markus, additional, Reihs, Robert, additional, Mueller, Heimo, additional, Sadhwani, Apaar, additional, Cai, Yuannan, additional, Flament, Isabelle, additional, Chen, Po-Hsuan Cameron, additional, Liu, Yun, additional, Stumpe, Martin C., additional, Xu, Zhaoyang, additional, Zatloukal, Kurt, additional, and Mermel, Craig H., additional

Published
2020
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44. Publisher Correction: Development and validation of a deep learning algorithm for improving Gleason scoring of prostate cancer

Author

Nagpal, Kunal, primary, Foote, Davis, additional, Liu, Yun, additional, Chen, Po-Hsuan Cameron, additional, Wulczyn, Ellery, additional, Tan, Fraser, additional, Olson, Niels, additional, Smith, Jenny L., additional, Mohtashamian, Arash, additional, Wren, James H., additional, Corrado, Greg S., additional, MacDonald, Robert, additional, Peng, Lily H., additional, Amin, Mahul B., additional, Evans, Andrew J., additional, Sangoi, Ankur R., additional, Mermel, Craig H., additional, Hipp, Jason D., additional, and Stumpe, Martin C., additional

Published
2019
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45. The landscape of somatic copy-number alteration across human cancers

Author

Beroukhim, Rameen, Mermel, Craig H., Porter, Dale, Wei, Guo, Raychaudhuri, Soumya, Donovan, Jerry, Barretina, Jordi, Boehm, Jesse S., Dobson, Jennifer, Urashima, Mitsuyoshi, Mc Henry, Kevin T., Pinchback, Reid M., Ligon, Azra H., Cho, Yoon-Jae, Haery, Leila, Greulich, Heidi, Reich, Michael, Winckler, Wendy, Lawrence, Michael S., Weir, Barbara A., Tanaka, Kumiko E., Chiang, Derek Y., Bass, Adam J., Loo, Alice, Hoffman, Carter, Prensner, John, Liefeld, Ted, Gao, Qing, Yecies, Derek, Signoretti, Sabina, Maher, Elizabeth, Kaye, Frederic J., Sasaki, Hidefumi, Tepper, Joel E., Fletcher, Jonathan A., Tabernero, Josep, Baselga, José, Tsao, Ming-Sound, Demichelis, Francesca, Rubin, Mark A., Janne, Pasi A., Daly, Mark J., Nucera, Carmelo, Levine, Ross L., Ebert, Benjamin L., Gabriel, Stacey, Rustgi, Anil K., Antonescu, Cristina R., Ladanyi, Marc, Letai, Anthony, Garraway, Levi A., Loda, Massimo, Beer, David G., True, Lawrence D., Okamoto, Aikou, Pomeroy, Scott L., Singer, Samuel, Golub, Todd R., Lander, Eric S., Getz, Gad, Sellers, William R., and Meyerson, Matthew

Published
2010
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47. Similar image search for histopathology: SMILY

Author

Hegde, Narayan, primary, Hipp, Jason D., additional, Liu, Yun, additional, Emmert-Buck, Michael, additional, Reif, Emily, additional, Smilkov, Daniel, additional, Terry, Michael, additional, Cai, Carrie J., additional, Amin, Mahul B., additional, Mermel, Craig H., additional, Nelson, Phil Q., additional, Peng, Lily H., additional, Corrado, Greg S., additional, and Stumpe, Martin C., additional

Published
2019
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48. Development and validation of a deep learning algorithm for improving Gleason scoring of prostate cancer

Author

Nagpal, Kunal, primary, Foote, Davis, additional, Liu, Yun, additional, Chen, Po-Hsuan Cameron, additional, Wulczyn, Ellery, additional, Tan, Fraser, additional, Olson, Niels, additional, Smith, Jenny L., additional, Mohtashamian, Arash, additional, Wren, James H., additional, Corrado, Greg S., additional, MacDonald, Robert, additional, Peng, Lily H., additional, Amin, Mahul B., additional, Evans, Andrew J., additional, Sangoi, Ankur R., additional, Mermel, Craig H., additional, Hipp, Jason D., additional, and Stumpe, Martin C., additional

Published
2019
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50. Computational Pathology: An Emerging Definition

Author

Louis, David N., Gerber, Georg K., Baron, Jason M., Bry, Lyn, Dighe, Anand S., Getz, Gad, Higgins, John M., Kuo, Frank C., Lane, William J., Michaelson, James S., Le, Long P., Mermel, Craig H., Gilbertson, John R., and Golden, Jeffrey A.

Subjects
Medical informatics, Natural language interfaces, Big data, Computational linguistics, Language processing, Health
Abstract

Advances in high-throughput laboratory and health information technologies are revolutionizing the disciplines of pathology and laboratory medicine. The ability to extract clinically actionable knowledge using computational methods from complex, high-dimensional [...]

Published
2014
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