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1,135 results on '"Akbani"'

4. Novel Colorimetric and Light Scatter Methods to Identify and Manage Peritoneal Dialysis-Associated Peritonitis at the Point-of-Care

Author

Govindji-Bhatt, Nishal, Kennedy, Stephnie M., Barker, Michael G., Kell, Darren, Henderson, Duncan, Goddard, Nicholas, Garcia, Ana Yepes, Milner, Adam S., Willett, Tom, Griffiths, Ryan, Foster, Peter, Kilgallon, William, Cant, Rachel, Knight, Christopher G., Lewis, David, Corbett, Richard, Akbani, Habib, Woodrow, Graham, Sood, Bhrigu, Iyasere, Osasuyi, Davies, Simon, Qazi, Junaid, Vardhan, Anand, Gillis, Laura, Wilkie, Martin, and Dobson, Curtis B.

Published
2024
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5. The Cancer Genome Atlas Comprehensive Molecular Characterization of Renal Cell Carcinoma

Author

Christopher J. Ricketts, Aguirre A. De Cubas, Huihui Fan, Christof C. Smith, Martin Lang, Ed Reznik, Reanne Bowlby, Ewan A. Gibb, Rehan Akbani, Rameen Beroukhim, Donald P. Bottaro, Toni K. Choueiri, Richard A. Gibbs, Andrew K. Godwin, Scott Haake, A. Ari Hakimi, Elizabeth P. Henske, James J. Hsieh, Thai H. Ho, Rupa S. Kanchi, Bhavani Krishnan, David J. Kwiatkowski, Wenbin Liu, Maria J. Merino, Gordon B. Mills, Jerome Myers, Michael L. Nickerson, Victor E. Reuter, Laura S. Schmidt, C. Simon Shelley, Hui Shen, Brian Shuch, Sabina Signoretti, Ramaprasad Srinivasan, Pheroze Tamboli, George Thomas, Benjamin G. Vincent, Cathy D. Vocke, David A. Wheeler, Lixing Yang, William Y. Kim, A. Gordon Robertson, Paul T. Spellman, W. Kimryn Rathmell, and W. Marston Linehan

Subjects
Biology (General), QH301-705.5
Published
2024
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6. Enhancing role-based trust management with a reputation system for MANETs

Author

Akbani Rehan and Korkmaz Turgay

Subjects
MANETs, trust management, access control, machine learning, Telecommunication, TK5101-6720, Electronics, TK7800-8360
Abstract

Abstract We start with role-based trust management (RBTM) and address some of the challenges associated with using RBTM in mobile ad hoc networks (MANETs). We then enhance RBTM with reputation systems (RSs), and propose a new hybrid trust management system (HTMS). In HTMS, the privilege level of an entity is determined not only by its role in the system, but also by its reputation score, which in turn is based on its behavior. If a privileged node becomes compromised and conducts several malicious or risky transactions, then its privilege level is quickly reduced to limit its access to resources and minimize the damage it can inflict further. The system uses a global, network-wide perspective to thwart global attacks. Such fine-grained variations of access control and dynamically assigning privilege levels would be very difficult to accomplish manually. We evaluated HTMS by comparing an implementation of it against an ideal response. We show that HTMS performs very close to the ideal if we can accurately estimate the proportion of malicious nodes in the network. We suggest using sampling to estimate this proportion. However, even if this estimate is not accurate, the results are still much better than using RBTM by itself. EDICS: SYS-ARCH; SYS-PROT; FOR-DETE; SYS-INTR.

Published
2011

7. Whole-genome characterization of lung adenocarcinomas lacking the RTK/RAS/RAF pathway

Author

Carrot-Zhang, Jian, Yao, Xiaotong, Devarakonda, Siddhartha, Deshpande, Aditya, Damrauer, Jeffrey S, Silva, Tiago Chedraoui, Wong, Christopher K, Choi, Hyo Young, Felau, Ina, Robertson, A Gordon, Castro, Mauro AA, Bao, Lisui, Rheinbay, Esther, Liu, Eric Minwei, Trieu, Tuan, Haan, David, Yau, Christina, Hinoue, Toshinori, Liu, Yuexin, Shapira, Ofer, Kumar, Kiran, Mungall, Karen L, Zhang, Hailei, Lee, Jake June-Koo, Berger, Ashton, Gao, Galen F, Zhitomirsky, Binyamin, Liang, Wen-Wei, Zhou, Meng, Moorthi, Sitapriya, Berger, Alice H, Collisson, Eric A, Zody, Michael C, Ding, Li, Cherniack, Andrew D, Getz, Gad, Elemento, Olivier, Benz, Christopher C, Stuart, Josh, Zenklusen, JC, Beroukhim, Rameen, Chang, Jason C, Campbell, Joshua D, Hayes, D Neil, Yang, Lixing, Laird, Peter W, Weinstein, John N, Kwiatkowski, David J, Tsao, Ming S, Travis, William D, Khurana, Ekta, Berman, Benjamin P, Hoadley, Katherine A, Robine, Nicolas, Network, TCGA Research, Arora, Kanika, Shah, Minita, Shelton, Jennifer, Bowlby, Reanne, Friedl, Verena, Goldman, Mary, Craft, Brian, Heiman, David I, Hajirasouliha, Iman, Ricketts, Camir, Anur, Pavana, Chiotti, Kami E, Caesar-Johnson, Samantha J, Demchok, John A, Ferguson, Martin L, Kemal, Anab, Tarnuzzer, Roy, Wang, Zhining, Yang, Liming, Spellman, Paul T, Raphael, Benjamin, Akbani, Rehan, Zhu, Jingchun, Jones, Steven JM, Shen, Hui, Meyerson, Matthew, Govindan, Ramaswamy, and Imielinski, Marcin

Subjects
Biological Sciences, Cancer Genomics, Genetics, Rare Diseases, Lung, Cancer, Human Genome, Lung Cancer, Biotechnology, 2.1 Biological and endogenous factors, Good Health and Well Being, Adenocarcinoma of Lung, Humans, Kelch-Like ECH-Associated Protein 1, Lung Neoplasms, Tachykinins, Whole Genome Sequencing, TCGA Research Network, TCGA, driver, genome analysis, lung adenocarcinoma, noncoding, oncogene, precision oncology, structural variation, tumor suppressor, whole genome sequencing, Biochemistry and Cell Biology, Medical Physiology, Biological sciences
Abstract

RTK/RAS/RAF pathway alterations (RPAs) are a hallmark of lung adenocarcinoma (LUAD). In this study, we use whole-genome sequencing (WGS) of 85 cases found to be RPA(-) by previous studies from The Cancer Genome Atlas (TCGA) to characterize the minority of LUADs lacking apparent alterations in this pathway. We show that WGS analysis uncovers RPA(+) in 28 (33%) of the 85 samples. Among the remaining 57 cases, we observe focal deletions targeting the promoter or transcription start site of STK11 (n = 7) or KEAP1 (n = 3), and promoter mutations associated with the increased expression of ILF2 (n = 6). We also identify complex structural variations associated with high-level copy number amplifications. Moreover, an enrichment of focal deletions is found in TP53 mutant cases. Our results indicate that RPA(-) cases demonstrate tumor suppressor deletions and genome instability, but lack unique or recurrent genetic lesions compensating for the lack of RPAs. Larger WGS studies of RPA(-) cases are required to understand this important LUAD subset.

Published
2021

8. NExUS: Bayesian simultaneous network estimation across unequal sample sizes

Author

Das, Priyam, Peterson, Christine, Do, Kim-Anh, Akbani, Rehan, and Baladandayuthapani, Veerabhadran

Subjects
Statistics - Applications, Statistics - Methodology
Abstract

Network-based analyses of high-throughput genomics data provide a holistic, systems-level understanding of various biological mechanisms for a common population. However, when estimating multiple networks across heterogeneous sub-populations, varying sample sizes pose a challenge in the estimation and inference, as network differences may be driven by differences in power. We are particularly interested in addressing this challenge in the context of proteomic networks for related cancers, as the number of subjects available for rare cancer (sub-)types is often limited. We develop NExUS (Network Estimation across Unequal Sample sizes), a Bayesian method that enables joint learning of multiple networks while avoiding artefactual relationship between sample size and network sparsity. We demonstrate through simulations that NExUS outperforms existing network estimation methods in this context, and apply it to learn network similarity and shared pathway activity for groups of cancers with related origins represented in The Cancer Genome Atlas (TCGA) proteomic data., Comment: 8 pages, 8 figues

Published
2018
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9. Pan-cancer analysis of whole genomes

Author

Aaltonen, Lauri A, Abascal, Federico, Abeshouse, Adam, Aburatani, Hiroyuki, Adams, David J, Agrawal, Nishant, Ahn, Keun Soo, Ahn, Sung-Min, Aikata, Hiroshi, Akbani, Rehan, Akdemir, Kadir C, Al-Ahmadie, Hikmat, Al-Sedairy, T, Al-Shahrour, Fatima, Alawi, Malik, Albert, Monique, Aldape, Kenneth, Alexandrov, Ludmil B, Ally, Adrian, Alsop, Kathryn, Alvarez, Eva G, Amary, Fernanda, Amin, Samirkumar B, Aminou, Brice, Ammerpohl, Ole, Anderson, Matthew J, Ang, Yeng, Antonello, Davide, Anur, Pavana, Aparicio, Samuel, Appelbaum, Elizabeth L, Arai, Yasuhito, Aretz, Axel, Arihiro, Koji, Ariizumi, Shun-ichi, Armenia, Joshua, Arnould, Laurent, Asa, Sylvia, Assenov, Yassen, Atwal, Gurnit, Aukema, Sietse, Auman, J Todd, Aure, Miriam RR, Awadalla, Philip, Aymerich, Marta, Bader, Gary D, Baez-Ortega, Adrian, Bailey, Matthew H, Bailey, Peter J, Balasundaram, Miruna, Balu, Saianand, Bandopadhayay, Pratiti, Banks, Rosamonde E, Barbi, Stefano, Barbour, Andrew P, Barenboim, Jonathan, Barnholtz-Sloan, Jill, Barr, Hugh, Barrera, Elisabet, Bartlett, John, Bartolome, Javier, Bassi, Claudio, Bathe, Oliver F, Baumhoer, Daniel, Bavi, Prashant, Baylin, Stephen B, Bazant, Wojciech, Beardsmore, Duncan, Beck, Timothy A, Behjati, Sam, Behren, Andreas, Niu, Beifang, Bell, Cindy, Beltran, Sergi, Benz, Christopher, Berchuck, Andrew, Bergmann, Anke K, Bergstrom, Erik N, Berman, Benjamin P, Berney, Daniel M, Bernhart, Stephan H, Beroukhim, Rameen, Berrios, Mario, Bersani, Samantha, Bertl, Johanna, Betancourt, Miguel, Bhandari, Vinayak, Bhosle, Shriram G, Biankin, Andrew V, Bieg, Matthias, Bigner, Darell, Binder, Hans, Birney, Ewan, Birrer, Michael, Biswas, Nidhan K, Bjerkehagen, Bodil, Bodenheimer, Tom, Boice, Lori, Bonizzato, Giada, and De Bono, Johann S

Subjects
Biological Sciences, Biomedical and Clinical Sciences, Bioinformatics and Computational Biology, Genetics, Oncology and Carcinogenesis, Biotechnology, Cancer Genomics, Human Genome, Prevention, Cancer, 2.1 Biological and endogenous factors, Cell Proliferation, Cellular Senescence, Chromothripsis, Cloud Computing, DNA Mutational Analysis, Evolution, Molecular, Female, Genome, Human, Genomics, Germ-Line Mutation, High-Throughput Nucleotide Sequencing, Humans, Information Dissemination, Male, Mutagenesis, Mutation, Neoplasms, Oncogenes, Promoter Regions, Genetic, RNA Splicing, Reproducibility of Results, Telomerase, Telomere, ICGC/TCGA Pan-Cancer Analysis of Whole Genomes Consortium, General Science & Technology
Abstract

Cancer is driven by genetic change, and the advent of massively parallel sequencing has enabled systematic documentation of this variation at the whole-genome scale1-3. Here we report the integrative analysis of 2,658 whole-cancer genomes and their matching normal tissues across 38 tumour types from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). We describe the generation of the PCAWG resource, facilitated by international data sharing using compute clouds. On average, cancer genomes contained 4-5 driver mutations when combining coding and non-coding genomic elements; however, in around 5% of cases no drivers were identified, suggesting that cancer driver discovery is not yet complete. Chromothripsis, in which many clustered structural variants arise in a single catastrophic event, is frequently an early event in tumour evolution; in acral melanoma, for example, these events precede most somatic point mutations and affect several cancer-associated genes simultaneously. Cancers with abnormal telomere maintenance often originate from tissues with low replicative activity and show several mechanisms of preventing telomere attrition to critical levels. Common and rare germline variants affect patterns of somatic mutation, including point mutations, structural variants and somatic retrotransposition. A collection of papers from the PCAWG Consortium describes non-coding mutations that drive cancer beyond those in the TERT promoter4; identifies new signatures of mutational processes that cause base substitutions, small insertions and deletions and structural variation5,6; analyses timings and patterns of tumour evolution7; describes the diverse transcriptional consequences of somatic mutation on splicing, expression levels, fusion genes and promoter activity8,9; and evaluates a range of more-specialized features of cancer genomes8,10-18.

Published
2020

10. The Immune Landscape of Cancer

Author

Thorsson, Vésteinn, Gibbs, David L, Brown, Scott D, Wolf, Denise, Bortone, Dante S, Yang, Tai-Hsien Ou, Porta-Pardo, Eduard, Gao, Galen F, Plaisier, Christopher L, Eddy, James A, Ziv, Elad, Culhane, Aedin C, Paull, Evan O, Sivakumar, IK Ashok, Gentles, Andrew J, Malhotra, Raunaq, Farshidfar, Farshad, Colaprico, Antonio, Parker, Joel S, Mose, Lisle E, Vo, Nam Sy, Liu, Jianfang, Liu, Yuexin, Rader, Janet, Dhankani, Varsha, Reynolds, Sheila M, Bowlby, Reanne, Califano, Andrea, Cherniack, Andrew D, Anastassiou, Dimitris, Bedognetti, Davide, Mokrab, Younes, Newman, Aaron M, Rao, Arvind, Chen, Ken, Krasnitz, Alexander, Hu, Hai, Malta, Tathiane M, Noushmehr, Houtan, Pedamallu, Chandra Sekhar, Bullman, Susan, Ojesina, Akinyemi I, Lamb, Andrew, Zhou, Wanding, Shen, Hui, Choueiri, Toni K, Weinstein, John N, Guinney, Justin, Saltz, Joel, Holt, Robert A, Rabkin, Charles S, Network, The Cancer Genome Atlas Research, Caesar-Johnson, Samantha J, Demchok, John A, Felau, Ina, Kasapi, Melpomeni, Ferguson, Martin L, Hutter, Carolyn M, Sofia, Heidi J, Tarnuzzer, Roy, Wang, Zhining, Yang, Liming, Zenklusen, Jean C, Zhang, Jiashan, Chudamani, Sudha, Liu, Jia, Lolla, Laxmi, Naresh, Rashi, Pihl, Todd, Sun, Qiang, Wan, Yunhu, Wu, Ye, Cho, Juok, DeFreitas, Timothy, Frazer, Scott, Gehlenborg, Nils, Getz, Gad, Heiman, David I, Kim, Jaegil, Lawrence, Michael S, Lin, Pei, Meier, Sam, Noble, Michael S, Saksena, Gordon, Voet, Doug, Zhang, Hailei, Bernard, Brady, Chambwe, Nyasha, Knijnenburg, Theo, Kramer, Roger, Leinonen, Kalle, Miller, Michael, Reynolds, Sheila, Shmulevich, Ilya, Thorsson, Vesteinn, Zhang, Wei, Akbani, Rehan, and Broom, Bradley M

Subjects
Biomedical and Clinical Sciences, Immunology, Cancer Genome Atlas Research Network
Abstract

(Immunity 48, 812–830.e1–e14; April 17, 2018) In the originally published version of this article, the authors neglected to include Younes Mokrab and Aaron M. Newman as co-authors and misspelled the names of authors Charles S. Rabkin and Ilya Shmulevich. The author names have been corrected here and online. In addition, the concluding sentence of the subsection “Immune Signature Compilation” in the Method Details in the original published article was deemed unclear because it did not specify differences among the gene set scoring methods. The concluding sentences now reads “Gene sets from Bindea et al., Senbabaoglu et al., and the MSigDB C7 collection were scored using single-sample gene set enrichment (ssGSEA) analysis (Barbie et al., 2009), as implemented in the GSVA R package (Hänzelmann et al., 2013). All other signatures were scored using methods found in the associated citations.”

Published
2019

11. Next-generation characterization of the Cancer Cell Line Encyclopedia

Author

Ghandi, Mahmoud, Huang, Franklin W, Jané-Valbuena, Judit, Kryukov, Gregory V, Lo, Christopher C, McDonald, E Robert, Barretina, Jordi, Gelfand, Ellen T, Bielski, Craig M, Li, Haoxin, Hu, Kevin, Andreev-Drakhlin, Alexander Y, Kim, Jaegil, Hess, Julian M, Haas, Brian J, Aguet, François, Weir, Barbara A, Rothberg, Michael V, Paolella, Brenton R, Lawrence, Michael S, Akbani, Rehan, Lu, Yiling, Tiv, Hong L, Gokhale, Prafulla C, de Weck, Antoine, Mansour, Ali Amin, Oh, Coyin, Shih, Juliann, Hadi, Kevin, Rosen, Yanay, Bistline, Jonathan, Venkatesan, Kavitha, Reddy, Anupama, Sonkin, Dmitriy, Liu, Manway, Lehar, Joseph, Korn, Joshua M, Porter, Dale A, Jones, Michael D, Golji, Javad, Caponigro, Giordano, Taylor, Jordan E, Dunning, Caitlin M, Creech, Amanda L, Warren, Allison C, McFarland, James M, Zamanighomi, Mahdi, Kauffmann, Audrey, Stransky, Nicolas, Imielinski, Marcin, Maruvka, Yosef E, Cherniack, Andrew D, Tsherniak, Aviad, Vazquez, Francisca, Jaffe, Jacob D, Lane, Andrew A, Weinstock, David M, Johannessen, Cory M, Morrissey, Michael P, Stegmeier, Frank, Schlegel, Robert, Hahn, William C, Getz, Gad, Mills, Gordon B, Boehm, Jesse S, Golub, Todd R, Garraway, Levi A, and Sellers, William R

Subjects
Genetics, Human Genome, Lung, Cancer, Biotechnology, Lung Cancer, Aetiology, 2.1 Biological and endogenous factors, Good Health and Well Being, Antineoplastic Agents, Biomarkers, Tumor, Cell Line, Tumor, DNA Methylation, Drug Resistance, Neoplasm, Ethnicity, Gene Editing, Histones, Humans, MicroRNAs, Molecular Targeted Therapy, Neoplasms, Protein Array Analysis, RNA Splicing, General Science & Technology
Abstract

Large panels of comprehensively characterized human cancer models, including the Cancer Cell Line Encyclopedia (CCLE), have provided a rigorous framework with which to study genetic variants, candidate targets, and small-molecule and biological therapeutics and to identify new marker-driven cancer dependencies. To improve our understanding of the molecular features that contribute to cancer phenotypes, including drug responses, here we have expanded the characterizations of cancer cell lines to include genetic, RNA splicing, DNA methylation, histone H3 modification, microRNA expression and reverse-phase protein array data for 1,072 cell lines from individuals of various lineages and ethnicities. Integration of these data with functional characterizations such as drug-sensitivity, short hairpin RNA knockdown and CRISPR-Cas9 knockout data reveals potential targets for cancer drugs and associated biomarkers. Together, this dataset and an accompanying public data portal provide a resource for the acceleration of cancer research using model cancer cell lines.

Published
2019

13. An Analysis of Vascular Access Thrombosis Events From the Proactive IV irOn Therapy in hemodiALysis Patients Trial

Author

Winnett, Georgia, Akbani, Habib, Winearls, Christopher, Wessels, Julie, Ayub, Waqar, Connor, Andrew, Brown, Alison, Moriarty, Jim, Chowdury, Paramit, Griffiths, Megan, Dasgupta, Indranil, Bhandari, Sunil, Doulton, Timothy, Macdougall, Iain, Barratt, Jonathan, Vilar, Enric, Mitra, Sandip, Ramakrishna, Babu, Nicholas, Johann, Ross, Calum, Khwaja, Arif, Hall, Matt, Kirk, Adam, Smith, Stuart, Jesky, Mark, Day, Clara, Alchi, Bassam, Stratton, Jon, Clarke, Helen, Walsh, Stephen, Brown, Rebecca, McCafferty, Kieran, Solomon, Laurie, Ramadoss, Suresh, Basanyake, Kolitha, Lawman, Sarah, Kalra, Philip, Balasubramaniam, Gowrie, Power, Albert, Banerjee, Debasish, Swift, Pauline, Wellberry-Smith, Matt, Goldsmith, Christopher, Ledson, Thomas, Mikhail, Ashraf, Benzimra, Ruth, Bell, Samira, Severn, Alison, Neary, John, Doyle, Arthur, Thomson, Peter, Shivashankar, Girish, Bolton, Stephanie, Quinn, Michael, Maxwell, Peter, Harty, John, Ford, Ian, Anker, Stefan, Farrington, Kenneth, McMurray, John, Tomson, Charles, Wheeler, David, Petrie, Mark, Connolly, Eugene, Jhund, Pardeep, MacDonald, Michael, Mark, Patrick, Walters, Matthew, Peacock, Janet, Isles, Chris, Reddan, Donal, Aziz, Jane, Boyle, Sarah, Burton, Claire, Clarke, Ross, Dinnett, Eleanor, Hillen, Neil, Kean, Sharon, Kerr, Claire, Murray, Heather, Reid, Amanda, Wetherall, Kirsty, Wilson, Robbie, White, Claire, Andani, Sadiq, Thomson, Peter C., Mark, Patrick B., Robertson, Michele, Anker, Stefan D., Jardine, Alan G., Kalra, Philip A., Wheeler, David C., Winearls, Christopher G., and Macdougall, Iain C.

Published
2022
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14. Integrative Molecular Characterization of Malignant Pleural Mesothelioma

Author

Hmeljak, Julija, Sanchez-Vega, Francisco, Hoadley, Katherine A, Shih, Juliann, Stewart, Chip, Heiman, David, Tarpey, Patrick, Danilova, Ludmila, Drill, Esther, Gibb, Ewan A, Bowlby, Reanne, Kanchi, Rupa, Osmanbeyoglu, Hatice U, Sekido, Yoshitaka, Takeshita, Jumpei, Newton, Yulia, Graim, Kiley, Gupta, Manaswi, Gay, Carl M, Diao, Lixia, Gibbs, David L, Thorsson, Vesteinn, Iype, Lisa, Kantheti, Havish, Severson, David T, Ravegnini, Gloria, Desmeules, Patrice, Jungbluth, Achim A, Travis, William D, Dacic, Sanja, Chirieac, Lucian R, Galateau-Sallé, Françoise, Fujimoto, Junya, Husain, Aliya N, Silveira, Henrique C, Rusch, Valerie W, Rintoul, Robert C, Pass, Harvey, Kindler, Hedy, Zauderer, Marjorie G, Kwiatkowski, David J, Bueno, Raphael, Tsao, Anne S, Creaney, Jenette, Lichtenberg, Tara, Leraas, Kristen, Bowen, Jay, Felau, Ina, Zenklusen, Jean Claude, Akbani, Rehan, Cherniack, Andrew D, Byers, Lauren A, Noble, Michael S, Fletcher, Jonathan A, Robertson, A Gordon, Shen, Ronglai, Aburatani, Hiroyuki, Robinson, Bruce W, Campbell, Peter, Ladanyi, Marc, Ally, Adrian, Anur, Pavana, Armenia, Joshua, Auman, J Todd, Balasundaram, Miruna, Balu, Saianand, Baylin, Stephen B, Becich, Michael, Behrens, Carmen, Beroukhim, Rameen, Bielski, Craig, Bodenheimer, Tom, Bootwalla, Moiz S, Brooks, Denise, Byers, Lauren Averett, Cárcano, Flávio M, Carlsen, Rebecca, Carvalho, Andre L, Cheung, Dorothy, Chirieac, Lucian, Cho, Juok, Chuah, Eric, Chudamani, Sudha, Cibulskis, Carrie, Cope, Leslie, Crain, Daniel, Curley, Erin, Rienzo, Assunta De, DeFreitas, Timothy, Demchok, John A, and Dhalla, Noreen

Subjects
Biomedical and Clinical Sciences, Oncology and Carcinogenesis, Immunology, Cancer, Human Genome, Lung Cancer, Lung, Rare Diseases, Genetics, Cancer Genomics, Biotechnology, 2.1 Biological and endogenous factors, Good Health and Well Being, Aged, Biomarkers, Tumor, Female, Histone-Lysine N-Methyltransferase, Humans, Kaplan-Meier Estimate, Lung Neoplasms, Male, Mesothelioma, Middle Aged, Mutation, Pleural Neoplasms, Prognosis, Protein Methyltransferases, Tumor Suppressor Proteins, Ubiquitin Thiolesterase, TCGA Research Network, Biochemistry and cell biology, Oncology and carcinogenesis
Abstract

Malignant pleural mesothelioma (MPM) is a highly lethal cancer of the lining of the chest cavity. To expand our understanding of MPM, we conducted a comprehensive integrated genomic study, including the most detailed analysis of BAP1 alterations to date. We identified histology-independent molecular prognostic subsets, and defined a novel genomic subtype with TP53 and SETDB1 mutations and extensive loss of heterozygosity. We also report strong expression of the immune-checkpoint gene VISTA in epithelioid MPM, strikingly higher than in other solid cancers, with implications for the immune response to MPM and for its immunotherapy. Our findings highlight new avenues for further investigation of MPM biology and novel therapeutic options. SIGNIFICANCE: Through a comprehensive integrated genomic study of 74 MPMs, we provide a deeper understanding of histology-independent determinants of aggressive behavior, define a novel genomic subtype with TP53 and SETDB1 mutations and extensive loss of heterozygosity, and discovered strong expression of the immune-checkpoint gene VISTA in epithelioid MPM.See related commentary by Aggarwal and Albelda, p. 1508.This article is highlighted in the In This Issue feature, p. 1494.

Published
2018

15. A Pan-Cancer Analysis Reveals High-Frequency Genetic Alterations in Mediators of Signaling by the TGF-β Superfamily

Author

Korkut, Anil, Zaidi, Sobia, Kanchi, Rupa S, Rao, Shuyun, Gough, Nancy R, Schultz, Andre, Li, Xubin, Lorenzi, Philip L, Berger, Ashton C, Robertson, Gordon, Kwong, Lawrence N, Datto, Mike, Roszik, Jason, Ling, Shiyun, Ravikumar, Visweswaran, Manyam, Ganiraju, Rao, Arvind, Shelley, Simon, Liu, Yuexin, Ju, Zhenlin, Hansel, Donna, de Velasco, Guillermo, Pennathur, Arjun, Andersen, Jesper B, O'Rourke, Colm J, Ohshiro, Kazufumi, Jogunoori, Wilma, Nguyen, Bao-Ngoc, Li, Shulin, Osmanbeyoglu, Hatice U, Ajani, Jaffer A, Mani, Sendurai A, Houseman, Andres, Wiznerowicz, Maciej, Chen, Jian, Gu, Shoujun, Ma, Wencai, Zhang, Jiexin, Tong, Pan, Cherniack, Andrew D, Deng, Chuxia, Resar, Linda, Network, The Cancer Genome Atlas Research, Caesar-Johnson, Samantha J, Demchok, John A, Felau, Ina, Kasapi, Melpomeni, Ferguson, Martin L, Hutter, Carolyn M, Sofia, Heidi J, Tarnuzzer, Roy, Wang, Zhining, Yang, Liming, Zenklusen, Jean C, Zhang, Jiashan, Chudamani, Sudha, Liu, Jia, Lolla, Laxmi, Naresh, Rashi, Pihl, Todd, Sun, Qiang, Wan, Yunhu, Wu, Ye, Cho, Juok, DeFreitas, Timothy, Frazer, Scott, Gehlenborg, Nils, Getz, Gad, Heiman, David I, Kim, Jaegil, Lawrence, Michael S, Lin, Pei, Meier, Sam, Noble, Michael S, Saksena, Gordon, Voet, Doug, Zhang, Hailei, Bernard, Brady, Chambwe, Nyasha, Dhankani, Varsha, Knijnenburg, Theo, Kramer, Roger, Leinonen, Kalle, Miller, Michael, Reynolds, Sheila, Shmulevich, Ilya, Thorsson, Vesteinn, Zhang, Wei, Akbani, Rehan, Broom, Bradley M, Hegde, Apurva M, Li, Jun, Liang, Han, Liu, Wenbin, and Lu, Yiling

Subjects
Biological Sciences, Genetics, Human Genome, Cancer Genomics, Biotechnology, Cancer, 2.1 Biological and endogenous factors, Bone Morphogenetic Protein 5, DNA Methylation, Humans, MicroRNAs, Mutation Rate, Neoplasms, Receptor, Transforming Growth Factor-beta Type I, Signal Transduction, Smad Proteins, Transforming Growth Factor beta, Cancer Genome Atlas Research Network, DNA methylation, Pan-Cancer, TCGA, TGF-β, TGF-β pathway, The Cancer Genome Atlas, cancer, microRNA, mutation hotspot, transcription, Biochemistry and Cell Biology, Biochemistry and cell biology
Abstract

We present an integromic analysis of gene alterations that modulate transforming growth factor β (TGF-β)-Smad-mediated signaling in 9,125 tumor samples across 33 cancer types in The Cancer Genome Atlas (TCGA). Focusing on genes that encode mediators and regulators of TGF-β signaling, we found at least one genomic alteration (mutation, homozygous deletion, or amplification) in 39% of samples, with highest frequencies in gastrointestinal cancers. We identified mutation hotspots in genes that encode TGF-β ligands (BMP5), receptors (TGFBR2, AVCR2A, and BMPR2), and Smads (SMAD2 and SMAD4). Alterations in the TGF-β superfamily correlated positively with expression of metastasis-associated genes and with decreased survival. Correlation analyses showed the contributions of mutation, amplification, deletion, DNA methylation, and miRNA expression to transcriptional activity of TGF-β signaling in each cancer type. This study provides a broad molecular perspective relevant for future functional and therapeutic studies of the diverse cancer pathways mediated by the TGF-β superfamily.

Published
2018

16. Comprehensive Characterization of Cancer Driver Genes and Mutations

Author

Bailey, Matthew H, Tokheim, Collin, Porta-Pardo, Eduard, Sengupta, Sohini, Bertrand, Denis, Weerasinghe, Amila, Colaprico, Antonio, Wendl, Michael C, Kim, Jaegil, Reardon, Brendan, Ng, Patrick Kwok-Shing, Jeong, Kang Jin, Cao, Song, Wang, Zixing, Gao, Jianjiong, Gao, Qingsong, Wang, Fang, Liu, Eric Minwei, Mularoni, Loris, Rubio-Perez, Carlota, Nagarajan, Niranjan, Cortés-Ciriano, Isidro, Zhou, Daniel Cui, Liang, Wen-Wei, Hess, Julian M, Yellapantula, Venkata D, Tamborero, David, Gonzalez-Perez, Abel, Suphavilai, Chayaporn, Ko, Jia Yu, Khurana, Ekta, Park, Peter J, Van Allen, Eliezer M, Liang, Han, Lawrence, Michael S, Godzik, Adam, Lopez-Bigas, Nuria, Stuart, Joshua M, Wheeler, David A, Getz, Gad, Chen, Amy, Lazar, Alexander J, Mills, Gordon B, Karchin, Rachel, Ding, Li, Caesar-Johnson, Samantha J, Demchok, John A, Felau, Ina, Kasapi, Melpomeni, Ferguson, Martin L, Hutter, Carolyn M, Sofia, Heidi J, Tarnuzzer, Roy, Wang, Zhining, Yang, Liming, Zenklusen, Jean C, Zhang, Jiashan, Chudamani, Sudha, Liu, Jia, Lolla, Laxmi, Naresh, Rashi, Pihl, Todd, Sun, Qiang, Wan, Yunhu, Wu, Ye, Cho, Juok, DeFreitas, Timothy, Frazer, Scott, Gehlenborg, Nils, Heiman, David I, Lin, Pei, Meier, Sam, Noble, Michael S, Saksena, Gordon, Voet, Doug, Zhang, Hailei, Bernard, Brady, Chambwe, Nyasha, Dhankani, Varsha, Knijnenburg, Theo, Kramer, Roger, Leinonen, Kalle, Liu, Yuexin, Miller, Michael, Reynolds, Sheila, Shmulevich, Ilya, Thorsson, Vesteinn, Zhang, Wei, Akbani, Rehan, Broom, Bradley M, Hegde, Apurva M, Ju, Zhenlin, Kanchi, Rupa S, Korkut, Anil, Li, Jun, and Ling, Shiyun

Subjects
Biological Sciences, Biomedical and Clinical Sciences, MC3 Working Group, Cancer Genome Atlas Research Network, Medical and Health Sciences, Developmental Biology, Biological sciences, Biomedical and clinical sciences
Abstract

(Cell 173, 371–385.e1–e9; April 5, 2018) It has come to our attention that we made two errors in preparation of this manuscript. First, in the STAR Methods, under the subheading of “Hypermutators and Immune Infiltrates” within the “Quantification and Statistical Analysis” section, we inadvertently referred to Figures S7A–S7C for data corresponding to sample stratification by hypermutator status alone in the last sentence. It should have referred to Figure S6A–S6C. Second, the lists of highly frequent missense mutations for COAD (colon adenocarcinoma) and READ (rectum adenocarcinoma) displayed in Figure S7 were incorrect because when we ordered the mutations in the initial analysis, we mistakenly combined the two cancer types COAD and READ for analysis, despite the fact that they were listed as two separate cancer types in the x-axis of the figure. After re-ordering the mutations by frequency for COAD and READ independently, information on highly frequent missense mutations for each of these cancer types is different and updated now in the revised Figure S7. These errors don't change the major conclusions of the paper and have been corrected online. We apologize for any confusion they may have caused. [Figure-presented]

Published
2018

17. The Cancer Genome Atlas Comprehensive Molecular Characterization of Renal Cell Carcinoma

Author

Ricketts, Christopher J, De Cubas, Aguirre A, Fan, Huihui, Smith, Christof C, Lang, Martin, Reznik, Ed, Bowlby, Reanne, Gibb, Ewan A, Akbani, Rehan, Beroukhim, Rameen, Bottaro, Donald P, Choueiri, Toni K, Gibbs, Richard A, Godwin, Andrew K, Haake, Scott, Hakimi, A Ari, Henske, Elizabeth P, Hsieh, James J, Ho, Thai H, Kanchi, Rupa S, Krishnan, Bhavani, Kwiatkowski, David J, Lui, Wembin, Merino, Maria J, Mills, Gordon B, Myers, Jerome, Nickerson, Michael L, Reuter, Victor E, Schmidt, Laura S, Shelley, C Simon, Shen, Hui, Shuch, Brian, Signoretti, Sabina, Srinivasan, Ramaprasad, Tamboli, Pheroze, Thomas, George, Vincent, Benjamin G, Vocke, Cathy D, Wheeler, David A, Yang, Lixing, Kim, William Y, Robertson, A Gordon, Spellman, Paul, Rathmell, W Kimryn, Linehan, W Marston, Caesar-Johnson, Samantha J, Demchok, John A, Felau, Ina, Kasapi, Melpomeni, Ferguson, Martin L, Hutter, Carolyn M, Sofia, Heidi J, Tarnuzzer, Roy, Wang, Zhining, Yang, Liming, Zenklusen, Jean C, Zhang, Jiashan, Chudamani, Sudha, Liu, Jia, Lolla, Laxmi, Naresh, Rashi, Pihl, Todd, Sun, Qiang, Wan, Yunhu, Wu, Ye, Cho, Juok, DeFreitas, Timothy, Frazer, Scott, Gehlenborg, Nils, Getz, Gad, Heiman, David I, Kim, Jaegil, Lawrence, Michael S, Lin, Pei, Meier, Sam, Noble, Michael S, Saksena, Gordon, Voet, Doug, Zhang, Hailei, Bernard, Brady, Chambwe, Nyasha, Dhankani, Varsha, Knijnenburg, Theo, Kramer, Roger, Leinonen, Kalle, Liu, Yuexin, Miller, Michael, Reynolds, Sheila, Shmulevich, Ilya, Thorsson, Vesteinn, Zhang, Wei, Broom, Bradley M, Hegde, Apurva M, Ju, Zhenlin, Korkut, Anil, Li, Jun, Liang, Han, and Ling, Shiyun

Subjects
Biological Sciences, Cancer Genome Atlas Research Network, Biochemistry and Cell Biology, Medical Physiology, Biological sciences
Abstract

(Cell Reports 23, 313–326; April 3, 2018) In the originally published version of this article, the author list contained two errors. Specifically, David J. Kwiatkowski was misspelled as David J. Kwaitkowski, and William Y. Kim was inadvertently written as William T. Kim. Both names have been corrected online. The authors regret this error.

Published
2018

18. Integrated Molecular Characterization of Testicular Germ Cell Tumors

Author

Shen, Hui, Shih, Juliann, Hollern, Daniel P, Wang, Linghua, Bowlby, Reanne, Tickoo, Satish K, Thorsson, Vésteinn, Mungall, Andrew J, Newton, Yulia, Hegde, Apurva M, Armenia, Joshua, Sánchez-Vega, Francisco, Pluta, John, Pyle, Louise C, Mehra, Rohit, Reuter, Victor E, Godoy, Guilherme, Jones, Jeffrey, Shelley, Carl S, Feldman, Darren R, Vidal, Daniel O, Lessel, Davor, Kulis, Tomislav, Cárcano, Flavio M, Leraas, Kristen M, Lichtenberg, Tara M, Brooks, Denise, Cherniack, Andrew D, Cho, Juok, Heiman, David I, Kasaian, Katayoon, Liu, Minwei, Noble, Michael S, Xi, Liu, Zhang, Hailei, Zhou, Wanding, ZenKlusen, Jean C, Hutter, Carolyn M, Felau, Ina, Zhang, Jiashan, Schultz, Nikolaus, Getz, Gad, Meyerson, Matthew, Stuart, Joshua M, Akbani, Rehan, Wheeler, David, Laird, Peter W, Nathanson, Katherine L, Cortessis, Victoria K, Hoadley, Katherine A, Wheeler, David A, Hughes, Daniel, Covington, Kyle, Jayaseelan, Joy C, Korchina, Viktoriya, Lewis, Lora, Hu, Jianhong, Doddapaneni, HarshaVardhan, Muzny, Donna, Gibbs, Richard, Hollern, Daniel, Vincent, Benjamin G, Chai, Shengjie, Smith, Christof C, Auman, J Todd, Shi, Yan, Meng, Shaowu, Skelly, Tara, Tan, Donghui, Veluvolu, Umadevi, Mieczkowski, Piotr A, Jones, Corbin D, Wilkerson, Matthew D, Balu, Saianand, Bodenheimer, Tom, Hoyle, Alan P, Jefferys, Stuart R, Mose, Lisle E, Simons, Janae V, Soloway, Matthew G, Roach, Jeffrey, Parker, Joel S, Hayes, D Neil, Perou, Charles M, Saksena, Gordon, Cibulskis, Carrie, Schumacher, Steven E, Beroukhim, Rameen, Gabriel, Stacey B, and Ally, Adrian

Subjects
Biological Sciences, Genetics, Urologic Diseases, Cancer, Human Genome, Biotechnology, Rare Diseases, DNA Copy Number Variations, DNA Methylation, Gene Expression Regulation, Neoplastic, Humans, Male, MicroRNAs, Neoplasms, Germ Cell and Embryonal, Proto-Oncogene Proteins c-kit, Seminoma, Testicular Neoplasms, ras Proteins, Cancer Genome Atlas Research Network, DNA methylation, KIT, The Cancer Genome Atlas, copy number, exome sequencing, miR-375, nonseminoma, seminoma, testicular germ cell tumors, Biochemistry and Cell Biology, Medical Physiology, Biological sciences
Abstract

We studied 137 primary testicular germ cell tumors (TGCTs) using high-dimensional assays of genomic, epigenomic, transcriptomic, and proteomic features. These tumors exhibited high aneuploidy and a paucity of somatic mutations. Somatic mutation of only three genes achieved significance-KIT, KRAS, and NRAS-exclusively in samples with seminoma components. Integrated analyses identified distinct molecular patterns that characterized the major recognized histologic subtypes of TGCT: seminoma, embryonal carcinoma, yolk sac tumor, and teratoma. Striking differences in global DNA methylation and microRNA expression between histology subtypes highlight a likely role of epigenomic processes in determining histologic fates in TGCTs. We also identified a subset of pure seminomas defined by KIT mutations, increased immune infiltration, globally demethylated DNA, and decreased KRAS copy number. We report potential biomarkers for risk stratification, such as miRNA specifically expressed in teratoma, and others with molecular diagnostic potential, such as CpH (CpA/CpC/CpT) methylation identifying embryonal carcinomas.

Published
2018

19. Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Author

Chiu, Hua-Sheng, Somvanshi, Sonal, Patel, Ektaben, Chen, Ting-Wen, Singh, Vivek P, Zorman, Barry, Patil, Sagar L, Pan, Yinghong, Chatterjee, Sujash S, Network, The Cancer Genome Atlas Research, Caesar-Johnson, Samantha J, Demchok, John A, Felau, Ina, Kasapi, Melpomeni, Ferguson, Martin L, Hutter, Carolyn M, Sofia, Heidi J, Tarnuzzer, Roy, Wang, Zhining, Yang, Liming, Zenklusen, Jean C, Zhang, Jiashan, Chudamani, Sudha, Liu, Jia, Lolla, Laxmi, Naresh, Rashi, Pihl, Todd, Sun, Qiang, Wan, Yunhu, Wu, Ye, Cho, Juok, DeFreitas, Timothy, Frazer, Scott, Gehlenborg, Nils, Getz, Gad, Heiman, David I, Kim, Jaegil, Lawrence, Michael S, Lin, Pei, Meier, Sam, Noble, Michael S, Saksena, Gordon, Voet, Doug, Zhang, Hailei, Bernard, Brady, Chambwe, Nyasha, Dhankani, Varsha, Knijnenburg, Theo, Kramer, Roger, Leinonen, Kalle, Liu, Yuexin, Miller, Michael, Reynolds, Sheila, Shmulevich, Ilya, Thorsson, Vesteinn, Zhang, Wei, Akbani, Rehan, Broom, Bradley M, Hegde, Apurva M, Ju, Zhenlin, Kanchi, Rupa S, Korkut, Anil, Li, Jun, Liang, Han, Ling, Shiyun, Liu, Wenbin, Lu, Yiling, Mills, Gordon B, Ng, Kwok-Shing, Rao, Arvind, Ryan, Michael, Wang, Jing, Weinstein, John N, Zhang, Jiexin, Abeshouse, Adam, Armenia, Joshua, Chakravarty, Debyani, Chatila, Walid K, de Bruijn, Ino, Gao, Jianjiong, Gross, Benjamin E, Heins, Zachary J, Kundra, Ritika, La, Konnor, Ladanyi, Marc, Luna, Augustin, Nissan, Moriah G, Ochoa, Angelica, Phillips, Sarah M, Reznik, Ed, Sanchez-Vega, Francisco, Sander, Chris, Schultz, Nikolaus, Sheridan, Robert, Sumer, S Onur, Sun, Yichao, Taylor, Barry S, Wang, Jioajiao, Zhang, Hongxin, and Anur, Pavana

Subjects
Biological Sciences, Bioinformatics and Computational Biology, Biotechnology, Breast Cancer, Cancer, Genetics, Cancer Genomics, Human Genome, Women's Health, 2.1 Biological and endogenous factors, Cell Line, Cell Line, Tumor, Gene Expression Regulation, Neoplastic, Gene Regulatory Networks, Genes, Tumor Suppressor, Humans, Neoplasms, Oncogenes, RNA, Long Noncoding, Cancer Genome Atlas Research Network, RNA-binding proteins, cancer gene, interactome, lncRNA, microRNA, modulation, noncoding RNA, pan-cancer, regulation, Biochemistry and Cell Biology, Medical Physiology, Biological sciences
Abstract

Long noncoding RNAs (lncRNAs) are commonly dysregulated in tumors, but only a handful are known to play pathophysiological roles in cancer. We inferred lncRNAs that dysregulate cancer pathways, oncogenes, and tumor suppressors (cancer genes) by modeling their effects on the activity of transcription factors, RNA-binding proteins, and microRNAs in 5,185 TCGA tumors and 1,019 ENCODE assays. Our predictions included hundreds of candidate onco- and tumor-suppressor lncRNAs (cancer lncRNAs) whose somatic alterations account for the dysregulation of dozens of cancer genes and pathways in each of 14 tumor contexts. To demonstrate proof of concept, we showed that perturbations targeting OIP5-AS1 (an inferred tumor suppressor) and TUG1 and WT1-AS (inferred onco-lncRNAs) dysregulated cancer genes and altered proliferation of breast and gynecologic cancer cells. Our analysis indicates that, although most lncRNAs are dysregulated in a tumor-specific manner, some, including OIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergistically dysregulate cancer pathways in multiple tumor contexts.

Published
2018

20. Somatic Mutational Landscape of Splicing Factor Genes and Their Functional Consequences across 33 Cancer Types

Author

Seiler, Michael, Peng, Shouyong, Agrawal, Anant A, Palacino, James, Teng, Teng, Zhu, Ping, Smith, Peter G, Network, The Cancer Genome Atlas Research, Caesar-Johnson, Samantha J, Demchok, John A, Felau, Ina, Kasapi, Melpomeni, Ferguson, Martin L, Hutter, Carolyn M, Sofia, Heidi J, Tarnuzzer, Roy, Wang, Zhining, Yang, Liming, Zenklusen, Jean C, Zhang, Jiashan, Chudamani, Sudha, Liu, Jia, Lolla, Laxmi, Naresh, Rashi, Pihl, Todd, Sun, Qiang, Wan, Yunhu, Wu, Ye, Cho, Juok, DeFreitas, Timothy, Frazer, Scott, Gehlenborg, Nils, Getz, Gad, Heiman, David I, Kim, Jaegil, Lawrence, Michael S, Lin, Pei, Meier, Sam, Noble, Michael S, Saksena, Gordon, Voet, Doug, Zhang, Hailei, Bernard, Brady, Chambwe, Nyasha, Dhankani, Varsha, Knijnenburg, Theo, Kramer, Roger, Leinonen, Kalle, Liu, Yuexin, Miller, Michael, Reynolds, Sheila, Shmulevich, Ilya, Thorsson, Vesteinn, Zhang, Wei, Akbani, Rehan, Broom, Bradley M, Hegde, Apurva M, Ju, Zhenlin, Kanchi, Rupa S, Korkut, Anil, Li, Jun, Liang, Han, Ling, Shiyun, Liu, Wenbin, Lu, Yiling, Mills, Gordon B, Ng, Kwok-Shing, Rao, Arvind, Ryan, Michael, Wang, Jing, Weinstein, John N, Zhang, Jiexin, Abeshouse, Adam, Armenia, Joshua, Chakravarty, Debyani, Chatila, Walid K, de Bruijn, Ino, Gao, Jianjiong, Gross, Benjamin E, Heins, Zachary J, Kundra, Ritika, La, Konnor, Ladanyi, Marc, Luna, Augustin, Nissan, Moriah G, Ochoa, Angelica, Phillips, Sarah M, Reznik, Ed, Sanchez-Vega, Francisco, Sander, Chris, Schultz, Nikolaus, Sheridan, Robert, Sumer, S Onur, Sun, Yichao, Taylor, Barry S, Wang, Jioajiao, Zhang, Hongxin, Anur, Pavana, Peto, Myron, and Spellman, Paul

Subjects
Biological Sciences, Bioinformatics and Computational Biology, Genetics, Cancer, Cancer Genomics, Human Genome, 2.1 Biological and endogenous factors, Cell Line, Tumor, Genes, Tumor Suppressor, Humans, Loss of Function Mutation, Mutation Rate, Neoplasms, Oncogenes, RNA Splicing, RNA Splicing Factors, Cancer Genome Atlas Research Network, FUBP1, RBM10, SF3B1, SRSF2, U2AF1, cancer, mutation, splicing, Biochemistry and Cell Biology, Medical Physiology, Biological sciences
Abstract

Hotspot mutations in splicing factor genes have been recently reported at high frequency in hematological malignancies, suggesting the importance of RNA splicing in cancer. We analyzed whole-exome sequencing data across 33 tumor types in The Cancer Genome Atlas (TCGA), and we identified 119 splicing factor genes with significant non-silent mutation patterns, including mutation over-representation, recurrent loss of function (tumor suppressor-like), or hotspot mutation profile (oncogene-like). Furthermore, RNA sequencing analysis revealed altered splicing events associated with selected splicing factor mutations. In addition, we were able to identify common gene pathway profiles associated with the presence of these mutations. Our analysis suggests that somatic alteration of genes involved in the RNA-splicing process is common in cancer and may represent an underappreciated hallmark of tumorigenesis.

Published
2018

21. Integrated Genomic Analysis of the Ubiquitin Pathway across Cancer Types

Author

Ge, Zhongqi, Leighton, Jake S, Wang, Yumeng, Peng, Xinxin, Chen, Zhongyuan, Chen, Hu, Sun, Yutong, Yao, Fan, Li, Jun, Zhang, Huiwen, Liu, Jianfang, Shriver, Craig D, Hu, Hai, Network, The Cancer Genome Atlas Research, Caesar-Johnson, Samantha J, Demchok, John A, Felau, Ina, Kasapi, Melpomeni, Ferguson, Martin L, Hutter, Carolyn M, Sofia, Heidi J, Tarnuzzer, Roy, Wang, Zhining, Yang, Liming, Zenklusen, Jean C, Zhang, Jiashan, Chudamani, Sudha, Liu, Jia, Lolla, Laxmi, Naresh, Rashi, Pihl, Todd, Sun, Qiang, Wan, Yunhu, Wu, Ye, Cho, Juok, DeFreitas, Timothy, Frazer, Scott, Gehlenborg, Nils, Getz, Gad, Heiman, David I, Kim, Jaegil, Lawrence, Michael S, Lin, Pei, Meier, Sam, Noble, Michael S, Saksena, Gordon, Voet, Doug, Zhang, Hailei, Bernard, Brady, Chambwe, Nyasha, Dhankani, Varsha, Knijnenburg, Theo, Kramer, Roger, Leinonen, Kalle, Liu, Yuexin, Miller, Michael, Reynolds, Sheila, Shmulevich, Ilya, Thorsson, Vesteinn, Zhang, Wei, Akbani, Rehan, Broom, Bradley M, Hegde, Apurva M, Ju, Zhenlin, Kanchi, Rupa S, Korkut, Anil, Liang, Han, Ling, Shiyun, Liu, Wenbin, Lu, Yiling, Mills, Gordon B, Ng, Kwok-Shing, Rao, Arvind, Ryan, Michael, Wang, Jing, Weinstein, John N, Zhang, Jiexin, Abeshouse, Adam, Armenia, Joshua, Chakravarty, Debyani, Chatila, Walid K, de Bruijn, Ino, Gao, Jianjiong, Gross, Benjamin E, Heins, Zachary J, Kundra, Ritika, La, Konnor, Ladanyi, Marc, Luna, Augustin, Nissan, Moriah G, Ochoa, Angelica, Phillips, Sarah M, Reznik, Ed, Sanchez-Vega, Francisco, Sander, Chris, Schultz, Nikolaus, Sheridan, Robert, Sumer, S Onur, and Sun, Yichao

Subjects
Biological Sciences, Bioinformatics and Computational Biology, Genetics, Cancer Genomics, Cancer, Human Genome, Biotechnology, Good Health and Well Being, Cell Line, Tumor, Gene Expression Regulation, Neoplastic, Genome, Human, Genomics, Humans, Metabolic Networks and Pathways, Neoplasms, Oncogene Proteins, Ubiquitination, Cancer Genome Atlas Research Network, FBXW7, The Cancer Genome Atlas, biomarker, cancer prognosis, pan-cancer analysis, therapeutic targets, tumor subtype, ubiquitin pathway, Biochemistry and Cell Biology, Medical Physiology, Biological sciences
Abstract

Protein ubiquitination is a dynamic and reversible process of adding single ubiquitin molecules or various ubiquitin chains to target proteins. Here, using multidimensional omic data of 9,125 tumor samples across 33 cancer types from The Cancer Genome Atlas, we perform comprehensive molecular characterization of 929 ubiquitin-related genes and 95 deubiquitinase genes. Among them, we systematically identify top somatic driver candidates, including mutated FBXW7 with cancer-type-specific patterns and amplified MDM2 showing a mutually exclusive pattern with BRAF mutations. Ubiquitin pathway genes tend to be upregulated in cancer mediated by diverse mechanisms. By integrating pan-cancer multiomic data, we identify a group of tumor samples that exhibit worse prognosis. These samples are consistently associated with the upregulation of cell-cycle and DNA repair pathways, characterized by mutated TP53, MYC/TERT amplification, and APC/PTEN deletion. Our analysis highlights the importance of the ubiquitin pathway in cancer development and lays a foundation for developing relevant therapeutic strategies.

Published
2018

22. lncRNA Epigenetic Landscape Analysis Identifies EPIC1 as an Oncogenic lncRNA that Interacts with MYC and Promotes Cell-Cycle Progression in Cancer

Author

Wang, Zehua, Yang, Bo, Zhang, Min, Guo, Weiwei, Wu, Zhiyuan, Wang, Yue, Jia, Lin, Li, Song, Network, The Cancer Genome Atlas Research, Caesar-Johnson, Samantha J, Demchok, John A, Felau, Ina, Kasapi, Melpomeni, Ferguson, Martin L, Hutter, Carolyn M, Sofia, Heidi J, Tarnuzzer, Roy, Wang, Zhining, Yang, Liming, Zenklusen, Jean C, Zhang, Jiashan, Chudamani, Sudha, Liu, Jia, Lolla, Laxmi, Naresh, Rashi, Pihl, Todd, Sun, Qiang, Wan, Yunhu, Wu, Ye, Cho, Juok, DeFreitas, Timothy, Frazer, Scott, Gehlenborg, Nils, Getz, Gad, Heiman, David I, Kim, Jaegil, Lawrence, Michael S, Lin, Pei, Meier, Sam, Noble, Michael S, Saksena, Gordon, Voet, Doug, Zhang, Hailei, Bernard, Brady, Chambwe, Nyasha, Dhankani, Varsha, Knijnenburg, Theo, Kramer, Roger, Leinonen, Kalle, Liu, Yuexin, Miller, Michael, Reynolds, Sheila, Shmulevich, Ilya, Thorsson, Vesteinn, Zhang, Wei, Akbani, Rehan, Broom, Bradley M, Hegde, Apurva M, Ju, Zhenlin, Kanchi, Rupa S, Korkut, Anil, Li, Jun, Liang, Han, Ling, Shiyun, Liu, Wenbin, Lu, Yiling, Mills, Gordon B, Ng, Kwok-Shing, Rao, Arvind, Ryan, Michael, Wang, Jing, Weinstein, John N, Zhang, Jiexin, Abeshouse, Adam, Armenia, Joshua, Chakravarty, Debyani, Chatila, Walid K, Bruijn, Inode, Gao, Jianjiong, Gross, Benjamin E, Heins, Zachary J, Kundra, Ritika, La, Konnor, Ladanyi, Marc, Luna, Augustin, Nissan, Moriah G, Ochoa, Angelica, Phillips, Sarah M, Reznik, Ed, Sanchez-Vega, Francisco, Sander, Chris, Schultz, Nikolaus, Sheridan, Robert, Sumer, S Onur, Sun, Yichao, Taylor, Barry S, Wang, Jioajiao, Zhang, Hongxin, Anur, Pavana, and Peto, Myron

Subjects
Biochemistry and Cell Biology, Biomedical and Clinical Sciences, Oncology and Carcinogenesis, Biological Sciences, Genetics, Breast Cancer, Cancer Genomics, Cancer, Women's Health, Human Genome, Animals, Binding Sites, Breast Neoplasms, Cell Cycle, Cell Line, Tumor, CpG Islands, DNA Methylation, Epigenesis, Genetic, Female, Gene Expression Regulation, Neoplastic, Humans, Mice, Neoplasm Transplantation, Prognosis, Promoter Regions, Genetic, Proto-Oncogene Proteins c-myc, RNA, Long Noncoding, Up-Regulation, Cancer Genome Atlas Research Network, CIMP, ENSG00000224271, EPIC1, LOC284930, MYC, P21, TCGA pan-cancer, breast cancer, long noncoding RNA, Neurosciences, Oncology & Carcinogenesis, Biochemistry and cell biology, Oncology and carcinogenesis
Abstract

We characterized the epigenetic landscape of genes encoding long noncoding RNAs (lncRNAs) across 6,475 tumors and 455 cancer cell lines. In stark contrast to the CpG island hypermethylation phenotype in cancer, we observed a recurrent hypomethylation of 1,006 lncRNA genes in cancer, including EPIC1 (epigenetically-induced lncRNA1). Overexpression of EPIC1 is associated with poor prognosis in luminal B breast cancer patients and enhances tumor growth in vitro and in vivo. Mechanistically, EPIC1 promotes cell-cycle progression by interacting with MYC through EPIC1's 129-283 nt region. EPIC1 knockdown reduces the occupancy of MYC to its target genes (e.g., CDKN1A, CCNA2, CDC20, and CDC45). MYC depletion abolishes EPIC1's regulation of MYC target and luminal breast cancer tumorigenesis in vitro and in vivo.

Published
2018

23. Comparative Molecular Analysis of Gastrointestinal Adenocarcinomas

Author

Liu, Yang, Sethi, Nilay S, Hinoue, Toshinori, Schneider, Barbara G, Cherniack, Andrew D, Sanchez-Vega, Francisco, Seoane, Jose A, Farshidfar, Farshad, Bowlby, Reanne, Islam, Mirazul, Kim, Jaegil, Chatila, Walid, Akbani, Rehan, Kanchi, Rupa S, Rabkin, Charles S, Willis, Joseph E, Wang, Kenneth K, McCall, Shannon J, Mishra, Lopa, Ojesina, Akinyemi I, Bullman, Susan, Pedamallu, Chandra Sekhar, Lazar, Alexander J, Sakai, Ryo, Network, The Cancer Genome Atlas Research, Caesar-Johnson, Samantha J, Demchok, John A, Felau, Ina, Kasapi, Melpomeni, Ferguson, Martin L, Hutter, Carolyn M, Sofia, Heidi J, Tarnuzzer, Roy, Wang, Zhining, Yang, Liming, Zenklusen, Jean C, Zhang, Jiashan, Chudamani, Sudha, Liu, Jia, Lolla, Laxmi, Naresh, Rashi, Pihl, Todd, Sun, Qiang, Wan, Yunhu, Wu, Ye, Cho, Juok, DeFreitas, Timothy, Frazer, Scott, Gehlenborg, Nils, Getz, Gad, Heiman, David I, Lawrence, Michael S, Lin, Pei, Meier, Sam, Noble, Michael S, Saksena, Gordon, Voet, Doug, Zhang, Hailei, Bernard, Brady, Chambwe, Nyasha, Dhankani, Varsha, Knijnenburg, Theo, Kramer, Roger, Leinonen, Kalle, Liu, Yuexin, Miller, Michael, Reynolds, Sheila, Shmulevich, Ilya, Thorsson, Vesteinn, Zhang, Wei, Broom, Bradley M, Hegde, Apurva M, Ju, Zhenlin, Korkut, Anil, Li, Jun, Liang, Han, Ling, Shiyun, Liu, Wenbin, Lu, Yiling, Mills, Gordon B, Ng, Kwok-Shing, Rao, Arvind, Ryan, Michael, Wang, Jing, Weinstein, John N, Zhang, Jiexin, Abeshouse, Adam, Armenia, Joshua, Chakravarty, Debyani, Chatila, Walid K, Bruijn, Inode, Gao, Jianjiong, Gross, Benjamin E, Heins, Zachary J, Kundra, Ritika, La, Konnor, and Ladanyi, Marc

Subjects
Biological Sciences, Genetics, Digestive Diseases, Rare Diseases, Colo-Rectal Cancer, Cancer Genomics, Human Genome, Cancer, Genetic Testing, Biotechnology, Adenocarcinoma, Aneuploidy, Chromosomal Instability, DNA Methylation, DNA Polymerase II, DNA-Binding Proteins, Epigenesis, Genetic, Female, Gastrointestinal Neoplasms, Gene Regulatory Networks, Heterogeneous-Nuclear Ribonucleoproteins, Humans, Male, Microsatellite Instability, MutL Protein Homolog 1, Mutation, Poly-ADP-Ribose Binding Proteins, Polymorphism, Single Nucleotide, Proto-Oncogene Proteins p21(ras), RNA-Binding Proteins, SOX9 Transcription Factor, Cancer Genome Atlas Research Network, cancer, colon, colorectal, epigenetic, esophagus, genomic, methylation, rectum, stomach, tumor, Neurosciences, Oncology and Carcinogenesis, Oncology & Carcinogenesis, Biochemistry and cell biology, Oncology and carcinogenesis
Abstract

We analyzed 921 adenocarcinomas of the esophagus, stomach, colon, and rectum to examine shared and distinguishing molecular characteristics of gastrointestinal tract adenocarcinomas (GIACs). Hypermutated tumors were distinct regardless of cancer type and comprised those enriched for insertions/deletions, representing microsatellite instability cases with epigenetic silencing of MLH1 in the context of CpG island methylator phenotype, plus tumors with elevated single-nucleotide variants associated with mutations in POLE. Tumors with chromosomal instability were diverse, with gastroesophageal adenocarcinomas harboring fragmented genomes associated with genomic doubling and distinct mutational signatures. We identified a group of tumors in the colon and rectum lacking hypermutation and aneuploidy termed genome stable and enriched in DNA hypermethylation and mutations in KRAS, SOX9, and PCBP1.

Published
2018

24. A Pan-Cancer Analysis of Enhancer Expression in Nearly 9000 Patient Samples

Author

Chen, Han, Li, Chunyan, Peng, Xinxin, Zhou, Zhicheng, Weinstein, John N, Network, The Cancer Genome Atlas Research, Caesar-Johnson, Samantha J, Demchok, John A, Felau, Ina, Kasapi, Melpomeni, Ferguson, Martin L, Hutter, Carolyn M, Sofia, Heidi J, Tarnuzzer, Roy, Wang, Zhining, Yang, Liming, Zenklusen, Jean C, Zhang, Jiashan, Chudamani, Sudha, Liu, Jia, Lolla, Laxmi, Naresh, Rashi, Pihl, Todd, Sun, Qiang, Wan, Yunhu, Wu, Ye, Cho, Juok, DeFreitas, Timothy, Frazer, Scott, Gehlenborg, Nils, Getz, Gad, Heiman, David I, Kim, Jaegil, Lawrence, Michael S, Lin, Pei, Meier, Sam, Noble, Michael S, Saksena, Gordon, Voet, Doug, Zhang, Hailei, Bernard, Brady, Chambwe, Nyasha, Dhankani, Varsha, Knijnenburg, Theo, Kramer, Roger, Leinonen, Kalle, Liu, Yuexin, Miller, Michael, Reynolds, Sheila, Shmulevich, Ilya, Thorsson, Vesteinn, Zhang, Wei, Akbani, Rehan, Broom, Bradley M, Hegde, Apurva M, Ju, Zhenlin, Kanchi, Rupa S, Korkut, Anil, Li, Jun, Liang, Han, Ling, Shiyun, Liu, Wenbin, Lu, Yiling, Mills, Gordon B, Ng, Kwok-Shing, Rao, Arvind, Ryan, Michael, Wang, Jing, Zhang, Jiexin, Abeshouse, Adam, Armenia, Joshua, Chakravarty, Debyani, Chatila, Walid K, de Bruijn, Ino, Gao, Jianjiong, Gross, Benjamin E, Heins, Zachary J, Kundra, Ritika, La, Konnor, Ladanyi, Marc, Luna, Augustin, Nissan, Moriah G, Ochoa, Angelica, Phillips, Sarah M, Reznik, Ed, Sanchez-Vega, Francisco, Sander, Chris, Schultz, Nikolaus, Sheridan, Robert, Sumer, S Onur, Sun, Yichao, Taylor, Barry S, Wang, Jioajiao, Zhang, Hongxin, Anur, Pavana, Peto, Myron, Spellman, Paul, Benz, Christopher, and Stuart, Joshua M

Subjects
Genetics, Cancer, Human Genome, 2.1 Biological and endogenous factors, Aetiology, Aneuploidy, B7-H1 Antigen, Chromatin, Databases, Genetic, Enhancer Elements, Genetic, Gene Expression Regulation, Neoplastic, Humans, Immunotherapy, Neoplasms, Sequence Analysis, RNA, Survival Rate, Cancer Genome Atlas Research Network, PD-L1 expression, The Cancer Genome Atlas, aneuploidy, chromatin state, enhancer expression, mutation burden, pan-cancer analysis, prognostic markers, Biological Sciences, Medical and Health Sciences, Developmental Biology
Abstract

The role of enhancers, a key class of non-coding regulatory DNA elements, in cancer development has increasingly been appreciated. Here, we present the detection and characterization of a large number of expressed enhancers in a genome-wide analysis of 8928 tumor samples across 33 cancer types using TCGA RNA-seq data. Compared with matched normal tissues, global enhancer activation was observed in most cancers. Across cancer types, global enhancer activity was positively associated with aneuploidy, but not mutation load, suggesting a hypothesis centered on "chromatin-state" to explain their interplay. Integrating eQTL, mRNA co-expression, and Hi-C data analysis, we developed a computational method to infer causal enhancer-gene interactions, revealing enhancers of clinically actionable genes. Having identified an enhancer ∼140 kb downstream of PD-L1, a major immunotherapy target, we validated it experimentally. This study provides a systematic view of enhancer activity in diverse tumor contexts and suggests the clinical implications of enhancers.

Published
2018

25. Genomic and Functional Approaches to Understanding Cancer Aneuploidy

Author

Taylor, Alison M, Shih, Juliann, Ha, Gavin, Gao, Galen F, Zhang, Xiaoyang, Berger, Ashton C, Schumacher, Steven E, Wang, Chen, Hu, Hai, Liu, Jianfang, Lazar, Alexander J, Network, The Cancer Genome Atlas Research, Caesar-Johnson, Samantha J, Demchok, John A, Felau, Ina, Kasapi, Melpomeni, Ferguson, Martin L, Hutter, Carolyn M, Sofia, Heidi J, Tarnuzzer, Roy, Wang, Zhining, Yang, Liming, Zenklusen, Jean C, Zhang, Jiashan, Chudamani, Sudha, Liu, Jia, Lolla, Laxmi, Naresh, Rashi, Pihl, Todd, Sun, Qiang, Wan, Yunhu, Wu, Ye, Cho, Juok, DeFreitas, Timothy, Frazer, Scott, Gehlenborg, Nils, Getz, Gad, Heiman, David I, Kim, Jaegil, Lawrence, Michael S, Lin, Pei, Meier, Sam, Noble, Michael S, Saksena, Gordon, Voet, Doug, Zhang, Hailei, Bernard, Brady, Chambwe, Nyasha, Dhankani, Varsha, Knijnenburg, Theo, Kramer, Roger, Leinonen, Kalle, Liu, Yuexin, Miller, Michael, Reynolds, Sheila, Shmulevich, Ilya, Thorsson, Vesteinn, Zhang, Wei, Akbani, Rehan, Broom, Bradley M, Hegde, Apurva M, Ju, Zhenlin, Kanchi, Rupa S, Korkut, Anil, Li, Jun, Liang, Han, Ling, Shiyun, Liu, Wenbin, Lu, Yiling, Mills, Gordon B, Ng, Kwok-Shing, Rao, Arvind, Ryan, Michael, Wang, Jing, Weinstein, John N, Zhang, Jiexin, Abeshouse, Adam, Armenia, Joshua, Chakravarty, Debyani, Chatila, Walid K, de Bruijn, Ino, Gao, Jianjiong, Gross, Benjamin E, Heins, Zachary J, Kundra, Ritika, La, Konnor, Ladanyi, Marc, Luna, Augustin, Nissan, Moriah G, Ochoa, Angelica, Phillips, Sarah M, Reznik, Ed, Sanchez-Vega, Francisco, Sander, Chris, Schultz, Nikolaus, Sheridan, Robert, Sumer, S Onur, Sun, Yichao, Taylor, Barry S, and Wang, Jioajiao

Subjects
Biological Sciences, Biomedical and Clinical Sciences, Bioinformatics and Computational Biology, Genetics, Oncology and Carcinogenesis, Human Genome, Lung Cancer, Cancer Genomics, Cancer, Lung, 2.1 Biological and endogenous factors, Aneuploidy, Carcinoma, Squamous Cell, Cell Cycle, Cell Proliferation, Chromosome Aberrations, Chromosome Deletion, Chromosomes, Human, Pair 3, Databases, Genetic, Genomics, Humans, Mutation Rate, Tumor Suppressor Protein p53, Cancer Genome Atlas Research Network, aneuploidy, cancer genomics, genome engineering, lung squamous cell carcinoma, Neurosciences, Oncology & Carcinogenesis, Biochemistry and cell biology, Oncology and carcinogenesis
Abstract

Aneuploidy, whole chromosome or chromosome arm imbalance, is a near-universal characteristic of human cancers. In 10,522 cancer genomes from The Cancer Genome Atlas, aneuploidy was correlated with TP53 mutation, somatic mutation rate, and expression of proliferation genes. Aneuploidy was anti-correlated with expression of immune signaling genes, due to decreased leukocyte infiltrates in high-aneuploidy samples. Chromosome arm-level alterations show cancer-specific patterns, including loss of chromosome arm 3p in squamous cancers. We applied genome engineering to delete 3p in lung cells, causing decreased proliferation rescued in part by chromosome 3 duplication. This study defines genomic and phenotypic correlates of cancer aneuploidy and provides an experimental approach to study chromosome arm aneuploidy.

Published
2018

26. Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

Author

Saltz, Joel, Gupta, Rajarsi, Hou, Le, Kurc, Tahsin, Singh, Pankaj, Nguyen, Vu, Samaras, Dimitris, Shroyer, Kenneth R, Zhao, Tianhao, Batiste, Rebecca, Van Arnam, John, Network, The Cancer Genome Atlas Research, Caesar-Johnson, Samantha J, Demchok, John A, Felau, Ina, Kasapi, Melpomeni, Ferguson, Martin L, Hutter, Carolyn M, Sofia, Heidi J, Tarnuzzer, Roy, Wang, Zhining, Yang, Liming, Zenklusen, Jean C, Zhang, Jiashan, Chudamani, Sudha, Liu, Jia, Lolla, Laxmi, Naresh, Rashi, Pihl, Todd, Sun, Qiang, Wan, Yunhu, Wu, Ye, Cho, Juok, DeFreitas, Timothy, Frazer, Scott, Gehlenborg, Nils, Getz, Gad, Heiman, David I, Kim, Jaegil, Lawrence, Michael S, Lin, Pei, Meier, Sam, Noble, Michael S, Saksena, Gordon, Voet, Doug, Zhang, Hailei, Bernard, Brady, Chambwe, Nyasha, Dhankani, Varsha, Knijnenburg, Theo, Kramer, Roger, Leinonen, Kalle, Liu, Yuexin, Miller, Michael, Reynolds, Sheila, Shmulevich, Ilya, Thorsson, Vesteinn, Zhang, Wei, Akbani, Rehan, Broom, Bradley M, Hegde, Apurva M, Ju, Zhenlin, Kanchi, Rupa S, Korkut, Anil, Li, Jun, Liang, Han, Ling, Shiyun, Liu, Wenbin, Lu, Yiling, Mills, Gordon B, Ng, Kwok-Shing, Rao, Arvind, Ryan, Michael, Wang, Jing, Weinstein, John N, Zhang, Jiexin, Abeshouse, Adam, Armenia, Joshua, Chakravarty, Debyani, Chatila, Walid K, de Bruijn, Ino, Gao, Jianjiong, Gross, Benjamin E, Heins, Zachary J, Kundra, Ritika, La, Konnor, Ladanyi, Marc, Luna, Augustin, Nissan, Moriah G, Ochoa, Angelica, Phillips, Sarah M, Reznik, Ed, Sanchez-Vega, Francisco, Sander, Chris, Schultz, Nikolaus, Sheridan, Robert, Sumer, S Onur, Sun, Yichao, Taylor, Barry S, and Wang, Jioajiao

Subjects
Biological Sciences, Machine Learning and Artificial Intelligence, Networking and Information Technology R&D (NITRD), Genetics, Cancer, Human Genome, Good Health and Well Being, Deep Learning, Humans, Image Interpretation, Computer-Assisted, Lymphocytes, Tumor-Infiltrating, Neoplasms, Cancer Genome Atlas Research Network, artificial intelligence, bioinformatics, computer vision, deep learning, digital pathology, immuno-oncology, lymphocytes, machine learning, tumor microenvironment, tumor-infiltrating lymphocytes, Biochemistry and Cell Biology, Medical Physiology, Biological sciences
Abstract

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumor-infiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment.

Published
2018

27. Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

Author

Campbell, Joshua D, Yau, Christina, Bowlby, Reanne, Liu, Yuexin, Brennan, Kevin, Fan, Huihui, Taylor, Alison M, Wang, Chen, Walter, Vonn, Akbani, Rehan, Byers, Lauren Averett, Creighton, Chad J, Coarfa, Cristian, Shih, Juliann, Cherniack, Andrew D, Gevaert, Olivier, Prunello, Marcos, Shen, Hui, Anur, Pavana, Chen, Jianhong, Cheng, Hui, Hayes, D Neil, Bullman, Susan, Pedamallu, Chandra Sekhar, Ojesina, Akinyemi I, Sadeghi, Sara, Mungall, Karen L, Robertson, A Gordon, Benz, Christopher, Schultz, Andre, Kanchi, Rupa S, Gay, Carl M, Hegde, Apurva, Diao, Lixia, Wang, Jing, Ma, Wencai, Sumazin, Pavel, Chiu, Hua-Sheng, Chen, Ting-Wen, Gunaratne, Preethi, Donehower, Larry, Rader, Janet S, Zuna, Rosemary, Al-Ahmadie, Hikmat, Lazar, Alexander J, Flores, Elsa R, Tsai, Kenneth Y, Zhou, Jane H, Rustgi, Anil K, Drill, Esther, Shen, Ronglei, Wong, Christopher K, Network, The Cancer Genome Atlas Research, Caesar-Johnson, Samantha J, Demchok, John A, Felau, Ina, Kasapi, Melpomeni, Ferguson, Martin L, Hutter, Carolyn M, Sofia, Heidi J, Tarnuzzer, Roy, Wang, Zhining, Yang, Liming, Zenklusen, Jean C, Zhang, Jiashan, Chudamani, Sudha, Liu, Jia, Lolla, Laxmi, Naresh, Rashi, Pihl, Todd, Sun, Qiang, Wan, Yunhu, Wu, Ye, Cho, Juok, DeFreitas, Timothy, Frazer, Scott, Gehlenborg, Nils, Getz, Gad, Heiman, David I, Kim, Jaegil, Lawrence, Michael S, Lin, Pei, Meier, Sam, Noble, Michael S, Saksena, Gordon, Voet, Doug, Zhang, Hailei, Bernard, Brady, Chambwe, Nyasha, Dhankani, Varsha, Knijnenburg, Theo, Kramer, Roger, Leinonen, Kalle, Miller, Michael, Reynolds, Sheila, Shmulevich, Ilya, Thorsson, Vesteinn, and Zhang, Wei

Subjects
Biological Sciences, Biotechnology, Cancer, Genetics, Cancer Genomics, Human Genome, Infectious Diseases, Sexually Transmitted Infections, 2.1 Biological and endogenous factors, Carcinoma, Squamous Cell, Cell Line, Tumor, DNA Methylation, Epithelial-Mesenchymal Transition, Gene Expression Regulation, Neoplastic, Genomics, Humans, Metabolic Networks and Pathways, Polymorphism, Genetic, Cancer Genome Atlas Research Network, bladder carcinoma with squamous differentiation, cervical squamous cell carcinoma, esophageal squamous cell carcinoma, genomics, head and neck squamous cell carcinoma, human papillomavirus, lung squamous cell carcinoma, proteomics, transcriptomics, Biochemistry and Cell Biology, Medical Physiology, Biological sciences
Abstract

This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs) from five sites associated with smoking and/or human papillomavirus (HPV). SCCs harbor 3q, 5p, and other recurrent chromosomal copy-number alterations (CNAs), DNA mutations, and/or aberrant methylation of genes and microRNAs, which are correlated with the expression of multi-gene programs linked to squamous cell stemness, epithelial-to-mesenchymal differentiation, growth, genomic integrity, oxidative damage, death, and inflammation. Low-CNA SCCs tended to be HPV(+) and display hypermethylation with repression of TET1 demethylase and FANCF, previously linked to predisposition to SCC, or harbor mutations affecting CASP8, RAS-MAPK pathways, chromatin modifiers, and immunoregulatory molecules. We uncovered hypomethylation of the alternative promoter that drives expression of the ΔNp63 oncogene and embedded miR944. Co-expression of immune checkpoint, T-regulatory, and Myeloid suppressor cells signatures may explain reduced efficacy of immune therapy. These findings support possibilities for molecular classification and therapeutic approaches.

Published
2018

28. Oncogenic Signaling Pathways in The Cancer Genome Atlas

Author

Sanchez-Vega, Francisco, Mina, Marco, Armenia, Joshua, Chatila, Walid K, Luna, Augustin, La, Konnor C, Dimitriadoy, Sofia, Liu, David L, Kantheti, Havish S, Saghafinia, Sadegh, Chakravarty, Debyani, Daian, Foysal, Gao, Qingsong, Bailey, Matthew H, Liang, Wen-Wei, Foltz, Steven M, Shmulevich, Ilya, Ding, Li, Heins, Zachary, Ochoa, Angelica, Gross, Benjamin, Gao, Jianjiong, Zhang, Hongxin, Kundra, Ritika, Kandoth, Cyriac, Bahceci, Istemi, Dervishi, Leonard, Dogrusoz, Ugur, Zhou, Wanding, Shen, Hui, Laird, Peter W, Way, Gregory P, Greene, Casey S, Liang, Han, Xiao, Yonghong, Wang, Chen, Iavarone, Antonio, Berger, Alice H, Bivona, Trever G, Lazar, Alexander J, Hammer, Gary D, Giordano, Thomas, Kwong, Lawrence N, McArthur, Grant, Huang, Chenfei, Tward, Aaron D, Frederick, Mitchell J, McCormick, Frank, Meyerson, Matthew, Network, The Cancer Genome Atlas Research, Caesar-Johnson, Samantha J, Demchok, John A, Felau, Ina, Kasapi, Melpomeni, Ferguson, Martin L, Hutter, Carolyn M, Sofia, Heidi J, Tarnuzzer, Roy, Wang, Zhining, Yang, Liming, Zenklusen, Jean C, Zhang, Jiashan, Chudamani, Sudha, Liu, Jia, Lolla, Laxmi, Naresh, Rashi, Pihl, Todd, Sun, Qiang, Wan, Yunhu, Wu, Ye, Cho, Juok, DeFreitas, Timothy, Frazer, Scott, Gehlenborg, Nils, Getz, Gad, Heiman, David I, Kim, Jaegil, Lawrence, Michael S, Lin, Pei, Meier, Sam, Noble, Michael S, Saksena, Gordon, Voet, Doug, Zhang, Hailei, Bernard, Brady, Chambwe, Nyasha, Dhankani, Varsha, Knijnenburg, Theo, Kramer, Roger, Leinonen, Kalle, Liu, Yuexin, Miller, Michael, Reynolds, Sheila, Thorsson, Vesteinn, Zhang, Wei, Akbani, Rehan, Broom, Bradley M, Hegde, Apurva M, and Ju, Zhenlin

Subjects
Biochemistry and Cell Biology, Bioinformatics and Computational Biology, Biological Sciences, Biomedical and Clinical Sciences, Genetics, Oncology and Carcinogenesis, Cancer, Cancer Genomics, Human Genome, 2.1 Biological and endogenous factors, Good Health and Well Being, Databases, Genetic, Genes, Neoplasm, Humans, Neoplasms, Phosphatidylinositol 3-Kinases, Signal Transduction, Transforming Growth Factor beta, Tumor Suppressor Protein p53, Wnt Proteins, Cancer Genome Atlas Research Network, PanCanAtlas, TCGA, cancer genome atlas, cancer genomics, combination therapy, pan-cancer, precision oncology, signaling pathways, therapeutics, whole exome sequencing, Medical and Health Sciences, Developmental Biology, Biological sciences, Biomedical and clinical sciences
Abstract

Genetic alterations in signaling pathways that control cell-cycle progression, apoptosis, and cell growth are common hallmarks of cancer, but the extent, mechanisms, and co-occurrence of alterations in these pathways differ between individual tumors and tumor types. Using mutations, copy-number changes, mRNA expression, gene fusions and DNA methylation in 9,125 tumors profiled by The Cancer Genome Atlas (TCGA), we analyzed the mechanisms and patterns of somatic alterations in ten canonical pathways: cell cycle, Hippo, Myc, Notch, Nrf2, PI-3-Kinase/Akt, RTK-RAS, TGFβ signaling, p53 and β-catenin/Wnt. We charted the detailed landscape of pathway alterations in 33 cancer types, stratified into 64 subtypes, and identified patterns of co-occurrence and mutual exclusivity. Eighty-nine percent of tumors had at least one driver alteration in these pathways, and 57% percent of tumors had at least one alteration potentially targetable by currently available drugs. Thirty percent of tumors had multiple targetable alterations, indicating opportunities for combination therapy.

Published
2018

29. Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation

Author

Malta, Tathiane M, Sokolov, Artem, Gentles, Andrew J, Burzykowski, Tomasz, Poisson, Laila, Weinstein, John N, Kamińska, Bożena, Huelsken, Joerg, Omberg, Larsson, Gevaert, Olivier, Colaprico, Antonio, Czerwińska, Patrycja, Mazurek, Sylwia, Mishra, Lopa, Heyn, Holger, Krasnitz, Alex, Godwin, Andrew K, Lazar, Alexander J, Network, The Cancer Genome Atlas Research, Caesar-Johnson, Samantha J, Demchok, John A, Felau, Ina, Kasapi, Melpomeni, Ferguson, Martin L, Hutter, Carolyn M, Sofia, Heidi J, Tarnuzzer, Roy, Wang, Zhining, Yang, Liming, Zenklusen, Jean C, Zhang, Jiashan, Chudamani, Sudha, Liu, Jia, Lolla, Laxmi, Naresh, Rashi, Pihl, Todd, Sun, Qiang, Wan, Yunhu, Wu, Ye, Cho, Juok, DeFreitas, Timothy, Frazer, Scott, Gehlenborg, Nils, Getz, Gad, Heiman, David I, Kim, Jaegil, Lawrence, Michael S, Lin, Pei, Meier, Sam, Noble, Michael S, Saksena, Gordon, Voet, Doug, Zhang, Hailei, Bernard, Brady, Chambwe, Nyasha, Dhankani, Varsha, Knijnenburg, Theo, Kramer, Roger, Leinonen, Kalle, Liu, Yuexin, Miller, Michael, Reynolds, Sheila, Shmulevich, Ilya, Thorsson, Vesteinn, Zhang, Wei, Akbani, Rehan, Broom, Bradley M, Hegde, Apurva M, Ju, Zhenlin, Kanchi, Rupa S, Korkut, Anil, Li, Jun, Liang, Han, Ling, Shiyun, Liu, Wenbin, Lu, Yiling, Mills, Gordon B, Ng, Kwok-Shing, Rao, Arvind, Ryan, Michael, Wang, Jing, Zhang, Jiexin, Abeshouse, Adam, Armenia, Joshua, Chakravarty, Debyani, Chatila, Walid K, de Bruijn, Ino, Gao, Jianjiong, Gross, Benjamin E, Heins, Zachary J, Kundra, Ritika, La, Konnor, Ladanyi, Marc, Luna, Augustin, Nissan, Moriah G, Ochoa, Angelica, Phillips, Sarah M, Reznik, Ed, and Sanchez-Vega, Francisco

Subjects
Biomedical and Clinical Sciences, Oncology and Carcinogenesis, Immunology, Rare Diseases, Stem Cell Research - Nonembryonic - Non-Human, Stem Cell Research - Nonembryonic - Human, Stem Cell Research, Cancer, Genetics, Machine Learning and Artificial Intelligence, Cancer Genomics, Human Genome, Stem Cell Research - Induced Pluripotent Stem Cell, Good Health and Well Being, Carcinogenesis, Cell Dedifferentiation, DNA Methylation, Databases, Genetic, Epigenesis, Genetic, Humans, Machine Learning, MicroRNAs, Neoplasm Metastasis, Neoplasms, Stem Cells, Transcriptome, Tumor Microenvironment, Cancer Genome Atlas Research Network, The Cancer Genome Atlas, cancer stem cells, dedifferentiation, epigenomic, genomic, machine learning, pan-cancer, stemness, Biological Sciences, Medical and Health Sciences, Developmental Biology, Biological sciences, Biomedical and clinical sciences
Abstract

Cancer progression involves the gradual loss of a differentiated phenotype and acquisition of progenitor and stem-cell-like features. Here, we provide novel stemness indices for assessing the degree of oncogenic dedifferentiation. We used an innovative one-class logistic regression (OCLR) machine-learning algorithm to extract transcriptomic and epigenetic feature sets derived from non-transformed pluripotent stem cells and their differentiated progeny. Using OCLR, we were able to identify previously undiscovered biological mechanisms associated with the dedifferentiated oncogenic state. Analyses of the tumor microenvironment revealed unanticipated correlation of cancer stemness with immune checkpoint expression and infiltrating immune cells. We found that the dedifferentiated oncogenic phenotype was generally most prominent in metastatic tumors. Application of our stemness indices to single-cell data revealed patterns of intra-tumor molecular heterogeneity. Finally, the indices allowed for the identification of novel targets and possible targeted therapies aimed at tumor differentiation.

Published
2018

30. Perspective on Oncogenic Processes at the End of the Beginning of Cancer Genomics

Author

Ding, Li, Bailey, Matthew H, Porta-Pardo, Eduard, Thorsson, Vesteinn, Colaprico, Antonio, Bertrand, Denis, Gibbs, David L, Weerasinghe, Amila, Huang, Kuan-lin, Tokheim, Collin, Cortés-Ciriano, Isidro, Jayasinghe, Reyka, Chen, Feng, Yu, Lihua, Sun, Sam, Olsen, Catharina, Kim, Jaegil, Taylor, Alison M, Cherniack, Andrew D, Akbani, Rehan, Suphavilai, Chayaporn, Nagarajan, Niranjan, Stuart, Joshua M, Mills, Gordon B, Wyczalkowski, Matthew A, Vincent, Benjamin G, Hutter, Carolyn M, Zenklusen, Jean Claude, Hoadley, Katherine A, Wendl, Michael C, Shmulevich, llya, Lazar, Alexander J, Wheeler, David A, Getz, Gad, Network, The Cancer Genome Atlas Research, Caesar-Johnson, Samantha J, Demchok, John A, Felau, Ina, Kasapi, Melpomeni, Ferguson, Martin L, Sofia, Heidi J, Tarnuzzer, Roy, Wang, Zhining, Yang, Liming, Zenklusen, Jean C, Zhang, Jiashan, Chudamani, Sudha, Liu, Jia, Lolla, Laxmi, Naresh, Rashi, Pihl, Todd, Sun, Qiang, Wan, Yunhu, Wu, Ye, Cho, Juok, DeFreitas, Timothy, Frazer, Scott, Gehlenborg, Nils, Heiman, David I, Lawrence, Michael S, Lin, Pei, Meier, Sam, Noble, Michael S, Saksena, Gordon, Voet, Doug, Zhang, Hailei, Bernard, Brady, Chambwe, Nyasha, Dhankani, Varsha, Knijnenburg, Theo, Kramer, Roger, Leinonen, Kalle, Liu, Yuexin, Miller, Michael, Reynolds, Sheila, Shmulevich, Ilya, Zhang, Wei, Broom, Bradley M, Hegde, Apurva M, Ju, Zhenlin, Kanchi, Rupa S, Korkut, Anil, Li, Jun, Liang, Han, Ling, Shiyun, Liu, Wenbin, Lu, Yiling, Ng, Kwok-Shing, Rao, Arvind, Ryan, Michael, Wang, Jing, Weinstein, John N, Zhang, Jiexin, and Abeshouse, Adam

Subjects
Biological Sciences, Biomedical and Clinical Sciences, Bioinformatics and Computational Biology, Genetics, Oncology and Carcinogenesis, Biotechnology, Cancer, Cancer Genomics, Human Genome, 2.1 Biological and endogenous factors, Good Health and Well Being, Carcinogenesis, DNA Repair, Databases, Genetic, Genes, Neoplasm, Genomics, Humans, Metabolic Networks and Pathways, Microsatellite Instability, Mutation, Neoplasms, Transcriptome, Tumor Microenvironment, Cancer Genome Atlas Research Network, TCGA, cancer, cancer genomics, omics, oncogenic process, Medical and Health Sciences, Developmental Biology, Biological sciences, Biomedical and clinical sciences
Abstract

The Cancer Genome Atlas (TCGA) has catalyzed systematic characterization of diverse genomic alterations underlying human cancers. At this historic junction marking the completion of genomic characterization of over 11,000 tumors from 33 cancer types, we present our current understanding of the molecular processes governing oncogenesis. We illustrate our insights into cancer through synthesis of the findings of the TCGA PanCancer Atlas project on three facets of oncogenesis: (1) somatic driver mutations, germline pathogenic variants, and their interactions in the tumor; (2) the influence of the tumor genome and epigenome on transcriptome and proteome; and (3) the relationship between tumor and the microenvironment, including implications for drugs targeting driver events and immunotherapies. These results will anchor future characterization of rare and common tumor types, primary and relapsed tumors, and cancers across ancestry groups and will guide the deployment of clinical genomic sequencing.

Published
2018

31. A Comprehensive Pan-Cancer Molecular Study of Gynecologic and Breast Cancers

Author

Berger, Ashton C, Korkut, Anil, Kanchi, Rupa S, Hegde, Apurva M, Lenoir, Walter, Liu, Wenbin, Liu, Yuexin, Fan, Huihui, Shen, Hui, Ravikumar, Visweswaran, Rao, Arvind, Schultz, Andre, Li, Xubin, Sumazin, Pavel, Williams, Cecilia, Mestdagh, Pieter, Gunaratne, Preethi H, Yau, Christina, Bowlby, Reanne, Robertson, A Gordon, Tiezzi, Daniel G, Wang, Chen, Cherniack, Andrew D, Godwin, Andrew K, Kuderer, Nicole M, Rader, Janet S, Zuna, Rosemary E, Sood, Anil K, Lazar, Alexander J, Ojesina, Akinyemi I, Adebamowo, Clement, Adebamowo, Sally N, Baggerly, Keith A, Chen, Ting-Wen, Chiu, Hua-Sheng, Lefever, Steve, Liu, Liang, MacKenzie, Karen, Orsulic, Sandra, Roszik, Jason, Shelley, Carl Simon, Song, Qianqian, Vellano, Christopher P, Wentzensen, Nicolas, Network, The Cancer Genome Atlas Research, Caesar-Johnson, Samantha J, Demchok, John A, Felau, Ina, Kasapi, Melpomeni, Ferguson, Martin L, Hutter, Carolyn M, Sofia, Heidi J, Tarnuzzer, Roy, Wang, Zhining, Yang, Liming, Zenklusen, Jean C, Zhang, Jiashan, Chudamani, Sudha, Liu, Jia, Lolla, Laxmi, Naresh, Rashi, Pihl, Todd, Sun, Qiang, Wan, Yunhu, Wu, Ye, Cho, Juok, DeFreitas, Timothy, Frazer, Scott, Gehlenborg, Nils, Getz, Gad, Heiman, David I, Kim, Jaegil, Lawrence, Michael S, Lin, Pei, Meier, Sam, Noble, Michael S, Saksena, Gordon, Voet, Doug, Zhang, Hailei, Bernard, Brady, Chambwe, Nyasha, Dhankani, Varsha, Knijnenburg, Theo, Kramer, Roger, Leinonen, Kalle, Miller, Michael, Reynolds, Sheila, Shmulevich, Ilya, Thorsson, Vesteinn, Zhang, Wei, Akbani, Rehan, Broom, Bradley M, Ju, Zhenlin, Li, Jun, Liang, Han, and Ling, Shiyun

Subjects
Biological Sciences, Bioinformatics and Computational Biology, Biomedical and Clinical Sciences, Oncology and Carcinogenesis, Breast Cancer, Cancer Genomics, Cancer, Human Genome, Genetics, Women's Health, Precision Medicine, 4.1 Discovery and preclinical testing of markers and technologies, Breast Neoplasms, DNA Copy Number Variations, Databases, Genetic, Female, Gene Expression Profiling, Gene Expression Regulation, Neoplastic, Gene Regulatory Networks, Genetic Predisposition to Disease, Genital Neoplasms, Female, Humans, Mutation, Organ Specificity, Prognosis, RNA, Long Noncoding, Receptors, Estrogen, Cancer Genome Atlas Research Network, TCGA, The Cancer Genome Atlas, breast cancer, cervical cancer, gynecologic cancer, omics, ovarian cancer, pan-gynecologic, uterine cancer, uterine carcinosarcoma, Neurosciences, Oncology & Carcinogenesis, Biochemistry and cell biology, Oncology and carcinogenesis
Abstract

We analyzed molecular data on 2,579 tumors from The Cancer Genome Atlas (TCGA) of four gynecological types plus breast. Our aims were to identify shared and unique molecular features, clinically significant subtypes, and potential therapeutic targets. We found 61 somatic copy-number alterations (SCNAs) and 46 significantly mutated genes (SMGs). Eleven SCNAs and 11 SMGs had not been identified in previous TCGA studies of the individual tumor types. We found functionally significant estrogen receptor-regulated long non-coding RNAs (lncRNAs) and gene/lncRNA interaction networks. Pathway analysis identified subtypes with high leukocyte infiltration, raising potential implications for immunotherapy. Using 16 key molecular features, we identified five prognostic subtypes and developed a decision tree that classified patients into the subtypes based on just six features that are assessable in clinical laboratories.

Published
2018

32. Cell-of-Origin Patterns Dominate the Molecular Classification of 10,000 Tumors from 33 Types of Cancer

Author

Hoadley, Katherine A, Yau, Christina, Hinoue, Toshinori, Wolf, Denise M, Lazar, Alexander J, Drill, Esther, Shen, Ronglai, Taylor, Alison M, Cherniack, Andrew D, Thorsson, Vésteinn, Akbani, Rehan, Bowlby, Reanne, Wong, Christopher K, Wiznerowicz, Maciej, Sanchez-Vega, Francisco, Robertson, A Gordon, Schneider, Barbara G, Lawrence, Michael S, Noushmehr, Houtan, Malta, Tathiane M, Network, The Cancer Genome Atlas, Caesar-Johnson, Samantha J, Demchok, John A, Felau, Ina, Kasapi, Melpomeni, Ferguson, Martin L, Hutter, Carolyn M, Sofia, Heidi J, Tarnuzzer, Roy, Wang, Zhining, Yang, Liming, Zenklusen, Jean C, Zhang, Jiashan, Chudamani, Sudha, Liu, Jia, Lolla, Laxmi, Naresh, Rashi, Pihl, Todd, Sun, Qiang, Wan, Yunhu, Wu, Ye, Cho, Juok, DeFreitas, Timothy, Frazer, Scott, Gehlenborg, Nils, Getz, Gad, Heiman, David I, Kim, Jaegil, Lin, Pei, Meier, Sam, Noble, Michael S, Saksena, Gordon, Voet, Doug, Zhang, Hailei, Bernard, Brady, Chambwe, Nyasha, Dhankani, Varsha, Knijnenburg, Theo, Kramer, Roger, Leinonen, Kalle, Liu, Yuexin, Miller, Michael, Reynolds, Sheila, Shmulevich, Ilya, Thorsson, Vesteinn, Zhang, Wei, Broom, Bradley M, Hegde, Apurva M, Ju, Zhenlin, Kanchi, Rupa S, Korkut, Anil, Li, Jun, Liang, Han, Ling, Shiyun, Liu, Wenbin, Lu, Yiling, Mills, Gordon B, Ng, Kwok-Shing, Rao, Arvind, Ryan, Michael, Wang, Jing, Weinstein, John N, Zhang, Jiexin, Abeshouse, Adam, Armenia, Joshua, Chakravarty, Debyani, Chatila, Walid K, de Bruijn, Ino, Gao, Jianjiong, Gross, Benjamin E, Heins, Zachary J, Kundra, Ritika, La, Konnor, Ladanyi, Marc, Luna, Augustin, Nissan, Moriah G, Ochoa, Angelica, and Phillips, Sarah M

Subjects
Biological Sciences, Bioinformatics and Computational Biology, Genetics, Biotechnology, Cancer, Networking and Information Technology R&D (NITRD), Cancer Genomics, Human Genome, 2.1 Biological and endogenous factors, Aneuploidy, Chromosomes, Cluster Analysis, CpG Islands, DNA Methylation, Databases, Factual, Humans, MicroRNAs, Mutation, Neoplasm Proteins, Neoplasms, RNA, Messenger, Cancer Genome Atlas Network, TCGA, cancer, cell-of-origin, genome, methylome, organs, proteome, subtypes, tissues, transcriptome, Medical and Health Sciences, Developmental Biology, Biological sciences, Biomedical and clinical sciences
Abstract

We conducted comprehensive integrative molecular analyses of the complete set of tumors in The Cancer Genome Atlas (TCGA), consisting of approximately 10,000 specimens and representing 33 types of cancer. We performed molecular clustering using data on chromosome-arm-level aneuploidy, DNA hypermethylation, mRNA, and miRNA expression levels and reverse-phase protein arrays, of which all, except for aneuploidy, revealed clustering primarily organized by histology, tissue type, or anatomic origin. The influence of cell type was evident in DNA-methylation-based clustering, even after excluding sites with known preexisting tissue-type-specific methylation. Integrative clustering further emphasized the dominant role of cell-of-origin patterns. Molecular similarities among histologically or anatomically related cancer types provide a basis for focused pan-cancer analyses, such as pan-gastrointestinal, pan-gynecological, pan-kidney, and pan-squamous cancers, and those related by stemness features, which in turn may inform strategies for future therapeutic development.

Published
2018

33. Pathogenic Germline Variants in 10,389 Adult Cancers

Author

Huang, Kuan-lin, Mashl, R Jay, Wu, Yige, Ritter, Deborah I, Wang, Jiayin, Oh, Clara, Paczkowska, Marta, Reynolds, Sheila, Wyczalkowski, Matthew A, Oak, Ninad, Scott, Adam D, Krassowski, Michal, Cherniack, Andrew D, Houlahan, Kathleen E, Jayasinghe, Reyka, Wang, Liang-Bo, Zhou, Daniel Cui, Liu, Di, Cao, Song, Kim, Young Won, Koire, Amanda, McMichael, Joshua F, Hucthagowder, Vishwanathan, Kim, Tae-Beom, Hahn, Abigail, Wang, Chen, McLellan, Michael D, Al-Mulla, Fahd, Johnson, Kimberly J, Network, The Cancer Genome Atlas Research, Caesar-Johnson, Samantha J, Demchok, John A, Felau, Ina, Kasapi, Melpomeni, Ferguson, Martin L, Hutter, Carolyn M, Sofia, Heidi J, Tarnuzzer, Roy, Wang, Zhining, Yang, Liming, Zenklusen, Jean C, Zhang, Jiashan, Chudamani, Sudha, Liu, Jia, Lolla, Laxmi, Naresh, Rashi, Pihl, Todd, Sun, Qiang, Wan, Yunhu, Wu, Ye, Cho, Juok, DeFreitas, Timothy, Frazer, Scott, Gehlenborg, Nils, Getz, Gad, Heiman, David I, Kim, Jaegil, Lawrence, Michael S, Lin, Pei, Meier, Sam, Noble, Michael S, Saksena, Gordon, Voet, Doug, Zhang, Hailei, Bernard, Brady, Chambwe, Nyasha, Dhankani, Varsha, Knijnenburg, Theo, Kramer, Roger, Leinonen, Kalle, Liu, Yuexin, Miller, Michael, Shmulevich, Ilya, Thorsson, Vesteinn, Zhang, Wei, Akbani, Rehan, Broom, Bradley M, Hegde, Apurva M, Ju, Zhenlin, Kanchi, Rupa S, Korkut, Anil, Li, Jun, Liang, Han, Ling, Shiyun, Liu, Wenbin, Lu, Yiling, Mills, Gordon B, Ng, Kwok-Shing, Rao, Arvind, Ryan, Michael, Wang, Jing, Weinstein, John N, Zhang, Jiexin, Abeshouse, Adam, Armenia, Joshua, Chakravarty, Debyani, Chatila, Walid K, de Bruijn, Ino, and Gao, Jianjiong

Subjects
Biological Sciences, Biomedical and Clinical Sciences, Genetics, Oncology and Carcinogenesis, Cancer Genomics, Cancer, Rare Diseases, Prevention, Human Genome, 2.1 Biological and endogenous factors, DNA Copy Number Variations, Databases, Genetic, Gene Deletion, Gene Frequency, Genetic Predisposition to Disease, Genotype, Germ Cells, Germ-Line Mutation, Humans, Loss of Heterozygosity, Mutation, Missense, Neoplasms, Polymorphism, Single Nucleotide, Proto-Oncogene Proteins c-met, Proto-Oncogene Proteins c-ret, Tumor Suppressor Proteins, Cancer Genome Atlas Research Network, LOH, cancer predisposition, germline and somatic genomes, variant pathogenicity, Medical and Health Sciences, Developmental Biology, Biological sciences, Biomedical and clinical sciences
Abstract

We conducted the largest investigation of predisposition variants in cancer to date, discovering 853 pathogenic or likely pathogenic variants in 8% of 10,389 cases from 33 cancer types. Twenty-one genes showed single or cross-cancer associations, including novel associations of SDHA in melanoma and PALB2 in stomach adenocarcinoma. The 659 predisposition variants and 18 additional large deletions in tumor suppressors, including ATM, BRCA1, and NF1, showed low gene expression and frequent (43%) loss of heterozygosity or biallelic two-hit events. We also discovered 33 such variants in oncogenes, including missenses in MET, RET, and PTPN11 associated with high gene expression. We nominated 47 additional predisposition variants from prioritized VUSs supported by multiple evidences involving case-control frequency, loss of heterozygosity, expression effect, and co-localization with mutations and modified residues. Our integrative approach links rare predisposition variants to functional consequences, informing future guidelines of variant classification and germline genetic testing in cancer.

Published
2018

34. Systematic Analysis of Splice-Site-Creating Mutations in Cancer

Author

Jayasinghe, Reyka G, Cao, Song, Gao, Qingsong, Wendl, Michael C, Vo, Nam Sy, Reynolds, Sheila M, Zhao, Yanyan, Climente-González, Héctor, Chai, Shengjie, Wang, Fang, Varghese, Rajees, Huang, Mo, Liang, Wen-Wei, Wyczalkowski, Matthew A, Sengupta, Sohini, Li, Zhi, Payne, Samuel H, Fenyö, David, Miner, Jeffrey H, Walter, Matthew J, Network, The Cancer Genome Atlas Research, Caesar-Johnson, Samantha J, Demchok, John A, Felau, Ina, Kasapi, Melpomeni, Ferguson, Martin L, Hutter, Carolyn M, Sofia, Heidi J, Tarnuzzer, Roy, Wang, Zhining, Yang, Liming, Zenklusen, Jean C, Zhang, Jiashan, Chudamani, Sudha, Liu, Jia, Lolla, Laxmi, Naresh, Rashi, Pihl, Todd, Sun, Qiang, Wan, Yunhu, Wu, Ye, Cho, Juok, DeFreitas, Timothy, Frazer, Scott, Gehlenborg, Nils, Getz, Gad, Heiman, David I, Kim, Jaegil, Lawrence, Michael S, Lin, Pei, Meier, Sam, Noble, Michael S, Saksena, Gordon, Voet, Doug, Zhang, Hailei, Bernard, Brady, Chambwe, Nyasha, Dhankani, Varsha, Knijnenburg, Theo, Kramer, Roger, Leinonen, Kalle, Liu, Yuexin, Miller, Michael, Reynolds, Sheila, Shmulevich, Ilya, Thorsson, Vesteinn, Zhang, Wei, Akbani, Rehan, Broom, Bradley M, Hegde, Apurva M, Ju, Zhenlin, Kanchi, Rupa S, Korkut, Anil, Li, Jun, Liang, Han, Ling, Shiyun, Liu, Wenbin, Lu, Yiling, Mills, Gordon B, Ng, Kwok-Shing, Rao, Arvind, Ryan, Michael, Wang, Jing, Weinstein, John N, Zhang, Jiexin, Abeshouse, Adam, Armenia, Joshua, Chakravarty, Debyani, Chatila, Walid K, de Bruijn, Ino, Gao, Jianjiong, Gross, Benjamin E, Heins, Zachary J, Kundra, Ritika, La, Konnor, Ladanyi, Marc, Luna, Augustin, Nissan, Moriah G, Ochoa, Angelica, and Phillips, Sarah M

Subjects
Biological Sciences, Bioinformatics and Computational Biology, Genetics, Cancer Genomics, Immunotherapy, Cancer, Rare Diseases, Human Genome, 5.1 Pharmaceuticals, 2.1 Biological and endogenous factors, BRCA1 Protein, GATA3 Transcription Factor, HEK293 Cells, Humans, Mutation, Neoplasms, Poly (ADP-Ribose) Polymerase-1, Programmed Cell Death 1 Receptor, RNA Splice Sites, Tumor Suppressor Protein p53, X-linked Nuclear Protein, Cancer Genome Atlas Research Network, RNA, mutations of clinical relevance, splicing, Biochemistry and Cell Biology, Medical Physiology, Biological sciences
Abstract

For the past decade, cancer genomic studies have focused on mutations leading to splice-site disruption, overlooking those having splice-creating potential. Here, we applied a bioinformatic tool, MiSplice, for the large-scale discovery of splice-site-creating mutations (SCMs) across 8,656 TCGA tumors. We report 1,964 originally mis-annotated mutations having clear evidence of creating alternative splice junctions. TP53 and GATA3 have 26 and 18 SCMs, respectively, and ATRX has 5 from lower-grade gliomas. Mutations in 11 genes, including PARP1, BRCA1, and BAP1, were experimentally validated for splice-site-creating function. Notably, we found that neoantigens induced by SCMs are likely several folds more immunogenic compared to missense mutations, exemplified by the recurrent GATA3 SCM. Further, high expression of PD-1 and PD-L1 was observed in tumors with SCMs, suggesting candidates for immune blockade therapy. Our work highlights the importance of integrating DNA and RNA data for understanding the functional and the clinical implications of mutations in human diseases.

Published
2018

35. Driver Fusions and Their Implications in the Development and Treatment of Human Cancers

Author

Gao, Qingsong, Liang, Wen-Wei, Foltz, Steven M, Mutharasu, Gnanavel, Jayasinghe, Reyka G, Cao, Song, Liao, Wen-Wei, Reynolds, Sheila M, Wyczalkowski, Matthew A, Yao, Lijun, Yu, Lihua, Sun, Sam Q, Group, The Fusion Analysis Working, Network, The Cancer Genome Atlas Research, Caesar-Johnson, Samantha J, Demchok, John A, Felau, Ina, Kasapi, Melpomeni, Ferguson, Martin L, Hutter, Carolyn M, Sofia, Heidi J, Tarnuzzer, Roy, Wang, Zhining, Yang, Liming, Zenklusen, Jean C, Zhang, Jiashan, Chudamani, Sudha, Liu, Jia, Lolla, Laxmi, Naresh, Rashi, Pihl, Todd, Sun, Qiang, Wan, Yunhu, Wu, Ye, Cho, Juok, DeFreitas, Timothy, Frazer, Scott, Gehlenborg, Nils, Getz, Gad, Heiman, David I, Kim, Jaegil, Lawrence, Michael S, Lin, Pei, Meier, Sam, Noble, Michael S, Saksena, Gordon, Voet, Doug, Zhang, Hailei, Bernard, Brady, Chambwe, Nyasha, Dhankani, Varsha, Knijnenburg, Theo, Kramer, Roger, Leinonen, Kalle, Liu, Yuexin, Miller, Michael, Reynolds, Sheila, Shmulevich, Ilya, Thorsson, Vesteinn, Zhang, Wei, Akbani, Rehan, Broom, Bradley M, Hegde, Apurva M, Ju, Zhenlin, Kanchi, Rupa S, Korkut, Anil, Li, Jun, Liang, Han, Ling, Shiyun, Liu, Wenbin, Lu, Yiling, Mills, Gordon B, Ng, Kwok-Shing, Rao, Arvind, Ryan, Michael, Wang, Jing, Weinstein, John N, Zhang, Jiexin, Abeshouse, Adam, Armenia, Joshua, Chakravarty, Debyani, Chatila, Walid K, de Bruijn, Ino, Gao, Jianjiong, Gross, Benjamin E, Heins, Zachary J, Kundra, Ritika, La, Konnor, Ladanyi, Marc, Luna, Augustin, Nissan, Moriah G, Ochoa, Angelica, Phillips, Sarah M, Reznik, Ed, Sanchez-Vega, Francisco, Sander, Chris, Schultz, Nikolaus, Sheridan, Robert, Sumer, S Onur, and Sun, Yichao

Subjects
Biological Sciences, Digestive Diseases, Rare Diseases, Biotechnology, Cancer Genomics, Genetics, Cancer, Human Genome, 5.1 Pharmaceuticals, Good Health and Well Being, Antineoplastic Agents, Carcinogenesis, Cell Line, Tumor, Humans, Molecular Targeted Therapy, Neoplasms, Oncogene Fusion, Oncogene Proteins, Fusion, Fusion Analysis Working Group, Cancer Genome Atlas Research Network, RNA, cancer, fusion, gene fusions, translocation, Biochemistry and Cell Biology, Medical Physiology, Biological sciences
Abstract

Gene fusions represent an important class of somatic alterations in cancer. We systematically investigated fusions in 9,624 tumors across 33 cancer types using multiple fusion calling tools. We identified a total of 25,664 fusions, with a 63% validation rate. Integration of gene expression, copy number, and fusion annotation data revealed that fusions involving oncogenes tend to exhibit increased expression, whereas fusions involving tumor suppressors have the opposite effect. For fusions involving kinases, we found 1,275 with an intact kinase domain, the proportion of which varied significantly across cancer types. Our study suggests that fusions drive the development of 16.5% of cancer cases and function as the sole driver in more than 1% of them. Finally, we identified druggable fusions involving genes such as TMPRSS2, RET, FGFR3, ALK, and ESR1 in 6.0% of cases, and we predicted immunogenic peptides, suggesting that fusions may provide leads for targeted drug and immune therapy.

Published
2018

36. Molecular Characterization and Clinical Relevance of Metabolic Expression Subtypes in Human Cancers

Author

Peng, Xinxin, Chen, Zhongyuan, Farshidfar, Farshad, Xu, Xiaoyan, Lorenzi, Philip L, Wang, Yumeng, Cheng, Feixiong, Tan, Lin, Mojumdar, Kamalika, Du, Di, Ge, Zhongqi, Li, Jun, Thomas, George V, Birsoy, Kivanc, Liu, Lingxiang, Zhang, Huiwen, Zhao, Zhongming, Marchand, Calena, Weinstein, John N, Network, The Cancer Genome Atlas Research, Caesar-Johnson, Samantha J, Demchok, John A, Felau, Ina, Kasapi, Melpomeni, Ferguson, Martin L, Hutter, Carolyn M, Sofia, Heidi J, Tarnuzzer, Roy, Wang, Zhining, Yang, Liming, Zenklusen, Jean C, Zhang, Jiashan, Chudamani, Sudha, Liu, Jia, Lolla, Laxmi, Naresh, Rashi, Pihl, Todd, Sun, Qiang, Wan, Yunhu, Wu, Ye, Cho, Juok, DeFreitas, Timothy, Frazer, Scott, Gehlenborg, Nils, Getz, Gad, Heiman, David I, Kim, Jaegil, Lawrence, Michael S, Lin, Pei, Meier, Sam, Noble, Michael S, Saksena, Gordon, Voet, Doug, Zhang, Hailei, Bernard, Brady, Chambwe, Nyasha, Dhankani, Varsha, Knijnenburg, Theo, Kramer, Roger, Leinonen, Kalle, Liu, Yuexin, Miller, Michael, Reynolds, Sheila, Shmulevich, Ilya, Thorsson, Vesteinn, Zhang, Wei, Akbani, Rehan, Broom, Bradley M, Hegde, Apurva M, Ju, Zhenlin, Kanchi, Rupa S, Korkut, Anil, Liang, Han, Ling, Shiyun, Liu, Wenbin, Lu, Yiling, Mills, Gordon B, Ng, Kwok-Shing, Rao, Arvind, Ryan, Michael, Wang, Jing, Zhang, Jiexin, Abeshouse, Adam, Armenia, Joshua, Chakravarty, Debyani, Chatila, Walid K, de Bruijn, Ino, Gao, Jianjiong, Gross, Benjamin E, Heins, Zachary J, Kundra, Ritika, La, Konnor, Ladanyi, Marc, Luna, Augustin, Nissan, Moriah G, Ochoa, Angelica, Phillips, Sarah M, and Reznik, Ed

Subjects
Biological Sciences, Nutrition, Cancer, Genetics, Human Genome, Cancer Genomics, 2.1 Biological and endogenous factors, Cell Line, Tumor, Core Binding Factor Alpha 2 Subunit, Drug Resistance, Neoplasm, HEK293 Cells, Humans, Metabolic Networks and Pathways, Neoplasms, Snail Family Transcription Factors, Transcriptome, Cancer Genome Atlas Research Network, The Cancer Genome Atlas, carbohydrate metabolism, drug sensitivity, master regulator, prognostic markers, somatic drivers, therapeutic targets, tumor subtypes, Biochemistry and Cell Biology, Medical Physiology, Biological sciences
Abstract

Metabolic reprogramming provides critical information for clinical oncology. Using molecular data of 9,125 patient samples from The Cancer Genome Atlas, we identified tumor subtypes in 33 cancer types based on mRNA expression patterns of seven major metabolic processes and assessed their clinical relevance. Our metabolic expression subtypes correlated extensively with clinical outcome: subtypes with upregulated carbohydrate, nucleotide, and vitamin/cofactor metabolism most consistently correlated with worse prognosis, whereas subtypes with upregulated lipid metabolism showed the opposite. Metabolic subtypes correlated with diverse somatic drivers but exhibited effects convergent on cancer hallmark pathways and were modulated by highly recurrent master regulators across cancer types. As a proof-of-concept example, we demonstrated that knockdown of SNAI1 or RUNX1-master regulators of carbohydrate metabolic subtypes-modulates metabolic activity and drug sensitivity. Our study provides a system-level view of metabolic heterogeneity within and across cancer types and identifies pathway cross-talk, suggesting related prognostic, therapeutic, and predictive utility.

Published
2018

37. Machine Learning Detects Pan-cancer Ras Pathway Activation in The Cancer Genome Atlas

Author

Way, Gregory P, Sanchez-Vega, Francisco, La, Konnor, Armenia, Joshua, Chatila, Walid K, Luna, Augustin, Sander, Chris, Cherniack, Andrew D, Mina, Marco, Ciriello, Giovanni, Schultz, Nikolaus, Network, The Cancer Genome Atlas Research, Caesar-Johnson, Samantha J, Demchok, John A, Felau, Ina, Kasapi, Melpomeni, Ferguson, Martin L, Hutter, Carolyn M, Sofia, Heidi J, Tarnuzzer, Roy, Wang, Zhining, Yang, Liming, Zenklusen, Jean C, Zhang, Jiashan, Chudamani, Sudha, Liu, Jia, Lolla, Laxmi, Naresh, Rashi, Pihl, Todd, Sun, Qiang, Wan, Yunhu, Wu, Ye, Cho, Juok, DeFreitas, Timothy, Frazer, Scott, Gehlenborg, Nils, Getz, Gad, Heiman, David I, Kim, Jaegil, Lawrence, Michael S, Lin, Pei, Meier, Sam, Noble, Michael S, Saksena, Gordon, Voet, Doug, Zhang, Hailei, Bernard, Brady, Chambwe, Nyasha, Dhankani, Varsha, Knijnenburg, Theo, Kramer, Roger, Leinonen, Kalle, Liu, Yuexin, Miller, Michael, Reynolds, Sheila, Shmulevich, Ilya, Thorsson, Vesteinn, Zhang, Wei, Akbani, Rehan, Broom, Bradley M, Hegde, Apurva M, Ju, Zhenlin, Kanchi, Rupa S, Korkut, Anil, Li, Jun, Liang, Han, Ling, Shiyun, Liu, Wenbin, Lu, Yiling, Mills, Gordon B, Ng, Kwok-Shing, Rao, Arvind, Ryan, Michael, Wang, Jing, Weinstein, John N, Zhang, Jiexin, Abeshouse, Adam, Chakravarty, Debyani, de Bruijn, Ino, Gao, Jianjiong, Gross, Benjamin E, Heins, Zachary J, Kundra, Ritika, Ladanyi, Marc, Nissan, Moriah G, Ochoa, Angelica, Phillips, Sarah M, Reznik, Ed, Sheridan, Robert, Sumer, S Onur, Sun, Yichao, Taylor, Barry S, and Wang, Jioajiao

Subjects
Biological Sciences, Bioinformatics and Computational Biology, Genetics, Precision Medicine, Cancer, Networking and Information Technology R&D (NITRD), Machine Learning and Artificial Intelligence, Cancer Genomics, Human Genome, Good Health and Well Being, Cell Line, Tumor, Gene Expression Regulation, Neoplastic, Genome, Human, Humans, Machine Learning, Neoplasms, Signal Transduction, ras Proteins, Cancer Genome Atlas Research Network, Gene expression, HRAS, KRAS, NF1, NRAS, Ras, TCGA, drug sensitivity, machine learning, pan-cancer, Biochemistry and Cell Biology, Medical Physiology, Biological sciences
Abstract

Precision oncology uses genomic evidence to match patients with treatment but often fails to identify all patients who may respond. The transcriptome of these "hidden responders" may reveal responsive molecular states. We describe and evaluate a machine-learning approach to classify aberrant pathway activity in tumors, which may aid in hidden responder identification. The algorithm integrates RNA-seq, copy number, and mutations from 33 different cancer types across The Cancer Genome Atlas (TCGA) PanCanAtlas project to predict aberrant molecular states in tumors. Applied to the Ras pathway, the method detects Ras activation across cancer types and identifies phenocopying variants. The model, trained on human tumors, can predict response to MEK inhibitors in wild-type Ras cell lines. We also present data that suggest that multiple hits in the Ras pathway confer increased Ras activity. The transcriptome is underused in precision oncology and, combined with machine learning, can aid in the identification of hidden responders.

Published
2018

38. An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics

Author

Liu, Jianfang, Lichtenberg, Tara, Hoadley, Katherine A, Poisson, Laila M, Lazar, Alexander J, Cherniack, Andrew D, Kovatich, Albert J, Benz, Christopher C, Levine, Douglas A, Lee, Adrian V, Omberg, Larsson, Wolf, Denise M, Shriver, Craig D, Thorsson, Vesteinn, Network, The Cancer Genome Atlas Research, Caesar-Johnson, Samantha J, Demchok, John A, Felau, Ina, Kasapi, Melpomeni, Ferguson, Martin L, Hutter, Carolyn M, Sofia, Heidi J, Tarnuzzer, Roy, Wang, Zhining, Yang, Liming, Zenklusen, Jean C, Zhang, Jiashan, Chudamani, Sudha, Liu, Jia, Lolla, Laxmi, Naresh, Rashi, Pihl, Todd, Sun, Qiang, Wan, Yunhu, Wu, Ye, Cho, Juok, DeFreitas, Timothy, Frazer, Scott, Gehlenborg, Nils, Getz, Gad, Heiman, David I, Kim, Jaegil, Lawrence, Michael S, Lin, Pei, Meier, Sam, Noble, Michael S, Saksena, Gordon, Voet, Doug, Zhang, Hailei, Bernard, Brady, Chambwe, Nyasha, Dhankani, Varsha, Knijnenburg, Theo, Kramer, Roger, Leinonen, Kalle, Liu, Yuexin, Miller, Michael, Reynolds, Sheila, Shmulevich, Ilya, Zhang, Wei, Akbani, Rehan, Broom, Bradley M, Hegde, Apurva M, Ju, Zhenlin, Kanchi, Rupa S, Korkut, Anil, Li, Jun, Liang, Han, Ling, Shiyun, Liu, Wenbin, Lu, Yiling, Mills, Gordon B, Ng, Kwok-Shing, Rao, Arvind, Ryan, Michael, Wang, Jing, Weinstein, John N, Zhang, Jiexin, Abeshouse, Adam, Armenia, Joshua, Chakravarty, Debyani, Chatila, Walid K, de Bruijn, Ino, Gao, Jianjiong, Gross, Benjamin E, Heins, Zachary J, Kundra, Ritika, La, Konnor, Ladanyi, Marc, Luna, Augustin, Nissan, Moriah G, Ochoa, Angelica, Phillips, Sarah M, Reznik, Ed, Sanchez-Vega, Francisco, Sander, Chris, Schultz, Nikolaus, Sheridan, Robert, and Sumer, S Onur

Subjects
Biological Sciences, Bioinformatics and Computational Biology, Biomedical and Clinical Sciences, Oncology and Carcinogenesis, Genetics, Cancer, Cancer Genomics, Precision Medicine, Networking and Information Technology R&D (NITRD), Women's Health, Human Genome, Biotechnology, Good Health and Well Being, Databases, Genetic, Genomics, Humans, Kaplan-Meier Estimate, Neoplasms, Proportional Hazards Models, Cancer Genome Atlas Research Network, Cox proportional hazards regression model, TCGA, The Cancer Genome Atlas, clinical data resource, disease-free interval, disease-specific survival, follow-up time, overall survival, progression-free interval, translational research, Medical and Health Sciences, Developmental Biology, Biological sciences, Biomedical and clinical sciences
Abstract

For a decade, The Cancer Genome Atlas (TCGA) program collected clinicopathologic annotation data along with multi-platform molecular profiles of more than 11,000 human tumors across 33 different cancer types. TCGA clinical data contain key features representing the democratized nature of the data collection process. To ensure proper use of this large clinical dataset associated with genomic features, we developed a standardized dataset named the TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR), which includes four major clinical outcome endpoints. In addition to detailing major challenges and statistical limitations encountered during the effort of integrating the acquired clinical data, we present a summary that includes endpoint usage recommendations for each cancer type. These TCGA-CDR findings appear to be consistent with cancer genomics studies independent of the TCGA effort and provide opportunities for investigating cancer biology using clinical correlates at an unprecedented scale.

Published
2018

39. Genomic and Molecular Landscape of DNA Damage Repair Deficiency across The Cancer Genome Atlas

Author

Knijnenburg, Theo A, Wang, Linghua, Zimmermann, Michael T, Chambwe, Nyasha, Gao, Galen F, Cherniack, Andrew D, Fan, Huihui, Shen, Hui, Way, Gregory P, Greene, Casey S, Liu, Yuexin, Akbani, Rehan, Feng, Bin, Donehower, Lawrence A, Miller, Chase, Shen, Yang, Karimi, Mostafa, Chen, Haoran, Kim, Pora, Jia, Peilin, Shinbrot, Eve, Zhang, Shaojun, Liu, Jianfang, Hu, Hai, Bailey, Matthew H, Yau, Christina, Wolf, Denise, Zhao, Zhongming, Weinstein, John N, Li, Lei, Ding, Li, Mills, Gordon B, Laird, Peter W, Wheeler, David A, Shmulevich, Ilya, Network, The Cancer Genome Atlas Research, Caesar-Johnson, Samantha J, Demchok, John A, Felau, Ina, Kasapi, Melpomeni, Ferguson, Martin L, Hutter, Carolyn M, Sofia, Heidi J, Tarnuzzer, Roy, Wang, Zhining, Yang, Liming, Zenklusen, Jean C, Zhang, Jiashan, Chudamani, Sudha, Liu, Jia, Lolla, Laxmi, Naresh, Rashi, Pihl, Todd, Sun, Qiang, Wan, Yunhu, Wu, Ye, Cho, Juok, DeFreitas, Timothy, Frazer, Scott, Gehlenborg, Nils, Getz, Gad, Heiman, David I, Kim, Jaegil, Lawrence, Michael S, Lin, Pei, Meier, Sam, Noble, Michael S, Saksena, Gordon, Voet, Doug, Zhang, Hailei, Bernard, Brady, Dhankani, Varsha, Knijnenburg, Theo, Kramer, Roger, Leinonen, Kalle, Miller, Michael, Reynolds, Sheila, Thorsson, Vesteinn, Zhang, Wei, Broom, Bradley M, Hegde, Apurva M, Ju, Zhenlin, Kanchi, Rupa S, Korkut, Anil, Li, Jun, Liang, Han, Ling, Shiyun, Liu, Wenbin, Lu, Yiling, Ng, Kwok-Shing, Rao, Arvind, Ryan, Michael, Wang, Jing, and Zhang, Jiexin

Subjects
Biological Sciences, Bioinformatics and Computational Biology, Genetics, Rare Diseases, Ovarian Cancer, Cancer, Human Genome, Cancer Genomics, Orphan Drug, Women's Health, 2.1 Biological and endogenous factors, Cell Line, Tumor, DNA Damage, Gene Silencing, Genome, Human, Humans, Loss of Heterozygosity, Machine Learning, Mutation, Neoplasms, Recombinational DNA Repair, Tumor Suppressor Proteins, Cancer Genome Atlas Research Network, DNA damage footprints, DNA damage repair, The Cancer Genome Atlas PanCanAtlas project, epigenetic silencing, integrative statistical analysis, mutational signatures, protein structure analysis, somatic copy-number alterations, somatic mutations, Biochemistry and Cell Biology, Medical Physiology, Biological sciences
Abstract

DNA damage repair (DDR) pathways modulate cancer risk, progression, and therapeutic response. We systematically analyzed somatic alterations to provide a comprehensive view of DDR deficiency across 33 cancer types. Mutations with accompanying loss of heterozygosity were observed in over 1/3 of DDR genes, including TP53 and BRCA1/2. Other prevalent alterations included epigenetic silencing of the direct repair genes EXO5, MGMT, and ALKBH3 in ∼20% of samples. Homologous recombination deficiency (HRD) was present at varying frequency in many cancer types, most notably ovarian cancer. However, in contrast to ovarian cancer, HRD was associated with worse outcomes in several other cancers. Protein structure-based analyses allowed us to predict functional consequences of rare, recurrent DDR mutations. A new machine-learning-based classifier developed from gene expression data allowed us to identify alterations that phenocopy deleterious TP53 mutations. These frequent DDR gene alterations in many human cancers have functional consequences that may determine cancer progression and guide therapy.

Published
2018

40. Scalable Open Science Approach for Mutation Calling of Tumor Exomes Using Multiple Genomic Pipelines

Author

Ellrott, Kyle, Bailey, Matthew H, Saksena, Gordon, Covington, Kyle R, Kandoth, Cyriac, Stewart, Chip, Hess, Julian, Ma, Chiotti, Kami E, McLellan, Michael, Sofia, Heidi J, Hutter, Carolyn, Getz, Gad, Wheeler, David, Ding, Li, Group, MC3 Working, Network, The Cancer Genome Atlas Research, Caesar-Johnson, Samantha J, Demchok, John A, Felau, Ina, Kasapi, Melpomeni, Ferguson, Martin L, Hutter, Carolyn M, Tarnuzzer, Roy, Wang, Zhining, Yang, Liming, Zenklusen, Jean C, Zhang, Jiashan, Chudamani, Sudha, Liu, Jia, Lolla, Laxmi, Naresh, Rashi, Pihl, Todd, Sun, Qiang, Wan, Yunhu, Wu, Ye, Cho, Juok, DeFreitas, Timothy, Frazer, Scott, Gehlenborg, Nils, Heiman, David I, Kim, Jaegil, Lawrence, Michael S, Lin, Pei, Meier, Sam, Noble, Michael S, Voet, Doug, Zhang, Hailei, Bernard, Brady, Chambwe, Nyasha, Dhankani, Varsha, Knijnenburg, Theo, Kramer, Roger, Leinonen, Kalle, Liu, Yuexin, Miller, Michael, Reynolds, Sheila, Shmulevich, Ilya, Thorsson, Vesteinn, Zhang, Wei, Akbani, Rehan, Broom, Bradley M, Hegde, Apurva M, Ju, Zhenlin, Kanchi, Rupa S, Korkut, Anil, Li, Jun, Liang, Han, Ling, Shiyun, Liu, Wenbin, Lu, Yiling, Mills, Gordon B, Ng, Kwok-Shing, Rao, Arvind, Ryan, Michael, Wang, Jing, Weinstein, John N, Zhang, Jiexin, Abeshouse, Adam, Armenia, Joshua, Chakravarty, Debyani, Chatila, Walid K, de Bruijn, Ino, Gao, Jianjiong, Gross, Benjamin E, Heins, Zachary J, Kundra, Ritika, La, Konnor, Ladanyi, Marc, Luna, Augustin, Nissan, Moriah G, Ochoa, Angelica, Phillips, Sarah M, Reznik, Ed, Sanchez-Vega, Francisco, Sander, Chris, and Schultz, Nikolaus

Subjects
Biological Sciences, Bioinformatics and Computational Biology, Genetics, Rare Diseases, Cancer Genomics, Human Genome, Cancer, Genetic Testing, Networking and Information Technology R&D (NITRD), 2.1 Biological and endogenous factors, Good Health and Well Being, Algorithms, Exome, Genomics, High-Throughput Nucleotide Sequencing, Humans, Information Dissemination, Mutation, Neoplasms, Sequence Analysis, DNA, Software, Exome Sequencing, MC3 Working Group, Cancer Genome Atlas Research Network, PanCanAtlas project, TCGA, large-scale, open science, pan-cancer, reproducible computing, somatic mutation calling, Biochemistry and Cell Biology, Biochemistry and cell biology
Abstract

The Cancer Genome Atlas (TCGA) cancer genomics dataset includes over 10,000 tumor-normal exome pairs across 33 different cancer types, in total >400 TB of raw data files requiring analysis. Here we describe the Multi-Center Mutation Calling in Multiple Cancers project, our effort to generate a comprehensive encyclopedia of somatic mutation calls for the TCGA data to enable robust cross-tumor-type analyses. Our approach accounts for variance and batch effects introduced by the rapid advancement of DNA extraction, hybridization-capture, sequencing, and analysis methods over time. We present best practices for applying an ensemble of seven mutation-calling algorithms with scoring and artifact filtering. The dataset created by this analysis includes 3.5 million somatic variants and forms the basis for PanCan Atlas papers. The results have been made available to the research community along with the methods used to generate them. This project is the result of collaboration from a number of institutes and demonstrates how team science drives extremely large genomics projects.

Published
2018

41. Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas

Author

Schaub, Franz X, Dhankani, Varsha, Berger, Ashton C, Trivedi, Mihir, Richardson, Anne B, Shaw, Reid, Zhao, Wei, Zhang, Xiaoyang, Ventura, Andrea, Liu, Yuexin, Ayer, Donald E, Hurlin, Peter J, Cherniack, Andrew D, Eisenman, Robert N, Bernard, Brady, Grandori, Carla, Network, The Cancer Genome Atlas, Caesar-Johnson, Samantha J, Demchok, John A, Felau, Ina, Kasapi, Melpomeni, Ferguson, Martin L, Hutter, Carolyn M, Sofia, Heidi J, Tarnuzzer, Roy, Wang, Zhining, Yang, Liming, Zenklusen, Jean C, Zhang, Jiashan, Chudamani, Sudha, Liu, Jia, Lolla, Laxmi, Naresh, Rashi, Pihl, Todd, Sun, Qiang, Wan, Yunhu, Wu, Ye, Cho, Juok, DeFreitas, Timothy, Frazer, Scott, Gehlenborg, Nils, Getz, Gad, Heiman, David I, Kim, Jaegil, Lawrence, Michael S, Lin, Pei, Meier, Sam, Noble, Michael S, Saksena, Gordon, Voet, Doug, Zhang, Hailei, Chambwe, Nyasha, Knijnenburg, Theo, Kramer, Roger, Leinonen, Kalle, Miller, Michael, Reynolds, Sheila, Shmulevich, Ilya, Thorsson, Vesteinn, Zhang, Wei, Akbani, Rehan, Broom, Bradley M, Hegde, Apurva M, Ju, Zhenlin, Kanchi, Rupa S, Korkut, Anil, Li, Jun, Liang, Han, Ling, Shiyun, Liu, Wenbin, Lu, Yiling, Mills, Gordon B, Ng, Kwok-Shing, Rao, Arvind, Ryan, Michael, Wang, Jing, Weinstein, John N, Zhang, Jiexin, Abeshouse, Adam, Armenia, Joshua, Chakravarty, Debyani, Chatila, Walid K, de Bruijn, Ino, Gao, Jianjiong, Gross, Benjamin E, Heins, Zachary J, Kundra, Ritika, La, Konnor, Ladanyi, Marc, Luna, Augustin, Nissan, Moriah G, Ochoa, Angelica, Phillips, Sarah M, Reznik, Ed, Sanchez-Vega, Francisco, Sander, Chris, and Schultz, Nikolaus

Subjects
Biological Sciences, Bioinformatics and Computational Biology, Cancer Genomics, Cancer, Human Genome, Genetics, Biotechnology, 2.1 Biological and endogenous factors, Basic Helix-Loop-Helix Leucine Zipper Transcription Factors, Basic Helix-Loop-Helix Transcription Factors, Biomarkers, Tumor, Carcinogenesis, Chromatin, Computational Biology, Genes, myc, Genomics, Humans, Neoplasms, Oncogenes, Proteomics, Proto-Oncogene Proteins c-myc, Repressor Proteins, Signal Transduction, Transcription Factors, Cancer Genome Atlas Network, MAX, MNT, MYC genomic alterations, TCGA, The Cancer Genome Atlas, Biochemistry and Cell Biology, Biochemistry and cell biology
Abstract

Although the MYC oncogene has been implicated in cancer, a systematic assessment of alterations of MYC, related transcription factors, and co-regulatory proteins, forming the proximal MYC network (PMN), across human cancers is lacking. Using computational approaches, we define genomic and proteomic features associated with MYC and the PMN across the 33 cancers of The Cancer Genome Atlas. Pan-cancer, 28% of all samples had at least one of the MYC paralogs amplified. In contrast, the MYC antagonists MGA and MNT were the most frequently mutated or deleted members, proposing a role as tumor suppressors. MYC alterations were mutually exclusive with PIK3CA, PTEN, APC, or BRAF alterations, suggesting that MYC is a distinct oncogenic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such as immune response and growth factor signaling; chromatin, translation, and DNA replication/repair were conserved pan-cancer. This analysis reveals insights into MYC biology and is a reference for biomarkers and therapeutics for cancers with alterations of MYC or the PMN.

Published
2018

42. A genome-wide association study suggests correlations of common genetic variants with peritoneal solute transfer rates in patients with kidney failure receiving peritoneal dialysis

Author

Pisoni, Ronald, Robinson, Bruce, Johnson, David, Cho, Yeoungjee, Wong, Muh Geot, Mather, Amanda, Cooper, Bruce, Devuyst, Olivier, Morelle, Johann, Goffin, Eric, Bammens, Bert, Bovy, Philippe, Margetts, Peter, Perl, Jeffrey, Taylor, Paul, Jain, Arsh, Jassal, Vanita, Stenvinkel, Peter, Heimburger, Olof, Kuan, Ying, Harron, Camille, Dasgupta, Indranil, Stoves, John, Akbani, Habib, Abeygunasekara, Sumith, Sharples, Edward, Mead, Paul, Hayat, Amer, Morgan, Neal, Cramp, Hilary, Robertson, Susan, Fielding, Richard, Brown, Edwina, Collinson, Helen, Ande, Pravene, Doulton, Tim, MacDougall, Iain, Cairns, Hugh, Vilar, Enric, Vardhan, Anand, Chess, James, Sandhu, Kanwaljit, Wilkie, Martin, McHaffie, Gavin, Lewis, Robert, Kamesh, Lavanya, Buck, Kate, Peel, Robert, Taylor, Jo, Johnston, Paul, Leung, Janson, Bingham, Coralie, Anijeet, Hameed, Asghar, Ramzana, Ranakrishna, Satish, Nair, Sunita, Iggo, Neil, Lewis, David, Udayaraj, Uday, Dawson, Susan, Woordrow, Graham, Chandrasekar, Thangavelu, Hamer, Rizwan, Barratt, Jonathan, Baines, Richard, Davies, Simon, Donovan, Kieron, Jones, Colin, Ynares, Christina, Dukes, Carl, Imam, Talha H., Corapi, Kristin, Nigwekar, Sagar, Khawar, Osman, Weiner, Daniel, Lau, Wei Ling, Harley, Kevin, Ghaffari, Arshia, Saxena, Ramesh, Abraham, Josephine, Mehrotra, Rajnish, Himmelfarb, Jonathan, Cavanaugh, Kerri L., Golper, Thomas A., Burkart, John M., Pirkle, James L., Miller, Brent, Jang, Judy, Turner, Jeffrey, Stanaway, Ian B., Jarvik, Gail P., Lambie, Mark, Johnson, David W., and Davies, Simon J.

Published
2021
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43. The Integrated Genomic Landscape of Thymic Epithelial Tumors

Author

Radovich, Milan, Pickering, Curtis R, Felau, Ina, Ha, Gavin, Zhang, Hailei, Jo, Heejoon, Hoadley, Katherine A, Anur, Pavana, Zhang, Jiexin, McLellan, Mike, Bowlby, Reanne, Matthew, Thomas, Danilova, Ludmila, Hegde, Apurva M, Kim, Jaegil, Leiserson, Mark DM, Sethi, Geetika, Lu, Charles, Ryan, Michael, Su, Xiaoping, Cherniack, Andrew D, Robertson, Gordon, Akbani, Rehan, Spellman, Paul, Weinstein, John N, Hayes, D Neil, Raphael, Ben, Lichtenberg, Tara, Leraas, Kristen, Zenklusen, Jean Claude, Network, The Cancer Genome Atlas, Ally, Adrian, Appelbaum, Elizabeth L, Auman, J Todd, Balasundaram, Miruna, Balu, Saianand, Behera, Madhusmita, Beroukhim, Rameen, Berrios, Mario, Blandino, Giovanni, Bodenheimer, Tom, Bootwalla, Moiz S, Bowen, Jay, Brooks, Denise, Carcano, Flavio M, Carlsen, Rebecca, Carvalho, Andre L, Castro, Patricia, Chalabreysse, Lara, Chin, Lynda, Cho, Juok, Choe, Gina, Chuah, Eric, Chudamani, Sudha, Cibulskis, Carrie, Cope, Leslie, Cordes, Matthew G, Crain, Daniel, Curley, Erin, Defreitas, Timothy, Demchok, John A, Detterbeck, Frank, Dhalla, Noreen, Dienemann, Hendrik, Edenfield, W Jeff, Facciolo, Francesco, Ferguson, Martin L, Frazer, Scott, Fronick, Catrina C, Fulton, Lucinda A, Fulton, Robert S, Gabriel, Stacey B, Gardner, Johanna, Gastier-Foster, Julie M, Gehlenborg, Nils, Gerken, Mark, Getz, Gad, Heiman, David I, Hobensack, Shital, Holbrook, Andrea, Holt, Robert A, Hoyle, Alan P, Hutter, Carolyn M, Ittmann, Michael, Jefferys, Stuart R, Jones, Corbin D, Jones, Steven JM, Kasaian, Katayoon, Kimes, Patrick K, Lai, Phillip H, Laird, Peter W, Lawrence, Michael S, Lin, Pei, Liu, Jia, Lolla, Laxmi, Lu, Yiling, Ma, Yussanne, Maglinte, Dennis T, and Mallery, David

Subjects
Biological Sciences, Biomedical and Clinical Sciences, Genetics, Biotechnology, Human Genome, Cancer, Rare Diseases, Autoimmune Disease, 2.1 Biological and endogenous factors, Inflammatory and immune system, Adolescent, Adult, Aged, Aged, 80 and over, Female, Genomics, Humans, Male, Middle Aged, Mutation, Neoplasms, Glandular and Epithelial, Thymoma, Thymus Neoplasms, Transcription Factors, TFII, Young Adult, Cancer Genome Atlas Network, TCGA, autoimmunity, genomics, myasthenia gravis, proteomics, thymic carcinoma, thymic epithelial tumors, thymoma, transcriptomics, Neurosciences, Oncology and Carcinogenesis, Oncology & Carcinogenesis, Biochemistry and cell biology, Oncology and carcinogenesis
Abstract

Thymic epithelial tumors (TETs) are one of the rarest adult malignancies. Among TETs, thymoma is the most predominant, characterized by a unique association with autoimmune diseases, followed by thymic carcinoma, which is less common but more clinically aggressive. Using multi-platform omics analyses on 117 TETs, we define four subtypes of these tumors defined by genomic hallmarks and an association with survival and World Health Organization histological subtype. We further demonstrate a marked prevalence of a thymoma-specific mutated oncogene, GTF2I, and explore its biological effects on multi-platform analysis. We further observe enrichment of mutations in HRAS, NRAS, and TP53. Last, we identify a molecular link between thymoma and the autoimmune disease myasthenia gravis, characterized by tumoral overexpression of muscle autoantigens, and increased aneuploidy.

Published
2018

44. A Case Report of Extensively Drug Resistant Typhoid in Karachi, Pakistan: A Major Health Concern to Curb the Outbreak

Author

Sarah Kamran Akbani and Fazeela Bibi

Subjects
Medicine
Abstract

The disease burden of extensively drug resistant typhoid in developing countries is a major emerging issue that cannot be ignored. Since its emergence from multidrug strains, the majority of typhoid cases in Karachi, Pakistan, have been extensively drug resistant, mostly infecting younger patients. In the study, the authors analysed one such case in an adolescent male and discussed how, by the implementation of national health policies, the spread of these infectious diseases could be prevented and the overall burden on the healthcare system decreased in areas with already limited resources.

Published
2021
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45. Comprehensive and Integrated Genomic Characterization of Adult Soft Tissue Sarcomas

Author

Network, The Cancer Genome Atlas Research, Abeshouse, Adam, Adebamowo, Clement, Adebamowo, Sally N, Akbani, Rehan, Akeredolu, Teniola, Ally, Adrian, Anderson, Matthew L, Anur, Pavana, Appelbaum, Elizabeth L, Armenia, Joshua, Auman, J Todd, Bailey, Matthew H, Baker, Laurence, Balasundaram, Miruna, Balu, Saianand, Barthel, Floris P, Bartlett, John, Baylin, Stephen B, Behera, Madhusmita, Belyaev, Dmitry, Bennett, Joesph, Benz, Christopher, Beroukhim, Rameen, Birrer, Michael, Bocklage, Thèrése, Bodenheimer, Tom, Boice, Lori, Bootwalla, Moiz S, Bowen, Jay, Bowlby, Reanne, Boyd, Jeff, Brohl, Andrew S, Brooks, Denise, Byers, Lauren, Carlsen, Rebecca, Castro, Patricia, Chen, Hsiao-Wei, Cherniack, Andrew D, Chibon, Fréderic, Chin, Lynda, Cho, Juok, Chuah, Eric, Chudamani, Sudha, Cibulskis, Carrie, Cooper, Lee AD, Cope, Leslie, Cordes, Matthew G, Crain, Daniel, Curley, Erin, Danilova, Ludmila, Dao, Fanny, Davis, Ian J, Davis, Lara E, Defreitas, Timothy, Delman, Keith, Demchok, John A, Demetri, George D, Demicco, Elizabeth G, Dhalla, Noreen, Diao, Lixia, Ding, Li, DiSaia, Phil, Dottino, Peter, Doyle, Leona A, Drill, Esther, Dubina, Michael, Eschbacher, Jennifer, Fedosenko, Konstantin, Felau, Ina, Ferguson, Martin L, Frazer, Scott, Fronick, Catrina C, Fulidou, Victoria, Fulton, Lucinda A, Fulton, Robert S, Gabriel, Stacey B, Gao, Jianjiong, Gao, Qingsong, Gardner, Johanna, Gastier-Foster, Julie M, Gay, Carl M, Gehlenborg, Nils, Gerken, Mark, Getz, Gad, Godwin, Andrew K, Godwin, Eryn M, Gordienko, Elena, Grilley-Olson, Juneko E, Gutman, David A, Gutmann, David H, Hayes, D Neil, Hegde, Apurva M, Heiman, David I, Heins, Zachary, Helsel, Carmen, Hepperla, Austin J, Higgins, Kelly, Hoadley, Katherine A, and Hobensack, Shital

Subjects
Biological Sciences, Biomedical and Clinical Sciences, Genetics, Oncology and Carcinogenesis, Biotechnology, Orphan Drug, Cancer Genomics, Human Genome, Rare Diseases, Cancer, 2.1 Biological and endogenous factors, Adult, Aged, Aged, 80 and over, Cluster Analysis, DNA Copy Number Variations, Epigenomics, Genome, Human, Genome-Wide Association Study, Humans, Middle Aged, Mutation, Sarcoma, Young Adult, Cancer Genome Atlas Research Network. Electronic address: elizabeth.demicco@sinaihealthsystem.ca, Cancer Genome Atlas Research Network, DNA methylation, The Cancer Genome Atlas, dedifferentiated liposarcoma, genomics, immune infiltration, leiomyosarcoma, molecular subtype, myxofibrosarcoma, pleomorphism, undifferentiated pleomorphic sarcoma, Medical and Health Sciences, Developmental Biology, Biological sciences, Biomedical and clinical sciences
Abstract

Sarcomas are a broad family of mesenchymal malignancies exhibiting remarkable histologic diversity. We describe the multi-platform molecular landscape of 206 adult soft tissue sarcomas representing 6 major types. Along with novel insights into the biology of individual sarcoma types, we report three overarching findings: (1) unlike most epithelial malignancies, these sarcomas (excepting synovial sarcoma) are characterized predominantly by copy-number changes, with low mutational loads and only a few genes (TP53, ATRX, RB1) highly recurrently mutated across sarcoma types; (2) within sarcoma types, genomic and regulomic diversity of driver pathways defines molecular subtypes associated with patient outcome; and (3) the immune microenvironment, inferred from DNA methylation and mRNA profiles, associates with outcome and may inform clinical trials of immune checkpoint inhibitors. Overall, this large-scale analysis reveals previously unappreciated sarcoma-type-specific changes in copy number, methylation, RNA, and protein, providing insights into refining sarcoma therapy and relationships to other cancer types.

Published
2017

46. Comprehensive Molecular Characterization of Muscle-Invasive Bladder Cancer

Author

Robertson, A Gordon, Kim, Jaegil, Al-Ahmadie, Hikmat, Bellmunt, Joaquim, Guo, Guangwu, Cherniack, Andrew D, Hinoue, Toshinori, Laird, Peter W, Hoadley, Katherine A, Akbani, Rehan, Castro, Mauro AA, Gibb, Ewan A, Kanchi, Rupa S, Gordenin, Dmitry A, Shukla, Sachet A, Sanchez-Vega, Francisco, Hansel, Donna E, Czerniak, Bogdan A, Reuter, Victor E, Su, Xiaoping, Carvalho, Benilton de Sa, Chagas, Vinicius S, Mungall, Karen L, Sadeghi, Sara, Pedamallu, Chandra Sekhar, Lu, Yiling, Klimczak, Leszek J, Zhang, Jiexin, Choo, Caleb, Ojesina, Akinyemi I, Bullman, Susan, Leraas, Kristen M, Lichtenberg, Tara M, Wu, Catherine J, Schultz, Nicholaus, Getz, Gad, Meyerson, Matthew, Mills, Gordon B, McConkey, David J, Network, TCGA Research, Albert, Monique, Alexopoulou, Iakovina, Ally, Adrian, Antic, Tatjana, Aron, Manju, Balasundaram, Miruna, Bartlett, John, Baylin, Stephen B, Beaver, Allison, Birol, Inanc, Boice, Lori, Bootwalla, Moiz S, Bowen, Jay, Bowlby, Reanne, Brooks, Denise, Broom, Bradley M, Bshara, Wiam, Burks, Eric, Cárcano, Flavio M, Carlsen, Rebecca, Carvalho, Benilton S, Carvalho, Andre L, Castle, Eric P, Castro, Patricia, Catto, James W, Chesla, David W, Chuah, Eric, Chudamani, Sudha, Cortessis, Victoria K, Cottingham, Sandra L, Crain, Daniel, Curley, Erin, Daneshmand, Siamak, Demchok, John A, Dhalla, Noreen, Djaladat, Hooman, Eckman, John, Egea, Sophie C, Engel, Jay, Felau, Ina, Ferguson, Martin L, Gardner, Johanna, Gastier-Foster, Julie M, Gerken, Mark, Gomez-Fernandez, Carmen R, and Harr, Jodi

Subjects
Biomedical and Clinical Sciences, Oncology and Carcinogenesis, Biotechnology, Genetics, Urologic Diseases, Cancer Genomics, Human Genome, Cancer, 2.1 Biological and endogenous factors, Aged, Cluster Analysis, DNA Methylation, Humans, MicroRNAs, Middle Aged, Muscle, Smooth, RNA, Long Noncoding, Survival Analysis, Urinary Bladder, Urinary Bladder Neoplasms, TCGA Research Network, APOBEC mutation, DNA methylation, basal mRNA subtype, lncRNA transcriptome, luminal mRNA subtype, microRNA, muscle-invasive bladder cancer, neoantigen, neuronal subtype, regulon, Biological Sciences, Medical and Health Sciences, Developmental Biology, Biological sciences, Biomedical and clinical sciences
Abstract

We report a comprehensive analysis of 412 muscle-invasive bladder cancers characterized by multiple TCGA analytical platforms. Fifty-eight genes were significantly mutated, and the overall mutational load was associated with APOBEC-signature mutagenesis. Clustering by mutation signature identified a high-mutation subset with 75% 5-year survival. mRNA expression clustering refined prior clustering analyses and identified a poor-survival "neuronal" subtype in which the majority of tumors lacked small cell or neuroendocrine histology. Clustering by mRNA, long non-coding RNA (lncRNA), and miRNA expression converged to identify subsets with differential epithelial-mesenchymal transition status, carcinoma in situ scores, histologic features, and survival. Our analyses identified 5 expression subtypes that may stratify response to different treatments.

Published
2017

47. Integrative Analysis Identifies Four Molecular and Clinical Subsets in Uveal Melanoma

Author

Robertson, A Gordon, Shih, Juliann, Yau, Christina, Gibb, Ewan A, Oba, Junna, Mungall, Karen L, Hess, Julian M, Uzunangelov, Vladislav, Walter, Vonn, Danilova, Ludmila, Lichtenberg, Tara M, Kucherlapati, Melanie, Kimes, Patrick K, Tang, Ming, Penson, Alexander, Babur, Ozgun, Akbani, Rehan, Bristow, Christopher A, Hoadley, Katherine A, Iype, Lisa, Chang, Matthew T, Network, TCGA Research, Abdel-Rahman, Mohamed H, Ally, Adrian, Auman, J Todd, Balasundaram, Miruna, Balu, Saianand, Benz, Christopher, Beroukhim, Rameen, Birol, Inanc, Bodenheimer, Tom, Bowen, Jay, Bowlby, Reanne, Brooks, Denise, Carlsen, Rebecca, Cebulla, Colleen M, Cherniack, Andrew D, Chin, Lynda, Cho, Juok, Chuah, Eric, Chudamani, Sudha, Cibulskis, Carrie, Cibulskis, Kristian, Cope, Leslie, Coupland, Sarah E, Defreitas, Timothy, Demchok, John A, Desjardins, Laurence, Dhalla, Noreen, Esmaeli, Bita, Felau, Ina, Ferguson, Martin L, Frazer, Scott, Gabriel, Stacey B, Gastier-Foster, Julie M, Gehlenborg, Nils, Gerken, Mark, Gershenwald, Jeffrey E, Getz, Gad, Griewank, Klaus G, Grimm, Elizabeth A, Hayes, D Neil, Hegde, Apurva M, Heiman, David I, Helsel, Carmen, Hobensack, Shital, Holt, Robert A, Hoyle, Alan P, Hu, Xin, Hutter, Carolyn M, Jager, Martine J, Jefferys, Stuart R, Jones, Corbin D, Jones, Steven JM, Kandoth, Cyriac, Kasaian, Katayoon, Kim, Jaegil, Kucherlapati, Raju, Lander, Eric, Lawrence, Michael S, Lazar, Alexander J, Lee, Semin, Leraas, Kristen M, Lin, Pei, Liu, Jia, Liu, Wenbin, Lolla, Laxmi, and Lu, Yiling

Subjects
Biological Sciences, Biomedical and Clinical Sciences, Genetics, Human Genome, Rare Diseases, Eye Disease and Disorders of Vision, Cancer, Biomarkers, Tumor, DNA Copy Number Variations, DNA Methylation, Eukaryotic Initiation Factor-1, Gene Expression Regulation, Neoplastic, Humans, Melanoma, Monosomy, Mutation, Phosphoproteins, Prognosis, RNA Splicing Factors, Serine-Arginine Splicing Factors, Tumor Suppressor Proteins, Ubiquitin Thiolesterase, Uveal Neoplasms, TCGA Research Network, EIF1AX, GNA11, GNAQ, SF3B1, SRSF2, TCGA, molecular subtypes, monosomy 3, noncoding RNA, uveal melanoma, Neurosciences, Oncology and Carcinogenesis, Oncology & Carcinogenesis, Biochemistry and cell biology, Oncology and carcinogenesis
Abstract

Comprehensive multiplatform analysis of 80 uveal melanomas (UM) identifies four molecularly distinct, clinically relevant subtypes: two associated with poor-prognosis monosomy 3 (M3) and two with better-prognosis disomy 3 (D3). We show that BAP1 loss follows M3 occurrence and correlates with a global DNA methylation state that is distinct from D3-UM. Poor-prognosis M3-UM divide into subsets with divergent genomic aberrations, transcriptional features, and clinical outcomes. We report change-of-function SRSF2 mutations. Within D3-UM, EIF1AX- and SRSF2/SF3B1-mutant tumors have distinct somatic copy number alterations and DNA methylation profiles, providing insight into the biology of these low- versus intermediate-risk clinical mutation subtypes.

Published
2017

48. Integrated Molecular Characterization of Uterine Carcinosarcoma

Author

Cherniack, Andrew D, Shen, Hui, Walter, Vonn, Stewart, Chip, Murray, Bradley A, Bowlby, Reanne, Hu, Xin, Ling, Shiyun, Soslow, Robert A, Broaddus, Russell R, Zuna, Rosemary E, Robertson, Gordon, Laird, Peter W, Kucherlapati, Raju, Mills, Gordon B, Network, The Cancer Genome Atlas Research, Akbani, Rehan, Ally, Adrian, Auman, J Todd, Balasundaram, Miruna, Balu, Saianand, Baylin, Stephen B, Beroukhim, Rameen, Bodenheimer, Tom, Bogomolniy, Faina, Boice, Lori, Bootwalla, Moiz S, Bowen, Jay, Broaddus, Russell, Brooks, Denise, Carlsen, Rebecca, Cho, Juok, Chuah, Eric, Chudamani, Sudha, Cibulskis, Kristian, Cline, Melissa, Dao, Fanny, David, Mutch, Demchok, John A, Dhalla, Noreen, Dowdy, Sean, Felau, Ina, Ferguson, Martin L, Frazer, Scott, Frick, Jessica, Gabriel, Stacey, Gastier-Foster, Julie M, Gehlenborg, Nils, Gerken, Mark, Getz, Gad, Gupta, Manaswi, Haussler, David, Hayes, D Neil, Heiman, David I, Hess, Julian, Hoadley, Katherine A, Hoffmann, Robert, Holt, Robert A, Hoyle, Alan P, Huang, Mei, Hutter, Carolyn M, Jefferys, Stuart R, Jones, Steven JM, Jones, Corbin D, Kanchi, Rupa S, Kandoth, Cyriac, Kasaian, Katayoon, Kerr, Sarah, Kim, Jaegil, Lai, Phillip H, Lander, Eric, Lawrence, Michael S, Lee, Darlene, Leraas, Kristen M, Leshchiner, Ignaty, Levine, Douglas A, Lichtenberg, Tara M, Lin, Pei, Liu, Jia, Liu, Wenbin, Liu, Yuexin, Lolla, Laxmi, Lu, Yiling, Ma, Yussanne, Maglinte, Dennis T, Marra, Marco A, Mayo, Michael, Meng, Shaowu, Meyerson, Matthew, Mieczkowski, Piotr A, Moore, Richard A, Mose, Lisle E, Mungall, Andrew J, and Mungall, Karen

Subjects
Biological Sciences, Bioinformatics and Computational Biology, Biomedical and Clinical Sciences, Genetics, Oncology and Carcinogenesis, Reproductive Medicine, Human Genome, Biotechnology, Rare Diseases, Cancer Genomics, Women's Health, Cancer, Precision Medicine, 2.1 Biological and endogenous factors, Carcinosarcoma, DNA Copy Number Variations, Epithelial-Mesenchymal Transition, Female, Humans, Mutation, Uterine Neoplasms, Cancer Genome Atlas Research Network, EMT, TGGA, The Cancer Genome Atlas, UCS, endometrial cancer, epithelial-to-mesenchymal transition, gynecologic cancer, gynecologic oncology, translational science, uterine carcinosarcoma, Neurosciences, Oncology & Carcinogenesis, Biochemistry and cell biology, Oncology and carcinogenesis
Abstract

We performed genomic, epigenomic, transcriptomic, and proteomic characterizations of uterine carcinosarcomas (UCSs). Cohort samples had extensive copy-number alterations and highly recurrent somatic mutations. Frequent mutations were found in TP53, PTEN, PIK3CA, PPP2R1A, FBXW7, and KRAS, similar to endometrioid and serous uterine carcinomas. Transcriptome sequencing identified a strong epithelial-to-mesenchymal transition (EMT) gene signature in a subset of cases that was attributable to epigenetic alterations at microRNA promoters. The range of EMT scores in UCS was the largest among all tumor types studied via The Cancer Genome Atlas. UCSs shared proteomic features with gynecologic carcinomas and sarcomas with intermediate EMT features. Multiple somatic mutations and copy-number alterations in genes that are therapeutic targets were identified.

Published
2017

49. Integrative Genomic Analysis of Cholangiocarcinoma Identifies Distinct IDH-Mutant Molecular Profiles

Author

Farshidfar, Farshad, Zheng, Siyuan, Gingras, Marie-Claude, Newton, Yulia, Shih, Juliann, Robertson, A Gordon, Hinoue, Toshinori, Hoadley, Katherine A, Gibb, Ewan A, Roszik, Jason, Covington, Kyle R, Wu, Chia-Chin, Shinbrot, Eve, Stransky, Nicolas, Hegde, Apurva, Yang, Ju Dong, Reznik, Ed, Sadeghi, Sara, Pedamallu, Chandra Sekhar, Ojesina, Akinyemi I, Hess, Julian M, Auman, J Todd, Rhie, Suhn K, Bowlby, Reanne, Borad, Mitesh J, Network, The Cancer Genome Atlas, Akbani, Rehan, Allotey, Loretta K, Ally, Adrian, Alvaro, Domenico, Andersen, Jesper B, Appelbaum, Elizabeth L, Arora, Arshi, Balasundaram, Miruna, Balu, Saianand, Bardeesy, Nabeel, Bathe, Oliver F, Baylin, Stephen B, Beroukhim, Rameen, Berrios, Mario, Bodenheimer, Tom, Boice, Lori, Bootwalla, Moiz S, Bowen, Jay, Bragazzi, Maria Consiglia, Brooks, Denise, Cardinale, Vincenzo, Carlsen, Rebecca, Carpino, Guido, Carvalho, Andre L, Chaiteerakij, Roongruedee, Chandan, Vishal C, Cherniack, Andrew D, Chin, Lynda, Cho, Juok, Choe, Gina, Chuah, Eric, Chudamani, Sudha, Cibulskis, Carrie, Cordes, Matthew G, Crain, Daniel, Curley, Erin, De Rose, Agostino Maria, Defreitas, Timothy, Demchok, John A, Deshpande, Vikram, Dhalla, Noreen, Ding, Li, Evason, Kimberley, Felau, Ina, Ferguson, Martin L, Foo, Wai Chin, Franchitto, Antonio, Frazer, Scott, Fronick, Catrina C, Fulton, Lucinda A, Fulton, Robert S, Gabriel, Stacey B, Gardner, Johanna, Gastier-Foster, Julie M, Gaudio, Eugenio, Gehlenborg, Nils, Genovese, Giannicola, Gerken, Mark, Getz, Gad, Giama, Nasra H, Gibbs, Richard A, Giuliante, Felice, Grazi, Gian Luca, Hayes, D Neil, Hegde, Apurva M, and Heiman, David I

Subjects
Biological Sciences, Bioinformatics and Computational Biology, Genetics, Liver Disease, Cancer Genomics, Cancer, Liver Cancer, Digestive Diseases - (Gallbladder), Digestive Diseases, Human Genome, Rare Diseases, Precision Medicine, 4.1 Discovery and preclinical testing of markers and technologies, 2.1 Biological and endogenous factors, Adult, Aged, Aged, 80 and over, Bile Duct Neoplasms, Cholangiocarcinoma, Chromatin, DNA Methylation, DNA-Binding Proteins, Female, Gene Expression Regulation, Neoplastic, Genomics, Humans, Isocitrate Dehydrogenase, Liver, Liver Neoplasms, Male, Middle Aged, Mitochondria, Mutation, Nuclear Proteins, Pancreatic Neoplasms, Promoter Regions, Genetic, RNA, Long Noncoding, RNA, Messenger, Transcription Factors, Cancer Genome Atlas Network, ARID1A, DNA methylation, IDH, RNA sequencing, TCGA, cholangiocarcinoma, integrative genomics, lncRNAs, multi-omics, whole exome, Biochemistry and Cell Biology, Medical Physiology, Biological sciences
Abstract

Cholangiocarcinoma (CCA) is an aggressive malignancy of the bile ducts, with poor prognosis and limited treatment options. Here, we describe the integrated analysis of somatic mutations, RNA expression, copy number, and DNA methylation by The Cancer Genome Atlas of a set of predominantly intrahepatic CCA cases and propose a molecular classification scheme. We identified an IDH mutant-enriched subtype with distinct molecular features including low expression of chromatin modifiers, elevated expression of mitochondrial genes, and increased mitochondrial DNA copy number. Leveraging the multi-platform data, we observed that ARID1A exhibited DNA hypermethylation and decreased expression in the IDH mutant subtype. More broadly, we found that IDH mutations are associated with an expanded histological spectrum of liver tumors with molecular features that stratify with CCA. Our studies reveal insights into the molecular pathogenesis and heterogeneity of cholangiocarcinoma and provide classification information of potential therapeutic significance.

Published
2017

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