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58 results on '"Lawrence, Michael"'

1. Mesoscale DNA features impact APOBEC3A and APOBEC3B deaminase activity and shape tumor mutational landscapes

Author

Sanchez, Ambrocio, Ortega, Pedro, Sakhtemani, Ramin, Manjunath, Lavanya, Oh, Sunwoo, Bournique, Elodie, Becker, Alexandrea, Kim, Kyumin, Durfee, Cameron, Temiz, Nuri Alpay, Chen, Xiaojiang S, Harris, Reuben S, Lawrence, Michael S, and Buisson, Rémi

Subjects
Biological Sciences, Bioinformatics and Computational Biology, Genetics, Cancer, Aetiology, 2.1 Biological and endogenous factors, Generic health relevance, Humans, Mutation, Neoplasms, Cytidine Deaminase, DNA, Minor Histocompatibility Antigens, Cytosine, Proteins
Abstract

Antiviral DNA cytosine deaminases APOBEC3A and APOBEC3B are major sources of mutations in cancer by catalyzing cytosine-to-uracil deamination. APOBEC3A preferentially targets single-stranded DNAs, with a noted affinity for DNA regions that adopt stem-loop secondary structures. However, the detailed substrate preferences of APOBEC3A and APOBEC3B have not been fully established, and the specific influence of the DNA sequence on APOBEC3A and APOBEC3B deaminase activity remains to be investigated. Here, we find that APOBEC3B also selectively targets DNA stem-loop structures, and they are distinct from those subjected to deamination by APOBEC3A. We develop Oligo-seq, an in vitro sequencing-based method to identify specific sequence contexts promoting APOBEC3A and APOBEC3B activity. Through this approach, we demonstrate that APOBEC3A and APOBEC3B deaminase activity is strongly regulated by specific sequences surrounding the targeted cytosine. Moreover, we identify the structural features of APOBEC3B and APOBEC3A responsible for their substrate preferences. Importantly, we determine that APOBEC3B-induced mutations in hairpin-forming sequences within tumor genomes differ from the DNA stem-loop sequences mutated by APOBEC3A. Together, our study provides evidence that APOBEC3A and APOBEC3B can generate distinct mutation landscapes in cancer genomes, driven by their unique substrate selectivity.

Published
2024

2. Heritable changes in chromatin contacts linked to transgenerational obesity

Author

Blumberg, Bruce, Cheng-An Chang, Richard, Egusquiza, Riann, Amato, Angélica, Li, Zhuorui, Joloya, Erika, Wheeler, Hailey, Nguyen, Angela, Shioda, Keiko, Odajima, Junko, Lawrence, Michael, and Shioda, Toshi

Subjects
Biochemistry and Cell Biology, Biomedical and Clinical Sciences, Biological Sciences, Stem Cell Research - Nonembryonic - Non-Human, Genetics, Stem Cell Research, Obesity, Prevention, Nutrition, Underpinning research, 1.1 Normal biological development and functioning
Abstract

Burgeoning evidence demonstrates that effects of environmental exposures can be transmitted to subsequent generations through the germline without DNA mutations1,2. This phenomenon remains controversial because underlying mechanisms have not been identified. Therefore, understanding how effects of environmental exposures are transmitted to unexposed generations without DNA mutations is a fundamental unanswered question in biology. Here, we used an established murine model of male-specific transgenerational obesity to show that exposure to the obesogen tributyltin (TBT) elicited heritable changes in chromatin interactions (CIs) in primordial germ cells (PGCs). New CIs were formed within the Ide gene encoding Insulin Degrading Enzyme in the directly exposed PGCs, then stably maintained in PGCs of the subsequent (unexposed) two generations. Concomitantly, Ide mRNA expression was decreased in livers of male descendants from the exposed dams. These males were hyperinsulinemic and hyperglycemic, phenocopying Ide-deficient mice that are predisposed to adult-onset, diet-induced obesity. Creation of new CIs in PGCs, suppression of hepatic Ide mRNA, increased fat mass, hyperinsulinemia and hyperglycemia were male-specific. Our results provide a plausible molecular mechanism underlying transmission of the transgenerational predisposition to obesity caused by gestational exposure to an environmental obesogen. They also provide an entry point for future studies aimed at understanding how environmental exposures alter chromatin structure to influence physiology across multiple generations in mammals.

Published
2023

3. An extended APOBEC3A mutation signature in cancer

Author

Langenbucher, Adam, Bowen, Danae, Sakhtemani, Ramin, Bournique, Elodie, Wise, Jillian F, Zou, Lee, Bhagwat, Ashok S, Buisson, Rémi, and Lawrence, Michael S

Subjects
Biological Sciences, Bioinformatics and Computational Biology, Genetics, Human Genome, Cancer, Base Sequence, Cell Line, Tumor, Cell Transformation, Neoplastic, Cytidine Deaminase, DNA, Escherichia coli, HEK293 Cells, Humans, Minor Histocompatibility Antigens, Mutagenesis, Mutation, Neoplasms, Nucleic Acid Conformation, Proteins
Abstract

APOBEC mutagenesis, a major driver of cancer evolution, is known for targeting TpC sites in DNA. Recently, we showed that APOBEC3A (A3A) targets DNA hairpin loops. Here, we show that DNA secondary structure is in fact an orthogonal influence on A3A substrate optimality and, surprisingly, can override the TpC sequence preference. VpC (non-TpC) sites in optimal hairpins can outperform TpC sites as mutational hotspots. This expanded understanding of APOBEC mutagenesis illuminates the genomic Twin Paradox, a puzzling pattern of closely spaced mutation hotspots in cancer genomes, in which one is a canonical TpC site but the other is a VpC site, and double mutants are seen only in trans, suggesting a two-hit driver event. Our results clarify this paradox, revealing that both hotspots in these twins are optimal A3A substrates. Our findings reshape the notion of a mutation signature, highlighting the additive roles played by DNA sequence and DNA structure.

Published
2021

4. Quantification of ongoing APOBEC3A activity in tumor cells by monitoring RNA editing at hotspots

Author

Jalili, Pégah, Bowen, Danae, Langenbucher, Adam, Park, Shinho, Aguirre, Kevin, Corcoran, Ryan B, Fleischman, Angela G, Lawrence, Michael S, Zou, Lee, and Buisson, Rémi

Subjects
Biological Sciences, Biomedical and Clinical Sciences, Oncology and Carcinogenesis, Cancer Genomics, Human Genome, Rare Diseases, Cancer, Genetics, 2.1 Biological and endogenous factors, Cell Line, Cell Line, Tumor, Cytidine Deaminase, Enzyme Assays, Gene Expression Regulation, Neoplastic, HEK293 Cells, Humans, Mutation, Neoplasms, Proteins, RNA Editing, RNA Interference, Exome Sequencing
Abstract

APOBEC3A is a cytidine deaminase driving mutagenesis, DNA replication stress and DNA damage in cancer cells. While the APOBEC3A-induced vulnerability of cancers offers an opportunity for therapy, APOBEC3A protein and mRNA are difficult to quantify in tumors due to their low abundance. Here, we describe a quantitative and sensitive assay to measure the ongoing activity of APOBEC3A in tumors. Using hotspot RNA mutations identified from APOBEC3A-positive tumors and droplet digital PCR, we develop an assay to quantify the RNA-editing activity of APOBEC3A. This assay is superior to APOBEC3A protein- and mRNA-based assays in predicting the activity of APOBEC3A on DNA. Importantly, we demonstrate that the RNA mutation-based APOBEC3A assay is applicable to clinical samples from cancer patients. Our study presents a strategy to follow the dysregulation of APOBEC3A in tumors, providing opportunities to investigate the role of APOBEC3A in tumor evolution and to target the APOBEC3A-induced vulnerability in therapy.

Published
2020

5. Passenger hotspot mutations in cancer driven by APOBEC3A and mesoscale genomic features

Author

Buisson, Rémi, Langenbucher, Adam, Bowen, Danae, Kwan, Eugene E, Benes, Cyril H, Zou, Lee, and Lawrence, Michael S

Subjects
Biological Sciences, Bioinformatics and Computational Biology, Cancer, Human Genome, Genetics, Biotechnology, Stem Cell Research, Cell Transformation, Neoplastic, Computational Biology, Cytidine Deaminase, Genomics, HEK293 Cells, Humans, Mutation, Neoplasms, Proteins, General Science & Technology
Abstract

Cancer drivers require statistical modeling to distinguish them from passenger events, which accumulate during tumorigenesis but provide no fitness advantage to cancer cells. The discovery of driver genes and mutations relies on the assumption that exact positional recurrence is unlikely by chance; thus, the precise sharing of mutations across patients identifies drivers. Examining the mutation landscape in cancer genomes, we found that many recurrent cancer mutations previously designated as drivers are likely passengers. Our integrated bioinformatic and biochemical analyses revealed that these passenger hotspot mutations arise from the preference of APOBEC3A, a cytidine deaminase, for DNA stem-loops. Conversely, recurrent APOBEC-signature mutations not in stem-loops are enriched in well-characterized driver genes and may predict new drivers. This demonstrates that mesoscale genomic features need to be integrated into computational models aimed at identifying mutations linked to diseases.

Published
2019

6. Stromal Microenvironment Shapes the Intratumoral Architecture of Pancreatic Cancer

Author

Ligorio, Matteo, Sil, Srinjoy, Malagon-Lopez, Jose, Nieman, Linda T, Misale, Sandra, Di Pilato, Mauro, Ebright, Richard Y, Karabacak, Murat N, Kulkarni, Anupriya S, Liu, Ann, Vincent Jordan, Nicole, Franses, Joseph W, Philipp, Julia, Kreuzer, Johannes, Desai, Niyati, Arora, Kshitij S, Rajurkar, Mihir, Horwitz, Elad, Neyaz, Azfar, Tai, Eric, Magnus, Neelima KC, Vo, Kevin D, Yashaswini, Chittampalli N, Marangoni, Francesco, Boukhali, Myriam, Fatherree, Jackson P, Damon, Leah J, Xega, Kristina, Desai, Rushil, Choz, Melissa, Bersani, Francesca, Langenbucher, Adam, Thapar, Vishal, Morris, Robert, Wellner, Ulrich F, Schilling, Oliver, Lawrence, Michael S, Liss, Andrew S, Rivera, Miguel N, Deshpande, Vikram, Benes, Cyril H, Maheswaran, Shyamala, Haber, Daniel A, Fernandez-Del-Castillo, Carlos, Ferrone, Cristina R, Haas, Wilhelm, Aryee, Martin J, and Ting, David T

Subjects
Biochemistry and Cell Biology, Biomedical and Clinical Sciences, Oncology and Carcinogenesis, Biological Sciences, Rare Diseases, Genetics, Digestive Diseases, Stem Cell Research, Pancreatic Cancer, Cancer, Stem Cell Research - Nonembryonic - Human, 2.1 Biological and endogenous factors, Aetiology, Animals, Cancer-Associated Fibroblasts, Carcinoma, Pancreatic Ductal, Cell Proliferation, Coculture Techniques, Epithelial-Mesenchymal Transition, Female, HEK293 Cells, Heterografts, Humans, Mice, Mice, Inbred NOD, Mice, SCID, Mitogen-Activated Protein Kinases, Pancreatic Neoplasms, RNA-Seq, STAT3 Transcription Factor, Stromal Cells, Transfection, Tumor Microenvironment, mass spectrometry, pancreatic cancer, pancreatic ductal adenocarcinoma, single cell RNA-sequencing, single cell spatial analysis, stromal microenvironment, tumor architecture, Medical and Health Sciences, Developmental Biology, Biological sciences, Biomedical and clinical sciences
Abstract

Single-cell technologies have described heterogeneity across tissues, but the spatial distribution and forces that drive single-cell phenotypes have not been well defined. Combining single-cell RNA and protein analytics in studying the role of stromal cancer-associated fibroblasts (CAFs) in modulating heterogeneity in pancreatic cancer (pancreatic ductal adenocarcinoma [PDAC]) model systems, we have identified significant single-cell population shifts toward invasive epithelial-to-mesenchymal transition (EMT) and proliferative (PRO) phenotypes linked with mitogen-activated protein kinase (MAPK) and signal transducer and activator of transcription 3 (STAT3) signaling. Using high-content digital imaging of RNA in situ hybridization in 195 PDAC tumors, we quantified these EMT and PRO subpopulations in 319,626 individual cancer cells that can be classified within the context of distinct tumor gland "units." Tumor gland typing provided an additional layer of intratumoral heterogeneity that was associated with differences in stromal abundance and clinical outcomes. This demonstrates the impact of the stroma in shaping tumor architecture by altering inherent patterns of tumor glands in human PDAC.

Published
2019

7. 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, Cancer, Biotechnology, Human Genome, Cancer Genomics, 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

8. 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

9. 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

10. 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, Breast Cancer, Human Genome, Cancer, Biotechnology, Women's Health, Genetics, Cancer Genomics, 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

11. 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 Genomics, Human Genome, Cancer, 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

12. 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, Human Genome, Genetics, Biotechnology, Cancer Genomics, Cancer, 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

13. 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, Cancer Genomics, Human Genome, Genetics, Cancer, Women's Health, Breast Cancer, 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

14. 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, Cancer, Human Genome, Rare Diseases, Genetic Testing, Biotechnology, Cancer Genomics, Colo-Rectal Cancer, Digestive Diseases, 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

15. 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

16. 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, Cancer Genomics, Human Genome, Cancer, Lung, Lung Cancer, 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

17. 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, Cancer, Genetics, Human Genome, Networking and Information Technology R&D (NITRD), Machine Learning and Artificial Intelligence, 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

18. 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, Sexually Transmitted Infections, Infectious Diseases, Human Genome, Genetics, Biotechnology, Cancer Genomics, Cancer, 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

19. 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 Genomics, Human Genome, Cancer, 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

20. 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, Stem Cell Research - Nonembryonic - Human, Stem Cell Research - Nonembryonic - Non-Human, Rare Diseases, Machine Learning and Artificial Intelligence, Human Genome, Genetics, Stem Cell Research, Stem Cell Research - Induced Pluripotent Stem Cell, Cancer Genomics, Cancer, 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

21. 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, Human Genome, Biotechnology, Cancer, Cancer Genomics, 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

22. 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, Cancer, Human Genome, Genetics, Precision Medicine, Women's Health, Cancer Genomics, Breast Cancer, 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

23. 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, Human Genome, Cancer Genomics, Networking and Information Technology R&D (NITRD), Biotechnology, Cancer, 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

24. 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, Rare Diseases, Prevention, Cancer Genomics, 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

25. 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, Cancer Genomics, Human Genome, Rare Diseases, Cancer, Immunotherapy, Genetics, 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

26. 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, Human Genome, Genetics, Biotechnology, Cancer Genomics, Cancer, 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

27. 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, Cancer Genomics, Human Genome, Cancer, Nutrition, Genetics, 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

28. 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, Cancer, Networking and Information Technology R&D (NITRD), Human Genome, Cancer Genomics, Precision Medicine, Machine Learning and Artificial Intelligence, 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

29. 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, Precision Medicine, Women's Health, Networking and Information Technology R&D (NITRD), Human Genome, Genetics, Biotechnology, Cancer Genomics, Cancer, 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

30. 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, Women's Health, Orphan Drug, Rare Diseases, Human Genome, Ovarian Cancer, Cancer Genomics, Cancer, 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

31. 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, Cancer, Human Genome, Rare Diseases, Genetic Testing, Networking and Information Technology R&D (NITRD), Cancer Genomics, 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

32. 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, Human Genome, Cancer, 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

33. 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, Human Genome, Autoimmune Disease, Cancer, Rare Diseases, Biotechnology, 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

34. APOBEC3A and APOBEC3B Activities Render Cancer Cells Susceptible to ATR Inhibition

Author

Buisson, Rémi, Lawrence, Michael S, Benes, Cyril H, and Zou, Lee

Subjects
Biochemistry and Cell Biology, Biomedical and Clinical Sciences, Oncology and Carcinogenesis, Biological Sciences, Genetics, Biotechnology, Cancer, 5.1 Pharmaceuticals, Development of treatments and therapeutic interventions, Ataxia Telangiectasia Mutated Proteins, Cytidine Deaminase, DNA Replication, Gene Expression Regulation, Neoplastic, Humans, Minor Histocompatibility Antigens, Mutation, Neoplasms, Proteins, Signal Transduction, Stress, Physiological, Tumor Cells, Cultured, Oncology & Carcinogenesis, Biochemistry and cell biology, Oncology and carcinogenesis
Abstract

The apolipoprotein B mRNA editing enzyme catalytic polypeptide-like APOBEC3A and APOBEC3B have emerged as key mutation drivers in cancer. Here, we show that APOBEC3A and APOBEC3B activities impose a unique type of replication stress by inducing abasic sites at replication forks. In contrast to cells under other types of replication stress, APOBEC3A-expressing cells were selectively sensitive to ATR inhibitors (ATRi), but not to a variety of DNA replication inhibitors and DNA-damaging drugs. In proliferating cells, APOBEC3A modestly elicited ATR but not ATM. ATR inhibition in APOBEC3A-expressing cells resulted in a surge of abasic sites at replication forks, revealing an ATR-mediated negative feedback loop during replication. The surge of abasic sites upon ATR inhibition associated with increased accumulation of single-stranded DNA, a substrate of APOBEC3A, triggering an APOBEC3A-driven feed-forward loop that ultimately drove cells into replication catastrophe. In a panel of cancer cell lines, ATRi selectively induced replication catastrophe in those harboring high APOBEC3A and/or APOBEC3B activities, showing that APOBEC3A and APOBEC3B activities conferred susceptibility to ATRi. Our results define an APOBEC-driven replication stress in cancer cells that may offer an opportunity for ATR-targeted therapy. Cancer Res; 77(17); 4567-78. ©2017 AACR.

Published
2017

35. Comprehensive and Integrative Genomic Characterization of Hepatocellular Carcinoma

Author

Ally, Adrian, Balasundaram, Miruna, Carlsen, Rebecca, Chuah, Eric, Clarke, Amanda, Dhalla, Noreen, Holt, Robert A, Jones, Steven JM, Lee, Darlene, Ma, Yussanne, Marra, Marco A, Mayo, Michael, Moore, Richard A, Mungall, Andrew J, Schein, Jacqueline E, Sipahimalani, Payal, Tam, Angela, Thiessen, Nina, Cheung, Dorothy, Wong, Tina, Brooks, Denise, Robertson, A Gordon, Bowlby, Reanne, Mungall, Karen, Sadeghi, Sara, Xi, Liu, Covington, Kyle, Shinbrot, Eve, Wheeler, David A, Gibbs, Richard A, Donehower, Lawrence A, Wang, Linghua, Bowen, Jay, Gastier-Foster, Julie M, Gerken, Mark, Helsel, Carmen, Leraas, Kristen M, Lichtenberg, Tara M, Ramirez, Nilsa C, Wise, Lisa, Zmuda, Erik, Gabriel, Stacey B, Meyerson, Matthew, Cibulskis, Carrie, Murray, Bradley A, Shih, Juliann, Beroukhim, Rameen, Cherniack, Andrew D, Schumacher, Steven E, Saksena, Gordon, Pedamallu, Chandra Sekhar, Chin, Lynda, Getz, Gad, Noble, Michael, Zhang, Hailei, Heiman, David, Cho, Juok, Gehlenborg, Nils, Voet, Douglas, Lin, Pei, Frazer, Scott, Defreitas, Timothy, Meier, Sam, Lawrence, Michael, Kim, Jaegil, Creighton, Chad J, Muzny, Donna, Doddapaneni, HarshaVardhan, Hu, Jianhong, Wang, Min, Morton, Donna, Korchina, Viktoriya, Han, Yi, Dinh, Huyen, Lewis, Lora, Bellair, Michelle, Liu, Xiuping, Santibanez, Jireh, Glenn, Robert, Lee, Sandra, Hale, Walker, Parker, Joel S, Wilkerson, Matthew D, Hayes, D Neil, Reynolds, Sheila M, Shmulevich, Ilya, Zhang, Wei, Liu, Yuexin, Iype, Lisa, Makhlouf, Hala, Torbenson, Michael S, Kakar, Sanjay, Yeh, Matthew M, Jain, Dhanpat, Kleiner, David E, Dhanasekaran, Renumathy, El-Serag, Hashem B, and Yim, Sun Young

Subjects
Human Genome, Digestive Diseases, Biotechnology, Rare Diseases, Genetics, Liver Disease, Liver Cancer, Cancer, Aetiology, 2.1 Biological and endogenous factors, 4.1 Discovery and preclinical testing of markers and technologies, Detection, screening and diagnosis, Good Health and Well Being, Carcinoma, Hepatocellular, DNA Methylation, Genomics, Humans, Isocitrate Dehydrogenase, Liver Neoplasms, MicroRNAs, Mutation, Cancer Genome Atlas Research Network. Electronic address: wheeler@bcm.edu, Cancer Genome Atlas Research Network, IDH1/2, TP53, cancer subtyping, expression profile, hepatocellular carcinoma, metabolic reprogramming, promoter hypermethylation, significantly mutated genes, sonic hedgehog signaling, stem cell phenotype, Biological Sciences, Medical and Health Sciences, Developmental Biology
Abstract

Liver cancer has the second highest worldwide cancer mortality rate and has limited therapeutic options. We analyzed 363 hepatocellular carcinoma (HCC) cases by whole-exome sequencing and DNA copy number analyses, and we analyzed 196 HCC cases by DNA methylation, RNA, miRNA, and proteomic expression also. DNA sequencing and mutation analysis identified significantly mutated genes, including LZTR1, EEF1A1, SF3B1, and SMARCA4. Significant alterations by mutation or downregulation by hypermethylation in genes likely to result in HCC metabolic reprogramming (ALB, APOB, and CPS1) were observed. Integrative molecular HCC subtyping incorporating unsupervised clustering of five data platforms identified three subtypes, one of which was associated with poorer prognosis in three HCC cohorts. Integrated analyses enabled development of a p53 target gene expression signature correlating with poor survival. Potential therapeutic targets for which inhibitors exist include WNT signaling, MDM4, MET, VEGFA, MCL1, IDH1, TERT, and immune checkpoint proteins CTLA-4, PD-1, and PD-L1.

Published
2017

36. 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, Cancer, Biotechnology, 2.1 Biological and endogenous factors, Aetiology, 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

37. Systematic Functional Interrogation of Rare Cancer Variants Identifies Oncogenic Alleles

Author

Kim, Eejung, Ilic, Nina, Shrestha, Yashaswi, Zou, Lihua, Kamburov, Atanas, Zhu, Cong, Yang, Xiaoping, Lubonja, Rakela, Tran, Nancy, Nguyen, Cindy, Lawrence, Michael S, Piccioni, Federica, Bagul, Mukta, Doench, John G, Chouinard, Candace R, Wu, Xiaoyun, Hogstrom, Larson, Natoli, Ted, Tamayo, Pablo, Horn, Heiko, Corsello, Steven M, Lage, Kasper, Root, David E, Subramanian, Aravind, Golub, Todd R, Getz, Gad, Boehm, Jesse S, and Hahn, William C

Subjects
Biological Sciences, Biomedical and Clinical Sciences, Bioinformatics and Computational Biology, Genetics, Oncology and Carcinogenesis, Cancer, Human Genome, Biotechnology, 2.1 Biological and endogenous factors, Aetiology, Alleles, Animals, Cell Line, Tumor, Cell Transformation, Neoplastic, Disease Models, Animal, Gene Expression Profiling, Genetic Association Studies, Genetic Predisposition to Disease, Genetic Variation, Heterografts, High-Throughput Screening Assays, Humans, Male, Mice, Neoplasms, Oncogenes, Reproducibility of Results, Biochemistry and cell biology, Oncology and carcinogenesis
Abstract

UnlabelledCancer genome characterization efforts now provide an initial view of the somatic alterations in primary tumors. However, most point mutations occur at low frequency, and the function of these alleles remains undefined. We have developed a scalable systematic approach to interrogate the function of cancer-associated gene variants. We subjected 474 mutant alleles curated from 5,338 tumors to pooled in vivo tumor formation assays and gene expression profiling. We identified 12 transforming alleles, including two in genes (PIK3CB, POT1) that have not been shown to be tumorigenic. One rare KRAS allele, D33E, displayed tumorigenicity and constitutive activation of known RAS effector pathways. By comparing gene expression changes induced upon expression of wild-type and mutant alleles, we inferred the activity of specific alleles. Because alleles found to be mutated only once in 5,338 tumors rendered cells tumorigenic, these observations underscore the value of integrating genomic information with functional studies.SignificanceExperimentally inferring the functional status of cancer-associated mutations facilitates the interpretation of genomic information in cancer. Pooled in vivo screen and gene expression profiling identified functional variants and demonstrated that expression of rare variants induced tumorigenesis. Variant phenotyping through functional studies will facilitate defining key somatic events in cancer. Cancer Discov; 6(7); 714-26. ©2016 AACR.See related commentary by Cho and Collisson, p. 694This article is highlighted in the In This Issue feature, p. 681.

Published
2016

38. Comprehensive Molecular Portraits of Invasive Lobular Breast Cancer

Author

Ciriello, Giovanni, Gatza, Michael L, Beck, Andrew H, Wilkerson, Matthew D, Rhie, Suhn K, Pastore, Alessandro, Zhang, Hailei, McLellan, Michael, Yau, Christina, Kandoth, Cyriac, Bowlby, Reanne, Shen, Hui, Hayat, Sikander, Fieldhouse, Robert, Lester, Susan C, Tse, Gary MK, Factor, Rachel E, Collins, Laura C, Allison, Kimberly H, Chen, Yunn-Yi, Jensen, Kristin, Johnson, Nicole B, Oesterreich, Steffi, Mills, Gordon B, Cherniack, Andrew D, Robertson, Gordon, Benz, Christopher, Sander, Chris, Laird, Peter W, Hoadley, Katherine A, King, Tari A, Perou, Charles M, Akbani, Rehan, Auman, J Todd, Balasundaram, Miruna, Balu, Saianand, Barr, Thomas, Beck, Andrew, Benz, Stephen, Berrios, Mario, Beroukhim, Rameen, Bodenheimer, Tom, Boice, Lori, Bootwalla, Moiz S, Bowen, Jay, Brooks, Denise, Chin, Lynda, Cho, Juok, Chudamani, Sudha, Davidsen, Tanja, Demchok, John A, Dennison, Jennifer B, Ding, Li, Felau, Ina, Ferguson, Martin L, Frazer, Scott, Gabriel, Stacey B, Gao, JianJiong, Gastier-Foster, Julie M, Gehlenborg, Nils, Gerken, Mark, Getz, Gad, Gibson, William J, Hayes, D Neil, Heiman, David I, Holbrook, Andrea, Holt, Robert A, Hoyle, Alan P, Hu, Hai, Huang, Mei, Hutter, Carolyn M, Hwang, E Shelley, Jefferys, Stuart R, Jones, Steven JM, Ju, Zhenlin, Kim, Jaegil, Lai, Phillip H, Lawrence, Michael S, Leraas, Kristen M, Lichtenberg, Tara M, Lin, Pei, Ling, Shiyun, Liu, Jia, Liu, Wenbin, Lolla, Laxmi, Lu, Yiling, Ma, Yussanne, Maglinte, Dennis T, Mardis, Elaine, Marks, Jeffrey, Marra, Marco A, and McAllister, Cynthia

Subjects
Biomedical and Clinical Sciences, Oncology and Carcinogenesis, Breast Cancer, Clinical Research, Genetics, Clinical Trials and Supportive Activities, Cancer, Antigens, CD, Breast Neoplasms, Cadherins, Carcinoma, Ductal, Breast, Carcinoma, Lobular, Female, Hepatocyte Nuclear Factor 3-alpha, Humans, Models, Molecular, Mutation, Oligonucleotide Array Sequence Analysis, Oncogene Protein v-akt, Transcriptome, TCGA Research Network, Biological Sciences, Medical and Health Sciences, Developmental Biology, Biological sciences, Biomedical and clinical sciences
Abstract

Invasive lobular carcinoma (ILC) is the second most prevalent histologic subtype of invasive breast cancer. Here, we comprehensively profiled 817 breast tumors, including 127 ILC, 490 ductal (IDC), and 88 mixed IDC/ILC. Besides E-cadherin loss, the best known ILC genetic hallmark, we identified mutations targeting PTEN, TBX3, and FOXA1 as ILC enriched features. PTEN loss associated with increased AKT phosphorylation, which was highest in ILC among all breast cancer subtypes. Spatially clustered FOXA1 mutations correlated with increased FOXA1 expression and activity. Conversely, GATA3 mutations and high expression characterized luminal A IDC, suggesting differential modulation of ER activity in ILC and IDC. Proliferation and immune-related signatures determined three ILC transcriptional subtypes associated with survival differences. Mixed IDC/ILC cases were molecularly classified as ILC-like and IDC-like revealing no true hybrid features. This multidimensional molecular atlas sheds new light on the genetic bases of ILC and provides potential clinical options.

Published
2015

39. Paired exome analysis of Barrett's esophagus and adenocarcinoma

Author

Stachler, Matthew D, Taylor-Weiner, Amaro, Peng, Shouyong, McKenna, Aaron, Agoston, Agoston T, Odze, Robert D, Davison, Jon M, Nason, Katie S, Loda, Massimo, Leshchiner, Ignaty, Stewart, Chip, Stojanov, Petar, Seepo, Sara, Lawrence, Michael S, Ferrer-Torres, Daysha, Lin, Jules, Chang, Andrew C, Gabriel, Stacey B, Lander, Eric S, Beer, David G, Getz, Gad, Carter, Scott L, and Bass, Adam J

Subjects
Biological Sciences, Genetics, Rare Diseases, Digestive Diseases, Cancer, Clinical Research, Adenocarcinoma, Barrett Esophagus, Class I Phosphatidylinositol 3-Kinases, Cyclin-Dependent Kinase Inhibitor p16, DNA Mutational Analysis, Esophageal Neoplasms, Exome, Gene Amplification, Gene Frequency, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Phosphatidylinositol 3-Kinases, Point Mutation, Tumor Suppressor Protein p53, Medical and Health Sciences, Developmental Biology, Agricultural biotechnology, Bioinformatics and computational biology
Abstract

Barrett's esophagus is thought to progress to esophageal adenocarcinoma (EAC) through a stepwise progression with loss of CDKN2A followed by TP53 inactivation and aneuploidy. Here we present whole-exome sequencing from 25 pairs of EAC and Barrett's esophagus and from 5 patients whose Barrett's esophagus and tumor were extensively sampled. Our analysis showed that oncogene amplification typically occurred as a late event and that TP53 mutations often occurred early in Barrett's esophagus progression, including in non-dysplastic epithelium. Reanalysis of additional EAC exome data showed that the majority (62.5%) of EACs emerged following genome doubling and that tumors with genomic doubling had different patterns of genomic alterations, with more frequent oncogenic amplification and less frequent inactivation of tumor suppressors, including CDKN2A. These data suggest that many EACs emerge not through the gradual accumulation of tumor-suppressor alterations but rather through a more direct path whereby a TP53-mutant cell undergoes genome doubling, followed by the acquisition of oncogenic amplifications.

Published
2015

40. Comprehensive genomic characterization of head and neck squamous cell carcinomas

Author

Lawrence, Michael S, Sougnez, Carrie, Lichtenstein, Lee, Cibulskis, Kristian, Lander, Eric, Gabriel, Stacey B, Getz, Gad, Ally, Adrian, Balasundaram, Miruna, Birol, Inanc, Bowlby, Reanne, Brooks, Denise, Butterfield, Yaron SN, Carlsen, Rebecca, Cheng, Dean, Chu, Andy, Dhalla, Noreen, Guin, Ranabir, Holt, Robert A, Jones, Steven JM, Lee, Darlene, Li, Haiyan I, Marra, Marco A, Mayo, Michael, Moore, Richard A, Mungall, Andrew J, Gordon Robertson, A, Schein, Jacqueline E, Sipahimalani, Payal, Tam, Angela, Thiessen, Nina, Wong, Tina, Protopopov, Alexei, Santoso, Netty, Lee, Semin, Parfenov, Michael, Zhang, Jianhua, Mahadeshwar, Harshad S, Tang, Jiabin, Ren, Xiaojia, Seth, Sahil, Haseley, Psalm, Zeng, Dong, Yang, Lixing, Xu, Andrew W, Song, Xingzhi, Pantazi, Angeliki, Bristow, Christopher A, Hadjipanayis, Angela, Seidman, Jonathan, Chin, Lynda, Park, Peter J, Kucherlapati, Raju, Akbani, Rehan, Casasent, Tod, Liu, Wenbin, Lu, Yiling, Mills, Gordon, Motter, Thomas, Weinstein, John, Diao, Lixia, Wang, Jing, Hong Fan, You, Liu, Jinze, Wang, Kai, Todd Auman, J, Balu, Saianand, Bodenheimer, Thomas, Buda, Elizabeth, Neil Hayes, D, Hoadley, Katherine A, Hoyle, Alan P, Jefferys, Stuart R, Jones, Corbin D, Kimes, Patrick K, Liu, Yufeng, Marron, JS, Meng, Shaowu, Mieczkowski, Piotr A, Mose, Lisle E, Parker, Joel S, Perou, Charles M, Prins, Jan F, Roach, Jeffrey, Shi, Yan, Simons, Janae V, Singh, Darshan, Soloway, Matthew G, Tan, Donghui, Veluvolu, Umadevi, Walter, Vonn, Waring, Scot, Wilkerson, Matthew D, Wu, Junyuan, Zhao, Ni, Cherniack, Andrew D, Hammerman, Peter S, Tward, Aaron D, Sekhar Pedamallu, Chandra, and Saksena, Gordon

Subjects
Biological Sciences, Biomedical and Clinical Sciences, Genetics, Oncology and Carcinogenesis, Human Genome, Biotechnology, Dental/Oral and Craniofacial Disease, Cancer, 2.1 Biological and endogenous factors, Aetiology, Good Health and Well Being, Carcinoma, Squamous Cell, DNA Copy Number Variations, DNA, Neoplasm, Female, Gene Expression Regulation, Neoplastic, Genome, Human, Genomics, Head and Neck Neoplasms, Humans, Male, Molecular Targeted Therapy, Mutation, Oncogenes, RNA, Neoplasm, Signal Transduction, Squamous Cell Carcinoma of Head and Neck, Transcription Factors, Cancer Genome Atlas Network, General Science & Technology
Abstract

The Cancer Genome Atlas profiled 279 head and neck squamous cell carcinomas (HNSCCs) to provide a comprehensive landscape of somatic genomic alterations. Here we show that human-papillomavirus-associated tumours are dominated by helical domain mutations of the oncogene PIK3CA, novel alterations involving loss of TRAF3, and amplification of the cell cycle gene E2F1. Smoking-related HNSCCs demonstrate near universal loss-of-function TP53 mutations and CDKN2A inactivation with frequent copy number alterations including amplification of 3q26/28 and 11q13/22. A subgroup of oral cavity tumours with favourable clinical outcomes displayed infrequent copy number alterations in conjunction with activating mutations of HRAS or PIK3CA, coupled with inactivating mutations of CASP8, NOTCH1 and TP53. Other distinct subgroups contained loss-of-function alterations of the chromatin modifier NSD1, WNT pathway genes AJUBA and FAT1, and activation of oxidative stress factor NFE2L2, mainly in laryngeal tumours. Therapeutic candidate alterations were identified in most HNSCCs.

Published
2015

41. Assessing the clinical utility of cancer genomic and proteomic data across tumor types

Author

Yuan, Yuan, Van Allen, Eliezer M, Omberg, Larsson, Wagle, Nikhil, Amin-Mansour, Ali, Sokolov, Artem, Byers, Lauren A, Xu, Yanxun, Hess, Kenneth R, Diao, Lixia, Han, Leng, Huang, Xuelin, Lawrence, Michael S, Weinstein, John N, Stuart, Josh M, Mills, Gordon B, Garraway, Levi A, Margolin, Adam A, Getz, Gad, and Liang, Han

Subjects
Biological Sciences, Biomedical and Clinical Sciences, Bioinformatics and Computational Biology, Genetics, Oncology and Carcinogenesis, Clinical Research, Human Genome, Biotechnology, Rare Diseases, Cancer, Biomarkers, Tumor, DNA, Neoplasm, Databases, Genetic, Genetic Markers, Genetic Predisposition to Disease, Humans, Neoplasms, Prevalence, Proteome, Risk Assessment, Survival Analysis
Abstract

Molecular profiling of tumors promises to advance the clinical management of cancer, but the benefits of integrating molecular data with traditional clinical variables have not been systematically studied. Here we retrospectively predict patient survival using diverse molecular data (somatic copy-number alteration, DNA methylation and mRNA, microRNA and protein expression) from 953 samples of four cancer types from The Cancer Genome Atlas project. We find that incorporating molecular data with clinical variables yields statistically significantly improved predictions (FDR < 0.05) for three cancers but those quantitative gains were limited (2.2-23.9%). Additional analyses revealed little predictive power across tumor types except for one case. In clinically relevant genes, we identified 10,281 somatic alterations across 12 cancer types in 2,928 of 3,277 patients (89.4%), many of which would not be revealed in single-tumor analyses. Our study provides a starting point and resources, including an open-access model evaluation platform, for building reliable prognostic and therapeutic strategies that incorporate molecular data.

Published
2014

42. A Pan-Cancer Analysis of Transcriptome Changes Associated with Somatic Mutations in U2AF1 Reveals Commonly Altered Splicing Events

Author

Brooks, Angela N, Choi, Peter S, de Waal, Luc, Sharifnia, Tanaz, Imielinski, Marcin, Saksena, Gordon, Pedamallu, Chandra Sekhar, Sivachenko, Andrey, Rosenberg, Mara, Chmielecki, Juliann, Lawrence, Michael S, DeLuca, David S, Getz, Gad, and Meyerson, Matthew

Subjects
Biological Sciences, Biomedical and Clinical Sciences, Bioinformatics and Computational Biology, Genetics, Oncology and Carcinogenesis, Human Genome, Rare Diseases, Hematology, Cancer, Lung Cancer, Lung, 2.1 Biological and endogenous factors, Aetiology, Acute Disease, Adenocarcinoma, Gene Expression Regulation, Neoplastic, HEK293 Cells, HeLa Cells, Humans, Leukemia, Myeloid, Lung Neoplasms, Mutation, Neoplasms, Nuclear Proteins, RNA Splice Sites, RNA Splicing, Reverse Transcriptase Polymerase Chain Reaction, Ribonucleoproteins, Splicing Factor U2AF, Transcriptome, Hela Cells, General Science & Technology
Abstract

Although recurrent somatic mutations in the splicing factor U2AF1 (also known as U2AF35) have been identified in multiple cancer types, the effects of these mutations on the cancer transcriptome have yet to be fully elucidated. Here, we identified splicing alterations associated with U2AF1 mutations across distinct cancers using DNA and RNA sequencing data from The Cancer Genome Atlas (TCGA). Using RNA-Seq data from 182 lung adenocarcinomas and 167 acute myeloid leukemias (AML), in which U2AF1 is somatically mutated in 3-4% of cases, we identified 131 and 369 splicing alterations, respectively, that were significantly associated with U2AF1 mutation. Of these, 30 splicing alterations were statistically significant in both lung adenocarcinoma and AML, including three genes in the Cancer Gene Census, CTNNB1, CHCHD7, and PICALM. Cell line experiments expressing U2AF1 S34F in HeLa cells and in 293T cells provide further support that these altered splicing events are caused by U2AF1 mutation. Consistent with the function of U2AF1 in 3' splice site recognition, we found that S34F/Y mutations cause preferences for CAG over UAG 3' splice site sequences. This report demonstrates consistent effects of U2AF1 mutation on splicing in distinct cancer cell types.

Published
2014

43. SHP2 inhibition restores sensitivity in ALK-rearranged non-small-cell lung cancer resistant to ALK inhibitors

Author

Dardaei, Leila, Wang, Hui Qin, Singh, Manrose, Fordjour, Paul, Shaw, Katherine X, Yoda, Satoshi, Kerr, Grainne, Yu, Kristine, Liang, Jinsheng, Cao, Yichen, Chen, Yan, Lawrence, Michael S, Langenbucher, Adam, Gainor, Justin F, Friboulet, Luc, Dagogo-Jack, Ibiayi, Myers, David T, Labrot, Emma, Ruddy, David, Parks, Melissa, Lee, Dana, DiCecca, Richard H, Moody, Susan, Hao, Huaixiang, Mohseni, Morvarid, LaMarche, Matthew, Williams, Juliet, Hoffmaster, Keith, Caponigro, Giordano, Shaw, Alice T, Hata, Aaron N, Benes, Cyril H, Li, Fang, and Engelman, Jeffrey A

Subjects
Protein-tyrosine kinase -- Physiological aspects -- Health aspects, Insulin receptors -- Physiological aspects -- Health aspects, Drug resistance -- Research, Medical research, Anaplastic lymphoma kinase (ALK) inhibitor -- Dosage and administration, Non-small cell lung cancer -- Drug therapy -- Development and progression, Biological sciences, Health
Abstract

Author(s): Leila Dardaei [1, 2]; Hui Qin Wang [3]; Manrose Singh [1]; Paul Fordjour [3]; Katherine X Shaw [1]; Satoshi Yoda [1, 2]; Grainne Kerr [4]; Kristine Yu [3]; Jinsheng [...]

Published
2018
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44. Whole-exome sequencing and clinical interpretation of formalin-fixed, paraffin-embedded tumor samples to guide precision cancer medicine

Author

Van Allen, Eliezer M., Wagle, Nikhil, Stojanov, Petar, Perrin, Danielle L., Cibulskis, Kristian, Marlow, Sara, Jane-Valbuena, Judit, Friedrich, Dennis C., Kryukov, Gregory, Carter, Scott L., McKenna, Aaron, Sivachenko, Andrey, Rosenberg, Mara, Kiezun, Adam, Voet, Douglas, Lawrence, Michael, Lichtenstein, Lee T., Gentry, Jeff G., Huang, Franklin W., Fostel, Jennifer, Farlow, Deborah, Barbie, David, Gandhi, Leena, Lander, Eric S., Gray, Stacy W., Joffe, Steven, Janne, Pasi, Garber, Judy, MacConaill, Laura, Lindeman, Neal, Rollins, Barrett, Kantoff, Philip, Fisher, Sheila A., Gabriel, Stacey, Getz, Gad, and Garraway, Levi A.

Subjects
Nucleotide sequencing -- Usage -- Research, DNA sequencing -- Usage -- Research, Cancer -- Diagnosis -- Genetic aspects -- Research, Biological sciences, Health
Abstract

Translating whole-exome sequencing (WES) for prospective clinical use may have an impact on the care of patients with cancer; however, multiple innovations are necessary for clinical implementation. These include rapid and robust WES of DNA derived from formalin-fixed, paraffin-embedded tumor tissue, analytical output similar to data from frozen samples and clinical interpretation of WES data for prospective use. Here, we describe a prospective clinical WES platform for archival formalin-fixed, paraffin-embedded tumor samples. The platform employs computational methods for effective clinical analysis and interpretation of WES data. When applied retrospectively to 511 exomes, the interpretative framework revealed a 'long tail' of somatic alterations in clinically important genes. Prospective application of this approach identified clinically relevant alterations in 15 out of 16 patients. In one patient, previously undetected findings guided clinical trial enrollment, leading to an objective clinical response. Overall, this methodology may inform the widespread implementation of precision cancer medicine., Massively parallel sequencing approaches such as WES have elucidated the landscape of genetic alterations in many tumor types and revealed biological insights relevant to clinical contexts (1). The increased practical [...]

Published
2014
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46. The insulin and EGF receptor structures: new insights into ligand-induced receptor activation

Author

Ward, Colin W., Lawrence, Michael C., Streltsov, Victor A., Adams, Timothy E., and McKern, Neil M.

Subjects
Ligands (Biochemistry) -- Research, Insulin -- Receptors, Insulin -- Research, Biological sciences, Chemistry
Abstract

The insulin receptor (IR) and epidermal growth factor receptor (EGFR; also known as ErbB) families exhibit similarities in the composition of their ectodomains. The past five years have seen structures determined for all members of the EGFR family including some complexes with ligand or monoclonal antibody fragments. These structures have led to a clearer understanding of their mechanism of activation and inhibition. By contrast, obtaining equivalent understanding of the IR family has lagged behind. However, within the past year, structures of partial and complete ectodomains of the IR have been published that show that the extracellular region of the receptor adopts an unexpected 'inverted V' conformation relative to the cell membrane. This is very different from the folded-over (tethered) conformation of the unactivated EGFR and provides insight into the potential mechanism of activation of the IR. The insulin and EGFR families

Published
2007

47. PSGL-1 derived from human neutrophils is a high-efficiency ligand for endothelium-expressed E-selectin under flow

Author

Zou, Xiaoyan, Patil, Vivek R. Shinde, Dagia, Nilesh M., Smith, Lee A., Wargo, Maureen J., Interliggi, Kimberly A., Lloyd, Christopher M., Tees, David F.J., Walcheck, Bruce, Lawrence, Michael B., and Goetz, Douglas J.

Subjects
Leukocytes -- Research, Leukocytes -- Physiological aspects, Ligands (Biochemistry) -- Research, Ligands (Biochemistry) -- Physiological aspects, Glycoproteins -- Research, Biological sciences
Abstract

P-selectin glycoprotein ligand-1 (PSGL-1) has been proposed as an important tethering ligand for E-selectin and is expressed at a modest level on human leukocytes. Sialyl Lewis x ([sLe.sup.x])-like glycans bind to E-selectin and are expressed at a relatively high level on circulating leukocytes. It is unclear whether PSGL-1 has unique biochemical attributes that contribute to its role as an E-selectin ligand. To probe this issue, we conjugated microspheres with either [sLe.sup.x] or PSGL-1 purified from myeloid cells (neutrophils and HL-60) and compared their adhesion to endothelial expressed E-selectin under defined shear conditions. We found that both [sLe.sup.x] and PSGL-1 microspheres adhere to 4 h of IL-l[beta]-activated human umbilical vein endothelial cells predominantly through E-selectin. Analysis of the adhesion revealed that the rate of initial tethering of the PSGL-1 microspheres to E-selectin was significantly greater than the rate of initial tethering of the [sLe.sup.x] microspheres despite the fact that the [sLe.sup.x] microspheres tested had higher ligand densities than the PSGL-1 microspheres. We also found that pretreatment of the PSGL-1 or [sLe.sup.x] microspheres with HECA-452 had no significant effect on initial tethering to E-selectin. These results support the hypotheses that 1) PSGL-1 is a high-efficiency tethering ligand for E-selectin, 2) ligand biochemistry can significantly influence initial tethering to E-selectin, and 3) PSGL-1 tethering to E-selectin can occur via non-HECA-452 reactive epitopes. adhesion; leukocyte; inflammation

Published
2005

48. Substrate-assisted catalysis in sialic acid aldolase

Author

Smith, Brian J., Lawrence, Michael C., and Barbosa, Joao A.R.G.

Subjects
Organic compounds -- Synthesis, Catalysis -- Research, Sialic acids -- Research, Enzymes -- Synthesis, Conformational analysis -- Research, Biological sciences, Chemistry
Abstract

The reversible aldol condensation of pyruvate and N-acetylmannosamine is catalyzed by sialic acid aldolase with an apparent lack of stereospecificity. As part of this reaction, two conformations are formed from the modeling of Schiff base and enamine intermediates in the active site of the protein. The acceptor-aldehyde group is located in different sides of the enamine in the conformations, although the remainder of the substrate has extremely similar geometries.

Published
1999

49. Threshold levels of fluid shear promote leukocyte adhesion through selectins (CD62L,P,E)

Author

Lawrence, Michael B., Kansas, Geoffrey S., Kunkel, Eric J., and Ley, Klaus

Subjects
Cell adhesion molecules -- Physiological aspects, Leukocytes -- Physiological aspects, Cell adhesion -- Physiological aspects, Biological sciences
Abstract

Leukocyte rolling mediated by P-, E-, L-selectin was analyzed via intravital microscopy and laminar flow assays. Analysis of leukocyte rolling indicated the role of a threshold level of shear force in promoting cell adhesion via P- and E-selectins. P- and L-selectin-dependent leukocyte rolling in the venules of mouse cremaster muscles also required a minimum shear rate. Furthermore, low blood flow velocities reduced rolling leukocyte flux in wild-type and L-selectin-deficient mice.

Published
1997

50. A thermodynamic study of ligand binding to the first three domains of the human insulin receptor: relationship between the receptor [alpha]-chain C-terminal peptide and the site 1 insulin mimetic peptides

Author

Menting, John G., Ward, Colin W., Margetts, Mai B., and Lawrence, Michael C.

Subjects
Alanine -- Chemical properties, Calorimetry -- Usage, Gene mutations -- Analysis, Ligand binding (Biochemistry) -- Analysis, Insulin -- Receptors, Insulin -- Chemical properties, Insulin -- Structure, Biological sciences, Chemistry
Abstract

Isothermal titration calorimetry is used to directly assay the binding of the insulin receptor (IR) [alpha]CT peptide to an IR construct (IR485) consisting of the three N-terminal domains of the receptor monomer. The mutation of the critical IR-alanineCT peptide residue Phe714 to alanine which does not affect the affinity of peptide for IR485 indicated that the resultant loss of insulin binding related to mutation is attributed to loss of interaction of the phenylalanine side chain with insulin.

Published
2009

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