Author: "Yang, Liming" / Topic: biological sciences - Searchworks@Jio Institute Digital Library Search Results

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35 results on '"Yang, Liming"'

1. Analysis of germline-driven ancestry-associated gene expression in cancers

Author: Chambwe, Nyasha, Sayaman, Rosalyn W, Hu, Donglei, Huntsman, Scott, Network, The Cancer Genome Analysis, Carrot-Zhang, Jian, Berger, Ashton C, Han, Seunghun, Meyerson, Matthew, Damrauer, Jeffrey S, Hoadley, Katherine A, Felau, Ina, Demchok, John A, Mensah, Michael KA, Tarnuzzer, Roy, Wang, Zhining, Yang, Liming, Knijnenburg, Theo A, Robertson, A Gordon, Yau, Christina, Benz, Christopher, Huang, Kuan-lin, Newberg, Justin Y, Frampton, Garrett M, Mashl, R Jay, Ding, Li, Romanel, Alessandro, Demichelis, Francesca, Zhou, Wanding, Laird, Peter W, Shen, Hui, Wong, Christopher K, Stuart, Joshua M, Lazar, Alexander J, Le, Xiuning, Oak, Ninad, Kemal, Anab, Caesar-Johnson, Samantha, Zenklusen, Jean C, Ziv, Elad, Beroukhim, Rameen, and Cherniack, Andrew D
Subjects: Biological Sciences, Health Sciences, Genetics, Human Genome, Biotechnology, Cancer, Good Health and Well Being, Gene Expression, Germ Cells, Humans, Neoplasms, Quantitative Trait Loci, RNA, Messenger, Cancer Genome Analysis Network, Bioinformatics, Computer sciences, Genomics, RNAseq, Sequence analysis
Abstract: Differential mRNA expression between ancestry groups can be explained by both genetic and environmental factors. We outline a computational workflow to determine the extent to which germline genetic variation explains cancer-specific molecular differences across ancestry groups. Using multi-omics datasets from The Cancer Genome Atlas (TCGA), we enumerate ancestry-informative markers colocalized with cancer-type-specific expression quantitative trait loci (e-QTLs) at ancestry-associated genes. This approach is generalizable to other settings with paired germline genotyping and mRNA expression data for a multi-ethnic cohort. For complete details on the use and execution of this protocol, please refer to Carrot-Zhang et al. (2020), Robertson et al. (2021), and Sayaman et al. (2021).
Published: 2022

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

Author: Carrot-Zhang, Jian, Yao, Xiaotong, Devarakonda, Siddhartha, Deshpande, Aditya, Damrauer, Jeffrey S, Silva, Tiago Chedraoui, Wong, Christopher K, Choi, Hyo Young, Felau, Ina, Robertson, A Gordon, Castro, Mauro AA, Bao, Lisui, Rheinbay, Esther, Liu, Eric Minwei, Trieu, Tuan, Haan, David, Yau, Christina, Hinoue, Toshinori, Liu, Yuexin, Shapira, Ofer, Kumar, Kiran, Mungall, Karen L, Zhang, Hailei, Lee, Jake June-Koo, Berger, Ashton, Gao, Galen F, Zhitomirsky, Binyamin, Liang, Wen-Wei, Zhou, Meng, Moorthi, Sitapriya, Berger, Alice H, Collisson, Eric A, Zody, Michael C, Ding, Li, Cherniack, Andrew D, Getz, Gad, Elemento, Olivier, Benz, Christopher C, Stuart, Josh, Zenklusen, JC, Beroukhim, Rameen, Chang, Jason C, Campbell, Joshua D, Hayes, D Neil, Yang, Lixing, Laird, Peter W, Weinstein, John N, Kwiatkowski, David J, Tsao, Ming S, Travis, William D, Khurana, Ekta, Berman, Benjamin P, Hoadley, Katherine A, Robine, Nicolas, Network, TCGA Research, Arora, Kanika, Shah, Minita, Shelton, Jennifer, Bowlby, Reanne, Friedl, Verena, Goldman, Mary, Craft, Brian, Heiman, David I, Hajirasouliha, Iman, Ricketts, Camir, Anur, Pavana, Chiotti, Kami E, Caesar-Johnson, Samantha J, Demchok, John A, Ferguson, Martin L, Kemal, Anab, Tarnuzzer, Roy, Wang, Zhining, Yang, Liming, Spellman, Paul T, Raphael, Benjamin, Akbani, Rehan, Zhu, Jingchun, Jones, Steven JM, Shen, Hui, Meyerson, Matthew, Govindan, Ramaswamy, and Imielinski, Marcin
Subjects: Biological Sciences, Cancer, Lung, Biotechnology, Genetics, Rare Diseases, Human Genome, Lung Cancer, Good Health and Well Being, Adenocarcinoma of Lung, Humans, Kelch-Like ECH-Associated Protein 1, Lung Neoplasms, Tachykinins, Whole Genome Sequencing, TCGA Research Network, TCGA, driver, genome analysis, lung adenocarcinoma, noncoding, oncogene, precision oncology, structural variation, tumor suppressor, whole genome sequencing, Biochemistry and Cell Biology, Medical Physiology, Biological sciences
Abstract: RTK/RAS/RAF pathway alterations (RPAs) are a hallmark of lung adenocarcinoma (LUAD). In this study, we use whole-genome sequencing (WGS) of 85 cases found to be RPA(-) by previous studies from The Cancer Genome Atlas (TCGA) to characterize the minority of LUADs lacking apparent alterations in this pathway. We show that WGS analysis uncovers RPA(+) in 28 (33%) of the 85 samples. Among the remaining 57 cases, we observe focal deletions targeting the promoter or transcription start site of STK11 (n = 7) or KEAP1 (n = 3), and promoter mutations associated with the increased expression of ILF2 (n = 6). We also identify complex structural variations associated with high-level copy number amplifications. Moreover, an enrichment of focal deletions is found in TP53 mutant cases. Our results indicate that RPA(-) cases demonstrate tumor suppressor deletions and genome instability, but lack unique or recurrent genetic lesions compensating for the lack of RPAs. Larger WGS studies of RPA(-) cases are required to understand this important LUAD subset.
Published: 2021

3. Comprehensive Analysis of Genetic Ancestry and Its Molecular Correlates in Cancer

Author: Carrot-Zhang, Jian, Chambwe, Nyasha, Damrauer, Jeffrey S, Knijnenburg, Theo A, Robertson, A Gordon, Yau, Christina, Zhou, Wanding, Berger, Ashton C, Huang, Kuan-lin, Newberg, Justin Y, Mashl, R Jay, Romanel, Alessandro, Sayaman, Rosalyn W, Demichelis, Francesca, Felau, Ina, Frampton, Garrett M, Han, Seunghun, Hoadley, Katherine A, Kemal, Anab, Laird, Peter W, Lazar, Alexander J, Le, Xiuning, Oak, Ninad, Shen, Hui, Wong, Christopher K, Zenklusen, Jean C, Ziv, Elad, Network, Cancer Genome Atlas Analysis, Aguet, Francois, Ding, Li, Demchok, John A, Mensah, Michael KA, Caesar-Johnson, Samantha, Tarnuzzer, Roy, Wang, Zhining, Yang, Liming, Alfoldi, Jessica, Karczewski, Konrad J, MacArthur, Daniel G, Meyerson, Matthew, Benz, Christopher, Stuart, Joshua M, Cherniack, Andrew D, and Beroukhim, Rameen
Subjects: Biological Sciences, Biomedical and Clinical Sciences, Oncology and Carcinogenesis, Genetics, Human Genome, Clinical Research, Cancer, Biotechnology, DNA Methylation, DNA-Binding Proteins, Ethnicity, F-Box-WD Repeat-Containing Protein 7, Gene Expression Regulation, Neoplastic, Genetic Predisposition to Disease, Genetics, Population, Genome, Human, Genomics, High-Throughput Nucleotide Sequencing, Humans, MicroRNAs, Mutation, Neoplasm Proteins, Neoplasms, Transcription Factors, Von Hippel-Lindau Tumor Suppressor Protein, Cancer Genome Atlas Analysis Network, TCGA, admixture, ancestry, cancer, eQTL, genomics, mRNA, methylation, miRNA, mutation, Neurosciences, Oncology & Carcinogenesis, Biochemistry and cell biology, Oncology and carcinogenesis
Abstract: We evaluated ancestry effects on mutation rates, DNA methylation, and mRNA and miRNA expression among 10,678 patients across 33 cancer types from The Cancer Genome Atlas. We demonstrated that cancer subtypes and ancestry-related technical artifacts are important confounders that have been insufficiently accounted for. Once accounted for, ancestry-associated differences spanned all molecular features and hundreds of genes. Biologically significant differences were usually tissue specific but not specific to cancer. However, admixture and pathway analyses suggested some of these differences are causally related to cancer. Specific findings included increased FBXW7 mutations in patients of African origin, decreased VHL and PBRM1 mutations in renal cancer patients of African origin, and decreased immune activity in bladder cancer patients of East Asian origin.
Published: 2020

4. 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, Cancer Genomics, Human Genome, Biotechnology, 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

5. 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

6. 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

7. 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

8. 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, Human Genome, Cancer, Cancer Genomics, 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

9. 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, Biotechnology, Cancer, Genetics, Cancer Genomics, 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

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

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

12. 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

13. Genomic and Functional Approaches to Understanding Cancer Aneuploidy

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

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

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

16. 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, Human Genome, Cancer, Cancer Genomics, 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

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

18. 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

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

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

20. 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

21. 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, Rare Diseases, Human Genome, Cancer Genomics, Prevention, Cancer, 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

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

23. 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, Rare Diseases, Genetics, Human Genome, Cancer Genomics, Digestive Diseases, Biotechnology, 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

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

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

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

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

Author: Knijnenburg, Theo A, Wang, Linghua, Zimmermann, Michael T, Chambwe, Nyasha, Gao, Galen F, Cherniack, Andrew D, Fan, Huihui, Shen, Hui, Way, Gregory P, Greene, Casey S, Liu, Yuexin, Akbani, Rehan, Feng, Bin, Donehower, Lawrence A, Miller, Chase, Shen, Yang, Karimi, Mostafa, Chen, Haoran, Kim, Pora, Jia, Peilin, Shinbrot, Eve, Zhang, Shaojun, Liu, Jianfang, Hu, Hai, Bailey, Matthew H, Yau, Christina, Wolf, Denise, Zhao, Zhongming, Weinstein, John N, Li, Lei, Ding, Li, Mills, Gordon B, Laird, Peter W, Wheeler, David A, Shmulevich, Ilya, Network, The Cancer Genome Atlas Research, Caesar-Johnson, Samantha J, Demchok, John A, Felau, Ina, Kasapi, Melpomeni, Ferguson, Martin L, Hutter, Carolyn M, Sofia, Heidi J, Tarnuzzer, Roy, Wang, Zhining, Yang, Liming, Zenklusen, Jean C, Zhang, Jiashan, Chudamani, Sudha, Liu, Jia, Lolla, Laxmi, Naresh, Rashi, Pihl, Todd, Sun, Qiang, Wan, Yunhu, Wu, Ye, Cho, Juok, DeFreitas, Timothy, Frazer, Scott, Gehlenborg, Nils, Getz, Gad, Heiman, David I, Kim, Jaegil, Lawrence, Michael S, Lin, Pei, Meier, Sam, Noble, Michael S, Saksena, Gordon, Voet, Doug, Zhang, Hailei, Bernard, Brady, Dhankani, Varsha, Knijnenburg, Theo, Kramer, Roger, Leinonen, Kalle, Miller, Michael, Reynolds, Sheila, Thorsson, Vesteinn, Zhang, Wei, Broom, Bradley M, Hegde, Apurva M, Ju, Zhenlin, Kanchi, Rupa S, Korkut, Anil, Li, Jun, Liang, Han, Ling, Shiyun, Liu, Wenbin, Lu, Yiling, Ng, Kwok-Shing, Rao, Arvind, Ryan, Michael, Wang, Jing, and Zhang, Jiexin
Subjects: Biological Sciences, Bioinformatics and Computational Biology, Genetics, Rare Diseases, Women's Health, Cancer Genomics, Human Genome, Ovarian Cancer, Cancer, Orphan Drug, 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

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

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

30. Body temperature as a mouse pharmacodynamic response to bombesin receptor subtype-3 agonists and other potential obesity treatments

Author: Metzger, Joseph M., Gagen, Karen, Raustad, Kate A., Yang, Liming, White, Amanda, Wang, Sheng-Ping, Craw, Stephanie, Liu, Ping, Lanza, Thomas, Lin, Linus S., Nargund, Ravi P., Guan, Xiao-Ming, Strack, Alison M., and Reitman, Marc L.
Subjects: Obesity -- Drug therapy, Obesity -- Research, Bombesin -- Physiological aspects, Bombesin -- Research, Agonists (Biochemistry) -- Health aspects, Agonists (Biochemistry) -- Research, Biological sciences
Abstract: Treatment of rodents with a bombesin receptor subtype-3 (BRS-3) agonist reduces food intake and increases fasting metabolic rate, causing weight loss with continued treatment. In small mammals, core body temperature ([T.sub.b]) is regulated in part by nutritional status, with a reduced [T.sub.b] during fasting. We report that fed Brs3 knockout mice have a lower [T.sub.b], which is discordant with their nutritional status. Treatment of wild-type mice with a BRS-3 agonist increased [T.sub.b], more so when the baseline [T.sub.b] was reduced such as by fasting or during the inactive phase of the light cycle. With repeated BRS-3 agonist dosing, the [T.sub.b] increase attenuated despite continued weight loss efficacy. The increase in [T.sub.b] was not prevented by inhibitors of prostaglandin E (PGE) production but was partially reduced by a [beta]-adrenergic blocker. These results demonstrate that BRS-3 has a role in body temperature regulation, presumably secondary to its effect on energy metabolism, including effects on sympathetic tone. By making use of this phenomenon, the reversal of the fasting [T.sub.b] reduction was developed into a sensitive single-dose pharmacodynamic assay for BRS-3 agonism and other antiobesity compounds acting by various mechanisms, including sibutramine, cannabinoid-1, and melanin-concentrating hormone-1 receptor blockers, and melanocortin, [[beta].sub.3]-adrenergic, and cholecystokinin-1 receptor agonists. These drugs increased both the fasted [T.sub.b] and the fasted, resting metabolic rates. The [T.sub.b] assay is a robust, information-rich assay that is simpler and has a greater throughput than measuring metabolic rate and is a practical, effective tool for drug discovery. tachyphylaxis; metabolic rate; thermoregulation; fasting; drug discovery doi: 10.1152/ajpendo.00404.2010.
Published: 2010

31. Angiotensin II induces carbon monoxide production in the perfused kidney: relationship to protein kinase C activation

Author: Li, Ping, Jiang, Houli, Yang, LiMing, Quan, Shuo, Dinocca, Sandra, Rodriguez, Francisca, Abraham, Nader G., and Nasjletti, Alberto
Subjects: Angiotensin -- Research, Biological sciences
Abstract: Angiotensin II induces carbon monoxide production in the perfused kidney: relationship to protein kinase C activation. Am J Physiol Renal Physiol 287: F914-F920, 2004. First published July 13, 2004; doi: 10.1152/ajprenal.00073.2004.--Heme oxygenase (HO)-derived carbon monoxide (CO) attenuates vascular reactivity to constrictor stimuli. ANG II produces vasoconstriction and induces HO-1 isoform expression. However, direct evidence that ANG II promotes HO product generation is lacking. Therefore, we examined the effects of ANG II on CO release and HO isoform expression in isolated rat kidneys. Kidneys were perfused with oxygenated Krebs buffer. ANG II (1 [micro]mol/l) increased (P < 0.05) perfusion pressure from 97 [+ or -] 9 to 150 [+ or -] 14 mmHg; it also increased (P < 0.05) the concentration of CO in the venous effluent (from 27.1 [+ or -] 11.9 to 45.6 [+ or -] 11.7, 62.5 [+ or -] 16.7, 94.8 [+ or -] 20.7, and 101.9 [+ or -] 13.1 nmol/l after 30, 60, 90, and 120 min, respectively). The pressor effect of ANG II was blunted (P < 0.05) in kidneys perfused with buffer containing losartan (10 [micro] mol/l) or PKC inhibitors staurosporine (0.1 [micro] mol/1) or calphostin C (1 [micro] mol/l). Kidneys perfused with buffer containing ANG II for 120 min also displayed increased (P < 0.05) HO-1 expression. Stannous mesoporphyrin (30 [micro] mol/l) decreased CO release (P < 0.05) in preparations perfused with and without ANG II; the HO inhibitor also increased (P < 0.05) perfusion pressure, more so in kidneys perfused with that without ANG II. We conclude that ANG II stimulates CO production and release in isolated, perfused rat kidneys. This action of ANG II is linked to the activation of A[T.sub.1] receptors and involves PKC activation and upregulation of renal HO-1 but not of HO-2 protein expression. The study suggests upregulation of renal HO-1 and CO release are protagonic events in a counterregulatory mechanism that attenuates ANG II-induced renal vasoconstriction. heme oxygenase; renal hemodynamics; vasoconstriction
Published: 2004

32. Medium perfusion enables engineering of compact and contractile cardiac tissue

Author: Radisic, Milica, Yang, Liming, Boublik, Jan, Cohen, Richard J., Langer, Robert, Freed, Lisa E., and Vunjak-Novakovic, Gordana
Subjects: Cardiology -- Research, Proteins -- Research, Biological sciences
Abstract: We hypothesized that functional constructs with physiological cell densities can be engineered in vitro by mimicking convective-diffusive oxygen transport normally present in vivo. To test this hypothesis, we designed an in vitro culture system that maintains efficient oxygen supply to the cells at all times during cell seeding and construct cultivation and characterized in detail construct metabolism, structure, and function. Neonatal rat cardiomyocytes suspended in Matrigel were cultured on collagen sponges at a high initial density (1.35 X [10.sup.8] cells/[cm.sup.3]) for 7 days with interstitial flow of medium; constructs cultured in orbitally mixed dishes, neonatal rat ventricles, and freshly isolated cardiomyocytes served as controls. Constructs were assessed at timed intervals with respect to cell number, distribution, viability, metabolic activity, cell cycle, presence of contractile proteins (sarcomeric [alpha]-actin, troponin I, and tropomyosin), and contractile function in response to electrical stimulation [excitation threshold (ET), maximum capture rate (MCR), response to a gap junctional blocker]. Interstitial flow of culture medium through the central 5-mm-diameter x 1.5-mm-thick region resulted in a physiological density of viable and differentiated, aerobically metabolizing cells, whereas dish culture resulted in constructs with only a 100--to 200-[micro]m-thick surface layer containing viable and differentiated but anaerobically metabolizing cells around an acellular interior. Perfusion resulted in significantly higher numbers of live cells, higher cell viability, and significantly more cells in the S phase compared with dish-grown constructs. In response to electrical stimulation, perfused constructs contracted synchronously, had lower ETs, and recovered their baseline function levels of ET and MCR after treatment with a gap junctional blocker; dish-grown constructs exhibited arrhythmic contractile patterns and failed to recover their baseline MCR levels. tissue engineering; cardiac muscle; contractile proteins; contractile function
Published: 2004

33. CO modulates pulmonary vascular response to acute hypoxia: relation to endothelin

Author: Zhang, Fan, Kaide, Jun Ichi, Yang, LiMing, Jiang, Houli, Quan, Shuo, Kemp, Rowena, Gong, Weiying, Balazy, Michael, Abraham, Nader G., and Nasjletti, Alberto
Subjects: Vasoconstriction -- Research, Biological sciences
Abstract: Pulmonary intralobar arteries express heme oxygenase (HO)-1 and -2 and release carbon monoxide (CO) during incubation in Krebs buffer. Acute hypoxia elicits isometric tension development (0.77 [+ or -] 0.06 mN/mm) in pulmonary vascular rings treated with 15 [micro]mol/l chromium mesoporphyrin (CrMP), an inhibitor of HO-dependent CO synthesis, but has not effect in untreated vessels. Acute hypoxia also induces contraction of pulmonary vessels taken from rats injected with HO-2 antisense oligodeoxynucleotides (ODN), which decrease pulmonary HO-2 vascular expression and CO release. Hypoxia-induced contraction of vessels treated with CrMP is attenuated (P < 0.05) by endothelium removal, by CO (1-100 [micro]mol/l) in the bathing buffer, and by endothelin-1 (ET-1) receptor blockade with L-754142 (10 [micro]mol/l). CrMP increases ET-1 levels in pulmonary intralobar arteries, particularly during incubation in hypooxygenated media. CrMP also causes a leftward shift in the concentration-response curve to ET-1, which is offset by exogenous CO. In anesthetized rats, pretreatment with CrMP (40 [micro]mol/kg iv) intensifies the elevation of pulmonary artery pressure elicited by breathing a hypoxic gas mixture. However, acute hypoxia does not elicit augmentation of pulmonary arterial pressure in rats pretreated concurrently with CrMP and the ET-1 receptor antagonist L-745142 (15 mg/kg iv). These data suggest that a product of HO activity, most likely CO, inhibits hypoxia-induced pulmonary vasoconstriction by reducing ET-1 vascular levels and sensitivity. hypoxic pulmonary vasoconstriction; heme oxygenase
Published: 2004

34. Modulation of cGMP by human HO-1 retrovirus gene transfer in pulmonary microvessel endothelial cells

Author: Abraham, Nader G., Quan, Shuo, Mieyal, Paul A., Yang, Liming, Burke-Wolin, Theresa, Mingone, Christopher J., Goodman, Alvin I., Nasjletti, Alberto, and Wolin, Michael S.
Subjects: Carbon monoxide -- Health aspects, Cell research, Biological sciences
Abstract: Carbon monoxide (CO) stimulates guanylate cyclase (GC) and increases guanosine 3', 5'-cyclic monophosphate (cGMP) levels. We transfected rat-lung pulmonary endothelial cells with a retrovirus-mediated human heme oxygenase (hHO)-i gene. Pulmonary cells that expressed hHO-1 exhibited a fourfold increase in HO activity associated with decreases in the steady-state levels of heme and cGMP without changes in soluble GC (sGC) and endothelial nitric oxide synthase (NOS) proteins or basal nitrite production. Heme elicited significant increases in CO production and intracellular cGMP levels in both pulmonary endothelial and pulmonary hHO-1-expressing cells. [[N.sup.[omega]]-nitro-L-arginine methyl ester (L-NAME), an inhibitor of NOS, significantly decreased cGMP levels in heine-treated pulmonary endothelial cells but not heme-treated hHO-1-expressing cells. In the presence of exogenous heine, CO and cGMP levels in hHO-1-expressing cells exceeded the corresponding levels in pulmonary endothelial cells. Acute exposure of endothelial cells to Sn[Cl.sub.2], which is an inducer of HO-1, increased cGMP levels, whereas chronic exposure decreased heine and cGMP levels. These results indicate that prolonged overexpression of HO-1 ultimately decreases sGC activity by limiting the availability of cellular heine. Heme activates sGC and enhances cGMP levels via a mechanism that is largely insensitive to NOS inhibition. guanosine 3',5'-cyclic monophosphate; retroviral vector; soluble guanylate cyclase; heine oxygenase
Published: 2002

35. Retrovirus-mediated HO gene transfer into endothelial cells protects against oxidant-induced injury

Author: Yang, Liming, Quan, Shuo, and Abraham, Nader G.
Subjects: Endothelium -- Cytology, Retroviruses -- Research, Guanosine -- Research, Oxidizing agents -- Research, Heme -- Research, Biological sciences
Abstract: The successful transduction of human heme oxygenase (HO)-1 gene into rat lung microvessel endothelium using replication-defective retroviral vector was reported. A 2.1-fold increase in HO-1 protein level was exhibited by cells transduced with human HO-1 gene. The results showed that the induction of HO-1 in response to injurious stimuli represents an important mechanism for moderating the severity of cell damage. Clinical applications loom as a possibility if HO activity is regulated in this manner.
Published: 1999

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35 results on '"Yang, Liming"'

1. Analysis of germline-driven ancestry-associated gene expression in cancers

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

3. Comprehensive Analysis of Genetic Ancestry and Its Molecular Correlates in Cancer

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

5. Comprehensive Characterization of Cancer Driver Genes and Mutations

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

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

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

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

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

11. Comparative Molecular Analysis of Gastrointestinal Adenocarcinomas

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

13. Genomic and Functional Approaches to Understanding Cancer Aneuploidy

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

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

16. Oncogenic Signaling Pathways in The Cancer Genome Atlas

17. Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation

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

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

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

21. Pathogenic Germline Variants in 10,389 Adult Cancers

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

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

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

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

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

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

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

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

30. Body temperature as a mouse pharmacodynamic response to bombesin receptor subtype-3 agonists and other potential obesity treatments

31. Angiotensin II induces carbon monoxide production in the perfused kidney: relationship to protein kinase C activation

32. Medium perfusion enables engineering of compact and contractile cardiac tissue

33. CO modulates pulmonary vascular response to acute hypoxia: relation to endothelin

34. Modulation of cGMP by human HO-1 retrovirus gene transfer in pulmonary microvessel endothelial cells

35. Retrovirus-mediated HO gene transfer into endothelial cells protects against oxidant-induced injury

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35 results on '"Yang, Liming"'

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