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

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41 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. 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

6. 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

7. 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, Cancer Genomics, Human Genome, Cancer, Genetics, Biotechnology, Good Health and Well Being, Cell Line, Tumor, Gene Expression Regulation, Neoplastic, Genome, Human, Genomics, Humans, Metabolic Networks and Pathways, Neoplasms, Oncogene Proteins, Ubiquitination, Cancer Genome Atlas Research Network, FBXW7, The Cancer Genome Atlas, biomarker, cancer prognosis, pan-cancer analysis, therapeutic targets, tumor subtype, ubiquitin pathway, Biochemistry and Cell Biology, Medical Physiology, Biological sciences
Abstract: Protein ubiquitination is a dynamic and reversible process of adding single ubiquitin molecules or various ubiquitin chains to target proteins. Here, using multidimensional omic data of 9,125 tumor samples across 33 cancer types from The Cancer Genome Atlas, we perform comprehensive molecular characterization of 929 ubiquitin-related genes and 95 deubiquitinase genes. Among them, we systematically identify top somatic driver candidates, including mutated FBXW7 with cancer-type-specific patterns and amplified MDM2 showing a mutually exclusive pattern with BRAF mutations. Ubiquitin pathway genes tend to be upregulated in cancer mediated by diverse mechanisms. By integrating pan-cancer multiomic data, we identify a group of tumor samples that exhibit worse prognosis. These samples are consistently associated with the upregulation of cell-cycle and DNA repair pathways, characterized by mutated TP53, MYC/TERT amplification, and APC/PTEN deletion. Our analysis highlights the importance of the ubiquitin pathway in cancer development and lays a foundation for developing relevant therapeutic strategies.
Published: 2018

8. 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

9. 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, Rare Diseases, Cancer, Human Genome, Colo-Rectal Cancer, Genetic Testing, Cancer Genomics, Biotechnology, Digestive Diseases, Adenocarcinoma, Aneuploidy, Chromosomal Instability, DNA Methylation, DNA Polymerase II, DNA-Binding Proteins, Epigenesis, Genetic, Female, Gastrointestinal Neoplasms, Gene Regulatory Networks, Heterogeneous-Nuclear Ribonucleoproteins, Humans, Male, Microsatellite Instability, MutL Protein Homolog 1, Mutation, Poly-ADP-Ribose Binding Proteins, Polymorphism, Single Nucleotide, Proto-Oncogene Proteins p21(ras), RNA-Binding Proteins, SOX9 Transcription Factor, Cancer Genome Atlas Research Network, cancer, colon, colorectal, epigenetic, esophagus, genomic, methylation, rectum, stomach, tumor, Neurosciences, Oncology and Carcinogenesis, Oncology & Carcinogenesis, Biochemistry and cell biology, Oncology and carcinogenesis
Abstract: We analyzed 921 adenocarcinomas of the esophagus, stomach, colon, and rectum to examine shared and distinguishing molecular characteristics of gastrointestinal tract adenocarcinomas (GIACs). Hypermutated tumors were distinct regardless of cancer type and comprised those enriched for insertions/deletions, representing microsatellite instability cases with epigenetic silencing of MLH1 in the context of CpG island methylator phenotype, plus tumors with elevated single-nucleotide variants associated with mutations in POLE. Tumors with chromosomal instability were diverse, with gastroesophageal adenocarcinomas harboring fragmented genomes associated with genomic doubling and distinct mutational signatures. We identified a group of tumors in the colon and rectum lacking hypermutation and aneuploidy termed genome stable and enriched in DNA hypermethylation and mutations in KRAS, SOX9, and PCBP1.
Published: 2018

10. 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

11. Genomic and Functional Approaches to Understanding Cancer Aneuploidy

Author: Taylor, Alison M, Shih, Juliann, Ha, Gavin, Gao, Galen F, Zhang, Xiaoyang, Berger, Ashton C, Schumacher, Steven E, Wang, Chen, Hu, Hai, Liu, Jianfang, Lazar, Alexander J, Network, The Cancer Genome Atlas Research, Caesar-Johnson, Samantha J, Demchok, John A, Felau, Ina, Kasapi, Melpomeni, Ferguson, Martin L, Hutter, Carolyn M, Sofia, Heidi J, Tarnuzzer, Roy, Wang, Zhining, Yang, Liming, Zenklusen, Jean C, Zhang, Jiashan, Chudamani, Sudha, Liu, Jia, Lolla, Laxmi, Naresh, Rashi, Pihl, Todd, Sun, Qiang, Wan, Yunhu, Wu, Ye, Cho, Juok, DeFreitas, Timothy, Frazer, Scott, Gehlenborg, Nils, Getz, Gad, Heiman, David I, Kim, Jaegil, Lawrence, Michael S, Lin, Pei, Meier, Sam, Noble, Michael S, Saksena, Gordon, Voet, Doug, Zhang, Hailei, Bernard, Brady, Chambwe, Nyasha, Dhankani, Varsha, Knijnenburg, Theo, Kramer, Roger, Leinonen, Kalle, Liu, Yuexin, Miller, Michael, Reynolds, Sheila, Shmulevich, Ilya, Thorsson, Vesteinn, Zhang, Wei, Akbani, Rehan, Broom, Bradley M, Hegde, Apurva M, Ju, Zhenlin, Kanchi, Rupa S, Korkut, Anil, Li, Jun, Liang, Han, Ling, Shiyun, Liu, Wenbin, Lu, Yiling, Mills, Gordon B, Ng, Kwok-Shing, Rao, Arvind, Ryan, Michael, Wang, Jing, Weinstein, John N, Zhang, Jiexin, Abeshouse, Adam, Armenia, Joshua, Chakravarty, Debyani, Chatila, Walid K, de Bruijn, Ino, Gao, Jianjiong, Gross, Benjamin E, Heins, Zachary J, Kundra, Ritika, La, Konnor, Ladanyi, Marc, Luna, Augustin, Nissan, Moriah G, Ochoa, Angelica, Phillips, Sarah M, Reznik, Ed, Sanchez-Vega, Francisco, Sander, Chris, Schultz, Nikolaus, Sheridan, Robert, Sumer, S Onur, Sun, Yichao, Taylor, Barry S, and Wang, Jioajiao
Subjects: Biological Sciences, Biomedical and Clinical Sciences, Bioinformatics and Computational Biology, Genetics, Oncology and Carcinogenesis, Cancer Genomics, Cancer, Lung Cancer, Human Genome, Lung, 2.1 Biological and endogenous factors, Aneuploidy, Carcinoma, Squamous Cell, Cell Cycle, Cell Proliferation, Chromosome Aberrations, Chromosome Deletion, Chromosomes, Human, Pair 3, Databases, Genetic, Genomics, Humans, Mutation Rate, Tumor Suppressor Protein p53, Cancer Genome Atlas Research Network, aneuploidy, cancer genomics, genome engineering, lung squamous cell carcinoma, Neurosciences, Oncology & Carcinogenesis, Biochemistry and cell biology, Oncology and carcinogenesis
Abstract: Aneuploidy, whole chromosome or chromosome arm imbalance, is a near-universal characteristic of human cancers. In 10,522 cancer genomes from The Cancer Genome Atlas, aneuploidy was correlated with TP53 mutation, somatic mutation rate, and expression of proliferation genes. Aneuploidy was anti-correlated with expression of immune signaling genes, due to decreased leukocyte infiltrates in high-aneuploidy samples. Chromosome arm-level alterations show cancer-specific patterns, including loss of chromosome arm 3p in squamous cancers. We applied genome engineering to delete 3p in lung cells, causing decreased proliferation rescued in part by chromosome 3 duplication. This study defines genomic and phenotypic correlates of cancer aneuploidy and provides an experimental approach to study chromosome arm aneuploidy.
Published: 2018

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

13. 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

14. The Immune Landscape of Cancer

Author: Thorsson, Vésteinn, Gibbs, David L, Brown, Scott D, Wolf, Denise, Bortone, Dante S, Ou Yang, Tai-Hsien, Porta-Pardo, Eduard, Gao, Galen F, Plaisier, Christopher L, Eddy, James A, Ziv, Elad, Culhane, Aedin C, Paull, Evan O, Sivakumar, IK Ashok, Gentles, Andrew J, Malhotra, Raunaq, Farshidfar, Farshad, Colaprico, Antonio, Parker, Joel S, Mose, Lisle E, Vo, Nam Sy, Liu, Jianfang, Liu, Yuexin, Rader, Janet, Dhankani, Varsha, Reynolds, Sheila M, Bowlby, Reanne, Califano, Andrea, Cherniack, Andrew D, Anastassiou, Dimitris, Bedognetti, Davide, Mokrab, Younes, Newman, Aaron M, Rao, Arvind, Chen, Ken, Krasnitz, Alexander, Hu, Hai, Malta, Tathiane M, Noushmehr, Houtan, Pedamallu, Chandra Sekhar, Bullman, Susan, Ojesina, Akinyemi I, Lamb, Andrew, Zhou, Wanding, Shen, Hui, Choueiri, Toni K, Weinstein, John N, Guinney, Justin, Saltz, Joel, Holt, Robert A, Rabkin, Charles S, Lazar, Alexander J, Serody, Jonathan S, Demicco, Elizabeth G, Disis, Mary L, Vincent, Benjamin G, Shmulevich, Ilya, 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 Julia, Chudamani, Sudha, Liu, Jia, Lolla, Laxmi, Naresh, Rashi, Pihl, Todd, Sun, Qiang, Wan, Yunhu, Wu, Ye, Cho, Juok, DeFreitas, Timothy, Frazer, Scott, Gehlenborg, Nils, Getz, Gad, Heiman, David I, Kim, Jaegil, Lawrence, Michael S, Lin, Pei, Meier, Sam, Noble, Michael S, Saksena, Gordon, Voet, Doug, Zhang, Hailei, Bernard, Brady, Chambwe, Nyasha, Knijnenburg, Theo, Kramer, Roger, Leinonen, Kalle, Miller, Michael, and Reynolds, Sheila
Subjects: Biomedical and Clinical Sciences, Oncology and Carcinogenesis, Immunology, Immunotherapy, Genetics, Human Genome, Cancer, Cancer Genomics, 2.1 Biological and endogenous factors, Inflammatory and immune system, Adolescent, Adult, Aged, Aged, 80 and over, Child, Female, Genomics, Humans, Interferon-gamma, Macrophages, Male, Middle Aged, Neoplasms, Prognosis, Th1-Th2 Balance, Transforming Growth Factor beta, Wound Healing, Young Adult, Cancer Genome Atlas Research Network, cancer genomics, immune subtypes, immuno-oncology, immunomodulatory, immunotherapy, integrative network analysis, tumor immunology, tumor microenvironment
Abstract: We performed an extensive immunogenomic analysis of more than 10,000 tumors comprising 33 diverse cancer types by utilizing data compiled by TCGA. Across cancer types, we identified six immune subtypes-wound healing, IFN-γ dominant, inflammatory, lymphocyte depleted, immunologically quiet, and TGF-β dominant-characterized by differences in macrophage or lymphocyte signatures, Th1:Th2 cell ratio, extent of intratumoral heterogeneity, aneuploidy, extent of neoantigen load, overall cell proliferation, expression of immunomodulatory genes, and prognosis. Specific driver mutations correlated with lower (CTNNB1, NRAS, or IDH1) or higher (BRAF, TP53, or CASP8) leukocyte levels across all cancers. Multiple control modalities of the intracellular and extracellular networks (transcription, microRNAs, copy number, and epigenetic processes) were involved in tumor-immune cell interactions, both across and within immune subtypes. Our immunogenomics pipeline to characterize these heterogeneous tumors and the resulting data are intended to serve as a resource for future targeted studies to further advance the field.
Published: 2018

15. Oncogenic Signaling Pathways in The Cancer Genome Atlas

Author: Sanchez-Vega, Francisco, Mina, Marco, Armenia, Joshua, Chatila, Walid K, Luna, Augustin, La, Konnor C, Dimitriadoy, Sofia, Liu, David L, Kantheti, Havish S, Saghafinia, Sadegh, Chakravarty, Debyani, Daian, Foysal, Gao, Qingsong, Bailey, Matthew H, Liang, Wen-Wei, Foltz, Steven M, Shmulevich, Ilya, Ding, Li, Heins, Zachary, Ochoa, Angelica, Gross, Benjamin, Gao, Jianjiong, Zhang, Hongxin, Kundra, Ritika, Kandoth, Cyriac, Bahceci, Istemi, Dervishi, Leonard, Dogrusoz, Ugur, Zhou, Wanding, Shen, Hui, Laird, Peter W, Way, Gregory P, Greene, Casey S, Liang, Han, Xiao, Yonghong, Wang, Chen, Iavarone, Antonio, Berger, Alice H, Bivona, Trever G, Lazar, Alexander J, Hammer, Gary D, Giordano, Thomas, Kwong, Lawrence N, McArthur, Grant, Huang, Chenfei, Tward, Aaron D, Frederick, Mitchell J, McCormick, Frank, Meyerson, Matthew, Network, The Cancer Genome Atlas Research, Caesar-Johnson, Samantha J, Demchok, John A, Felau, Ina, Kasapi, Melpomeni, Ferguson, Martin L, Hutter, Carolyn M, Sofia, Heidi J, Tarnuzzer, Roy, Wang, Zhining, Yang, Liming, Zenklusen, Jean C, Zhang, Jiashan, Chudamani, Sudha, Liu, Jia, Lolla, Laxmi, Naresh, Rashi, Pihl, Todd, Sun, Qiang, Wan, Yunhu, Wu, Ye, Cho, Juok, DeFreitas, Timothy, Frazer, Scott, Gehlenborg, Nils, Getz, Gad, Heiman, David I, Kim, Jaegil, Lawrence, Michael S, Lin, Pei, Meier, Sam, Noble, Michael S, Saksena, Gordon, Voet, Doug, Zhang, Hailei, Bernard, Brady, Chambwe, Nyasha, Dhankani, Varsha, Knijnenburg, Theo, Kramer, Roger, Leinonen, Kalle, Liu, Yuexin, Miller, Michael, Reynolds, Sheila, Thorsson, Vesteinn, Zhang, Wei, Akbani, Rehan, Broom, Bradley M, Hegde, Apurva M, and Ju, Zhenlin
Subjects: Biochemistry and Cell Biology, Bioinformatics and Computational Biology, Biological Sciences, Biomedical and Clinical Sciences, Genetics, Oncology and Carcinogenesis, Cancer Genomics, Human Genome, Cancer, 2.1 Biological and endogenous factors, Good Health and Well Being, Databases, Genetic, Genes, Neoplasm, Humans, Neoplasms, Phosphatidylinositol 3-Kinases, Signal Transduction, Transforming Growth Factor beta, Tumor Suppressor Protein p53, Wnt Proteins, Cancer Genome Atlas Research Network, PanCanAtlas, TCGA, cancer genome atlas, cancer genomics, combination therapy, pan-cancer, precision oncology, signaling pathways, therapeutics, whole exome sequencing, Medical and Health Sciences, Developmental Biology, Biological sciences, Biomedical and clinical sciences
Abstract: Genetic alterations in signaling pathways that control cell-cycle progression, apoptosis, and cell growth are common hallmarks of cancer, but the extent, mechanisms, and co-occurrence of alterations in these pathways differ between individual tumors and tumor types. Using mutations, copy-number changes, mRNA expression, gene fusions and DNA methylation in 9,125 tumors profiled by The Cancer Genome Atlas (TCGA), we analyzed the mechanisms and patterns of somatic alterations in ten canonical pathways: cell cycle, Hippo, Myc, Notch, Nrf2, PI-3-Kinase/Akt, RTK-RAS, TGFβ signaling, p53 and β-catenin/Wnt. We charted the detailed landscape of pathway alterations in 33 cancer types, stratified into 64 subtypes, and identified patterns of co-occurrence and mutual exclusivity. Eighty-nine percent of tumors had at least one driver alteration in these pathways, and 57% percent of tumors had at least one alteration potentially targetable by currently available drugs. Thirty percent of tumors had multiple targetable alterations, indicating opportunities for combination therapy.
Published: 2018

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

17. 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

18. 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, Group, The 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, 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, Akbani, Rehan, Broom, Bradley M, Hegde, Apurva M, Ju, Zhenlin, Kanchi, Rupa S, Korkut, Anil, Li, Jun, Ling, Shiyun, Liu, Wenbin, Lu, Yiling, Mills, Gordon B, Ng, Kwok-Shing, Rao, Arvind, Ryan, Michael, Wang, Jing, Weinstein, John N, and Zhang, Jiexin
Subjects: Biological Sciences, Bioinformatics and Computational Biology, Biomedical and Clinical Sciences, Oncology and Carcinogenesis, Cancer Genomics, Human Genome, Networking and Information Technology R&D (NITRD), Cancer, Precision Medicine, Genetic Testing, Genetics, 2.1 Biological and endogenous factors, Good Health and Well Being, Algorithms, B7-H1 Antigen, Computational Biology, Databases, Genetic, Entropy, Humans, Microsatellite Instability, Mutation, Neoplasms, Principal Component Analysis, Programmed Cell Death 1 Receptor, MC3 Working Group, Cancer Genome Atlas Research Network, driver gene discovery, mutations of clinical relevance, oncology, structure analysis, Medical and Health Sciences, Developmental Biology, Biological sciences, Biomedical and clinical sciences
Abstract: Identifying molecular cancer drivers is critical for precision oncology. Multiple advanced algorithms to identify drivers now exist, but systematic attempts to combine and optimize them on large datasets are few. We report a PanCancer and PanSoftware analysis spanning 9,423 tumor exomes (comprising all 33 of The Cancer Genome Atlas projects) and using 26 computational tools to catalog driver genes and mutations. We identify 299 driver genes with implications regarding their anatomical sites and cancer/cell types. Sequence- and structure-based analyses identified >3,400 putative missense driver mutations supported by multiple lines of evidence. Experimental validation confirmed 60%-85% of predicted mutations as likely drivers. We found that >300 MSI tumors are associated with high PD-1/PD-L1, and 57% of tumors analyzed harbor putative clinically actionable events. Our study represents the most comprehensive discovery of cancer genes and mutations to date and will serve as a blueprint for future biological and clinical endeavors.
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, Cancer, Rare Diseases, Prevention, Cancer Genomics, Human Genome, 2.1 Biological and endogenous factors, DNA Copy Number Variations, Databases, Genetic, Gene Deletion, Gene Frequency, Genetic Predisposition to Disease, Genotype, Germ Cells, Germ-Line Mutation, Humans, Loss of Heterozygosity, Mutation, Missense, Neoplasms, Polymorphism, Single Nucleotide, Proto-Oncogene Proteins c-met, Proto-Oncogene Proteins c-ret, Tumor Suppressor Proteins, Cancer Genome Atlas Research Network, LOH, cancer predisposition, germline and somatic genomes, variant pathogenicity, Medical and Health Sciences, Developmental Biology, Biological sciences, Biomedical and clinical sciences
Abstract: We conducted the largest investigation of predisposition variants in cancer to date, discovering 853 pathogenic or likely pathogenic variants in 8% of 10,389 cases from 33 cancer types. Twenty-one genes showed single or cross-cancer associations, including novel associations of SDHA in melanoma and PALB2 in stomach adenocarcinoma. The 659 predisposition variants and 18 additional large deletions in tumor suppressors, including ATM, BRCA1, and NF1, showed low gene expression and frequent (43%) loss of heterozygosity or biallelic two-hit events. We also discovered 33 such variants in oncogenes, including missenses in MET, RET, and PTPN11 associated with high gene expression. We nominated 47 additional predisposition variants from prioritized VUSs supported by multiple evidences involving case-control frequency, loss of heterozygosity, expression effect, and co-localization with mutations and modified residues. Our integrative approach links rare predisposition variants to functional consequences, informing future guidelines of variant classification and germline genetic testing in cancer.
Published: 2018

22. 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, 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, Broom, Bradley M, Hegde, Apurva M, Ju, Zhenlin, Korkut, Anil, Li, Jun, Liang, Han, Ling, Shiyun, Liu, Wenbin, and Lu, Yiling
Subjects: Biological Sciences, Human Genome, Kidney Disease, Cancer, Biotechnology, Rare Diseases, Genetics, Cancer Genomics, Orphan Drug, 4.1 Discovery and preclinical testing of markers and technologies, 2.1 Biological and endogenous factors, Good Health and Well Being, Biomarkers, Tumor, Carcinoma, Renal Cell, Cyclin-Dependent Kinase Inhibitor p16, DNA-Binding Proteins, Genome, Human, Humans, Kidney Neoplasms, Metabolic Networks and Pathways, Nuclear Proteins, PTEN Phosphohydrolase, Phenotype, Survival Analysis, Transcription Factors, Tumor Suppressor Proteins, Ubiquitin Thiolesterase, Cancer Genome Atlas Research Network, CDKN2A, DNA hypermethylation, PanCanAtlas, TCGA, chromatin remodeling, chromophobe renal cell carcinoma, clear cell renal cell carcinoma, immune signature, papillary renal cell carcinoma, Biochemistry and Cell Biology, Medical Physiology, Biological sciences
Abstract: Renal cell carcinoma (RCC) is not a single disease, but several histologically defined cancers with different genetic drivers, clinical courses, and therapeutic responses. The current study evaluated 843 RCC from the three major histologic subtypes, including 488 clear cell RCC, 274 papillary RCC, and 81 chromophobe RCC. Comprehensive genomic and phenotypic analysis of the RCC subtypes reveals distinctive features of each subtype that provide the foundation for the development of subtype-specific therapeutic and management strategies for patients affected with these cancers. Somatic alteration of BAP1, PBRM1, and PTEN and altered metabolic pathways correlated with subtype-specific decreased survival, while CDKN2A alteration, increased DNA hypermethylation, and increases in the immune-related Th2 gene expression signature correlated with decreased survival within all major histologic subtypes. CIMP-RCC demonstrated an increased immune signature, and a uniform and distinct metabolic expression pattern identified a subset of metabolically divergent (MD) ChRCC that associated with extremely poor survival.
Published: 2018

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

Author: Jayasinghe, Reyka G, Cao, Song, Gao, Qingsong, Wendl, Michael C, Vo, Nam Sy, Reynolds, Sheila M, Zhao, Yanyan, Climente-González, Héctor, Chai, Shengjie, Wang, Fang, Varghese, Rajees, Huang, Mo, Liang, Wen-Wei, Wyczalkowski, Matthew A, Sengupta, Sohini, Li, Zhi, Payne, Samuel H, Fenyö, David, Miner, Jeffrey H, Walter, Matthew J, Network, The Cancer Genome Atlas Research, Caesar-Johnson, Samantha J, Demchok, John A, Felau, Ina, Kasapi, Melpomeni, Ferguson, Martin L, Hutter, Carolyn M, Sofia, Heidi J, Tarnuzzer, Roy, Wang, Zhining, Yang, Liming, Zenklusen, Jean C, Zhang, Jiashan, Chudamani, Sudha, Liu, Jia, Lolla, Laxmi, Naresh, Rashi, Pihl, Todd, Sun, Qiang, Wan, Yunhu, Wu, Ye, Cho, Juok, DeFreitas, Timothy, Frazer, Scott, Gehlenborg, Nils, Getz, Gad, Heiman, David I, Kim, Jaegil, Lawrence, Michael S, Lin, Pei, Meier, Sam, Noble, Michael S, Saksena, Gordon, Voet, Doug, Zhang, Hailei, Bernard, Brady, Chambwe, Nyasha, Dhankani, Varsha, Knijnenburg, Theo, Kramer, Roger, Leinonen, Kalle, Liu, Yuexin, Miller, Michael, Reynolds, Sheila, Shmulevich, Ilya, Thorsson, Vesteinn, Zhang, Wei, Akbani, Rehan, Broom, Bradley M, Hegde, Apurva M, Ju, Zhenlin, Kanchi, Rupa S, Korkut, Anil, Li, Jun, Liang, Han, Ling, Shiyun, Liu, Wenbin, Lu, Yiling, Mills, Gordon B, Ng, Kwok-Shing, Rao, Arvind, Ryan, Michael, Wang, Jing, Weinstein, John N, Zhang, Jiexin, Abeshouse, Adam, Armenia, Joshua, Chakravarty, Debyani, Chatila, Walid K, de Bruijn, Ino, Gao, Jianjiong, Gross, Benjamin E, Heins, Zachary J, Kundra, Ritika, La, Konnor, Ladanyi, Marc, Luna, Augustin, Nissan, Moriah G, Ochoa, Angelica, and Phillips, Sarah M
Subjects: Biological Sciences, Bioinformatics and Computational Biology, Cancer Genomics, Human Genome, Rare Diseases, Cancer, Immunotherapy, Genetics, 5.1 Pharmaceuticals, 2.1 Biological and endogenous factors, BRCA1 Protein, GATA3 Transcription Factor, HEK293 Cells, Humans, Mutation, Neoplasms, Poly (ADP-Ribose) Polymerase-1, Programmed Cell Death 1 Receptor, RNA Splice Sites, Tumor Suppressor Protein p53, X-linked Nuclear Protein, Cancer Genome Atlas Research Network, RNA, mutations of clinical relevance, splicing, Biochemistry and Cell Biology, Medical Physiology, Biological sciences
Abstract: For the past decade, cancer genomic studies have focused on mutations leading to splice-site disruption, overlooking those having splice-creating potential. Here, we applied a bioinformatic tool, MiSplice, for the large-scale discovery of splice-site-creating mutations (SCMs) across 8,656 TCGA tumors. We report 1,964 originally mis-annotated mutations having clear evidence of creating alternative splice junctions. TP53 and GATA3 have 26 and 18 SCMs, respectively, and ATRX has 5 from lower-grade gliomas. Mutations in 11 genes, including PARP1, BRCA1, and BAP1, were experimentally validated for splice-site-creating function. Notably, we found that neoantigens induced by SCMs are likely several folds more immunogenic compared to missense mutations, exemplified by the recurrent GATA3 SCM. Further, high expression of PD-1 and PD-L1 was observed in tumors with SCMs, suggesting candidates for immune blockade therapy. Our work highlights the importance of integrating DNA and RNA data for understanding the functional and the clinical implications of mutations in human diseases.
Published: 2018

24. 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

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

Author: Peng, Xinxin, Chen, Zhongyuan, Farshidfar, Farshad, Xu, Xiaoyan, Lorenzi, Philip L, Wang, Yumeng, Cheng, Feixiong, Tan, Lin, Mojumdar, Kamalika, Du, Di, Ge, Zhongqi, Li, Jun, Thomas, George V, Birsoy, Kivanc, Liu, Lingxiang, Zhang, Huiwen, Zhao, Zhongming, Marchand, Calena, Weinstein, John N, Network, The Cancer Genome Atlas Research, Caesar-Johnson, Samantha J, Demchok, John A, Felau, Ina, Kasapi, Melpomeni, Ferguson, Martin L, Hutter, Carolyn M, Sofia, Heidi J, Tarnuzzer, Roy, Wang, Zhining, Yang, Liming, Zenklusen, Jean C, Zhang, Jiashan, Chudamani, Sudha, Liu, Jia, Lolla, Laxmi, Naresh, Rashi, Pihl, Todd, Sun, Qiang, Wan, Yunhu, Wu, Ye, Cho, Juok, DeFreitas, Timothy, Frazer, Scott, Gehlenborg, Nils, Getz, Gad, Heiman, David I, Kim, Jaegil, Lawrence, Michael S, Lin, Pei, Meier, Sam, Noble, Michael S, Saksena, Gordon, Voet, Doug, Zhang, Hailei, Bernard, Brady, Chambwe, Nyasha, Dhankani, Varsha, Knijnenburg, Theo, Kramer, Roger, Leinonen, Kalle, Liu, Yuexin, Miller, Michael, Reynolds, Sheila, Shmulevich, Ilya, Thorsson, Vesteinn, Zhang, Wei, Akbani, Rehan, Broom, Bradley M, Hegde, Apurva M, Ju, Zhenlin, Kanchi, Rupa S, Korkut, Anil, Liang, Han, Ling, Shiyun, Liu, Wenbin, Lu, Yiling, Mills, Gordon B, Ng, Kwok-Shing, Rao, Arvind, Ryan, Michael, Wang, Jing, Zhang, Jiexin, Abeshouse, Adam, Armenia, Joshua, Chakravarty, Debyani, Chatila, Walid K, de Bruijn, Ino, Gao, Jianjiong, Gross, Benjamin E, Heins, Zachary J, Kundra, Ritika, La, Konnor, Ladanyi, Marc, Luna, Augustin, Nissan, Moriah G, Ochoa, Angelica, Phillips, Sarah M, and Reznik, Ed
Subjects: Biological Sciences, Cancer Genomics, Human Genome, Cancer, Nutrition, Genetics, 2.1 Biological and endogenous factors, Cell Line, Tumor, Core Binding Factor Alpha 2 Subunit, Drug Resistance, Neoplasm, HEK293 Cells, Humans, Metabolic Networks and Pathways, Neoplasms, Snail Family Transcription Factors, Transcriptome, Cancer Genome Atlas Research Network, The Cancer Genome Atlas, carbohydrate metabolism, drug sensitivity, master regulator, prognostic markers, somatic drivers, therapeutic targets, tumor subtypes, Biochemistry and Cell Biology, Medical Physiology, Biological sciences
Abstract: Metabolic reprogramming provides critical information for clinical oncology. Using molecular data of 9,125 patient samples from The Cancer Genome Atlas, we identified tumor subtypes in 33 cancer types based on mRNA expression patterns of seven major metabolic processes and assessed their clinical relevance. Our metabolic expression subtypes correlated extensively with clinical outcome: subtypes with upregulated carbohydrate, nucleotide, and vitamin/cofactor metabolism most consistently correlated with worse prognosis, whereas subtypes with upregulated lipid metabolism showed the opposite. Metabolic subtypes correlated with diverse somatic drivers but exhibited effects convergent on cancer hallmark pathways and were modulated by highly recurrent master regulators across cancer types. As a proof-of-concept example, we demonstrated that knockdown of SNAI1 or RUNX1-master regulators of carbohydrate metabolic subtypes-modulates metabolic activity and drug sensitivity. Our study provides a system-level view of metabolic heterogeneity within and across cancer types and identifies pathway cross-talk, suggesting related prognostic, therapeutic, and predictive utility.
Published: 2018

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

Author: Way, Gregory P, Sanchez-Vega, Francisco, La, Konnor, Armenia, Joshua, Chatila, Walid K, Luna, Augustin, Sander, Chris, Cherniack, Andrew D, Mina, Marco, Ciriello, Giovanni, Schultz, Nikolaus, Network, The Cancer Genome Atlas Research, Caesar-Johnson, Samantha J, Demchok, John A, Felau, Ina, Kasapi, Melpomeni, Ferguson, Martin L, Hutter, Carolyn M, Sofia, Heidi J, Tarnuzzer, Roy, Wang, Zhining, Yang, Liming, Zenklusen, Jean C, Zhang, Jiashan, Chudamani, Sudha, Liu, Jia, Lolla, Laxmi, Naresh, Rashi, Pihl, Todd, Sun, Qiang, Wan, Yunhu, Wu, Ye, Cho, Juok, DeFreitas, Timothy, Frazer, Scott, Gehlenborg, Nils, Getz, Gad, Heiman, David I, Kim, Jaegil, Lawrence, Michael S, Lin, Pei, Meier, Sam, Noble, Michael S, Saksena, Gordon, Voet, Doug, Zhang, Hailei, Bernard, Brady, Chambwe, Nyasha, Dhankani, Varsha, Knijnenburg, Theo, Kramer, Roger, Leinonen, Kalle, Liu, Yuexin, Miller, Michael, Reynolds, Sheila, Shmulevich, Ilya, Thorsson, Vesteinn, Zhang, Wei, Akbani, Rehan, Broom, Bradley M, Hegde, Apurva M, Ju, Zhenlin, Kanchi, Rupa S, Korkut, Anil, Li, Jun, Liang, Han, Ling, Shiyun, Liu, Wenbin, Lu, Yiling, Mills, Gordon B, Ng, Kwok-Shing, Rao, Arvind, Ryan, Michael, Wang, Jing, Weinstein, John N, Zhang, Jiexin, Abeshouse, Adam, Chakravarty, Debyani, de Bruijn, Ino, Gao, Jianjiong, Gross, Benjamin E, Heins, Zachary J, Kundra, Ritika, Ladanyi, Marc, Nissan, Moriah G, Ochoa, Angelica, Phillips, Sarah M, Reznik, Ed, Sheridan, Robert, Sumer, S Onur, Sun, Yichao, Taylor, Barry S, and Wang, Jioajiao
Subjects: Biological Sciences, Bioinformatics and Computational Biology, Genetics, Cancer, Networking and Information Technology R&D (NITRD), Human Genome, Cancer Genomics, Precision Medicine, Machine Learning and Artificial Intelligence, Good Health and Well Being, Cell Line, Tumor, Gene Expression Regulation, Neoplastic, Genome, Human, Humans, Machine Learning, Neoplasms, Signal Transduction, ras Proteins, Cancer Genome Atlas Research Network, Gene expression, HRAS, KRAS, NF1, NRAS, Ras, TCGA, drug sensitivity, machine learning, pan-cancer, Biochemistry and Cell Biology, Medical Physiology, Biological sciences
Abstract: Precision oncology uses genomic evidence to match patients with treatment but often fails to identify all patients who may respond. The transcriptome of these "hidden responders" may reveal responsive molecular states. We describe and evaluate a machine-learning approach to classify aberrant pathway activity in tumors, which may aid in hidden responder identification. The algorithm integrates RNA-seq, copy number, and mutations from 33 different cancer types across The Cancer Genome Atlas (TCGA) PanCanAtlas project to predict aberrant molecular states in tumors. Applied to the Ras pathway, the method detects Ras activation across cancer types and identifies phenocopying variants. The model, trained on human tumors, can predict response to MEK inhibitors in wild-type Ras cell lines. We also present data that suggest that multiple hits in the Ras pathway confer increased Ras activity. The transcriptome is underused in precision oncology and, combined with machine learning, can aid in the identification of hidden responders.
Published: 2018

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

28. 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, Cancer Genomics, Human Genome, Women's Health, Rare Diseases, Cancer, Ovarian 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

29. 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

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

Author: Schaub, Franz X, Dhankani, Varsha, Berger, Ashton C, Trivedi, Mihir, Richardson, Anne B, Shaw, Reid, Zhao, Wei, Zhang, Xiaoyang, Ventura, Andrea, Liu, Yuexin, Ayer, Donald E, Hurlin, Peter J, Cherniack, Andrew D, Eisenman, Robert N, Bernard, Brady, Grandori, Carla, Network, The Cancer Genome Atlas, Caesar-Johnson, Samantha J, Demchok, John A, Felau, Ina, Kasapi, Melpomeni, Ferguson, Martin L, Hutter, Carolyn M, Sofia, Heidi J, Tarnuzzer, Roy, Wang, Zhining, Yang, Liming, Zenklusen, Jean C, Zhang, Jiashan, Chudamani, Sudha, Liu, Jia, Lolla, Laxmi, Naresh, Rashi, Pihl, Todd, Sun, Qiang, Wan, Yunhu, Wu, Ye, Cho, Juok, DeFreitas, Timothy, Frazer, Scott, Gehlenborg, Nils, Getz, Gad, Heiman, David I, Kim, Jaegil, Lawrence, Michael S, Lin, Pei, Meier, Sam, Noble, Michael S, Saksena, Gordon, Voet, Doug, Zhang, Hailei, Chambwe, Nyasha, Knijnenburg, Theo, Kramer, Roger, Leinonen, Kalle, Miller, Michael, Reynolds, Sheila, Shmulevich, Ilya, Thorsson, Vesteinn, Zhang, Wei, Akbani, Rehan, Broom, Bradley M, Hegde, Apurva M, Ju, Zhenlin, Kanchi, Rupa S, Korkut, Anil, Li, Jun, Liang, Han, Ling, Shiyun, Liu, Wenbin, Lu, Yiling, Mills, Gordon B, Ng, Kwok-Shing, Rao, Arvind, Ryan, Michael, Wang, Jing, Weinstein, John N, Zhang, Jiexin, Abeshouse, Adam, Armenia, Joshua, Chakravarty, Debyani, Chatila, Walid K, de Bruijn, Ino, Gao, Jianjiong, Gross, Benjamin E, Heins, Zachary J, Kundra, Ritika, La, Konnor, Ladanyi, Marc, Luna, Augustin, Nissan, Moriah G, Ochoa, Angelica, Phillips, Sarah M, Reznik, Ed, Sanchez-Vega, Francisco, Sander, Chris, and Schultz, Nikolaus
Subjects: Biological Sciences, Bioinformatics and Computational Biology, Cancer Genomics, Human Genome, Cancer, Genetics, Biotechnology, 2.1 Biological and endogenous factors, Basic Helix-Loop-Helix Leucine Zipper Transcription Factors, Basic Helix-Loop-Helix Transcription Factors, Biomarkers, Tumor, Carcinogenesis, Chromatin, Computational Biology, Genes, myc, Genomics, Humans, Neoplasms, Oncogenes, Proteomics, Proto-Oncogene Proteins c-myc, Repressor Proteins, Signal Transduction, Transcription Factors, Cancer Genome Atlas Network, MAX, MNT, MYC genomic alterations, TCGA, The Cancer Genome Atlas, Biochemistry and Cell Biology, Biochemistry and cell biology
Abstract: Although the MYC oncogene has been implicated in cancer, a systematic assessment of alterations of MYC, related transcription factors, and co-regulatory proteins, forming the proximal MYC network (PMN), across human cancers is lacking. Using computational approaches, we define genomic and proteomic features associated with MYC and the PMN across the 33 cancers of The Cancer Genome Atlas. Pan-cancer, 28% of all samples had at least one of the MYC paralogs amplified. In contrast, the MYC antagonists MGA and MNT were the most frequently mutated or deleted members, proposing a role as tumor suppressors. MYC alterations were mutually exclusive with PIK3CA, PTEN, APC, or BRAF alterations, suggesting that MYC is a distinct oncogenic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such as immune response and growth factor signaling; chromatin, translation, and DNA replication/repair were conserved pan-cancer. This analysis reveals insights into MYC biology and is a reference for biomarkers and therapeutics for cancers with alterations of MYC or the PMN.
Published: 2018

31. Comprehensive Molecular Characterization of Papillary Renal-Cell Carcinoma

Author: Linehan, W Marston, Spellman, Paul T, Ricketts, Christopher J, Creighton, Chad J, Fei, Suzanne S, Davis, Caleb, Wheeler, David A, Murray, Bradley A, Schmidt, Laura, Vocke, Cathy D, Peto, Myron, Al Mamun, Abu Amar M, Shinbrot, Eve, Sethi, Anurag, Brooks, Samira, Rathmell, W Kimryn, Brooks, Angela N, Hoadley, Katherine A, Robertson, A Gordon, Brooks, Denise, Bowlby, Reanne, Sadeghi, Sara, Shen, Hui, Weisenberger, Daniel J, Bootwalla, Moiz, Baylin, Stephen B, Laird, Peter W, Cherniack, Andrew D, Saksena, Gordon, Haake, Scott, Li, Jun, Liang, Han, Lu, Yiling, Mills, Gordon B, Akbani, Rehan, Leiserson, Mark DM, Raphael, Benjamin J, Anur, Pavana, Bottaro, Donald, Albiges, Laurence, Barnabas, Nandita, Choueiri, Toni K, Czerniak, Bogdan, Godwin, Andrew K, Hakimi, A Ari, Ho, Thai H, Hsieh, James, Ittmann, Michael, Kim, William Y, Krishnan, Bhavani, Merino, Maria J, Mills Shaw, Kenna R, Reuter, Victor E, Reznik, Ed, Shelley, Carl S, Shuch, Brian, Signoretti, Sabina, Srinivasan, Ramaprasad, Tamboli, Pheroze, Thomas, George, Tickoo, Satish, Burnett, Kenneth, Crain, Daniel, Gardner, Johanna, Lau, Kevin, Mallery, David, Morris, Scott, Paulauskis, Joseph D, Penny, Robert J, Shelton, Candace, Shelton, W Troy, Sherman, Mark, Thompson, Eric, Yena, Peggy, Avedon, Melissa T, Bowen, Jay, Gastier-Foster, Julie M, Gerken, Mark, Leraas, Kristen M, Lichtenberg, Tara M, Ramirez, Nilsa C, Santos, Tracie, Wise, Lisa, Zmuda, Erik, Demchok, John A, Felau, Ina, Hutter, Carolyn M, Sheth, Margi, Sofia, Heidi J, Tarnuzzer, Roy, Wang, Zhining, Yang, Liming, Zenklusen, Jean C, Zhang, Jiashan, Ayala, Brenda, Baboud, Julien, Chudamani, Sudha, Liu, Jia, Lolla, Laxmi, and Naresh, Rashi
Subjects: Biomedical and Clinical Sciences, Oncology and Carcinogenesis, Cancer, Rare Diseases, Genetics, Biotechnology, Digestive Diseases, Kidney Disease, Good Health and Well Being, Carcinoma, Papillary, CpG Islands, DNA Methylation, Humans, Kidney Neoplasms, MicroRNAs, Mutation, NF-E2-Related Factor 2, Phenotype, Proto-Oncogene Proteins c-met, RNA, Messenger, RNA, Neoplasm, Sequence Analysis, RNA, Signal Transduction, Cancer Genome Atlas Research Network, Medical and Health Sciences, General & Internal Medicine, Biomedical and clinical sciences, Health sciences
Abstract: BackgroundPapillary renal-cell carcinoma, which accounts for 15 to 20% of renal-cell carcinomas, is a heterogeneous disease that consists of various types of renal cancer, including tumors with indolent, multifocal presentation and solitary tumors with an aggressive, highly lethal phenotype. Little is known about the genetic basis of sporadic papillary renal-cell carcinoma, and no effective forms of therapy for advanced disease exist.MethodsWe performed comprehensive molecular characterization of 161 primary papillary renal-cell carcinomas, using whole-exome sequencing, copy-number analysis, messenger RNA and microRNA sequencing, DNA-methylation analysis, and proteomic analysis.ResultsType 1 and type 2 papillary renal-cell carcinomas were shown to be different types of renal cancer characterized by specific genetic alterations, with type 2 further classified into three individual subgroups on the basis of molecular differences associated with patient survival. Type 1 tumors were associated with MET alterations, whereas type 2 tumors were characterized by CDKN2A silencing, SETD2 mutations, TFE3 fusions, and increased expression of the NRF2-antioxidant response element (ARE) pathway. A CpG island methylator phenotype (CIMP) was observed in a distinct subgroup of type 2 papillary renal-cell carcinomas that was characterized by poor survival and mutation of the gene encoding fumarate hydratase (FH).ConclusionsType 1 and type 2 papillary renal-cell carcinomas were shown to be clinically and biologically distinct. Alterations in the MET pathway were associated with type 1, and activation of the NRF2-ARE pathway was associated with type 2; CDKN2A loss and CIMP in type 2 conveyed a poor prognosis. Furthermore, type 2 papillary renal-cell carcinoma consisted of at least three subtypes based on molecular and phenotypic features. (Funded by the National Institutes of Health.).
Published: 2016

32. Comprehensive, Integrative Genomic Analysis of Diffuse Lower-Grade Gliomas

Author: Brat, Daniel J, Verhaak, Roel GW, Aldape, Kenneth D, Yung, WK Alfred, Salama, Sofie R, Cooper, Lee AD, Rheinbay, Esther, Miller, C Ryan, Vitucci, Mark, Morozova, Olena, Robertson, A Gordon, Noushmehr, Houtan, Laird, Peter W, Cherniack, Andrew D, Akbani, Rehan, Huse, Jason T, Ciriello, Giovanni, Poisson, Laila M, Barnholtz-Sloan, Jill S, Berger, Mitchel S, Brennan, Cameron, Colen, Rivka R, Colman, Howard, Flanders, Adam E, Giannini, Caterina, Grifford, Mia, Iavarone, Antonio, Jain, Rajan, Joseph, Isaac, Kim, Jaegil, Kasaian, Katayoon, Mikkelsen, Tom, Murray, Bradley A, O'Neill, Brian Patrick, Pachter, Lior, Parsons, Donald W, Sougnez, Carrie, Sulman, Erik P, Vandenberg, Scott R, Van Meir, Erwin G, von Deimling, Andreas, Zhang, Hailei, Crain, Daniel, Lau, Kevin, Mallery, David, Morris, Scott, Paulauskis, Joseph, Penny, Robert, Shelton, Troy, Sherman, Mark, Yena, Peggy, Black, Aaron, Bowen, Jay, Dicostanzo, Katie, Gastier-Foster, Julie, Leraas, Kristen M, Lichtenberg, Tara M, Pierson, Christopher R, Ramirez, Nilsa C, Taylor, Cynthia, Weaver, Stephanie, Wise, Lisa, Zmuda, Erik, Davidsen, Tanja, Demchok, John A, Eley, Greg, Ferguson, Martin L, Hutter, Carolyn M, Mills Shaw, Kenna R, Ozenberger, Bradley A, Sheth, Margi, Sofia, Heidi J, Tarnuzzer, Roy, Wang, Zhining, Yang, Liming, Zenklusen, Jean Claude, Ayala, Brenda, Baboud, Julien, Chudamani, Sudha, Jensen, Mark A, Liu, Jia, Pihl, Todd, Raman, Rohini, Wan, Yunhu, Wu, Ye, Ally, Adrian, Auman, J Todd, Balasundaram, Miruna, Balu, Saianand, Baylin, Stephen B, Beroukhim, Rameen, Bootwalla, Moiz S, Bowlby, Reanne, Bristow, Christopher A, Brooks, Denise, Butterfield, Yaron, Carlsen, Rebecca, Carter, Scott, Chin, Lynda, and Chu, Andy
Subjects: Biomedical and Clinical Sciences, Clinical Sciences, Oncology and Carcinogenesis, Clinical Research, Human Genome, Brain Cancer, Rare Diseases, Neurosciences, Biotechnology, Cancer Genomics, Genetics, Cancer, Brain Disorders, Adolescent, Adult, Aged, Chromosomes, Human, Pair 1, Chromosomes, Human, Pair 19, Cluster Analysis, DNA, Neoplasm, Female, Genes, p53, Glioblastoma, Glioma, Humans, Kaplan-Meier Estimate, Male, Middle Aged, Mutation, Neoplasm Grading, Proportional Hazards Models, Sequence Analysis, DNA, Signal Transduction, Cancer Genome Atlas Research Network, Medical and Health Sciences, General & Internal Medicine, Biomedical and clinical sciences, Health sciences
Abstract: BackgroundDiffuse low-grade and intermediate-grade gliomas (which together make up the lower-grade gliomas, World Health Organization grades II and III) have highly variable clinical behavior that is not adequately predicted on the basis of histologic class. Some are indolent; others quickly progress to glioblastoma. The uncertainty is compounded by interobserver variability in histologic diagnosis. Mutations in IDH, TP53, and ATRX and codeletion of chromosome arms 1p and 19q (1p/19q codeletion) have been implicated as clinically relevant markers of lower-grade gliomas.MethodsWe performed genomewide analyses of 293 lower-grade gliomas from adults, incorporating exome sequence, DNA copy number, DNA methylation, messenger RNA expression, microRNA expression, and targeted protein expression. These data were integrated and tested for correlation with clinical outcomes.ResultsUnsupervised clustering of mutations and data from RNA, DNA-copy-number, and DNA-methylation platforms uncovered concordant classification of three robust, nonoverlapping, prognostically significant subtypes of lower-grade glioma that were captured more accurately by IDH, 1p/19q, and TP53 status than by histologic class. Patients who had lower-grade gliomas with an IDH mutation and 1p/19q codeletion had the most favorable clinical outcomes. Their gliomas harbored mutations in CIC, FUBP1, NOTCH1, and the TERT promoter. Nearly all lower-grade gliomas with IDH mutations and no 1p/19q codeletion had mutations in TP53 (94%) and ATRX inactivation (86%). The large majority of lower-grade gliomas without an IDH mutation had genomic aberrations and clinical behavior strikingly similar to those found in primary glioblastoma.ConclusionsThe integration of genomewide data from multiple platforms delineated three molecular classes of lower-grade gliomas that were more concordant with IDH, 1p/19q, and TP53 status than with histologic class. Lower-grade gliomas with an IDH mutation either had 1p/19q codeletion or carried a TP53 mutation. Most lower-grade gliomas without an IDH mutation were molecularly and clinically similar to glioblastoma. (Funded by the National Institutes of Health.).
Published: 2015

33. Two novel gene-specific markers at the Pik locus facilitate the application of rice blast resistant alleles in breeding

Author: Lin Yan, Ping-sheng Ji, Chen Ziqiang, Jia-mi Luo, Chen Zaijie, Zonghua Wang, Yang Liming, Tian Dagang, and Feng Wang
Subjects: 0106 biological sciences, K-type alleles, Agriculture (General), Introgression, Locus (genetics), Plant Science, Biology, 01 natural sciences, Biochemistry, S1-972, Novel gene, Food Animals, Allele, Indel, Gene, Selection (genetic algorithm), Genetics, molecular marker, Ecology, rice, Chromosome, food and beverages, 04 agricultural and veterinary sciences, Pik-p, blast disease, Pi-1, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Animal Science and Zoology, Agronomy and Crop Science, 010606 plant biology & botany, Food Science
Abstract: Blast, a disease caused by Magnaporthe oryzae, is a major constraint for rice production worldwide. Introgression of durable blast resistance genes into high-yielding rice cultivars has been considered a priority to control the disease. The blast resistance Pik locus, located on chromosome 11, contains at least six important resistance genes, but these genes have not been widely employed in resistance breeding since existing markers hardly satisfy current breeding needs due to their limited scope of application. In this study, two PCR-based markers, Pikp-Del and Pi1-In, were developed to target the specific InDel (insertion/deletion) of the Pik-p and Pi-1 genes, respectively. The two markers precisely distinguished Pik-p, Pi-1, and the K-type alleles at the Pik locus, which is a necessary element for functional genes from rice varieties. Results also revealed that only several old varieties contain the two genes, of which nearly half carry the K-type alleles. Therefore, these identified varieties can serve as new gene sources for developing blast resistant rice. The two newly developed markers will be highly useful for the use of Pik-p, Pi-1 and other resistance genes at the Pik locus in marker-assisted selection (MAS) breeding programs.
Published: 2021

34. Ancestry-Specific Predisposing Germline Variants in Cancer

Author: Oak, Ninad, Cherniack, Andrew D., Mashl, R. Jay, Carrot-Zhang, Jian, Chambwe, Nyasha, Damrauer, Jeffrey, Knijnenburg, Theo, Robertson, A. Gordon, Yau, Christina, Zhou, Wanding, Berger, Ashton, Huang, Kuan-lin, Newberg, Justin, Romanel, Alessandro, Sayaman, Rosalyn, Demichelis, Francesca, Felau, Ina, Frampton, Garret, Han, Seunghun, Hoadley, Katherine, Kemal, Anab, Laird, Peter, Lazar, Alexander, Xiuning, Le, Shen, Hui, Wong, Christopher, Zenklusen, Jean, Ziv, Elad, Aguet, Francois, Ding, Li, Demchok, John, Mensah, Michael, Tarnuzzer, Roy, Wang, Zhining, Yang, Liming, Alfoldi, Jessica, Karczewski, Konrad, Macarthur, Daniel, Benz, Christopher, Stuart, Joshua, Hirsch, Fred R., Beroukhim, Rameen, Gümüş, Zeynep H., and Plon, Sharon E.
Subjects: Male, lcsh:QH426-470, SDHB, Genetic genealogy, lcsh:Medicine, Ataxia Telangiectasia Mutated Proteins, Biology, Germline, Loss of heterozygosity, Germline mutation, Risk Factors, Paraganglioma, Neoplasms, Genetics, Genetic predisposition, Carcinoma, medicine, Humans, Genetic Predisposition to Disease, Allele, Molecular Biology, Germ-Line Mutation, Genetics (clinical), BRCA2 Protein, Research, lcsh:R, BRIP1, medicine.disease, Fanconi Anemia Complementation Group Proteins, Succinate Dehydrogenase, lcsh:Genetics, Von Hippel-Lindau Tumor Suppressor Protein, Molecular Medicine, Female, RNA Helicases
Abstract: Background Distinct prevalence of inherited genetic predisposition may partially explain the difference of cancer risks across ancestries. Ancestry-specific analyses of germline genomes are required to inform cancer genetic risk and prognosis of diverse populations. Methods We conducted analyses using germline and somatic sequencing data generated by The Cancer Genome Atlas. Collapsing pathogenic and likely pathogenic variants to cancer predisposition genes (CPG), we analyzed the association between CPGs and cancer types within ancestral groups. We also identified the predisposition-associated two-hit events and gene expression effects in tumors. Results Genetic ancestry analysis classified the cohort of 9899 cancer cases into individuals of primarily European (N = 8184, 82.7%), African (N = 966, 9.8%), East Asian (N = 649, 6.6%), South Asian (N = 48, 0.5%), Native/Latin American (N = 41, 0.4%), and admixed (N = 11, 0.1%) ancestries. In the African ancestry, we discovered a potentially novel association of BRCA2 in lung squamous cell carcinoma (OR = 41.4 [95% CI, 6.1–275.6]; FDR = 0.002) previously identified in Europeans, along with a known association of BRCA2 in ovarian serous cystadenocarcinoma (OR = 8.5 [95% CI, 1.5–47.4]; FDR = 0.045). In the East Asian ancestry, we discovered one previously known association of BRIP1 in stomach adenocarcinoma (OR = 12.8 [95% CI, 1.8–90.8]; FDR = 0.038). Rare variant burden analysis further identified 7 suggestive associations in African ancestry individuals previously described in European ancestry, including SDHB in pheochromocytoma and paraganglioma, ATM in prostate adenocarcinoma, VHL in kidney renal clear cell carcinoma, FH in kidney renal papillary cell carcinoma, and PTEN in uterine corpus endometrial carcinoma. Most predisposing variants were found exclusively in one ancestry in the TCGA and gnomAD datasets. Loss of heterozygosity was identified for 7 out of the 15 African ancestry carriers of predisposing variants. Further, tumors from the SDHB or BRCA2 carriers showed simultaneous allelic-specific expression and low gene expression of their respective affected genes, and FH splice-site variant carriers showed mis-splicing of FH. Conclusions While several CPGs are shared across patients, many pathogenic variants are found to be ancestry-specific and trigger somatic effects. Studies using larger cohorts of diverse ancestries are required to pinpoint ancestry-specific genetic predisposition and inform genetic screening strategies.
Published: 2020

35. Proteomic analysis of the defense response to Magnaporthe oryzae in rice harboring the blast resistance gene Piz-t

Author: Chen Zaijie, Liu Yang, Yuming Luo, Feng Wang, Chen Ziqiang, Songbiao Chen, Yang Liming, Tian Dagang, and Yuanchang Zhou
Subjects: 0106 biological sciences, 0301 basic medicine, Transgene, Protein subunit, Soil Science, Virulence, Proteomic analysis, Plant Science, lcsh:Plant culture, 01 natural sciences, 03 medical and health sciences, lcsh:SB1-1110, Protein kinase A, Gene, Genetics, biology, Kinase, RuBisCO, Cytochrome P450, food and beverages, Resistance gene, Rice blast disease, 030104 developmental biology, iTRAQ, biology.protein, Piz-t, Original Article, Agronomy and Crop Science, 010606 plant biology & botany
Abstract: Background Rice blast (caused by Magnaporthe oryzae) is one of the most destructive diseases of rice. While many blast resistance (R) genes have been identified and deployed in rice cultivars, little is known about the R gene-mediated defense mechanism. We used a rice transgenic line harboring the resistance gene Piz-t to investigate the R gene-mediated resistance response to infection. Results We conducted comparative proteome profiling of the Piz-t transgenic Nipponbare line (NPB-Piz-t) and wild-type Nipponbare (NPB) inoculated with M. oryzae at 24, 48, 72 h post-inoculation (hpi) using isobaric tags for relative and absolute quantification (iTRAQ) analysis. Comparative analysis of the response of NPB-Piz-t to the avirulent isolate KJ201 and the virulent isolate RB22 identified 114 differentially expressed proteins (DEPs) between KJ201-inoculated NPB-Piz-t (KJ201-Piz-t) and mock-treated NPB-Piz-t (Mock-Piz-t), and 118 DEPs between RB22-inoculated NPB-Piz-t (RB22-Piz-t) and Mock-Piz-t. Among the DEPs, 56 occurred commonly in comparisons KJ201-Piz-t/Mock-Piz-t and RB22-Piz-t/Mock-Piz-t. In a comparison of the responses of NPB and NPB-Piz-t to isolate KJ201, 93 DEPs between KJ201-Piz-t and KJ201-NPB were identified. DEPs in comparisons KJ201-Piz-t/Mock-Piz-t, RB22-Piz-t/Mock-Piz-t and KJ201-Piz-t/KJ201-NPB contained a number of proteins that may be involved in rice response to pathogens, including pathogenesis-related (PR) proteins, hormonal regulation-related proteins, defense and stress response-related proteins, receptor-like kinase, and cytochrome P450. Comparative analysis further identified 7 common DEPs between the comparisons KJ201-Piz-t/KJ201-NPB and KJ201-Piz-t/RB22-Piz-t, including alcohol dehydrogenase I, receptor-like protein kinase, endochitinase, similar to rubisco large subunit, NADP-dependent malic enzyme, and two hypothetical proteins. Conclusions Our results provide a valuable resource for discovery of complex protein networks involved in the resistance response of rice to blast fungus. Electronic supplementary material The online version of this article (10.1186/s12284-018-0240-3) contains supplementary material, which is available to authorized users.
Published: 2018

36. Comprehensive Molecular Characterization of Papillary Renal-Cell Carcinoma

Author: Cancer Genome Atlas Research Network, Linehan, W Marston, Spellman, Paul T, Ricketts, Christopher J, Creighton, Chad J, Fei, Suzanne S, Davis, Caleb, Wheeler, David A, Murray, Bradley A, Schmidt, Laura, Vocke, Cathy D, Peto, Myron, Al Mamun, Abu Amar M, Shinbrot, Eve, Sethi, Anurag, Brooks, Samira, Rathmell, W Kimryn, Brooks, Angela N, Hoadley, Katherine A, Robertson, A Gordon, Brooks, Denise, Bowlby, Reanne, Sadeghi, Sara, Shen, Hui, Weisenberger, Daniel J, Bootwalla, Moiz, Baylin, Stephen B, Laird, Peter W, Cherniack, Andrew D, Saksena, Gordon, Haake, Scott, Li, Jun, Liang, Han, Lu, Yiling, Mills, Gordon B, Akbani, Rehan, Leiserson, Mark DM, Raphael, Benjamin J, Anur, Pavana, Bottaro, Donald, Albiges, Laurence, Barnabas, Nandita, Choueiri, Toni K, Czerniak, Bogdan, Godwin, Andrew K, Hakimi, A Ari, Ho, Thai H, Hsieh, James, Ittmann, Michael, Kim, William Y, Krishnan, Bhavani, Merino, Maria J, Mills Shaw, Kenna R, Reuter, Victor E, Reznik, Ed, Shelley, Carl S, Shuch, Brian, Signoretti, Sabina, Srinivasan, Ramaprasad, Tamboli, Pheroze, Thomas, George, Tickoo, Satish, Burnett, Kenneth, Crain, Daniel, Gardner, Johanna, Lau, Kevin, Mallery, David, Morris, Scott, Paulauskis, Joseph D, Penny, Robert J, Shelton, Candace, Shelton, W Troy, Sherman, Mark, Thompson, Eric, Yena, Peggy, Avedon, Melissa T, Bowen, Jay, Gastier-Foster, Julie M, Gerken, Mark, Leraas, Kristen M, Lichtenberg, Tara M, Ramirez, Nilsa C, Santos, Tracie, Wise, Lisa, Zmuda, Erik, Demchok, John A, Felau, Ina, Hutter, Carolyn M, Sheth, Margi, Sofia, Heidi J, Tarnuzzer, Roy, Wang, Zhining, Yang, Liming, Zenklusen, Jean C, Zhang, Jiashan, Ayala, Brenda, Baboud, Julien, Chudamani, Sudha, Liu, Jia, and Lolla, Laxmi
Subjects: Kidney Disease, NF-E2-Related Factor 2, Papillary, Messenger, Cancer Genome Atlas Research Network, Medical and Health Sciences, Rare Diseases, General & Internal Medicine, Genetics, Humans, Cancer, Carcinoma, DNA Methylation, Proto-Oncogene Proteins c-met, Kidney Neoplasms, MicroRNAs, Phenotype, Good Health and Well Being, Mutation, RNA, Neoplasm, CpG Islands, Digestive Diseases, Sequence Analysis, Signal Transduction, Biotechnology
Abstract: BackgroundPapillary renal-cell carcinoma, which accounts for 15 to 20% of renal-cell carcinomas, is a heterogeneous disease that consists of various types of renal cancer, including tumors with indolent, multifocal presentation and solitary tumors with an aggressive, highly lethal phenotype. Little is known about the genetic basis of sporadic papillary renal-cell carcinoma, and no effective forms of therapy for advanced disease exist.MethodsWe performed comprehensive molecular characterization of 161 primary papillary renal-cell carcinomas, using whole-exome sequencing, copy-number analysis, messenger RNA and microRNA sequencing, DNA-methylation analysis, and proteomic analysis.ResultsType 1 and type 2 papillary renal-cell carcinomas were shown to be different types of renal cancer characterized by specific genetic alterations, with type 2 further classified into three individual subgroups on the basis of molecular differences associated with patient survival. Type 1 tumors were associated with MET alterations, whereas type 2 tumors were characterized by CDKN2A silencing, SETD2 mutations, TFE3 fusions, and increased expression of the NRF2-antioxidant response element (ARE) pathway. A CpG island methylator phenotype (CIMP) was observed in a distinct subgroup of type 2 papillary renal-cell carcinomas that was characterized by poor survival and mutation of the gene encoding fumarate hydratase (FH).ConclusionsType 1 and type 2 papillary renal-cell carcinomas were shown to be clinically and biologically distinct. Alterations in the MET pathway were associated with type 1, and activation of the NRF2-ARE pathway was associated with type 2; CDKN2A loss and CIMP in type 2 conveyed a poor prognosis. Furthermore, type 2 papillary renal-cell carcinoma consisted of at least three subtypes based on molecular and phenotypic features. (Funded by the National Institutes of Health.).
Published: 2016

37. Comprehensive, Integrative Genomic Analysis of Diffuse Lower-Grade Gliomas

Author: Cancer Genome Atlas Research Network, Brat, Daniel J, Verhaak, Roel GW, Aldape, Kenneth D, Yung, WK Alfred, Salama, Sofie R, Cooper, Lee AD, Rheinbay, Esther, Miller, C Ryan, Vitucci, Mark, Morozova, Olena, Robertson, A Gordon, Noushmehr, Houtan, Laird, Peter W, Cherniack, Andrew D, Akbani, Rehan, Huse, Jason T, Ciriello, Giovanni, Poisson, Laila M, Barnholtz-Sloan, Jill S, Berger, Mitchel S, Brennan, Cameron, Colen, Rivka R, Colman, Howard, Flanders, Adam E, Giannini, Caterina, Grifford, Mia, Iavarone, Antonio, Jain, Rajan, Joseph, Isaac, Kim, Jaegil, Kasaian, Katayoon, Mikkelsen, Tom, Murray, Bradley A, O'Neill, Brian Patrick, Pachter, Lior, Parsons, Donald W, Sougnez, Carrie, Sulman, Erik P, Vandenberg, Scott R, Van Meir, Erwin G, von Deimling, Andreas, Zhang, Hailei, Crain, Daniel, Lau, Kevin, Mallery, David, Morris, Scott, Paulauskis, Joseph, Penny, Robert, Shelton, Troy, Sherman, Mark, Yena, Peggy, Black, Aaron, Bowen, Jay, Dicostanzo, Katie, Gastier-Foster, Julie, Leraas, Kristen M, Lichtenberg, Tara M, Pierson, Christopher R, Ramirez, Nilsa C, Taylor, Cynthia, Weaver, Stephanie, Wise, Lisa, Zmuda, Erik, Davidsen, Tanja, Demchok, John A, Eley, Greg, Ferguson, Martin L, Hutter, Carolyn M, Mills Shaw, Kenna R, Ozenberger, Bradley A, Sheth, Margi, Sofia, Heidi J, Tarnuzzer, Roy, Wang, Zhining, Yang, Liming, Zenklusen, Jean Claude, Ayala, Brenda, Baboud, Julien, Chudamani, Sudha, Jensen, Mark A, Liu, Jia, Pihl, Todd, Raman, Rohini, Wan, Yunhu, Wu, Ye, Ally, Adrian, Auman, J Todd, Balasundaram, Miruna, Balu, Saianand, Baylin, Stephen B, Beroukhim, Rameen, Bootwalla, Moiz S, Bowlby, Reanne, Bristow, Christopher A, Brooks, Denise, Butterfield, Yaron, Carlsen, Rebecca, Carter, Scott, and Chin, Lynda
Subjects: p53, Adult, Male, Adolescent, Cancer Genome Atlas Research Network, Kaplan-Meier Estimate, Medical and Health Sciences, Chromosomes, Rare Diseases, General & Internal Medicine, Genetics, Humans, Cluster Analysis, Proportional Hazards Models, Aged, Cancer, Pair 19, Human Genome, Glioma, DNA, Middle Aged, Brain Disorders, Brain Cancer, Genes, Mutation, Pair 1, Neoplasm, Female, Neoplasm Grading, Glioblastoma, Sequence Analysis, Human, Signal Transduction
Abstract: BackgroundDiffuse low-grade and intermediate-grade gliomas (which together make up the lower-grade gliomas, World Health Organization grades II and III) have highly variable clinical behavior that is not adequately predicted on the basis of histologic class. Some are indolent; others quickly progress to glioblastoma. The uncertainty is compounded by interobserver variability in histologic diagnosis. Mutations in IDH, TP53, and ATRX and codeletion of chromosome arms 1p and 19q (1p/19q codeletion) have been implicated as clinically relevant markers of lower-grade gliomas.MethodsWe performed genomewide analyses of 293 lower-grade gliomas from adults, incorporating exome sequence, DNA copy number, DNA methylation, messenger RNA expression, microRNA expression, and targeted protein expression. These data were integrated and tested for correlation with clinical outcomes.ResultsUnsupervised clustering of mutations and data from RNA, DNA-copy-number, and DNA-methylation platforms uncovered concordant classification of three robust, nonoverlapping, prognostically significant subtypes of lower-grade glioma that were captured more accurately by IDH, 1p/19q, and TP53 status than by histologic class. Patients who had lower-grade gliomas with an IDH mutation and 1p/19q codeletion had the most favorable clinical outcomes. Their gliomas harbored mutations in CIC, FUBP1, NOTCH1, and the TERT promoter. Nearly all lower-grade gliomas with IDH mutations and no 1p/19q codeletion had mutations in TP53 (94%) and ATRX inactivation (86%). The large majority of lower-grade gliomas without an IDH mutation had genomic aberrations and clinical behavior strikingly similar to those found in primary glioblastoma.ConclusionsThe integration of genomewide data from multiple platforms delineated three molecular classes of lower-grade gliomas that were more concordant with IDH, 1p/19q, and TP53 status than with histologic class. Lower-grade gliomas with an IDH mutation either had 1p/19q codeletion or carried a TP53 mutation. Most lower-grade gliomas without an IDH mutation were molecularly and clinically similar to glioblastoma. (Funded by the National Institutes of Health.).
Published: 2015

38. ROS Generated by Upconversion Nanoparticle-Mediated Photodynamic Therapy Induces Autophagy Via PI3K/AKT/ mTOR Signaling Pathway in M1 Peritoneal Macrophage.

Author: Han, Xiaobo, Zhong, Zhaoyu, Zheng, Yinghong, Liu, Zhongni, Jiang, Yueqing, Gao, Ziyu, Cong, Lin, Yang, Liming, Tian, Ye, Kou, Jiayuan, and Zhang, Zhiren
Subjects: ATHEROSCLEROSIS, REACTIVE oxygen species, PHOTODYNAMIC therapy, AUTOPHAGY, MACROPHAGES, TOR proteins, PHOSPHOINOSITIDES, PROTEIN kinase B, GENETICS
Abstract: Background/Aims: Atherosclerosis is a chronic inflammatory cardiovascular disease. Macrophages are major components of atherosclerotic plaques and play a key role in the development of atherosclerosis by secreting a variety of pro-inflammatory factors. Our previous studies have confirmed that upconversion nanoparticles encapsulating chlorin e6 (UCNPs-Ce6) mediated photodynamic therapy (PDT) can promote cholesterol efflux and induce apoptosis in THP-1 macrophages. In this study, we investigated whether reactive oxygen species (ROS) generated by UCNPs-Ce6-mediated PDT can induce autophagy to inhibit the expression of pro-inflammatory factor in M1 peritoneal macrophages. Methods: Peritoneal macrophages were collected from C57/BL6 mice injected with 3% thioglycollate broth medium and induced by lipopolysaccharides and interferon-γ. Intracellular ROS production was assessed by 2’-7’-dichloroforescein diacetate and flow cytometry. Autophagy was assayed by western blot, transmission electron microscopy and immunofluorescence. Pro-inflammatory cytokines were detected by enzyme-linked immunosorbent assay and western blot. Results: Model M1 peritoneal macrophages were established after 24 h induction. Protein expression levels of LC3 II and Beclin1, and degradation of p62 increased and peaked at 2 h in the PDT group. Meanwhile, levels of inflammatory cytokines iNOS, IL-12, and TNF-α markedly decreased after PDT. The increase in autophagy levels and decrease in pro-inflammatory cytokines were significantly inhibited by 3-methyladenine. Furthermore, ROS generated by UCNPs-Ce6 mediated PDT activated autophagy. The expression of autophagy related-protein and inflammatory cytokines iNOS, IL-12, and TNF-α were inhibited by the ROS inhibitor N-acetyl cysteine. Conclusions: ROS generated by UCNPs-Ce6-mediated PDT activated autophagy and inhibited the expression of pro-inflammatory factors of M1 peritoneal macrophage via the PI3K/AKT/mTOR signaling pathway. [ABSTRACT FROM AUTHOR]
Published: 2018
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39. Apoptosis of THP-1 Derived Macrophages Induced by Sonodynamic Therapy Using a New Sonosensitizer Hydroxyl Acetylated Curcumin.

Author: Zheng, Longbin, Sun, Xinyong, Zhu, Xing, Lv, Fengxiang, Zhong, Zhaoyu, Zhang, Feng, Guo, Wenhui, Cao, Wenwu, Yang, Liming, and Tian, Ye
Subjects: APOPTOSIS, CURCUMIN, SODIUM azide, CYTOLOGY, MITOCHONDRIAL membranes, MACROPHAGE activation
Abstract: Curcumin is extracted from the rhizomes of the traditional Chinese herb Curcuma longa. Our previous study indicated curcumin was able to function as a sonosensitizer. Hydroxyl acylated curcumin was synthesized from curcumin to eliminate the unstable hydroxy perssad in our group. The potential use of Hydroxyl acylated curcumin as a sonosensitizer for sonodynamic therapy (SDT) requires further exploration. This study investigated the sonodynamic effect of Hydroxyl acylated curcumin on THP-1 macrophage. THP-1 macrophages were cultured with Hydroxyl acylated curcumin at a concentration of 5.0 μg/mL for 4 hours and then exposed to pulse ultrasound irradiation (0.5 W/cm2 with 1.0 MHz ) for 5 min, 10 min and 15 min. Six hours later, cell viability decreased significantly by CCK-8 assay. After ultrasound irradiation, the ratio of apoptosis and necrosis in SDT group was higher than that in control, Hydroxyl acylated curcumin alone and ultrasound alone. Moreover, the apoptotic rate was higher than necrotic rate with the flow cytometry analysis. Furthermore, Hydroxyl acylated curcumin-SDT induced reactive oxygen species (ROS) generation in THP-1 macrophages immediately after the ultrasound treatment while ROS generation was reduced significantly with the scavenger of singlet oxygen Sodium azide (NaN3). Hydroxyl acylated curcumin-SDT led to a conspicuous loss of mitochondrial membrane potential (MMP) compared with other groups, while MMP was increased significantly with the scavenger of singlet oxygen Sodium azide (NaN3), ROS inhibitor N-acetyl cysteine (NAC) and Mitochondrial Permeability Transition Pore (MPTP) inhibitor Cyclosporin A (CsA). The cytochrome C, cleaved-Caspase-9, cleaved-Caspase-3 and cleaved-PARP upregulated after SDT through Western blotting. These findings suggested that Hydroxyl acylated curcumin under low-intensity ultrasound had sonodynamic effect on THP-1 macrophages via generation of intracellular singlet oxygen and mitochondria-caspase signaling pathway, indicating that Hydroxyl acylated curcumin could be used as a novel sonosensitizer in SDT for atherosclerosis. [ABSTRACT FROM AUTHOR]
Published: 2014
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40. Expression of human heme oxygenase-1 in the thick ascending limb attenuates angiotensin II-mediated increase in oxidative injury.

Author: Quan, Shuo, Yang, Liming, Shnouda, Sylvia, Schwartzman, Michal L, Nasjletti, Alberto, Goodman, Alvin I, and Abraham, Nader G
Subjects: *ANIMAL experimentation, *CELL culture, *COMPARATIVE studies, *DNA, *GENE expression, *GENES, *GENETICS, *GLUTATHIONE, *ISOENZYMES, *KIDNEY tubules, *RESEARCH methodology, *MEDICAL cooperation, *MEMBRANE proteins, *NUCLEOTIDES, *OXIDOREDUCTASES, *PROTEINS, *RATS, *RECOMBINANT proteins, *RESEARCH, *ANGIOTENSIN II, *OXIDATIVE stress, *EVALUATION research, *ION transport (Biology), *METALLOPORPHYRINS, *DINOPROSTONE, *WOUNDS & injuries
Abstract: Background: Heme oxygenase-1 (HO-1) catalyzes the conversion of heme to bilirubin, carbon monoxide (CO), and free iron, thus controlling the level of cellular heme. The medullary thick ascending limb of the loop of Henle (TALH) is situated in a site of markedly diminished oxygen tension and, as such, is highly vulnerable to ischemic insult. We hypothesize that selective upregulation of HO-1 in TALH by gene transfer attenuates oxidative stress caused by angiotensin II (Ang II).Methods: An adenoviral vector expressing the human HO-1 under the control of the TALH-specific promoter [Na(+)-K(+)-Cl(-) cotransporter (NKCC2 promoter)] was constructed and the cell specific expression of the recombinant adenovirus was examined using several types of cells, including endothelial, vascular smooth muscle, and TALH cells. The effects of HO-1 transduction on HO-1 expression, HO activity and the response to Ang II with respect to cyclooxygenase-2 (COX-2) up-regulation and oxidative injury [growth-stimulating hormone (GSH) levels and cell death] were determined.Results: Western blot and reverse transcription-polymerase chain reaction (RT-PCR) revealed that human HO-1 was selectively expressed in primary cultured TALH cells following infection with Ad-NKCC2-HO-1. In TALH cells infected with Ad-NKCC2-HO-1, Ang II-stimulated prostaglandin E(2) (PGE(2)) levels were reduced by 40%. Ang II caused a marked decrease in GSH levels and this decrease was greatly attenuated in TALH cells transduced with Ad-NKCC2-HO-1. Moreover, Ang II-mediated DNA degradation was completely blocked by the site-specific expression of human HO-1 gene.Conclusion: These results indicate that TALH cell survival after exposure to oxidative stress injury may be facilitated by selective upregulation of HO-1, thusly blocking inflammation and apoptosis. [ABSTRACT FROM AUTHOR]
Published: 2004
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41. Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling.

Author: Alizadeh, Ash A., Eisen, Michael B., Davis, R. Eric, Ma, Chi, Lossos, Izidore S., Rosenwald, Andreas, Boldrick, Jennifer C., Sabet, Hajeer, Tran, Truc, Yu, Xin, Powell, John I., Yang, Liming, Marti, Gerald E., Moore, Troy, Hudson Jr., James, Lu, Lisheng, Lewis, David B., Tibshirani, Robert, Sherlock, Gavin, and Chan, Wing C.
Subjects: LYMPHOMAS, MOLECULAR genetics, TUMOR genetics, CANCER treatment, GENE expression, MOLECULES, GENETICS
Abstract: Examines the molecular heterogeneity in the tumors of diffuse large B-cell lymphoma (DLBCL) patients. How researchers conducted a systematic characterization of gene expression in B-cell malignancies; Identification of two molecularly distinct forms of DLBCL; Observation that patients with germinal center B-like DLBCL had better survival rates than patients with activated B-like DLBCL; Conclusions.
Published: 2000
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