Search

Your search keyword '"Sander, Chris"' showing total 1,670 results
Start Over Author "Sander, Chris"
1,670 results on '"Sander, Chris"'

7. A deep learning algorithm to predict risk of pancreatic cancer from disease trajectories

Author

Placido, Davide, Yuan, Bo, Hjaltelin, Jessica X., Zheng, Chunlei, Haue, Amalie D., Chmura, Piotr J., Yuan, Chen, Kim, Jihye, Umeton, Renato, Antell, Gregory, Chowdhury, Alexander, Franz, Alexandra, Brais, Lauren, Andrews, Elizabeth, Marks, Debora S., Regev, Aviv, Ayandeh, Siamack, Brophy, Mary T., Do, Nhan V., Kraft, Peter, Wolpin, Brian M., Rosenthal, Michael H., Fillmore, Nathanael R., Brunak, Søren, and Sander, Chris

Published
2023
Full Text
View/download PDF

8. Inference of cell dynamics on perturbation data using adjoint sensitivity

Author

Ji, Weiqi, Yuan, Bo, Shen, Ciyue, Regev, Aviv, Sander, Chris, and Deng, Sili

Subjects
Quantitative Biology - Molecular Networks, Computer Science - Machine Learning
Abstract

Data-driven dynamic models of cell biology can be used to predict cell response to unseen perturbations. Recent work (CellBox) had demonstrated the derivation of interpretable models with explicit interaction terms, in which the parameters were optimized using machine learning techniques. While the previous work was tested only in a single biological setting, this work aims to extend the range of applicability of this model inference approach to a diversity of biological systems. Here we adapted CellBox in Julia differential programming and augmented the method with adjoint algorithms, which has recently been used in the context of neural ODEs. We trained the models using simulated data from both abstract and biology-inspired networks, which afford the ability to evaluate the recovery of the ground truth network structure. The resulting accuracy of prediction by these models is high both in terms of low error against data and excellent agreement with the network structure used for the simulated training data. While there is no analogous ground truth for real life biological systems, this work demonstrates the ability to construct and parameterize a considerable diversity of network models with high predictive ability. The expectation is that this kind of procedure can be used on real perturbation-response data to derive models applicable to diverse biological systems., Comment: Accepted as a workshop paper at ICLR 2021 SimDL Workshop

Published
2021

9. Protein design and variant prediction using autoregressive generative models.

Author

Shin, Jung-Eun, Riesselman, Adam J, Kollasch, Aaron W, McMahon, Conor, Simon, Elana, Sander, Chris, Manglik, Aashish, Kruse, Andrew C, and Marks, Debora S

Subjects
Humans, Proteins, Antibodies, Antigens, Protein Engineering, Computational Biology, Amino Acid Sequence, Genotype, Phenotype, Mutation, Algorithms, Neural Networks, Computer, Neural Networks, Computer
Abstract

The ability to design functional sequences and predict effects of variation is central to protein engineering and biotherapeutics. State-of-art computational methods rely on models that leverage evolutionary information but are inadequate for important applications where multiple sequence alignments are not robust. Such applications include the prediction of variant effects of indels, disordered proteins, and the design of proteins such as antibodies due to the highly variable complementarity determining regions. We introduce a deep generative model adapted from natural language processing for prediction and design of diverse functional sequences without the need for alignments. The model performs state-of-art prediction of missense and indel effects and we successfully design and test a diverse 105-nanobody library that shows better expression than a 1000-fold larger synthetic library. Our results demonstrate the power of the alignment-free autoregressive model in generalizing to regions of sequence space traditionally considered beyond the reach of prediction and design.

Published
2021

10. Artificial Intelligence and Early Detection of Pancreatic Cancer: 2020 Summative Review.

Author

Kenner, Barbara, Chari, Suresh T, Kelsen, David, Klimstra, David S, Pandol, Stephen J, Rosenthal, Michael, Rustgi, Anil K, Taylor, James A, Yala, Adam, Abul-Husn, Noura, Andersen, Dana K, Bernstein, David, Brunak, Søren, Canto, Marcia Irene, Eldar, Yonina C, Fishman, Elliot K, Fleshman, Julie, Go, Vay Liang W, Holt, Jane M, Field, Bruce, Goldberg, Ann, Hoos, William, Iacobuzio-Donahue, Christine, Li, Debiao, Lidgard, Graham, Maitra, Anirban, Matrisian, Lynn M, Poblete, Sung, Rothschild, Laura, Sander, Chris, Schwartz, Lawrence H, Shalit, Uri, Srivastava, Sudhir, and Wolpin, Brian

Subjects
Humans, Carcinoma, Pancreatic Ductal, Pancreatic Neoplasms, Prognosis, Survival Analysis, Interdisciplinary Communication, Genomics, Artificial Intelligence, Early Detection of Cancer, Biomarkers, Tumor, Cancer, Rare Diseases, Prevention, Pancreatic Cancer, Digestive Diseases, Good Health and Well Being, artificial intelligence, machine learning, pancreatic cancer, early detection, Clinical Sciences, Gastroenterology & Hepatology
Abstract

AbstractDespite considerable research efforts, pancreatic cancer is associated with a dire prognosis and a 5-year survival rate of only 10%. Early symptoms of the disease are mostly nonspecific. The premise of improved survival through early detection is that more individuals will benefit from potentially curative treatment. Artificial intelligence (AI) methodology has emerged as a successful tool for risk stratification and identification in general health care. In response to the maturity of AI, Kenner Family Research Fund conducted the 2020 AI and Early Detection of Pancreatic Cancer Virtual Summit (www.pdac-virtualsummit.org) in conjunction with the American Pancreatic Association, with a focus on the potential of AI to advance early detection efforts in this disease. This comprehensive presummit article was prepared based on information provided by each of the interdisciplinary participants on one of the 5 following topics: Progress, Problems, and Prospects for Early Detection; AI and Machine Learning; AI and Pancreatic Cancer-Current Efforts; Collaborative Opportunities; and Moving Forward-Reflections from Government, Industry, and Advocacy. The outcome from the robust Summit conversations, to be presented in a future white paper, indicate that significant progress must be the result of strategic collaboration among investigators and institutions from multidisciplinary backgrounds, supported by committed funders.

Published
2021

11. Systematic Assessment of Tumor Purity and Its Clinical Implications

Author

Haider, Syed, Tyekucheva, Svitlana, Prandi, Davide, Fox, Natalie S, Ahn, Jaeil, Xu, Andrew Wei, Pantazi, Angeliki, Park, Peter J, Laird, Peter W, Sander, Chris, Wang, Wenyi, Demichelis, Francesca, Loda, Massimo, and Boutros, Paul C

Subjects
Biomedical and Clinical Sciences, Oncology and Carcinogenesis, Human Genome, Aging, Genetics, Biotechnology, Prostate Cancer, Urologic Diseases, Cancer, Cancer Genome Atlas Research Network, Oncology and carcinogenesis
Abstract

PurposeThe tumor microenvironment is complex, comprising heterogeneous cellular populations. As molecular profiles are frequently generated using bulk tissue sections, they represent an admixture of multiple cell types (including immune, stromal, and cancer cells) interacting with each other. Therefore, these molecular profiles are confounded by signals emanating from many cell types. Accurate assessment of residual cancer cell fraction is crucial for parameterization and interpretation of genomic analyses, as well as for accurately interpreting the clinical properties of the tumor.Materials and methodsTo benchmark cancer cell fraction estimation methods, 10 estimators were applied to a clinical cohort of 333 patients with prostate cancer. These methods include gold-standard multiobserver pathology estimates, as well as estimates inferred from genome, epigenome, and transcriptome data. In addition, two methods based on genomic and transcriptomic profiles were used to quantify tumor purity in 4,497 tumors across 12 cancer types. Bulk mRNA and microRNA profiles were subject to in silico deconvolution to estimate cancer cell-specific mRNA and microRNA profiles.ResultsWe present a systematic comparison of 10 tumor purity estimation methods on a cohort of 333 prostate tumors. We quantify variation among purity estimation methods and demonstrate how this influences interpretation of clinico-genomic analyses. Our data show poor concordance between pathologic and molecular purity estimates, necessitating caution when interpreting molecular results. Limited concordance between DNA- and mRNA-derived purity estimates remained a general pan-cancer phenomenon when tested in an additional 4,497 tumors spanning 12 cancer types.ConclusionThe choice of tumor purity estimation method may have a profound impact on the interpretation of genomic assays. Taken together, these data highlight the need for improved assessment of tumor purity and quantitation of its influences on the molecular hallmarks of cancers.

Published
2020

12. Analyses of non-coding somatic drivers in 2,658 cancer whole genomes.

Author

Rheinbay, Esther, Nielsen, Morten Muhlig, Abascal, Federico, Wala, Jeremiah A, Shapira, Ofer, Tiao, Grace, Hornshøj, Henrik, Hess, Julian M, Juul, Randi Istrup, Lin, Ziao, Feuerbach, Lars, Sabarinathan, Radhakrishnan, Madsen, Tobias, Kim, Jaegil, Mularoni, Loris, Shuai, Shimin, Lanzós, Andrés, Herrmann, Carl, Maruvka, Yosef E, Shen, Ciyue, Amin, Samirkumar B, Bandopadhayay, Pratiti, Bertl, Johanna, Boroevich, Keith A, Busanovich, John, Carlevaro-Fita, Joana, Chakravarty, Dimple, Chan, Calvin Wing Yiu, Craft, David, Dhingra, Priyanka, Diamanti, Klev, Fonseca, Nuno A, Gonzalez-Perez, Abel, Guo, Qianyun, Hamilton, Mark P, Haradhvala, Nicholas J, Hong, Chen, Isaev, Keren, Johnson, Todd A, Juul, Malene, Kahles, Andre, Kahraman, Abdullah, Kim, Youngwook, Komorowski, Jan, Kumar, Kiran, Kumar, Sushant, Lee, Donghoon, Lehmann, Kjong-Van, Li, Yilong, Liu, Eric Minwei, Lochovsky, Lucas, Park, Keunchil, Pich, Oriol, Roberts, Nicola D, Saksena, Gordon, Schumacher, Steven E, Sidiropoulos, Nikos, Sieverling, Lina, Sinnott-Armstrong, Nasa, Stewart, Chip, Tamborero, David, Tubio, Jose MC, Umer, Husen M, Uusküla-Reimand, Liis, Wadelius, Claes, Wadi, Lina, Yao, Xiaotong, Zhang, Cheng-Zhong, Zhang, Jing, Haber, James E, Hobolth, Asger, Imielinski, Marcin, Kellis, Manolis, Lawrence, Michael S, von Mering, Christian, Nakagawa, Hidewaki, Raphael, Benjamin J, Rubin, Mark A, Sander, Chris, Stein, Lincoln D, Stuart, Joshua M, Tsunoda, Tatsuhiko, Wheeler, David A, Johnson, Rory, Reimand, Jüri, Gerstein, Mark, Khurana, Ekta, Campbell, Peter J, López-Bigas, Núria, PCAWG Drivers and Functional Interpretation Working Group, PCAWG Structural Variation Working Group, Weischenfeldt, Joachim, Beroukhim, Rameen, Martincorena, Iñigo, Pedersen, Jakob Skou, Getz, Gad, and PCAWG Consortium

Subjects
PCAWG Drivers and Functional Interpretation Working Group, PCAWG Structural Variation Working Group, PCAWG Consortium, Humans, Neoplasms, Gene Expression Regulation, Neoplastic, Mutation, Genome, Human, Databases, Genetic, DNA Breaks, INDEL Mutation, Genome-Wide Association Study, Gene Expression Regulation, Neoplastic, Genome, Human, Databases, Genetic, General Science & Technology
Abstract

The discovery of drivers of cancer has traditionally focused on protein-coding genes1-4. Here we present analyses of driver point mutations and structural variants in non-coding regions across 2,658 genomes from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium5 of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). For point mutations, we developed a statistically rigorous strategy for combining significance levels from multiple methods of driver discovery that overcomes the limitations of individual methods. For structural variants, we present two methods of driver discovery, and identify regions that are significantly affected by recurrent breakpoints and recurrent somatic juxtapositions. Our analyses confirm previously reported drivers6,7, raise doubts about others and identify novel candidates, including point mutations in the 5' region of TP53, in the 3' untranslated regions of NFKBIZ and TOB1, focal deletions in BRD4 and rearrangements in the loci of AKR1C genes. We show that although point mutations and structural variants that drive cancer are less frequent in non-coding genes and regulatory sequences than in protein-coding genes, additional examples of these drivers will be found as more cancer genomes become available.

Published
2020

13. Protein structure prediction assisted with sparse NMR data in CASP13

Author

Sala, Davide, Huang, Yuanpeng Janet, Cole, Casey A, Snyder, David A, Liu, Gaohua, Ishida, Yojiro, Swapna, GVT, Brock, Kelly P, Sander, Chris, Fidelis, Krzysztof, Kryshtafovych, Andriy, Inouye, Masayori, Tejero, Roberto, Valafar, Homayoun, Rosato, Antonio, and Montelione, Gaetano T

Subjects
Biological Sciences, Bioinformatics and Computational Biology, Rare Diseases, Algorithms, Computer Simulation, Crystallography, X-Ray, Magnetic Resonance Spectroscopy, Models, Molecular, Protein Conformation, Protein Folding, Proteins, Reproducibility of Results, CASP, contact prediction, protein modeling, residual dipolar coupling, simulated NMR spectra, sparse NMR data, structure prediction, Mathematical Sciences, Information and Computing Sciences, Bioinformatics, Biological sciences, Mathematical sciences
Abstract

CASP13 has investigated the impact of sparse NMR data on the accuracy of protein structure prediction. NOESY and 15 N-1 H residual dipolar coupling data, typical of that obtained for 15 N,13 C-enriched, perdeuterated proteins up to about 40 kDa, were simulated for 11 CASP13 targets ranging in size from 80 to 326 residues. For several targets, two prediction groups generated models that are more accurate than those produced using baseline methods. Real NMR data collected for a de novo designed protein were also provided to predictors, including one data set in which only backbone resonance assignments were available. Some NMR-assisted prediction groups also did very well with these data. CASP13 also assessed whether incorporation of sparse NMR data improves the accuracy of protein structure prediction relative to nonassisted regular methods. In most cases, incorporation of sparse, noisy NMR data results in models with higher accuracy. The best NMR-assisted models were also compared with the best regular predictions of any CASP13 group for the same target. For six of 13 targets, the most accurate model provided by any NMR-assisted prediction group was more accurate than the most accurate model provided by any regular prediction group; however, for the remaining seven targets, one or more regular prediction method provided a more accurate model than even the best NMR-assisted model. These results suggest a novel approach for protein structure determination, in which advanced prediction methods are first used to generate structural models, and sparse NMR data is then used to validate and/or refine these models.

Published
2019

15. FcγR-mediated SARS-CoV-2 infection of monocytes activates inflammation

Author

Junqueira, Caroline, Crespo, Ângela, Ranjbar, Shahin, de Lacerda, Luna B., Lewandrowski, Mercedes, Ingber, Jacob, Parry, Blair, Ravid, Sagi, Clark, Sarah, Schrimpf, Marie Rose, Ho, Felicia, Beakes, Caroline, Margolin, Justin, Russell, Nicole, Kays, Kyle, Boucau, Julie, Das Adhikari, Upasana, Vora, Setu M., Leger, Valerie, Gehrke, Lee, Henderson, Lauren A., Janssen, Erin, Kwon, Douglas, Sander, Chris, Abraham, Jonathan, Goldberg, Marcia B., Wu, Hao, Mehta, Gautam, Bell, Steven, Goldfeld, Anne E., Filbin, Michael R., and Lieberman, Judy

Published
2022
Full Text
View/download PDF

16. Rare Germline Variants Are Associated with Rapid Biochemical Recurrence After Radical Prostate Cancer Treatment: A Pan Prostate Cancer Group Study

Author

Burns, Daniel, Anokian, Ezequiel, Saunders, Edward J., Bristow, Robert G., Fraser, Michael, Reimand, Jüri, Schlomm, Thorsten, Sauter, Guido, Brors, Benedikt, Korbel, Jan, Weischenfeldt, Joachim, Waszak, Sebastian M., Corcoran, Niall M., Jung, Chol-Hee, Pope, Bernard J., Hovens, Chris M., Cancel-Tassin, Géraldine, Cussenot, Olivier, Loda, Massimo, Sander, Chris, Hayes, Vanessa M., Dalsgaard Sorensen, Karina, Lu, Yong-Jie, Hamdy, Freddie C., Foster, Christopher S., Gnanapragasam, Vincent, Butler, Adam, Lynch, Andy G., Massie, Charlie E., Woodcock, Dan J., Cooper, Colin S., Wedge, David C., Brewer, Daniel S., Kote-Jarai, Zsofia, and Eeles, Rosalind A.

Published
2022
Full Text
View/download PDF

17. Comprehensive Analysis of Alternative Splicing Across Tumors from 8,705 Patients.

Author

Kahles, André, Lehmann, Kjong-Van, Toussaint, Nora C, Hüser, Matthias, Stark, Stefan G, Sachsenberg, Timo, Stegle, Oliver, Kohlbacher, Oliver, Sander, Chris, Cancer Genome Atlas Research Network, and Rätsch, Gunnar

Subjects
Cancer Genome Atlas Research Network, Humans, Neoplasms, Sequence Analysis, RNA, Alternative Splicing, Polymorphism, Single Nucleotide, Quantitative Trait Loci, Exome Sequencing, CPTAC, GTEx, MS proteomics, RNA-seq, TCGA, TCGA Pan-Cancer Atlas, alternative splicing, cancer, exome, immunoediting, immunotherapy, neoantigens, splicing QTL, tumor-specific splicing, Genetics, Ovarian Cancer, Human Genome, Rare Diseases, Cancer, Neurosciences, Oncology and Carcinogenesis, Oncology & Carcinogenesis
Abstract

Our comprehensive analysis of alternative splicing across 32 The Cancer Genome Atlas cancer types from 8,705 patients detects alternative splicing events and tumor variants by reanalyzing RNA and whole-exome sequencing data. Tumors have up to 30% more alternative splicing events than normal samples. Association analysis of somatic variants with alternative splicing events confirmed known trans associations with variants in SF3B1 and U2AF1 and identified additional trans-acting variants (e.g., TADA1, PPP2R1A). Many tumors have thousands of alternative splicing events not detectable in normal samples; on average, we identified ≈930 exon-exon junctions ("neojunctions") in tumors not typically found in GTEx normals. From Clinical Proteomic Tumor Analysis Consortium data available for breast and ovarian tumor samples, we confirmed ≈1.7 neojunction- and ≈0.6 single nucleotide variant-derived peptides per tumor sample that are also predicted major histocompatibility complex-I binders ("putative neoantigens").

Published
2018

18. Sequencing of prostate cancers identifies new cancer genes, routes of progression and drug targets

Author

Wedge, David C, Gundem, Gunes, Mitchell, Thomas, Woodcock, Dan J, Martincorena, Inigo, Ghori, Mohammed, Zamora, Jorge, Butler, Adam, Whitaker, Hayley, Kote-Jarai, Zsofia, Alexandrov, Ludmil B, Van Loo, Peter, Massie, Charlie E, Dentro, Stefan, Warren, Anne Y, Verrill, Clare, Berney, Dan M, Dennis, Nening, Merson, Sue, Hawkins, Steve, Howat, William, Lu, Yong-Jie, Lambert, Adam, Kay, Jonathan, Kremeyer, Barbara, Karaszi, Katalin, Luxton, Hayley, Camacho, Niedzica, Marsden, Luke, Edwards, Sandra, Matthews, Lucy, Bo, Valeria, Leongamornlert, Daniel, McLaren, Stuart, Ng, Anthony, Yu, Yongwei, Zhang, Hongwei, Dadaev, Tokhir, Thomas, Sarah, Easton, Douglas F, Ahmed, Mahbubl, Bancroft, Elizabeth, Fisher, Cyril, Livni, Naomi, Nicol, David, Tavaré, Simon, Gill, Pelvender, Greenman, Christopher, Khoo, Vincent, Van As, Nicholas, Kumar, Pardeep, Ogden, Christopher, Cahill, Declan, Thompson, Alan, Mayer, Erik, Rowe, Edward, Dudderidge, Tim, Gnanapragasam, Vincent, Shah, Nimish C, Raine, Keiran, Jones, David, Menzies, Andrew, Stebbings, Lucy, Teague, Jon, Hazell, Steven, Corbishley, Cathy, CAMCAP Study Group, de Bono, Johann, Attard, Gerhardt, Isaacs, William, Visakorpi, Tapio, Fraser, Michael, Boutros, Paul C, Bristow, Robert G, Workman, Paul, Sander, Chris, The TCGA Consortium, Hamdy, Freddie C, Futreal, Andrew, McDermott, Ultan, Al-Lazikani, Bissan, Lynch, Andrew G, Bova, G Steven, Foster, Christopher S, Brewer, Daniel S, Neal, David E, Cooper, Colin S, and Eeles, Rosalind A

Subjects
Genetics, Human Genome, Aging, Cancer, Urologic Diseases, Biotechnology, Prostate Cancer, Aetiology, 5.1 Pharmaceuticals, 2.1 Biological and endogenous factors, Development of treatments and therapeutic interventions, Adult, Aged, Aged, 80 and over, BRCA2 Protein, Disease Progression, Hepatocyte Nuclear Factor 3-alpha, High-Throughput Nucleotide Sequencing, Humans, Male, Middle Aged, Mutation, Oncogenes, Prostatic Neoplasms, CAMCAP Study Group, TCGA Consortium, Biological Sciences, Medical and Health Sciences, Developmental Biology
Abstract

Prostate cancer represents a substantial clinical challenge because it is difficult to predict outcome and advanced disease is often fatal. We sequenced the whole genomes of 112 primary and metastatic prostate cancer samples. From joint analysis of these cancers with those from previous studies (930 cancers in total), we found evidence for 22 previously unidentified putative driver genes harboring coding mutations, as well as evidence for NEAT1 and FOXA1 acting as drivers through noncoding mutations. Through the temporal dissection of aberrations, we identified driver mutations specifically associated with steps in the progression of prostate cancer, establishing, for example, loss of CHD1 and BRCA2 as early events in cancer development of ETS fusion-negative cancers. Computational chemogenomic (canSAR) analysis of prostate cancer mutations identified 11 targets of approved drugs, 7 targets of investigational drugs, and 62 targets of compounds that may be active and should be considered candidates for future clinical trials.

Published
2018

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

Author

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

Subjects
Biological Sciences, Bioinformatics and Computational Biology, 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

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

Author

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

Subjects
Biological Sciences, Bioinformatics and Computational Biology, Genetics, 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

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

Author

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

Subjects
Biological Sciences, Bioinformatics and Computational Biology, 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

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

Author

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

Subjects
Biochemistry and Cell Biology, Biomedical and Clinical Sciences, Oncology and Carcinogenesis, Biological Sciences, 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

23. 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

24. 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

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

Author

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

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

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

Published
2018

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

Author

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

Subjects
Biological Sciences, 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

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

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

Published
2018

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

Author

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

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

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

Published
2018

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

34. Author Correction: Analyses of non-coding somatic drivers in 2,658 cancer whole genomes

Author

Rheinbay, Esther, Nielsen, Morten Muhlig, Abascal, Federico, Wala, Jeremiah A., Shapira, Ofer, Tiao, Grace, Hornshøj, Henrik, Hess, Julian M., Juul, Randi Istrup, Lin, Ziao, Feuerbach, Lars, Sabarinathan, Radhakrishnan, Madsen, Tobias, Kim, Jaegil, Mularoni, Loris, Shuai, Shimin, Lanzós, Andrés, Herrmann, Carl, Maruvka, Yosef E., Shen, Ciyue, Amin, Samirkumar B., Bandopadhayay, Pratiti, Bertl, Johanna, Boroevich, Keith A., Busanovich, John, Carlevaro-Fita, Joana, Chakravarty, Dimple, Chan, Calvin Wing Yiu, Craft, David, Dhingra, Priyanka, Diamanti, Klev, Fonseca, Nuno A., Gonzalez-Perez, Abel, Guo, Qianyun, Hamilton, Mark P., Haradhvala, Nicholas J., Hong, Chen, Isaev, Keren, Johnson, Todd A., Juul, Malene, Kahles, Andre, Kahraman, Abdullah, Kim, Youngwook, Komorowski, Jan, Kumar, Kiran, Kumar, Sushant, Lee, Donghoon, Lehmann, Kjong-Van, Li, Yilong, Liu, Eric Minwei, Lochovsky, Lucas, Park, Keunchil, Pich, Oriol, Roberts, Nicola D., Saksena, Gordon, Schumacher, Steven E., Sidiropoulos, Nikos, Sieverling, Lina, Sinnott-Armstrong, Nasa, Stewart, Chip, Tamborero, David, Tubio, Jose M. C., Umer, Husen M., Uusküla-Reimand, Liis, Wadelius, Claes, Wadi, Lina, Yao, Xiaotong, Zhang, Cheng-Zhong, Zhang, Jing, Haber, James E., Hobolth, Asger, Imielinski, Marcin, Kellis, Manolis, Lawrence, Michael S., von Mering, Christian, Nakagawa, Hidewaki, Raphael, Benjamin J., Rubin, Mark A., Sander, Chris, Stein, Lincoln D., Stuart, Joshua M., Tsunoda, Tatsuhiko, Wheeler, David A., Johnson, Rory, Reimand, Jüri, Gerstein, Mark, Khurana, Ekta, Campbell, Peter J., López-Bigas, Núria, Weischenfeldt, Joachim, Beroukhim, Rameen, Martincorena, Iñigo, Pedersen, Jakob Skou, and Getz, Gad

Published
2023
Full Text
View/download PDF

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

Author

Farshidfar, Farshad, Zheng, Siyuan, Gingras, Marie-Claude, Newton, Yulia, Shih, Juliann, Robertson, A Gordon, Hinoue, Toshinori, Hoadley, Katherine A, Gibb, Ewan A, Roszik, Jason, Covington, Kyle R, Wu, Chia-Chin, Shinbrot, Eve, Stransky, Nicolas, Hegde, Apurva, Yang, Ju Dong, Reznik, Ed, Sadeghi, Sara, Pedamallu, Chandra Sekhar, Ojesina, Akinyemi I, Hess, Julian M, Auman, J Todd, Rhie, Suhn K, Bowlby, Reanne, Borad, Mitesh J, Cancer Genome Atlas Network, Zhu, Andrew X, Stuart, Josh M, Sander, Chris, Akbani, Rehan, Cherniack, Andrew D, Deshpande, Vikram, Mounajjed, Taofic, Foo, Wai Chin, Torbenson, Michael S, Kleiner, David E, Laird, Peter W, Wheeler, David A, McRee, Autumn J, Bathe, Oliver F, Andersen, Jesper B, Bardeesy, Nabeel, Roberts, Lewis R, and Kwong, Lawrence N

Subjects
Cancer Genome Atlas Network, Liver, Chromatin, Mitochondria, Humans, Cholangiocarcinoma, Bile Duct Neoplasms, Liver Neoplasms, Pancreatic Neoplasms, Isocitrate Dehydrogenase, Nuclear Proteins, Transcription Factors, RNA, Messenger, Genomics, DNA Methylation, Gene Expression Regulation, Neoplastic, Mutation, Adult, Aged, Aged, 80 and over, Middle Aged, Female, Male, Promoter Regions, Genetic, RNA, Long Noncoding, ARID1A, DNA methylation, IDH, RNA sequencing, TCGA, cholangiocarcinoma, integrative genomics, lncRNAs, multi-omics, whole exome, RNA, Messenger, Gene Expression Regulation, Neoplastic, and over, Promoter Regions, Genetic, Long Noncoding, Biochemistry and Cell Biology, Medical Physiology
Abstract

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

Published
2017

36. Quantification of the effect of mutations using a global probability model of natural sequence variation

Author

Hopf, Thomas A., Ingraham, John B., Poelwijk, Frank J., Springer, Michael, Sander, Chris, and Marks, Debora S.

Subjects
Quantitative Biology - Biomolecules
Abstract

Modern biomedicine is challenged to predict the effects of genetic variation. Systematic functional assays of point mutants of proteins have provided valuable empirical information, but vast regions of sequence space remain unexplored. Fortunately, the mutation-selection process of natural evolution has recorded rich information in the diversity of natural protein sequences. Here, building on probabilistic models for correlated amino-acid substitutions that have been successfully applied to determine the three-dimensional structures of proteins, we present a statistical approach for quantifying the contribution of residues and their interactions to protein function, using a statistical energy, the evolutionary Hamiltonian. We find that these probability models predict the experimental effects of mutations with reasonable accuracy for a number of proteins, especially where the selective pressure is similar to the evolutionary pressure on the protein, such as antibiotics.

Published
2015
Full Text
View/download PDF

37. 3D RNA and functional interactions from evolutionary couplings

Author

Weinreb, Caleb, Riesselman, Adam J., Ingraham, John B., Gross, Torsten, Sander, Chris, and Marks, Debora S.

Subjects
Quantitative Biology - Biomolecules
Abstract

Non-coding RNAs are ubiquitous, but the discovery of new RNA gene sequences far outpaces research on their structure and functional interactions. We mine the evolutionary sequence record to derive precise information about function and structure of RNAs and RNA-protein complexes. As in protein structure prediction, we use maximum entropy global probability models of sequence co-variation to infer evolutionarily constrained nucleotide-nucleotide interactions within RNA molecules, and nucleotide-amino acid interactions in RNA-protein complexes. The predicted contacts allow all-atom blinded 3D structure prediction at good accuracy for several known RNA structures and RNA-protein complexes. For unknown structures, we predict contacts in 160 non-coding RNA families. Beyond 3D structure prediction, evolutionary couplings help identify important functional interactions, e.g., at switch points in riboswitches and at a complex nucleation site in HIV. Aided by accelerating sequence accumulation, evolutionary coupling analysis can accelerate the discovery of functional interactions and 3D structures involving RNA.

Published
2015

38. Graph Curvature and the Robustness of Cancer Networks

Author

Tannenbaum, Allen, Sander, Chris, Zhu, Liangjia, Sandhu, Romeil, Kolesov, Ivan, Reznik, Eduard, Senbabaoglu, Yasin, and Georgiou, Tryphon

Subjects
Quantitative Biology - Molecular Networks
Abstract

The importance of studying properties of networks is manifest in diverse fields ranging from biology, engineering, physics, chemistry, neuroscience, and medicine. The functionality of networks with regard to performance, throughput, reliability and robustness is strongly linked to the underlying geometric and topological properties of the network and this is the focus of this paper, especially as applied to certain biological networks. The fundamental mathematical abstraction of a network as a weighted graph brings to bear the tools of graph theory--a highly developed subject of mathematical research. But more importantly, recently proposed geometric notions of curvature on very general metric measure spaces allow us to utilize a whole new set of tools and ideas that help quantify functionality and robustness of graphs. In particular, robustness is closely connected to network entropy which, in turn, is very closely related to curvature. We will see that there are a number of alternative notions of discrete curvature that are compatible with the classical Riemannian definition, each having its own advantages and disadvantages, and are relevant to networks of interest. We will concentrate on the role of curvature for certain key cancer networks in order to quantitatively indicate their apparent functional robustness relative to their normal counterparts., Comment: 13 pages, 3 figures, 7 tables

Published
2015

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

Author

Roberts, Lewis R, Farshidfar, Farshad, Zheng, Siyuan, Gingras, Marie-Claude, Newton, Yulia, Shih, Juliann, Robertson, A Gordon, Hinoue, Toshinori, Hoadley, Katherine A, Gibbs, Richard, Roszik, Jason, Covington, Kyle, Wu, Chia-Chin, Shinbrot, Eve, Stransky, Nicolas, Hegde, Apurva M, Yang, Ju Dong, Reznik, Ed, Sadeghi, Sara, Pedamallu, Chandra Sekhar, Ojesina, Akinyemi I, Hess, Julian, Auman, J Todd, Rhie, Suhn K, Bowlby, Reanne, Borad, Mitesh J, Stuart, Josh, Sander, Chris, Akbani, Rehan, Cherniack, Andrew D, Laird, Peter W, Deshpande, Vikram, Mounajjed, Taofic, Foo, Wai Chin, Torbenson, Michael, Kleiner, David E, Wheeler, David A, Mcree, Autumn J, Bathe, Oliver F, Andersen, Jesper B, Bardeesy, Nabeel, and Kwong, Lawrence N

Subjects
Gastroenterology & Hepatology, Medical Biochemistry and Metabolomics, Clinical Sciences, Immunology
Published
2016

42. Sequence co-evolution gives 3D contacts and structures of protein complexes

Author

Hopf, Thomas A., Schärfe, Charlotta P. I., Rodrigues, João P. G. L. M., Green, Anna G., Sander, Chris, Bonvin, Alexandre M. J. J., and Marks, Debora S.

Subjects
Quantitative Biology - Biomolecules
Abstract

Protein-protein interactions are fundamental to many biological processes. Experimental screens have identified tens of thousands of interactions and structural biology has provided detailed functional insight for select 3D protein complexes. An alternative rich source of information about protein interactions is the evolutionary sequence record. Building on earlier work, we show that analysis of correlated evolutionary sequence changes across proteins identifies residues that are close in space with sufficient accuracy to determine the three-dimensional structure of the protein complexes. We evaluate prediction performance in blinded tests on 76 complexes of known 3D structure, predict protein-protein contacts in 32 complexes of unknown structure, and demonstrate how evolutionary couplings can be used to distinguish between interacting and non-interacting protein pairs in a large complex. With the current growth of sequence databases, we expect that the method can be generalized to genome-wide elucidation of protein-protein interaction networks and used for interaction predictions at residue resolution.

Published
2014
Full Text
View/download PDF

43. Pharmacologically controlling protein-protein interactions through epichaperomes for therapeutic vulnerability in cancer

Author

Joshi, Suhasini, Gomes, Erica DaGama, Wang, Tai, Corben, Adriana, Taldone, Tony, Gandu, Srinivasa, Xu, Chao, Sharma, Sahil, Buddaseth, Salma, Yan, Pengrong, Chan, Lon Yin L., Gokce, Askan, Rajasekhar, Vinagolu K., Shrestha, Lisa, Panchal, Palak, Almodovar, Justina, Digwal, Chander S., Rodina, Anna, Merugu, Swathi, Pillarsetty, NagaVaraKishore, Miclea, Vlad, Peter, Radu I., Wang, Wanyan, Ginsberg, Stephen D., Tang, Laura, Mattar, Marissa, de Stanchina, Elisa, Yu, Kenneth H., Lowery, Maeve, Grbovic-Huezo, Olivera, O’Reilly, Eileen M., Janjigian, Yelena, Healey, John H., Jarnagin, William R., Allen, Peter J., Sander, Chris, Erdjument-Bromage, Hediye, Neubert, Thomas A., Leach, Steven D., and Chiosis, Gabriela

Published
2021
Full Text
View/download PDF

45. Quantitative Proteome Landscape of the NCI-60 Cancer Cell Lines

Author

Guo, Tiannan, Luna, Augustin, Rajapakse, Vinodh N., Koh, Ching Chiek, Wu, Zhicheng, Liu, Wei, Sun, Yaoting, Gao, Huanhuan, Menden, Michael P., Xu, Chao, Calzone, Laurence, Martignetti, Loredana, Auwerx, Chiara, Buljan, Marija, Banaei-Esfahani, Amir, Ori, Alessandro, Iskar, Murat, Gillet, Ludovic, Bi, Ran, Zhang, Jiangnan, Zhang, Huanhuan, Yu, Chenhuan, Zhong, Qing, Varma, Sudhir, Schmitt, Uwe, Qiu, Peng, Zhang, Qiushi, Zhu, Yi, Wild, Peter J., Garnett, Mathew J., Bork, Peer, Beck, Martin, Liu, Kexin, Saez-Rodriguez, Julio, Elloumi, Fathi, Reinhold, William C., Sander, Chris, Pommier, Yves, and Aebersold, Ruedi

Published
2019
Full Text
View/download PDF

46. Comprehensive Molecular Portraits of Invasive Lobular Breast Cancer

Author

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

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

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

Published
2015

47. Pathway and network analysis of cancer genomes

Author

Creixell, Pau, Reimand, Jueri, Haider, Syed, Wu, Guanming, Shibata, Tatsuhiro, Vazquez, Miguel, Mustonen, Ville, Gonzalez-Perez, Abel, Pearson, John, Sander, Chris, Raphael, Benjamin J, Marks, Debora S, Ouellette, BF Francis, Valencia, Alfonso, Bader, Gary D, Boutros, Paul C, Stuart, Joshua M, Linding, Rune, Lopez-Bigas, Nuria, and Stein, Lincoln D

Subjects
Cancer, Gene Regulatory Networks, Genome, Humans, Neoplasms, Signal Transduction, Mutation Consequences and Pathway Analysis Working Group of the International Cancer Genome Consortium, Biological Sciences, Technology, Medical and Health Sciences, Developmental Biology
Abstract

Genomic information on tumors from 50 cancer types cataloged by the International Cancer Genome Consortium (ICGC) shows that only a few well-studied driver genes are frequently mutated, in contrast to many infrequently mutated genes that may also contribute to tumor biology. Hence there has been large interest in developing pathway and network analysis methods that group genes and illuminate the processes involved. We provide an overview of these analysis techniques and show where they guide mechanistic and translational investigations.

Published
2015

48. Perturbation Biology: inferring signaling networks in cellular systems

Author

Molinelli, Evan J., Korkut, Anil, Wang, Weiqing, Miller, Martin L., Gauthier, Nicholas P., Jing, Xiaohong, Kaushik, Poorvi, He, Qin, Mills, Gordon, Solit, David B., Pratilas, Christine A., Weigt, Martin, Braunstein, Alfredo, Pagnani, Andrea, Zecchina, Riccardo, and Sander, Chris

Subjects
Quantitative Biology - Molecular Networks, 92C42
Abstract

We present a new experimental-computational technology of inferring network models that predict the response of cells to perturbations and that may be useful in the design of combinatorial therapy against cancer. The experiments are systematic series of perturbations of cancer cell lines by targeted drugs, singly or in combination. The response to perturbation is measured in terms of levels of proteins and phospho-proteins and of cellular phenotype such as viability. Computational network models are derived de novo, i.e., without prior knowledge of signaling pathways, and are based on simple non-linear differential equations. The prohibitively large solution space of all possible network models is explored efficiently using a probabilistic algorithm, belief propagation, which is three orders of magnitude more efficient than Monte Carlo methods. Explicit executable models are derived for a set of perturbation experiments in Skmel-133 melanoma cell lines, which are resistant to the therapeutically important inhibition of Raf kinase. The resulting network models reproduce and extend known pathway biology. They can be applied to discover new molecular interactions and to predict the effect of novel drug perturbations, one of which is verified experimentally. The technology is suitable for application to larger systems in diverse areas of molecular biology.

Published
2013
Full Text
View/download PDF

49. A Hybrid Approach for Protein Structure Determination Combining Sparse NMR with Evolutionary Coupling Sequence Data

Author

Huang, Yuanpeng Janet, Brock, Kelly P., Sander, Chris, Marks, Debora S., Montelione, Gaetano T., COHEN, IRUN R., Series Editor, LAJTHA, ABEL, Series Editor, LAMBRIS, JOHN D., Series Editor, PAOLETTI, RODOLFO, Series Editor, Rezaei, Nima, Series Editor, Nakamura, Haruki, editor, Kleywegt, Gerard, editor, Burley, Stephen K., editor, and Markley, John L., editor

Published
2018
Full Text
View/download PDF

50. Direct-coupling analysis of residue co-evolution captures native contacts across many protein families

Author

Morcos, Faruck, Pagnani, Andrea, Lunt, Bryan, Bertolino, Arianna, Marks, Debora S., Sander, Chris, Zecchina, Riccardo, Onuchic, Jose' N., Hwa, Terence, and Weigt, Martin

Subjects
Quantitative Biology - Quantitative Methods, Condensed Matter - Statistical Mechanics, Quantitative Biology - Biomolecules
Abstract

The similarity in the three-dimensional structures of homologous proteins imposes strong constraints on their sequence variability. It has long been suggested that the resulting correlations among amino acid compositions at different sequence positions can be exploited to infer spatial contacts within the tertiary protein structure. Crucial to this inference is the ability to disentangle direct and indirect correlations, as accomplished by the recently introduced Direct Coupling Analysis (DCA) (Weigt et al. (2009) Proc Natl Acad Sci 106:67). Here we develop a computationally efficient implementation of DCA, which allows us to evaluate the accuracy of contact prediction by DCA for a large number of protein domains, based purely on sequence information. DCA is shown to yield a large number of correctly predicted contacts, recapitulating the global structure of the contact map for the majority of the protein domains examined. Furthermore, our analysis captures clear signals beyond intra- domain residue contacts, arising, e.g., from alternative protein conformations, ligand- mediated residue couplings, and inter-domain interactions in protein oligomers. Our findings suggest that contacts predicted by DCA can be used as a reliable guide to facilitate computational predictions of alternative protein conformations, protein complex formation, and even the de novo prediction of protein domain structures, provided the existence of a large number of homologous sequences which are being rapidly made available due to advances in genome sequencing., Comment: 28 pages, 7 figures, to appear in PNAS

Published
2011
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results