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author:("ishmulevich, Ilya")
1.  Comprehensive molecular characterization of gastric adenocarcinoma 
Bass, Adam J. | Thorsson, Vesteinn | Shmulevich, Ilya | Reynolds, Sheila M. | Miller, Michael | Bernard, Brady | Hinoue, Toshinori | Laird, Peter W. | Curtis, Christina | Shen, Hui | Weisenberger, Daniel J. | Schultz, Nikolaus | Shen, Ronglai | Weinhold, Nils | Kelsen, David P. | Bowlby, Reanne | Chu, Andy | Kasaian, Katayoon | Mungall, Andrew J. | Robertson, A. Gordon | Sipahimalani, Payal | Cherniack, Andrew | Getz, Gad | Liu, Yingchun | Noble, Michael S. | Pedamallu, Chandra | Sougnez, Carrie | Taylor-Weiner, Amaro | Akbani, Rehan | Lee, Ju-Seog | Liu, Wenbin | Mills, Gordon B. | Yang, Da | Zhang, Wei | Pantazi, Angeliki | Parfenov, Michael | Gulley, Margaret | Piazuelo, M. Blanca | Schneider, Barbara G. | Kim, Jihun | Boussioutas, Alex | Sheth, Margi | Demchok, John A. | Rabkin, Charles S. | Willis, Joseph E. | Ng, Sam | Garman, Katherine | Beer, David G. | Pennathur, Arjun | Raphael, Benjamin J. | Wu, Hsin-Ta | Odze, Robert | Kim, Hark K. | Bowen, Jay | Leraas, Kristen M. | Lichtenberg, Tara M. | Weaver, Stephanie | McLellan, Michael | Wiznerowicz, Maciej | Sakai, Ryo | Getz, Gad | Sougnez, Carrie | Lawrence, Michael S. | Cibulskis, Kristian | Lichtenstein, Lee | Fisher, Sheila | Gabriel, Stacey B. | Lander, Eric S. | Ding, Li | Niu, Beifang | Ally, Adrian | Balasundaram, Miruna | Birol, Inanc | Bowlby, Reanne | Brooks, Denise | Butterfield, Yaron S. N. | Carlsen, Rebecca | Chu, Andy | Chu, Justin | Chuah, Eric | Chun, Hye-Jung E. | Clarke, Amanda | Dhalla, Noreen | Guin, Ranabir | Holt, Robert A. | Jones, Steven J.M. | Kasaian, Katayoon | Lee, Darlene | Li, Haiyan A. | Lim, Emilia | Ma, Yussanne | Marra, Marco A. | Mayo, Michael | Moore, Richard A. | Mungall, Andrew J. | Mungall, Karen L. | Nip, Ka Ming | Robertson, A. Gordon | Schein, Jacqueline E. | Sipahimalani, Payal | Tam, Angela | Thiessen, Nina | Beroukhim, Rameen | Carter, Scott L. | Cherniack, Andrew D. | Cho, Juok | Cibulskis, Kristian | DiCara, Daniel | Frazer, Scott | Fisher, Sheila | Gabriel, Stacey B. | Gehlenborg, Nils | Heiman, David I. | Jung, Joonil | Kim, Jaegil | Lander, Eric S. | Lawrence, Michael S. | Lichtenstein, Lee | Lin, Pei | Meyerson, Matthew | Ojesina, Akinyemi I. | Pedamallu, Chandra Sekhar | Saksena, Gordon | Schumacher, Steven E. | Sougnez, Carrie | Stojanov, Petar | Tabak, Barbara | Taylor-Weiner, Amaro | Voet, Doug | Rosenberg, Mara | Zack, Travis I. | Zhang, Hailei | Zou, Lihua | Protopopov, Alexei | Santoso, Netty | Parfenov, Michael | Lee, Semin | Zhang, Jianhua | Mahadeshwar, Harshad S. | Tang, Jiabin | Ren, Xiaojia | Seth, Sahil | Yang, Lixing | Xu, Andrew W. | Song, Xingzhi | Pantazi, Angeliki | Xi, Ruibin | Bristow, Christopher A. | Hadjipanayis, Angela | Seidman, Jonathan | Chin, Lynda | Park, Peter J. | Kucherlapati, Raju | Akbani, Rehan | Ling, Shiyun | Liu, Wenbin | Rao, Arvind | Weinstein, John N. | Kim, Sang-Bae | Lee, Ju-Seog | Lu, Yiling | Mills, Gordon | Laird, Peter W. | Hinoue, Toshinori | Weisenberger, Daniel J. | Bootwalla, Moiz S. | Lai, Phillip H. | Shen, Hui | Triche, Timothy | Van Den Berg, David J. | Baylin, Stephen B. | Herman, James G. | Getz, Gad | Chin, Lynda | Liu, Yingchun | Murray, Bradley A. | Noble, Michael S. | Askoy, B. Arman | Ciriello, Giovanni | Dresdner, Gideon | Gao, Jianjiong | Gross, Benjamin | Jacobsen, Anders | Lee, William | Ramirez, Ricardo | Sander, Chris | Schultz, Nikolaus | Senbabaoglu, Yasin | Sinha, Rileen | Sumer, S. Onur | Sun, Yichao | Weinhold, Nils | Thorsson, Vésteinn | Bernard, Brady | Iype, Lisa | Kramer, Roger W. | Kreisberg, Richard | Miller, Michael | Reynolds, Sheila M. | Rovira, Hector | Tasman, Natalie | Shmulevich, Ilya | Ng, Santa Cruz Sam | Haussler, David | Stuart, Josh M. | Akbani, Rehan | Ling, Shiyun | Liu, Wenbin | Rao, Arvind | Weinstein, John N. | Verhaak, Roeland G.W. | Mills, Gordon B. | Leiserson, Mark D. M. | Raphael, Benjamin J. | Wu, Hsin-Ta | Taylor, Barry S. | Black, Aaron D. | Bowen, Jay | Carney, Julie Ann | Gastier-Foster, Julie M. | Helsel, Carmen | Leraas, Kristen M. | Lichtenberg, Tara M. | McAllister, Cynthia | Ramirez, Nilsa C. | Tabler, Teresa R. | Wise, Lisa | Zmuda, Erik | Penny, Robert | Crain, Daniel | Gardner, Johanna | Lau, Kevin | Curely, Erin | Mallery, David | Morris, Scott | Paulauskis, Joseph | Shelton, Troy | Shelton, Candace | Sherman, Mark | Benz, Christopher | Lee, Jae-Hyuk | Fedosenko, Konstantin | Manikhas, Georgy | Potapova, Olga | Voronina, Olga | Belyaev, Smitry | Dolzhansky, Oleg | Rathmell, W. Kimryn | Brzezinski, Jakub | Ibbs, Matthew | Korski, Konstanty | Kycler, Witold | ŁaŸniak, Radoslaw | Leporowska, Ewa | Mackiewicz, Andrzej | Murawa, Dawid | Murawa, Pawel | Spychała, Arkadiusz | Suchorska, Wiktoria M. | Tatka, Honorata | Teresiak, Marek | Wiznerowicz, Maciej | Abdel-Misih, Raafat | Bennett, Joseph | Brown, Jennifer | Iacocca, Mary | Rabeno, Brenda | Kwon, Sun-Young | Penny, Robert | Gardner, Johanna | Kemkes, Ariane | Mallery, David | Morris, Scott | Shelton, Troy | Shelton, Candace | Curley, Erin | Alexopoulou, Iakovina | Engel, Jay | Bartlett, John | Albert, Monique | Park, Do-Youn | Dhir, Rajiv | Luketich, James | Landreneau, Rodney | Janjigian, Yelena Y. | Kelsen, David P. | Cho, Eunjung | Ladanyi, Marc | Tang, Laura | McCall, Shannon J. | Park, Young S. | Cheong, Jae-Ho | Ajani, Jaffer | Camargo, M. Constanza | Alonso, Shelley | Ayala, Brenda | Jensen, Mark A. | Pihl, Todd | Raman, Rohini | Walton, Jessica | Wan, Yunhu | Demchok, John A. | Eley, Greg | Mills Shaw, Kenna R. | Sheth, Margi | Tarnuzzer, Roy | Wang, Zhining | Yang, Liming | Zenklusen, Jean Claude | Davidsen, Tanja | Hutter, Carolyn M. | Sofia, Heidi J. | Burton, Robert | Chudamani, Sudha | Liu, Jia
Nature  2014;513(7517):202-209.
Gastric cancer is a leading cause of cancer deaths, but analysis of its molecular and clinical characteristics has been complicated by histological and aetiological heterogeneity. Here we describe a comprehensive molecular evaluation of 295 primary gastric adenocarcinomas as part of The Cancer Genome Atlas (TCGA) project. We propose a molecular classification dividing gastric cancer into four subtypes: tumours positive for Epstein–Barr virus, which display recurrent PIK3CA mutations, extreme DNA hypermethylation, and amplification of JAK2, CD274 (also known as PD-L1) and PDCD1LG2 (also knownasPD-L2); microsatellite unstable tumours, which show elevated mutation rates, including mutations of genes encoding targetable oncogenic signalling proteins; genomically stable tumours, which are enriched for the diffuse histological variant and mutations of RHOA or fusions involving RHO-family GTPase-activating proteins; and tumours with chromosomal instability, which show marked aneuploidy and focal amplification of receptor tyrosine kinases. Identification of these subtypes provides a roadmap for patient stratification and trials of targeted therapies.
doi:10.1038/nature13480
PMCID: PMC4170219  PMID: 25079317
2.  Logic models to predict continuous outputs based on binary inputs with an application to personalized cancer therapy 
Scientific Reports  2016;6:36812.
Mining large datasets using machine learning approaches often leads to models that are hard to interpret and not amenable to the generation of hypotheses that can be experimentally tested. We present ‘Logic Optimization for Binary Input to Continuous Output’ (LOBICO), a computational approach that infers small and easily interpretable logic models of binary input features that explain a continuous output variable. Applying LOBICO to a large cancer cell line panel, we find that logic combinations of multiple mutations are more predictive of drug response than single gene predictors. Importantly, we show that the use of the continuous information leads to robust and more accurate logic models. LOBICO implements the ability to uncover logic models around predefined operating points in terms of sensitivity and specificity. As such, it represents an important step towards practical application of interpretable logic models.
doi:10.1038/srep36812
PMCID: PMC5120272  PMID: 27876821
3.  Biological Information as Set-Based Complexity 
Summary
It is not obvious what fraction of all the potential information residing in the molecules and structures of living systems is significant or meaningful to the system. Sets of random sequences or identically repeated sequences, for example, would be expected to contribute little or no useful information to a cell. This issue of quantitation of information is important since the ebb and flow of biologically significant information is essential to our quantitative understanding of biological function and evolution. Motivated specifically by these problems of biological information, we propose here a class of measures to quantify the contextual nature of the information in sets of objects, based on Kolmogorov's intrinsic complexity. Such measures discount both random and redundant information and are inherent in that they do not require a defined state space to quantify the information. The maximization of this new measure, which can be formulated in terms of the universal information distance, appears to have several useful and interesting properties, some of which we illustrate with examples.
doi:10.1109/TIT.2009.2037046
PMCID: PMC5110148  PMID: 27857450
4.  Augmentation of Response to Chemotherapy by microRNA-506 Through Regulation of RAD51 in Serous Ovarian Cancers 
Background:
Chemoresistance is a major challenge in cancer treatment. miR-506 is a potent inhibitor of the epithelial-to-mesenchymal transition (EMT), which is also associated with chemoresistance. We characterized the role of miR-506 in chemotherapy response in high-grade serous ovarian cancers.
Methods:
We used Kaplan-Meier and log-rank methods to analyze the relationship between miR-506 and progression-free and overall survival in The Cancer Genome Atlas (TCGA) (n = 468) and Bagnoli (n = 130) datasets, in vitro experiments to study whether miR-506 is associated with homologous recombination, and response to chemotherapy agents. We used an orthotopic ovarian cancer mouse model (n = 10 per group) to test the effect of miR-506 on cisplatin and PARP inhibitor sensitivity. All statistical tests were two-sided.
Results:
MiR-506 was associated with better response to therapy and longer progression-free and overall survival in two independent epithelial ovarian cancer patient cohorts (PFS: high vs low miR-506 expression; Bagnoli: hazard ratio [HR] = 3.06, 95% confidence interval [CI] = 1.90 to 4.70, P < .0001; TCGA: HR = 1.49, 95% CI = 1.00 to 2.25, P = 0.04). MiR-506 sensitized cells to DNA damage through directly targeting the double-strand DNA damage repair gene RAD51. Systemic delivery of miR-506 in 8–12 week old female athymic nude mice statistically significantly augmented the cisplatin and olaparib response (mean tumor weight ± SD, control miRNA plus cisplatin vs miR-506 plus cisplatin: 0.36±0.05g vs 0.07±0.02g, P < .001; control miRNA plus olaparib vs miR-506 plus olaparib: 0.32±0.13g vs 0.05±0.02g, P = .045, respectively), thus recapitulating the clinical observation.
Conclusions:
MiR-506 is a robust clinical marker for chemotherapy response and survival in serous ovarian cancers and has important therapeutic value in sensitizing cancer cells to chemotherapy.
doi:10.1093/jnci/djv108
PMCID: PMC4554255  PMID: 25995442
5.  Association of Somatic Mutations of ADAMTS Genes With Chemotherapy Sensitivity and Survival in High-Grade Serous Ovarian Carcinoma 
JAMA oncology  2015;1(4):486-494.
IMPORTANCE
Chemotherapy response in the majority of patients with ovarian cancer remains unpredictable.
OBJECTIVE
To identify novel molecular markers for predicting chemotherapy response in patients with ovarian cancer.
DESIGN, SETTING, AND PARTICIPANTS
Observational study of genomics and clinical data of high-grade serous ovarian cancer cases with genomic and clinical data made public between 2009 and 2014 via the Cancer Genome Atlas project.
MAIN OUTCOMES AND MEASURES
Chemotherapy response (primary outcome) and overall survival (OS), progression-free survival (PFS), and platinum-free duration (secondary outcome).
RESULTS
In 512 patients with ovarian cancer with available whole-exome sequencing data, mutations from 8 members of the ADAMTS family (ADAMTS mutations) with an overall mutation rate of approximately 10.4% were associated with a significantly higher chemotherapy sensitivity (100% for ADAMTS-mutated vs 64% for ADAMTS wild-type cases; P < .001) and longer platinum-free duration (median platinum-free duration, 21.7 months for ADAMTS-mutated vs 10.1 months for ADAMTS wild-type cases; P = .001). Moreover, ADAMTS mutations were associated with significantly better OS (hazard ratio [HR], 0.54 [95% CI, 0.42–0.89]; P = .01 and median OS, 58.0 months for ADAMTS-mutated vs 41.3 months for ADAMTS wild-type cases) and PFS (HR, 0.42 [95% CI, 0.38–0.70]; P < .001 and median PFS, 31.8 for ADAMTS-mutated vs 15.3 months for ADAMTS wild-type cases). After adjustment by BRCA1 or BRCA2 mutation, surgical stage, residual tumor, and patient age, ADAMTS mutations were significantly associated with better OS (HR, 0.53 [95% CI, 0.32–0.87]; P = .01), PFS (HR, 0.40 [95% CI, 0.25–0.62]; P < .001), and platinum-free survival (HR, 0.45 [95% CI, 0.28–0.73]; P = .001). ADAMTS-mutated cases exhibited a distinct mutation spectrum and were significantly associated with tumors with a higher genome-wide mutation rate than ADAMTS wild-type cases across the whole exome (median mutation number per sample, 121 for ADAMTS-mutated vs 69 for ADAMTS wild-type cases; P < .001).
CONCLUSIONS AND RELEVANCE
ADAMTS mutations may contribute to outcomes in ovarian cancer cases without BRCA1 or BRCA2 mutations and may have important clinical implications.
doi:10.1001/jamaoncol.2015.1432
PMCID: PMC4608536  PMID: 26181259
6.  Using Incomplete Trios to Boost Confidence in Family Based Association Studies 
Most currently available family based association tests are designed to account only for nuclear families with complete genotypes for parents as well as offspring. Due to the availability of increasingly less expensive generation of whole genome sequencing information, genetic studies are able to collect data for more families and from large family cohorts with the goal of improving statistical power. However, due to missing genotypes, many families are not included in the family based association tests, negating the benefits of large scale sequencing data. Here, we present the CIFBAT method to use incomplete families in Family Based Association Test (FBAT) to evaluate robustness against missing data. CIFBAT uses quantile intervals of the FBAT statistic by randomly choosing valid completions of incomplete family genotypes based on Mendelian inheritance rules. By considering all valid completions equally likely and computing quantile intervals over many randomized iterations, CIFBAT avoids assumption of a homogeneous population structure or any particular missingness pattern in the data. Using simulated data, we show that the quantile intervals computed by CIFBAT are useful in validating robustness of the FBAT statistic against missing data and in identifying genomic markers with higher precision. We also propose a novel set of candidate genomic markers for uterine related abnormalities from analysis of familial whole genome sequences, and provide validation for a previously established set of candidate markers for Type 1 diabetes. We have provided a software package that incorporates TDT, robustTDT, FBAT, and CIFBAT. The data format proposed for the software uses half the memory space that the standard FBAT format (PED) files use, making it efficient for large scale genome wide association studies.
doi:10.3389/fgene.2016.00034
PMCID: PMC4796035  PMID: 27047537
family based association tests; missing genotypes; randomized imputation; quantile intervals; population stratification; whole genome analysis; memory efficient data format
7.  CloudForest: A Scalable and Efficient Random Forest Implementation for Biological Data 
PLoS ONE  2015;10(12):e0144820.
Random Forest has become a standard data analysis tool in computational biology. However, extensions to existing implementations are often necessary to handle the complexity of biological datasets and their associated research questions. The growing size of these datasets requires high performance implementations. We describe CloudForest, a Random Forest package written in Go, which is particularly well suited for large, heterogeneous, genetic and biomedical datasets. CloudForest includes several extensions, such as dealing with unbalanced classes and missing values. Its flexible design enables users to easily implement additional extensions. CloudForest achieves fast running times by effective use of the CPU cache, optimizing for different classes of features and efficiently multi-threading. https://github.com/ilyalab/CloudForest.
doi:10.1371/journal.pone.0144820
PMCID: PMC4692062  PMID: 26679347
8.  Biocellion: accelerating computer simulation of multicellular biological system models 
Bioinformatics  2014;30(21):3101-3108.
Motivation: Biological system behaviors are often the outcome of complex interactions among a large number of cells and their biotic and abiotic environment. Computational biologists attempt to understand, predict and manipulate biological system behavior through mathematical modeling and computer simulation. Discrete agent-based modeling (in combination with high-resolution grids to model the extracellular environment) is a popular approach for building biological system models. However, the computational complexity of this approach forces computational biologists to resort to coarser resolution approaches to simulate large biological systems. High-performance parallel computers have the potential to address the computing challenge, but writing efficient software for parallel computers is difficult and time-consuming.
Results: We have developed Biocellion, a high-performance software framework, to solve this computing challenge using parallel computers. To support a wide range of multicellular biological system models, Biocellion asks users to provide their model specifics by filling the function body of pre-defined model routines. Using Biocellion, modelers without parallel computing expertise can efficiently exploit parallel computers with less effort than writing sequential programs from scratch. We simulate cell sorting, microbial patterning and a bacterial system in soil aggregate as case studies.
Availability and implementation: Biocellion runs on x86 compatible systems with the 64 bit Linux operating system and is freely available for academic use. Visit http://biocellion.com for additional information.
Contact: seunghwa.kang@pnnl.gov
doi:10.1093/bioinformatics/btu498
PMCID: PMC4609016  PMID: 25064572
9.  Integrated Genomic Characterization of Papillary Thyroid Carcinoma 
Agrawal, Nishant | Akbani, Rehan | Aksoy, B. Arman | Ally, Adrian | Arachchi, Harindra | Asa, Sylvia L. | Auman, J. Todd | Balasundaram, Miruna | Balu, Saianand | Baylin, Stephen B. | Behera, Madhusmita | Bernard, Brady | Beroukhim, Rameen | Bishop, Justin A. | Black, Aaron D. | Bodenheimer, Tom | Boice, Lori | Bootwalla, Moiz S. | Bowen, Jay | Bowlby, Reanne | Bristow, Christopher A. | Brookens, Robin | Brooks, Denise | Bryant, Robert | Buda, Elizabeth | Butterfield, Yaron S.N. | Carling, Tobias | Carlsen, Rebecca | Carter, Scott L. | Carty, Sally E. | Chan, Timothy A. | Chen, Amy Y. | Cherniack, Andrew D. | Cheung, Dorothy | Chin, Lynda | Cho, Juok | Chu, Andy | Chuah, Eric | Cibulskis, Kristian | Ciriello, Giovanni | Clarke, Amanda | Clayman, Gary L. | Cope, Leslie | Copland, John | Covington, Kyle | Danilova, Ludmila | Davidsen, Tanja | Demchok, John A. | DiCara, Daniel | Dhalla, Noreen | Dhir, Rajiv | Dookran, Sheliann S. | Dresdner, Gideon | Eldridge, Jonathan | Eley, Greg | El-Naggar, Adel K. | Eng, Stephanie | Fagin, James A. | Fennell, Timothy | Ferris, Robert L. | Fisher, Sheila | Frazer, Scott | Frick, Jessica | Gabriel, Stacey B. | Ganly, Ian | Gao, Jianjiong | Garraway, Levi A. | Gastier-Foster, Julie M. | Getz, Gad | Gehlenborg, Nils | Ghossein, Ronald | Gibbs, Richard A. | Giordano, Thomas J. | Gomez-Hernandez, Karen | Grimsby, Jonna | Gross, Benjamin | Guin, Ranabir | Hadjipanayis, Angela | Harper, Hollie A. | Hayes, D. Neil | Heiman, David I. | Herman, James G. | Hoadley, Katherine A. | Hofree, Matan | Holt, Robert A. | Hoyle, Alan P. | Huang, Franklin W. | Huang, Mei | Hutter, Carolyn M. | Ideker, Trey | Iype, Lisa | Jacobsen, Anders | Jefferys, Stuart R. | Jones, Corbin D. | Jones, Steven J.M. | Kasaian, Katayoon | Kebebew, Electron | Khuri, Fadlo R. | Kim, Jaegil | Kramer, Roger | Kreisberg, Richard | Kucherlapati, Raju | Kwiatkowski, David J. | Ladanyi, Marc | Lai, Phillip H. | Laird, Peter W. | Lander, Eric | Lawrence, Michael S. | Lee, Darlene | Lee, Eunjung | Lee, Semin | Lee, William | Leraas, Kristen M. | Lichtenberg, Tara M. | Lichtenstein, Lee | Lin, Pei | Ling, Shiyun | Liu, Jinze | Liu, Wenbin | Liu, Yingchun | LiVolsi, Virginia A. | Lu, Yiling | Ma, Yussanne | Mahadeshwar, Harshad S. | Marra, Marco A. | Mayo, Michael | McFadden, David G. | Meng, Shaowu | Meyerson, Matthew | Mieczkowski, Piotr A. | Miller, Michael | Mills, Gordon | Moore, Richard A. | Mose, Lisle E. | Mungall, Andrew J. | Murray, Bradley A. | Nikiforov, Yuri E. | Noble, Michael S. | Ojesina, Akinyemi I. | Owonikoko, Taofeek K. | Ozenberger, Bradley A. | Pantazi, Angeliki | Parfenov, Michael | Park, Peter J. | Parker, Joel S. | Paull, Evan O. | Pedamallu, Chandra Sekhar | Perou, Charles M. | Prins, Jan F. | Protopopov, Alexei | Ramalingam, Suresh S. | Ramirez, Nilsa C. | Ramirez, Ricardo | Raphael, Benjamin J. | Rathmell, W. Kimryn | Ren, Xiaojia | Reynolds, Sheila M. | Rheinbay, Esther | Ringel, Matthew D. | Rivera, Michael | Roach, Jeffrey | Robertson, A. Gordon | Rosenberg, Mara W. | Rosenthall, Matthew | Sadeghi, Sara | Saksena, Gordon | Sander, Chris | Santoso, Netty | Schein, Jacqueline E. | Schultz, Nikolaus | Schumacher, Steven E. | Seethala, Raja R. | Seidman, Jonathan | Senbabaoglu, Yasin | Seth, Sahil | Sharpe, Samantha | Mills Shaw, Kenna R. | Shen, John P. | Shen, Ronglai | Sherman, Steven | Sheth, Margi | Shi, Yan | Shmulevich, Ilya | Sica, Gabriel L. | Simons, Janae V. | Sipahimalani, Payal | Smallridge, Robert C. | Sofia, Heidi J. | Soloway, Matthew G. | Song, Xingzhi | Sougnez, Carrie | Stewart, Chip | Stojanov, Petar | Stuart, Joshua M. | Tabak, Barbara | Tam, Angela | Tan, Donghui | Tang, Jiabin | Tarnuzzer, Roy | Taylor, Barry S. | Thiessen, Nina | Thorne, Leigh | Thorsson, Vésteinn | Tuttle, R. Michael | Umbricht, Christopher B. | Van Den Berg, David J. | Vandin, Fabio | Veluvolu, Umadevi | Verhaak, Roel G.W. | Vinco, Michelle | Voet, Doug | Walter, Vonn | Wang, Zhining | Waring, Scot | Weinberger, Paul M. | Weinstein, John N. | Weisenberger, Daniel J. | Wheeler, David | Wilkerson, Matthew D. | Wilson, Jocelyn | Williams, Michelle | Winer, Daniel A. | Wise, Lisa | Wu, Junyuan | Xi, Liu | Xu, Andrew W. | Yang, Liming | Yang, Lixing | Zack, Travis I. | Zeiger, Martha A. | Zeng, Dong | Zenklusen, Jean Claude | Zhao, Ni | Zhang, Hailei | Zhang, Jianhua | Zhang, Jiashan (Julia) | Zhang, Wei | Zmuda, Erik | Zou., Lihua
Cell  2014;159(3):676-690.
Summary
Papillary thyroid carcinoma (PTC) is the most common type of thyroid cancer. Here, we describe the genomic landscape of 496 PTCs. We observed a low frequency of somatic alterations (relative to other carcinomas) and extended the set of known PTC driver alterations to include EIF1AX, PPM1D and CHEK2 and diverse gene fusions. These discoveries reduced the fraction of PTC cases with unknown oncogenic driver from 25% to 3.5%. Combined analyses of genomic variants, gene expression, and methylation demonstrated that different driver groups lead to different pathologies with distinct signaling and differentiation characteristics. Similarly, we identified distinct molecular subgroups of BRAF-mutant tumors and multidimensional analyses highlighted a potential involvement of oncomiRs in less-differentiated subgroups. Our results propose a reclassification of thyroid cancers into molecular subtypes that better reflect their underlying signaling and differentiation properties, which has the potential to improve their pathological classification and better inform the management of the disease.
doi:10.1016/j.cell.2014.09.050
PMCID: PMC4243044  PMID: 25417114
10.  Two mature products of MIR-491 coordinate to suppress key cancer hallmarks in glioblastoma 
Oncogene  2014;34(13):1619-1628.
MIR-491 is commonly co-deleted with its adjacent CDKN2A on chromosome 9p21.3 in glioblastoma (GBM). However, it is not known whether deletion of MIR-491 is only a passenger event or plays an important role. Small-RNA sequencing of samples from GBM patients demonstrated that both mature products of MIR-491 (miR-491-5p and -3p) are downregulated in tumors compared to normal brain. The integration of GBM data from The Cancer Genome Atlas (TCGA), miRNA target prediction and reporter assays showed that miR-491-5p directly targets EGFR, CDK6, and Bcl-xL, whereas miR-491-3p targets IGFBP2 and CDK6. Functionally, miR-491-3p inhibited glioma cell invasion; overexpression of both miR-491-5p and -3p inhibited proliferation of glioma cell lines and impaired the propagation of glioma stem cells (GSCs), thereby prolonging survival of xenograft mice. Moreover, knockdown of miR-491-5p in primary Ink4a-Arf-null mouse glial progenitor cells exacerbated cell proliferation and invasion. Therefore, MIR-491 is a tumor suppressor gene that, by utilizing both mature forms, coordinately controls key cancer hallmarks: proliferation, invasion, and stem cell propagation.
doi:10.1038/onc.2014.98
PMCID: PMC4205227  PMID: 24747968
miR-491; CDKN2A; glioma stem cell; GBM; IGFBP2; CDK6; EGFR
11.  A multilevel pan-cancer map links gene mutations to cancer hallmarks 
Background
A central challenge in cancer research is to create models that bridge the gap between the molecular level on which interventions can be designed and the cellular and tissue levels on which the disease phenotypes are manifested. This study was undertaken to construct such a model from functional annotations and explore its use when integrated with large-scale cancer genomics data.
Methods
We created a map that connects genes to cancer hallmarks via signaling pathways. We projected gene mutation and focal copy number data from various cancer types onto this map. We performed statistical analyses to uncover mutually exclusive and co-occurring oncogenic aberrations within this topology.
Results
Our analysis showed that although the genetic fingerprint of tumor types could be very different, there were less variations at the level of hallmarks, consistent with the idea that different genetic alterations have similar functional outcomes. Additionally, we showed how the multilevel map could help to clarify the role of infrequently mutated genes, and we demonstrated that mutually exclusive gene mutations were more prevalent in pathways, whereas many co-occurring gene mutations were associated with hallmark characteristics.
Conclusions
Overlaying this map with gene mutation and focal copy number data from various cancer types makes it possible to investigate the similarities and differences between tumor samples systematically at the levels of not only genes but also pathways and hallmarks.
Electronic supplementary material
The online version of this article (doi:10.1186/s40880-015-0050-6) contains supplementary material, which is available to authorized users.
doi:10.1186/s40880-015-0050-6
PMCID: PMC4593384  PMID: 26369414
Cancer systems biology; Cancer hallmarks; Gene mutations; Multilevel model
12.  Clinical Significance of CTNNB1 Mutation and Wnt Pathway Activation in Endometrioid Endometrial Carcinoma 
Background
Endometrioid endometrial carcinoma (EEC) is the most common form of endometrial carcinoma. The heterogeneous clinical course of EEC is an obstacle to individualized patient care.
Methods
We performed an integrated analysis on the multiple-dimensional data types including whole-exome and RNA sequencing, RPPA profiling, and clinical data from 271 EEC cases in The Cancer Genome Atlas (TCGA) to identify molecular fingerprints that may account for this clinical heterogeneity. Significance analysis of microarray was used to identify marker genes of each subtype that were subject to pathway analysis. Association of molecular subtypes with clinical features and mutation data was analyzed with the Mann Whitney, Chi-square, Fisher’s exact, and Kruskal-Wallis tests. Survival analysis was evaluated with log-rank test. All statistical tests were two-sided.
Results
Four transcriptome subtypes with distinct clinicopathologic characteristics and mutation spectra were identified from the TCGA dataset and validated in an independent sample cohort of 184 EEC cases. Cluster II consisted of younger, obese patients with low-grade EEC but diminished survival. CTNNB1 exon 3 mutations were present in 87.0% (47/54) of Cluster II (P < .001) that exhibited a low overall mutation rate; this was statistically significantly associated with Wnt/β-catenin signaling activation (P < .001). High expression levels of CTNNB1 (P = .001), MYC (P = .01), and CCND1 (P = .01) were associated with poorer overall survival in low-grade EEC tumors.
Conclusions
CTNNB1 exon 3 mutations are likely a driver that characterize an aggressive subset of low-grade and low-stage EEC occurring in younger women.
doi:10.1093/jnci/dju245
PMCID: PMC4200060  PMID: 25214561
13.  MiR-506 Suppresses Proliferation and Induces Senescence by Directly Targeting the CDK4/6-FOXM1 Axis in Ovarian Cancer 
The Journal of pathology  2014;233(3):308-318.
Ovarian carcinoma is the most lethal gynecological malignancy. Better understanding of the molecular pathogenesis of this disease and effective targeted therapies are needed to improve patient outcomes. MicroRNAs play important roles in cancer progression and have the potential for use as either therapeutic agents or targets. Studies in other cancers have suggested that miR-506 has antitumor activity, but its function has yet to be elucidated. We found that deregulation of miR-506 in ovarian carcinoma promotes an aggressive phenotype. Ectopic overexpression of miR-506 in ovarian cancer cells was sufficient to inhibit proliferation and to promote senescence. We also demonstrated that CDK4 and CDK6 are direct targets of miR-506, and that miR-506 can inhibit CDK4/6-FOXM1 signaling, which is activated in the majority of serous ovarian carcinomas. This newly recognized miR-506/CDK4/6-FOXM1 axis provides further insight into the pathogenesis of ovarian carcinoma and identifies a potential novel therapeutic agent.
doi:10.1002/path.4348
PMCID: PMC4144705  PMID: 24604117
miR-506; ovarian carcinoma; proliferation; senescence; FOXM1
14.  CTCF haploinsufficiency destabilizes DNA methylation and predisposes to cancer 
Cell reports  2014;7(4):1020-1029.
SUMMARY
Epigenetic alterations, particularly in DNA methylation, are ubiquitous in cancer, yet the molecular origins and the consequences of these alterations are poorly understood. The DNA binding protein CTCF regulates a diverse array of epigenetic processes and is frequently altered by hemizygous deletion or mutation in human cancer. To date, a causal role for CTCF in cancer has not been established. Here we show that Ctcf hemizygous knockout mice are markedly susceptible to spontaneous, radiation, and chemically induced cancer in a broad range of tissues. Ctcf+/− tumors are characterized by increased aggressiveness including invasion, metastatic dissemination, and mixed epithelial/mesenchymal differentiation. Molecular analysis of Ctcf+/− tumors indicates that Ctcf is haploinsufficient for tumor suppression. Tissues with hemizygous loss of CTCF exhibit increased variability in CpG methylation genome-wide. These findings establish CTCF as a prominent tumor suppressor gene and point to CTCF mediated epigenetic stability as a major barrier to neoplastic progression.
doi:10.1016/j.celrep.2014.04.004
PMCID: PMC4040130  PMID: 24794443
15.  Identification of copy number variants in whole-genome data using Reference Coverage Profiles 
The identification of DNA copy numbers from short-read sequencing data remains a challenge for both technical and algorithmic reasons. The raw data for these analyses are measured in tens to hundreds of gigabytes per genome; transmitting, storing, and analyzing such large files is cumbersome, particularly for methods that analyze several samples simultaneously. We developed a very efficient representation of depth of coverage (150–1000× compression) that enables such analyses. Current methods for analyzing variants in whole-genome sequencing (WGS) data frequently miss copy number variants (CNVs), particularly hemizygous deletions in the 1–100 kb range. To fill this gap, we developed a method to identify CNVs in individual genomes, based on comparison to joint profiles pre-computed from a large set of genomes. We analyzed depth of coverage in over 6000 high quality (>40×) genomes. The depth of coverage has strong sequence-specific fluctuations only partially explained by global parameters like %GC. To account for these fluctuations, we constructed multi-genome profiles representing the observed or inferred diploid depth of coverage at each position along the genome. These Reference Coverage Profiles (RCPs) take into account the diverse technologies and pipeline versions used. Normalization of the scaled coverage to the RCP followed by hidden Markov model (HMM) segmentation enables efficient detection of CNVs and large deletions in individual genomes. Use of pre-computed multi-genome coverage profiles improves our ability to analyze each individual genome. We make available RCPs and tools for performing these analyses on personal genomes. We expect the increased sensitivity and specificity for individual genome analysis to be critical for achieving clinical-grade genome interpretation.
doi:10.3389/fgene.2015.00045
PMCID: PMC4330915  PMID: 25741365
whole-genome sequencing; structural variation; depth of coverage; signal processing; clinical genomics
16.  Key nodes of a microRNA network associated with the integrated mesenchymal subtype of high-grade serous ovarian cancer 
Chinese Journal of Cancer  2015;34(1):28-40.
Metastasis is the main cause of cancer mortality. One of the initiating events of cancer metastasis of epithelial tumors is epithelial-to-mesenchymal transition (EMT), during which cells dedifferentiate from a relatively rigid cell structure/morphology to a flexible and changeable structure/morphology often associated with mesenchymal cells. The presence of EMT in human epithelial tumors is reflected by the increased expression of genes and levels of proteins that are preferentially present in mesenchymal cells. The combined presence of these genes forms the basis of mesenchymal gene signatures, which are the foundation for classifying a mesenchymal subtype of tumors. Indeed, tumor classification schemes that use clustering analysis of large genomic characterizations, like The Cancer Genome Atlas (TCGA), have defined mesenchymal subtype in a number of cancer types, such as high-grade serous ovarian cancer and glioblastoma. However, recent analyses have shown that gene expression-based classifications of mesenchymal subtypes often do not associate with poor survival. This “paradox” can be ameliorated using integrated analysis that combines multiple data types. We recently found that integrating mRNA and microRNA (miRNA) data revealed an integrated mesenchymal subtype that is consistently associated with poor survival in multiple cohorts of patients with serous ovarian cancer. This network consists of 8 major miRNAs and 214 mRNAs. Among the 8 miRNAs, 4 are known to be regulators of EMT. This review provides a summary of these 8 miRNAs, which were associated with the integrated mesenchymal subtype of serous ovarian cancer.
doi:10.5732/cjc.014.10284
PMCID: PMC4302087  PMID: 25556616
MicroRNA (miRNA); epithelial-to-mesenchymal transition (EMT); cancer; ovary; miR-506; miR-101
17.  Multiscale Representation of Genomic Signals 
Nature methods  2014;11(6):689-694.
Genomic information is encoded on a wide range of distance scales, ranging from tens of base pairs to megabases. We developed a multiscale framework to analyze and visualize the information content of genomic signals. Different types of signals, such as GC content or DNA methylation, are characterized by distinct patterns of signal enrichment or depletion across scales spanning several orders of magnitude. These patterns are associated with a variety of genomic annotations, including genes, nuclear lamina associated domains, and repeat elements. By integrating the information across all scales, as compared to using any single scale, we demonstrate improved prediction of gene expression from Polymerase II chromatin immunoprecipitation sequencing (ChIP-seq) measurements and we observed that gene expression differences in colorectal cancer are not most strongly related to gene body methylation, but rather to methylation patterns that extend beyond the single-gene scale.
doi:10.1038/nmeth.2924
PMCID: PMC4040162  PMID: 24727652
18.  Structure-based predictions broadly link transcription factor mutations to gene expression changes in cancers 
Nucleic Acids Research  2014;42(21):12973-12983.
Thousands of unique mutations in transcription factors (TFs) arise in cancers, and the functional and biological roles of relatively few of these have been characterized. Here, we used structure-based methods developed specifically for DNA-binding proteins to systematically predict the consequences of mutations in several TFs that are frequently mutated in cancers. The explicit consideration of protein–DNA interactions was crucial to explain the roles and prevalence of mutations in TP53 and RUNX1 in cancers, and resulted in a higher specificity of detection for known p53-regulated genes among genetic associations between TP53 genotypes and genome-wide expression in The Cancer Genome Atlas, compared to existing methods of mutation assessment. Biophysical predictions also indicated that the relative prevalence of TP53 missense mutations in cancer is proportional to their thermodynamic impacts on protein stability and DNA binding, which is consistent with the selection for the loss of p53 transcriptional function in cancers. Structure and thermodynamics-based predictions of the impacts of missense mutations that focus on specific molecular functions may be increasingly useful for the precise and large-scale inference of aberrant molecular phenotypes in cancer and other complex diseases.
doi:10.1093/nar/gku1031
PMCID: PMC4245936  PMID: 25378323
19.  The Somatic Genomic Landscape of Glioblastoma 
Cell  2013;155(2):462-477.
We describe the landscape of somatic genomic alterations based on multi-dimensional and comprehensive characterization of more than 500 glioblastoma tumors (GBMs). We identify several novel mutated genes as well as complex rearrangements of signature receptors including EGFR and PDGFRA. TERT promoter mutations are shown to correlate with elevated mRNA expression, supporting a role in telomerase reactivation. Correlative analyses confirm that the survival advantage of the proneural subtype is conferred by the G-CIMP phenotype, and MGMT DNA methylation may be a predictive biomarker for treatment response only in classical subtype GBM. Integrative analysis of genomic and proteomic profiles challenges the notion of therapeutic inhibition of a pathway as an alternative to inhibition of the target itself. These data will facilitate the discovery of therapeutic and diagnostic target candidates, the validation of research and clinical observations and the generation of unanticipated hypotheses that can advance our molecular understanding of this lethal cancer.
doi:10.1016/j.cell.2013.09.034
PMCID: PMC3910500  PMID: 24120142
20.  Association between BRCA2 but not BRCA1 Mutations and Beneficial Survival, Chemotherapy Sensitivity, and Gene Mutator Phenotype in Patients with Ovarian Cancer 
Context
Attempts to determine the clinical significance of BRCA1/2 mutations in ovarian cancer (OvCa) have produced conflicting results.
Objective
To determine the relationships between BRCA1/2 deficiency (i.e., mutation and promoter hypermethylation) and overall survival (OS), progression-free survival (PFS), chemotherapy response, and whole exome mutation rate in OvCa.
Design, Setting, and Patients
Observational study of multidimensional genomics and clinical data on 316 high-grade serous OvCa cases that were made public between 2009 and 2010 via The Cancer Genome Atlas project.
Main Outcome Measures
OS and PFS rates (primary outcomes) and chemotherapy response (secondary outcome).
Results
BRCA2 mutations (29 cases) were associated with significantly better OS (adjusted hazard ratio [HR], 0.33; 95% CI, 0.16–0.69, P=0.003; 5-year OS: 61% for BRCA2 mutated vs. 25% for BRCA wild-type [wt] cases) and PFS (adjusted HR, 0.40; 95% CI, 0.22–0.74, P=0.004; 3-year PFS: 44% for BRCA2 mutated vs. 16% for BRCA wt cases), whereas neither BRCA1 mutations (37 cases) nor BRCA1 methylation (33 cases) were associated with prognosis. Moreover, BRCA2 mutations were associated with a significantly higher primary chemotherapy sensitivity rate (100% for BRCA2 mutated vs. 82% [P=0.02] and 80% [P=0.05] for BRCA wt and BRCA1 mutated cases, respectively) and longer platinum-free duration (median platinum-free duration: 18.0 months for BRCA2 mutated vs. 11.7 [P=0.02] and 12.5 [P=0.04] months for BRCA wt and BRCA1 mutated cases, respectively). Further investigation revealed that BRCA2 mutated, but not BRCA1 mutated cases, exhibited a “mutator phenotype” by containing significantly more mutations than BRCA wt cases across the whole exome (median mutation number per sample: 84 for BRCA2 mutated vs. 52 for BRCA wt cases, false-discovery rate <0.1).
Conclusions
BRCA2 mutation, but not BRCA1 deficiency, is associated with improved survival, chemotherapy response, and genome instability compared with BRCA wild-type.
doi:10.1001/jama.2011.1456
PMCID: PMC4159096  PMID: 21990299
BRCA1; BRCA2; mutations; survival; platinum-based drug response
21.  Gene pair signatures in cell type transcriptomes reveal lineage control 
Nature methods  2013;10(6):577-583.
The distinct cell types of multicellular organisms arise due to constraints imposed by gene regulatory networks on the collective change of gene expression across the genome, creating self-stabilizing expression states, or attractors. We compiled a resource of curated human expression data comprising 166 cell types and 2,602 transcription regulating genes and developed a data driven method built around the concept of expression reversal defined at the level of gene pairs, such as those participating in toggle switch circuits. This approach allows us to organize the cell types into their ontogenetic lineage-relationships and to reflect regulatory relationships among genes that explain their ability to function as determinants of cell fate. We show that this method identifies genes belonging to regulatory circuits that control neuronal fate, pluripotency and blood cell differentiation, thus offering a novel large-scale perspective on lineage specification.
doi:10.1038/nmeth.2445
PMCID: PMC4131748  PMID: 23603899
22.  Large-scale molecular characterization and analysis of gastric cancer 
Chinese Journal of Cancer  2014;33(8):369-370.
doi:10.5732/cjc.014.10116
PMCID: PMC4135364  PMID: 25313412
23.  Quantitative analysis of colony morphology in yeast 
BioTechniques  2014;56(1):18-27.
Microorganisms often form multicellular structures such as biofilms and structured colonies that can influence the organism’s virulence, drug resistance, and adherence to medical devices. Phenotypic classification of these structures has traditionally relied on qualitative scoring systems that limit detailed phenotypic comparisons between strains. Automated imaging and quantitative analysis have the potential to improve the speed and accuracy of experiments designed to study the genetic and molecular networks underlying different morphological traits. For this reason, we have developed a platform that uses automated image analysis and pattern recognition to quantify phenotypic signatures of yeast colonies. Our strategy enables quantitative analysis of individual colonies, measured at a single time point or over a series of time-lapse images, as well as the classification of distinct colony shapes based on image-derived features. Phenotypic changes in colony morphology can be expressed as changes in feature space trajectories over time, thereby enabling the visualization and quantitative analysis of morphological development. To facilitate data exploration, results are plotted dynamically through an interactive Yeast Image Analysis web application (YIMAA; http://yimaa.cs.tut.fi) that integrates the raw and processed images across all time points, allowing exploration of the image-based features and principal components associated with morphological development.
doi:10.2144/000114123
PMCID: PMC3996921  PMID: 24447135
colony morphology; image analysis; software; yeast; phenotype; time-lapse
24.  Post-transcriptional regulatory network of epithelial-to-mesenchymal and mesenchymal-to-epithelial transitions 
Epithelial-to-mesenchymal transition (EMT) and its reverse process, mesenchymal-to-epithelial transition (MET), play important roles in embryogenesis, stem cell biology, and cancer progression. EMT can be regulated by many signaling pathways and regulatory transcriptional networks. Furthermore, post-transcriptional regulatory networks regulate EMT; these networks include the long non-coding RNA (lncRNA) and microRNA (miRNA) families. Specifically, the miR-200 family, miR-101, miR-506, and several lncRNAs have been found to regulate EMT. Recent studies have illustrated that several lncRNAs are overexpressed in various cancers and that they can promote tumor metastasis by inducing EMT. MiRNA controls EMT by regulating EMT transcription factors or other EMT regulators, suggesting that lncRNAs and miRNA are novel therapeutic targets for the treatment of cancer. Further efforts have shown that non-coding-mediated EMT regulation is closely associated with epigenetic regulation through promoter methylation (e.g., miR-200 or miR-506) and protein regulation (e.g., SET8 via miR-502). The formation of gene fusions has also been found to promote EMT in prostate cancer. In this review, we discuss the post-transcriptional regulatory network that is involved in EMT and MET and how targeting EMT and MET may provide effective therapeutics for human disease.
doi:10.1186/1756-8722-7-19
PMCID: PMC3973872  PMID: 24598126
Long non-coding RNA (lncRNA); microRNA (miRNA); Epithelial-to-mesenchymal transition (EMT); Mesenchymal-to-epithelial transition (MET)
25.  Integrated analyses identify a master microRNA regulatory network for the mesenchymal subtype in serous ovarian cancer 
Cancer cell  2013;23(2):186-199.
Summary
Integrated genomic analyses revealed a miRNA-regulatory network, which further defined a robust integrated mesenchymal subtype associated with poor overall survival in 459 cases of serous ovarian cancer (OvCa) from The Cancer Genome Atlas and 560 cases from independent cohorts. Eight key miRNAs, including miR-506, miR-141 and miR-200a, were predicted to regulate 89% of the targets in this network. Follow-up functional experiments illustrate that miR-506 augmented E-cadherin expression, inhibited cell migration and invasion, and prevented TGFβ-induced epithelial-mesenchymal transition (EMT) by targeting SNAI2, a transcriptional repressor of E-cadherin. In human OvCa, miR-506 expression was correlated with decreased SNAI2 and VIM, elevated E-cadherin, and beneficial prognosis. Nanoparticle delivery of miR-506 in orthotopic OvCa mouse models led to E-cadherin induction and reduced tumor growth.
doi:10.1016/j.ccr.2012.12.020
PMCID: PMC3603369  PMID: 23410973

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