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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
3.  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
4.  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
5.  Synthetic lethal screens as a means to understand and treat MYC-driven cancers 
Cold Spring Harbor perspectives in medicine  2014;4(3):10.1101/cshperspect.a014209 a014209.
While therapeutics against MYC could potentially be employed against a wide range of human cancers, MYC targeted therapies have proven difficult to develop. The convergence of breakthroughs in human genomics and in gene silencing using RNA interference (RNAi), have recently allowed functional interrogation of the genome and systematic identification of synthetic lethal interactions with hyper-active MYC. Here, we focus on the pathways that have emerged through RNAi screens and present evidence that a subset of genes exhibiting synthetic lethality with MYC are significantly interconnected and linked to chromatin, and transcriptional processes, as well as to DNA repair and cell-cycle checkpoints. Other synthetic lethal interactions with MYC point to novel pathways and potentially broaden the repertoire of targeted therapies. The elucidation of MYC synthetic lethal interactions is still in its infancy and how these interactions may be influenced by tissue specific programs and by concurrent genetic change will require further investigation. Nevertheless, we predict that these studies may lead the way to novel therapeutic approaches and new insights into the role of MYC in cancer.
doi:10.1101/cshperspect.a014209
PMCID: PMC3935389  PMID: 24591535
6.  Increasing Coverage of Transcription Factor Position Weight Matrices through Domain-level Homology 
PLoS ONE  2012;7(8):e42779.
Transcription factor-DNA interactions, central to cellular regulation and control, are commonly described by position weight matrices (PWMs). These matrices are frequently used to predict transcription factor binding sites in regulatory regions of DNA to complement and guide further experimental investigation. The DNA sequence preferences of transcription factors, encoded in PWMs, are dictated primarily by select residues within the DNA binding domain(s) that interact directly with DNA. Therefore, the DNA binding properties of homologous transcription factors with identical DNA binding domains may be characterized by PWMs derived from different species. Accordingly, we have implemented a fully automated domain-level homology searching method for identical DNA binding sequences.
By applying the domain-level homology search to transcription factors with existing PWMs in the JASPAR and TRANSFAC databases, we were able to significantly increase coverage in terms of the total number of PWMs associated with a given species, assign PWMs to transcription factors that did not previously have any associations, and increase the number of represented species with PWMs over an order of magnitude. Additionally, using protein binding microarray (PBM) data, we have validated the domain-level method by demonstrating that transcription factor pairs with matching DNA binding domains exhibit comparable DNA binding specificity predictions to transcription factor pairs with completely identical sequences.
The increased coverage achieved herein demonstrates the potential for more thorough species-associated investigation of protein-DNA interactions using existing resources. The PWM scanning results highlight the challenging nature of transcription factors that contain multiple DNA binding domains, as well as the impact of motif discovery on the ability to predict DNA binding properties. The method is additionally suitable for identifying domain-level homology mappings to enable utilization of additional information sources in the study of transcription factors. The domain-level homology search method, resulting PWM mappings, web-based user interface, and web API are publicly available at http://dodoma.systemsbiology.netdodoma.systemsbiology.net.
doi:10.1371/journal.pone.0042779
PMCID: PMC3428306  PMID: 22952610
7.  A Generalized Knowledge-Based Discriminatory Function for Biomolecular Interactions 
Proteins  2009;76(1):115-128.
Several novel and established knowledge-based discriminatory function formulations and reference state derivations have been evaluated to identify parameter sets capable of distinguishing native and near-native biomolecular interactions from incorrect ones. We developed the r·m·r function, a novel atomic level radial distribution function with mean reference state that averages over all pairwise atom types from a reduced atom type composition, using experimentally determined intermolecular complexes in the Cambridge Structural Database (CSD) and the Protein Data Bank (PDB) as the information sources. We demonstrate that r·m·r had the best discriminatory accuracy and power for protein-small molecule and protein-DNA interactions, regardless of whether the native complex was included or excluded from the test set. The superior performance of the r·m·r discriminatory function compared to seventeen alternative functions evaluated on publicly available test sets for protein-small molecule and protein-DNA interactions indicated that the function was not over optimized through back testing on a single class of biomolecular interactions. The initial success of the reduced composition and superior performance with the CSD as the distribution set over the PDB implies that further improvements and generality of the function are possible by deriving probabilities from subsets of the CSD, using structures that consist of only the atom types to be considered for given biomolecular interactions. The method is available as a web server module at http://protinfo.compbio.washington.edu.
doi:10.1002/prot.22323
PMCID: PMC2891153  PMID: 19127590
discriminatory function; knowledge-based; protein-small molecule; protein-DNA; protein-ligand; complexes; biomolecular interactions
8.  Conformational changes below the Tm: Molecular dynamics studies of the thermal pretransition of ribonuclease A† 
Biochemistry  2007;47(3):880-892.
Recent work suggests that some native conformations of proteins can vary with temperature. To obtain an atomic-level description of this structural and conformational variation, we have performed all-atom, explicit-solvent molecular dynamics simulations of bovine pancreatic ribonuclease A (RNase A) up to its melting temperature (Tm ≈ 337 K). RNase A has a thermal pretransition near 320 K [Stelea, S.D, Pancoska, P., Benight, A.S., Keiderling, T.A. (2001) Prot. Sci. 10, 970—978]. Our simulations identify a conformational change that coincides with this pretransition. Between 310 and 320 K, there is a small but significant decrease in the number of native contacts, β-sheet hydrogen bonding, and deviation of backbone conformation from the starting structure, and an increase in nonnative contacts. Native contacts are lost in β-sheet regions and in α1, partially due to movement of α1 away from the β-sheet core. At 330 and 340 K, a nonnative helical segment forms at residues 15–20, corresponding to a helix observed in the N-terminal domain-swapped dimer [Liu Y.S., Hart, P.J., Schulnegger, M.P., Eisenberg, D. (1998) Proc. Natl. Acad. Sci. USA, 95, 3437—3432]. The conformations observed at the higher temperatures possess native-like topology and overall conformation, with many native contacts, but they have a disrupted active site. We propose that these conformations may represent the native state at elevated temperature, or the N′ state. These simulations show that subtle, functionally important changes in protein conformation can occur below the Tm.
doi:10.1021/bi701565b
PMCID: PMC2532537  PMID: 18161991

Results 1-8 (8)