385 results on '"Margolin, Adam A."'
Search Results
2. Germline contamination and leakage in whole genome somatic single nucleotide variant detection
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Sendorek, Dorota H, Caloian, Cristian, Ellrott, Kyle, Bare, J Christopher, Yamaguchi, Takafumi N, Ewing, Adam D, Houlahan, Kathleen E, Norman, Thea C, Margolin, Adam A, Stuart, Joshua M, and Boutros, Paul C
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Biological Sciences ,Bioinformatics and Computational Biology ,Genetics ,Human Genome ,Cancer ,Generic health relevance ,Good Health and Well Being ,Algorithms ,Genome ,Human ,Germ Cells ,Humans ,Internet ,Neoplasms ,Polymorphism ,Single Nucleotide ,User-Computer Interface ,Whole Genome Sequencing ,Cancer genomics ,Next-generation sequencing ,Mutation calling ,Germline contamination ,Germline leakage ,Patient identifiability ,Single nucleotide variant ,SNV ,Mathematical Sciences ,Information and Computing Sciences ,Bioinformatics ,Biological sciences ,Information and computing sciences ,Mathematical sciences - Abstract
BackgroundThe clinical sequencing of cancer genomes to personalize therapy is becoming routine across the world. However, concerns over patient re-identification from these data lead to questions about how tightly access should be controlled. It is not thought to be possible to re-identify patients from somatic variant data. However, somatic variant detection pipelines can mistakenly identify germline variants as somatic ones, a process called "germline leakage". The rate of germline leakage across different somatic variant detection pipelines is not well-understood, and it is uncertain whether or not somatic variant calls should be considered re-identifiable. To fill this gap, we quantified germline leakage across 259 sets of whole-genome somatic single nucleotide variant (SNVs) predictions made by 21 teams as part of the ICGC-TCGA DREAM Somatic Mutation Calling Challenge.ResultsThe median somatic SNV prediction set contained 4325 somatic SNVs and leaked one germline polymorphism. The level of germline leakage was inversely correlated with somatic SNV prediction accuracy and positively correlated with the amount of infiltrating normal cells. The specific germline variants leaked differed by tumour and algorithm. To aid in quantitation and correction of leakage, we created a tool, called GermlineFilter, for use in public-facing somatic SNV databases.ConclusionsThe potential for patient re-identification from leaked germline variants in somatic SNV predictions has led to divergent open data access policies, based on different assessments of the risks. Indeed, a single, well-publicized re-identification event could reshape public perceptions of the values of genomic data sharing. We find that modern somatic SNV prediction pipelines have low germline-leakage rates, which can be further reduced, especially for cloud-sharing, using pre-filtering software.
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- 2018
3. Valection: design optimization for validation and verification studies
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Cooper, Christopher I, Yao, Delia, Sendorek, Dorota H, Yamaguchi, Takafumi N, P’ng, Christine, Houlahan, Kathleen E, Caloian, Cristian, Fraser, Michael, SMC-DNA Challenge Participants, Ellrott, Kyle, Margolin, Adam A, Bristow, Robert G, Stuart, Joshua M, and Boutros, Paul C
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Sequence Analysis ,DNA ,Software Validation ,Verification ,Validation ,Candidate-selection ,DNA sequencing ,SMC-DNA Challenge Participants ,Mathematical Sciences ,Biological Sciences ,Information and Computing Sciences ,Bioinformatics - Abstract
BackgroundPlatform-specific error profiles necessitate confirmatory studies where predictions made on data generated using one technology are additionally verified by processing the same samples on an orthogonal technology. However, verifying all predictions can be costly and redundant, and testing a subset of findings is often used to estimate the true error profile.ResultsTo determine how to create subsets of predictions for validation that maximize accuracy of global error profile inference, we developed Valection, a software program that implements multiple strategies for the selection of verification candidates. We evaluated these selection strategies on one simulated and two experimental datasets.ConclusionsValection is implemented in multiple programming languages, available at: http://labs.oicr.on.ca/boutros-lab/software/valection.
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- 2018
4. Combining accurate tumor genome simulation with crowdsourcing to benchmark somatic structural variant detection
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Lee, Anna Y, Ewing, Adam D, Ellrott, Kyle, Hu, Yin, Houlahan, Kathleen E, Bare, J Christopher, Espiritu, Shadrielle Melijah G, Huang, Vincent, Dang, Kristen, Chong, Zechen, Caloian, Cristian, Yamaguchi, Takafumi N, Kellen, Michael R, Chen, Ken, Norman, Thea C, Friend, Stephen H, Guinney, Justin, Stolovitzky, Gustavo, Haussler, David, Margolin, Adam A, Stuart, Joshua M, and Boutros, Paul C
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Biological Sciences ,Biomedical and Clinical Sciences ,Bioinformatics and Computational Biology ,Genetics ,Oncology and Carcinogenesis ,Cancer ,Human Genome ,Generic health relevance ,Algorithms ,Benchmarking ,Computer Simulation ,Crowdsourcing ,Databases ,Genetic ,Genetic Variation ,Genome ,Human ,Genomics ,High-Throughput Nucleotide Sequencing ,Humans ,Neoplasms ,Software ,Somatic mutations ,Simulation ,Structural variants ,Cancer genomics ,Whole-genome sequencing ,ICGC-TCGA DREAM Somatic Mutation Calling Challenge Participants ,Environmental Sciences ,Information and Computing Sciences ,Bioinformatics - Abstract
BackgroundThe phenotypes of cancer cells are driven in part by somatic structural variants. Structural variants can initiate tumors, enhance their aggressiveness, and provide unique therapeutic opportunities. Whole-genome sequencing of tumors can allow exhaustive identification of the specific structural variants present in an individual cancer, facilitating both clinical diagnostics and the discovery of novel mutagenic mechanisms. A plethora of somatic structural variant detection algorithms have been created to enable these discoveries; however, there are no systematic benchmarks of them. Rigorous performance evaluation of somatic structural variant detection methods has been challenged by the lack of gold standards, extensive resource requirements, and difficulties arising from the need to share personal genomic information.ResultsTo facilitate structural variant detection algorithm evaluations, we create a robust simulation framework for somatic structural variants by extending the BAMSurgeon algorithm. We then organize and enable a crowdsourced benchmarking within the ICGC-TCGA DREAM Somatic Mutation Calling Challenge (SMC-DNA). We report here the results of structural variant benchmarking on three different tumors, comprising 204 submissions from 15 teams. In addition to ranking methods, we identify characteristic error profiles of individual algorithms and general trends across them. Surprisingly, we find that ensembles of analysis pipelines do not always outperform the best individual method, indicating a need for new ways to aggregate somatic structural variant detection approaches.ConclusionsThe synthetic tumors and somatic structural variant detection leaderboards remain available as a community benchmarking resource, and BAMSurgeon is available at https://github.com/adamewing/bamsurgeon .
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- 2018
5. A Community Challenge for Inferring Genetic Predictors of Gene Essentialities through Analysis of a Functional Screen of Cancer Cell Lines
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Gönen, Mehmet, Weir, Barbara A, Cowley, Glenn S, Vazquez, Francisca, Guan, Yuanfang, Jaiswal, Alok, Karasuyama, Masayuki, Uzunangelov, Vladislav, Wang, Tao, Tsherniak, Aviad, Howell, Sara, Marbach, Daniel, Hoff, Bruce, Norman, Thea C, Airola, Antti, Bivol, Adrian, Bunte, Kerstin, Carlin, Daniel, Chopra, Sahil, Deran, Alden, Ellrott, Kyle, Gopalacharyulu, Peddinti, Graim, Kiley, Kaski, Samuel, Khan, Suleiman A, Newton, Yulia, Ng, Sam, Pahikkala, Tapio, Paull, Evan, Sokolov, Artem, Tang, Hao, Tang, Jing, Wennerberg, Krister, Xie, Yang, Zhan, Xiaowei, Zhu, Fan, Community, Broad-DREAM, Afsari, Bahman, Aittokallio, Tero, Boehm, Jesse S, Chang, Yu-Chuan, Chen, Tenghui, Chong, Zechen, Elmarakeby, Haitham, Fertig, Elana J, Gonçalves, Emanuel, Gong, Pinghua, Hafemeister, Christoph, Hahn, William C, Heath, Lenwood, Kędziorski, Łukasz, Khemka, Niraj, King, Erh-kan, Lauria, Mario, Liu, Mark, Machado, Daniel, Mamitsuka, Hiroshi, Margolin, Adam A, Mazurkiewicz, Mateusz, Menden, Michael P, Migacz, Szymon, Nie, Zhi, Praveen, Paurush, Priami, Corrado, Rizzetto, Simone, Rocha, Miguel, Root, David E, Rudd, Cameron, Rudnicki, Witold R, Saez-Rodriguez, Julio, Song, Lei, Stolovitzky, Gustavo, Stuart, Joshua M, Sun, Duanchen, and Szalai, Bence
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Biological Sciences ,Genetics ,Biotechnology ,Prevention ,Cancer ,Human Genome ,Algorithms ,Cell Line ,Tumor ,Gene Expression ,Genes ,Essential ,Genomics ,Humans ,RNA ,Small Interfering ,Broad-DREAM Community ,cancer genomics ,community challenge ,crowdsourcing ,functional screen ,machine learning ,oncogene ,Biochemistry and Cell Biology ,Biochemistry and cell biology - Abstract
We report the results of a DREAM challenge designed to predict relative genetic essentialities based on a novel dataset testing 98,000 shRNAs against 149 molecularly characterized cancer cell lines. We analyzed the results of over 3,000 submissions over a period of 4 months. We found that algorithms combining essentiality data across multiple genes demonstrated increased accuracy; gene expression was the most informative molecular data type; the identity of the gene being predicted was far more important than the modeling strategy; well-predicted genes and selected molecular features showed enrichment in functional categories; and frequently selected expression features correlated with survival in primary tumors. This study establishes benchmarks for gene essentiality prediction, presents a community resource for future comparison with this benchmark, and provides insights into factors influencing the ability to predict gene essentiality from functional genetic screens. This study also demonstrates the value of releasing pre-publication data publicly to engage the community in an open research collaboration.
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- 2017
6. Quantitative Analysis of Histological Tissue Image Based on Cytological Profiles and Spatial Statistics
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Chang, Young Hwan, Thibault, Guillaume, Azimi, Vahid, Johnson, Brett, Jorgens, Danielle, Link, Jason, Margolin, Adam, and Gray, Joe W
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Earth Sciences ,Biomedical and Clinical Sciences ,Oncology and Carcinogenesis ,Cancer ,Cell Nucleus ,Eosine Yellowish-(YS) ,Hematoxylin ,Humans ,Spatial Analysis ,Staining and Labeling - Abstract
The cellular heterogeneity and complex tissue architecture of most tumor samples is a major obstacle in image analysis on standard hematoxylin and eosin-stained (H&E) tissue sections. A mixture of cancer and normal cells complicates the interpretation of their cytological profiles. Furthermore, spatial arrangement and architectural organization of cells are generally not reflected in cellular characteristics analysis. To address these challenges, first we describe an automatic nuclei segmentation of H&E tissue sections. In the task of deconvoluting cellular heterogeneity, we adopt Landmark based Spectral Clustering (LSC) to group individual nuclei in such a way that nuclei in the same group are more similar. We next devise spatial statistics for analyzing spatial arrangement and organization, which are not detectable by individual cellular characteristics. Our quantitative, spatial statistics analysis could benefit H&E section analysis by refining and complementing cellular characteristics analysis.
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- 2016
7. The metabolome regulates the epigenetic landscape during naive-to-primed human embryonic stem cell transition
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Sperber, Henrik, Mathieu, Julie, Wang, Yuliang, Ferreccio, Amy, Hesson, Jennifer, Xu, Zhuojin, Fischer, Karin A, Devi, Arikketh, Detraux, Damien, Gu, Haiwei, Battle, Stephanie L, Showalter, Megan, Valensisi, Cristina, Bielas, Jason H, Ericson, Nolan G, Margaretha, Lilyana, Robitaille, Aaron M, Margineantu, Daciana, Fiehn, Oliver, Hockenbery, David, Blau, C Anthony, Raftery, Daniel, Margolin, Adam A, Hawkins, R David, Moon, Randall T, Ware, Carol B, and Ruohola-Baker, Hannele
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Genetics ,Stem Cell Research - Embryonic - Human ,Regenerative Medicine ,Stem Cell Research ,Underpinning research ,1.1 Normal biological development and functioning ,Generic health relevance ,Animals ,Blotting ,Western ,Cell Differentiation ,Cells ,Cultured ,Embryonic Stem Cells ,Epigenesis ,Genetic ,Gas Chromatography-Mass Spectrometry ,Gene Expression Profiling ,Gene Knockdown Techniques ,Histones ,Human Embryonic Stem Cells ,Humans ,Lysine ,Mass Spectrometry ,Metabolome ,Metabolomics ,Methylation ,Mice ,Niacinamide ,Nicotinamide N-Methyltransferase ,Proteomics ,Reverse Transcriptase Polymerase Chain Reaction ,S-Adenosylmethionine ,Signal Transduction ,Biological Sciences ,Medical and Health Sciences ,Developmental Biology - Abstract
For nearly a century developmental biologists have recognized that cells from embryos can differ in their potential to differentiate into distinct cell types. Recently, it has been recognized that embryonic stem cells derived from both mice and humans exhibit two stable yet epigenetically distinct states of pluripotency: naive and primed. We now show that nicotinamide N-methyltransferase (NNMT) and the metabolic state regulate pluripotency in human embryonic stem cells (hESCs). Specifically, in naive hESCs, NNMT and its enzymatic product 1-methylnicotinamide are highly upregulated, and NNMT is required for low S-adenosyl methionine (SAM) levels and the H3K27me3 repressive state. NNMT consumes SAM in naive cells, making it unavailable for histone methylation that represses Wnt and activates the HIF pathway in primed hESCs. These data support the hypothesis that the metabolome regulates the epigenetic landscape of the earliest steps in human development.
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- 2015
8. The NIH BD2K center for big data in translational genomics
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Paten, Benedict, Diekhans, Mark, Druker, Brian J, Friend, Stephen, Guinney, Justin, Gassner, Nadine, Guttman, Mitchell, Kent, W James, Mantey, Patrick, Margolin, Adam A, Massie, Matt, Novak, Adam M, Nothaft, Frank, Pachter, Lior, Patterson, David, Smuga-Otto, Maciej, Stuart, Joshua M, Veer, Laura Van’t, Wold, Barbara, and Haussler, David
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Distributed Computing and Systems Software ,Information and Computing Sciences ,Human Genome ,Networking and Information Technology R&D (NITRD) ,Genetics ,Biotechnology ,Generic health relevance ,Good Health and Well Being ,Computational Biology ,Datasets as Topic ,Genomics ,Humans ,Knowledge Bases ,National Institutes of Health (U.S.) ,Translational Research ,Biomedical ,United States ,computational genomics ,genomics ,big data ,APIs ,genome informatics ,Engineering ,Medical and Health Sciences ,Medical Informatics ,Biomedical and clinical sciences ,Health sciences ,Information and computing sciences - Abstract
The world's genomics data will never be stored in a single repository - rather, it will be distributed among many sites in many countries. No one site will have enough data to explain genotype to phenotype relationships in rare diseases; therefore, sites must share data. To accomplish this, the genetics community must forge common standards and protocols to make sharing and computing data among many sites a seamless activity. Through the Global Alliance for Genomics and Health, we are pioneering the development of shared application programming interfaces (APIs) to connect the world's genome repositories. In parallel, we are developing an open source software stack (ADAM) that uses these APIs. This combination will create a cohesive genome informatics ecosystem. Using containers, we are facilitating the deployment of this software in a diverse array of environments. Through benchmarking efforts and big data driver projects, we are ensuring ADAM's performance and utility.
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- 2015
9. Combining tumor genome simulation with crowdsourcing to benchmark somatic single-nucleotide-variant detection
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Ewing, Adam D, Houlahan, Kathleen E, Hu, Yin, Ellrott, Kyle, Caloian, Cristian, Yamaguchi, Takafumi N, Bare, J Christopher, P'ng, Christine, Waggott, Daryl, Sabelnykova, Veronica Y, Kellen, Michael R, Norman, Thea C, Haussler, David, Friend, Stephen H, Stolovitzky, Gustavo, Margolin, Adam A, Stuart, Joshua M, and Boutros, Paul C
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Genetics ,Cancer ,Human Genome ,Algorithms ,Benchmarking ,Crowdsourcing ,Genome ,Humans ,Neoplasms ,Polymorphism ,Single Nucleotide ,ICGC-TCGA DREAM Somatic Mutation Calling Challenge participants ,Biological Sciences ,Technology ,Medical and Health Sciences ,Developmental Biology - Abstract
The detection of somatic mutations from cancer genome sequences is key to understanding the genetic basis of disease progression, patient survival and response to therapy. Benchmarking is needed for tool assessment and improvement but is complicated by a lack of gold standards, by extensive resource requirements and by difficulties in sharing personal genomic information. To resolve these issues, we launched the ICGC-TCGA DREAM Somatic Mutation Calling Challenge, a crowdsourced benchmark of somatic mutation detection algorithms. Here we report the BAMSurgeon tool for simulating cancer genomes and the results of 248 analyses of three in silico tumors created with it. Different algorithms exhibit characteristic error profiles, and, intriguingly, false positives show a trinucleotide profile very similar to one found in human tumors. Although the three simulated tumors differ in sequence contamination (deviation from normal cell sequence) and in subclonality, an ensemble of pipelines outperforms the best individual pipeline in all cases. BAMSurgeon is available at https://github.com/adamewing/bamsurgeon/.
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- 2015
10. Multivariate dependence and genetic networks inference
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Margolin, Adam A., Wang, Kai, Califano, Andrea, and Nemenman, Ilya
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Quantitative Biology - Quantitative Methods ,Quantitative Biology - Molecular Networks - Abstract
A critical task in systems biology is the identification of genes that interact to control cellular processes by transcriptional activation of a set of target genes. Many methods have been developed to use statistical correlations in high-throughput datasets to infer such interactions. However, cellular pathways are highly cooperative, often requiring the joint effect of many molecules, and few methods have been proposed to explicitly identify such higher-order interactions, partially due to the fact that the notion of multivariate statistical dependency itself remains imprecisely defined. We define the concept of dependence among multiple variables using maximum entropy techniques and introduce computational tests for their identification. Synthetic network results reveal that this procedure uncovers dependencies even in undersampled regimes, when the joint probability distribution cannot be reliably estimated. Analysis of microarray data from human B cells reveals that third-order statistics, but not second-order ones, uncover relationships between genes that interact in a pathway to cooperatively regulate a common set of targets., Comment: 35 pages, expanded version of q-bio/0406015
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- 2010
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11. Toward better benchmarking: challenge-based methods assessment in cancer genomics.
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Boutros, Paul C, Margolin, Adam A, Stuart, Joshua M, Califano, Andrea, and Stolovitzky, Gustavo
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Humans ,Neoplasms ,DNA Mutational Analysis ,Genomics ,Genome ,Human ,Reference Standards ,Benchmarking ,Genetic Association Studies ,Genome ,Human ,Bioinformatics ,Environmental Sciences ,Biological Sciences ,Information and Computing Sciences - Abstract
Rapid technological development has created an urgent need for improved evaluation of algorithms for the analysis of cancer genomics data. We outline how challenge-based assessment may help fill this gap by leveraging crowd-sourcing to distribute effort and reduce bias.
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- 2014
12. Multiplatform Analysis of 12 Cancer Types Reveals Molecular Classification within and across Tissues of Origin
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Hoadley, Katherine A, Yau, Christina, Wolf, Denise M, Cherniack, Andrew D, Tamborero, David, Ng, Sam, Leiserson, Max DM, Niu, Beifang, McLellan, Michael D, Uzunangelov, Vladislav, Zhang, Jiashan, Kandoth, Cyriac, Akbani, Rehan, Shen, Hui, Omberg, Larsson, Chu, Andy, Margolin, Adam A, Veer, Laura J van’t, Lopez-Bigas, Nuria, Laird, Peter W, Raphael, Benjamin J, Ding, Li, Robertson, A Gordon, Byers, Lauren A, Mills, Gordon B, Weinstein, John N, Van Waes, Carter, Chen, Zhong, Collisson, Eric A, Network, The Cancer Genome Atlas Research, Benz, Christopher C, Perou, Charles M, and Stuart, Joshua M
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Biological Sciences ,Biomedical and Clinical Sciences ,Genetics ,Oncology and Carcinogenesis ,Human Genome ,Rare Diseases ,Biotechnology ,Cancer ,Orphan Drug ,Urologic Diseases ,Cancer Genomics ,2.1 Biological and endogenous factors ,Cluster Analysis ,Humans ,Neoplasms ,Transcriptome ,Cancer Genome Atlas Research Network ,Medical and Health Sciences ,Developmental Biology ,Biological sciences ,Biomedical and clinical sciences - Abstract
Recent genomic analyses of pathologically defined tumor types identify "within-a-tissue" disease subtypes. However, the extent to which genomic signatures are shared across tissues is still unclear. We performed an integrative analysis using five genome-wide platforms and one proteomic platform on 3,527 specimens from 12 cancer types, revealing a unified classification into 11 major subtypes. Five subtypes were nearly identical to their tissue-of-origin counterparts, but several distinct cancer types were found to converge into common subtypes. Lung squamous, head and neck, and a subset of bladder cancers coalesced into one subtype typified by TP53 alterations, TP63 amplifications, and high expression of immune and proliferation pathway genes. Of note, bladder cancers split into three pan-cancer subtypes. The multiplatform classification, while correlated with tissue-of-origin, provides independent information for predicting clinical outcomes. All data sets are available for data-mining from a unified resource to support further biological discoveries and insights into novel therapeutic strategies.
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- 2014
13. Assessing the clinical utility of cancer genomic and proteomic data across tumor types
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Yuan, Yuan, Van Allen, Eliezer M, Omberg, Larsson, Wagle, Nikhil, Amin-Mansour, Ali, Sokolov, Artem, Byers, Lauren A, Xu, Yanxun, Hess, Kenneth R, Diao, Lixia, Han, Leng, Huang, Xuelin, Lawrence, Michael S, Weinstein, John N, Stuart, Josh M, Mills, Gordon B, Garraway, Levi A, Margolin, Adam A, Getz, Gad, and Liang, Han
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Biological Sciences ,Biomedical and Clinical Sciences ,Bioinformatics and Computational Biology ,Genetics ,Oncology and Carcinogenesis ,Clinical Research ,Human Genome ,Biotechnology ,Rare Diseases ,Cancer ,Biomarkers ,Tumor ,DNA ,Neoplasm ,Databases ,Genetic ,Genetic Markers ,Genetic Predisposition to Disease ,Humans ,Neoplasms ,Prevalence ,Proteome ,Risk Assessment ,Survival Analysis - Abstract
Molecular profiling of tumors promises to advance the clinical management of cancer, but the benefits of integrating molecular data with traditional clinical variables have not been systematically studied. Here we retrospectively predict patient survival using diverse molecular data (somatic copy-number alteration, DNA methylation and mRNA, microRNA and protein expression) from 953 samples of four cancer types from The Cancer Genome Atlas project. We find that incorporating molecular data with clinical variables yields statistically significantly improved predictions (FDR < 0.05) for three cancers but those quantitative gains were limited (2.2-23.9%). Additional analyses revealed little predictive power across tumor types except for one case. In clinically relevant genes, we identified 10,281 somatic alterations across 12 cancer types in 2,928 of 3,277 patients (89.4%), many of which would not be revealed in single-tumor analyses. Our study provides a starting point and resources, including an open-access model evaluation platform, for building reliable prognostic and therapeutic strategies that incorporate molecular data.
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- 2014
14. Pan-cancer transcriptional signatures predictive of oncogenic mutations reveal that Fbw7 regulates cancer cell oxidative metabolism
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Davis, Ryan J., Gönen, Mehmet, Margineantu, Daciana H., Handeli, Shlomo, Swanger, Jherek, Hoellerbauer, Pia, Paddison, Patrick J., Gu, Haiwei, Raftery, Daniel, Grim, Jonathan E., Hockenbery, David M., Margolin, Adam A., and Clurman, Bruce E.
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- 2018
15. Genome-wide discovery of modulators of transcriptional interactions in human B lymphocytes
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Wang, Kai, Nemenman, Ilya, Banerjee, Nilanjana, Margolin, Adam, and Califano, Andrea
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Quantitative Biology - Molecular Networks ,Quantitative Biology - Genomics ,Quantitative Biology - Quantitative Methods - Abstract
Transcriptional interactions in a cell are modulated by a variety of mechanisms that prevent their representation as pure pairwise interactions between a transcription factor and its target(s). These include, among others, transcription factor activation by phosphorylation and acetylation, formation of active complexes with one or more co-factors, and mRNA/protein degradation and stabilization processes. This paper presents a first step towards the systematic, genome-wide computational inference of genes that modulate the interactions of specific transcription factors at the post-transcriptional level. The method uses a statistical test based on changes in the mutual information between a transcription factor and each of its candidate targets, conditional on the expression of a third gene. The approach was first validated on a synthetic network model, and then tested in the context of a mammalian cellular system. By analyzing 254 microarray expression profiles of normal and tumor related human B lymphocytes, we investigated the post transcriptional modulators of the MYC proto-oncogene, an important transcription factor involved in tumorigenesis. Our method discovered a set of 100 putative modulator genes, responsible for modulating 205 regulatory relationships between MYC and its targets. The set is significantly enriched in molecules with function consistent with their activities as modulators of cellular interactions, recapitulates established MYC regulation pathways, and provides a notable repertoire of novel regulators of MYC function. The approach has broad applicability and can be used to discover modulators of any other transcription factor, provided that adequate expression profile data are available., Comment: 15 pages, 3 figures, 2 tables; minor changes following referees' comments; accepted to RECOMB06
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- 2005
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16. Conditional Network Analysis Identifies Candidate Regulator Genes in Human B Cells
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Wang, Kai, Banerjee, Nilanjana, Margolin, Adam, Nemenman, Ilya, Basso, Katia, Favera, Riccardo, and Califano, Andrea
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Quantitative Biology - Molecular Networks ,Quantitative Biology - Genomics ,Quantitative Biology - Quantitative Methods - Abstract
Cellular phenotypes are determined by the dynamical activity of networks of co-regulated genes. Elucidating such networks is crucial for the understanding of normal cell physiology as well as for the dissection of complex pathologic phenotypes. Existing methods for such "reverse engineering" of genetic networks from microarray expression data have been successful only in prokaryotes (E. coli) and lower eukaryotes (S. cerevisiae) with relatively simple genomes. Additionally, they have mostly attempted to reconstruct average properties about the network connectivity without capturing the highly conditional nature of the interactions. In this paper we extend the ARACNE algorithm, which we recently introduced and successfully applied to the reconstruction of whole-genome transcriptional networks from mammalian cells, precisely to link the existence of specific network structures to the expression or lack thereof of specific regulator genes. This is accomplished by analyzing thousands of alternative network topologies generated by constraining the data set on the presence or absence of putative regulator genes. By considering interactions that are consistently supported across several such constraints, we identify many transcriptional interactions that would not have been detectable by the original method. By selecting genes that produce statistically significant changes in network topology, we identify novel candidate regulator genes. Further analysis shows that transcription factors, kinases, phosphatases, and other gene families known to effect biochemical interactions, are significantly overrepresented among the set of candidate regulator genes identified in silico, indirectly supporting the validity of the approach., Comment: Submitted to RECOMB 2005 (11 pages, 4 figures, 2 tables)
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- 2004
17. ARACNE: An Algorithm for the Reconstruction of Gene Regulatory Networks in a Mammalian Cellular Context
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Margolin, Adam A., Nemenman, Ilya, Basso, Katia, Klein, Ulf, Wiggins, Chris, Stolovitzky, Gustavo, Favera, Riccardo Dalla, and Califano, Andrea
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Quantitative Biology - Molecular Networks ,Quantitative Biology - Genomics ,Quantitative Biology - Quantitative Methods - Abstract
Background: Elucidating gene regulatory networks is crucial for understanding normal cell physiology and complex pathologic phenotypes. Existing computational methods for the genome-wide ``reverse engineering'' of such networks have been successful only for lower eukaryotes with simple genomes. Here we present ARACNE, a novel algorithm, using microarray expression profiles, specifically designed to scale up to the complexity of regulatory networks in mammalian cells, yet general enough to address a wider range of network deconvolution problems. This method uses an information theoretic approach to eliminate the majority of indirect interactions inferred by co-expression methods. Results: We prove that ARACNE reconstructs the network exactly (asymptotically) if the effect of loops in the network topology is negligible, and we show that the algorithm works well in practice, even in the presence of numerous loops and complex topologies. We assess ARACNE's ability to reconstruct transcriptional regulatory networks using both a realistic synthetic dataset and a microarray dataset from human B cells. On synthetic datasets ARACNE achieves very low error rates and outperforms established methods, such as Relevance Networks and Bayesian Networks. Application to the deconvolution of genetic networks in human B cells demonstrates ARACNE's ability to infer validated transcriptional targets of the c MYC proto-oncogene. We also study the effects of mis estimation of mutual information on network reconstruction, and show that algorithms based on mutual information ranking are more resilient to estimation errors., Comment: accepted version; minor revisions following referee suggestions; 28 pages, 9 figures; detailed version of q-bio.MN/0410036
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- 2004
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18. On The Reconstruction of Interaction Networks with Applications to Transcriptional Regulation
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Margolin, Adam A., Nemenman, Ilya, Wiggins, Chris, Stolovitzky, Gustavo, and Califano, Andrea
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Quantitative Biology - Molecular Networks ,Quantitative Biology - Genomics ,Quantitative Biology - Quantitative Methods - Abstract
A novel information-theoretic method for reconstruction of interaction networks is introduced. We prove that the method is exact for some class of networks. Performance tests on large synthetic transcriptional regulatory networks produce very encouraging results., Comment: 4 pages, 1 figure; NIPS'04 workshop on Computational Biology; extended abstract of q-bio.MN/0410037; minor changes following post-workshop discussions
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- 2004
19. Enabling transparent and collaborative computational analysis of 12 tumor types within The Cancer Genome Atlas
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Omberg, Larsson, Ellrott, Kyle, Yuan, Yuan, Kandoth, Cyriac, Wong, Chris, Kellen, Michael R, Friend, Stephen H, Stuart, Josh, Liang, Han, and Margolin, Adam A
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Biological Sciences ,Genetics ,Cancer ,Human Genome ,Cooperative Behavior ,Genome ,Humans ,Neoplasms ,Reproducibility of Results ,Medical and Health Sciences ,Developmental Biology ,Agricultural biotechnology ,Bioinformatics and computational biology - Abstract
The Cancer Genome Atlas Pan-Cancer Analysis Working Group collaborated on the Synapse software platform to share and evolve data, results and methodologies while performing integrative analysis of molecular profiling data from 12 tumor types. The group's work serves as a pilot case study that provides (i) a template for future large collaborative studies; (ii) a system to support collaborative projects; and (iii) a public resource of highly curated data, results and automated systems for the evaluation of community-developed models.
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- 2013
20. A Community Challenge for Inferring Genetic Predictors of Gene Essentialities through Analysis of a Functional Screen of Cancer Cell Lines
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Afsari, Bahman, Airola, Antti, Aittokallio, Tero, Bivol, Adrian, Boehm, Jesse S., Bunte, Kerstin, Carlin, Daniel, Chang, Yu-Chuan, Chen, Tenghui, Chong, Zechen, Chopra, Sahil, Cowley, Glenn S., Deran, Alden, Ellrott, Kyle, Elmarakeby, Haitham, Fertig, Elana J., Gonçalves, Emanuel, Gönen, Mehmet, Gong, Pinghua, Gopalacharyulu, Peddinti, Graim, Kiley, Guan, Yuanfang, Hafemeister, Christoph, Hahn, William C., Heath, Lenwood, Hoff, Bruce, Howell, Sara, Jaiswal, Alok, Karasuyama, Masayuki, Kaski, Samuel, Kędziorski, Łukasz, Khan, Suleiman A., Khemka, Niraj, King, Erh-kan, Lauria, Mario, Liu, Mark, Machado, Daniel, Mamitsuka, Hiroshi, Marbach, Daniel, Margolin, Adam A., Mazurkiewicz, Mateusz, Menden, Michael P., Migacz, Szymon, Newton, Yulia, Ng, Sam, Nie, Zhi, Norman, Thea C., Pahikkala, Tapio, Paull, Evan, Praveen, Paurush, Priami, Corrado, Rizzetto, Simone, Rocha, Miguel, Root, David E., Rudd, Cameron, Rudnicki, Witold R., Saez-Rodriguez, Julio, Sokolov, Artem, Song, Lei, Stolovitzky, Gustavo, Stuart, Joshua M., Sun, Duanchen, Szalai, Bence, Tang, Hao, Tang, Jing, Tsherniak, Aviad, Uzunangelov, Vladislav, Vazquez, Francisca, Wang, Tao, Wang, Difei, Weir, Barbara A., Wennerberg, Krister, Wu, Ling-yun, Xiao, Guanghua, Xie, Yang, Ye, Jieping, Ye, Yuting, Zhan, Xiaowei, Zhou, Wanding, and Zhu, Fan
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- 2017
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21. Chromatin and Transcriptional Analysis of Mesoderm Progenitor Cells Identifies HOPX as a Regulator of Primitive Hematopoiesis
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Palpant, Nathan J., Wang, Yuliang, Hadland, Brandon, Zaunbrecher, Rebecca J., Redd, Meredith, Jones, Daniel, Pabon, Lil, Jain, Rajan, Epstein, Jonathan, Ruzzo, Walter L., Zheng, Ying, Bernstein, Irwin, Margolin, Adam, and Murry, Charles E.
- Published
- 2017
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22. A deep learning system accurately classifies primary and metastatic cancers using passenger mutation patterns
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Jiao, Wei, Atwal, Gurnit, Polak, Paz, Karlic, Rosa, Cuppen, Edwin, Al-Shahrour, Fatima, Bailey, Peter J, Biankin, Andrew V, Boutros, Paul C, Campbell, Peter J, Chang, David K, Cooke, Susanna L, Deshpande, Vikram, Faltas, Bishoy M, Faquin, William C, Garraway, Levi, Getz, Gad, Grimmond, Sean M, Haider, Syed, Hoadley, Katherine A, Kaiser, Vera B, Kato, Mamoru, Kübler, Kirsten, Lazar, Alexander J, Li, Constance H, Louis, David N, Margolin, Adam, Martin, Sancha, Nahal-Bose, Hardeep K, Nielsen, G Petur, Nik-Zainal, Serena, Omberg, Larsson, P’ng, Christine, Perry, Marc D, Rheinbay, Esther, Rubin, Mark A, Semple, Colin A, Sgroi, Dennis C, Shibata, Tatsuhiro, Siebert, Reiner, Smith, Jaclyn, Stein, Lincoln D, Stobbe, Miranda D, Sun, Ren X, Thai, Kevin, Wright, Derek W, Wu, Chin-Lee, Yuan, Ke, Zhang, Junjun, Danyi, Alexandra, de Ridder, Jeroen, van Herpen, Carla, Lolkema, Martijn P, Steeghs, Neeltje, Morris, Quaid D, Aaltonen, Lauri A, Abascal, Federico, Abeshouse, Adam, Aburatani, Hiroyuki, 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Johan, Stadler, Peter F, Staib, Peter, Stark, Stefan G, Stebbings, Lucy, Stefánsson, Ólafur Andri, Stegle, Oliver, Stenhouse, Alasdair, Stewart, Chip, Stilgenbauer, Stephan, Stratton, Michael R, Stretch, Jonathan R, Struck, Adam J, Stuart, Joshua M, Stunnenberg, Henk G, Su, Hong, Su, Xiaoping, Sungalee, Stephanie, Susak, Hana, Suzuki, Akihiro, Sweep, Fred, Szczepanowski, Monika, Sültmann, Holger, Yugawa, Takashi, Tam, Angela, Tamborero, David, Tan, Benita Kiat Tee, Tan, Donghui, Tan, Patrick, Tanaka, Hiroko, Taniguchi, Hirokazu, Tanskanen, Tomas J, Tarabichi, Maxime, Tarnuzzer, Roy, Tarpey, Patrick, Taschuk, Morgan L, Tatsuno, Kenji, Tavaré, Simon, Taylor, Darrin F, Taylor-Weiner, Amaro, Teague, Jon W, Teh, Bin Tean, Tembe, Varsha, Temes, Javier, Thayer, Sarah P, Thiessen, Nina, Thomas, Gilles, Thomas, Sarah, Thompson, Alan, Thompson, Alastair M, Thompson, John FF, Thompson, R Houston, Thorne, Heather, Thorne, Leigh B, Thorogood, Adrian, Tiao, Grace, Tijanic, Nebojsa, Timms, Lee E, Tirabosco, Roberto, Tojo, Marta, Tommasi, Stefania, Toon, Christopher W, Toprak, Umut H, Torrents, David, Tortora, Giampaolo, Tost, Jörg, Totoki, Yasushi, Townend, David, Traficante, Nadia, Treilleux, Isabelle, Trotta, Jean-Rémi, Trümper, Lorenz HP, Tsao, Ming, Tsunoda, Tatsuhiko, MC Tubio, Jose, Tucker, Olga, Turkington, Richard, Turner, Daniel J, Tutt, Andrew, Ueno, Masaki, Ueno, Naoto T, Umbricht, Christopher, Umer, Husen M, Underwood, Timothy J, Urban, Lara, Urushidate, Tomoko, Ushiku, Tetsuo, Uusküla-Reimand, Liis, Valencia, Alfonso, Van Den Berg, David J, Van Laere, Steven, Van Loo, Peter, Van Meir, Erwin G, Van den Eynden, Gert G, Van der Kwast, Theodorus, Vasudev, Naveen, Vazquez, Miguel, Vedururu, Ravikiran, Veluvolu, Umadevi, Vembu, Shankar, Verbeke, Lieven PC, Vermeulen, Peter, Verrill, Clare, Viari, Alain, Vicente, David, Vicentini, Caterina, VijayRaghavan, K, Viksna, Juris, Vilain, Ricardo E, Villasante, Izar, Vincent-Salomon, Anne, Visakorpi, Tapio, Voet, Douglas, Vyas, Paresh, Vázquez-García, Ignacio, Waddell, Nick M, Waddell, Nicola, Wadelius, Claes, Wadi, Lina, Wagener, Rabea, Wala, Jeremiah A, Wang, Jian, Wang, Jiayin, Wang, Linghua, Wang, Qi, Wang, Wenyi, Wang, Yumeng, Wang, Zhining, Waring, Paul M, Warnatz, Hans-Jörg, Warrell, Jonathan, Warren, Anne Y, Waszak, Sebastian M, Wedge, David C, Weichenhan, Dieter, Weinberger, Paul, Weinstein, John N, Weischenfeldt, Joachim, Weisenberger, Daniel J, Welch, Ian, Wendl, Michael C, Werner, Johannes, Whalley, Justin P, Wheeler, David A, Whitaker, Hayley C, Wigle, Dennis, Wilkerson, Matthew D, Williams, Ashley, Wilmott, James S, Wilson, Gavin W, Wilson, Julie M, Wilson, Richard K, Winterhoff, Boris, Wintersinger, Jeffrey A, Wiznerowicz, Maciej, Wolf, Stephan, Wong, Bernice H, Wong, Tina, Wong, Winghing, Woo, Youngchoon, Wood, Scott, Wouters, Bradly G, Wright, Adam J, Wright, Mark H, Wu, Dai-Ying, Wu, Guanming, Wu, Jianmin, Wu, Kui, Wu, Yang, Wu, Zhenggang, Xi, Liu, Xia, Tian, Xiang, Qian, Xiao, Xiao, Xing, Rui, Xiong, Heng, Xu, Qinying, Xu, Yanxun, Xue, Hong, Yachida, Shinichi, Yakneen, Sergei, Yamaguchi, Rui, Yamaguchi, Takafumi N, Yamamoto, Masakazu, Yamamoto, Shogo, Yamaue, Hiroki, Yang, Fan, Yang, Huanming, Yang, Jean Y, Yang, Liming, Yang, Lixing, Yang, Shanlin, Yang, Tsun-Po, Yang, Yang, Yao, Xiaotong, Yaspo, Marie-Laure, Yates, Lucy, Yau, Christina, Ye, Chen, Ye, Kai, Yellapantula, Venkata D, Yoon, Christopher J, Yoon, Sung-Soo, Yousif, Fouad, Yu, Jun, Yu, Kaixian, Yu, Willie, Yu, Yingyan, Yuan, Yuan, Yuen, Denis, Yung, Christina K, Zaikova, Olga, Zamora, Jorge, Zapatka, Marc, Zenklusen, Jean C, Zenz, Thorsten, Zeps, Nikolajs, Zhang, Cheng-Zhong, Zhang, Fan, Zhang, Hailei, Zhang, Hongwei, Zhang, Hongxin, Zhang, Jiashan, Zhang, Jing, Zhang, Xiuqing, Zhang, Xuanping, Zhang, Yan, Zhang, Zemin, Zhao, Zhongming, Zheng, Liangtao, Zheng, Xiuqing, Zhou, Wanding, Zhou, Yong, Zhu, Bin, Zhu, Hongtu, Zhu, Jingchun, Zhu, Shida, Zou, Lihua, Zou, Xueqing, deFazio, Anna, van As, Nicholas, van Deurzen, Carolien HM, van de Vijver, Marc J, Veer, L van’t, and von Mering, Christian
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Human Biology & Physiology ,Ecology,Evolution & Ethology ,Tumour Biology ,Genetics & Genomics ,Structural Biology & Biophysics ,Computational & Systems Biology - Abstract
In cancer, the primary tumour’s organ of origin and histopathology are the strongest determinants of its clinical behaviour, but in 3% of cases a patient presents with a metastatic tumour and no obvious primary. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, we train a deep learning classifier to predict cancer type based on patterns of somatic passenger mutations detected in whole genome sequencing (WGS) of 2606 tumours representing 24 common cancer types produced by the PCAWG Consortium. Our classifier achieves an accuracy of 91% on held-out tumor samples and 88% and 83% respectively on independent primary and metastatic samples, roughly double the accuracy of trained pathologists when presented with a metastatic tumour without knowledge of the primary. Surprisingly, adding information on driver mutations reduced accuracy. Our results have clinical applicability, underscore how patterns of somatic passenger mutations encode the state of the cell of origin, and can inform future strategies to detect the source of circulating tumour DNA.
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- 2023
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23. Integrated Genomic Analysis of Diverse Induced Pluripotent Stem Cells from the Progenitor Cell Biology Consortium
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Salomonis, Nathan, Dexheimer, Phillip J., Omberg, Larsson, Schroll, Robin, Bush, Stacy, Huo, Jeffrey, Schriml, Lynn, Ho Sui, Shannan, Keddache, Mehdi, Mayhew, Christopher, Shanmukhappa, Shiva Kumar, Wells, James, Daily, Kenneth, Hubler, Shane, Wang, Yuliang, Zambidis, Elias, Margolin, Adam, Hide, Winston, Hatzopoulos, Antonis K., Malik, Punam, Cancelas, Jose A., Aronow, Bruce J., and Lutzko, Carolyn
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- 2016
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24. Supplementary Table 4 from Functional Precision Medicine Identifies Novel Druggable Targets and Therapeutic Options in Head and Neck Cancer
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Xu, Chang, primary, Nikolova, Olga, primary, Basom, Ryan S., primary, Mitchell, Ryan M., primary, Shaw, Reid, primary, Moser, Russell D., primary, Park, Heuijoon, primary, Gurley, Kay E., primary, Kao, Michael C., primary, Green, Carlos L., primary, Schaub, Franz X., primary, Diaz, Robert L., primary, Swan, Hallie A., primary, Jang, In S., primary, Guinney, Justin, primary, Gadi, Vijayakrishna K., primary, Margolin, Adam A., primary, Grandori, Carla, primary, Kemp, Christopher J., primary, and Méndez, Eduardo, primary
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- 2023
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25. Supplementary Table 10 from Functional Precision Medicine Identifies Novel Druggable Targets and Therapeutic Options in Head and Neck Cancer
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Xu, Chang, primary, Nikolova, Olga, primary, Basom, Ryan S., primary, Mitchell, Ryan M., primary, Shaw, Reid, primary, Moser, Russell D., primary, Park, Heuijoon, primary, Gurley, Kay E., primary, Kao, Michael C., primary, Green, Carlos L., primary, Schaub, Franz X., primary, Diaz, Robert L., primary, Swan, Hallie A., primary, Jang, In S., primary, Guinney, Justin, primary, Gadi, Vijayakrishna K., primary, Margolin, Adam A., primary, Grandori, Carla, primary, Kemp, Christopher J., primary, and Méndez, Eduardo, primary
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- 2023
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26. Supplementary Tables S18-20 from Functional Kinomics Identifies Candidate Therapeutic Targets in Head and Neck Cancer
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Moser, Russell, primary, Xu, Chang, primary, Kao, Michael, primary, Annis, James, primary, Lerma, Luisa Angelica, primary, Schaupp, Christopher M., primary, Gurley, Kay E., primary, Jang, In Sock, primary, Biktasova, Asel, primary, Yarbrough, Wendell G., primary, Margolin, Adam A., primary, Grandori, Carla, primary, Kemp, Christopher J., primary, and Méndez, Eduardo, primary
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- 2023
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27. Supplementary Table Legends from Functional Kinomics Identifies Candidate Therapeutic Targets in Head and Neck Cancer
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Moser, Russell, primary, Xu, Chang, primary, Kao, Michael, primary, Annis, James, primary, Lerma, Luisa Angelica, primary, Schaupp, Christopher M., primary, Gurley, Kay E., primary, Jang, In Sock, primary, Biktasova, Asel, primary, Yarbrough, Wendell G., primary, Margolin, Adam A., primary, Grandori, Carla, primary, Kemp, Christopher J., primary, and Méndez, Eduardo, primary
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- 2023
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28. Supplementary Table S21 from Functional Kinomics Identifies Candidate Therapeutic Targets in Head and Neck Cancer
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Moser, Russell, primary, Xu, Chang, primary, Kao, Michael, primary, Annis, James, primary, Lerma, Luisa Angelica, primary, Schaupp, Christopher M., primary, Gurley, Kay E., primary, Jang, In Sock, primary, Biktasova, Asel, primary, Yarbrough, Wendell G., primary, Margolin, Adam A., primary, Grandori, Carla, primary, Kemp, Christopher J., primary, and Méndez, Eduardo, primary
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- 2023
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29. Supplementary Figures from Functional Precision Medicine Identifies Novel Druggable Targets and Therapeutic Options in Head and Neck Cancer
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Xu, Chang, primary, Nikolova, Olga, primary, Basom, Ryan S., primary, Mitchell, Ryan M., primary, Shaw, Reid, primary, Moser, Russell D., primary, Park, Heuijoon, primary, Gurley, Kay E., primary, Kao, Michael C., primary, Green, Carlos L., primary, Schaub, Franz X., primary, Diaz, Robert L., primary, Swan, Hallie A., primary, Jang, In S., primary, Guinney, Justin, primary, Gadi, Vijayakrishna K., primary, Margolin, Adam A., primary, Grandori, Carla, primary, Kemp, Christopher J., primary, and Méndez, Eduardo, primary
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- 2023
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30. Supplementary Figure S1 from Functional Kinomics Identifies Candidate Therapeutic Targets in Head and Neck Cancer
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Moser, Russell, primary, Xu, Chang, primary, Kao, Michael, primary, Annis, James, primary, Lerma, Luisa Angelica, primary, Schaupp, Christopher M., primary, Gurley, Kay E., primary, Jang, In Sock, primary, Biktasova, Asel, primary, Yarbrough, Wendell G., primary, Margolin, Adam A., primary, Grandori, Carla, primary, Kemp, Christopher J., primary, and Méndez, Eduardo, primary
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- 2023
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31. Supplementary Tables S1-8 from Functional Kinomics Identifies Candidate Therapeutic Targets in Head and Neck Cancer
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Moser, Russell, primary, Xu, Chang, primary, Kao, Michael, primary, Annis, James, primary, Lerma, Luisa Angelica, primary, Schaupp, Christopher M., primary, Gurley, Kay E., primary, Jang, In Sock, primary, Biktasova, Asel, primary, Yarbrough, Wendell G., primary, Margolin, Adam A., primary, Grandori, Carla, primary, Kemp, Christopher J., primary, and Méndez, Eduardo, primary
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- 2023
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32. Supplementary Table 6-7 from Functional Precision Medicine Identifies Novel Druggable Targets and Therapeutic Options in Head and Neck Cancer
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Xu, Chang, primary, Nikolova, Olga, primary, Basom, Ryan S., primary, Mitchell, Ryan M., primary, Shaw, Reid, primary, Moser, Russell D., primary, Park, Heuijoon, primary, Gurley, Kay E., primary, Kao, Michael C., primary, Green, Carlos L., primary, Schaub, Franz X., primary, Diaz, Robert L., primary, Swan, Hallie A., primary, Jang, In S., primary, Guinney, Justin, primary, Gadi, Vijayakrishna K., primary, Margolin, Adam A., primary, Grandori, Carla, primary, Kemp, Christopher J., primary, and Méndez, Eduardo, primary
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- 2023
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33. Supplementary Table 1-3 from Functional Precision Medicine Identifies Novel Druggable Targets and Therapeutic Options in Head and Neck Cancer
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Xu, Chang, primary, Nikolova, Olga, primary, Basom, Ryan S., primary, Mitchell, Ryan M., primary, Shaw, Reid, primary, Moser, Russell D., primary, Park, Heuijoon, primary, Gurley, Kay E., primary, Kao, Michael C., primary, Green, Carlos L., primary, Schaub, Franz X., primary, Diaz, Robert L., primary, Swan, Hallie A., primary, Jang, In S., primary, Guinney, Justin, primary, Gadi, Vijayakrishna K., primary, Margolin, Adam A., primary, Grandori, Carla, primary, Kemp, Christopher J., primary, and Méndez, Eduardo, primary
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- 2023
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34. An open protocol for modeling T Cell Clonotype repertoires using TCRβ CDR3 sequences
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Gurun, Burcu, primary, Horton, Wesley, additional, Murugan, Dhaarini, additional, Zhu, Biqing, additional, Leyshock, Patrick, additional, Kumar, Sushil, additional, Byrne, Katelyn T., additional, Vonderheide, Robert H., additional, Margolin, Adam A., additional, Mori, Motomi, additional, Spellman, Paul T., additional, Coussens, Lisa M., additional, and Speed, Terence P., additional
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- 2023
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35. Additional file 1 of An open protocol for modeling T Cell Clonotype repertoires using TCRβ CDR3 sequences
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Gurun, Burcu, Horton, Wesley, Murugan, Dhaarini, Zhu, Biqing, Leyshock, Patrick, Kumar, Sushil, Byrne, Katelyn T., Vonderheide, Robert H., Margolin, Adam A., Mori, Motomi, Spellman, Paul T., Coussens, Lisa M., and Speed, Terence P.
- Abstract
Additional file 1: Supplementary Figure 1. ST Count Distribution in presence of DNA. Supplementary Figure 2. Normalization reduces spread in presence of DNA. Supplementary Figure 3. Concordance analysis of T cell repertoire metrics. Supplementary Figure 4. Competition between gDNA and ST during TCR sequencing. Supplementary Figure 5. Reproducibility analysis: Drop-outs are frequent even for the top clones. Supplementary Table 1. Primer Sequences. Supplementary Figure 6. TCR sequencing pipeline schema. Supplementary Figure 7. Monoclonal amplification check.
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- 2023
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36. Additional file 2 of An open protocol for modeling T Cell Clonotype repertoires using TCRβ CDR3 sequences
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Gurun, Burcu, Horton, Wesley, Murugan, Dhaarini, Zhu, Biqing, Leyshock, Patrick, Kumar, Sushil, Byrne, Katelyn T., Vonderheide, Robert H., Margolin, Adam A., Mori, Motomi, Spellman, Paul T., Coussens, Lisa M., and Speed, Terence P.
- Abstract
Additional file 2.
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- 2023
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37. Combined burden and functional impact tests for cancer driver discovery using DriverPower
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Shuai, Shimin, Abascal, Federico, Amin, Samirkumar B, Bader, Gary D, Bandopadhayay, Pratiti, Barenboim, Jonathan, Beroukhim, Rameen, Bertl, Johanna, Boroevich, Keith A, Brunak, Søren, Campbell, Peter J, Carlevaro-Fita, Joana, Chakravarty, Dimple, Chan, Calvin Wing Yiu, Chen, Ken, Choi, Jung Kyoon, Deu-Pons, Jordi, Dhingra, Priyanka, Diamanti, Klev, Feuerbach, Lars, Fink, J Lynn, Fonseca, Nuno A, Frigola, Joan, Gambacorti-Passerini, Carlo, Garsed, Dale W, Gerstein, Mark, Getz, Gad, Guo, Qianyun, Gut, Ivo G, Haan, David, Hamilton, Mark P, Haradhvala, Nicholas J, Harmanci, Arif O, Helmy, Mohamed, Herrmann, Carl, Hess, Julian M, Hobolth, Asger, Hodzic, Ermin, Hong, Chen, Hornshøj, Henrik, Isaev, Keren, Izarzugaza, Jose MG, Johnson, Rory, Johnson, Todd A, Juul, Malene, Juul, Randi Istrup, Kahles, Andre, Kahraman, Abdullah, Kellis, Manolis, Khurana, Ekta, Kim, Jaegil, Kim, Jong K, Kim, Youngwook, Komorowski, Jan, Korbel, Jan O, Kumar, Sushant, Lanzós, Andrés, Larsson, Erik, Lawrence, Michael S, Lee, Donghoon, Lehmann, Kjong-Van, Li, Shantao, Li, Xiaotong, Lin, Ziao, Liu, Eric Minwei, Lochovsky, Lucas, Lou, Shaoke, Madsen, Tobias, Marchal, Kathleen, Martincorena, Iñigo, Martinez-Fundichely, Alexander, Maruvka, Yosef E, McGillivray, Patrick D, Meyerson, William, Muiños, Ferran, Mularoni, Loris, Nakagawa, Hidewaki, Nielsen, Morten Muhlig, Paczkowska, Marta, Park, Keunchil, Park, Kiejung, Pedersen, Jakob Skou, Pons, Tirso, Pulido-Tamayo, Sergio, Raphael, Benjamin J, Reimand, Jüri, Reyes-Salazar, Iker, Reyna, Matthew A, Rheinbay, Esther, Rubin, Mark A, Rubio-Perez, Carlota, Sahinalp, S Cenk, Saksena, Gordon, Salichos, Leonidas, Sander, Chris, Schumacher, Steven E, Shackleton, Mark, Shapira, Ofer, Shen, Ciyue, Shrestha, Raunak, Sidiropoulos, Nikos, Sieverling, Lina, Sinnott-Armstrong, Nasa, Stein, Lincoln D, Stuart, Joshua M, Tamborero, David, Tiao, Grace, Tsunoda, Tatsuhiko, Umer, Husen M, Uusküla-Reimand, Liis, Valencia, Alfonso, Vazquez, Miguel, Verbeke, Lieven PC, Wadelius, Claes, Wadi, Lina, Wang, Jiayin, Warrell, Jonathan, Waszak, Sebastian M, Weischenfeldt, Joachim, Wheeler, David A, Wu, Guanming, Yu, Jun, Zhang, Jing, Zhang, Xuanping, Zhang, Yan, Zhao, Zhongming, Zou, Lihua, von Mering, Christian, Gallinger, Steven, Aaltonen, Lauri A, Abeshouse, Adam, Aburatani, Hiroyuki, Adams, David J, Agrawal, Nishant, Ahn, Keun Soo, Ahn, Sung-Min, Aikata, Hiroshi, Akbani, Rehan, Akdemir, Kadir C, Al-Ahmadie, Hikmat, Al-Sedairy, Sultan T, Al-Shahrour, Fatima, Alawi, Malik, Albert, Monique, Aldape, Kenneth, Alexandrov, Ludmil B, Ally, Adrian, Alsop, Kathryn, Alvarez, Eva G, Amary, Fernanda, Aminou, Brice, Ammerpohl, Ole, Anderson, Matthew J, Ang, Yeng, Antonello, Davide, Anur, Pavana, Aparicio, Samuel, Appelbaum, Elizabeth L, Arai, Yasuhito, Aretz, Axel, Arihiro, Koji, Ariizumi, Shun-ichi, Armenia, Joshua, Arnould, Laurent, Asa, Sylvia, Assenov, Yassen, Atwal, Gurnit, Aukema, Sietse, Auman, J Todd, Aure, Miriam RR, Awadalla, Philip, Aymerich, Marta, Baez-Ortega, Adrian, Bailey, Matthew H, Bailey, Peter J, Balasundaram, Miruna, Balu, Saianand, Banks, Rosamonde E, Barbi, Stefano, Barbour, Andrew P, Barnholtz-Sloan, Jill, Barr, Hugh, Barrera, Elisabet, Bartlett, John, Bartolome, Javier, Bassi, Claudio, Bathe, Oliver F, Baumhoer, Daniel, Bavi, Prashant, Baylin, Stephen B, Bazant, Wojciech, Beardsmore, Duncan, Beck, Timothy A, Behjati, Sam, Behren, Andreas, Niu, Beifang, Bell, Cindy, Beltran, Sergi, Benz, Christopher, Berchuck, Andrew, Bergmann, Anke K, Bergstrom, Erik N, Berman, Benjamin P, Berney, Daniel M, Bernhart, Stephan H, Berrios, Mario, Bersani, Samantha, Betancourt, Miguel, Bhandari, Vinayak, Bhosle, Shriram G, Biankin, Andrew V, Bieg, Matthias, Bigner, Darell, Binder, Hans, Birney, Ewan, Birrer, Michael, Biswas, Nidhan K, Bjerkehagen, Bodil, Bodenheimer, Tom, Boice, Lori, Bonizzato, Giada, De Bono, Johann S, Boot, Arnoud, Bootwalla, Moiz S, Borg, Ake, Borkhardt, Arndt, Borozan, Ivan, Borst, Christoph, Bosenberg, Marcus, Bosio, Mattia, Boultwood, Jacqueline, Bourque, Guillaume, Boutros, Paul C, Bova, G Steven, Bowen, David T, Bowlby, Reanne, Bowtell, David DL, Boyault, Sandrine, Boyce, Rich, Boyd, Jeffrey, Brazma, Alvis, Brennan, Paul, Brewer, Daniel S, Brinkman, Arie B, Bristow, Robert G, Broaddus, Russell R, Brock, Jane E, Brock, Malcolm, Broeks, Annegien, Brooks, Angela N, Brooks, Denise, Brors, Benedikt, Bruxner, Timothy JC, Bruzos, Alicia L, Buchanan, Alex, Buchhalter, Ivo, Buchholz, Christiane, Bullman, Susan, Burke, Hazel, Burkhardt, Birgit, Burns, Kathleen H, Busanovich, John, Bustamante, Carlos D, Butler, Adam P, Butte, Atul J, Byrne, Niall J, Børresen-Dale, Anne-Lise, Caesar-Johnson, Samantha J, Cafferkey, Andy, Cahill, Declan, Calabrese, Claudia, Caldas, Carlos, Calvo, Fabien, Camacho, Niedzica, Campo, Elias, Cantù, Cinzia, Cao, Shaolong, Carey, Thomas E, Carlsen, Rebecca, Cataldo, Ivana, Cazzola, Mario, Cebon, Jonathan, Cerfolio, Robert, Chadwick, Dianne E, Chalmers, Don, Chan, Kin, Chan-Seng-Yue, Michelle, Chandan, Vishal S, Chang, David K, Chanock, Stephen J, Chantrill, Lorraine A, Chateigner, Aurélien, Chatterjee, Nilanjan, Chayama, Kazuaki, Chen, Hsiao-Wei, Chen, Jieming, Chen, Yiwen, Chen, Zhaohong, Cherniack, Andrew D, Chien, Jeremy, Chiew, Yoke-Eng, Chin, Suet-Feung, Cho, Juok, Cho, Sunghoon, Choi, Wan, Chomienne, Christine, Chong, Zechen, Choo, Su Pin, Chou, Angela, Christ, Angelika N, Christie, Elizabeth L, Chuah, Eric, Cibulskis, Carrie, Cibulskis, Kristian, Cingarlini, Sara, Clapham, Peter, Claviez, Alexander, Cleary, Sean, Cloonan, Nicole, Cmero, Marek, Collins, Colin C, Connor, Ashton A, Cooke, Susanna L, Cooper, Colin S, Cope, Leslie, Corbo, Vincenzo, Cordes, Matthew G, Cordner, Stephen M, Cortés-Ciriano, Isidro, Covington, Kyle, Cowin, Prue A, Craft, Brian, Craft, David, Creighton, Chad J, Cun, Yupeng, Curley, Erin, Cutcutache, Ioana, Czajka, Karolina, Czerniak, Bogdan, Dagg, Rebecca A, Danilova, Ludmila, Davi, Maria Vittoria, Davidson, Natalie R, Davies, Helen, Davis, Ian J, Davis-Dusenbery, Brandi N, Dawson, Kevin J, De La Vega, Francisco M, De Paoli-Iseppi, Ricardo, Defreitas, Timothy, Dei Tos, Angelo P, Delaneau, Olivier, Demchok, John A, Demeulemeester, Jonas, Demidov, German M, Demircioğlu, Deniz, Dennis, Nening M, Denroche, Robert E, Dentro, Stefan C, Desai, Nikita, Deshpande, Vikram, Deshwar, Amit G, Desmedt, Christine, Dhalla, Noreen, Dhani, Neesha C, Dhir, Rajiv, DiBiase, Anthony, Ding, Li, Ding, Shuai, Dinh, Huy Q, Dirix, Luc, Doddapaneni, HarshaVardhan, Donmez, Nilgun, Dow, Michelle T, Drapkin, Ronny, Drechsel, Oliver, Drews, Ruben M, Serge, Serge, Dudderidge, Tim, Dueso-Barroso, Ana, Dunford, Andrew J, Dunn, Michael, Dursi, Lewis Jonathan, Duthie, Fraser R, Dutton-Regester, Ken, Eagles, Jenna, Easton, Douglas F, Edmonds, Stuart, Edwards, Paul A, Edwards, Sandra E, Eeles, Rosalind A, Ehinger, Anna, Eils, Juergen, Eils, Roland, El-Naggar, Adel, Eldridge, Matthew, Ellrott, Kyle, Erkek, Serap, Escaramis, Georgia, Espiritu, Shadrielle 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Human Biology & Physiology ,Ecology,Evolution & Ethology ,Tumour Biology ,Genetics & Genomics ,Structural Biology & Biophysics ,Computational & Systems Biology - Abstract
The discovery of driver mutations is one of the key motivations for cancer genome sequencing. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2658 cancers across 38 tumour types, we describe DriverPower, a software package that uses mutational burden and functional impact evidence to identify driver mutations in coding and non-coding sites within cancer whole genomes. Using a total of 1373 genomic features derived from public sources, DriverPower’s background mutation model explains up to 93% of the regional variance in the mutation rate across multiple tumour types. By incorporating functional impact scores, we are able to further increase the accuracy of driver discovery. Testing across a collection of 2583 cancer genomes from the PCAWG project, DriverPower identifies 217 coding and 95 non-coding driver candidates. Comparing to six published methods used by the PCAWG Drivers and Functional Interpretation Working Group, DriverPower has the highest F1 score for both coding and non-coding driver discovery. This demonstrates that DriverPower is an effective framework for computational driver discovery.
- Published
- 2023
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38. Integrative pathway enrichment analysis of multivariate omics data
- Author
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Paczkowska, Marta, Barenboim, Jonathan, Sintupisut, Nardnisa, Fox, Natalie S, Zhu, Helen, Abd-Rabbo, Diala, Mee, Miles W, Boutros, Paul C, Abascal, Federico, Amin, Samirkumar B, Bader, Gary D, Beroukhim, Rameen, Bertl, Johanna, Boroevich, Keith A, Brunak, Søren, Campbell, Peter J, Carlevaro-Fita, Joana, Chakravarty, Dimple, Chan, Calvin Wing Yiu, Chen, Ken, Choi, Jung Kyoon, Deu-Pons, Jordi, Dhingra, Priyanka, Diamanti, Klev, Feuerbach, Lars, Fink, J Lynn, Fonseca, Nuno A, Frigola, Joan, Gambacorti-Passerini, Carlo, Garsed, Dale W, Gerstein, Mark, Getz, Gad, Gonzalez-Perez, Abel, Guo, Qianyun, Gut, Ivo G, Haan, David, Hamilton, Mark P, Haradhvala, Nicholas J, Harmanci, Arif O, Helmy, Mohamed, Herrmann, Carl, Hess, Julian M, Hobolth, Asger, Hodzic, Ermin, Hong, Chen, Hornshøj, Henrik, Isaev, Keren, Izarzugaza, Jose MG, Johnson, Rory, Johnson, Todd A, Juul, Malene, Juul, Randi Istrup, Kahles, Andre, Kahraman, Abdullah, Kellis, Manolis, Khurana, Ekta, Kim, Jaegil, Kim, Jong K, Kim, 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Qian, Xiao, Xiao, Xing, Rui, Xiong, Heng, Xu, Qinying, Xu, Yanxun, Xue, Hong, Yachida, Shinichi, Yakneen, Sergei, Yamaguchi, Rui, Yamaguchi, Takafumi N, Yamamoto, Masakazu, Yamamoto, Shogo, Yamaue, Hiroki, Yang, Fan, Yang, Huanming, Yang, Jean Y, Yang, Liming, Yang, Lixing, Yang, Shanlin, Yang, Tsun-Po, Yang, Yang, Yao, Xiaotong, Yaspo, Marie-Laure, Yates, Lucy, Yau, Christina, Ye, Chen, Ye, Kai, Yellapantula, Venkata D, Yoon, Christopher J, Yoon, Sung-Soo, Yousif, Fouad, Yu, Kaixian, Yu, Willie, Yu, Yingyan, Yuan, Ke, Yuan, Yuan, Yuen, Denis, Yung, Christina K, Zaikova, Olga, Zamora, Jorge, Zapatka, Marc, Zenklusen, Jean C, Zenz, Thorsten, Zeps, Nikolajs, Zhang, Cheng-Zhong, Zhang, Fan, Zhang, Hailei, Zhang, Hongwei, Zhang, Hongxin, Zhang, Jiashan, Zhang, Junjun, Zhang, Xiuqing, Zhang, Zemin, Zheng, Liangtao, Zheng, Xiuqing, Zhou, Wanding, Zhou, Yong, Zhu, Bin, Zhu, Hongtu, Zhu, Jingchun, Zhu, Shida, Zou, Xueqing, deFazio, Anna, van As, Nicholas, van Deurzen, Carolien HM, van de Vijver, Marc J, and Veer, L van’t
- Subjects
Human Biology & Physiology ,Ecology,Evolution & Ethology ,Tumour Biology ,Genetics & Genomics ,Structural Biology & Biophysics ,Computational & Systems Biology - Abstract
Multi-omics datasets represent distinct aspects of the central dogma of molecular biology. Such high-dimensional molecular profiles pose challenges to data interpretation and hypothesis generation. ActivePathways is an integrative method that discovers significantly enriched pathways across multiple datasets using statistical data fusion, rationalizes contributing evidence and highlights associated genes. As part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2658 cancers across 38 tumor types, we integrated genes with coding and non-coding mutations and revealed frequently mutated pathways and additional cancer genes with infrequent mutations. We also analyzed prognostic molecular pathways by integrating genomic and transcriptomic features of 1780 breast cancers and highlighted associations with immune response and anti-apoptotic signaling. Integration of ChIP-seq and RNA-seq data for master regulators of the Hippo pathway across normal human tissues identified processes of tissue regeneration and stem cell regulation. ActivePathways is a versatile method that improves systems-level understanding of cellular organization in health and disease through integration of multiple molecular datasets and pathway annotations.
- Published
- 2023
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39. Pathway and network analysis of more than 2500 whole cancer genomes
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Reyna, Matthew A, Haan, David, Paczkowska, Marta, Verbeke, Lieven PC, Vazquez, Miguel, Kahraman, Abdullah, Pulido-Tamayo, Sergio, Barenboim, Jonathan, Wadi, Lina, Dhingra, Priyanka, Shrestha, Raunak, Getz, Gad, Lawrence, Michael S, Pedersen, Jakob Skou, Rubin, Mark A, Wheeler, David A, Brunak, Søren, Izarzugaza, Jose MG, Khurana, Ekta, Marchal, Kathleen, von Mering, Christian, Sahinalp, S Cenk, Valencia, Alfonso, Abascal, Federico, Amin, Samirkumar B, Bader, Gary D, Bandopadhayay, Pratiti, Beroukhim, Rameen, Bertl, Johanna, Boroevich, Keith A, Busanovich, John, Campbell, Peter J, Carlevaro-Fita, Joana, Chakravarty, Dimple, Chan, Calvin Wing Yiu, Chen, Ken, Choi, Jung Kyoon, Deu-Pons, Jordi, Diamanti, Klev, Feuerbach, Lars, Fink, J Lynn, Fonseca, Nuno A, Frigola, Joan, Gambacorti-Passerini, Carlo, Garsed, Dale W, Gerstein, Mark, Guo, Qianyun, Gut, Ivo G, Hamilton, Mark P, Haradhvala, Nicholas J, Harmanci, Arif O, Helmy, Mohamed, Herrmann, Carl, Hess, Julian M, Hobolth, Asger, Hodzic, 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Human Biology & Physiology ,Ecology,Evolution & Ethology ,Tumour Biology ,Genetics & Genomics ,Structural Biology & Biophysics ,Computational & Systems Biology - Abstract
The catalog of cancer driver mutations in protein-coding genes has greatly expanded in the past decade. However, non-coding cancer driver mutations are less well-characterized and only a handful of recurrent non-coding mutations, most notably TERT promoter mutations, have been reported. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2658 cancer across 38 tumor types, we perform multi-faceted pathway and network analyses of non-coding mutations across 2583 whole cancer genomes from 27 tumor types compiled by the ICGC/TCGA PCAWG project that was motivated by the success of pathway and network analyses in prioritizing rare mutations in protein-coding genes. While few non-coding genomic elements are recurrently mutated in this cohort, we identify 93 genes harboring non-coding mutations that cluster into several modules of interacting proteins. Among these are promoter mutations associated with reduced mRNA expression in TP53, TLE4, and TCF4. We find that biological processes had variable proportions of coding and non-coding mutations, with chromatin remodeling and proliferation pathways altered primarily by coding mutations, while developmental pathways, including Wnt and Notch, altered by both coding and non-coding mutations. RNA splicing is primarily altered by non-coding mutations in this cohort, and samples containing non-coding mutations in well-known RNA splicing factors exhibit similar gene expression signatures as samples with coding mutations in these genes. These analyses contribute a new repertoire of possible cancer genes and mechanisms that are altered by non-coding mutations and offer insights into additional cancer vulnerabilities that can be investigated for potential therapeutic treatments.
- Published
- 2023
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40. Divergent mutational processes distinguish hypoxic and normoxic tumours
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Bhandari, Vinayak, Li, Constance H, Bristow, Robert G, Boutros, Paul C, Aaltonen, Lauri A, Abascal, Federico, Abeshouse, Adam, Aburatani, Hiroyuki, Adams, David J, Agrawal, Nishant, Ahn, Keun Soo, Ahn, Sung-Min, Aikata, Hiroshi, Akbani, Rehan, Akdemir, Kadir C, Al-Ahmadie, Hikmat, Al-Sedairy, Sultan T, Al-Shahrour, Fatima, Alawi, Malik, Albert, Monique, Aldape, Kenneth, Alexandrov, Ludmil B, Ally, Adrian, Alsop, Kathryn, Alvarez, Eva G, Amary, Fernanda, Amin, Samirkumar B, Aminou, Brice, Ammerpohl, Ole, Anderson, Matthew J, Ang, Yeng, Antonello, Davide, Anur, Pavana, Aparicio, Samuel, Appelbaum, Elizabeth L, Arai, Yasuhito, Aretz, Axel, Arihiro, Koji, Ariizumi, Shun-ichi, Armenia, Joshua, Arnould, Laurent, Asa, Sylvia, Assenov, Yassen, Atwal, Gurnit, Aukema, Sietse, Auman, J Todd, Aure, Miriam RR, Awadalla, Philip, Aymerich, Marta, Bader, Gary D, Baez-Ortega, Adrian, Bailey, Matthew H, Bailey, Peter J, Balasundaram, Miruna, Balu, Saianand, Bandopadhayay, Pratiti, Banks, Rosamonde E, 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Human Biology & Physiology ,Ecology,Evolution & Ethology ,Tumour Biology ,Genetics & Genomics ,Structural Biology & Biophysics ,Computational & Systems Biology - Abstract
Many primary tumours have low levels of molecular oxygen (hypoxia), and hypoxic tumours respond poorly to therapy. Pan-cancer molecular hallmarks of tumour hypoxia remain poorly understood, with limited comprehension of its associations with specific mutational processes, non-coding driver genes and evolutionary features. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2658 cancers across 38 tumour types, we quantify hypoxia in 1188 tumours spanning 27 cancer types. Elevated hypoxia associates with increased mutational load across cancer types, irrespective of underlying mutational class. The proportion of mutations attributed to several mutational signatures of unknown aetiology directly associates with the level of hypoxia, suggesting underlying mutational processes for these signatures. At the gene level, driver mutations in TP53, MYC and PTEN are enriched in hypoxic tumours, and mutations in PTEN interact with hypoxia to direct tumour evolutionary trajectories. Overall, hypoxia plays a critical role in shaping the genomic and evolutionary landscapes of cancer.
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- 2023
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41. Additional file 3 of An open protocol for modeling T Cell Clonotype repertoires using TCRβ CDR3 sequences
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Gurun, Burcu, Horton, Wesley, Murugan, Dhaarini, Zhu, Biqing, Leyshock, Patrick, Kumar, Sushil, Byrne, Katelyn T., Vonderheide, Robert H., Margolin, Adam A., Mori, Motomi, Spellman, Paul T., Coussens, Lisa M., and Speed, Terence P.
- Abstract
Additional file 3.
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- 2023
- Full Text
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42. Genomic footprints of activated telomere maintenance mechanisms in cancer
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Michael, Koster, Roelof, Kote-Jarai, Zsofia, Koures, Antonios, Kovacevic, Milena, Kremeyer, Barbara, Kretzmer, Helene, Kreuz, Markus, Krishnamurthy, Savitri, Kube, Dieter, Kumar, Pardeep, Kumar, Sushant, Kumar, Yogesh, Kundra, Ritika, Kübler, Kirsten, Küppers, Ralf, Lagergren, Jesper, Lai, Phillip H, Laird, Peter W, Lakhani, Sunil R, Lalansingh, Christopher M, Lalonde, Emilie, Lamaze, Fabien C, Lambert, Adam, Lander, Eric, Landgraf, Pablo, Landoni, Luca, Langerød, Anita, Lanzós, Andrés, Larsimont, Denis, Larsson, Erik, Lathrop, Mark, Lau, Loretta MS, Lawerenz, Chris, Lawlor, Rita T, Lawrence, Michael S, Lazar, Alexander J, Lazic, Ana Mijalkovic, Le, Xuan, Lee, Darlene, Lee, Donghoon, Lee, Hee Jin, Lee, Jeong-Yeon, Lee, Juhee, Lee, Ming Ta Michael, Lee-Six, Henry, Lehmann, Kjong-Van, Lehrach, Hans, Lenze, Dido, Leonard, Conrad R, Leongamornlert, Daniel A, Leshchiner, Ignaty, Letourneau, Louis, Letunic, Ivica, Levine, Douglas A, Lewis, Lora, Ley, Tim, Li, Chang, Li, Constance H, Li, Haiyan Irene, Li, Jun, Li, Lin, Li, Shantao, Li, Siliang, Li, Xiaobo, Li, Xiaotong, Li, Xinyue, Liang, Han, Liang, Sheng-Ben, Lichter, Peter, Lin, Pei, Lin, Ziao, Linehan, WM, Lingjærde, Ole Christian, Liu, Dongbing, Liu, Eric Minwei, Liu, Fei-Fei Fei, Liu, Fenglin, Liu, Jia, Liu, Xingmin, Livingstone, Julie, Livitz, Dimitri, Livni, Naomi, Lochovsky, Lucas, Loeffler, Markus, Long, Georgina V, Lopez-Guillermo, Armando, Lou, Shaoke, Louis, David N, Lovat, Laurence B, Lu, Yiling, Lu, Yong-Jie, Lu, Youyong, Luchini, Claudio, Lungu, Ilinca, Luo, Xuemei, Luxton, Hayley J, Lype, Lisa, López, Cristina, López-Otín, Carlos, Z, Eric, Ma, Yussanne, MacGrogan, Gaetan, MacRae, Shona, Madsen, Tobias, Maejima, Kazuhiro, Mafficini, Andrea, Maglinte, Dennis T, Maitra, Arindam, Majumder, Partha P, Malcovati, Luca, Malikic, Salem, Malleo, Giuseppe, Mann, Graham J, Mantovani-Löffler, Luisa, Marchal, Kathleen, Marchegiani, Giovanni, Mardis, Elaine R, Margolin, Adam A, Marin, Maximillian G, Markowski, Julia, Marks, Jeffrey, Marques-Bonet, Tomas, Marra, Marco A, Marsden, Luke, Martens, John WM, Martin, Sancha, Martin-Subero, Jose I, Maruvka, Yosef E, Mashl, R Jay, Massie, Charlie E, Matthew, Thomas J, Matthews, Lucy, Mayer, Erik, Mayes, Simon, Mayo, Michael, Mbabaali, Faridah, McCune, Karen, McDermott, Ultan, McGillivray, Patrick D, McLellan, Michael D, McPherson, John D, McPherson, John R, McPherson, Treasa A, Meier, Samuel R, Meng, Alice, Meng, Shaowu, Menzies, Andrew, Merrett, Neil D, Merson, Sue, Meyerson, William, Mieczkowski, Piotr A, Mihaiescu, George L, Mijalkovic, Sanja, Mikkelsen, Tom, Milella, Michele, Mileshkin, Linda, Miller, Christopher A, Miller, David K, Miller, Jessica K, Mills, Gordon B, Milovanovic, Ana, Minner, Sarah, Miotto, Marco, Arnau, Gisela Mir, Mirabello, Lisa, Mitchell, Chris, Mitchell, Thomas J, Miyoshi, Naoki, Mizuno, Shinichi, Molnár-Gábor, Fruzsina, Moore, Malcolm J, Moore, Richard A, Morganella, Sandro, Morris, Quaid D, Morrison, Carl, Mose, Lisle E, Moser, 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Okusaka, Takuji, Omberg, Larsson, Ong, Choon Kiat, Ott, German, Ouellette, BF Francis, P’ng, Christine, Paczkowska, Marta, Paiella, Salvatore, Pairojkul, Chawalit, Pajic, Marina, Pan-Hammarström, Qiang, Papaemmanuil, Elli, Papatheodorou, Irene, Paramasivam, Nagarajan, Park, Ji Wan, Park, Joong-Won, Park, Keunchil, Park, Kiejung, Parker, Joel S, Parsons, Simon L, Pass, Harvey, Pasternack, Danielle, Pastore, Alessandro, Patch, Ann-Marie, Pauporté, Iris, Pea, Antonio, Pedamallu, Chandra Sekhar, Pedersen, Jakob Skou, Pederzoli, Paolo, Peifer, Martin, Pennell, Nathan A, Perou, Charles M, Perry, Marc D, Petersen, Gloria M, Peto, Myron, Petrelli, Nicholas, Petryszak, Robert, Pfister, Stefan M, Phillips, Mark, Pich, Oriol, Pickett, Hilda A, Pihl, Todd D, Pillay, Nischalan, Pinder, Sarah, Pinese, Mark, Pinho, Andreia V, Pitkänen, Esa, Pivot, Xavier, Piñeiro-Yáñez, Elena, Planko, Laura, Plass, Christoph, Polak, Paz, Pons, Tirso, Popescu, Irinel, Potapova, Olga, Prasad, Aparna, Preston, Shaun R, 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Paresh, Vázquez-García, Ignacio, Waddell, Nick M, Wadelius, Claes, Wadi, Lina, Wagener, Rabea, Wang, Jian, Wang, Jiayin, Wang, Linghua, Wang, Qi, Wang, Wenyi, Wang, Yumeng, Wang, Zhining, Waring, Paul M, Warnatz, Hans-Jörg, Warrell, Jonathan, Warren, Anne Y, Waszak, Sebastian M, Wedge, David C, Weichenhan, Dieter, Weinberger, Paul, Weinstein, John N, Weisenberger, Daniel J, Welch, Ian, Wendl, Michael C, Werner, Johannes, Whalley, Justin P, Wheeler, David A, Whitaker, Hayley C, Wigle, Dennis, Wilkerson, Matthew D, Williams, Ashley, Wilmott, James S, Wilson, Gavin W, Wilson, Julie M, Wilson, Richard K, Winterhoff, Boris, Wintersinger, Jeffrey A, Wiznerowicz, Maciej, Wolf, Stephan, Wong, Bernice H, Wong, Tina, Wong, Winghing, Woo, Youngchoon, Wood, Scott, Wouters, Bradly G, Wright, Adam J, Wright, Derek W, Wright, Mark H, Wu, Chin-Lee, Wu, Dai-Ying, Wu, Guanming, Wu, Jianmin, Wu, Kui, Wu, Yang, Wu, Zhenggang, Xi, Liu, Xia, Tian, Xiang, Qian, Xiao, Xiao, Xing, Rui, Xiong, Heng, Xu, Qinying, Xu, Yanxun, Xue, Hong, Yachida, Shinichi, Yakneen, Sergei, Yamaguchi, Rui, Yamaguchi, Takafumi N, Yamamoto, Masakazu, Yamamoto, Shogo, Yamaue, Hiroki, Yang, Fan, Yang, Huanming, Yang, Jean Y, Yang, Liming, Yang, Shanlin, Yang, Tsun-Po, Yang, Yang, Yaspo, Marie-Laure, Yates, Lucy, Yau, Christina, Ye, Chen, Ye, Kai, Yellapantula, Venkata D, Yoon, Christopher J, Yousif, Fouad, Yu, Jun, Yu, Kaixian, Yu, Willie, Yu, Yingyan, Yuan, Ke, Yuan, Yuan, Yuen, Denis, Yung, Christina K, Zaikova, Olga, Zapatka, Marc, Zenklusen, Jean C, Zenz, Thorsten, Zeps, Nikolajs, Zhang, Fan, Zhang, Hailei, Zhang, Hongwei, Zhang, Hongxin, Zhang, Jiashan, Zhang, Jing, Zhang, Junjun, Zhang, Xiuqing, Zhang, Xuanping, Zhang, Yan, Zhang, Zemin, Zhao, Zhongming, Zheng, Liangtao, Zheng, Xiuqing, Zhou, Wanding, Zhou, Yong, Zhu, Bin, Zhu, Hongtu, Zhu, Jingchun, Zhu, Shida, Zou, Lihua, Zou, Xueqing, deFazio, Anna, van As, Nicholas, van Deurzen, Carolien HM, van de Vijver, Marc J, Veer, L van’t, and von Mering, Christian
- Subjects
Human Biology & Physiology ,Ecology,Evolution & Ethology ,Tumour Biology ,Genetics & Genomics ,Structural Biology & Biophysics ,Computational & Systems Biology - Abstract
Cancers require telomere maintenance mechanisms for unlimited replicative potential. They achieve this through TERT activation or alternative telomere lengthening associated with ATRX or DAXX loss. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, we dissect whole-genome sequencing data of over 2500 matched tumor-control samples from 36 different tumor types aggregated within the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium to characterize the genomic footprints of these mechanisms. While the telomere content of tumors with ATRX or DAXX mutations (ATRX/DAXXtrunc) is increased, tumors with TERT modifications show a moderate decrease of telomere content. One quarter of all tumor samples contain somatic integrations of telomeric sequences into non-telomeric DNA. This fraction is increased to 80% prevalence in ATRX/DAXXtrunc tumors, which carry an aberrant telomere variant repeat (TVR) distribution as another genomic marker. The latter feature includes enrichment or depletion of the previously undescribed singleton TVRs TTCGGG and TTTGGG, respectively. Our systematic analysis provides new insight into the recurrent genomic alterations associated with telomere maintenance mechanisms in cancer.
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- 2023
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43. Addendum: The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity
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Barretina, Jordi, Caponigro, Giordano, Stransky, Nicolas, Venkatesan, Kavitha, Margolin, Adam A., Kim, Sungjoon, Wilson, Christopher J., Lehár, Joseph, Kryukov, Gregory V., Sonkin, Dmitriy, Reddy, Anupama, Liu, Manway, Murray, Lauren, Berger, Michael F., Monahan, John E., Morais, Paula, Meltzer, Jodi, Korejwa, Adam, Jané-Valbuena, Judit, Mapa, Felipa A., Thibault, Joseph, Bric-Furlong, Eva, Raman, Pichai, Shipway, Aaron, Engels, Ingo H., Cheng, Jill, Yu, Guoying K., Yu, Jianjun, Aspesi, Jr, Peter, de Silva, Melanie, Jagtap, Kalpana, Jones, Michael D., Wang, Li, Hatton, Charles, Palescandolo, Emanuele, Gupta, Supriya, Mahan, Scott, Sougnez, Carrie, Onofrio, Robert C., Liefeld, Ted, MacConaill, Laura, Winckler, Wendy, Reich, Michael, Li, Nanxin, Mesirov, Jill P., Gabriel, Stacey B., Getz, Gad, Ardlie, Kristin, Chan, Vivien, Myer, Vic E., Weber, Barbara L., Porter, Jeff, Warmuth, Markus, Finan, Peter, Harris, Jennifer L., Meyerson, Matthew, Golub, Todd R., Morrissey, Michael P., Sellers, William R., Schlegel, Robert, and Garraway, Levi A.
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- 2019
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44. Population-level distribution and putative immunogenicity of cancer neoepitopes
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Wood, Mary A., Paralkar, Mayur, Paralkar, Mihir P., Nguyen, Austin, Struck, Adam J., Ellrott, Kyle, Margolin, Adam, Nellore, Abhinav, and Thompson, Reid F.
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- 2018
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45. Genome-Wide Discovery of Modulators of Transcriptional Interactions in Human B Lymphocytes
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Wang, Kai, Nemenman, Ilya, Banerjee, Nilanjana, Margolin, Adam A., Califano, Andrea, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Istrail, Sorin, editor, Pevzner, Pavel, editor, Waterman, Michael, editor, Apostolico, Alberto, editor, Guerra, Concettina, editor, and Pevzner, Pavel A., editor
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- 2006
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46. Chemical Genomics Identifies Small-Molecule MCL1 Repressors and BCL-xL as a Predictor of MCL1 Dependency
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Wei, Guo, Margolin, Adam A., Haery, Leila, Brown, Emily, Cucolo, Lisa, Julian, Bina, Shehata, Shyemaa, Kung, Andrew L., Beroukhim, Rameen, and Golub, Todd R.
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- 2012
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47. An open protocol for modeling T Cell Clonotype repertoires using TCRβ CDR3 sequences
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Gurun, Burcu, primary, Horton, Wesley, additional, Murugan, Dhaarini, additional, Zhu, Biqing, additional, Leyshock, Patrick, additional, Kumar, Sushil, additional, Byrne, Katelyn T., additional, Vonderheide, Robert H., additional, Margolin, Adam A., additional, Mori, Motomi, additional, Spellman, Paul T, additional, Coussens, Lisa M., additional, and Speed, Terence P, additional
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- 2022
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48. Identifying the Proteins to Which Small-Molecule Probes and Drugs Bind in Cells
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Ong, Shao-En, Schenone, Monica, Margolin, Adam A., Li, Xiaoyu, Do, Kathy, Doud, Mary K., Mani, D. R., Kuai, Letian, Wang, Xiang, Wood, John L., Tolliday, Nicola J., Koehler, Angela N., Marcaurelle, Lisa A., Golub, Todd R., Gould, Robert J., Schreiber, Stuart L., and Carr, Steven A.
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- 2009
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49. Chlp-on-Chip Significance Analysis Reveals Large-Scale Binding and Regulation by Human Transcription Factor Oncogenes
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Margolin, Adam A., Palomero, Teresa, Sumazin, Pavel, Califano, Andrea, Ferrando, Adolfo A., Stolovitzky, Gustavo, and Honig, Barry H.
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- 2009
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50. NOTCH1 Directly Regulates c-MYC and Activates a Feed-Forward-Loop Transcriptional Network Promoting Leukemic Cell Growth
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Palomero, Teresa, Lim, Wei Keat, Odom, Duncan T., Sulis, Maria Luisa, Real, Pedro J., Margolin, Adam, Barnes, Kelly C., O'Neil, Jennifer, Neuberg, Donna, Weng, Andrew P., Aster, Jon C., Sigaux, Francois, Soulier, Jean, Look, A. Thomas, Young, Richard A., Califano, Andrea, and Ferrando, Adolfo A.
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- 2006
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