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1. Oncoprotein-specific molecular interaction maps (SigMaps) for cancer network analyses

Author

Broyde, Joshua, Simpson, David R, Murray, Diana, Paull, Evan O, Chu, Brennan W, Tagore, Somnath, Jones, Sunny J, Griffin, Aaron T, Giorgi, Federico M, Lachmann, Alexander, Jackson, Peter, Sweet-Cordero, E Alejandro, Honig, Barry, and Califano, Andrea

Subjects
Biochemistry and Cell Biology, Bioinformatics and Computational Biology, Biomedical and Clinical Sciences, Biological Sciences, Oncology and Carcinogenesis, Biotechnology, Cancer, Genetics, Algorithms, Animals, Gene Regulatory Networks, Humans, Mice, Mutation, Neoplasms, Oncogene Proteins, Organoids, Proto-Oncogene Proteins p21(ras), RNA, Small Interfering, ROC Curve, Signal Transduction
Abstract

Tumor-specific elucidation of physical and functional oncoprotein interactions could improve tumorigenic mechanism characterization and therapeutic response prediction. Current interaction models and pathways, however, lack context specificity and are not oncoprotein specific. We introduce SigMaps as context-specific networks, comprising modulators, effectors and cognate binding-partners of a specific oncoprotein. SigMaps are reconstructed de novo by integrating diverse evidence sources-including protein structure, gene expression and mutational profiles-via the OncoSig machine learning framework. We first generated a KRAS-specific SigMap for lung adenocarcinoma, which recapitulated published KRAS biology, identified novel synthetic lethal proteins that were experimentally validated in three-dimensional spheroid models and established uncharacterized crosstalk with RAB/RHO. To show that OncoSig is generalizable, we first inferred SigMaps for the ten most mutated human oncoproteins and then for the full repertoire of 715 proteins in the COSMIC Cancer Gene Census. Taken together, these SigMaps show that the cell's regulatory and signaling architecture is highly tissue specific.

Published
2021

2. The Immune Landscape of Cancer

Author

Thorsson, Vésteinn, Gibbs, David L, Brown, Scott D, Wolf, Denise, Bortone, Dante S, Yang, Tai-Hsien Ou, Porta-Pardo, Eduard, Gao, Galen F, Plaisier, Christopher L, Eddy, James A, Ziv, Elad, Culhane, Aedin C, Paull, Evan O, Sivakumar, IK Ashok, Gentles, Andrew J, Malhotra, Raunaq, Farshidfar, Farshad, Colaprico, Antonio, Parker, Joel S, Mose, Lisle E, Vo, Nam Sy, Liu, Jianfang, Liu, Yuexin, Rader, Janet, Dhankani, Varsha, Reynolds, Sheila M, Bowlby, Reanne, Califano, Andrea, Cherniack, Andrew D, Anastassiou, Dimitris, Bedognetti, Davide, Mokrab, Younes, Newman, Aaron M, Rao, Arvind, Chen, Ken, Krasnitz, Alexander, Hu, Hai, Malta, Tathiane M, Noushmehr, Houtan, Pedamallu, Chandra Sekhar, Bullman, Susan, Ojesina, Akinyemi I, Lamb, Andrew, Zhou, Wanding, Shen, Hui, Choueiri, Toni K, Weinstein, John N, Guinney, Justin, Saltz, Joel, Holt, Robert A, Rabkin, Charles S, Network, The Cancer Genome Atlas Research, Caesar-Johnson, Samantha J, Demchok, John A, Felau, Ina, Kasapi, Melpomeni, Ferguson, Martin L, Hutter, Carolyn M, Sofia, Heidi J, Tarnuzzer, Roy, Wang, Zhining, Yang, Liming, Zenklusen, Jean C, Zhang, Jiashan, Chudamani, Sudha, Liu, Jia, Lolla, Laxmi, Naresh, Rashi, Pihl, Todd, Sun, Qiang, Wan, Yunhu, Wu, Ye, Cho, Juok, DeFreitas, Timothy, Frazer, Scott, Gehlenborg, Nils, Getz, Gad, Heiman, David I, Kim, Jaegil, Lawrence, Michael S, Lin, Pei, Meier, Sam, Noble, Michael S, Saksena, Gordon, Voet, Doug, Zhang, Hailei, Bernard, Brady, Chambwe, Nyasha, Knijnenburg, Theo, Kramer, Roger, Leinonen, Kalle, Miller, Michael, Reynolds, Sheila, Shmulevich, Ilya, Thorsson, Vesteinn, Zhang, Wei, Akbani, Rehan, and Broom, Bradley M

Subjects
Biomedical and Clinical Sciences, Immunology, Cancer Genome Atlas Research Network
Abstract

(Immunity 48, 812–830.e1–e14; April 17, 2018) In the originally published version of this article, the authors neglected to include Younes Mokrab and Aaron M. Newman as co-authors and misspelled the names of authors Charles S. Rabkin and Ilya Shmulevich. The author names have been corrected here and online. In addition, the concluding sentence of the subsection “Immune Signature Compilation” in the Method Details in the original published article was deemed unclear because it did not specify differences among the gene set scoring methods. The concluding sentences now reads “Gene sets from Bindea et al., Senbabaoglu et al., and the MSigDB C7 collection were scored using single-sample gene set enrichment (ssGSEA) analysis (Barbie et al., 2009), as implemented in the GSVA R package (Hänzelmann et al., 2013). All other signatures were scored using methods found in the associated citations.”

Published
2019

3. The Immune Landscape of Cancer

Author

Thorsson, Vésteinn, Gibbs, David L, Brown, Scott D, Wolf, Denise, Bortone, Dante S, Ou Yang, Tai-Hsien, Porta-Pardo, Eduard, Gao, Galen F, Plaisier, Christopher L, Eddy, James A, Ziv, Elad, Culhane, Aedin C, Paull, Evan O, Sivakumar, IK Ashok, Gentles, Andrew J, Malhotra, Raunaq, Farshidfar, Farshad, Colaprico, Antonio, Parker, Joel S, Mose, Lisle E, Vo, Nam Sy, Liu, Jianfang, Liu, Yuexin, Rader, Janet, Dhankani, Varsha, Reynolds, Sheila M, Bowlby, Reanne, Califano, Andrea, Cherniack, Andrew D, Anastassiou, Dimitris, Bedognetti, Davide, Mokrab, Younes, Newman, Aaron M, Rao, Arvind, Chen, Ken, Krasnitz, Alexander, Hu, Hai, Malta, Tathiane M, Noushmehr, Houtan, Pedamallu, Chandra Sekhar, Bullman, Susan, Ojesina, Akinyemi I, Lamb, Andrew, Zhou, Wanding, Shen, Hui, Choueiri, Toni K, Weinstein, John N, Guinney, Justin, Saltz, Joel, Holt, Robert A, Rabkin, Charles S, Lazar, Alexander J, Serody, Jonathan S, Demicco, Elizabeth G, Disis, Mary L, Vincent, Benjamin G, Shmulevich, Ilya, Caesar-Johnson, Samantha J, Demchok, John A, Felau, Ina, Kasapi, Melpomeni, Ferguson, Martin L, Hutter, Carolyn M, Sofia, Heidi J, Tarnuzzer, Roy, Wang, Zhining, Yang, Liming, Zenklusen, Jean C, Zhang, Jiashan Julia, Chudamani, Sudha, Liu, Jia, Lolla, Laxmi, Naresh, Rashi, Pihl, Todd, Sun, Qiang, Wan, Yunhu, Wu, Ye, Cho, Juok, DeFreitas, Timothy, Frazer, Scott, Gehlenborg, Nils, Getz, Gad, Heiman, David I, Kim, Jaegil, Lawrence, Michael S, Lin, Pei, Meier, Sam, Noble, Michael S, Saksena, Gordon, Voet, Doug, Zhang, Hailei, Bernard, Brady, Chambwe, Nyasha, Knijnenburg, Theo, Kramer, Roger, Leinonen, Kalle, Miller, Michael, and Reynolds, Sheila

Subjects
Biomedical and Clinical Sciences, Oncology and Carcinogenesis, Immunology, Cancer, Immunotherapy, Cancer Genomics, Genetics, Human Genome, 2.1 Biological and endogenous factors, Inflammatory and immune system, Adolescent, Adult, Aged, Aged, 80 and over, Child, Female, Genomics, Humans, Interferon-gamma, Macrophages, Male, Middle Aged, Neoplasms, Prognosis, Th1-Th2 Balance, Transforming Growth Factor beta, Wound Healing, Young Adult, Cancer Genome Atlas Research Network, cancer genomics, immune subtypes, immuno-oncology, immunomodulatory, immunotherapy, integrative network analysis, tumor immunology, tumor microenvironment
Abstract

We performed an extensive immunogenomic analysis of more than 10,000 tumors comprising 33 diverse cancer types by utilizing data compiled by TCGA. Across cancer types, we identified six immune subtypes-wound healing, IFN-γ dominant, inflammatory, lymphocyte depleted, immunologically quiet, and TGF-β dominant-characterized by differences in macrophage or lymphocyte signatures, Th1:Th2 cell ratio, extent of intratumoral heterogeneity, aneuploidy, extent of neoantigen load, overall cell proliferation, expression of immunomodulatory genes, and prognosis. Specific driver mutations correlated with lower (CTNNB1, NRAS, or IDH1) or higher (BRAF, TP53, or CASP8) leukocyte levels across all cancers. Multiple control modalities of the intracellular and extracellular networks (transcription, microRNAs, copy number, and epigenetic processes) were involved in tumor-immune cell interactions, both across and within immune subtypes. Our immunogenomics pipeline to characterize these heterogeneous tumors and the resulting data are intended to serve as a resource for future targeted studies to further advance the field.

Published
2018

4. Revealing cancer subtypes with higher-order correlations applied to imaging and omics data

Author

Graim, Kiley, Liu, Tiffany Ting, Achrol, Achal S, Paull, Evan O, Newton, Yulia, Chang, Steven D, Harsh, Griffith R, Cordero, Sergio P, Rubin, Daniel L, and Stuart, Joshua M

Subjects
Biomedical and Clinical Sciences, Oncology and Carcinogenesis, Brain Cancer, Brain Disorders, Rare Diseases, Urologic Diseases, Clinical Research, Cancer, 4.1 Discovery and preclinical testing of markers and technologies, Detection, screening and diagnosis, Computational Biology, DNA Copy Number Variations, Genotype, Glioblastoma, Humans, Magnetic Resonance Imaging, Mutation, Phenotype, Molecular subtyping, Community detection, MRI, Magnetic resonance imaging, Clustering, Genetics, Medical Biochemistry and Metabolomics, Genetics & Heredity, Medical biochemistry and metabolomics
Abstract

BackgroundPatient stratification to identify subtypes with different disease manifestations, severity, and expected survival time is a critical task in cancer diagnosis and treatment. While stratification approaches using various biomarkers (including high-throughput gene expression measurements) for patient-to-patient comparisons have been successful in elucidating previously unseen subtypes, there remains an untapped potential of incorporating various genotypic and phenotypic data to discover novel or improved groupings.MethodsHere, we present HOCUS, a unified analytical framework for patient stratification that uses a community detection technique to extract subtypes out of sparse patient measurements. HOCUS constructs a patient-to-patient network from similarities in the data and iteratively groups and reconstructs the network into higher order clusters. We investigate the merits of using higher-order correlations to cluster samples of cancer patients in terms of their associations with survival outcomes.ResultsIn an initial test of the method, the approach identifies cancer subtypes in mutation data of glioblastoma, ovarian, breast, prostate, and bladder cancers. In several cases, HOCUS provides an improvement over using the molecular features directly to compare samples. Application of HOCUS to glioblastoma images reveals a size and location classification of tumors that improves over human expert-based stratification.ConclusionsSubtypes based on higher order features can reveal comparable or distinct groupings. The distinct solutions can provide biologically- and treatment-relevant solutions that are just as significant as solutions based on the original data.

Published
2017

5. Common and cell-type specific responses to anti-cancer drugs revealed by high throughput transcript profiling.

Author

Niepel, Mario, Hafner, Marc, Duan, Qiaonan, Wang, Zichen, Paull, Evan O, Chung, Mirra, Lu, Xiaodong, Stuart, Joshua M, Golub, Todd R, Subramanian, Aravind, Ma'ayan, Avi, and Sorger, Peter K

Subjects
Cell Line, Tumor, Humans, Antineoplastic Agents, Gene Expression Profiling, Drug Synergism, High-Throughput Screening Assays, Genetics, Cancer, Pediatric Research Initiative, 5.1 Pharmaceuticals, 1.1 Normal biological development and functioning, Cell Line, Tumor, MD Multidisciplinary
Abstract

More effective use of targeted anti-cancer drugs depends on elucidating the connection between the molecular states induced by drug treatment and the cellular phenotypes controlled by these states, such as cytostasis and death. This is particularly true when mutation of a single gene is inadequate as a predictor of drug response. The current paper describes a data set of ~600 drug cell line pairs collected as part of the NIH LINCS Program ( http://www.lincsproject.org/ ) in which molecular data (reduced dimensionality transcript L1000 profiles) were recorded across dose and time in parallel with phenotypic data on cellular cytostasis and cytotoxicity. We report that transcriptional and phenotypic responses correlate with each other in general, but whereas inhibitors of chaperones and cell cycle kinases induce similar transcriptional changes across cell lines, changes induced by drugs that inhibit intra-cellular signaling kinases are cell-type specific. In some drug/cell line pairs significant changes in transcription are observed without a change in cell growth or survival; analysis of such pairs identifies drug equivalence classes and, in one case, synergistic drug interactions. In this case, synergy involves cell-type specific suppression of an adaptive drug response.

Published
2017

6. Prophetic Granger Causality to infer gene regulatory networks

Author

Carlin, Daniel E, Paull, Evan O, Graim, Kiley, Wong, Christopher K, Bivol, Adrian, Ryabinin, Peter, Ellrott, Kyle, Sokolov, Artem, and Stuart, Joshua M

Subjects
Biochemistry and Cell Biology, Bioinformatics and Computational Biology, Biological Sciences, Cancer, Bioengineering, Biotechnology, Genetics, Causality, Computational Biology, Gene Regulatory Networks, Humans, Machine Learning, Models, Theoretical, Neoplasms, Systems Biology, General Science & Technology
Abstract

We introduce a novel method called Prophetic Granger Causality (PGC) for inferring gene regulatory networks (GRNs) from protein-level time series data. The method uses an L1-penalized regression adaptation of Granger Causality to model protein levels as a function of time, stimuli, and other perturbations. When combined with a data-independent network prior, the framework outperformed all other methods submitted to the HPN-DREAM 8 breast cancer network inference challenge. Our investigations reveal that PGC provides complementary information to other approaches, raising the performance of ensemble learners, while on its own achieves moderate performance. Thus, PGC serves as a valuable new tool in the bioinformatics toolkit for analyzing temporal datasets. We investigate the general and cell-specific interactions predicted by our method and find several novel interactions, demonstrating the utility of the approach in charting new tumor wiring.

Published
2017

7. Inferring causal molecular networks: empirical assessment through a community-based effort

Author

Hill, Steven M., Heiser, Laura M., Cokelaer, Thomas, Unger, Michael, Nesser, Nicole K., Carlin, Daniel E., Zhang, Yang, Sokolov, Artem, Paull, Evan O., Wong, Chris K., Graim, Kiley, Bivol, Adrian, Wang, Haizhou, Zhu, Fan, Afsari, Bahman, Danilova, Ludmila V., Favorov, Alexander V., Lee, Wai Shing, Taylor, Dane, Hu, Chenyue W., Long, Byron L., Noren, David P., Bisberg, Alexander J., Mills, Gordon B., Gray, Joe W., Kellen, Michael, Norman, Thea, Friend, Stephen, Qutub, Amina A., Fertig, Elana J., Guan, Yuanfang, Song, Mingzhou, Stuart, Joshua M., Spellman, Paul T., Koeppl, Heinz, Stolovitzky, Gustavo, Saez-Rodriguez, Julio, Mukherjee, Sach, Hill, Steven M., Heiser, Laura M., Cokelaer, Thomas, Unger, Michael, Nesser, Nicole K., Carlin, Daniel E., Zhang, Yang, Sokolov, Artem, Paull, Evan O., Wong, Chris K., Graim, Kiley, Bivol, Adrian, Wang, Haizhou, Zhu, Fan, Afsari, Bahman, Danilova, Ludmila V., Favorov, Alexander V., Lee, Wai Shing, Taylor, Dane, Hu, Chenyue W., Long, Byron L., Noren, David P., Bisberg, Alexander J., Mills, Gordon B., Gray, Joe W., Kellen, Michael, Norman, Thea, Friend, Stephen, Qutub, Amina A., Fertig, Elana J., Guan, Yuanfang, Song, Mingzhou, Stuart, Joshua M., Spellman, Paul T., Koeppl, Heinz, Stolovitzky, Gustavo, Saez-Rodriguez, Julio, and Mukherjee, Sach

Abstract

It remains unclear whether causal, rather than merely correlational, relationships in molecular networks can be inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge, which focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective, and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess inferred molecular networks in a causal sense.

Published
2024

8. Phosphoproteome Integration Reveals Patient-Specific Networks in Prostate Cancer.

Author

Drake, Justin M, Paull, Evan O, Graham, Nicholas A, Lee, John K, Smith, Bryan A, Titz, Bjoern, Stoyanova, Tanya, Faltermeier, Claire M, Uzunangelov, Vladislav, Carlin, Daniel E, Fleming, Daniel Teo, Wong, Christopher K, Newton, Yulia, Sudha, Sud, Vashisht, Ajay A, Huang, Jiaoti, Wohlschlegel, James A, Graeber, Thomas G, Witte, Owen N, and Stuart, Joshua M

Subjects
Humans, Phosphoproteins, Proteome, Signal Transduction, Algorithms, Male, Transcriptome, Prostatic Neoplasms, Castration-Resistant, Precision Medicine, Prostatic Neoplasms, Castration-Resistant, Developmental Biology, Biological Sciences, Medical and Health Sciences
Abstract

We used clinical tissue from lethal metastatic castration-resistant prostate cancer (CRPC) patients obtained at rapid autopsy to evaluate diverse genomic, transcriptomic, and phosphoproteomic datasets for pathway analysis. Using Tied Diffusion through Interacting Events (TieDIE), we integrated differentially expressed master transcriptional regulators, functionally mutated genes, and differentially activated kinases in CRPC tissues to synthesize a robust signaling network consisting of druggable kinase pathways. Using MSigDB hallmark gene sets, six major signaling pathways with phosphorylation of several key residues were significantly enriched in CRPC tumors after incorporation of phosphoproteomic data. Individual autopsy profiles developed using these hallmarks revealed clinically relevant pathway information potentially suitable for patient stratification and targeted therapies in late stage prostate cancer. Here, we describe phosphorylation-based cancer hallmarks using integrated personalized signatures (pCHIPS) that shed light on the diversity of activated signaling pathways in metastatic CRPC while providing an integrative, pathway-based reference for drug prioritization in individual patients.

Published
2016

9. Inferring causal molecular networks: empirical assessment through a community-based effort

Author

Hill, Steven M, Heiser, Laura M, Cokelaer, Thomas, Unger, Michael, Nesser, Nicole K, Carlin, Daniel E, Zhang, Yang, Sokolov, Artem, Paull, Evan O, Wong, Chris K, Graim, Kiley, Bivol, Adrian, Wang, Haizhou, Zhu, Fan, Afsari, Bahman, Danilova, Ludmila V, Favorov, Alexander V, Lee, Wai Shing, Taylor, Dane, Hu, Chenyue W, Long, Byron L, Noren, David P, Bisberg, Alexander J, Mills, Gordon B, Gray, Joe W, Kellen, Michael, Norman, Thea, Friend, Stephen, Qutub, Amina A, Fertig, Elana J, Guan, Yuanfang, Song, Mingzhou, Stuart, Joshua M, Spellman, Paul T, Koeppl, Heinz, Stolovitzky, Gustavo, Saez-Rodriguez, Julio, and Mukherjee, Sach

Subjects
Biochemistry and Cell Biology, Biological Sciences, Networking and Information Technology R&D (NITRD), Bioengineering, Cancer, 2.1 Biological and endogenous factors, Generic health relevance, Algorithms, Causality, Computational Biology, Computer Simulation, Gene Expression Profiling, Gene Regulatory Networks, Humans, Models, Biological, Neoplasms, Protein Interaction Mapping, Signal Transduction, Software, Systems Biology, Tumor Cells, Cultured, HPN-DREAM Consortium, Technology, Medical and Health Sciences, Developmental Biology, Biological sciences
Abstract

It remains unclear whether causal, rather than merely correlational, relationships in molecular networks can be inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge, which focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective, and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess inferred molecular networks in a causal sense.

Published
2016

10. Pathway-Based Genomics Prediction using Generalized Elastic Net.

Author

Sokolov, Artem, Carlin, Daniel E, Paull, Evan O, Baertsch, Robert, and Stuart, Joshua M

Subjects
Animals, Humans, Proteome, Chromosome Mapping, Protein Interaction Mapping, Signal Transduction, Models, Genetic, Computer Simulation, Pattern Recognition, Automated, Models, Genetic, Pattern Recognition, Automated, Mathematical Sciences, Biological Sciences, Information and Computing Sciences, Bioinformatics
Abstract

We present a novel regularization scheme called The Generalized Elastic Net (GELnet) that incorporates gene pathway information into feature selection. The proposed formulation is applicable to a wide variety of problems in which the interpretation of predictive features using known molecular interactions is desired. The method naturally steers solutions toward sets of mechanistically interlinked genes. Using experiments on synthetic data, we demonstrate that pathway-guided results maintain, and often improve, the accuracy of predictors even in cases where the full gene network is unknown. We apply the method to predict the drug response of breast cancer cell lines. GELnet is able to reveal genetic determinants of sensitivity and resistance for several compounds. In particular, for an EGFR/HER2 inhibitor, it finds a possible trans-differentiation resistance mechanism missed by the corresponding pathway agnostic approach.

Published
2016

11. ONE-CLASS DETECTION OF CELL STATES IN TUMOR SUBTYPES

Author

Altman, Russ B, Dunker, A Keith, Hunter, Lawrence, Ritchie, Marylyn D, Murray, Tiffany A, Klein, Teri E, SOKOLOV, ARTEM, PAULL, EVAN O, and STUART, JOSHUA M

Subjects
Biological Sciences, Biomedical and Clinical Sciences, Oncology and Carcinogenesis, Stem Cell Research, Human Genome, Breast Cancer, Cancer, Genetics, Generic health relevance, Good Health and Well Being, Breast Neoplasms, Computational Biology, Embryonic Stem Cells, Female, Gene Expression Profiling, Humans, Logistic Models, Neoplasms, Neoplastic Stem Cells, Precision Medicine, Support Vector Machine, Urinary Bladder Neoplasms, One-class models, Pan-Cancer
Abstract

The cellular composition of a tumor greatly influences the growth, spread, immune activity, drug response, and other aspects of the disease. Tumor cells are usually comprised of a heterogeneous mixture of subclones, each of which could contain their own distinct character. The presence of minor subclones poses a serious health risk for patients as any one of them could harbor a fitness advantage with respect to the current treatment regimen, fueling resistance. It is therefore vital to accurately assess the make-up of cell states within a tumor biopsy. Transcriptome-wide assays from RNA sequencing provide key data from which cell state signatures can be detected. However, the challenge is to find them within samples containing mixtures of cell types of unknown proportions. We propose a novel one-class method based on logistic regression and show that its performance is competitive to two established SVM-based methods for this detection task. We demonstrate that one-class models are able to identify specific cell types in heterogeneous cell populations better than their binary predictor counterparts. We derive one-class predictors for the major breast and bladder subtypes and reaffirm the connection between these two tissues. In addition, we use a one-class predictor to quantitatively associate an embryonic stem cell signature with an aggressive breast cancer subtype that reveals shared stemness pathways potentially important for treatment.

Published
2016

12. Discovering causal pathways linking genomic events to transcriptional states using Tied Diffusion Through Interacting Events (TieDIE)

Author

Paull, Evan O, Carlin, Daniel E, Niepel, Mario, Sorger, Peter K, Haussler, David, and Stuart, Joshua M

Subjects
Biochemistry and Cell Biology, Bioinformatics and Computational Biology, Genetics, Biological Sciences, Human Genome, Cancer, Aetiology, 2.1 Biological and endogenous factors, Breast Neoplasms, Cell Line, Tumor, Female, Gene Expression Profiling, Gene Expression Regulation, Neoplastic, Gene Regulatory Networks, Genomics, Humans, Neoplasms, Protein Interaction Mapping, Signal Transduction, Software, Transcription Factors, Mathematical Sciences, Information and Computing Sciences, Bioinformatics, Biological sciences, Information and computing sciences, Mathematical sciences
Abstract

MotivationIdentifying the cellular wiring that connects genomic perturbations to transcriptional changes in cancer is essential to gain a mechanistic understanding of disease initiation, progression and ultimately to predict drug response. We have developed a method called Tied Diffusion Through Interacting Events (TieDIE) that uses a network diffusion approach to connect genomic perturbations to gene expression changes characteristic of cancer subtypes. The method computes a subnetwork of protein-protein interactions, predicted transcription factor-to-target connections and curated interactions from literature that connects genomic and transcriptomic perturbations.ResultsApplication of TieDIE to The Cancer Genome Atlas and a breast cancer cell line dataset identified key signaling pathways, with examples impinging on MYC activity. Interlinking genes are predicted to correspond to essential components of cancer signaling and may provide a mechanistic explanation of tumor character and suggest subtype-specific drug targets.AvailabilitySoftware is available from the Stuart lab's wiki: https://sysbiowiki.soe.ucsc.edu/tiedie.Contactjstuart@ucsc.edu.Supplementary informationSupplementary data are available at Bioinformatics online.

Published
2013

13. Comprehensive molecular characterization of urothelial bladder carcinoma

Author

Weinstein, John N., Akbani, Rehan, Broom, Bradley M., Wang, Wenyi, Verhaak, Roeland G. W., McConkey, David, Lerner, Seth, Morgan, Margaret, Creighton, Chad J., Smith, Carolyn, Kwiatkowski, David J., Cherniack, Andrew D., Kim, Jaegil, Pedamallu, Chandra Sekhar, Noble, Michael S., Al-Ahmadie, Hikmat A., Reuter, Victor E., Rosenberg, Jonathan E., Bajorin, Dean F., Bochner, Bernard H., Solit, David B., Koppie, Theresa, Robinson, Brian, Gordenin, Dmitry A., Fargo, David, Klimczak, Leszek J., Roberts, Steven A., Au, Jessie, Laird, Peter W., Hinoue, Toshinori, Schultz, Nikolaus, Ramirez, Ricardo, Hansel, Donna, Hoadley, Katherine A., Kim, William Y., Damrauer, Jeffrey S., Baylin, Stephen B., Mungall, Andrew J., Robertson, Gordon A., Chu., Andy, Sougnez, Carrie, Cibulskis, Kristian, Lichtenstein, Lee, Sivachenko, Andrey, Stewart, Chip, Lawrence, Michael S., Getz, Gad, Lander, Eric, Gabriel., Stacey B., Donehower, Lawrence, Carter, Scott L., Saksena, Gordon, Schumacher, Steven E., Freeman, Samuel S., Jung, Joonil, Bhatt, Ami S., Pugh, Trevor, Beroukhim, Rameen, Gabriel, Stacey B., Meyerson, Matthew, Chu, Andy, Ally, Adrian, Balasundaram, Miruna, Butterfield, Yaron S. N., Dhalla, Noreen, Hirst, Carrie, Holt, Robert A., Jones, Steven J. M., Lee, Darlene, Li, Haiyan I., Marra, Marco A., Mayo, Michael, Moore, Richard A., Schein, Jacqueline E., Sipahimalani, Payal, Tam, Angela, Thiessen, Nina, Wong, Tina, Wye, Natasja, Bowlby, Reanne, Chuah, Eric, Guin, Ranabir, Shen, Hui, Bootwalla, Moiz S., Jr, Timothy Triche, Lai, Phillip H., Van Den Berg, David J., Weisenberger, Daniel J., Balu, Saianand, Bodenheimer, Tom, Damrauer Alan P. Hoyle, Jeffrey S., Jefferys, Stuart R., Meng, Shaowu, Mose, Lisle E., Simons, Janae V., Soloway, Mathew G., Wu, Junyuan, Parker, Joel S., Hayes, Neil D., Roach, Jeffrey, Buda, Elizabeth, Jones, Corbin D., Mieczkowski, Piotr A., Tan, Donghui, Veluvolu, Umadevi, Waring, Scot, Auman, Todd J., Perou, Charles M., Wilkerson, Matthew D., Santoso, Netty, Parfenov, Michael, Ren, Xiaojia, Pantazi, Angeliki, Hadjipanayis, Angela, Seidman, Jonathan, Kucherlapati, Raju, Lee, Semin, Yang, Lixing, Park, Peter J., Xu, Andrew Wei, Protopopov, Alexei, Zhang, Jianhua, Bristow, Christopher, Mahadeshwar, Harshad S., Seth, Sahil, Song, Xingzhi, Tang, Jiabin, Zeng, Dong, Chin, Lynda, Guo, Charles, Casasent, Tod D., Liu, Wenbin, Ju, Zhenlin, Motter, Thomas, Peng, Bo, Ryan, Michael, Su, Xiaoping, Yang, Ji-Yeon, Lorenzi, Philip L., Yao, Hui, Zhang, Nianxiang, Zhang, Jiexin, Mills, Gordon B., Cho, Juok, DiCara, Daniel, Frazer, Scott, Gehlenborg, Nils, Heiman, David I., Lin, Pei, Liu, Yingchun, Stojanov, Petar, Voet, Doug, Zhang, Hailei, Zou, Lihua, Bernard, Brady, Kreisberg, Dick, Reynolds, Sheila, Rovira, Hector, Shmulevich, Ilya, Gao, Jianjiong, Jacobsen, Anders, Aksoy, Arman B., Antipin, Yevgeniy, Ciriello, Giovanni, Dresdner, Gideon, Gross, Benjamin, Lee, William, Reva, Boris, Shen, Ronglai, Sinha, Rileen, Sumer, Onur S., Weinhold, Nils, Ladanyi, Marc, Sander, Chris, Benz, Christopher, Carlin, Daniel, Haussler, David, Ng, Sam, Paull, Evan O., Stuart, Joshua, Zhu, Jing, Liu, Yuexin, Zhang, Wei, Taylor, Barry S., Lichtenberg, Tara M., Zmuda, Erik, Barr, Thomas, Black, Aaron D., George, Myra, Hanf, Benjamin, Helsel, Carmen, McAllister, Cynthia, Ramirez, Nilsa C., Tabler, Teresa R., Weaver, Stephanie, Wise, Lisa, Bowen, Jay, Gastier-Foster, Julie M., Jian, Weiguo, Tello, Sebrina, Ittman, Michael, Castro, Patricia, McClenden, Whitney D., Gibbs, Richard, Saller, Charles, Tarvin, Katherine, DiPiero, Jennifer M., Owens, Jennifer, Bollag, Roni, Li, Qiang, Weinberger, Paul, Czerwinski, Christine, Huelsenbeck-Dill, Lori, Iacocca, Mary, Petrelli, Nicholas, Rabeno, Brenda, Swanson, Pat, Shelton, Troy, Curley, Erin, Gardner, Johanna, Mallery, David, Penny, Robert, Van Bang, Nguyen, Thi Hanh, Phan, Kohl, Bernard, Van Le, Xuan, Duc Phu, Bui, Thorp, Richard, Tien, Nguyen Viet, Vinh, Le Quang, Sandusky, George, Burks, Eric, Christ, Kimberly, Gee, Jason, Holway, Antonia, Moinzadeh, Alireza, Sorcini, Andrea, Sullivan, Travis, Garcia-Grossman, Ilana R., Regazzi, Ashley M., Boice, Lori, Rathmell, Wendy Kimryn, Thorne, Leigh, Bastacky, Sheldon, Davies, Benjamin, Dhir, Rajiv, Gingrich, Jeffrey, Hrebinko, Ronald, Maranchie, Jodi, Nelson, Joel, Parwani, Anil, Bshara, Wiam, Gaudioso, Carmelo, Morrison, Carl, Alexopoulou, Vina, Bartlett, John, Engel, Jay, Kodeeswaran, Sugy, Antic, Tatjana, O’Donnell, Peter H., Smith, Norm D., Steinberg, Gary D., Egea, Sophie, Gomez-Fernandez, Carmen, Herbert, Lynn, Jorda, Merce, Soloway, Mark, Beaver, Allison, Carter, Suzie, Kapur, Payal, Lewis, Cheryl, Lotan, Yair, Bondaruk, Jolanta, Czerniak, Bogdan, Skinner, Eila, Aldape, Kenneth, Jensen, Mark A., Kahn, Ari B., Pihl, Todd D., Pot, David A., Srinivasan, Deepak, Wan, Yunhu, Ferguson, Martin L., Zenklusen, Jean Claude, Davidsen, Tanja, Demchok, John A., Mills Shaw, Kenna R., Sheth, Margi, Tarnuzzer, Roy, Wang, Zhining, Yang, Liming, Hutter, Carolyn, Ozenberger, Bradley A., Sofia, Heidi J., and Eley, Greg

Published
2014
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14. A modular master regulator landscape controls cancer transcriptional identity

Author

Paull, Evan O., primary, Aytes, Alvaro, additional, Jones, Sunny J., additional, Subramaniam, Prem S., additional, Giorgi, Federico M., additional, Douglass, Eugene F., additional, Tagore, Somnath, additional, Chu, Brennan, additional, Vasciaveo, Alessandro, additional, Zheng, Siyuan, additional, Verhaak, Roel, additional, Abate-Shen, Cory, additional, Alvarez, Mariano J., additional, and Califano, Andrea, additional

Published
2021
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15. The Master Regulator Protein BAZ2B Can Reprogram Human Hematopoietic Lineage-Committed Progenitors into a Multipotent State

Author

Arumugam, Karthik, primary, Shin, William, additional, Schiavone, Valentina, additional, Vlahos, Lukas, additional, Tu, Xiaochuan, additional, Carnevali, Davide, additional, Kesner, Jordan, additional, Paull, Evan O., additional, Romo, Neus, additional, Subramaniam, Prem, additional, Worley, Jeremy, additional, Tan, Xiangtian, additional, Califano, Andrea, additional, and Cosma, Maria Pia, additional

Published
2020
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16. Comprehensive molecular characterization of clear cell renal cell carcinoma

Author

Creighton, Chad J., Morgan, Margaret, Gunaratne, Preethi H., Wheeler, David A., Gibbs, Richard A., Robertson, Gordon A., Chu, Andy, Beroukhim, Rameen, Cibulskis, Kristian, Signoretti, Sabina, Hsin-Ta Wu, Fabio Vandin, Raphael, Benjamin J., Verhaak, Roel G. W., Tamboli, Pheroze, Torres-Garcia, Wandaliz, Akbani, Rehan, Weinstein, John N., Reuter, Victor, Hsieh, James J., Brannon, Rose A., Ari Hakimi, A., Jacobsen, Anders, Ciriello, Giovanni, Reva, Boris, Ricketts, Christopher J., Linehan, Marston W., Stuart, Joshua M., Rathmell, Kimryn W., Shen, Hui, Laird, Peter W., Muzny, Donna, Davis, Caleb, Xi, Liu, Chang, Kyle, Kakkar, Nipun, Treviño, Lisa R., Benton, Susan, Reid, Jeffrey G., Morton, Donna, Doddapaneni, Harsha, Han, Yi, Lewis, Lora, Dinh, Huyen, Kovar, Christie, Zhu, Yiming, Santibanez, Jireh, Wang, Min, Hale, Walker, Kalra, Divya, Getz, Gad, Lawrence, Michael S., Sougnez, Carrie, Carter, Scott L., Sivachenko, Andrey, Lichtenstein, Lee, Stewart, Chip, Voet, Doug, Fisher, Sheila, Gabriel, Stacey B., Lander, Eric, Schumacher, Steve E., Tabak, Barbara, Saksena, Gordon, Onofrio, Robert C., Cherniack, Andrew D., Gentry, Jeff, Ardlie, Kristin, Meyerson, Matthew, Chun, Hye-Jung E., Mungall, Andrew J., Sipahimalani, Payal, Stoll, Dominik, Ally, Adrian, Balasundaram, Miruna, Butterfield, Yaron S. N., Carlsen, Rebecca, Carter, Candace, Chuah, Eric, Coope, Robin J. N., Dhalla, Noreen, Gorski, Sharon, Guin, Ranabir, Hirst, Carrie, Hirst, Martin, Holt, Robert A., Lebovitz, Chandra, Lee, Darlene, Li, Haiyan I., Mayo, Michael, Moore, Richard A., Pleasance, Erin, Plettner, Patrick, Schein, Jacqueline E., Shafiei, Arash, Slobodan, Jared R., Tam, Angela, Thiessen, Nina, Varhol, Richard J., Wye, Natasja, Zhao, Yongjun, Birol, Inanc, Jones, Steven J. M., Marra, Marco A., Auman, Todd J., Tan, Donghui, Jones, Corbin D., Hoadley, Katherine A., Mieczkowski, Piotr A., Mose, Lisle E., Jefferys, Stuart R., Topal, Michael D., Liquori, Christina, Turman, Yidi J., Shi, Yan, Waring, Scot, Buda, Elizabeth, Walsh, Jesse, Wu, Junyuan, Bodenheimer, Tom, Hoyle, Alan P., Simons, Janae V., Soloway, Mathew G., Balu, Saianand, Parker, Joel S., Hayes, Neil D., Perou, Charles M., Kucherlapati, Raju, Park, Peter, Triche, Timothy, Jr, Weisenberger, Daniel J., Lai, Phillip H., Bootwalla, Moiz S., Maglinte, Dennis T., Mahurkar, Swapna, Berman, Benjamin P., Van Den Berg, David J., Cope, Leslie, Baylin, Stephen B., Noble, Michael S., DiCara, Daniel, Zhang, Hailei, Cho, Juok, Heiman, David I., Gehlenborg, Nils, Mallard, William, Lin, Pei, Frazer, Scott, Stojanov, Petar, Liu, Yingchun, Zhou, Lihua, Kim, Jaegil, Chin, Lynda, Vandin, Fabio, Wu, Hsin-Ta, Benz, Christopher, Yau, Christina, Reynolds, Sheila M., Shmulevich, Ilya, Verhaak, Roel G.W., Vegesna, Rahul, Kim, Hoon, Zhang, Wei, Cogdell, David, Jonasch, Eric, Ding, Zhiyong, Lu, Yiling, Zhang, Nianxiang, Unruh, Anna K., Casasent, Tod D., Wakefield, Chris, Tsavachidou, Dimitra, Mills, Gordon B., Schultz, Nikolaus, Antipin, Yevgeniy, Gao, Jianjiong, Cerami, Ethan, Gross, Benjamin, Aksoy, Arman B., Sinha, Rileen, Weinhold, Nils, Sumer, Onur S., Taylor, Barry S., Shen, Ronglai, Ostrovnaya, Irina, Berger, Michael F., Ladanyi, Marc, Sander, Chris, Fei, Suzanne S., Stout, Andrew, Spellman, Paul T., Rubin, Daniel L., Liu, Tiffany T., Ng, Sam, Paull, Evan O., Carlin, Daniel, Goldstein, Theodore, Waltman, Peter, Ellrott, Kyle, Zhu, Jing, Haussler, David, Xiao, Weimin, Shelton, Candace, Gardner, Johanna, Penny, Robert, Sherman, Mark, Mallery, David, Morris, Scott, Paulauskis, Joseph, Burnett, Ken, Shelton, Troy, Kaelin, William G., Choueiri, Toni, Atkins, Michael B., Curley, Erin, Tickoo, Satish, Thorne, Leigh, Boice, Lori, Huang, Mei, Fisher, Jennifer C., Vocke, Cathy D., Peterson, James, Worrell, Robert, Merino, Maria J., Schmidt, Laura S., Czerniak, Bogdan A., Aldape, Kenneth D., Wood, Christopher G., Boyd, Jeff, Weaver, JoEllen, Iacocca, Mary V., Petrelli, Nicholas, Witkin, Gary, Brown, Jennifer, Czerwinski, Christine, Huelsenbeck-Dill, Lori, Rabeno, Brenda, Myers, Jerome, Morrison, Carl, Bergsten, Julie, Eckman, John, Harr, Jodi, Smith, Christine, Tucker, Kelinda, Zach, Leigh Anne, Bshara, Wiam, Gaudioso, Carmelo, Dhir, Rajiv, Maranchie, Jodi, Nelson, Joel, Parwani, Anil, Potapova, Olga, Fedosenko, Konstantin, Cheville, John C., Thompson, Houston R., Mosquera, Juan M., Rubin, Mark A., Blute, Michael L., Pihl, Todd, Jensen, Mark, Sfeir, Robert, Kahn, Ari, Chu, Anna, Kothiyal, Prachi, Snyder, Eric, Pontius, Joan, Ayala, Brenda, Backus, Mark, Walton, Jessica, Baboud, Julien, Berton, Dominique, Nicholls, Matthew, Srinivasan, Deepak, Raman, Rohini, Girshik, Stanley, Kigonya, Peter, Alonso, Shelley, Sanbhadti, Rashmi, Barletta, Sean, Pot, David, Sheth, Margi, Demchok, John A., Davidsen, Tanja, Wang, Zhining, Yang, Liming, Tarnuzzer, Roy W., Zhang, Jiashan, Eley, Greg, Ferguson, Martin L., Mills Shaw, Kenna R., Guyer, Mark S., Ozenberger, Bradley A., and Sofia, Heidi J.

Published
2013
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17. Comprehensive molecular portraits of human breast tumours

Author

Koboldt, Daniel C., Fulton, Robert S., McLellan, Michael D., Schmidt, Heather, Kalicki-Veizer, Joelle, McMichael, Joshua F., Fulton, Lucinda L., Dooling, David J., Ding, Li, Mardis, Elaine R., Wilson, Richard K., Ally, Adrian, Balasundaram, Miruna, Butterfield, Yaron S. N., Carlsen, Rebecca, Carter, Candace, Chu, Andy, Chuah, Eric, Chun, Hye-Jung E., Coope, Robin J. N., Dhalla, Noreen, Guin, Ranabir, Hirst, Carrie, Hirst, Martin, Holt, Robert A., Lee, Darlene, Li, Haiyan I., Mayo, Michael, Moore, Richard A., Mungall, Andrew J., Pleasance, Erin, Gordon Robertson, A., Schein, Jacqueline E., Shafiei, Arash, Sipahimalani, Payal, Slobodan, Jared R., Stoll, Dominik, Tam, Angela, Thiessen, Nina, Varhol, Richard J., Wye, Natasja, Zeng, Thomas, Zhao, Yongjun, Birol, Inanc, Jones, Steven J. M., Marra, Marco A., Cherniack, Andrew D., Saksena, Gordon, Onofrio, Robert C., Pho, Nam H., Carter, Scott L., Schumacher, Steven E., Tabak, Barbara, Hernandez, Bryan, Gentry, Jeff, Nguyen, Huy, Crenshaw, Andrew, Ardlie, Kristin, Beroukhim, Rameen, Winckler, Wendy, Getz, Gad, Gabriel, Stacey B., Meyerson, Matthew, Chin, Lynda, Park, Peter J., Kucherlapati, Raju, Hoadley, Katherine A., Todd Auman, J., Fan, Cheng, Turman, Yidi J., Shi, Yan, Li, Ling, Topal, Michael D., He, Xiaping, Chao, Hann-Hsiang, Prat, Aleix, Silva, Grace O., Iglesia, Michael D., Zhao, Wei, Usary, Jerry, Berg, Jonathan S., Adams, Michael, Booker, Jessica, Wu, Junyuan, Gulabani, Anisha, Bodenheimer, Tom, Hoyle, Alan P., Simons, Janae V., Soloway, Matthew G., Mose, Lisle E., Jefferys, Stuart R., Balu, Saianand, Parker, Joel S., Neil Hayes, D., Perou, Charles M., Malik, Simeen, Mahurkar, Swapna, Shen, Hui, Weisenberger, Daniel J., Triche, Timothy, Jr, Lai, Phillip H., Bootwalla, Moiz S., Maglinte, Dennis T., Berman, Benjamin P., Van Den Berg, David J., Baylin, Stephen B., Laird, Peter W., Creighton, Chad J., Donehower, Lawrence A., Noble, Michael, Voet, Doug, Gehlenborg, Nils, DiCara, Daniel, Zhang, Juinhua, Zhang, Hailei, Wu, Chang-Jiun, Liu, Spring Yingchun, Lawrence, Michael S., Zou, Lihua, Sivachenko, Andrey, Lin, Pei, Stojanov, Petar, Jing, Rui, Cho, Juok, Sinha, Raktim, Park, Richard W., Nazaire, Marc-Danie, Robinson, Jim, Thorvaldsdottir, Helga, Mesirov, Jill, Reynolds, Sheila, Kreisberg, Richard B., Bernard, Brady, Bressler, Ryan, Erkkila, Timo, Lin, Jake, Thorsson, Vesteinn, Zhang, Wei, Shmulevich, Ilya, Ciriello, Giovanni, Weinhold, Nils, Schultz, Nikolaus, Gao, Jianjiong, Cerami, Ethan, Gross, Benjamin, Jacobsen, Anders, Sinha, Rileen, Arman Aksoy, B., Antipin, Yevgeniy, Reva, Boris, Shen, Ronglai, Taylor, Barry S., Ladanyi, Marc, Sander, Chris, Anur, Pavana, Spellman, Paul T., Lu, Yiling, Liu, Wenbin, Verhaak, Roel R. G., Mills, Gordon B., Akbani, Rehan, Zhang, Nianxiang, Broom, Bradley M., Casasent, Tod D., Wakefield, Chris, Unruh, Anna K., Baggerly, Keith, Coombes, Kevin, Weinstein, John N., Haussler, David, Benz, Christopher C., Stuart, Joshua M., Benz, Stephen C., Zhu, Jingchun, Szeto, Christopher C., Scott, Gary K., Yau, Christina, Paull, Evan O., Carlin, Daniel, Wong, Christopher, Sokolov, Artem, Thusberg, Janita, Mooney, Sean, Ng, Sam, Goldstein, Theodore C., Ellrott, Kyle, Grifford, Mia, Wilks, Christopher, Ma, Singer, Craft, Brian, Yan, Chunhua, Hu, Ying, Meerzaman, Daoud, Gastier-Foster, Julie M., Bowen, Jay, Ramirez, Nilsa C., Black, Aaron D., Pyatt, Robert E., White, Peter, Zmuda, Erik J., Frick, Jessica, Lichtenberg, Tara M., Brookens, Robin, George, Myra M., Gerken, Mark A., Harper, Hollie A., Leraas, Kristen M., Wise, Lisa J., Tabler, Teresa R., McAllister, Cynthia, Barr, Thomas, Hart-Kothari, Melissa, Tarvin, Katie, Saller, Charles, Sandusky, George, Mitchell, Colleen, Iacocca, Mary V., Brown, Jennifer, Rabeno, Brenda, Czerwinski, Christine, Petrelli, Nicholas, Dolzhansky, Oleg, Abramov, Mikhail, Voronina, Olga, Potapova, Olga, Marks, Jeffrey R., Suchorska, Wiktoria M., Murawa, Dawid, Kycler, Witold, Ibbs, Matthew, Korski, Konstanty, Spychała, Arkadiusz, Murawa, Paweł, Brzeziński, Jacek J., Perz, Hanna, Łaźniak, Radosław, Teresiak, Marek, Tatka, Honorata, Leporowska, Ewa, Bogusz-Czerniewicz, Marta, Malicki, Julian, Mackiewicz, Andrzej, Wiznerowicz, Maciej, Van Le, Xuan, Kohl, Bernard, Viet Tien, Nguyen, Thorp, Richard, Van Bang, Nguyen, Sussman, Howard, Duc Phu, Bui, Hajek, Richard, Phi Hung, Nguyen, Viet The Phuong, Tran, Quyet Thang, Huynh, Zaki Khan, Khurram, Penny, Robert, Mallery, David, Curley, Erin, Shelton, Candace, Yena, Peggy, Ingle, James N., Couch, Fergus J., Lingle, Wilma L., King, Tari A., Maria Gonzalez-Angulo, Ana, Dyer, Mary D., Liu, Shuying, Meng, Xiaolong, Patangan, Modesto, Waldman, Frederic, Stöppler, Hubert, Kimryn Rathmell, W., Thorne, Leigh, Huang, Mei, Boice, Lori, Hill, Ashley, Morrison, Carl, Gaudioso, Carmelo, Bshara, Wiam, Daily, Kelly, Egea, Sophie C., Pegram, Mark D., Gomez-Fernandez, Carmen, Dhir, Rajiv, Bhargava, Rohit, Brufsky, Adam, Shriver, Craig D., Hooke, Jeffrey A., Leigh Campbell, Jamie, Mural, Richard J., Hu, Hai, Somiari, Stella, Larson, Caroline, Deyarmin, Brenda, Kvecher, Leonid, Kovatich, Albert J., Ellis, Matthew J., Stricker, Thomas, White, Kevin, Olopade, Olufunmilayo, Luo, Chunqing, Chen, Yaqin, Bose, Ron, Chang, Li-Wei, Beck, Andrew H., Pihl, Todd, Jensen, Mark, Sfeir, Robert, Kahn, Ari, Chu, Anna, Kothiyal, Prachi, Wang, Zhining, Snyder, Eric, Pontius, Joan, Ayala, Brenda, Backus, Mark, Walton, Jessica, Baboud, Julien, Berton, Dominique, Nicholls, Matthew, Srinivasan, Deepak, Raman, Rohini, Girshik, Stanley, Kigonya, Peter, Alonso, Shelley, Sanbhadti, Rashmi, Barletta, Sean, Pot, David, Sheth, Margi, Demchok, John A., Mills Shaw, Kenna R., Yang, Liming, Eley, Greg, Ferguson, Martin L., Tarnuzzer, Roy W., Zhang, Jiashan, Dillon, Laura A. L., Buetow, Kenneth, Fielding, Peter, Ozenberger, Bradley A., Guyer, Mark S., Sofia, Heidi J., and Palchik, Jacqueline D.

Published
2012
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18. Oncoprotein-specific molecular interaction maps (SigMaps) for cancer network analyses

Author

Broyde, Joshua, primary, Simpson, David R., additional, Murray, Diana, additional, Paull, Evan O., additional, Chu, Brennan W., additional, Tagore, Somnath, additional, Jones, Sunny J., additional, Griffin, Aaron T., additional, Giorgi, Federico M., additional, Lachmann, Alexander, additional, Jackson, Peter, additional, Sweet-Cordero, E. Alejandro, additional, Honig, Barry, additional, and Califano, Andrea, additional

Published
2020
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19. A Modular Master Regulator Landscape Determines the Impact of Genetic Alterations on the Transcriptional Identity of Cancer Cells

Author

Paull, Evan O., primary, Aytes, Alvaro, additional, Subramaniam, Prem, additional, Giorgi, Federico M., additional, Douglass, Eugene F., additional, Chu, Brennan, additional, Jones, Sunny J., additional, Zheng, Siyuan, additional, Verhaak, Roel, additional, Abate-Shen, Cory, additional, Alvarez, Mariano J., additional, and Califano, Andrea, additional

Published
2019
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20. Single-cell based elucidation of molecularly-distinct glioblastoma states and drug sensitivity

Author

Ding, Hongxu, primary, Burgenske, Danielle M., additional, Zhao, Wenting, additional, Subramaniam, Prem S., additional, Bakken, Katrina K., additional, He, Lihong, additional, Alvarez, Mariano J., additional, Laise, Pasquale, additional, Paull, Evan O., additional, Spinazzi, Eleonora F., additional, Dovas, Athanassios, additional, Marie, Tamara, additional, Upadhyayula, Pavan, additional, Cruz, Filemon Dela, additional, Diolaiti, Daniel, additional, Kung, Andrew, additional, Bruce, Jeffrey N., additional, Canoll, Peter, additional, Sims, Peter A., additional, Sarkaria, Jann N., additional, and Califano, Andrea, additional

Published
2019
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21. The Immune Landscape of Cancer

Author

Onderzoeksgroep 6, Pathologie Pathologen staf, Thorsson, Vésteinn, Gibbs, David L., Brown, Scott D., Wolf, Denise, Bortone, Dante S., Ou Yang, Tai Hsien, Porta-Pardo, Eduard, Gao, Galen F., Plaisier, Christopher L., Eddy, James A., Ziv, Elad, Culhane, Aedin C., Paull, Evan O., Sivakumar, I. K.Ashok, Gentles, Andrew J., Malhotra, Raunaq, Farshidfar, Farshad, Colaprico, Antonio, Parker, Joel S., Mose, Lisle E., Vo, Nam Sy, Liu, Jianfang, Liu, Yuexin, Rader, Janet, Dhankani, Varsha, Reynolds, Sheila M., Bowlby, Reanne, Califano, Andrea, Cherniack, Andrew D., Anastassiou, Dimitris, Bedognetti, Davide, Rao, Arvind, Chen, Ken, Krasnitz, Alexander, Hu, Hai, Malta, Tathiane M., Noushmehr, Houtan, Pedamallu, Chandra Sekhar, Bullman, Susan, Ojesina, Akinyemi I., Lamb, Andrew, Zhou, Wanding, Shen, Hui, Choueiri, Toni K., Weinstein, John N., Guinney, Justin, Saltz, Joel, Holt, Robert, Zhang, Jiashan (Julia), de Krijger, Ronald, The Cancer Genome Atlas Research Network, Onderzoeksgroep 6, Pathologie Pathologen staf, Thorsson, Vésteinn, Gibbs, David L., Brown, Scott D., Wolf, Denise, Bortone, Dante S., Ou Yang, Tai Hsien, Porta-Pardo, Eduard, Gao, Galen F., Plaisier, Christopher L., Eddy, James A., Ziv, Elad, Culhane, Aedin C., Paull, Evan O., Sivakumar, I. K.Ashok, Gentles, Andrew J., Malhotra, Raunaq, Farshidfar, Farshad, Colaprico, Antonio, Parker, Joel S., Mose, Lisle E., Vo, Nam Sy, Liu, Jianfang, Liu, Yuexin, Rader, Janet, Dhankani, Varsha, Reynolds, Sheila M., Bowlby, Reanne, Califano, Andrea, Cherniack, Andrew D., Anastassiou, Dimitris, Bedognetti, Davide, Rao, Arvind, Chen, Ken, Krasnitz, Alexander, Hu, Hai, Malta, Tathiane M., Noushmehr, Houtan, Pedamallu, Chandra Sekhar, Bullman, Susan, Ojesina, Akinyemi I., Lamb, Andrew, Zhou, Wanding, Shen, Hui, Choueiri, Toni K., Weinstein, John N., Guinney, Justin, Saltz, Joel, Holt, Robert, Zhang, Jiashan (Julia), de Krijger, Ronald, and The Cancer Genome Atlas Research Network

Published
2018

24. The number of titrated microRNA species dictates ceRNA regulation

Author

Chiu, Hua-Sheng, primary, Martínez, María Rodríguez, additional, . Komissarova, Elena V, additional, Llobet-Navas, David, additional, Bansal, Mukesh, additional, Paull, Evan O, additional, Silva, José, additional, Yang, Xuerui, additional, Sumazin, Pavel, additional, and Califano, Andrea, additional

Published
2018
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25. Inferring causal molecular networks: empirical assessment through a community-based effort

Author

Hill, Steven M., Heiser, Laura M., Cokelaer, Thomas, Unger, Michael, Nesser, Nicole K., Carlin, Daniel E., Zhang, Yang, Sokolov, Artem, Paull, Evan O., Wong, Chris K., Graim, Kiley, Bivol, Adrian, Wang, Haizhou, Zhu, Fan, Afsari, Bahman, and Koeppl, Heinz

Abstract

It remains unclear whether causal, rather than merely correlational, relationships in molecular networks can be inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge, which focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective, and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess inferred molecular networks in a causal sense., Nature Methods, 13 (4), ISSN:1548-7105, ISSN:1548-7091

Published
2016
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26. ONE-CLASS DETECTION OF CELL STATES IN TUMOR SUBTYPES.

Author

Sokolov, Artem, Sokolov, Artem, Paull, Evan O, Stuart, Joshua M, Sokolov, Artem, Sokolov, Artem, Paull, Evan O, and Stuart, Joshua M

Abstract

The cellular composition of a tumor greatly influences the growth, spread, immune activity, drug response, and other aspects of the disease. Tumor cells are usually comprised of a heterogeneous mixture of subclones, each of which could contain their own distinct character. The presence of minor subclones poses a serious health risk for patients as any one of them could harbor a fitness advantage with respect to the current treatment regimen, fueling resistance. It is therefore vital to accurately assess the make-up of cell states within a tumor biopsy. Transcriptome-wide assays from RNA sequencing provide key data from which cell state signatures can be detected. However, the challenge is to find them within samples containing mixtures of cell types of unknown proportions. We propose a novel one-class method based on logistic regression and show that its performance is competitive to two established SVM-based methods for this detection task. We demonstrate that one-class models are able to identify specific cell types in heterogeneous cell populations better than their binary predictor counterparts. We derive one-class predictors for the major breast and bladder subtypes and reaffirm the connection between these two tissues. In addition, we use a one-class predictor to quantitatively associate an embryonic stem cell signature with an aggressive breast cancer subtype that reveals shared stemness pathways potentially important for treatment.

Published
2016

27. Abstract 3882: Patient-specific druggable networks in lethal prostate cancer through proteome-guided multi-omic integration

Author

Drake, Justin M., primary, Paull, Evan O., additional, Graham, Nicholas A., additional, Lee, John K., additional, Smith, Bryan A., additional, Stoyanova, Tanya, additional, Faltermeier, Claire M., additional, Carlin, Daniel E., additional, Vashisht, Ajay, additional, Huang, Jiaoti, additional, Wohlschlegel, James A., additional, Graeber, Thomas G., additional, Witte, Owen N., additional, and Stuart, Joshua M., additional

Published
2016
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29. Revealing cancer subtypes with higher-order correlations applied to imaging and omics data.

Author

Kiley, Kiley, Tiffany Ting Liu, Achrol, Achal S., Paull, Evan O., Newton, Yulia, Chang, Steven D., Harsh IV, Griffith R., Cordero, Sergio P., Rubin, Daniel L., and Stuart, Joshua M.

Subjects
TUMOR classification, CANCER patients, GENETIC mutation, BREAST cancer, OVARIAN cancer, MAGNETIC resonance imaging
Abstract

Background: Patient stratification to identify subtypes with different disease manifestations, severity, and expected survival time is a critical task in cancer diagnosis and treatment. While stratification approaches using various biomarkers (including high-throughput gene expression measurements) for patient-to-patient comparisons have been successful in elucidating previously unseen subtypes, there remains an untapped potential of incorporating various genotypic and phenotypic data to discover novel or improved groupings. Methods: Here, we present HOCUS, a unified analytical framework for patient stratification that uses a community detection technique to extract subtypes out of sparse patient measurements. HOCUS constructs a patient-to-patient network from similarities in the data and iteratively groups and reconstructs the network into higher order clusters. We investigate the merits of using higher-order correlations to cluster samples of cancer patients in terms of their associations with survival outcomes. Results: In an initial test of the method, the approach identifies cancer subtypes in mutation data of glioblastoma, ovarian, breast, prostate, and bladder cancers. In several cases, HOCUS provides an improvement over using the molecular features directly to compare samples. Application of HOCUS to glioblastoma images reveals a size and location classification of tumors that improves over human expert-based stratification. Conclusions: Subtypes based on higher order features can reveal comparable or distinct groupings. The distinct solutions can provide biologically- and treatment-relevant solutions that are just as significant as solutions based on the original data. [ABSTRACT FROM AUTHOR]

Published
2017
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30. Integrated Genomic Characterization of Papillary Thyroid Carcinoma

Author

Agrawal, Nishant, primary, Akbani, Rehan, additional, Aksoy, B. Arman, additional, Ally, Adrian, additional, Arachchi, Harindra, additional, Asa, Sylvia L., additional, Auman, J. Todd, additional, Balasundaram, Miruna, additional, Balu, Saianand, additional, Baylin, Stephen B., additional, Behera, Madhusmita, additional, Bernard, Brady, additional, Beroukhim, Rameen, additional, Bishop, Justin A., additional, Black, Aaron D., additional, Bodenheimer, Tom, additional, Boice, Lori, additional, Bootwalla, Moiz S., additional, Bowen, Jay, additional, Bowlby, Reanne, additional, Bristow, Christopher A., additional, Brookens, Robin, additional, Brooks, Denise, additional, Bryant, Robert, additional, Buda, Elizabeth, additional, Butterfield, Yaron S.N., additional, Carling, Tobias, additional, Carlsen, Rebecca, additional, Carter, Scott L., additional, Carty, Sally E., additional, Chan, Timothy A., additional, Chen, Amy Y., additional, Cherniack, Andrew D., additional, Cheung, Dorothy, additional, Chin, Lynda, additional, Cho, Juok, additional, Chu, Andy, additional, Chuah, Eric, additional, Cibulskis, Kristian, additional, Ciriello, Giovanni, additional, Clarke, Amanda, additional, Clayman, Gary L., additional, Cope, Leslie, additional, Copland, John A., additional, Covington, Kyle, additional, Danilova, Ludmila, additional, Davidsen, Tanja, additional, Demchok, John A., additional, DiCara, Daniel, additional, Dhalla, Noreen, additional, Dhir, Rajiv, additional, Dookran, Sheliann S., additional, Dresdner, Gideon, additional, Eldridge, Jonathan, additional, Eley, Greg, additional, El-Naggar, Adel K., additional, Eng, Stephanie, additional, Fagin, James A., additional, Fennell, Timothy, additional, Ferris, Robert L., additional, Fisher, Sheila, additional, Frazer, Scott, additional, Frick, Jessica, additional, Gabriel, Stacey B., additional, Ganly, Ian, additional, Gao, Jianjiong, additional, Garraway, Levi A., additional, Gastier-Foster, Julie M., additional, Getz, Gad, additional, Gehlenborg, Nils, additional, Ghossein, Ronald, additional, Gibbs, Richard A., additional, Giordano, Thomas J., additional, Gomez-Hernandez, Karen, additional, Grimsby, Jonna, additional, Gross, Benjamin, additional, Guin, Ranabir, additional, Hadjipanayis, Angela, additional, Harper, Hollie A., additional, Hayes, D. Neil, additional, Heiman, David I., additional, Herman, James G., additional, Hoadley, Katherine A., additional, Hofree, Matan, additional, Holt, Robert A., additional, Hoyle, Alan P., additional, Huang, Franklin W., additional, Huang, Mei, additional, Hutter, Carolyn M., additional, Ideker, Trey, additional, Iype, Lisa, additional, Jacobsen, Anders, additional, Jefferys, Stuart R., additional, Jones, Corbin D., additional, Jones, Steven J.M., additional, Kasaian, Katayoon, additional, Kebebew, Electron, additional, Khuri, Fadlo R., additional, Kim, Jaegil, additional, Kramer, Roger, additional, Kreisberg, Richard, additional, Kucherlapati, Raju, additional, Kwiatkowski, David J., additional, Ladanyi, Marc, additional, Lai, Phillip H., additional, Laird, Peter W., additional, Lander, Eric, additional, Lawrence, Michael S., additional, Lee, Darlene, additional, Lee, Eunjung, additional, Lee, Semin, additional, Lee, William, additional, Leraas, Kristen M., additional, Lichtenberg, Tara M., additional, Lichtenstein, Lee, additional, Lin, Pei, additional, Ling, Shiyun, additional, Liu, Jinze, additional, Liu, Wenbin, additional, Liu, Yingchun, additional, LiVolsi, Virginia A., additional, Lu, Yiling, additional, Ma, Yussanne, additional, Mahadeshwar, Harshad S., additional, Marra, Marco A., additional, Mayo, Michael, additional, McFadden, David G., additional, Meng, Shaowu, additional, Meyerson, Matthew, additional, Mieczkowski, Piotr A., additional, Miller, Michael, additional, Mills, Gordon, additional, Moore, Richard A., additional, Mose, Lisle E., additional, Mungall, Andrew J., additional, Murray, Bradley A., additional, Nikiforov, Yuri E., additional, Noble, Michael S., additional, Ojesina, Akinyemi I., additional, Owonikoko, Taofeek K., additional, Ozenberger, Bradley A., additional, Pantazi, Angeliki, additional, Parfenov, Michael, additional, Park, Peter J., additional, Parker, Joel S., additional, Paull, Evan O., additional, Pedamallu, Chandra Sekhar, additional, Perou, Charles M., additional, Prins, Jan F., additional, Protopopov, Alexei, additional, Ramalingam, Suresh S., additional, Ramirez, Nilsa C., additional, Ramirez, Ricardo, additional, Raphael, Benjamin J., additional, Rathmell, W. Kimryn, additional, Ren, Xiaojia, additional, Reynolds, Sheila M., additional, Rheinbay, Esther, additional, Ringel, Matthew D., additional, Rivera, Michael, additional, Roach, Jeffrey, additional, Robertson, A. Gordon, additional, Rosenberg, Mara W., additional, Rosenthal, Matthew, additional, Sadeghi, Sara, additional, Saksena, Gordon, additional, Sander, Chris, additional, Santoso, Netty, additional, Schein, Jacqueline E., additional, Schultz, Nikolaus, additional, Schumacher, Steven E., additional, Seethala, Raja R., additional, Seidman, Jonathan, additional, Senbabaoglu, Yasin, additional, Seth, Sahil, additional, Sharpe, Samantha, additional, Shaw, Kenna R. Mills, additional, Shen, John P., additional, Shen, Ronglai, additional, Sherman, Steven, additional, Sheth, Margi, additional, Shi, Yan, additional, Shmulevich, Ilya, additional, Sica, Gabriel L., additional, Simons, Janae V., additional, Sinha, Rileen, additional, Sipahimalani, Payal, additional, Smallridge, Robert C., additional, Sofia, Heidi J., additional, Soloway, Matthew G., additional, Song, Xingzhi, additional, Sougnez, Carrie, additional, Stewart, Chip, additional, Stojanov, Petar, additional, Stuart, Joshua M., additional, Sumer, S. Onur, additional, Sun, Yichao, additional, Tabak, Barbara, additional, Tam, Angela, additional, Tan, Donghui, additional, Tang, Jiabin, additional, Tarnuzzer, Roy, additional, Taylor, Barry S., additional, Thiessen, Nina, additional, Thorne, Leigh, additional, Thorsson, Vésteinn, additional, Tuttle, R. Michael, additional, Umbricht, Christopher B., additional, Van Den Berg, David J., additional, Vandin, Fabio, additional, Veluvolu, Umadevi, additional, Verhaak, Roel G.W., additional, Vinco, Michelle, additional, Voet, Doug, additional, Walter, Vonn, additional, Wang, Zhining, additional, Waring, Scot, additional, Weinberger, Paul M., additional, Weinhold, Nils, additional, Weinstein, John N., additional, Weisenberger, Daniel J., additional, Wheeler, David, additional, Wilkerson, Matthew D., additional, Wilson, Jocelyn, additional, Williams, Michelle, additional, Winer, Daniel A., additional, Wise, Lisa, additional, Wu, Junyuan, additional, Xi, Liu, additional, Xu, Andrew W., additional, Yang, Liming, additional, Yang, Lixing, additional, Zack, Travis I., additional, Zeiger, Martha A., additional, Zeng, Dong, additional, Zenklusen, Jean Claude, additional, Zhao, Ni, additional, Zhang, Hailei, additional, Zhang, Jianhua, additional, Zhang, Jiashan (Julia), additional, Zhang, Wei, additional, Zmuda, Erik, additional, and Zou, Lihua, additional

Published
2014
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33. Inferring causal molecular networks: empirical assessment through a community-based effort

Author

Carlin, Daniel E., Hill, Steven M., Meerzaman, Daoud, Kannan, Venkateshan, Afsari, Bahman, Hase, Takeshi, Budak, Gungor, Lee, Wai Shing, Caglar, Mehmet, Stuart, Joshua M., Coort, Susan, Haider, Saad, Friend, Stephen, Carlon, Azzurra, Zairis, Sakellarios, Cai, Binghuang, Sichani, Omid Askari, Komatsoulis, George, Sambo, Francesco, Kursa, Miron Bartosz, Kikuchi, Kaito, Nesser, Nicole K., Anton, Bernat, Wang, Haizhou, Huang, Xun, Bonneau, Richard, Knapp, Bettina, Berlow, Noah, Wan, Qian, Graim, Kiley, Paull, Evan O., Guan, Yuanfang, Gao, Xi, Lu, Songjian, Trifoglio, Emanuele, Neapolitan, Richard E., Hafemeister, Christoph, Finotello, Francesca, Linger, Michael, Bonet, Jaume, Saez-Rodriguez, Julio, Zhang, Yang, Zi, Zhike, Min, Wenwen, Al-Ouran, Rami, Giaretta, Alberto, Strunz, Sonja, Bagheri, Neda, Di Camillo, Barbara, Bohler, Anwesha, Hu, Ying, Creighton, Chad J., Poglayen, Daniel, Song, Mingzhou, Ghosh, Samik, Kaderali, Lars, Arodz, Tomasz, Evelo, Chris, Bivol, Adrian, Kitano, Hiroaki, Zengerling, Michael, Qutub, Amina A., Pal, Ranadip, Sanavia, Tiziana, Xue, Albert Y., Liu, Yu, Cokelaer, Thomas, Gray, Joe W., Mills, Gordon B., Fertig, Elana J., Palinkas, Aljoscha, Tegnér, Jesper, Li, Yichao, Chen, Lujia, Mukherjee, Sach, Emmett, Kevin, Hodgson, Jay, Jiang, Xia, Oliva, Baldo, Yamanaka, Ryota, Yan, Chunhua, Spellman, Paul T., Welch, Lonnie, Großeholz, Ruth, Kellen, Michael, Sharifi-Zarchi, Ali, Ciaccio, Mark F., Guinney, Justin, Thobe, Kirste, Norman, Thea, Zenil, Hector, Hu, Chenyue W., Krämer, Andreas, Cooper, Gregory, Taylor, Dane, Bisberg, Alexander J., Long, Byron L., Streck, Adam, Kacprowski, Tim, Manfrini, Marco, Sokolov, Artem, Jalili, Mahdi, Bunescu, Razvan, Liang, Xiaoyu, Kang, Mingon, Müller, Christian Lorenz, Heiser, Laura M., Zhu, Fan, Hoff, Bruce, Kutmon, Martina, Noren, David P., Dutta-Moscato, Joyeeta, Wong, Chris K., Lu, Xinghua, Favorov, Alexander V., Hahn, Oliver, Finkle, Justin D., Planas-Iglesias, Joan, Liu, Zhaoqi, Fassia, Mohammad-Kasim H., Stolovitzky, Gustavo, Daneshmand, Seyed-Mohammad-Hadi, Unger, Michael, Cai, Chunhui, Koeppl, Heinz, Matos, Marta R. A., Kim, Dong-Chul, Gao, Jean, Hsu, Chih Hao, Danilova, Ludmila V., Toffolo, Gianna Maria, Wu, Jia J., De La Fuente, Alberto, Slawek, Janusz, and Opiyo, Stephen Obol

Subjects
3. Good health
Abstract

Inferring molecular networks is a central challenge in computational biology. However, it has remained unclear whether causal, rather than merely correlational, relationships can be effectively inferred in complex biological settings. Here we describe the HPN-DREAM network inference challenge that focused on learning causal influences in signaling networks. We used phosphoprotein data from cancer cell lines as well as in silico data from a nonlinear dynamical model. Using the phosphoprotein data, we scored more than 2,000 networks submitted by challenge participants. The networks spanned 32 biological contexts and were scored in terms of causal validity with respect to unseen interventional data. A number of approaches were effective and incorporating known biology was generally advantageous. Additional sub-challenges considered time-course prediction and visualization. Our results constitute the most comprehensive assessment of causal network inference in a mammalian setting carried out to date and suggest that learning causal relationships may be feasible in complex settings such as disease states. Furthermore, our scoring approach provides a practical way to empirically assess the causal validity of inferred molecular networks.

34. A modular master regulator landscape controls cancer transcriptional identity

Author

Alessandro Vasciaveo, Andrea Califano, Brennan Chu, Somnath Tagore, Sunny J. Jones, Cory Abate-Shen, Federico M. Giorgi, Evan O. Paull, Siyuan Zheng, Prem S. Subramaniam, Mariano J. Alvarez, Eugene F Douglass, Alvaro Aytes, Roel G.W. Verhaak, Paull, Evan O, Aytes, Alvaro, Jones, Sunny J, Subramaniam, Prem S, Giorgi, Federico M, Douglass, Eugene F, Tagore, Somnath, Chu, Brennan, Vasciaveo, Alessandro, Zheng, Siyuan, Verhaak, Roel, Abate-Shen, Cory, Alvarez, Mariano J, and Califano, Andrea

Subjects
Transcription, Genetic, Somatic cell, multiomic, Mice, Nude, cancer systems biology, Computational biology, Adenocarcinoma, Biology, Article, General Biochemistry, Genetics and Molecular Biology, 03 medical and health sciences, 0302 clinical medicine, integrative genomic, Cell Line, Tumor, Neoplasms, cancer genetic, Gene expression, medicine, Transcriptional regulation, Animals, Humans, network analysi, Gene Regulatory Networks, transcriptional regulation, genomic alteration, 030304 developmental biology, 0303 health sciences, Genome, Human, Reproducibility of Results, Cancer, medicine.disease, Phenotype, Gene Expression Regulation, Neoplastic, HEK293 Cells, Cancer systems biology, Colonic Neoplasms, Mutation, Cancer cell, Identity (object-oriented programming), pan-cancer analysi, 030217 neurology & neurosurgery
Abstract

Despite considerable efforts, the mechanisms linking genomic alterations to the transcriptional identity of cancer cells remain elusive. Integrative genomic analysis, using a network-based approach, identified 407 master regulator (MR) proteins responsible for canalizing the genetics of individual samples from 20 cohorts in The Cancer Genome Atlas (TCGA) into 112 transcriptionally distinct tumor subtypes. MR proteins could be further organized into 24 pan-cancer, master regulator block modules (MRBs), each regulating key cancer hallmarks and predictive of patient outcome in multiple cohorts. Of all somatic alterations detected in each individual sample, >50% were predicted to induce aberrant MR activity, yielding insight into mechanisms linking tumor genetics and transcriptional identity and establishing non-oncogene dependencies. Genetic and pharmacological validation assays confirmed the predicted effect of upstream mutations and MR activity on downstream cellular identity and phenotype. Thus, co-analysis of mutational and gene expression profiles identified elusive subtypes and provided testable hypothesis for mechanisms mediating the effect of genetic alterations.

Published
2021

35. ONE-CLASS DETECTION OF CELL STATES IN TUMOR SUBTYPES.

Author

Sokolov A, Paull EO, and Stuart JM

Subjects
Breast Neoplasms classification, Breast Neoplasms genetics, Breast Neoplasms pathology, Computational Biology methods, Computational Biology statistics & numerical data, Embryonic Stem Cells pathology, Female, Gene Expression Profiling statistics & numerical data, Humans, Logistic Models, Neoplasms genetics, Neoplastic Stem Cells pathology, Precision Medicine, Support Vector Machine, Urinary Bladder Neoplasms classification, Urinary Bladder Neoplasms genetics, Urinary Bladder Neoplasms pathology, Neoplasms classification, Neoplasms pathology
Abstract

The cellular composition of a tumor greatly influences the growth, spread, immune activity, drug response, and other aspects of the disease. Tumor cells are usually comprised of a heterogeneous mixture of subclones, each of which could contain their own distinct character. The presence of minor subclones poses a serious health risk for patients as any one of them could harbor a fitness advantage with respect to the current treatment regimen, fueling resistance. It is therefore vital to accurately assess the make-up of cell states within a tumor biopsy. Transcriptome-wide assays from RNA sequencing provide key data from which cell state signatures can be detected. However, the challenge is to find them within samples containing mixtures of cell types of unknown proportions. We propose a novel one-class method based on logistic regression and show that its performance is competitive to two established SVM-based methods for this detection task. We demonstrate that one-class models are able to identify specific cell types in heterogeneous cell populations better than their binary predictor counterparts. We derive one-class predictors for the major breast and bladder subtypes and reaffirm the connection between these two tissues. In addition, we use a one-class predictor to quantitatively associate an embryonic stem cell signature with an aggressive breast cancer subtype that reveals shared stemness pathways potentially important for treatment.

Published
2016

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