title: A Multi-Method Approach for Proteomic Network Inference in 11 Human Cancers.
abstract: Protein expression and post-translational modification levels are tightly regulated in neoplastic cells to maintain cellular processes known as 'cancer hallmarks'. The first Pan-Cancer initiative of The Cancer Genome Atlas (TCGA) Research Network has aggregated protein expression profiles for 3,467 patient samples from 11 tumor types using the antibody based reverse phase protein array (RPPA) technology. The resultant proteomic data can be utilized to computationally infer protein-protein interaction (PPI) networks and to study the commonalities and differences across tumor types. In this study, we compare the performance of 13 established network inference methods in their capacity to retrieve the curated Pathway Commons interactions from RPPA data. We observe that no single method has the best performance in all tumor types, but a group of six methods, including diverse techniques such as correlation, mutual information, and regression, consistently rank highly among the tested methods. We utilize the high performing methods to obtain a consensus network; and identify four robust and densely connected modules that reveal biological processes as well as suggest antibody-related technical biases. Mapping the consensus network interactions to Reactome gene lists confirms the pan-cancer importance of signal transduction pathways, innate and adaptive immune signaling, cell cycle, metabolism, and DNA repair; and also suggests several biological processes that may be specific to a subset of tumor types. Our results illustrate the utility of the RPPA platform as a tool to study proteomic networks in cancer.
authors.affiliation: Computational Biology Program, Memorial Sloan Kettering Cancer Center, New York, New York, United States of America.
authors.forename: Yasin
authors.initials: Y
authors.lastname: Şenbabaoğlu
authors.affiliation: Computational Biology Program, Memorial Sloan Kettering Cancer Center, New York, New York, United States of America.
authors.forename: Selçuk Onur
authors.initials: SO
authors.lastname: Sümer
authors.affiliation: Computational Biology Program, Memorial Sloan Kettering Cancer Center, New York, New York, United States of America.
authors.forename: Francisco
authors.initials: F
authors.lastname: Sánchez-Vega
authors.affiliation: Computational Biology Program, Memorial Sloan Kettering Cancer Center, New York, New York, United States of America.
authors.forename: Debra
authors.initials: D
authors.lastname: Bemis
authors.affiliation: Computational Biology Program, Memorial Sloan Kettering Cancer Center, New York, New York, United States of America.
authors.forename: Giovanni
authors.initials: G
authors.lastname: Ciriello
authors.affiliation: Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York, United States of America.
authors.forename: Nikolaus
authors.initials: N
authors.lastname: Schultz
authors.affiliation: Computational Biology Program, Memorial Sloan Kettering Cancer Center, New York, New York, United States of America.
authors.forename: Chris
authors.initials: C
authors.lastname: Sander
mesh_terms: D016000:Cluster Analysis; D030562:Databases, Protein; D020869:Gene Expression Profiling; D006801:Humans; D009363:Neoplasm Proteins / Q000032:analysis / Q000235:genetics / Q000378:metabolism*; D009369:Neoplasms / Q000235:genetics / Q000378:metabolism*; D025341:Principal Component Analysis; D060066:Protein Interaction Maps / Q000502:physiology*; D040901:Proteomics / Q000379:methods*; D012984:Software
chemical_list: D009363:Neoplasm Proteins