| Literature DB >> 34591613 |
Fan Zheng1,2, Marcus R Kelly1,2, Dana J Ramms2,3,4, Marissa L Heintschel5, Kai Tao6,7, Beril Tutuncuoglu2,8,9,10, John J Lee1, Keiichiro Ono1, Helene Foussard8,9,10, Michael Chen1, Kari A Herrington11, Erica Silva1, Sophie N Liu1, Jing Chen1, Christopher Churas1, Nicholas Wilson1, Anton Kratz1,2, Rudolf T Pillich1,2, Devin N Patel1,2, Jisoo Park1,2, Brent Kuenzi1,2, Michael K Yu1, Katherine Licon1,2, Dexter Pratt1, Jason F Kreisberg1,2, Minkyu Kim2,8,9,10, Danielle L Swaney2,8,9,10, Xiaolin Nan6,7,12, Stephanie I Fraley5, J Silvio Gutkind2,3,4, Nevan J Krogan2,8,9,10, Trey Ideker1,2,3,5.
Abstract
A major goal of cancer research is to understand how mutations distributed across diverse genes affect common cellular systems, including multiprotein complexes and assemblies. Two challenges—how to comprehensively map such systems and how to identify which are under mutational selection—have hindered this understanding. Accordingly, we created a comprehensive map of cancer protein systems integrating both new and published multi-omic interaction data at multiple scales of analysis. We then developed a unified statistical model that pinpoints 395 specific systems under mutational selection across 13 cancer types. This map, called NeST (Nested Systems in Tumors), incorporates canonical processes and notable discoveries, including a PIK3CA-actomyosin complex that inhibits phosphatidylinositol 3-kinase signaling and recurrent mutations in collagen complexes that promote tumor proliferation. These systems can be used as clinical biomarkers and implicate a total of 548 genes in cancer evolution and progression. This work shows how disparate tumor mutations converge on protein assemblies at different scales.Entities:
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Year: 2021 PMID： 34591613 PMCID： PMC9126298 DOI： 10.1126/science.abf3067
Source DB: PubMed Journal: Science ISSN： 0036-8075 Impact factor: 63.714