Cristea, S. | Search bioRxiv

bioRxiv2018

Cyclin D-Cdk4,6 drives cell cycle progression via the retinoblastoma protein’s C-terminal helix

The cyclin-dependent kinases Cdk4 and Cdk6 form complexes with D-type cyclins to drive cell proliferation. A well-known target of cyclin D-Cdk4,6 is the retinoblastoma protein, Rb, which inhibits cell cycle progression until its inactivation by phosphorylation. However, the role of cyclin D-Cdk4,6 phosphorylation of Rb in cell cycle progression is unclear because Rb can be phosphorylated by other cyclin-Cdk complexes and cyclin D-Cdk4,6 complexes have other targets that may drive cell division. Here, we show that cyclin D-Cdk4,6 docks one side of an alpha-helix in the C-terminus of Rb, which is not recognized by cyclins E, A, and B. This helix-based docking mechanism is shared by the p107 and p130 Rb-family members across metazoans. Mutation of the Rb C-terminal helix prevents phosphorylation, promotes G1 arrest, and enhances Rbs tumor suppressive function. Our work conclusively demonstrates that the cyclin D-Rb interaction drives cell division and defines a new class of cyclin-based docking mechanisms.

cell biology↗

bioRxiv2018

ModulOmics: Integrating Multi-Omics Data to Identify Cancer Driver Modules

The identification of molecular pathways driving cancer progression is a fundamental unsolved problem in tumorigenesis, which can substantially further our understanding of cancer mechanisms and inform the development of targeted therapies. Most current approaches to address this problem use primarily somatic mutations, not fully exploiting additional layers of biological information. Here, we describe ModulOmics, a method to de novo identify cancer driver pathways, or modules, by integrating multiple data types (protein-protein interactions, mutual exclusivity of mutations or copy number alterations, transcriptional co-regulation, and RNA co-expression) into a single probabilistic model. To efficiently search the exponential space of candidate modules, ModulOmics employs a two-step optimization procedure that combines integer linear programming with stochastic search. Across several cancer types, ModulOmics identifies highly functionally connected modules enriched with cancer driver genes, outperforming state-of-the-art methods. For breast cancer subtypes, the inferred modules recapitulate known molecular mechanisms and suggest novel subtype-specific functionalities. These findings are supported by an independent patient cohort, as well as independent proteomic and phosphoproteomic datasets.

cancer biology↗