selected functional events; oncogenic signatures; epigenetic alterations; cancer targeted combination therapies; therapeutically actionable signatures
Mina Marco, Iyer Arvind, Tavernari Daniele, Raynaud Franck, Ciriello Giovanni (2020), Discovering functional evolutionary dependencies in human cancers, in Nature Genetics
, 52(11), 1198-1207.
Saghafinia Sadegh, Mina Marco, Riggi Nicolo, Hanahan Douglas, Ciriello Giovanni (2018), Pan-Cancer Landscape of Aberrant DNA Methylation across Human Tumors, in Cell Reports
, 25(4), 1066-1080.e8.
Sanchez-Vega Francisco, Mina Marco, Armenia Joshua, Chatila Walid K., Luna Augustin, La Konnor C., Dimitriadoy Sofia, Liu David L., Kantheti Havish S., Saghafinia Sadegh, Chakravarty Debyani, Daian Foysal, Gao Qingsong, Bailey Matthew H., Liang Wen-Wei, Foltz Steven M., Shmulevich Ilya, Ding Li, Heins Zachary, Ochoa Angelica, Gross Benjamin, Gao Jianjiong, Zhang Hongxin, Kundra Ritika, et al. (2018), Oncogenic Signaling Pathways in The Cancer Genome Atlas, in Cell
, 173(2), 321-337.e10.
Mina Marco, Raynaud Franck, Tavernari Daniele, Battistello Elena, Sungalee Stephanie, Saghafinia Sadegh, Laessle Titouan, Sanchez-Vega Francisco, Schultz Nikolaus, Oricchio Elisa, Ciriello Giovanni (2017), Conditional Selection of Genomic Alterations Dictates Cancer Evolution and Oncogenic Dependencies, in Cancer Cell
, 32(2), 155-168.e6.
Cancer displays great molecular diversity both between and within patients’ tumors. This heterogeneity results in inconsistent and unpredictable therapeutic response. Large genomic studies have allowed a comprehensive exploration of the genetic and epigenetic bases of cancer. However, the quantity and variety of modifications observed severely challenge the identification of the few events that are functional and ultimately can be targeted for therapy. Finally, cancer phenotypes are determined by more than one functional event acting in concert, and these genetic contexts determine prognosis and treatment response. The overall goal of this project is to identify functional and actionable sets of molecular alterations, here termed signatures, to design therapeutic protocols matching drug combinations with genetically defined tumor classes.The proposed research builds on computational biology and cancer genomics work from our group [1, 2, 3] and the largest collection of cancer genomes from The Cancer Genome Atlas (TCGA) including 11144 tumors from 33 cancer types. This dataset has been only recently finalized and as a group leader in the consortium, we are in a unique position to access this data and successfully pursue the proposed goals. The project’s aims are:AIM 1: From thousands of molecular alterations to selected functional events in cancer. Here, we will distill a set of selected functional events integrating genetic and epigenetic modifications. We will complement analyses of copy number changes and mutations with a novel approach to determine aberrant patterns of DNA methylation. Finally, we will integrate multiple types of events to identify signatures of alterations characteristic of tumor subclasses and reflecting distinct oncogenic mechanisms (oncogenic signatures). AIM 2: From oncogenic selected events to therapeutically actionable targets. Here, we will classify genetic and epigenetic events as therapeutically actionable and/or response-associated to selected drug compounds. We will integrate this classification into the context of signatures to map clinically relevant events with single or drug combinations in well characterized molecular contexts. This approach will ultimately generate hypotheses testable in the laboratory.AIM 3: From therapeutic targets to actionable signatures: a new paradigm to harness tumor interdependencies. Here, we will identify human tumor subclasses characterized by signatures of clinically relevant events. We will analyze these classes at different levels of granularity to account for functionally similar alterations and compounds. We will nominate actionable signatures predictive of drug response in specific genetic contexts and establish a framework for pre-clinical therapeutic studies. Results from this project will demonstrate the potential of combining systematic analyses based on actionable alterations across large tumor cohorts to design therapeutic protocols tailored to specific patient populations.