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Alexandra Capellini,1,* Matthew Williams,1,* Kenan Onel,1– 3 Kuan-Lin Huang1– 3 1Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, USA, New York, NY, 10029, USA; 2Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, USA, New York, NY, 10029, USA; 3Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, USA, New York, NY, 10029, USA*These authors contributed equally to this workCorrespondence: Kenan Onel; Kuan-Lin HuangDepartment of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, Box 1498, New York, NY, 10029, USATel +1 212-824-6134Email kenan.onel@mssm.edu; kuan-lin.huang@mssm.eduAbstract: The canonical model for hereditary cancer predisposition is a cancer predisposition gene (CPG) that drives either one or both of two fundamental hallmarks of cancer, defective genomic integrity and deregulated cell proliferation, ultimately resulting in the accumulation of mutations within cells. Thus, the genes most commonly associated with cancer-predisposing genetic syndromes are tumor suppressor genes that regulate DNA repair (eg, BRCA1, BRCA2, MMR genes) and/or cell cycle (eg, APC, RB1). In recent years, however, the spectrum of high-penetrance CPGs has expanded considerably to include genes in non-canonical pathways such as oncogenic signaling, metabolism, and protein translation. We propose here that, given the variety of pathways that may ultimately affect genome integrity and cell proliferation, the model of cancer genetic predisposition needs to be expanded to account for diverse mechanisms. This synthesis calls for modeling and multi-omic studies applying novel experimental and computational approaches to understand cancer genetic predisposition.Keywords: cancer, genetics, predisposition, multi-omics, genomics, oncogenesis |
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