학술논문
Comparative oncogenomics identifies combinations of driver genes and drug targets in BRCA1-mutated breast cancer
Document Type
Original Paper
Author
Annunziato, Stefano; de Ruiter, Julian R.; Henneman, Linda; Brambillasca, Chiara S.; Lutz, Catrin; Vaillant, François; Ferrante, Federica; Drenth, Anne Paulien; van der Burg, Eline; Siteur, Bjørn; van Gerwen, Bas; de Bruijn, Roebi; van Miltenburg, Martine H.; Huijbers, Ivo J.; van de Ven, Marieke; Visvader, Jane E.; Lindeman, Geoffrey J.; Wessels, Lodewyk F. A.; Jonkers, Jos
Source
Nature Communications. 10(1)
Subject
Language
English
ISSN
2041-1723
Abstract
BRCA1-mutated breast cancer is primarily driven by DNA copy-number alterations (CNAs) containing large numbers of candidate driver genes. Validation of these candidates requires novel approaches for high-throughput in vivo perturbation of gene function. Here we develop genetically engineered mouse models (GEMMs) of BRCA1-deficient breast cancer that permit rapid introduction of putative drivers by either retargeting of GEMM-derived embryonic stem cells, lentivirus-mediated somatic overexpression or in situ CRISPR/Cas9-mediated gene disruption. We use these approaches to validate Myc, Met, Pten and Rb1 as bona fide drivers in BRCA1-associated mammary tumorigenesis. Iterative mouse modeling and comparative oncogenomics analysis show that MYC-overexpression strongly reshapes the CNA landscape of BRCA1-deficient mammary tumors and identify MCL1 as a collaborating driver in these tumors. Moreover, MCL1 inhibition potentiates the in vivo efficacy of PARP inhibition (PARPi), underscoring the therapeutic potential of this combination for treatment of BRCA1-mutated cancer patients with poor response to PARPi monotherapy.
It is difficult to identify cancer driver genes in cancers, for instance BRCA1 mutated breast cancer, that are characterised by large scale genomic alterations. Here, the authors develop genetically engineered mouse models of BRCA1-deficient breast cancer that allow highthroughput in vivo perturbation of candidate driver genes, validating drivers Myc, Met, Pten and Rb1, and identifying MCL1 as a collaborating driver whose targeting can impact efficacy of PARP inhibition.
It is difficult to identify cancer driver genes in cancers, for instance BRCA1 mutated breast cancer, that are characterised by large scale genomic alterations. Here, the authors develop genetically engineered mouse models of BRCA1-deficient breast cancer that allow highthroughput in vivo perturbation of candidate driver genes, validating drivers Myc, Met, Pten and Rb1, and identifying MCL1 as a collaborating driver whose targeting can impact efficacy of PARP inhibition.