Genomic profiling of murine mammary tumors identifies potential personalized drug targets for p53-deficient mammary cancers

Abstract

Targeted therapies against basal-like breast tumors, which are typically 'triple-negative breast cancers (TNBCs)', remain an important unmet clinical need. Somatic TP53 mutations are the most common genetic event in basal-like breast tumors and TNBC. To identify additional drivers and possible drug targets of this subtype, a comparative study between human and murine tumors was performed by utilizing a murine Trp53-null mammary transplant tumor model. We show that two subsets of murine Trp53-null mammary transplant tumors resemble aspects of the human basal-like subtype. DNA-microarray, whole-genome and exome-based sequencing approaches were used to interrogate the secondary genetic aberrations of these tumors, which were then compared to human basal-like tumors to identify conserved somatic genetic features. DNA copy-number variation produced the largest number of conserved candidate personalized drug targets. These candidates were filtered using a DNA-RNA Pearson correlation cut-off and a requirement that the gene was deemed essential in at least 5% of human breast cancer cell lines from an RNA-mediated interference screen database. Five potential personalized drug target genes, which were spontaneously amplified loci in both murine and human basal-like tumors, were identified: Cul4a, Lamp1, Met, Pnpla6 and Tubgcp3 As a proof of concept, inhibition of Met using crizotinib caused Met-amplified murine tumors to initially undergo complete regression. This study identifies Met as a promising drug target in a subset of murine Trp53-null tumors, thus identifying a potential shared driver with a subset of human basal-like breast cancers. Our results also highlight the importance of comparative genomic studies for discovering personalized drug targets and for providing a preclinical model for further investigations of key tumor signaling pathways.

Keywords: Basal-like breast cancer; Exome sequencing; Genetically engineered mouse models; Personalized genomics; Whole-genome sequencing; p53.