Highly variable outcomes are observed in patients with estrogen receptor positive (ER+) breast cancer who undergo preoperative estrogen deprivation therapy with aromatase inhibitors (AI). In this study, 46 tumor and normal genomes and 31 exomes of participants selected from two clinical trials of neoadjuvant AI therapy on ER+ breast cancer were sequenced to identify somatic alterations that correlate with response to AI, to screen for therapeutic targets and to elucidate the genetic landscape of ER+ breast cancer. Of the significantly mutated genes, GATA3 mutations correlated with low post-treatment Ki67 and up-regulation of IGF1R mRNA. TP53 mutations associated with multiple markers of poor outcome and mutations in MAP3K1 associated with inferior clinical response. Mutations in MAP3K1 and MAP2K4 were mutually exclusive and positively associated with mutations in PIK3CA. The majority of tumors were multiclonal, as defined by distinct mutation clusters. A number of potential therapeutic targets were provided by both common and rare variants. Potential therapeutically relevant ... (Show More)

Highly variable outcomes are observed in patients with estrogen receptor positive (ER+) breast cancer who undergo preoperative estrogen deprivation therapy with aromatase inhibitors (AI). In this study, 46 tumor and normal genomes and 31 exomes of participants selected from two clinical trials of neoadjuvant AI therapy on ER+ breast cancer were sequenced to identify somatic alterations that correlate with response to AI, to screen for therapeutic targets and to elucidate the genetic landscape of ER+ breast cancer. Of the significantly mutated genes, GATA3 mutations correlated with low post-treatment Ki67 and up-regulation of IGF1R mRNA. TP53 mutations associated with multiple markers of poor outcome and mutations in MAP3K1 associated with inferior clinical response. Mutations in MAP3K1 and MAP2K4 were mutually exclusive and positively associated with mutations in PIK3CA. The majority of tumors were multiclonal, as defined by distinct mutation clusters. A number of potential therapeutic targets were provided by both common and rare variants. Potential therapeutically relevant mutations of tryosine kinase included ERBB2, KIT, PDGFRA, DDR1, CSF1R, and MET.