Goldstein, Theodore C.

503 Background: To correlate clinical features of estrogen receptor positive breast cancer with somatic mutations, massively parallel sequencing (MPS) was applied to tumor and normal DNAs accrued from patients treated with neoadjuvant aromatase inhibitors (AI). Methods: MPS was applied to 77 baseline tumor biopsy samples from the preoperative letrozole trial (JACS 2009: 208, 906) and the Z1031 trial (JCO 2011: 29, 2342) followed by targeted sequencing in another 240 trial samples. Standard statistical approaches were used to compare mutation status and clinical parameters and pathway-based correlation was used to assess interactions between signaling perturbations induced by gene mutations and response to neoadjuvant AI. Results: Eighteen genes were significantly mutated above background. Aside from PIK3CA mutations, the list is dominated by loss-of-function mutations in tumor suppressor genes. Five (RUNX1, CBFP, MYH9, MLL3 and SF3B1) have been previously linked to benign and malignant hematopoietic disorders. Clinical correlation revealed that TP53 mutation was associated with PAM50 LumB status, high-grade histology and high proliferation rates whereas loss-of-function mutations in MAP3K1 associate with PAM50 lumA status, low proliferation rates, and low grade histology. Mutations in GATA3 were associated with greater suppression of proliferation upon AI treatment suggesting mutGATA3 may predict endocrine response. Notably, mutations in MAP3K1 were more common in PIK3CA mutant cases, suggesting cooperation. Pathway analysis demonstrated that rare MAP2K4 mutations produce similar pathway perturbations as MAP3K1 mutation, a logical finding since MAP2K4 is a substrate for MAP3K1. Signaling network patterns driven by lncRNA MALAT1 mutations were associated with multiple poor clinical outcome features. Rare mutations in druggable kinases included two in the kinase domain of HER2. Conclusions: Tumor heterogeneity in luminal-type breast cancer is driven by specific patterns of somatic mutations, however most druggable or potentially prognostic mutations are infrequent. Prospective clinical trials based on these findings will require comprehensive genome sequencing approaches and large scale investigations.

Abstract Patient-derived tumor xenograft (PDX) mouse models have emerged as an important oncology research platform to study tumor evolution, mechanisms of drug response and resistance, and tailoring chemotherapeutic approaches for individual patients. The lack of robust standards for reporting on PDX models has hampered the ability of researchers to find relevant PDX models and associated data. Here we present the PDX models minimal information standard (PDX-MI) for reporting on the generation, quality assurance, and use of PDX models. PDX-MI defines the minimal information for describing the clinical attributes of a patient's tumor, the processes of implantation and passaging of tumors in a host mouse strain, quality assurance methods, and the use of PDX models in cancer research. Adherence to PDX-MI standards will facilitate accurate search results for oncology models and their associated data across distributed repository databases and promote reproducibility in research studies using these models. Cancer Res; 77(21); e62–66. ©2017 AACR.