Abstract S1-01: Whole exome and transcriptome sequencing of resistant ER+ metastatic breast cancer

Abstract

Background: While great strides have been made in the treatment of estrogen receptor-positive (ER+) metastatic breast cancer (MBC), therapeutic resistance invariably occurs. A better understanding of the underlying resistance mechanisms is critical to enable durable control of this disease. Methods: We performed whole exome sequencing (WES) and transcriptome sequencing (RNA-seq) on metastatic tumor biopsies from 88 patients with ER+ MBC who had developed resistance to one or more ER-directed therapies. For 27 of these patients, we sequenced the treatment-naïve primary tumors for comparison to the resistant specimens. Tumors were analyzed for point mutations, insertions/deletions, copy number alterations, translocations, and gene expression. Detailed clinicopathologic data was collected for each patient and linked to the genomic information. Results: WES of all metastatic samples demonstrated several recurrently altered genes whose incidence differed significantly from primary, treatment-naïve ER+ breast cancers sequenced in the TCGA study (TCGA). These include ESR1 mutations (n=17, 19.3%; 32.86 fold enrichment, q.value<7.5e-12), CCND1 amplification (n=52, 59.1%; 2.3 fold enrichment, q.value<0.0073), and MAP2K4 biallelic inactivation (n=14, 15.9%; 3.04 fold enrichment, q.value< 0.054). Comparing to matched primary samples from the same patient, many alterations were found to be acquired in several cases, including for ESR1, ERBB2, PIK3CA, PTEN, RB1, AKT1, and others. Initial analysis of RNA-seq data from metastatic samples (n=59) allowed classification of individual resistance mechanisms into broader resistance modes based on the observed transcriptional state. Conclusions: We present a genomic landscape of resistant ER+ MBC using WES and RNA-seq. Multiple genes were recurrently altered in these tumors at significantly higher rates than in ER+ primary breast cancer. When compared with matched primary tumors from the same patient, alterations in these and other genes were often found to be acquired after treatment, suggesting a role in resistance to ER-directed therapies and/or metastasis. Potential resistance mechanisms appear to fall into several categories; integrating RNA-seq data may enhance the ability to identify these categories even when genomic alterations are not identified. Multiple clinically relevant genomic and molecular alterations are identified in metastatic biopsies– with implications for choice of next therapy, clinical trial eligibility, and novel drug targets. Citation Format: Cohen O, Kim D, Oh C, Waks A, Oliver N, Helvie K, Marini L, Rotem A, Lloyd M, Stover D, Adalsteinsson V, Freeman S, Ha G, Cibulskis C, Anderka K, Tamayo P, Johannessen C, Krop I, Garraway L, Winer E, Lin N, Wagle N. Whole exome and transcriptome sequencing of resistant ER+ metastatic breast cancer [abstract]. In: Proceedings of the 2016 San Antonio Breast Cancer Symposium; 2016 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2017;77(4 Suppl):Abstract nr S1-01.

KDM5 Histone Demethylase Activity Links Cellular Transcriptomic Heterogeneity to Therapeutic Resistance

“…We found numerous genetic variants present in both fulvestrant- and tamoxifen-resistant cells, and GSEA showed that the expression of genes downstream of some of the genetic variants were significantly altered (Figure 6I and Table S6). Several of the genetic variants found in both FULVR and TAMR cells were related to glutamate metabolism (e.g., HIF1A , PCDHGA12 , TMX4 , and TNR ) and almost all of them were also detected in metastatic lesions of breast cancer patients resistant to endocrine therapies (Cohen et al, 2017) confirming their physiologic relevance.…”

“…Normalized microarray gene expression and clinical data of a cohort of 132 primary tumors from tamoxifen-treated patients followed up more than 5-years were obtained from GEO accession number GSE9893 (Chanrion et al, 2008). We also analyzed an unpublished dataset of RNA-seq gene expression RPKM (Reads Per Kilobase of transcript, per Million mapped reads) of a cohort of 109 ER + distant metastases that are part of the Metastatic Breast Cancer Project (Cohen et al, 2017). Breast cancer patients were >18 years of age and all but one female.…”

Prevalence and mutational determinants of high tumor mutation burden in breast cancer

“…For the subsequent hypermutated cohort analysis, we also included 8 additional patients (from a cohort of 222 patients) from our ongoing study of estrogen receptor (ER)-positive metastatic breast cancer in the Center for Cancer Precision Medicine at Dana-Farber Cancer Institute (DFCI-CCPM). 24 Prior to any study procedures, all patients provided written informed consent to whole exome sequencing of tumor and normal DNA, as approved by the Dana-Farber/Harvard Cancer Center Institutional Review Board (DF/HCC Protocol 05-246). Metastatic core biopsies were obtained from patients and samples were immediately snap frozen in optimal cutting temperature and stored in -80°C.…”

“…We hypothesized that hypermutated breast cancers may respond to immune checkpoint inhibitors regardless of the underlying mutational signature. To test this, we examined the treatment histories of 222 patients with metastatic breast cancer from our prospective metastatic biopsy cohort at DFCI 24 . We identified 8 pts (3.6%) with hypermutated breast cancer, of whom four had received treatment with anti-PD-1/PD-L1 based therapies.…”

A network modeling approach to elucidate drug resistance mechanisms and predict combinatorial drug treatments in breast cancer

“…The breast cancer model and its predictions will serve as a basis for guiding and interpreting drug resistance and drug combination studies in the context of ER+ and/or HER2+ breast cancer. The model can be expanded to incorporate multiple genetic alterations observed in breast cancer patient cohorts (38,40,76,77) by introducing these alterations into the model appropriately (e.g., as a node activation or inactivation). The inclusion of the most probable intrinsic or acquired resistance mechanisms to a treatment, informed by pre-, on-and post-treatment genetic characterization of tumors (77), will allow the identification and ranking of the combinatorial interventions that are effective even in the presence of tumor drug resistance.…”