1. Integrative analyses identify modulators of response to neoadjuvant aromatase inhibitors in patients with early breast cancer
- Author
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Anita K. Dunbier, Jorge S. Reis-Filho, Alexey Larionov, Ash Nerurkar, Lesley-Ann Martin, Ricardo Ribas, Elena Lopez-Knowles, Zara Ghazoui, Peter Osin, J Michael Dixon, Paul M. Wilkerson, Mitch Dowsett, Alan Mackay, Aradhana Rani, Helen Anderson, Lorna Renshaw, and William R. Miller
- Subjects
Oncology ,medicine.medical_specialty ,Antineoplastic Agents, Hormonal ,DNA Copy Number Variations ,Microarray ,medicine.medical_treatment ,Estrogen receptor ,Breast Neoplasms ,Mice ,Breast cancer ,Cell Line, Tumor ,Internal medicine ,Antineoplastic Combined Chemotherapy Protocols ,Biomarkers, Tumor ,medicine ,Animals ,Choline Kinase ,Cluster Analysis ,Humans ,Aromatase ,Neoadjuvant therapy ,Cell Proliferation ,Neoplasm Staging ,Medicine(all) ,Chromosome Aberrations ,Hormone response element ,Comparative Genomic Hybridization ,biology ,Aromatase Inhibitors ,Gene Expression Profiling ,Reproducibility of Results ,Prognosis ,medicine.disease ,Neoadjuvant Therapy ,3. Good health ,Gene Expression Regulation, Neoplastic ,Gene expression profiling ,Treatment Outcome ,Receptors, Estrogen ,biology.protein ,Female ,Research Article ,Comparative genomic hybridization - Abstract
Introduction Aromatase inhibitors (AIs) are a vital component of estrogen receptor positive (ER+) breast cancer treatment. De novo and acquired resistance, however, is common. The aims of this study were to relate patterns of copy number aberrations to molecular and proliferative response to AIs, to study differences in the patterns of copy number aberrations between breast cancer samples pre- and post-AI neoadjuvant therapy, and to identify putative biomarkers for resistance to neoadjuvant AI therapy using an integrative analysis approach. Methods Samples from 84 patients derived from two neoadjuvant AI therapy trials were subjected to copy number profiling by microarray-based comparative genomic hybridisation (aCGH, n = 84), gene expression profiling (n = 47), matched pre- and post-AI aCGH (n = 19 pairs) and Ki67-based AI-response analysis (n = 39). Results Integrative analysis of these datasets identified a set of nine genes that, when amplified, were associated with a poor response to AIs, and were significantly overexpressed when amplified, including CHKA, LRP5 and SAPS3. Functional validation in vitro, using cell lines with and without amplification of these genes (SUM44, MDA-MB134-VI, T47D and MCF7) and a model of acquired AI-resistance (MCF7-LTED) identified CHKA as a gene that when amplified modulates estrogen receptor (ER)-driven proliferation, ER/estrogen response element (ERE) transactivation, expression of ER-regulated genes and phosphorylation of V-AKT murine thymoma viral oncogene homolog 1 (AKT1). Conclusions These data provide a rationale for investigation of the role of CHKA in further models of de novo and acquired resistance to AIs, and provide proof of concept that integrative genomic analyses can identify biologically relevant modulators of AI response. Electronic supplementary material The online version of this article (doi:10.1186/s13058-015-0532-0) contains supplementary material, which is available to authorized users.
- Published
- 2015
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