Effect of Aromatase Inhibition on Functional Gene Modules in Estrogen Receptor–Positive Breast Cancer and Their Relationship with Antiproliferative Response

Purpose: To investigate potential associations between gene modules representing key biologic processes and response to aromatase inhibitors (AI) in estrogen receptor–positive (ER+) breast cancer. Patients and Methods: Paired gene expression and Ki67 protein expression were available from 69 postmenopausal women with ER+ early breast cancer, at baseline and 2 weeks post-anastrozole treatment, in the presurgical setting. Functional gene modules (n = 26) were retrieved from published studies and their module scores were computed before and after elimination of proliferation-associated genes (PAG). Ki67 and module scores were assessed at baseline and 2 weeks post-anastrozole. Unsupervised clustering was used to assess associations between modules and Ki67. Results: Proliferation-based modules were highly correlated with Ki67 expression both pretreatment and on-treatment. At baseline with and without PAGs, Ki67 expression was significantly inversely correlated with ERG, ESR1.2, SET, and PIK3CA modules. Modules measuring estrogen signaling strongly predicted antiproliferative response to therapy with and without PAGs. Baseline expression of insulin-like growth factor-1 (IGF-I) module predicted a poor change in Ki67-implicating genes within the module as involved in de novo resistance to AIs. High expression of Immune.2.STAT1 module pretreatment predicted poor antiproliferative response to therapy. A significant association between estrogen-regulated genes modules (ESR1, ESR1-2, SET, and ERG) was evident post AI. Conclusions: Multiple processes and pathways are affected by AI treatment in ER+ breast cancer. Modules closely associated with ESR1 expression were predictive of good antiproliferative response to AIs, but modules representing immune activity and IGF-I/MAPK were predictive of poor Ki67 response, supporting their therapeutic targeting in combination with AIs. Clin Cancer Res; 20(9); 2485–94. ©2014 AACR.