Shared and distinct molecular signatures of resistance to different endocrine therapies (Alliance A011106).

Abstract

566 Background: Endocrine therapy (ET) resistance (ETR) remains a primary challenge in ER+ breast cancer. Analyzing pretreatment tumor transcriptomes across trials with early response endpoints can reveal shared and specific ETR signatures. This study utilizes baseline RNA data from the Phase III ALTERNATE trial (Alliance A011106, NCT01953588; Anastrozole [A], Fulvestrant [F], or AF) and the ACOSOG Z1031B trial (NCT00824941) to identify predictors of early Ki67 response in postmenopausal ER+/HER2– patients. Methods: ETR was defined as week-4 on-treatment Ki67 >10%. Baseline gene expression from ALTERNATE was analyzed to identify differentially expressed (DE) genes (Wilcoxon test, P<0.05) and Hallmark pathways associated with ETR, both across and within individual treatment arms. Feature selection was performed using mixOmics. A Pan-Endocrine Therapy Signature (PETS) was developed by uniting DE genes identified across all three ALTERNATE arms and Z1031B. All statistical analyses were conducted in R (P<0.05). Results: Overall ETR rate in the ALTERNATE RNA-seq cohort (n=733) was 26%. ETR was associated with high Risk of Recurrence (ROR), Oncotype RS, and Mammaprint scores (calculated from RNA-seq data in research setting). In luminal tumors (n=649), ETR was linked to chr 3q13.33, 8q24.13, and 20q13.12 cytoband upregulation and 17q21, 18q23, 3p21.1, and 10q24.32 cytoband downregulation. ETR tumors showed T-cell, E2F target, and interferon-γ enrichment; sensitive tumors favored early estrogen response and muscle differentiation. At individual gene level, high MYBL2 , PIF1 , TROAP and with low HJURP predicted ETR across all samples (AUC>0.70). A deep learning model using all protein-coding genes achieved AUC 0.82 (training) and 0.79 (test) in predicting ETR. Cross-trial integration identified ETR-associated PETS, enriched for genomic instability. PETS performed comparably to established signatures and strongly correlated with MYBL2 signature (r=0.93). Top ETR predictors were MYBL2 , AURKB , and EME1 for Arm A and IL4I1 , TNFAIP6 , and ANLN for Arm AF. AF-resistant tumors were enriched for systemic lupus and RIG-I–like receptor signaling; sensitive tumors favored PI3K–AKT, EGFR TKI resistance, AMPK, and insulin signaling. Conclusions: Baseline transcriptomics identify shared and therapy-specific ETR markers. The 15-gene PETS defines a convergent resistance signature, performing similar to established signatures in predicting ETR and correlating with MYBL2. Enrichment of cell-cycle and immune pathways in resistant tumors may suggest patient stratification approach for alternative or combinatorial strategies to overcome early ETR in ER+ breast cancer. Acknowledgement: https://acknowledgments.alliancefound.org. Support: U10CA180821, U10CA180882, U24CA1. Clinical trial information: NCT01953588 .