Integrating Genomics and Real-World Data to Predict Response to T-DXd in Metastatic Breast Cancer

Background: Metastatic breast cancer (MBC) is a major clinical challenge. Fam-trastuzumab deruxtecan-nxki (T-DXd), an antibody-drug conjugate targeting HER2, has shown efficacy across HER2+ and HER2-low subtypes. However, predictive biomarkers beyond HER2 status are underexplored in real-world settings. We leveraged real-world data from the Tempus database1 to evaluate associations between somatic mutations and clinical outcomes in a clinically annotated cohort of patients with MBC treated with T-DXd, aiming to identify genomic correlates of response and survival to inform patient selection and therapeutic strategies. Methods: We analyzed 124 patients with MBC with baseline tumor-normal matched sequencing data (Tempus xT 2). Endpoints included real-world best overall response (rwBOR), progression-free survival (rwPFS), time to next treatment (rwTTNT), and overall survival (rwOS). Patients were classified as responders (CR/PR) or nonresponders (SD/PD) based on curated rwBOR. Oncoplots identified frequently mutated genes by rwBOR and HER2 status. Logistic regression assessed rwBOR (nonresponder as reference); Cox proportional hazard models evaluated rwPFS, rwTTNT, and rwOS, adjusting for age at T-DXd initiation, ER/PR status, care plan, sampling time, and tissue location. Models were run for all variants and for pathogenic-only subsets, stratified by HER2 status. Genes with >4% mutation frequency were included (50-60 genes for all-variant models; 11-13 genes for pathogenic-only models). Results: The most frequently mutated genes were TP53 (48%), PIK3CA (31%), and GATA3 (17%). In all-variant models, DYNC2H1 was associated with worse rwBOR (HER2+: P= 0.006; HER2-low: P=0.049), rwOS (HER2-low: P=0.014), and rwTTNT (HER2-low: P=0.004). PIK3CA mutations correlated with improved rwBOR (HER2+: P=0.016), rwPFS (all: P=0.01; HER2-low: P=0.007), and rwOS (HER2-low: P=0.002). Additional genes with consistent associations included SPEN, POLQ, MED12, MAP2K4, ARID1B, SYNE1, KMT2C, and RB1. Pathogenic-only analyses confirmed PIK3CA as a key predictor across multiple endpoints. While most models yielded FDR-adjusted P values >0.2, we prioritized genes with nominal P<0.1 and consistent prognostic direction across endpoints as indicative of potential signal. Conclusions: Mutations such as PIK3CA were consistently correlated with improved outcome across HER2 subtypes, suggesting potential as predictive/prognostic biomarkers. Conversely, DYNC2H1 mutations correlated with poorer outcomes, particularly in HER2-low patients, implicating potential resistance mechanisms. These findings support integrating genomic data into real-world evidence frameworks to enhance patient stratification, personalize treatment, and guide biomarker-driven clinical trials in MBC. References: 1. www.tempus.com 2. Tempus-xT.v4_Validation Citation Format: Abraham Apfel, Alka A. Potdar, Yuanqing Ye, Viswanath Devanarayan, Evvie Jagoda, Shelley MacNeil, Yan Zhang, Pallavi Sachdev. Integrating genomics and real-world data to predict fam-trastuzumab deruxtecan response in metastatic breast cancer across HER2 subtypes [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 1038.