03/19/2026

An RNA-Based Survival Model Predicting Real-World Response to Trastuzumab Deruxtecan

AACR 2026 PRESENTATION
Authors Klemen Žiberna, Anže Lovše, Žan Kuralt, Janez Kokošar, Marcel Levstek, Luka Ausec, Miha Štajdohar, Rafael Rosengarten, Mark Uhlik, Joshua Wheeler

Abstract
Antibody-drug conjugates (ADCs) such as trastuzumab deruxtecan (T-DXd, Enhertu) have redefined therapy for HER2-expressing breast cancer, yet clinical benefit remains unpredictable across HER2-positive, -low, and -ultralow disease. Current IHC/FISH diagnostics quantify receptor abundance but fail to capture the molecular state that governs ADC sensitivity.To address this gap, we developed an RNA-based survival model for Enhertu using the Genialis Supermodel, a large molecular foundation model. The Supermodel maps gene expression into hundreds of biomodules, algorithmic representations of biology that capture diverse oncologic hallmarks including signaling pathways, stress responses, and drug-target mechanisms. We used biomodules specific to ADC mechanisms-of-action as input features in predictive models that learn biological patterns associated with T-DXd response.In a real-world clinical cohort (n≈90 T-DXd-treated patients) from the Tempus real-world multimodal database, we performed survival modeling of time-to-next-treatment (rwTTNT). Stratified nested cross-validation was used to assess model robustness and predictive performance. Prognostic specificity was assessed in prior-line rwTTNT and in an independent clinically matched cohort. The model showed statistically significant discrimination (C-index 0.632, HR 2.22 [95% CI 1.14-4.35], p = 0.017). Predicted-benefit patients had longer rwTTNT (345 vs 245 days), and no prognostic signal appeared in control cohorts (C-index ≈ 0.5), suggesting predictive specificity. Top predictive features aligned with ADC biology, including TOP3B and TOP2A (topoisomerase payload), ATM and TP53 (DNA damage response), HIF1A (hypoxia), ESR1 (hormone signaling), and XBP1/NFATC1 (stress and immune regulation).This Enhertu survival model applies biologically structured AI to real-world RNA-seq data to reveal treatment-specific patterns of response. Integrating large-scale embeddings, mechanistic biomodules, and survival modeling, we identified biological programs related to DNA repair and stress response associated with T-DXd benefit.

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