Biology-informed predictive modeling and resistance monitoring in trastuzumab deruxtecan–treated metastatic breast cancer.
Abstract
1023 Background: The antibody-drug conjugate (ADC), trastuzumab deruxtecan (T-DXd), produces meaningful responses in metastatic breast cancer (mBC), yet current biomarkers inadequately identify likely responders or elucidate resistance mechanisms. We developed a biology-informed survival model to predict T-DXd benefit in real-world data, assess treatment specificity, and characterize resistance evolution at time of progression. Methods: We analyzed RNA-seq and clinical outcomes from 150 de-identified T-DXd–treated mBC patients in the Tempus real-world multimodal database. Baseline expression profiles were embedded into biology-structured biomodules using a large molecular foundation model trained on over 1-million transcriptomes, capturing processes relevant to ADC activity: DNA damage response (DDR), intracellular trafficking, cellular stress adaptation, and tumor microenvironment signaling. These biomodules informed a survival model trained to predict T-DXd–specific clinical benefit. Predictive specificity was evaluated in a matched cohort of T-DXd–eligible mBC patients treated with taxane-based chemotherapy and HER2-targeting therapy. Longitudinal tumor profiling from 15 patients with progressive disease were analyzed to monitor resistance evolution. Results: The survival model predicted and identified biologically distinct benefit groups within the T-DXd cohort, with minimal separation observed in a control standard-of-care cohort, confirming treatment-specific predictive value (HR 2.22 [95% CI 1.14–4.35], p = 0.017). Predicted-benefit patients had longer rwTTNT (median=345 vs 245 days), and no prognostic signal appeared in control cohorts (C-index = 0.51), suggesting predictive specificity. Longitudinal analysis of progressed patients revealed reproducible biological shifts at progression, including attenuation of programs associated with effective ADC engagement and upregulation of adaptive stress-response pathways. These molecular transitions correlated with clinical deterioration and earlier transition to subsequent therapy. Conclusions: This study delineates the biology associated with durable T-DXd benefit and captures resistance evolution at progression in real-world patients. These findings highlight opportunities to refine patient selection and identify therapeutically actionable biology driving acquired resistance to T-DXd in the mBC setting.
Article Details
Journal Info
Journal of Clinical Oncology
Lippincott Williams & Wilkins
Authors (11)
Josh Wheeler
Genialis, Inc., Boston, MA
Klemen Žiberna
Genialis, Inc, Boston, MA
Ben Terdich
Tempus AI, Inc., Chicago, IL
Frasier Glenn
Tempus AI, Inc., Chicago, IL
Michelle M. Stein
Tempus AI, Inc., Chicago, IL
Jonathan R. Dry
Tempus AI, Chicago, IL
Justin Guinney
Tempus AI, Inc., Chicago, IL
Michelle Ting-Lin
Tempus AI, Inc., Chicago, IL
Luka Ausec
Miha Riley Štajdohar
Genialis, Inc, Boston, MA
Mark Uhlik
Genialis, Inc, Boston, MA