Abstract 3351: From bench to bedside: generalizable AI model for ADC biomarker evaluation in NSCLC
Antibody-drug conjugates (ADCs) targeting trophoblast cell-surface antigen 2 (TROP-2) and hepatocyte growth factor receptor (cMET) are promising therapies for non-small cell lung cancer (NSCLC). However, their clinical application requires robust and rapid biomarker evaluation that addresses expression heterogeneity and avoid interobserver variability. Current approaches based on pathologist assessments are limited by subjectivity and scalability. This study aimed to develop a generalizable AI model for ADC biomarker evaluation, trained on TROP-2 and inferred on cMET, to validate its adaptability across markers. Additionally, the model’s performance was compared with expert pathologists to assess its clinical utility. Finally, biomarker prevalence in the two main NSCLC subtypes, namely adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC), was evaluated.
