Spatial multiomics profiling can reveal how tumor heterogeneity and microenvironment influence ADC efficacy. Combining spatial transcriptomics, proteomics, and AI-based analysis improves the identification of tissue factors influencing therapy response. This integrative approach potentially enables prediction of response, resistance, and optimized patient selection, which we review here.
ABSTRACT
Antibody–drug conjugates (ADCs) have transformed the therapeutic landscape of solid tumors; however, responses remain heterogeneous and complex to predict. In addition, a growing number of multiple ADC targets are either approved or in late-stage clinical development, such as NECTIN-4, HER2, or TROP2 for metastatic urothelial cancer. Spatial multiomics—representing next-generation methods that couple high-plex RNA sequencing and multiplex protein imaging with precise x–y–z coordinates within tissues—offer a direct way to correlate (ADC) antigen expression, cell state information, and micro-anatomical context with patient treatment outcomes. In this review, we highlight suitability and technological advancements in current spatial transcriptomics and proteomics approaches to decode modes of action and resistance to ADCs and extract biological insights, particularly in metastatic urothelial cancer—and propose an integrative framework that combines spatial readouts with machine and/or deep learning-driven analytics to stratify patients, forecast on- and off-target toxicities, and guide next-generation linker–payload designs or combination therapies.