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Updated: May 6, 2026
Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection
Published on: July 6, 2022
Spatial Multiomics Reveal Insights Into ADC Efficacy
Osman Goni1, Niklas Klümper2, Maria Del Mar Muñiz Moreno3
1Division of Nephrology and Clinical Immunology, Medical Faculty, RWTH Aachen University, Aachen, Germany.
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.
Insights
Spatial multiomics can predict antibody-drug conjugate (ADC) treatment responses in solid tumors. This approach correlates antigen expression and cell states with outcomes, aiding in patient stratification and therapy optimization for cancers like metastatic urothelial carcinoma.
Area of Science:
- Oncology
- Molecular Biology
- Bioinformatics
Background:
- Antibody-drug conjugates (ADCs) show promise in solid tumor treatment but exhibit variable patient responses.
- Multiple ADC targets (e.g., NECTIN-4, HER2, TROP2) are under investigation for metastatic urothelial cancer.
- Predicting treatment efficacy and toxicity remains a challenge in ADC therapy.
Purpose of the Study:
- To review the utility of spatial multiomics in understanding ADC mechanisms and resistance.
- To highlight advancements in spatial transcriptomics and proteomics for ADC research.
- To propose an integrated framework for patient stratification and therapy development using spatial data.
Main Methods:
- Spatial multiomics integrates high-plex RNA sequencing and multiplex protein imaging with spatial coordinates.
- Analysis correlates ADC antigen expression, cell states, and tissue microenvironment with treatment outcomes.
- Machine learning and deep learning approaches are applied to spatial data for predictive analytics.
Main Results:
- Spatial multiomics provides direct correlation between molecular profiles, tissue architecture, and ADC response.
- Technological advancements enable detailed analysis of ADC action and resistance mechanisms.
- The framework facilitates patient stratification and prediction of on-/off-target toxicities.
Conclusions:
- Spatial multiomics is crucial for decoding ADC efficacy and resistance in solid tumors, especially metastatic urothelial cancer.
- Integrating spatial data with AI-driven analytics can personalize ADC treatment strategies.
- This approach can guide the design of next-generation ADCs and combination therapies.
