Abstract B045: Deep learning–guided spatial dissection of melanoma uncovers compartmentalized tumor states associated with response and resistance to immunotherapy and its combination with MAPK inhibitors
Kalpit ShahAbstract
Purpose:
Intratumoral heterogeneity is a key determinant of immunotherapy resistance, yet current biomarkers largely rely on single-modality features—such as CD8+ T cell infiltration, IFNG expression, or checkpoint ligand staining—without accounting for the spatial complexity of the tumor microenvironment. Composite biomarkers that integrate both tumor-intrinsic programs and their spatial organization remain underdeveloped. To address this, we combined machine learning–based molecular classification with spatial transcriptomics to uncover how transcriptional tumor states are physically structured within the tumor architecture and how this organization governs response or resistance to immunotherapy.
Methods:
We leveraged non-negative matrix factorization (NMF) and tumor microenvirnoment metabolism signatures with bulk RNA-seq data from >700 melanoma tumors across multiple phase I–III clinical trials. These programs were mapped onto spatial transcriptomics data using Tangram 2.0, a deep learning model that aligns single-cell or deconvolved bulk data to spatial coordinates. We analyzed cell–cell interactions, tumor-stromal patterning, and radial gene gradients to interpret functional spatial architecture.
Results:
Spatial mapping revealed that melanoma tumors often harbor multiple transcriptional programs arranged in discrete, non-overlapping tissue regions. Notably, undifferentiated (UR) and differentiated (DC) programs—associated with distinct therapeutic responses—co-existed within the same tumor but remained compartmentalized. Only spatial domains with DC-like features exhibited MAPK inhibitor–induced MHC-I expression and immune cell infiltration, whereas UR regions remained immune-excluded and were enriched for fibroblasts and extracellular matrix remodeling. These findings suggest that spatial segregation of tumor states, rather than overall tumor composition, may predict response to immunotherapy. Conclusions: This study presents a machine learning–enabled spatial framework that links transcriptional identity to physical tissue structure in melanoma. By uncovering how spatial architecture constrains therapeutic response, our work highlights the value of AI-driven models like Tangram for interpreting spatial data and guiding precision immuno-oncology.
Citation Format:
Kalpit Shah. Deep learning–guided spatial dissection of melanoma uncovers compartmentalized tumor states associated with response and resistance to immunotherapy and its combination with MAPK inhibitors [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Artificial Intelligence and Machine Learning; 2025 Jul 10-12; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(13_Suppl):Abstract nr B045.