Extracellular matrix-driven patient stratification and network modeling reveal distinct molecular grades with potential clinical implications.

The extracellular matrix (ECM) critically shapes tumor fate and treatment outcome, serving as a potent prognostic factor. Yet, its compositional heterogeneity across tumors makes it difficult to assess its impact on tumor dynamics. To address this, we introduce an ECM-guided patient stratification pipeline through integration of multi-omic data in lung cancer patients. We obtained four patient groups, representing ECM-grades that showed distinct clinical features, mutation profiles, and cellular heterogeneity. Investigation of patient-specific ECM-induced intracellular signaling via network modeling revealed strong enrichment of pathways and transcriptional regulators related to epithelial-mesenchymal transition (EMT) and cancer stemness in higher ECM-grades. Drug proximity analysis on ECM-grade specific networks predicted olaparib as an ECM-grade dependent therapeutic while erlotinib to be ECM-insensitive which were validated experimentally on lung tumor cells with distinct mutational profiles in response to differing ECM microenvironments. Overall, our ECM-mediated stratification approach is a robust system for capturing ECM heterogeneity and identifying patient groups that can be selectively targeted by distinct therapeutic strategies.