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

Area of Science:

• Oncology
• Biomedical Engineering
• Genomics

Background:

• The extracellular matrix (ECM) significantly influences tumor progression and treatment response.
• Tumor ECM heterogeneity poses challenges in assessing its prognostic value.
• Current methods struggle to capture the dynamic impact of ECM on tumor behavior.

Purpose of the Study:

• To develop an ECM-guided patient stratification pipeline for lung cancer.
• To integrate multi-omic data for comprehensive ECM analysis.
• To identify distinct patient groups based on ECM characteristics and their clinical implications.

Main Methods:

• Integration of multi-omic data from lung cancer patients.
• Development of an ECM-guided patient stratification pipeline.
• Network modeling for intracellular signaling analysis.
• Experimental validation of drug responses in different ECM microenvironments.

Main Results:

• Four distinct patient groups (ECM-grades) with varied clinical features and mutation profiles were identified.
• Higher ECM-grades showed enrichment of epithelial-mesenchymal transition (EMT) and cancer stemness pathways.
• Drug proximity analysis predicted ECM-grade-dependent efficacy for olaparib and ECM-insensitivity for erlotinib, which were experimentally validated.

Conclusions:

• The ECM-mediated stratification approach effectively captures ECM heterogeneity in lung cancer.
• This system identifies patient groups amenable to specific therapeutic strategies.
• The findings support personalized treatment approaches based on ECM characteristics.