Tumor Collagens Predict Genetic Features and Patient Outcomes

The extracellular matrix (ECM) is a critical determinant of tumor fate that reflects the output from myriad cell types in the tumor. The impact of ECM composition on patient outcomes remains largely unknown. Collagens constitute the principal substrate of the tumor ECM and yet have been largely overlooked as simple structural proteins. When RNA transcription from solid tumors in The Cancer Genome Atlas (TCGA) was retrospectively analyzed for expression patterns across the 43 human collagen genes, strong associations with survival, specific immunoenvironments, somatic gene mutations, copy number variations, and aneuploidy were revealed in all cancer types. Clustering by matrisome and collagen expression grouped tumors by tissue of origin. We developed a machine learning classifier that predicts aneuploidy, and chromosome arm copy number alteration (CNA) status based on collagen expression alone with high accuracy in many cancer types, suggesting a strong relationship between ECM context and specific molecular alterations. These findings have broad implications in defining the relationship between cancer-related genetic defects and the tumor microenvironment to improve prognosis and therapeutic targeting for patient care, opening new avenues of investigation to define tumor ecosystems.