Classification of tumors by collagen expression reveals genotype-tumor ECM interactions

Although the relationship between genotype and the tumor environment remains unclear, tumors evolve in specific contexts such that the genotypes and tumor environment shape each other to create an ecosystem. We aim to improve classification of tumors by considering the whole tumor ecosystem by combining genotypes with components of the extracellular matrix (ECM) to develop new prognostic markers and improve targeting of treatments. Collagens are major components of the tumor environment that do far more than just form structures. The function of the full panoply of 43 collagen genes in tumors remains underappreciated. Using the TCGA dataset, we find that mRNA expression of the 43 collagen genes classifies tumors by their cell of origin very similarly as published reports. K-means clustering in each tumor type by collagen mRNA expression revealed classifications strongly associated with overall survival, specific pathways, and immune cell signatures. The collagen defined groups were strongly associated with specific somatic mutations, copy number changes, ploidy and aneuploidy levels. The collagen clusters also revealed specific immunoenvironments, suggesting which tumors were most likely to respond to immunotherapy. To further evaluate these enrichments, we developed a machine learning model to predict which tumors have high or low aneuploidy and specific gain or loss of chromosome arms based on collagen expression, highlighting the connection between collagen expression and specific cancer genomes with areas under the curve above 0.8 for many tumor types including all the GI tumors. Notably, clusters with high total collagen were typically associated with lower aneuploidy levels and tumors with high aneuploidy were grouped in clusters with a mix of minor collagens, and lower collagen type I. These data support a model where tumors with high collagen type I environments have lower aneuploidy and ploidy levels compared to tumors with higher expression of tissue specific minor collagens. The classifier is driven by expression of minor, non-collagen type I, collagens that typically have very specific expression in normal tissue and become dysregulated in many tumors. These findings strongly suggest minor collagens are critical components defining disease progression, the cancer genome and should be included in pre-clinical studies to model the actual human tumor environment and to improve drug development. Preliminary proof-of-concept data for one such minor collagen, COL7A1, will be presented. In sum, these findings demonstrate how classification of tumors by collagens identified strong links between specific cancer genomes and the tumor ECM. Citation Format: Alexander Brodsky, Kevin S. Guo, Amanda Khoo, Vahid Agbortoko, Dongfang Yang, Elizabeth Y. Wu, Ian Y. Wong. Classification of tumors by collagen expression reveals genotype-tumor ECM interactions [abstract]. In: Proceedings of the AACR Virtual Special Conference on the Evolving Tumor Microenvironment in Cancer Progression: Mechanisms and Emerging Therapeutic Opportunities; in association with the Tumor Microenvironment (TME) Working Group; 2021 Jan 11-12. Philadelphia (PA): AACR; Cancer Res 2021;81(5 Suppl):Abstract nr PO019.