Abstract 4271: Most expression quantitative trait loci discovered in tumors cannot be attributed to cancer cells
Authors: Paul Geeleher, Fan Wang, Zheny Zhang, Robert L. Grossman, Cathal Seoighe, R. Stephanie Huang
Journal: Cancer Research (2018)

Abstract Genome-wide association studies (GWAS) have identified hundreds of inherited genetic variants that increase cancer risk. However, most cancer risk variants are in non-coding regions of the genome and modulate risk by affecting gene regulation. Thus, determining how inherited genetic variation affects gene expression in cancer is critically important to understanding disease development. Consequently, by leveraging large genomics datasets like The Cancer Genome Atlas (TCGA), previous studies have examined the effect of inherited genetic variation on gene expression in tumors; an approach known as expression quantitative trait locus (eQTL) mapping. However, tumors are mixtures of both cancer and normal cells, for example, immune cells and stroma. No previous cancer eQTL study has accounted for this mixture of cell types, essentially having treated bulk tumor expression as representative of gene expression in cancer cells. Building on research in deconvolution of mixtures of cell types, we have developed a new approach that can accurately account for the effect of tumor-infiltrating normal cells on cancer eQTLs. The approach involves first estimating the proportion of tumor-infiltrating normal cells (tumor purity) using a combined estimate from genomics data and H&amp;E staining. Then, we developed a statistical model that can account for the effect of tumor purity on eQTLs by modeling the interaction of the tumor purity estimate and genotype. Intuitively, this works by determining how the magnitude of the association between gene expression and genotype changes as a function of tumor purity, then extrapolating this effect to 100% cancer cells. The model's accuracy was validated using simulated data and on expression profiles from purified cell types. We used this model to map eQTLs in the TCGA breast cancer cohort. Remarkably, while 57,189 eQTLs were identified in TCGA breast cancer patients using a conventional model, only 8,833 (15.4%) could be attributed to cancer cells when tumor purity was accounted for. Analysis of the Genotype-Tissue Expression (GTEx) data provided evidence that almost 50% of tumor eQTLs inferred using a conventional approach affect gene expression in tumor-infiltrating immune cells and fibroblasts. We also investigated the eQTL profiles of cancer risk variants from a GWAS for breast cancer risk. Strikingly, for 33% of breast cancer risk variants identified as eQTLs using a conventional approach, we found strong evidence of an effect in tumor-infiltrating normal cells, but no evidence of an effect in cancer cells. This suggests cancer risk is mediated by the effect of inherited genetic variation on gene regulation in the cells of the tumor microenvironment, as well as in cancer and pre-cancer cells. Our findings profoundly challenge the current interpretation of inherited genetic regulation in cancer and should be considered in functional validation of results from all cancer risk GWAS. Citation Format: Paul Geeleher, Fan Wang, Zheny Zhang, Robert L. Grossman, Cathal Seoighe, R. Stephanie Huang. Most expression quantitative trait loci discovered in tumors cannot be attributed to cancer cells [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 4271.