Genetic Modifiers of the Breast Tumor Microenvironment
Authors: Michael J. Flister, Carmen Bergom
Journal: Trends in cancer (2018)

Breast tumors are interactive systems that consist of malignant cancer cells and nonmalignant cell types within the TME (such as endothelial cells, fibroblasts, and leukocytes). Crosstalk between cellular compartments influences disease risk and outcome. Although genetic modifiers of the TME have long been suspected, they remain largely uncharacterized. It is unclear what portion of heritable breast cancer risk is influenced by host TME modifiers. In this review, a candidate host TME modifier is defined as having a significant association with breast cancer and a reported biological role in at least one TME cell type (such as endothelial cells, fibroblasts, and leukocytes). In a careful review of >170 genetic loci associated with human breast cancer, 24 candidates were identified to likely impact breast cancer risk through the TME. Multiple nonmalignant cell types in the tumor microenvironment (TME) impact breast cancer risk, metastasis, and response to therapy, yet most heritable mechanisms that influence TME cell function and breast cancer outcomes are largely unknown. Breast cancer risk is ∼30% heritable and >170 genetic loci have been associated with breast cancer traits. However, the majority of candidate genes have poorly defined mechanistic roles in breast cancer biology. Research indicates that breast cancer risk modifiers directly impact cancer cells, yet it is equally plausible that some modifier alleles impact the nonmalignant TME. The objective of this review is to examine the list of current breast cancer candidate genes that may modify breast cancer risk and outcome through the TME. Multiple nonmalignant cell types in the tumor microenvironment (TME) impact breast cancer risk, metastasis, and response to therapy, yet most heritable mechanisms that influence TME cell function and breast cancer outcomes are largely unknown. Breast cancer risk is ∼30% heritable and >170 genetic loci have been associated with breast cancer traits. However, the majority of candidate genes have poorly defined mechanistic roles in breast cancer biology. Research indicates that breast cancer risk modifiers directly impact cancer cells, yet it is equally plausible that some modifier alleles impact the nonmalignant TME. The objective of this review is to examine the list of current breast cancer candidate genes that may modify breast cancer risk and outcome through the TME. Breast cancer is the most common female malignancy and is the second most common cause of cancer death among females in the US, with more than 40 000 deaths each year. 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Despite the evidence that genetic modifiers of the host TME impact breast cancer risk and outcome, the focused research on TME modifiers is very limited and there are many unresolved questions (see Outstanding Questions). One salient point from the existing literature is that host TME modifier candidates are likely to have complex interactions across multiple molecular pathways, cell types, and physiological functions (Figure 1, Key Figure). It is also highly plausible that some genetic modifiers impact both cancer cells and multiple TME cell types. For example, multiple breast cancer candidates (e.g., FGFR2, TGFβR2, and MKL1) [8Easton D.F. et al.Genome-wide association study identifies novel breast cancer susceptibility loci.Nature. 2007; 447: 1087-1093Crossref PubMed Scopus (1788) Google Scholar, 13Hunter D.J. et al.A genome-wide association study identifies alleles in FGFR2 associated with risk of sporadic postmenopausal breast cancer.Nat. 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For example, the phenotypic effects of a genetic modifier of cytotoxic T lymphocyte function might be dampened or amplified in a patient that has co-inherited a modifier of lymphocyte trafficking. The challenges to disentangling the complexities of host TME modifiers are further compounded by limitations to the current tools for assessing the heritable genetic modifiers of breast cancer. Genetic association and mapping studies of breast cancer risk and outcome are suitable for nominating candidate regions, but are unable to establish the cell type specificity of a genetic modifier without functional testing. However, despite the preponderance of studies that have experimentally validated cancer cell-autonomous mechanisms [20Bojesen S.E. et al.Multiple independent variants at the TERT locus are associated with telomere length and risks of breast and ovarian cancer.Nat. 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However, a drawback of eQTL analyses is their basis upon mixed RNA extracted from tumor biopsies that contain variable amounts of cancer cells and TME cell types. Thus, similar to GWAS and other genetic mapping strategies, eQTL analyses are limited in their ability to distinguish host TME modifiers from other types of genetic risk factors. Finally, because eQTL analyses of tumor biopsies are based on RNA that is derived from multiple cell types, it is also foreseeable that differences in cell type-specific expression of the same gene might mask the detection of eQTL that exist within only a specific TME cell type. We propose that there are several existing strategies that could be adapted for discovering and characterizing host TME modifiers of breast cancer risk and outcome. One such strategy is to combine eQTL analyses with cell purification techniques or laser-capture microscopy. Both techniques have previously been used to quantify cell type-specific RNA expression in the breast TME [54Liu H. et al.Discovery of stromal regulatory networks that suppress Ras-sensitized epithelial cell proliferation.Dev. Cell. 2017; 41: 392-407Abstract Full Text Full Text PDF PubMed Google Scholar, 55Saleh S.M.I. et al.Identification of interacting stromal axes in triple-negative breast cancer.Cancer Res. 2017; 77: 4673-4683Crossref PubMed Scopus (14) Google Scholar, 56Finak G. et al.Stromal gene expression predicts clinical outcome in breast cancer.Nat. Med. 2008; 14: 518-527Crossref PubMed Scopus (1134) Google Scholar, 78Allinen M. et al.Molecular characterization of the tumor microenvironment in breast cancer.Cancer Cell. 2004; 6: 17-32Abstract Full Text Full Text PDF PubMed Scopus (942) Google Scholar]; however, to our knowledge, none of the previous studies incorporated genotypic information and therefore TME-specific eQTL analyses have yet to be reported. Another promising strategy to identify host TME modifiers is to perform a modified eQTL analysis at the protein level, using multiplex immunofluorescent assays to correlate cell type-specific protein expression with patient genotypes. The capacity of this approach could be expanded using high density tissue microarrays and quantitative immunofluorescent imaging, which offers highly sensitive and spatially resolved detection of protein expression at the cellular and subcellular levels [79LeBaron M.J. et al.In vivo response-based identification of direct hormone target cell populations using high-density tissue arrays.Endocrinology. 2007; 148: 989-1008Crossref PubMed Scopus (0) Google Scholar, 80LeBaron M.J. et al.Ultrahigh density microarrays of solid samples.Nat. Methods. 2005; 2: 511-513Crossref PubMed Scopus (0) Google Scholar, 81Rui H. LeBaron M.J. Creating tissue microarrays by cutting-edge matrix assembly.Expert Rev. Med. 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