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• W2802537011 abstract "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. Approximately 30% of breast cancer risk is heritable [1Moller S. et al.The heritability of breast cancer among women in the Nordic twin study of cancer.Cancer Epidemiol. Biomarkers Prev. 2016; 25: 145-150Crossref PubMed Scopus (10) Google Scholar], of which 5–10% of cases can be attributed to rare alleles (such as BRCA1, BRCA2, PTEN, and TP53) that are highly penetrant and others that are moderately penetrant (such as ATM, BRIP1, CHEK2, and PALB2) [2Stratton M.R. Rahman N. The emerging landscape of breast cancer susceptibility.Nat. Genet. 2008; 40: 17-22Crossref PubMed Scopus (337) Google Scholar, 3Foulkes W.D. Inherited susceptibility to common cancers.New Engl. J. Med. 2008; 359: 2143-2153Crossref PubMed Scopus (0) Google Scholar]. A larger group of 182 common alleles have been identified by genome-wide association studies (GWAS) [4Ahmed S. et al.Newly discovered breast cancer susceptibility loci on 3p24 and 17q23.2.Nat. 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Genet. 2013; 92: 489-503Abstract Full Text Full Text PDF PubMed Scopus (142) Google Scholar, 31Dunning A.M. et al.Breast cancer risk variants at 6q25 display different phenotype associations and regulate ESR1, RMND1 and CCDC170.Nat. Genet. 2016; 48: 374-386Crossref PubMed Google Scholar, 32Couch F.J. et al.Identification of four novel susceptibility loci for oestrogen receptor negative breast cancer.Nat. Commun. 2016; 7: 11375Crossref PubMed Scopus (18) Google Scholar, 33Lawrenson K. et al.Functional mechanisms underlying pleiotropic risk alleles at the 19p13.1 breast-ovarian cancer susceptibility locus.Nat. Commun. 2016; 7: 12675Crossref PubMed Scopus (29) Google Scholar, 34Wyszynski A. et al.An intergenic risk locus containing an enhancer deletion in 2q35 modulates breast cancer risk by deregulating IGFBP5 expression.Hum. Mol. Genet. 2016; 25: 3863-3876Crossref PubMed Scopus (5) Google Scholar, 35Michailidou K. et al.Association analysis identifies 65 new breast cancer risk loci.Nature. 2017; 551: 92-94Crossref PubMed Scopus (348) Google Scholar, 36Milne R.L. et al.Identification of ten variants associated with risk of estrogen-receptor-negative breast cancer.Nat. Genet. 2017; 49: 1767-1778Crossref PubMed Scopus (98) Google Scholar], which confer lower relative risks (RR) of breast cancer (<1.5-fold RR) compared with risk modifiers that are highly penetrant (>5-fold RR) and moderately penetrant (1.5–5-fold RR) [2Stratton M.R. Rahman N. The emerging landscape of breast cancer susceptibility.Nat. Genet. 2008; 40: 17-22Crossref PubMed Scopus (337) Google Scholar, 3Foulkes W.D. Inherited susceptibility to common cancers.New Engl. J. Med. 2008; 359: 2143-2153Crossref PubMed Scopus (0) Google Scholar]. Only a small fraction of breast cancer heritability can be explained by the current list of genetic candidates [22Michailidou K. et al.Large-scale genotyping identifies 41 new loci associated with breast cancer risk.Nat. Genet. 2013; 45: 353-361Crossref PubMed Scopus (709) Google Scholar], indicating that additional rare and common modifier alleles likely exist. As might be expected, modifier alleles with high to moderate penetrance are typically linked directly with the malignant transformation of breast epithelial cells through disruption of pathways regulating DNA damage repair, cell cycle, and apoptosis, whereas GWAS candidates fall within a diverse range of molecular and cellular pathways [37Nielsen F.C. et al.Hereditary breast and ovarian cancer: new genes in confined pathways.Nat. Rev. Cancer. 2016; 16: 599-612Crossref PubMed Scopus (145) Google Scholar]. Adding to the complexity is the preponderance of loci with multiple candidates in linkage disequilibrium (LD), which are ‘co-inherited’ and therefore considered equally culpable candidates in the development of breast cancer [38Flister M.J. et al.Identifying multiple causative genes at a single GWAS locus.Genome Res. 2013; 23: 1996-2002Crossref PubMed Scopus (62) Google Scholar]. Although breast cancer risk modifiers directly impact cancer cells, some modifier alleles can plausibly impact breast cancer risk through the nonmalignant tumor microenvironment (TME). To date, at least two host TME modifier loci of breast cancer have been experimentally validated, and evidence of several more TME modifier loci exist. In one example, the Mcs5a rat mammary tumor risk locus was shown to modify mammary carcinoma progression via the immune system, which was driven by FBXO10 and was dependent upon T lymphocytes [39Smits B.M. et al.The non-protein coding breast cancer susceptibility locus Mcs5a acts in a non-mammary cell-autonomous fashion through the immune system and modulates T-cell homeostasis and functions.Breast Cancer Res. 2011; 13: R81Crossref PubMed Scopus (15) Google Scholar]. A homologous mechanism has been replicated in human T lymphocytes [40Xu X. et al.Human MCS5A1 candidate breast cancer susceptibility gene FBXO10 is induced by cellular stress and correlated with lens epithelium-derived growth factor (LEDGF).Mol. Carcinog. 2014; 53: 300-313Crossref PubMed Scopus (4) Google Scholar] and associated with human breast cancer risk [41Samuelson D.J. et al.Rat Mcs5a is a compound quantitative trait locus with orthologous human loci that associate with breast cancer risk.Proc. Natl. Acad. Sci. U. S. A. 2007; 104: 6299-6304Crossref PubMed Scopus (0) Google Scholar]. In another example, a newly developed genetic mapping strategy, consomic/congenic xenograft model (CXM), was used to identify a host TME modifier locus that is linked with DLL4 and impacts breast cancer growth and metastasis in the rat by inducing dysfunctional angiogenesis, which was independent of tumor cell changes [42Flister M.J. et al.CXM – a new tool for mapping breast cancer risk in the tumor microenvironment.Cancer Res. 2014; 74: 6419-6429Crossref PubMed Scopus (5) Google Scholar, 43Flister M.J. et al.Host genetic modifiers of nonproductive angiogenesis inhibit breast cancer.Breast Cancer Res. Treat. 2017; 165: 53-64Crossref PubMed Scopus (1) Google Scholar, 44Jagtap J. et al.Methods for detecting host genetic modifiers of tumor vascular function using dynamic near-infrared fluorescence imaging.Biomed. Opt. Express. 2018; 9: 543-556Crossref PubMed Scopus (0) Google Scholar]. Further evidence of host TME modifiers exist in mouse genetic mapping studies, including three modifier loci (Mmtg1-3) that were linked with mammary tumor angiogenesis [45Le Voyer T. et al.Three loci modify growth of a transgene-induced mammary tumor: suppression of proliferation associated with decreased microvessel density.Genomics. 2001; 74: 253-261Crossref PubMed Scopus (0) Google Scholar] and PTPRJ, a mediator of angiogenesis [46Takahashi T. et al.A mutant receptor tyrosine phosphatase, CD148, causes defects in vascular development.Mol. Cell. Biol. 2003; 23: 1817-1831Crossref PubMed Scopus (0) Google Scholar] that was originally discovered for its role in susceptibility to colon cancer, and has since been linked with breast cancer risk [47Lesueur F. et al.Allelic association of the human homologue of the mouse modifier Ptprj with breast cancer.Hum. Mol. Genet. 2005; 14: 2349-2356Crossref PubMed Scopus (63) Google Scholar]. In addition, a MHC-linked modifier locus in a mouse mammary tumor virus (MMTV)-induced mammary tumor model were found to be largely dependent on systemic factors, such as infiltrating immune cells and inflammatory cytokines [48Dux A. Demant P. MHC-controlled susceptibility to C3H-MTV-induced mouse mammary tumors is predominantly systemic rather than local.Int. J. Cancer. 1987; 40: 372-377Crossref PubMed Scopus (12) Google Scholar]. As in human breast cancer, many mammary tumor modifier loci in the mouse and rat remain uncharacterized and overlap with quantitative trait loci (QTL) for TME-related phenotypes, such as angiogenesis and immunity. Thus, there are likely many more uncharacterized host TME modifiers of breast tumor risk and progression. The breast TME is comprised of multiple nonmalignant cell types that interact with malignant tumor cells at all disease stages, including tumor initiation, metastatic progression, and response to therapy [49Liu W. et al.Microenvironmental influences on metastasis suppressor expression and function during a metastatic cell’s journey.Cancer Microenviron. 2014; 7: 117-131Crossref PubMed Scopus (11) Google Scholar, 50Olson O.C. Joyce J.A. Microenvironment-mediated resistance to anticancer therapies.Cell Res. 2013; 23: 179-181Crossref PubMed Scopus (17) Google Scholar, 51Quail D.F. Joyce J.A. Microenvironmental regulation of tumor progression and metastasis.Nat. Med. 2013; 19: 1423-1437Crossref PubMed Scopus (2629) Google Scholar, 52Polyak K. Kalluri R. The role of the microenvironment in mammary gland development and cancer.Cold Spring Harb. Perspect. Biol. 2010; 2a003244Crossref PubMed Google Scholar, 53McAllister S.S. Weinberg R.A. The tumour-induced systemic environment as a critical regulator of cancer progression and metastasis.Nat. Cell Biol. 2014; 16: 717-727Crossref PubMed Scopus (414) Google Scholar]. Expression studies of human breast tumor samples have identified stromal networks that predict breast cancer risk and outcome, demonstrating the importance of 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]. Fibroblasts are a major component of the TME and are essential for maintaining normal mammary gland homeostasis [57Wiseman B.S. Werb Z. Stromal effects on mammary gland development and breast cancer.Science. 2002; 296: 1046-1049Crossref PubMed Scopus (569) Google Scholar]. In the malignant setting, cancer-associated fibroblasts (CAFs) modulate multiple aspects of tumor pathophysiology, including malignant progression of cancer cells (proliferation, survival, and invasion), fibrosis, angiogenesis, and tumor-associated immunity [58Kalluri R. The biology and function of fibroblasts in cancer.Nat. Rev. Cancer. 2016; 16: 582-598Crossref PubMed Scopus (993) Google Scholar]. Tumor angiogenesis is necessary for growth and progression of breast tumors and is coordinated by cancer cells and multiple nonmalignant TME cell types, including endothelial cells, fibroblasts, and infiltrating leukocytes [59Hanahan D. Folkman J. Patterns and emerging mechanisms of the angiogenic switch during tumorigenesis.Cell. 1996; 86: 353-364Abstract Full Text Full Text PDF PubMed Scopus (5730) Google Scholar, 60Bergers G. Benjamin L.E. Tumorigenesis and the angiogenic switch.Nat. Rev. Cancer. 2003; 3: 401-410Crossref PubMed Scopus (2544) Google Scholar]. A denser tumor vasculature is correlated with increased tumor growth and hematogenous metastasis, which is due to enhanced oxygen supply, nutrients, and routes for metastatic dissemination [61Nico B. et al.Evaluation of microvascular density in tumors: pro and contra.Histol. Histopathol. 2008; 23: 601-607PubMed Google Scholar]. Likewise, tumor lymphatic vessels provide routes for tumor cell metastasis, and invasion of tumor-associated lymphatic vessels highly correlates with poor clinical outcomes [60Bergers G. Benjamin L.E. Tumorigenesis and the angiogenic switch.Nat. Rev. Cancer. 2003; 3: 401-410Crossref PubMed Scopus (2544) Google Scholar, 62Ran S. et al.Lymphangiogenesis and lymphatic metastasis in breast cancer.Pathophysiology. 2010; 17: 229-251Abstract Full Text Full Text PDF PubMed Scopus (155) Google Scholar]. Tumor-associated blood and lymphatic vessels are also the primary routes for trafficking innate and adaptive immune cells, which play both pro- and antitumorigenic roles during tumor initiation, progression, and response to therapy [63Fridman W.H. et al.The immune contexture in human tumours: impact on clinical outcome.Nat. Rev. Cancer. 2012; 12: 298-306Crossref PubMed Scopus (2075) Google Scholar]. It is now widely accepted that the TME affects most aspects of breast cancer biology, yet we do not fully understand the heritable genetic modifiers that influence human breast cancer through the host TME. 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. Genet. 2007; 39: 870-874Crossref PubMed Scopus (1183) Google Scholar, 22Michailidou K. et al.Large-scale genotyping identifies 41 new loci associated with breast cancer risk.Nat. Genet. 2013; 45: 353-361Crossref PubMed Scopus (709) Google Scholar, 64Udler M.S. et al.FGFR2 variants and breast cancer risk: fine-scale mapping using African American studies and analysis of chromatin conformation.Hum. Mol. Genet. 2009; 18: 1692-1703Crossref PubMed Scopus (0) Google Scholar, 65Barnholtz-Sloan J.S. et al.FGFR2 and other loci identified in genome-wide association studies are associated with breast cancer in African-American and younger women.Carcinogenesis. 2010; 31: 1417-1423Crossref PubMed Scopus (0) Google Scholar, 66Scollen S. et al.TGF-beta signaling pathway and breast cancer susceptibility.Cancer Epidemiol. 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Cancer. 1996; 74: 1423-1426Crossref PubMed Google Scholar, 74Loibl S. et al.Immunohistochemical evaluation of endothelial nitric oxide synthase expression in primary breast cancer.Breast. 2005; 14: 230-235Abstract Full Text Full Text PDF PubMed Scopus (0) Google Scholar, 75Yang H. et al.Reduced expression of Toll-like receptor 4 inhibits human breast cancer cells proliferation and inflammatory cytokines secretion.J. Exp. Clin. Cancer Res. 2010; 29: 92Crossref PubMed Scopus (0) Google Scholar]. Thus, it is possible that a single genetic modifier might elicit complex physiological changes across multiple cell types and the combined effects of these cell type-specific alterations are ultimately manifested at the phenotypic level. One could also envision seemingly unrelated TME modifiers that are not connected at the molecular level, but might interact at the cellular or tissue levels by modifying the density or physiological poise of cellular mediators within the TME. 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. Genet. 2013; 45: 371-384Crossref PubMed Scopus (374) Google Scholar, 30French J.D. et al.Functional variants at the 11q13 risk locus for breast cancer regulate cyclin D1 expression through long-range enhancers.Am. J. Hum. Genet. 2013; 92: 489-503Abstract Full Text Full Text PDF PubMed Scopus (142) Google Scholar, 76Freedman M.L. et al.Principles for the post-GWAS functional characterization of cancer risk loci.Nat. Genet. 2011; 43: 513-518Crossref PubMed Scopus (286) Google Scholar], very few experimental models exist to identify and test the genetic modifiers that might impact the host TME. Another common method for identifying genetic modifiers of breast cancer is to scan for expression QTL (eQTL) [77Li Q. et al.Integrative eQTL-based analyses reveal the biology of breast cancer risk loci.Cell. 2013; 152: 633-641Abstract Full Text Full Text PDF PubMed Scopus (208) Google Scholar]. 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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• W2802537011 created "2018-05-17" @default.
• W2802537011 creator A5020694375 @default.
• W2802537011 creator A5052008011 @default.
• W2802537011 date "2018-06-01" @default.
• W2802537011 modified "2023-10-16" @default.
• W2802537011 title "Genetic Modifiers of the Breast Tumor Microenvironment" @default.
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