Fine-Scale Mapping of the 5q11.2 Breast Cancer Locus Reveals at Least Three Independent Risk Variants Regulating MAP3K1

Abstract

Genome-wide association studies (GWASs) have revealed SNP rs889312 on 5q11.2 to be associated with breast cancer risk in women of European ancestry. In an attempt to identify the biologically relevant variants, we analyzed 909 genetic variants across 5q11.2 in 103,991 breast cancer individuals and control individuals from 52 studies in the Breast Cancer Association Consortium. Multiple logistic regression analyses identified three independent risk signals: the strongest associations were with 15 correlated variants (iCHAV1), where the minor allele of the best candidate, rs62355902, associated with significantly increased risks of both estrogen-receptor-positive (ER(+): odds ratio [OR] = 1.24, 95% confidence interval [CI] = 1.21-1.27, ptrend = 5.7 × 10(-44)) and estrogen-receptor-negative (ER(-): OR = 1.10, 95% CI = 1.05-1.15, ptrend = 3.0 × 10(-4)) tumors. After adjustment for rs62355902, we found evidence of association of a further 173 variants (iCHAV2) containing three subsets with a range of effects (the strongest was rs113317823 [pcond = 1.61 × 10(-5)]) and five variants composing iCHAV3 (lead rs11949391; ER(+): OR = 0.90, 95% CI = 0.87-0.93, pcond = 1.4 × 10(-4)). Twenty-six percent of the prioritized candidate variants coincided with four putative regulatory elements that interact with the MAP3K1 promoter through chromatin looping and affect MAP3K1 promoter activity. Functional analysis indicated that the cancer risk alleles of four candidates (rs74345699 and rs62355900 [iCHAV1], rs16886397 [iCHAV2a], and rs17432750 [iCHAV3]) increased MAP3K1 transcriptional activity. Chromatin immunoprecipitation analysis revealed diminished GATA3 binding to the minor (cancer-protective) allele of rs17432750, indicating a mechanism for its action. We propose that the cancer risk alleles act to increase MAP3K1 expression in vivo and might promote breast cancer cell survival.

SNP variants at the MAP3K1/SETD9 locus 5q11.2 associate with somatic PIK3CA variants in breast cancers

“…20 A possible SETD9 involvement is suggested by the strong SNV associations with SETD9 overexpression; moreover, 5q11.2 SNV eQTL to SETD9 has been reported also in normal blood. 18 However, we found a synergy of PI3KCA SM and SNV only for MAP3K1 overexpression.…”

“…20 A possible SETD9 involvement is suggested by the strong SNV associations with SETD9 overexpression; moreover, 5q11.2 SNV eQTL to SETD9 has been reported also in normal blood. 18 However, we found a synergy of PI3KCA SM and SNV only for MAP3K1 overexpression.Two of our findings indicate that a complex BC risk SNV structure is present in the 5q11.2 region. First, only the SNV in the boundary of MAP3K1/SETD9 genes (but not the reference risk SNP rs889312, which they are in high LD with) were found associated with PI3KCA SM.…”

“…In this short report, we show that germline SNVs located near the MAP3K1/SETD9 genes associate with PIK3CA SM in ER+ BC with OR values (1.75 and 2.97 for rs331499 and rs252913, respectively) much higher than their OR of association with BC or BC subtypes (OR about 1.1, 18 as the OR of most cancer-risk SNV 4 ). SNV data are coherent with gene expression data: the SNV associations with MAP3K1/SETD9 overexpression are increased when the distance from the target gene is reduced, and, for MAP3K1, are stronger in PIK3CA SM BC samples.…”

“…Hence, their opposite alleles should be associated with BCs in which PIK3CA is not mutated to build up to their overall increased BC risk. 18 This 'reverse' phase should not surprise because the SNV/PIK3CA SM associations found have an allele unbalancing effect one order of magnitude stronger than the reported SNV/BC risk. However, it predicts the presence of multiple classes of SNV BC risk in the 5q11.2 segment that split when probed for PIK3CA somatic variants.…”

The long range regulation of breast cancer associated genes

“…A second study using extensive imputation to expand the pool of iCOGs genotypes added another 15 new loci, explaining a further 2% of familial breast cancer risk [36]. A number of iCOGS follow up studies have been performed involving fine mapping of the loci to identify the target genes mediating the SNP-associated risk (detailed in Section 1.4.2) [5,36,[59][60][61][62]. One of the most interesting findings to come out of GWAS in general and further confirmed by analysis of the COGs data, is that the majority of risk-associated SNPs fall in non-coding regions of the genome [34].…”