Identification of novel breast cancer susceptibility loci in meta-analyses conducted among Asian and European descendants

Shu, Xiang; Long, Jirong; Cai, Qiuyin; Kweon, Sun-Seog; Choi, Ji Yeob; Kubo, Michiaki; Park, Sung Sup; Bolla, Manjeet K.; Dennis, Joe; Wang, Qin; Yang, Yaohua; Shi, Jiajun; Guo, Xingyi; Li, Bingshan; Tao, Ran; Aronson, Kristan J.; Chan, Kelvin Y.K.; Chan, Tsun Leung; Gao, Yu Tang; Hartman, Mikael; Ho, Weang Kee; Ito, Hidemi; Iwasaki, Motoki; Iwata, Hiroji; John, Esther M.; Kasuga, Yoshio; Khoo, Ui‐Soon; Kim, Mi Kyung; Kong, Sun Young; Kurian, Allison W.; Kwong, Ava; Lee, Eun Sook; Li, Jingmei; Lophatananon, Artitaya; Low, Siew Kee; Mariapun, Shivaani; Matsuda, Koichi; Matsuo, Keitaro; Muir, Kenneth; Noh, Dong Young; Park, Boyoung; Park, Min Ho; Shen, Chen; Shin, Min Ho; Spinelli, John J.; Takahashi, Atsushi; Tseng, Chiu-Chen; Tsugane, Shoichiro; Wu, Anna H.; Xiang, Yong Bing; Yamaji, Taiki; Zheng, Ying; Milne, Roger L.; Dunning, Alison M.; Pharoah, Paul D.P.; García‐Closas, Montserrat; Teo, Soo‐Hwang; Kang, Daehee; Easton, Douglas F.; Simard, Jacques; Wang, Zheng

doi:10.1038/s41467-020-15046-w

Cited by 57 publications

(66 citation statements)

References 49 publications

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“…Family studies using linkage analysis have identified several rare mutations with strong effects (i.e., highly penetrant), notably at BRCA1, BRCA2, PALB2, ATM , and CHEK2 loci, conferring lifetime risk of breast cancer 31 . The large-scale genome-wide association studies (GWAS) have identified more than 200 susceptibility loci, each of which confers a small risk for breast cancer development 32 . However, the mechanism steering these genetic associations remains largely unknown because most variants are located in non-coding regions and are not in strong linkage disequilibrium with known protein‐coding variants 33 .…”

Section: Discussionmentioning

confidence: 99%

Coexistence of inhibitory and activating killer-cell immunoglobulin-like receptors to the same cognate HLA-C2 and Bw4 ligands confer breast cancer risk

Ashouri

Rajalingam

Barani

et al. 2021

Sci Rep

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Human leukocyte antigen (HLA) class I-specific killer-cell immunoglobulin-like receptors (KIR) regulate natural killer (NK) cell function in eliminating malignancy. Breast cancer (BC) patients exhibit reduced NK-cytotoxicity in peripheral blood. To test the hypothesis that certain KIR-HLA combinations impairing NK-cytotoxicity predispose to BC risk, we analyzed KIR and HLA polymorphisms in 162 women with BC and 278 controls. KIR-Bx genotypes increased significantly in BC than controls (83.3% vs. 71.9%, OR 1.95), and the increase was more pronounced in advanced-cancer (OR 5.3). No difference was observed with inhibitory KIR (iKIR) and HLA-ligand combinations. The activating KIR (aKIR) and HLA-ligand combinations, 2DS1 + C2 (OR 2.98) and 3DS1 + Bw4 (OR 2.6), were significantly increased in advanced-BC. All patients with advanced-cancer carrying 2DS1 + C2 or 3DS1 + Bw4 also have their iKIR counterparts 2DL1 and 3DL1, respectively. Contrarily, the 2DL1 + C2 and 3DL1 + Bw4 pairs without their aKIR counterparts are significantly higher in controls. These data suggest that NK cells expressing iKIR to the cognate HLA-ligands in the absence of putative aKIR counterpart are instrumental in antitumor response. These data provide a new framework for improving the utility of genetic risk scores for individualized surveillance.

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Section: Discussionmentioning

confidence: 99%

Coexistence of inhibitory and activating killer-cell immunoglobulin-like receptors to the same cognate HLA-C2 and Bw4 ligands confer breast cancer risk

Ashouri

Rajalingam

Barani

et al. 2021

Sci Rep

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“…We identified seven associations for breast cancer (in addition to the eight reported above) that were new in the context of the breast cancer data set used in this study but have since been reported for breast cancer at P < x 10 -8 in the meta-analysis by Zhang et al 13 described above (five of the seven associations; Supplementary Table 5) or in a trans-ancestry genome-wide association meta-analysis by Shu et al 15 (two of the seven associations; Supplementary Table 5). Shu et al combined the breast cancer data set used in this study with a data set that included over 24,000 breast cancer cases and 24,000 controls of Asian ancestry.…”

Section: Cancer Susceptibility Loci Not Previously Identified For Onementioning

confidence: 83%

“…This result is consistent with emerging evidence that the cell of origin of HGSOCs, the most common and aggressive histotype of ovarian cancer, is in the fallopian tube 67,68 , and suggests that combining data from other cancers improves the ranking of genes associated with ovarian cancer risk that are also fallopian tube-specific. We identified nine lead SNP-breast cancer associations in our study that were new in the context of the breast cancer data set used here but have recently been identified in two larger breast cancer GWAS 13,15 . These breast and ovarian cancer findings, in particular, demonstrate that cross-cancer GWAS meta-analysis can be a powerful approach to the identification of new cancer susceptibility loci.…”

Section: Discussionmentioning

confidence: 99%

“…Only variants that had either been genotyped or imputed with a quality score > 0.8 were used to define lead SNPs. A comprehensive list of previously reported cancer susceptibility loci for breast, prostate, ovarian, and endometrial cancers (including susceptibility loci known to be shared by at least two of these cancers) was drawn up based on lead SNPs reported in 11 publications [6][7][8][9][10]13,15,48,[74][75][76] .…”

Section: Independent Lead Snps Genomic Regions and Known Cancer Susmentioning

confidence: 99%

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Combining genome-wide studies of breast, prostate, ovarian and endometrial cancers maps cross-cancer susceptibility loci and identifies new genetic associations

Kar¹,

Lindströem²,

Hung³

et al. 2020

Preprint

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ABSTRACTWe report a meta-analysis of breast, prostate, ovarian, and endometrial cancer genome-wide association data (effective sample size: 237,483 cases/317,006 controls). This identified 465 independent lead variants (P<5×10−8) across 192 genomic regions. Four lead variants were >1Mb from previously identified risk loci for the four cancers and an additional 23 lead variant-cancer associations were novel for one of the cancers. Bayesian models supported pleiotropic effects involving at least two cancers at 222/465 lead variants in 118/192 regions. Gene-level association analysis identified 13 shared susceptibility genes (P<2.6×10−6) in 13 regions not previously implicated in any of the four cancers and not uncovered by our variant-level meta-analysis. Several lead variants had opposite effects across cancers, including a cluster of such variants in the TP53 pathway. Fifty-four lead variants were associated with blood cell traits and suggested genetic overlaps with clonal hematopoiesis. Our study highlights the remarkable pervasiveness of pleiotropy across hormone-related cancers, further illuminating their shared genetic and mechanistic origins at variant- and gene-level resolution.

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“…In the last decade, GWAS conducted on many different tumor types, including pancreatic [22], ovarian [23], lung [24], prostate [25], and breast cancer [26], identified numerous risk alleles, most of which are common and individually confer only a modest increase in disease risk. For instance, GWAS revealed 31 novel genetic susceptibility loci associated with the genetic predisposition for breast cancer [27] and 12 novel loci for prostate cancer [28]. Notably, the vast majority of genetic variants identified through GWAS (>90%) are located in the non-coding regions of the genome [29].…”

Section: Introductionmentioning

confidence: 99%

Exploring the effects of genetic variation on gene regulation in cancer in the context of 3D genome structure

Osman

Shawky

Bryliński

2020

Preprint

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Numerous genome-wide association studies (GWAS) conducted to date revealed genetic variants associated with various diseases, including breast and prostate cancers. Despite the availability of these large-scale data, relatively few variants have been functionally characterized, mainly because the majority of single-nucleotide polymorphisms (SNPs) map to the non-coding regions of the human genome. The functional characterization of these non-coding variants and the identification of their target genes remain challenging. In this communication, we explore the potential functional mechanisms of non-coding SNPs by integrating GWAS with the high-resolution chromosome conformation capture (Hi-C) data for breast and prostate cancers. We show that more genetic variants map to regulatory elements through the 3D genome structure than the 1D linear genome lacking physical chromatin interactions. Importantly, the association of enhancers, transcription factors, and their target genes with breast and prostate cancers tends to be higher when these regulatory elements are mapped to high-risk SNPs through spatial interactions compared to simply using a linear proximity. Finally, we demonstrate that topologically associating domains (TADs) carrying high-risk SNPs also contain gene regulatory elements whose association with cancer is generally higher than those belonging to control TADs containing no high-risk variants. Our results suggest that many SNPs may contribute to the cancer development by affecting the expression of certain tumor-related genes through long-range chromatin interactions with gene regulatory elements. Integrating large-scale genetic datasets with the 3D genome structure offers an attractive and unique approach to systematically investigate the functional mechanisms of genetic variants in disease risk and progression.

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Identification of novel breast cancer susceptibility loci in meta-analyses conducted among Asian and European descendants

Cited by 57 publications

References 49 publications

Coexistence of inhibitory and activating killer-cell immunoglobulin-like receptors to the same cognate HLA-C2 and Bw4 ligands confer breast cancer risk

Coexistence of inhibitory and activating killer-cell immunoglobulin-like receptors to the same cognate HLA-C2 and Bw4 ligands confer breast cancer risk

Combining genome-wide studies of breast, prostate, ovarian and endometrial cancers maps cross-cancer susceptibility loci and identifies new genetic associations

Exploring the effects of genetic variation on gene regulation in cancer in the context of 3D genome structure

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