An additional 13 studies that focused on fish intake or at-risk populations were summarized to increase readers’ understanding of the topic based on this review, DHA and EPA interact with certain genetic variants to decrease breast, colorectal and prostate cancer risk, although data was limited and identified polymorphisms were heterogeneous. The evidence to date demonstrates that omega-3 long-chain polyunsaturated fatty acids (n-3 LC-PUFA) may decrease cancer risk by affecting genetic variants of inflammatory pathways, oxidative stress and tumor apoptosis. Collectively, data supports the notion that once a genetic variant is identified, the benefits of a targeted, personalized therapeutic regimen that includes DHA and/or EPA should be considered.This scoping review examines the interaction of docosahexaenoic acid (DHA) and eicosapentaenoic acid (EPA) and genetic variants of various types of cancers. A comprehensive search was performed to identify controlled and observational studies conducted through August 2017. Eighteen unique studies were included: breast cancer ( Cancer is a growing global burden. In 2018, there was an estimated 18.1 million cancer cases and 9.6 million cancer deaths worldwide [Omega-3 long-chain polyunsaturated fatty acids (n-3 LC-PUFA), such docosahexaenoic acid (DHA) and eicosapentaenoic acid (EPA), are important fatty acids that may play a role in preventing some cancers and act as adjunct therapy to chemotherapeutics, immunotherapeutics, or radiation . The samN-3 LC-PUFA have pleiotropic effects and enhance cancer cell apoptosis, modulate various eicosanoid pathways leading to reduced inflammation, such as suppressing cyclooxygenase-2 (COX-2) synthesis and the inhibition of arachidonic acid-derived eicosanoids . ImportaThis scoping review was undertaken to identify human studies that examined the effects of preformed n-3 LC-PUFA, namely DHA and EPA, on genetic variants of any type of cancer. Both observational and interventional studies are included. The goal is to better understand associations between preformed n-3 LC-PUFA intake or tissue levels and genotype-specific cancer risk, and to understand variability in individual responses to n-3 LC-PUFA supplementation in order to provide a targeted, personalized therapeutic approach to cancer treatment.Human studies were included if they met the following criteria: (1) peer-reviewed publications of trials, including controlled trials (randomized or non-randomized) or observational trials; (2) involving healthy subjects or cancer patients; where (3) n-3 LC-PUFA (DHA and/or EPA) were given as an intervention or DHA and/or EPA blood or tissue levels were measured or the intake of DHA and/or EPA was reported; (4) and the association with risk of cancer by genotype or expression of genes involved in carcinogenesis was measured. All types of control/comparators were considered for inclusion.Case reports, protocols, conference abstracts, letters, commentaries as well as animal or cell-line studies were excluded. Studies in which the independent effects of DHA and/or EPA could not be isolated due to combination with another active ingredient or not separately analyzed in the diet or tissue from other n-3 LC-PUFA  were also excluded. PubMed, Embase, Scopus, World of Science and Cochrane’s CENTRAL were searched from their database inception through August 2017. All searches were restricted to the peer-reviewed studies involving human subjects. See Two investigators (L.T. and M.V.E.) independently screened titles and abstracts of all citations yielded from the literature search in duplicate using the pre-defined study eligibility criteria. Conflicts regarding inclusion of citations were resolved by consensus between the two investigators and when consensus could not be reached, a third party was consulted (K.Y.M). Full texts were acquired for any abstract meeting all eligibility criteria or for those where eligibility could not be determined. Full-texts of abstracts reporting only fish intake were acquired to see if DHA or EPA intake or tissue levels were reported. If not reported in the full text, authors of these studies were contacted; contacted authors either did not respond or replied that these levels were not measured. During this process, recognizing the limited data available for preformed DHA and EPA, the authors elected to summarize and include in the evidence map, studies reporting only fish intake since fish is the primary source of EPA and DHA. Studies reporting only fish intake as well as studies reporting estrogen receptor (ER) status or progesterone receptor (PR) status of breast cancer tumors, and studies involving participants with familial adenomatous polyposis (FAP), were summarized to provide additional insights into those meeting the inclusion criteria. FAP is an inherited disorder associated with mutations in the adenomatous polyposis coli (APC) gene and colorectal cancer (CRC). Thus, FAP patients are particularly at risk for developing CRC.ER+ is the most common breast cancer with 2 out of 3 cases being hormone receptor positive, and most of these ER+. The BReast CAncer susceptibility gene (BRCA) is a genetic mutation predisposing one to an increased risk of breast and/or ovarian cancer. Generally, BRCA 2 tumors are ER+. Given the relationship between ER+ and BRCA 2, it was of interest to see if any studies report associations with BRCA or ER status and n-3 LC-PUFA in blood or diet.n = 2), gastric cancer (n = 1), exocrine pancreatic cancer (n = 1), chronic lymphocytic leukemia , prostate cancer , and colorectal cancer . Thirteen additional studies, as described above, were added to enhance the understanding of the research topic, i.e., fish intake only (n = 6); studies that involved subjects with FAP (n = 2); and studies that report on the estrogen receptor (ER) or progesterone receptor (PR) status of breast cancer tumors (n = 5) were summarized. The initial database search yielded 1916 distinct citations. An additional 174 citations, identified via other sources, were also included for screening. Eighteen unique studies within 21 publications fit the eligibility criteria within our scoping review . Cancer n = 9). Five studies were either randomized controlled trials (RCTs) or post-hoc analyses of RCTs.  conducted an interventional, non-randomized, not placebo-controlled trial investigating DHA incorporation into red blood cells (RBC) after 2g DHA supplementation for 10 days in 11 healthy controls and 34 breast cancer patients. Statistically higher DHA incorporation in RBC membranes of BRCA 1 and 2 mutation carriers versus healthy controls was found 17 conduc. Preclinincreased the risk of ER+ breast cancer [Results for an additional five studies that reported on ER/PR status are summarized see . Four oft cancer , althougdecreased risk of ER+ breast cancer [Bassett et al. (2016) conducted a case-cohort study involving 571 breast cancer cases and 2492 healthy controls. High dietary intake of both EPA and DHA was also associated with t cancer .increased risk of colon cancer [increased CRC risk, while subjects with homozygous APC 1822 (TT) and low levels of n-3 LC-PUFA intake had a decreased CRC risk [increased CRC risk. Further analysis revealed that this PARP gene modified associations between marine n-3 LC-PUFA and CRC risk only amongst rectal cases [decreased CRC risk, while a high genetic risk score combined with an increased dark fish or total EPA and DHA consumption resulted in an increased CRC risk. When the genetic risk score was limited to six single-nucleotide polymorphisms (SNPs) associated with the transforming growth factor-beta (TGF-β) pathway, no interaction was found [Six studies within seven reports involving CRC fit the inclusion criteria as shown in n cancer . AnotherCRC risk . Stern eal cases . Kantor as found . decreased CRC risk [Song et al. (2015) conducted a prospective cohort study from which 1125 CRC cases were categorized as having microsatellite instability-high (MSI) tumors (a distinct phenotype of CRC) or microsatellite-stable (MSS) tumors. The authors found those with CpG island methylator phenotype (CIMP)-low/negative status and MSI tumors with increasing marine n-3 LC-PUFA intake (quartile 1 versus quartile 4) were associated with significantly CRC risk . increased in the placebo group and decreased in the EPA group. In the EPA group, 614 genes were identified as differentially expressed in tumor cells from patients with decreased CCL2 plasma levels compared to patients with no change or an increase in CCL2 plasma levels. Furthermore, EPA-treated patients whose plasma CCL2 levels decreased had a significantly better disease-free survival compared with individuals in whom the plasma CCL2 level increased after supplementation. A decrease in the synthesis of CCL2 and CCL2 receptor expression was also shown by Volpato with EPA treatment in vitro, suggesting a potential mechanism of action. Additionally, although EPA is known to inhibit PGE2 synthesis and EP4 receptor activation in CRC cells, Volpato’s in vitro data demonstrated EPA’s effects on CCL2 were PGE2-EP4 receptor-independent.A post-hoc analysis of an RCT investigated the expression of chemokine C-C motif ligand 2 (CCL2), a pro-inflammatory chemokine, in 55 CRC patients with liver metastases randomized to 2g EPA daily or placebo for an average of 30 days prior to surgery . They fodecreased risk of CRC while variant carriers had no risk reduction with similar intake [An additional four case-cohort studies/case-controlled studies ,34,35,36r intake .decrease in the number of polyps, diameter of polyps and global polyp burden at six months versus the placebo group [Two studies involving participants with FAP were also analyzed see . Almendibo group . increased risk of aggressive PCA with low EPA and DHA intake (quartile 1) versus carriers of the MPO GG genotype [decreased risk of PCA. Furthermore, carriers of COX-2 rs4648310 (+8897 AG or GG) with low n-3 LC-PUFA intake were associated with an increased risk of PCA, and this was reversed by increasing n-3 LC-PUFA intake [decreased risk of PCA [increases in prostate cancer risk were observed for EPA among men > 62 years of age [Seven studies within eight reports involving PCA were identified see . Four ofgenotype . Fradet A intake . Hedelink of PCA . Khankars of age .Cui et al. (2016) conducted a retrospective cohort investigating the association between fatty acid desaturase (FADS) rs174537 and Cg27386326 methylation status with n-3 LC-PUFA composition and markers of n-3 LC-PUFA biosynthesis in specimens from 60 PCA patients undergoing radical prostatectomy. When comparing FADS rs174537 GG versus TT genotypes, they found that lower DHA levels in the PCA tissue was associated with the TT genotype . An RCT, involving 84 PCA patients, investigated how daily supplementation of 1098 mg EPA and 549 mg DHA or 30 mg lycopene or placebo for three months modulated several gene expression pathways. A canonical pathway analysis revealed statistically significant differences in the modulation of eight gene expression pathways in the fish oil group versus the placebo group at 3 months ,40.decreased prostate cancer tissue cell-cycle progression (CCP) score at post-intervention versus the Western diet group [Galet et al. (2014) conducted a post-hoc analysis of an RCT that randomized 55 PCA patients to either a low-fat diet plus 1000 mg EPA and 1835 mg DHA or a Western diet for 4–6 weeks prior to a radical prostectomy. The low-fat diet supplemented with n-3 LC-PUFA resulted in a et group . increased risk of advanced PCA risk, while an inverse association between dark fish intake and advanced PCA risk was found among carriers of the GG allele, but not among C allele carriers [Catsburg et al. (2012) conducted a case-control study in which 497 localized and 936 advanced PCA cases and 760 controls were genotyped. Carriers of the C allele of prostaglandin-endoperoxide synthase 2 (PTGS2) 765 G/C with high white fish intake had an carriers .decrease in Nf-KB activity in subjects with higher Nf-KB activity levels at baseline. There was also a significant decrease in mRNA abundance of 31 genes with higher Nf-KB activity at baseline in response to n-3 LC-PUFA intake [Four studies involving other cancer types were also included in this review see . FahrmanA intake .decreased gastric cancer risk was associated with increased seafood intake (≥1 time/week) in carriers of toll-like receptor 4 (TLR4) gene CC/CT rs10116253 and TT/TC rs1927911 [In an RCT by Cury-Boaventura et al. (2012) involving 25 surgical gastric or colon cancer, a preoperative fish oil infusion (0.2 g/kg for 3 days) altered the expression of seven genes related to cell death demonstrating a protective effect on postoperative lymphocyte apoptosis while the medium/long-chain triglycerides infusion altered 12 genes with both pro-and antiapoptotic effects associated with postoperative lymphocyte and neutrophil apoptosis . In a cas1927911 .Morales et al. (2007) conducted a case-case study involving 121 pancreatic cancer patients and found that patients with K-ras-mutated tumors had a significantly lower intake of n-3 LC-PUFA compared to those with non-K-ras-mutated tumors .The current scoping review suggests that human research regarding the interaction between genetic variants, dietary n-3 LC-PUFA  or tissue status, and cancer risk and/or treatment is at an inaugural stage. Several databases devoted to the genomics of cancer demonstrates how much this research is in its infancy. The Catalogue of Somatic Mutations in Cancer, for example, houses data on over a million samples, including almost 3 million coding mutations and over 100 million abnormal expression variants . In contTwo studies in the current review directly considered the interaction between genetic variants, breast cancer, and n-3 LC-PUFA intake. Consistent with the hypothesis that n-3 LC-PUFA may protect against cancer by promoting oxidative stress, the companion studies of Gago-Dominquez et al. (2004)  and CescAnother identified study considered the interaction between the BRCA gene mutation or tumor hormone status and n-3 LC-PUFA or fish intake  and showAs BRCA status is commonly related to tumor ER status , we examOngoing trials currently registered in Clinicaltrials.gov appear to largely focus on the use of n-3 LC-PUFA as adjunctive treatment for breast cancer  and prevention related to tumor hormone status . Some of these trials may provide additional insights regarding genetic variants and dietary n-3 LC-PUFA.reduced risk for CRC [While a growing body of mechanistic evidence suggests that EPA and DHA play a protective role in CRC risk , evidenc for CRC ,28,32, w for CRC ,30,31. decreased CRC risk per increase in daily fish consumption [Studies reporting protective effects of dietary DHA/EPA ,28,32 exsumption . increased CRC risk for the wild-type and heterozygous APC 1822, but with a decreased CRC risk in those homozygous for the variant allele. This study among others, points to the complexity of genotypic nutrient interactions, and highlights the need for more intervention trials to include genotyping in evaluating therapeutic responses. APC gene defects are responsible for FAP which increases CRC risk [Looking at the tumor suppressor APC gene, Theodoratou and co-workers found that low n-3 LC-PUFA intake was associated with an CRC risk . StudiesCRC risk . In contCRC risk . decreased risk of proximal colon cancer and a statistically significant increased risk of distal colon cancer with increased n-3 LC-PUFA intake in their meta-analysis, but did not examine genetic variants [reduced risk of CRC among higher fish consumers, although genetic variants were also not included. [Mixed results of genotype interactions between dietary n-3 LC-PUFA versus fish consumption, may help explain the inconsistent findings of the systematic reviews and meta-analyses of CRC risk. Chen and co-workers (2015) found a non-statistically significant variants . In contncluded. . Most rencluded. . increase in aggressive prostate cancer risk among men with low n-3 LC-PUFA. Due to the unsaturated nature of n-3 LC-PUFA, these fatty acids are highly peroxidizable and capable of generating reactive oxygen species (ROS), which alters cellular redox states. Many tumor cells exhibit altered ROS pathways, which enable higher intracellular ROS levels in response to dietary n-3 LC-PUFA and may induce tumor cell apoptosis [Of three studies finding protective benefits of n-3 LC-PUFA in Prostate cancer, Cheng and co-workers found evidence supporting increased oxidative stress as the mechanism responsible. Specifically, they examined the interaction between MPO variants and serum levels of n-3 LC-PUFA . Those wpoptosis . decreased risk of advanced prostate cancer with higher dark fish (higher in n-3 LC-PUFA) consumption. The authors hypothesize that the C allele may be responsible for increased PTGS2 activity (hence greater inflammation) and that interactions with heterocyclic amines may have further increased inflammatory pathways and prostate cancer risk. The remainder of studies reporting a protective benefit of fish or n-3 LC-PUFA intake investigated genetic variants associated with inflammatory pathways, again including PTGS2 and COX-2. Catsburg and co-workers (2012) found PTGS2 765 G/C genetic variants with high white fish consumption were positively associated with risk only among carriers of the C allele . While tdecreased prostate cancer risk by 72% [Looking at variants of COX-2, Hedelin et al. (2006) found high intake of salmon-type fish but not n-3 LC-PUFA per se, among subjects who were heterozygous or homozygous for allele (C) of the SNP (rs5275: +6365 T/C) k by 72% . It shouk by 72% .increased risk for prostate cancer when intake of n-3 LC-PUFA was low, but higher n-3 LC-PUFA intake was strongly protective [Similarly, Fradet and co-workers found men with the COX-2 variant rs4648310 (+8897 A/G) to be at otective . SNP rs4otective , althougThe results of three ongoing intervention trials  promise to provide additional insights into the role on dietary n-3 LC-PUFA and prostate cancer risk and treatment (NCT02333435), with at least two of these being likely to provide evidence regarding the interaction of n-3 LC-PUFA with genetic variants . Studies involving patients with CLL , gastricIn this scoping review, we have endeavored to provide a comprehensive summary of key study design elements on the topic of n-3 LC-PUFA and cancer that assist the reader in judging various aspects of evidence quality and strength. Scoping reviews are not intended to provide a systematic assessment of the quality or strength of evidence in a particular area ,64, so tDue to the nature of examining a broad research question on n-3 LC-PUFA and cancer, our review identified three main cancers, yet culminated in a heterogeneous collection of studies with various polymorphisms that precludes a simple summary, but does identify varied data and mechanisms of action as well as limitations of the existing evidence base. For example, Hou et al. (2016) suggest that the discrepancy between findings for CRC risk and fish consumption versus n-3 LC-PUFA intake may be due to the source of n-3 LC-PUFA, e.g., from fish oil, purified EPA, DHA, or a combination of the two resulting in variable ratios of EPA and DHA, along with the administration of different doses . This obEvidence from this scoping review suggests that dietary n-3 LC-PUFA may interact with genetic variants of inflammatory signals, apoptotic gene expression markers, and cell cycle regulation factors in a manner that may decrease the risk of breast, colorectal and prostate cancer. However, depending upon the genetic polymorphism identified, a few studies found an increased risk or no benefit. Results reported in the current review require replication in large cohorts and well-powered intervention trials with genotyping to further elucidate the role of dietary n-3 LC-PUFA and genetic variation in cancer risk. Common mechanisms for n-3 LC-PUFA’ anti-neoplastic effects include the inhibition of inflammatory pathways, e.g. COX-2 activity, PTGS, CCL2, and enhanced oxidative stress pathways and apoptosis, e.g., myloperoxidase genes and NF-kB. This review highlights variability in individual responses to n-3 LC-PUFA supplementation, some of which appear to be dose dependent. Once a genetic variant is identified, a targeted, personalized therapeutic approach that includes DHA and/or EPA may be possible as an adjuvant to immunotherapy or chemotherapy. The expansion of research designed to evaluate n-3 LC-PUFA interactions with genetic variants of other leading cancers, such as gastric, pancreatic, lung and bronchus, is also needed.


ERCC6 , and implicated its interaction with ERCC8 . We found significant upregulation of ERCC6 (p = 7.95 × 10–6) and ERCC8 (p = 4.67 × 10–6) in breast cancer and similar frequencies of ERCC6 (1.8%) and ERCC8 (0.3%) mutations in breast tumors to known breast cancer susceptibility genes such as BLM (1.9%) and LSP1 (0.3%). Our integrative genomics approach suggests that ERCC6 may be a previously unreported low- to moderate-risk breast cancer susceptibility gene, which may also interact with ERCC8.Up to 30% of all breast cancer cases may be inherited and up to 85% of those may be due to segregation of susceptibility genes with low and moderate risk [odds ratios (OR) ≤ 3] for  breast cancer. The majority of low/moderate-risk genes, particularly those with minor allele frequencies (MAF) of < 30%, have not been identified and/or validated due to limitations of conventional association testing approaches, which include the agnostic nature of Genome Wide Association Studies (GWAS). To overcome these limitations, we used a hypothesis-driven integrative genomics approach to test the association of breast cancer with candidate genes by analyzing multi-omics data. Our candidate-gene association analyses of GWAS datasets suggested an increased risk of breast cancer with  There will be an estimated 276,480 new cases of invasive breast cancer and 42,170 deaths due to breast cancer among US women in 20201. Breast cancer is a complex disorder with genetic and environmental factors playing synergistic roles in its etiology.Breast cancer is the most commonly-reported cancer and the second leading cause of cancer death among women in the United States (US). Approximately, 1 in 8 women in the US will develop breast cancer in their lifetime and 1 in 35 will die from the disease2. About 15% of inherited breast cancer cases may be due to segregation of rare germline mutations [minor allele frequency (MAF) ≤ 1%] in high-penetrance  susceptibility genes such as BRCA1, BRCA2, TP53, and PTEN3. Medium-penetrance (3 ≤ RR ≤ 10) genes such as CHEK2, ATM, PALB2 (suggested by some as being high-penetrance5), BRIP1 (disputed as a breast cancer susceptibility gene by some6) have also been identified and account for about 8% of inherited breast cancers cases2. Nearly all high- and medium-penetrance genes were identified through family-based genetic epidemiologic investigations and gene resequencing7. Early  onset of the disease is a well-established hallmark of inherited breast cancers that are due to segregation of mutations in high- and medium-penetrance genes. Low-penetrance [RR or odds ratio (OR) < 3] genes, such as FGFR2, CASP8, MAP3K1 and LSP1, with MAF of about 30% in the general population and accounting for about 15% of inherited breast cancer cases have also been identified through genome-wide association studies (GWAS)7. Susceptibility genes accounting for the remaining of about 62% of inherited breast cancers  are yet to be identified and/or validated  breast cancer. We report on the analysis of raw multiomics data from several relevant GWAS, transcriptome, and somatic mutation datasets to test the association of breast cancer with two candidate genes, ERCC6 and ERCC8.Inherent limitations of GWAS, namely their agnostic nature necessitating stringent p-value thresholds, multiple-testing corrections, and exceedingly large sample size requirements for detecting small effects and gene–gene interactions, may be responsible for the apparent inability to identify and replicate the remainder of low- to medium-penetrance breast cancer susceptibility genes, particularly those with MAF < 30%. Integrative approaches enable extraction of deeper biological insights than what can be achieved through single-dimensional or conventional analyses. Inspired by our hypothesis-driven integrative genetic epidemiologic investigations of DNA repair disorders, which enabled us to decipher the biologic mechanisms that underlie the association between mutations in a subset of DNA repair genes and certain clinical outcomes associated with these disorders11 dataset analyzed in our study  are described in the Methods section and were published in our previous report12. All ERCC6 SNPs analyzed in CGEMS were in Hardy–Weinberg Equilibrium (HWE) and in strong Linkage Disequilibrium (LD) among the controls , rs3750749 (NC_000010.11:g.49476182A > G), and rs4253082 (NC_000010.11:g.49509540C > T), were associated with an increased risk for breast cancer in CGEMS  , 1/6 , and 4/4   and the ERCC8 1/1 and ERCC6 1/6 diplotype combination  compared with the reference category (Table ERCC6 and ERCC8 at the diplotype level (pinteraction = 0.010)  are described in the Methods section. All ERCC6 and ERCC8 SNPs analyzed in our NSABP dataset were in HWE and in strong LD among the controls  was associated with an increased risk for breast cancer among heterozygotes  (Table ERCC8 SNPs or ERCC6 and ERCC8 Haplotypes and diplotypes (Supplementary Table ERCC6 and ERCC8 diplotypes in NSABP.One 16 and the subset dataset used in our study (465 cases and 1394 controls)12 have been published and are described in the Methods section. All ERCC6 and ERCC8 SNPs typed on these subjects were in HWE and in strong LD among the controls   and rs2228528 (NC_000010.11:g.49524234C > T)    and the ERCC8 0/5 and ERCC6 0/0 diplotype combination  compared with the reference category (Table ERCC6 and ERCC8 at the diplotype level (pinteraction = 0.034)  are described in the Methods section. All ERCC6 and ERCC8 SNPs typed on these subjects were in HWE and in strong LD among the controls  of breast cancer among heterozygotes in BPC3  with breast cancer was detected in PBCS (results not shown).One ERCC8 SNPs, haplotypes or diplotypes were associated with a statistically-significant increased risk of breast cancer (Supplementary Table ERCC6 and ERCC8 at the diplotype level (pinteraction = 0.047) (Table None of 7) Table C.19, and one from a case–control study of IDBC, E-TAMB-27620, revealed significant upregulation of ERCC6 and ERCC8. In GSE10780, upregulation of ERCC6  and ERCC8  was comparable to that of BRCA1   and ERCC8 (p = 4.67 × 10–6).Individual analysis of raw data from two gene expression microarray datasets, one containing invasive ductal breast cancers (IDBC) and normal adjacent tissue, GSE1078021  breast tumor data revealed that 95% of genes mutated in breast tumors had mutation frequencies that were below 1%  in breast tumors was similar to those of known breast cancer susceptibility genes, such as BRCA1 (2.9%), BRCA2 (2.9%), BLM (1.9%), FGFR2 (1.5%), and CHEK2 (1.0%)  in breast tumors was also similar to some of the other known breast cancer susceptibility genes such as STK11 (0.3%) and LSP1 (0.3%)  Fig. a. The mu ERCC8 0.% in breaERCC6 and ERCC8 to those of breast cancer susceptibility genes with high-penetrance , medium-penetrance , and low-penetrance , and to the rest of the genome. This analysis revealed that high-penetrance breast cancer susceptibility genes had higher mutation frequencies than medium- and low-penetrance genes  control genes . We identified 200 moderate-impact and 41 high-impact mutations in ERCC6 and similar numbers in ERCC8. The ratios of high- to moderate-impact mutations for ERCC6 (0.20) and ERCC8 (0.22) were similar to those for BRCA1 (0.30), BRCA2 (0.30), CHEK2 (0.28), and BLM (0.20) , and show the location of two of our significant ERCC6 SNPs located in exons, rs2228528 and rs2229760, with respect to the reported mutations in TCGA.Using data on all cancers in TCGA, we compared the ratio of high-impact mutations  to moderate-impact mutations  in ERCC6 and ERCC8 with peri- and post-menopausal breast cancer. Our candidate-gene association study involving individual analysis of four GWAS datasets  found 30% to threefold increased risk of breast cancer (OR 1.30 with rs2228528 in WHI to OR 2.91 with rs3750751 in CGEMS) conferred by six ERCC6 variations. The MAF of these six ERCC6 variations ranged from 11.8 for rs3750751 to 40.9% for rs4838519 in the general Caucasian population [based on MAF reported in the NCBI (https://www.ncbi.nlm.nih.gov/snp/)].Using a hypothesis-driven (candidate-gene) integrative genetic epidemiologic approach to analysis of raw multi-omics data, we tested the association of ERCC6 haplotype, Hap 5, contained the variant alleles at two of the associated SNPs, rs3750749 and rs4253082. Furthermore, Hap 5 was one of the haplotype pairs in diplotype 2/5, which was associated with nearly threefold increase in risk. One of the other ERCC6 diplotypes associated with nearly threefold increase in risk in CGEMS, 4/4, contained the variant allele at the third associated SNP, rs375075. Similarly, the associated haplotype in the WHI, Hap 2, contained the variant alleles at the two associated SNPs, namely rs2228528 and rs1012553; Hap 2 was one of the haplotype pairs in diplotype 2/4, which was associated with 2.5-fold increased risk in this dataset. In BPC3, Hap 6 was the only haplotype associated with a borderline significant increased risk and was also the only common haplotype that contained the variant allele at the associated SNP, rs3750751.Our findings were consistent at the SNP, haplotype and diplotype level within each dataset. In CGEMS, the associated ERCC6 diplotypes with main effects were those highlighted in joint effects analyses. For example, in CGEMS, joint effect analysis highlighted diplotype 2/5, which had a significant main effect in diplotype analysis. Similarly, in WHI, joint effect analysis highlighted diplotype 2/4. Even though it is difficult to compare associated haplotypes and diplotypes across different datasets (due to different genotyping platforms and different SNPs typed in each dataset as well as LD between typed and untyped SNPs), consistency can also be noted across the analyzed datasets. One ERCC6 SNP (rs3750751) was significant in 2 of 3 GWAS where it was typed (this SNP is discussed in detail below).The results of joint effect analyses were also compatible with main effect analyses within each dataset in that ERCC6 and ERCC8 in breast cancer, which is notable given the large sample size . The similarity of ERCC6 (and ERCC8) to BRCA1 in terms of both expression level and fold-change estimate is also noteworthy. BRCA1 levels are normally elevated in tumor biopsies from breast cancer patients who do not carry a germline mutation in the BRCA122. This is beleived to be due to the involvement of BRCA1 in cell cycle control and DNA repair processes23.In order to gain additional support for our findings, we conducted analyses of transcriptome and TCGA data. In general, somatic transcript levels and mutation frequencies do not necessarily parallel germline mutation profiles. Therefore, our findings from these analyses, although consistent with the results of our analyses of GWAS datasets, should be interpreted only as additional evidence. Analysis of transcriptome datasets showed statistically- significant upregulation of https://prot-shiny-vm.broadinstitute.org:3838/CPTAC-BRCA2016/) and found elevated expression of both, similar to some of our other comparison proteins such as CHEK2 (Data not shown).We also assessed protein expression levels of ERCC6 and ERCC8 in breast cancer using the Broad Institute proteome database  also provided supporting evidence for involvement of ERCC8 variations on breast cancer risk did not reach statistical significance in some of our GWAS datasets, we cannot rule out a main or modifying effect for ERCC8 given our findings of possible interaction between ERCC6 and ERCC8 in three of four GWAS analyzed. The findings from our analyses of transcriptome and TCGA datasets further suggest a potential main or modifying effect for ERCC8 in breast cancer susceptibility.Although, the main effects of ERCC6 SNP (rs3750751) was significant in 2 of 3 GWAS where it was typed. While heterozygotes at this SNP had 35% increased risk in BPC3 , homozygotes for the variant allele at rs3750751 had close to threefold increased risk of breast cancer in CGEMS. The frequency of heterozygotes at ERCC6 rs3750751 ranged from 14 to 16% among the controls in our GWAS datasets. The homozygote variant frequency for this SNP was less than 1% among controls in our datasets. The minor allele frequency (MAF) of rs3750751 was 7.2–8.6% among the controls in our datasets (Supplementary Table https://www.ncbi.nlm.nih.gov/snp/).One  rs375075 was signERCC6 rs3750751 has not been previously implicated in breast cancer risk, however it has been reported to be associated with an increased risk of bladder cancer in one study24. rs3750751 lies in the 3′ untranslated region (3′-UTR), which has been proposed to be involved in post-transcription regulation of protein expression25. Recent studies implicate the role of micro RNA (miRNA)s in such regulation, suggesting that SNP variants located in 3′ UTRs may destroy or create miRNA binding sites26, thus influencing tumor susceptibility27. Each miRNA may be able to repress hundreds of gene targets post-transcriptionally, therefore, they are powerful regulators of gene expression28. miRNAs are involved in regulating a diverse set of biological processes including growth, differentiation and apoptosis29. The other ERCC6 variations associated with breast cancer in our study were rs3750749, rs4253082, and rs1012553, all located in the introns, and rs2228528 and rs2229760, which are missense variants in the exons. An interaction with smoking (pack-years) was noted for rs4253082 in one bladder cancer study30 and a possible association of rs2228528 with muscle-invasive bladder tumors was noted in another study31. To establish the role of these SNPs in cancer susceptibility and elucidate the underlying mechanism of carcinogenesis, functional assays are needed.43, a few of which have been validated. One validated low-penetrance breast cancer susceptibility gene, FGFR210, has been found to be associated with 26% increased risk of breast cancer  and has a MAF of > 30% in the general population. In comparison, our findings suggest 30% to threefold increased risk of breast cancer for ERCC6 variations with MAF of 11.8–40.9% in the general population. Lack of identification of ERCC6 in previous studies may be due to inherent limitations of GWAS and candidate gene studies. A major limitation of GWAS is that they are agnostic and exploratory, hence the high probability of false positive associations and the need for very large sample sizes for detection of weak main effects and gene–gene interactions. To alleviate the multiple-testing large false positive burden, stringent threshold levels for significance  have been recommended for GWAS. This stringent threshold levels may lead to many true positive associations being missed. Candidate-gene approaches, which study association between variations in a small number of genes with the risk of disease, are more powerful since they are hypothesis-driven and have a lower probability of false-positive associations . However, selection of suitable candidate genes is challenging and may be imprecise.Analysis of different breast cancer GWAS to date have led to associations with 182 variantsERCC6 and ERCC8 as candidates for our integrative genomic study reported here was based on our previous genetic epidemiologic studies of DNA repair disorders46, which led to the hypothesis that genes involved in the nucleotide excision repair (NER) pathway may be involved in breast cancer susceptibility. ERCC6 and ERCC8 code for the main components of the transcription-coupled (TC) repair sub-pathway of the NER, which repairs damage to actively-transcribed regions of DNA48 caused by ultraviolet radiation, chemicals and free radicals49. Free radicals and reactive oxygen species (ROS) are associated with oxidative stress, a mechanism relevant to cancer. It has been observed that TC-NER is induced as the result of damage caused by oxidative stress, and that ERCC6 accumulates at sites of locally induced oxidative damage in vivo in a transcription-dependent manner50. It has also been observed that cells with mutations in ERCC6 and ERCC8 are sensitive to ROS51. The effect of ERCC6 and ERCC8 variants analyzed in our study on TC-NER is not known. However, given that TC-NER is a critical protective pathway against genotoxic agents52, it is plausible that certain variations in ERCC6 and ERCC8 may affect protein function, and repair efficiency of TC-NER, hence increase susceptibility to cancer.Selection of ERCC6 and ERCC8 diplotypes. Our findings of interaction are biologically plausible as these genes work together in the same biological pathway and sub-pathways. Our results suggest that TC-NER and global genome (GG)-NER may be good candidates for future pathway-based association studies of breast cancer.In our study, we found some evidence for interaction between 53. Currently, genetic screening and counseling regarding personalized preventive and management strategies  are available to individuals from families and ethnic groups with identified high- and medium-penetrance mutations3. Multigene panel tests for breast cancer exist and are offered to women who meet the criteria for clinical genetic testing such as early age of onset, bilateral and/or triple-negative disease, and a family history54. In theory, incorporation of additional low- and moderate-risk genes into multigene panel tests, following substantial validation and efficacy studies, may improve prevention strategies.The public health significance of this study stems from its potential to provide leads for inclusion of additional low- to moderate-risk breast cancer susceptibility genes  in the panel of susceptibility genes for risk scoresERCC6 associations were consistent in all four datasets despite the differences in ER and PR status of the subjects further strengthens our results.Strengths of our study include its unique integrative nature and uniform application of inclusion/exclusion factors to all GWAS and transcriptome datasets. One limitation of our study, which was related to the secondary nature of our datasets, was lack of information on individual estrogen receptor (ER) and progesterone receptor (PR) status in all datasets and inclusion of only ER-negative cases in BPC3. Stratifying by receptor status may modify the associations observed in our study or identify new associations. Nevertheless, the fact that our findings with respect to Another limitation of our study  was lack of information on all risk-modifying environmental and reproductive/behavioral variables. We were limited by the variables which were provided to us as part of each dataset. While we had information on nearly all confounders in WHI, we were only provided with 5-year age categories and family history information in CGEMS. Despite this limitation, we utilized all information provided to us on all reproductive and behavioral variables in order to properly adjust our odds ratios for the effect of confounders. Our future studies to replicate and confirm these findings would need to involve primary data collection of pre-, peri- and post-menopausal breast cancer cases. Future studies would also need to validate and extend our findings to other populations and racial/ethnic groups.ERCC6 may be a previously-unreported low- to moderate-risk breast cancer susceptibility gene, and that it may interact with ERCC8. Our results suggest that NER may be a good candidate for future pathway-based association studies of breast cancer. Our findings have the potential to provide a deeper insight into the genetic basis of common breast cancer and, following proper replication and validation, generate leads for improved prevention methods.Using a hypothesis-driven integrative genetic epidemiologic approach to analysis of multi-omics data, we propose that 46, ERCC6 and ERCC852, were selected as part of a panel of candidate genes for association studies with breast cancer. All SNPs within ERCC6 and ERCC8 (including those in the upstream and downstream regulatory regions) which were typed in selected GWAS datasets (described below) in at least 80% of the subjects were used for analysis. After quality control exclusions, our roster of SNPs within the two candidate genes included 12 ERCC6 and 2 ERCC8 SNPs in CGEMS, 17 ERCC6 and 3 ERCC8 SNPs in NSABP, 14 ERCC6 and 8 ERCC8 SNPs in WHI, and 16 ERCC6 and 3 ERCC8 SNPs in BPC3. Information about these SNPs along with their chromosomal positions [obtained from the reference sequence in the National Center for Biotechnology Information (NCBI) (https://www.ncbi.nlm.nih.gov/snp/) Build 38.p12] and physical locations within the gene are listed in Supplementary Table Based on our previous genetic epidemiologic studies of DNA repair disorders55.We selected case–control GWAS datasets which contained cases with invasive ductal breast cancer (IDBC) diagnosed peri- or post-menopause (≥ 50 years of age) and healthy controls  appropriately-matched to the cases based on a number of criteria by the original investigators. Race was restricted to Caucasian to avoid population stratification11 , a nested case–control  within the National Surgical Adjuvant Breast and Bowel Project (NSABP)14, a nested case–control (465 cases and 1394 controls) within the Women's Health Initiative (WHI)16 Hormone Therapy Trials, and a nested case–control  within the Breast and Prostate Cancer Cohort Consortium (BPC3)18. Institutional Review Board (IRB) approval was obtained from University at Albany [by RM] for analyses of secondary datasets (Protocol #11-298 and #11-E-177) reported in this paper. Detailed information about each secondary dataset is provided below.Raw data from four GWAS datasets fitting our inclusion criteria were downloaded from the National Institutes of Health (NIH) after obtaining approvals from the corresponding data repository committees and stored on a computer and/or the server in RM’s laboratory at the University at Albany. The datasets analyzed in this study included the Cancer Genetic Markers of Susceptibility (CGEMS)11, a case–control study nested within the Nurse’s Health Study (NHS) cohort containing genotype information at 528,178 single nucleotide polymorphism (SNP) loci on 1145 post-menopausal women of European ancestry with invasive breast cancer and 1,142 controls was downloaded from the NIH [accession #6175-13 for version phs000147/GRU]. Detailed information about the design and original analyses of CGEMS data is available from published reports33. For this dataset, we restricted the analysis to cases diagnosed at ≥ 55 years of age (N = 1089) and frequency-matched controls (N = 1093). Besides 5-year age categories, the only other phenotype variable available to us on these subjects was presence or absence of a first-degree family history of breast cancer.Raw data from CGEMS14 and P-213) were downloaded from the NIH (accession #10821-11 for version phs000305/APTC). This dataset contained genotype information at 601,273 SNPs on 592 breast cancer cases and 1171 controls of European (Caucasian) ancestry. We restricted our analysis to women diagnosed with breast cancer ≥ 50 years of age (N = 430) and appropriately-matched controls (N = 822). In NSABP14, controls were matched to cases based on several factors by the original study investigators; the matching factors included age at trial entry, time in the study, history of lobular carcinoma in situ, and 5-year predicted breast cancer risk based on the Gail model.Raw data from a nested case–control study within the PGRN-RIKEN Mayo National Surgical Adjuvant Breast and Bowel Project (NSABP) Prevention Trials  subgroup of WHI] and 733,202 SNPs [in Women’s Health Initiative Memory Study (WHIMS) group] on a total of 10,634 subjects (4894 in GARNET and 5740 in WHIMS) was downloaded [NIH accession #11295-10 for version phs000200/HMB-IRB and #11296-10 for phs000200/HMB-IRB-NPU]. We created a nested case–control dataset of women diagnosed with invasive breast cancer ≥ 50 years of age (N = 465) and healthy controls (N = 1394) frequency-matched to the cases based on age in 3:1 control to case ratio. Variables provided to us on WHI subjects included a number of confounders of breast cancer risk such as family history of breast cancer, parity, oral contraceptive use, breast feeding, and body mass index, which we adjusted for in our analyses. The demographic characteristics of the 465 cases in our study and the entire control population that gave rise to the 1394 controls included in our analyses were published in our previous report12.Raw data from the Women's Health Initiative (WHI)18 was downloaded from the NIH . The parent BPC3 GWAS included cases and controls from eight studies, but the subset BPC3 dataset which was made available to us contained genotype information at 550,000 loci on 1309 ER-negative breast cancer cases and 1351 appropriately-matched controls from three of the original BPC3 cohorts, namely the Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial, the European Prospective Investigation into Cancer and Nutrition (EPIC), and the Polish Breast Cancer Study (PBCS). We restricted our analysis to breast cancer cases diagnosed ≥ 50 years of age (N = 977) and appropriately-matched healthy controls (N = 1026) in this subset BPC3-PLCO/EPIC/PBCS dataset, referred to as BPC3 in this paper. Phenotype variables provided to us in this dataset included family history of breast cancer, which was adjusted for in our analyses.Raw data from the Breast and Prostate Cancer Cohort Consortium (BPC3)56 and a χ2 test of independence. Individual haplotypes and diplotypes (haplotype pairs) were determined for all subjects using PHASE57. Logistic regression models were used to calculate odd ratios (OR) and 95% confidence intervals (CI) for association between breast cancer and each SNP, haplotype and diplotype, while adjusting for covariates. Unconditional logistic regression was used for analyses of CGEMS, WHI and BPC3 data, while conditional logistic regression was used to analyze NSABP. The CGEMS, NSABP, and WHI datasets contained combination of both estrogen receptor (ER)-positive and -negative cases where as the BPC3 dataset made accessible to us contained ER-negative cases only. All datasets contained combination of progesterone (PR)-positive and –negative cases. The individual ER and PR status of cases were not made available to us for any of the GWAS datasets. Joint effects of ERCC6 and ERCC8 diplotypes on risk of breast cancer were examined and multiplicative interaction at the diplotype level was tested by assessing statistical significance of the interaction terms in the logistic regression models using the Wald test. All association testing was done using SAS version 9.3  and SPSS version 24 (IBM SPSS Statistics).For each gene, linkage disequilibrium (LD) patterns and deviations from Hardy–Weinberg Equilibrium (HWE) were assessed among the controls in each dataset using Haploview19 and E-TABM 27620, containing normal and cancerous breast tissue, were downloaded from Gene Expression Omnibus (GEO) and Array Express, respectively, and used for meta-analysis. As per the inclusion criteria, both datasets ascertained cases of IDBC among peri- and post-menopausal Caucasian women. Control samples in GSE1078019 contained normal adjacent tissue from breast cancer cases. Controls in E-TABM 27620 had no personal history of breast cancer and contributed histologically normal breast tissue. Other inclusion criteria were presence of more than five samples per group separated by more than two standard deviations (SD) difference between the groups in Principle Component Analysis (PCA) using first two axes of the PCA. GSE10780 contained 42 IDBC cases and 143 control samples and E-TAMB-276 contained 23 IDBC cases and 10 healthy controls.Raw data from two gene expression microarray datasets, GSE1078019 and E-TABM-27620 by calculating the p-values for each gene in each experiment using student’s t-test. P-values for genes with discordant fold differences between datasets were changed to “one” and Fisher’s method was used to calculate meta-analysis p-values. Fold changes were averaged. All statistical analyses of gene expression microarray data were done using Partek Genomics version 6.6 .The data was geometric-mean normalized and analyzed using non-parametric T-test. Meta-analysis was conducted on GSE1078021. We analyzed TCGA breast tumor data by comparing frequency of mutations in ERCC6 and ERCC8 with the frequencies in known breast cancer susceptibility genes and the rest of the genome. We also compared the ratio of high-impact mutations  to moderate-impact mutations  in ERCC6 and ERCC8 versus several comparison groups in the TCGA. One comparison group included all known high-, medium- and low-penetrance breast cancer susceptibility genes. Another comparison set included a group of size-matched  control genes . All analyses of the TCGA data were done through the TCGA portal21 .We sought to validate our findings by examining frequency of somatic mutations in The Cancer Genome Atlas (TCGA)ERCC6, ERCC8, and the BRCA genes using https://www.cbioportal.org/) and assessed protein expression levels of ERCC6 and ERCC8 in breast cancer using the Broad Institute proteome database (https://prot-shiny-vm.broadinstitute.org:3838/CPTAC-BRCA2016/).Additionally, we created the somatic mutation landscape of Supplementary Information.


ORs), by the Cox regression model. For overall breast cancer patients, the more frequency of red meat  and salted food  were statistically significantly associated with a greater risk of breast cancer. Beans , white meat , aquatic products , vegetables , fruit , and green tea  were significantly associated with a lower risk of breast cancer. For luminal breast cancer patients, beans , white meat , green tea , and milk  were protective factors. While for nonluminal breast cancer, red meat was not included in the equation, and beans , white meat , vegetables , and milk  still showed a significantly reduced risk of nonluminal breast cancer. Different dietary factors revealed different effects on the etiology of breast cancer. Red meat may be a specific risk factor for luminal‐type breast cancer.To explore the associations between dietary factors and breast cancer (BC) molecular subtypes. The retrospective cases were confirmed by pathological diagnosis with breast cancer were gathered in two major hospitals in Xuzhou city, China, from 2015 to 2016. These cases were classified by the meeting standard of 13th St Gallen: luminal A, luminal B, Her‐2 overexpression, and triple‐negative breast cancer (TNBC) subtypes. A 1:2 paired retrospective case–control study with 210 cases and 420 controls was conducted to evaluate individual dietary intake, by food frequency questionnaire (FFQ) and estimate odds ratios ( The paper was to explore the associations between dietary factors and breast cancer molecular subtypes by food frequency questionnaire. The result showed that different dietary factors revealed different effects on the etiology of breast cancer, and red meat may be a specific risk factor for luminal‐type breast cancer. Moreover, Her‐2 ++ without FISH test was considered by default to be negative.The patients were classified by molecular subtypes according to the definition of the 11th St Gallen: luminal A subtype , luminal B subtype , Her‐2 overexpression subtype , and TNBC . Due to the similar etiological characteristics between luminal A and luminal B breast cancer, they were combined as luminal type, and the other two types were nonluminal type.2.3The questionnaire collected fundamental and clinicopathological profiles included age at diagnosis, region, menstrual history, reproductive history, family history, TNM status, histological type, and expressions of ER, PR, Her‐2, and Ki67. Results of Her‐2 fluorescence in situ hybridization (FISH) testing were also available. In addition, we took a national semi‐quantitative food frequency questionnaire method (FFQ) to reflect individual long‐term dietary patterns. Referring to the results of the current dietary studies of breast cancer, the food was sorted into beans, red meat, white meat, aquatic products, vegetables, and fruits. The face‐to‐face survey was completed by graduate students trained in uniform standards at hospital or community health center for reducing information bias. To correct the dietary questionnaires for measurement errors, measuring aid was used to investigate the frequency and amount of intake of these foods in the past year. For analysis, the frequency was finally converted into times of consumption per year.2.4x¯±s). The Cox regression model was used to analyze the influencing factors of breast cancer and its subtypes. The test level was 0.05. The study protocol was approved by removed for blind peer review. This study was prepared based on strengthening the reporting of observational studies in epidemiology (STROBE) statement for case–control studies. The study was approved by the Institutional Review Board of the Affiliated Hospital of Xuzhou Medical University (XYFY2015‐KL071‐01). Written informed consent was obtained from all adult subjects.Epidata version 3.1 and SPSS version 16.0 were used to establish and analyze the database. Measurement data were expressed by , and salted products  were determined to be independent risk factors for breast cancer versus control. Annual frequency intake of beans , white meat , aquatic products , fruit , green tea , and vegetables  were statistically significantly associated with a lower risk for breast cancer , family history of cancer and passive smoking, etc., annual intake of red meat  and salted products  were again determined to be significant risk factors. Annual intake of beans , white meat , green tea , and milk  were seen to result in a reduced risk for luminal breast cancer , white meat , vegetables , and milk  were significantly protective factors against nonluminal breast cancer . Written informed consent was obtained from all adult subjects.The authors declare no conflict of interest.Guohai Yuan was involved in data collection, analysis of results, and writing the draft manuscript. Jingjing Zhang, Yi Ren, Wei Ding, Yan Du, and Lu Zhang were involved in development of the study design and data analysis. Jihong Shao was involved in development of the analysis of results and contributed to submitting the manuscript. All authors are in agreement with the manuscript and declare that the content has not been published elsewhere.


To the Editor:The recent article by Dania and colleagues reported that black women had a 33% increased risk for subsequent invasive breast cancer after diagnosis of lobular carcinoma in situ (LCIS) during 90 months of follow-up . The autThe authors did not consider the role of vitamin D in affecting risk of invasive breast cancer. Black women living in the USA have much lower 25-hydroxyvitamin D [25(OH)D] concentrations than do white women due to their darker skin  in overall cancer incidence.There is evidence that breast cancer risk among black women is affected by 25(OH) D concentrations. For example, a prospective study from the Black Women’s Health Study found that the incidence rate ratio for invasive breast cancer for lowest vs. highest quartile of predicted 25(OH) D was 1.23  [The evidence that vitamin D reduces risk of breast cancer was reviewed recently . The strThe proposed mechanisms whereby vitamin D metabolites reduce the risk of breast, colorectal, prostate, and overall cancer include surveillance of cells, reduced angiogenesis around tumors, and anti-metastasis actions . How


Measuring systemic chronic inflammatory markers in the blood may be one way of understanding the role of inflammation in breast cancer risk, and might provide an intermediate outcome marker in prevention studies. Here, we present the results of a systematic review of prospective epidemiologic studies that examined associations between systemic inflammatory biomarkers measured in blood and breast cancer risk. From 1 January 2014 to 20 April 2020, we identified 18 unique studies (from 16 publications) that examined the association of systemic inflammatory biomarkers measured in blood with breast cancer risk using prospectively collected epidemiologic data. Only one marker, C-reactive protein, was studied extensively (measured in 13 of the 16 publications), and had some evidence of a positive association with breast cancer risk. Evidence associating other inflammatory biomarkers and more comprehensive panels of markers with the development of breast cancer is limited. Future prospective evidence from expanded panels of systemic blood inflammatory biomarkers is needed to establish strong and independent links with breast cancer risk, along with mechanistic studies to understand inflammatory pathways and demonstrate how breast tissue responds to chronic inflammation. This knowledge could ultimately support the development and evaluation of mechanistically driven interventions to reduce inflammation and prevent breast cancer. BRCA1 and BRCA2 mutations [The relationship between chronic inflammation and cancer is complex and bidirectional. In this review, we focus on the role of inflammation in the development of breast cancer. The mechanisms through which chronic inflammation might lead to cancer have been detailed elsewhere ,2 and inutations . These oChronic inflammation can occur in response to lifestyle and environmental factors that are associated with breast cancer. For example, weight gain in adulthood and being overweight or obese after menopause are associated with both chronic inflammation and breast cancer risk ,6,7. TheMeasuring systemic chronic inflammatory markers in the blood may be one way of understanding the role of inflammation in breast cancer risk and might provide intermediate outcome markers in prevention studies. Inflammatory markers, such as C-reactive protein (CRP), interleukin-6 (IL-6), and tumor necrosis factor-α (TNF- α) are shown to markedly increase in response to infection and tissue damage, as well as in active state of disease . Variatin = 5 assessed breast cancer survival rather than incidence), exposure ascertainment (n = 1 inflammatory biomarker measured in urine rather than blood), or study design (n = 8 case-control study). Case-control studies were excluded because temporality cannot be clearly established for biomarkers that may be affected by the disease process. The remaining 16 publications were included in our review. We searched the reference lists of the included publications for additional eligible publications, but no additional studies were identified. We extracted data on study population, study design, sample size, exposure assessment, confounding assessment, and relevant effect estimates, and the corresponding 95% confidence intervals (CIs) from the included publications. We did not conduct a quantitative meta-analysis because there was substantial heterogeneity across studies in terms of which systemic blood biomarkers were evaluated.We conducted a systematic review of prospective epidemiologic studies investigating the association between circulating inflammatory biomarkers measured in blood and breast cancer risk. We performed a search of the PubMed database to identify studies published from 1 January 2014 to 20 April 2020. We selected these dates because three meta-analyses were published in 2015, which searched the literature up until December 2014 ,22 or FeWe identified 18 unique studies from 16 publications  that ex2 = 45–47%) [p = 0.08–0.17) [ 45–47%) ,22,23. T2 , but not for postmenopausal women with a BMI ≥ 25 kg/m2  [2 , but not for postmenopausal women with a BMI < 25 kg/m2  [Three of the 12 new studies found an overall statistically significant positive association between CRP and overall breast cancer risk, including a population-based cohort study of 44,715 women (baseline age range = 48–67 years) in Denmark  , a populegories) . In cont6, 1.21) ,32,35, t6, 1.21)  and HR 16, 1.21)  and HR 02 , but not for women with a BMI ≥ 25 kg/m2  [A statistically significant positive association was found between fibrinogen and breast cancer risk in the WHS cohort  , but not4, 1.14) .Statistically significant positive associations with breast cancer risk were found for IL-1β (a pro-inflammatory cytokine) in a Swedish nested case-control study of postmenopausal women  , and forSeveral studies found a negative association between specific inflammatory biomarkers in blood and breast cancer risk. A nested case-control study in the European Prospective Investigation into Cancer and the nutrition (EPIC)-Varese cohort found a statistically significant negative association between adiponectin  and breTwo studies considered composite scores of inflammatory biomarkers ,27. UsinWe systematically reviewed the literature on circulating blood biomarkers of inflammation available since the last meta-analysis published in 2015. Only one marker, CRP, has been studied extensively. Three meta-analyses published in 2015 each found a consistent, positive association between elevated blood levels of CRP and breast cancer risk. However, for the new studies identified in this updated review, only half of the studies (6 of 12) reported point estimates >1.10 ,31,32,36CRP is a sensitive and widely used systemic marker of inflammation that, compared with other inflammatory markers, had several advantages as a chronic inflammation marker for past epidemiologic studies, including the availability of reliable assays and temporal stability . Yet, itA growing number of studies have examined whether different medical conditions that are related to chronic inflammation are also associated with breast cancer risk by comparing women with these conditions to women without, in terms of the incidence of breast cancer . HoweverThere is a fair amount of evidence in experimental models of breast cancer that obesity and fatty diet are linked to inflammation within the mammary tissue (macrophage-induced crown-like structures or CLS), morphological increases in fibrous tissue, upregulation of aromatase activity (associated with increased number of fat cells expressing the gene) and other biomarkers of inflammation, such as CC-chemokine ligand 2 (CCL2). CCL2 is an inflammatory cytokine that recruits macrophages to sites of injury. Macrophage-associated CLS has been linked to increased proinflammatory mediators  and aromatase expression, and may be an indicator of mammary tumor risk in rodent models . CaloricCritical to understanding the role of chronic inflammation and breast cancer risk is the need to appreciate that breast tissue is metabolically active and can change in form and function with metabolic disease and risk factors. Therefore, along with the need for studies that examine systemic inflammation, studies are needed that examine how the breast responds to chronic inflammation. This will require non-invasive ways to measure breast tissue changes. While standard screening methods, including mammography and magnetic resonance imaging (MRI) without background parenchymal enhancement (BPE), do not capture the metabolic activity in breast tissue, there are alternative non-screening methods that can be used to evaluate metabolic changes in breast tissue in association with inflammation. Such non-screening and non-diagnostic methods include thermography, Diffuse Optical Tomography (DOT), and optical spectroscopy (OS) ,58,59. AThese alternative non-invasive methods  do not expose the breast tissue to radiation and can be used to assess changes over short durations of time. Therefore, if validated in different settings, such methods could be used as an outcome for intervention studies, as they represent a non-invasive method to measure vascular changes in the breast tissue. A key advantage of combining these non-invasive measures of breast tissue changes with blood biomarkers is that, in combination, they may be a powerful biosensor and/or intermediate outcome for intervention studies. In addition to being advantageous for intervention studies, these non-invasive measures of breast tissue changes could also be used in etiologic studies to explore the role of chronic inflammation in breast tissue composition and metabolic function.The development of a validated panel of chronic inflammatory markers that can be utilized as an intermediate outcome, with or without non-invasive breast tissue measurements, would be useful in intervention studies. Moreover, the use of validated non-invasive biomarkers that are predictive of breast cancer would enhance breast cancer risk reduction and risk stratification. This would be similar to how other chronic diseases are monitored through blood tests combined with intermediate measures . However, this review found that only CRP has been extensively studied in association with breast cancer risk. Few epidemiologic studies have evaluated other inflammatory biomarkers, individually or in panels, that may be more specific to breast-related changes. Future prospective studies utilizing expanded panels of systemic blood inflammatory biomarkers, along with serially collected measurements, are thus needed to strengthen the evidence on inflammation with breast cancer risk. In addition to building the epidemiologic evidence, it will also be important to conduct mechanistic studies to understand inflammatory pathways and demonstrate how breast tissue responds to chronic inflammation, such as through altered metabolic activity. This knowledge could ultimately support the development and evaluation of mechanistically driven interventions to reduce inflammation and prevent breast cancer.


Polygenic risk scores (PRS) have been shown to predict breast cancer risk in European women, but their utility in Asian women is unclear. Here we evaluate the best performing PRSs for European-ancestry women using data from 17,262 breast cancer cases and 17,695 controls of Asian ancestry from 13 case-control studies, and 10,255 Chinese women from a prospective cohort (413 incident breast cancers). Compared to women in the middle quintile of the risk distribution, women in the highest 1% of PRS distribution have a ~2.7-fold risk and women in the lowest 1% of PRS distribution has ~0.4-fold risk of developing breast cancer. There is no evidence of heterogeneity in PRS performance in Chinese, Malay and Indian women. A PRS developed for European-ancestry women is also predictive of breast cancer risk in Asian women and can help in developing risk-stratified screening programmes in Asia. Polygenic risk scores predict the likelihood that an individual will develop a certain cancer, however these are often specific for a given population. Here, the authors show that a risk score developed to assess the risk of breast cancer in European women can also predict risk in Asian populations. By contrast, screening in the majority of Asian countries is opportunistic and suffers from poor uptake, contributing to delayed detection and poor survival2. In addition, there are concerns about the appropriate starting age of screening, as women are recommended to start screening at age 50 in many Asian countries, even though the peak breast cancer incidence in Asian populations is between 40 and 50 years of age3. Taken together with the rapidly increasing incidence of breast cancer in Asia4, there is thus an urgent need to develop an appropriate screening strategy for Asian women.In the majority of high-income Western countries, breast cancer screening is systematic and population-based, and this has contributed to an improvement in survivalBRCA1 and BRCA2 can lead to better management of risk, but these only explain a small fraction of breast cancer cases in the general population5. Risk profiles based on a combination of low penetrance but common breast cancer susceptibility single nucleotide polymorphisms (SNPs), summarised as polygenic risk scores (PRS), have been shown to be an important predictor of disease risk8. A 313-SNP PRS developed in European populations has improved predictive power compared to earlier PRS based on fewer SNPs;7 this PRS demonstrated similar associations with disease risk in eleven independent prospective studies. Studies in European populations have demonstrated that PRS substantially improve discrimination, in comparison to risk prediction models based on classical risk factors alone9. In particular, using the recent extension of the BOADICEA model, it has been demonstrated that the 313-SNP PRS provides greater level of risk stratification in the population than epidemiological risk factors alone, and that the greatest level of risk stratification is achieved when both the PRS and epidemiological risk factors are considered jointly10. Screening trials13 in women of predominantly European descent are ongoing to evaluate personalised breast cancer screening programme based on a woman’s individual risk of disease, as a means of improving screening efficiency14.Provision of genetic counselling and genetic testing for rare variants in breast cancer predisposition genes such as 19. Only ~20% of the existing breast cancer genome-wide association study (GWAS) data are from women of Asian ancestry21. This limits the precision in the relative risk estimates for individual variants, which is critical for development of predictive PRS. Furthermore, Asian populations are ethnically and genetically diverse22, and genetic associations with breast cancer risk may vary by ancestry. Here, we evaluate the predictive ability of the 313-SNP PRS developed for European women for predicting breast cancer risk in Asian women, using data from 17,262 cases and 17,695 control women of Asian ancestry, from 10 studies based in Asian countries and three studies from North America, participating in the Breast Cancer Association Consortium (BCAC); and 10,255 Chinese women from a prospective cohort. We also evaluate the heterogeneity in the associations with breast cancer risk by ethnicity. We show that European ancestry-based PRS is predictive of breast cancer risk in Asian women.Although there have been several efforts to create an Asian-specific PRS, these have been limited by the smaller sample size of Asian genetic studies7.To ensure accurate determination of PRS in the ethnic-specific analyses, 26 of the 313 SNPs with imputation accuracy scores <0.9, based in the Malaysian Breast Cancer Genetic Study (MyBrCa) and Singapore Breast Cancer Cohort (SGBCC) of 6900 cases and 7606 controls, combined, were excluded. Hence, the PRS was constructed using 287 SNPs for all BCAC studies , ER-positive PRS (PRSER+) and ER-negative PRS (PRSER−), while the standard deviations (SDs) were slightly lower in Asian controls [versus European controls] for all three PRSs , was similar across the three ethnic groups  for overall breast cancer  and ER-positive breast cancer , but higher (p < 0.05) for ER-negative breast cancer , using data on 10,255 women, of whom 413 had developed breast cancer (Supplementary Table 24 (Table 25) is used to define women at sufficient risk to justify screening, Chinese and Malay women in the highest 1% of the PRS distribution would reach this threshold by age 35, while Indian women in the highest 1% would reach the threshold at age 39 years.Absolute lifetime and 10-year breast cancer risks by 287 SNP PRS percentile were derived by combining the estimated overall breast cancer ORs from BCAC Asian studies  at some point in their life. The maximum 10-year absolute risk for Chinese women in the highest 25%, Malay women in the highest 16% and Indian women in the highest 17% of the PRS distribution were greater than 2.3%. Offering screening to these women would capture ~40%, ~27% and ~28% of all breast cancer cases in the Chinese, Malay and Indian populations, respectively . We also recalculate the 287-SNP PRS using the same samples. The list of SNPs and corresponding weights as reported in the literature are given in Supplementary Table We compared the predictive performance of the 287-SNP PRS for overall breast cancer with five PRSsTo date, the utility of incorporating common genetic variants into breast cancer risk prediction models has predominantly been investigated in women of European descent. Previous efforts in Asian studies thus far have focused on the development of Asian-specific PRS, and have been limited by small sample size. Given the difficulties of defining population-specific PRS, a more practical question is whether the PRS developed using data from women of European ancestry is predictive of risk for women of Asian ancestry. In this study, using the largest available data of Asian women, we independently evaluated the predictive performance of PRS developed based on 287 variants.Our study showed that the European-ancestry PRS was predictive of overall breast cancer risk for Asians. The magnitudes of association were generally consistent across the ten participating case–control Asian studies and the prospective Singaporean Chinese study. The association was also consistent across the three ethnic groups in Malaysia and Singapore, suggesting that the PRS is associated with similar relative risk estimates in all three ethnicities, though the confidence intervals for Malays and Indians are wide.27, achieved AUC of 0.562  may reflect the different allele frequency distributions: of the 287 SNPs that are common in women of European ancestry , 43 are rare in Asian women and therefore contribute minimally to the PRS. In this paper, we have standardised the PRS to the European SD to enable comparison of the performance of the PRS in European and Asian populations. A more relevant approach is to standardised the PRS to the Asian SD, in which case the overall breast cancer OR per unit increase in PRS would be decreased to 1.48 (95% CI: 1.44–1.52). Taken together, these results highlight the need to calibrate the PRS distribution to enable risk models developed based on one population (e.g. Europeans) to be used in another population (e.g. Asians).26 leading to a greater variation in risk of breast cancer. Larger studies of Asian women in non-Asian countries are needed to provide more reliable estimates.The 287-SNP PRS had a lower predictive performance for overall breast cancer among Asians from the three North American studies, compared to the Asian or European studies  would never reach this threshold . It is important to note, however, that the estimated association of family history on breast cancer risk (OR = 1.35) is lower compared with other studies (OR = 1.8–3.9 in European studies34 and OR = 1.52–2.1 in Asian studies26). This might be due to inaccuracies in the family history data. The control women in the largest study (MyBrCa) contributing to these analyses, accounted for ~30% of the total data, were recruited through opportunistic screening which may be enriched for family history relative to the cases. In addition, there was evidence of heterogeneity  in the effect sizes of association between family history and breast cancer risk across Asian studies.Our study has some limitations worth noting. Although we used the largest dataset of Asian women available to date to evaluate the performance of PRS, the sample size was still too limited to provide precise relative risk estimates for the extremes of the PRS distribution, particularly for ER-specific disease. The majority of the data in the BCAC dataset were generated with the OncoArray, however, ~27% samples were genotyped using iCOGS array, which has lower genome-coverage. Of the 287 SNPs, 42 SNPs have imputation score between 0.75 and 0.9, while 53 SNPs have imputation score below 0.75 in the iCOGs dataset. This may explain in part the evidence for some heterogeneity in effect sizes between iCOGS and OncoArray datasets. The attenuation (10%) in the effect size of family history of breast cancer on breast cancer risk after adjusting for the 287-SNP PRS is consistent with the predicted contribution of the SNPs to the twofold familial risk of breast cancer for 287 SNPs  may be used as the basis for Asian-specific breast cancer risk prediction models that include the PRS as well as other predictors of breast cancer risk. These models will allow for higher levels of risk stratification to be achieved, as recently demonstrated in women of European ancestry33). SCHS is a population-based prospective cohort study. Of the total of 10,255 women aged 43–75 years who had not had any cancer diagnosis prior to recruitment, 413 registry-confirmed breast cancers developed over 195,317.2 person years of prospective follow-up. Follow-up started 6 months after recruitment and was censored at age of breast cancer diagnosis, age at last known non-breast cancer status, or age on 31 December 2015, whichever came first. Supplementary Table 7 and (b) ten nested case–control studies within prospective cohorts in BCAC, comprising 11,225 cases and 17,788 controls, included in the test dataset in Mavaddat et al.7, but excluding subjects <80 years old and for whom age was unknown. All studies were approved by the relevant institutional ethics committees and review boards, and all participants provided written informed consent.The study participants were 45,233 women of Asian ancestry from three sources: (a) 32,238 women  participating in 10 Asian studies in Breast Cancer Association Consortium (BCAC); (b) 2719 women (1507 invasive cases and 1212 controls) of Asian ancestry participating in 3 north American population-based case–control studies in BCAC; and (c) 10,266 women of Chinese ethnicity participating in Singapore Chinese Health Study , were excluded. For first-degree relative pairs, the control was removed from the case–control pairs; otherwise the sample with the lower call rate was excluded. SNPs with assay call rate <95% and deviation from Hardy–Weinberg equilibrium in controls at p < 10−7 in controls or p < 10−12 for cases were excluded. The iCOGS and OncoArray datasets were imputed separately using a two-stage imputation approach, using SHAPEIT235 for phasing and IMPUTE236 for imputation, with 1000 Genomes Project (Phase 3) data as the reference panel37.All samples in BCAC studies were genotyped using one of two arrays: the ~211,155-SNP iCOGS array and the ~533,000-SNP OncoArraySamples in the prospective study (SCHS) were genotyped using Illumina Global Screening Array. Samples with call rate < 95% and extremes in heterozygosity were excluded. For first- and second-degree relative pairs, the sample with the lower call rate was excluded. Data were imputed using IMPUTE2 with 1000 Genomes Project (Phase 3) as reference panel. Only non-monomorphic SNPs in East Asian population in the reference panel were imputed.36. This metric takes values between 0 and 1, with higher values indicating higher imputation certainty and 1 implying perfect imputation.Post-imputation quality was based on the imputation accuracy score INFOSCORE as provided by IMPUTE234. Individuals with >40% estimated East Asian ancestry were retained. In the second stage, principal components were generated on the Asian ancestry individuals using a subset of uncorrelated SNPs. Similar ancestry informative principal components were generated on the SCHS dataset.Principal components analyses were used to identify ethnic outliers and define ancestry informative covariates. For the BCAC data, continental ancestry was derived by combining the data with the 1000 Genomes Project reference data7. SNPs with an imputation accuracy score <0.9, based in the MyBrCa and SGBCC studies, combined, were excluded; to ensure accurate determination of PRSs in the ethnic-specific analyses.The analyses were based on the 313-SNP PRS developed in women of European ancestryxk is the dosage of risk allele (0–2) for SNP k and βk is the corresponding weight. To avoid bias due to overfitting, we used the weights previously derived for women European ancestry7. The ER-specific PRSs (denoted as PRSER+ for ER-positive PRS and PRSER− for ER-negative PRS) used same set of SNPs but weights from the hybrid method as reported in Mavaddat et al.7; the hybrid method assigns subtype-specific weights to a subset of SNPs for which the effect sizes differ significantly by subtype. The list of SNPs and the corresponding weight are provided in Supplementary Data We derived PRS for overall breast cancer using Eq. 1\documenLogistic regression models were used to estimate ORs for the association between the standardised PRSs and breast cancer risk. The overall breast cancer PRS was used as predictor in association analyses between overall breast cancer and PRS while for subtype-specific analyses, ER-specific PRS were used as predictors. The PRS were treated as either a continuous or categorical predictor in the model. When used as a categorical variable, the PRS was categorised into the following PRS percentile ranges based on the PRS distribution in controls: 1%, 1–5%, 5–10%, 10–20%, 20–40%, 40–60%, 60–80%, 80–90%, 90–95%, 95–99% and 99–100%. The 40–60% category was used as the reference. For ethnic-specific analyses, analyses were stratified by ethnicity  using only the MyBrCA and SGBCC datasets. All models were adjusted for first ten principal components and study/array/batch; here samples from the same study that were genotyped in two batches (as was the case for MyBrCa and SGBCC) or on both arrays were treated as different strata for the purposes of adjustment. A Cox proportional hazard model was used for the evaluation of the PRS association with overall breast cancer risk in the prospective cohort and HRs per SD of the PRS were estimated.The discriminatory accuracy of models for predicting breast cancer risk was evaluated using the area under the receiver operating characteristic curve (AUC), adjusted by study. Estimated ORs by PRS quantiles were compared with the predicted ORs under the model in which the PRS is considered as a continuous covariate and the log (OR) is linearly related to the PRS. To determine the proportion of the familial breast cancer risk that could be explained by PRS, we estimated the OR for the association of first-degree family history and breast cancer risk first adjusted for first 10 principal components and study/array/batch, and then additionally adjusted for the PRS.To evaluate the effect modification of the PRS (as a continuous covariate) by age and family history of breast cancer in first-degree relatives, we included additional interaction terms in the logistic regression model.38. The proportion of the familial relative risk  due to the PRS was therefore estimated by using Eq.  was then used to compare the estimated effect sizes with those reported in Mavaddat et al. (2019)n by Eq. :3\documen in Eq. 4\documenu) is the breast cancer incidence associated with PRS at age u, Sg(u) is the probability of being breast cancer free at age u, and Sm(u) is the probability of not dying from a cause other than breast cancer to age u. The PRS-specific breast cancer incidences, u), were calculated iteratively by assuming that the average age-specific breast cancer incidence over all PRS percentiles agreed with the population breast cancer incidence6. We calculated lifetime and 10-year absolute risks using Singaporean mortality and breast cancer incidence for Chinese, Malays and Indians24. The recommended screening age at 50 years old in many Asian countries is based on European or North American guidelines29 and the average 10-year risk of breast cancer for women of European ancestry at age 50 years old is 2.3%25. Hence, we determined the proportion of women in the general population who would have the 10-year risk of breast cancer above this threshold, using method as described in Pharoah et al.38. To do this, the maximum 10-year absolute risk, adjusting for competing mortality, for women age 20–70, was calculated for each PRS centile category , assuming an OR per 1 SD of the PRS of 1.48 (the estimated effect size in Asian studies).The age-specific absolute risks of developing breast cancer, adjusting for competing mortality, in each PRS percentile was calculated using Eq. 5\documen15, 51 SNPs17, 44 SNPs19, 6 SNPs26 and 46 SNPs27. The PRSs were derived using Eq. (We compared the predictive performance of the European ancestry-based PRS with PRSs that were previously developed or evaluated in Asian populations. The five Asian ancestry-derived PRSs included 5 SNPslogistic and comproc in Stata, Cox proportional hazard model was done using Coxph in R.All statistical analyses were conducted using R v.3.0.3 or Stata v.14.2. Logistic regression and AUC were done using Further information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Reporting Summary


The intrauterine and early life environments have been linked to the etiology of breast cancer in prior studies. We prospectively examined whether maternal and newborn anthropometric factors as reported by the mother are related to an increased incidence of adult breast cancer in the daughter. We used data from 35,133 mother-daughter dyads of the Nurses’ Health Study (NHS) II and the Nurses’ Mothers’ Cohort Study. In 2001, living mothers of NHS II participants who were free of cancer completed a questionnaire on their pregnancy with the nurse and their nurse daughter’s early life experience. During 403,786 years of follow-up, 865 daughters developed incident cases of invasive breast cancer. Nurses with a birthweight of ≥4000 g had a 32% greater risk for breast cancer (multivariable-adjusted hazard ratio (HR) = 1.32, 95% confidence interval (CI) = 1.02–1.71, p-trend = 0.09) compared with those with birthweights of 3000–3499 g. Higher birth length tended to increase risk of premenopausal breast cancer (p for trend = 0.05). We further noted a modest U-shaped relation between maternal weight gain during pregnancy and premenopausal breast cancer incidence in the daughter. Fetal growth may contribute to shaping later life risk for breast cancer, especially prior to menopause. Known and suspected risk factors for breast cancer operating during adult life leave substantial variation in rates of this tumor unexplained. Over the past couple of decades perinatal and early life characteristics have emerged as novel breast cancer risk factors with consistency across different study populations but with some international divergence.Breast cancer is the most common malignancy among women worldwide, with an estimated 1.67 million new cancer cases diagnosed in 20126, although this is not true for all studies7. Intrauterine exposure to sex steroid hormones, growth hormone, insulin, insulin-like growth factors (IGF)-1, and IGF-2, and epigenetic variation are potential key pathways linking anthropometric variables in early life to adult breast cancer risk8. Because investigations from retrospective case-control studies relying on information collected from the cases and controls themselves leave room for differential misclassification, data from prospective longitudinal analyses are warranted to overcome this bias. In previous literature, fewer data have been available on maternal factors such as pre-pregnancy body mass index (BMI), and weight gain during pregnancy in relation to the risk of breast cancer in the daughter. However, with the global epidemic of obesity maternal body mass prior to and during pregnancy has substantially increased during the past couple of decades, with 39% of women with normal, 59% with overweight, and 56% with obese prepregnancy BMI exceeding the current U.S. Institute of Medicine (IOM) recommendations for gestational weight gain9.The implications of these rapidly changing intrauterine conditions for the daughters’ future breast cancer risk need to be investigated.In prior epidemiologic studies, high birthweight has been associated with greater risk of breast cancerWe therefore used data from the prospective Nurses’ Mothers’ Cohort Study to explore the relation of anthropometric variables in newborns including birthweight and birth length to their risk of developing breast cancer in adulthood; we also examined whether maternal pre-pregnancy BMI, height, and weight gain during pregnancy as reported by the mothers were associated with the incidence of breast cancer among daughters participating in the Nurses’ Health Study (NHS) II.10. Follow-up questionnaires were mailed to participating nurses every two years updating information on lifestyle factors and health. In 2001, 35 830 living mothers of NHSII who were cancer-free participants completed a questionnaire on their pregnancy with their nurse daughter and on her early life exposures forming the Nurses’ Mothers’ Cohort Study11. NHS II participants who were adopted or whose adoption status was unknown and those with missing information on the exposures of interest were excluded from the respective analyses.The NHS II cohort was established in 1989 when 116,680 nurses aged between 25 and 42 years from 14 U.S. states completed a mailed questionnaire on lifestyle factors and medical historyThe institutional review boards of the Brigham and Women’s Hospital and Harvard School of Public Health, Boston approved the study protocol. Response to the questionnaire was considered implied consent.2. Pre-pregnancy BMI was categorized following standard World Health Organization definitions of underweight (<18.5 kg/m2), normal weight (18.5–24.9 kg/m2), overweight (25–29.9 kg/m2), and obese (≥30 kg/m2). Mothers were also asked to report their gestational weight gain in pre-specified categories . Mothers further reported birthweight and birth length of their daughters as open-ended values. Birthweights recalled by the nurses’ mothers were highly concordant with birthweight information obtained from birth certificates (r = 0.85)12. Birthweight was coded using standard categories . Birth length was categorized in equally spaced intervals capturing the narrow distribution . Ponderal index was calculated from birth weight and length [Ponderal index = birthweight (grams) x 100/³] and categorized in quintiles.On the Mothers’ questionnaire, the nurses’ mothers were asked to report their height and weight before pregnancy in open-ended questions; this information was used to calculate pre-pregnancy BMI as kg/m13.Covariate information on tobacco use during pregnancy and gestational age was obtained from the Mothers’ questionnaire. Additional information on daughter’s characteristics, including race/ethnicity, family history of breast cancer, month and year of birth of the nurse, menopausal status, adult caloric intake, alcohol consumption, smoking, and BMI was available from the NHS II questionnaires. The derivation of menopausal status in this cohort has been previously describedOn follow-up questionnaires administered every two years, NHS participants were asked whether they had been diagnosed with breast cancer in the previous two years. To confirm the diagnosis, participants who reported a breast cancer diagnosis were asked for permission to review their relevant medical records and pathology reports. Due to the high accuracy of self-reported breast cancer diagnosis (>98%), nurse participants who reported a diagnosis of breast cancer but for whom medical records could not be obtained were classified as breast cancer cases.Deaths were identified by reports from the next of kin, postal authorities, or the National Death Index.2, 18.5–24.9 kg/m2, 25.0–30.0 kg/m2, >30 kg/m2), pregnancy weight gain , maternal smoking during pregnancy , and gestational age . In separate models, we included adult characteristics of the nurses including alcohol consumption, energy intake, smoking habits, and most recent pre-diagnostic (current) BMI during adulthood, all of which were updated during follow-up.Cox proportional hazards regression models were used to estimate the hazard ratios (HRs) and 95% confidence intervals (CIs) of the incidence of breast cancer in the daughters across categories of infant birthweight, birth length, maternal pre-pregnancy BMI, height, and gestational weight gain. Participants contributed follow-up time from the return of the Nurses’ Mothers questionnaire in 2001 to the date of death, cancer diagnosis (except non-melanoma skin cancer), or end of follow-up on June 1, 2013. The basic model adjusted for the nurse’s age and follow-up cycle. Multivariable-adjusted models additionally included race , family history of breast cancer , maternal pre-pregnancy BMI . The study has been conducted and reported in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelinesDuring 403,786 person-years of follow-up between 2001 and 2013, we documented 865 newly diagnosed invasive cases of breast cancer among NHS II participants. The distribution of perinatal and adult characteristics of the nurse daughters and of the maternal anthropometric pregnancy characteristics across categories of birthweight and pregnancy weight gain is shown in Tables Compared with daughters with birthweights of 3000–3499 g, those with a birthweight of ≥4000 g had a 32% greater incidence of adult breast cancer  compared to those whose mothers were normal-weight  and an increased risk of breast cancer in adulthood has been observed in most prior studies7. Unlike in our study, the birth length – breast cancer association was not modified by menopausal status in that meta-analysis7. The mechanisms underlying any association between birth length and subsequent breast cancer are likely similar to those connecting birthweight and breast cancer.Our study revealed a statistically significant dose-response relation between birth length and premenopausal breast cancer. A previous meta-analysis suggested a positive association between birth length and later breast cancer in studies based on birth records, but not in studies based on parental recall or self-reports, likely reflecting the difficulty in precisely capturing birth lengthet al.17 examining the relation between maternal weight gain during pregnancy reported by the mother and breast cancer risk in the daughters in two population-based case-control studies reported a higher breast cancer risk in daughters  whose mothers gained 25–34 pounds compared to mothers who gained 15–24 pounds17. Notably, women whose mothers gained 35 pounds or more during pregnancy were not at an increased risk of breast cancer in that study17. We previously analyzed the relation between maternally reported pre-pregnancy BMI and gestational weight gain with breast cancer risk in adult daughters in a retrospective case-control study nested within the NHS I and NHS II and found no association18. Similar to the results in our current study, Sanderson et al.17 did not observe a link between maternal pre-pregnancy BMI and breast cancer risk in the daughters. However, both our study and that by Sanderson and colleagues were based on pregnancies several decades in the past, when in particular pre-pregnancy BMI, but also gestational weight gain recommendations were considerably lower than currently. Hence, these associations need to be further evaluated in more recent studies including a larger proportion of mothers with high pre-pregnancy BMI and maternal weight gain, reflecting current distributions.We observed a modest U-shaped relation between maternal weight gain during pregnancy and the daughter’s premenopausal breast cancer risk. A previous analysis by Sanderson Strengths of our study include its prospective design, the large number of study participants, and adjustment for a number of important potential perinatal confounding variables. Moreover, our study was based on a two-generation design providing the unique opportunity to use data collected directly from the mothers and combine them with data provided by the nurses.12. Similarly, other studies have reported good agreement between pregnancy-related factors recorded during pregnancy and maternal recall21. The nurses’ adult life variables did not appreciably affect associations observed. However, we cannot rule out unmeasured confounding or residual confounding through covariates measured with error. Finally, with mothers being born between 1921 and 1964, we had limited power to examine substantial pregnancy weight gains and high values of pre-pregnancy BMI which have become more prevalent in recent years.A limitation of our study is that gestational and newborn characteristics had to be recalled by the mothers likely introducing random misclassification. However, the validity of birthweight reported by the mothers appears to be high when compared with birthweight recorded on state birth records (r = 0.85)In summary, a high birthweight and possibly a high birth length may predict an increased risk of breast cancer in later life, particularly of premenopausal breast cancer. Whether maternal factors during pregnancy and other infant factors are related to adult breast cancer risk requires further clarification in other prospective studies with good exposure validity.