Stratification of women according to their risk of breast cancer based on polygenic risk scores (PRSs) could improve screening and prevention strategies. Our aim was to develop PRSs, optimized for prediction of estrogen receptor (ER)-specific disease, from the largest available genome-wide association dataset and to empirically validate the PRSs in prospective studies. The development dataset comprised 94,075 case subjects and 75,017 control subjects of European ancestry from 69 studies, divided into training and validation sets. Samples were genotyped using genome-wide arrays, and single-nucleotide polymorphisms (SNPs) were selected by stepwise regression or lasso penalized regression. The best performing PRSs were validated in an independent test set comprising 11,428 case subjects and 18,323 control subjects from 10 prospective studies and 190,040 women from UK Biobank (3,215 incident breast cancers). For the best PRSs (313 SNPs), the odds ratio for overall disease per 1 standard deviation in ten prospective studies was 1.61 (95%CI: 1.57–1.65) with area under receiver-operator curve (AUC) = 0.630 (95%CI: 0.628–0.651). The lifetime risk of overall breast cancer in the top centile of the PRSs was 32.6%. Compared with women in the middle quintile, those in the highest 1% of risk had 4.37- and 2.78-fold risks, and those in the lowest 1% of risk had 0.16- and 0.27-fold risks, of developing ER-positive and ER-negative disease, respectively. Goodness-of-fit tests indicated that this PRS was well calibrated and predicts disease risk accurately in the tails of the distribution. This PRS is a powerful and reliable predictor of breast cancer risk that may improve breast cancer prevention programs.

Background:Data for multiple common susceptibility alleles for breast cancer may be combined to identify women at different levels of breast cancer risk. Such stratification could guide preventive and screening strategies. However, empirical evidence for genetic risk stratification is lacking.Methods:We investigated the value of using 77 breast cancer-associated single nucleotide polymorphisms (SNPs) for risk stratification, in a study of 33 673 breast cancer cases and 33 381 control women of European origin. We tested all possible pair-wise multiplicative interactions and constructed a 77-SNP polygenic risk score (PRS) for breast cancer overall and by estrogen receptor (ER) status. Absolute risks of breast cancer by PRS were derived from relative risk estimates and UK incidence and mortality rates.Results:There was no strong evidence for departure from a multiplicative model for any SNP pair. Women in the highest 1% of the PRS had a three-fold increased risk of developing breast cancer compared with women in the middle quintile (odds ratio [OR] = 3.36, 95% confidence interval [CI] = 2.95 to 3.83). The ORs for ER-positive and ER-negative disease were 3.73 (95% CI = 3.24 to 4.30) and 2.80 (95% CI = 2.26 to 3.46), respectively. Lifetime risk of breast cancer for women in the lowest and highest quintiles of the PRS were 5.2% and 16.6% for a woman without family history, and 8.6% and 24.4% for a woman with a first-degree family history of breast cancer.Conclusions:The PRS stratifies breast cancer risk in women both with and without a family history of breast cancer. The observed level of risk discrimination could inform targeted screening and prevention strategies. Further discrimination may be achievable through combining the PRS with lifestyle/environmental factors, although these were not considered in this report.

BACKGROUNDGenetic testing for breast cancer susceptibility is widely used, but for many genes, evidence of an association with breast cancer is weak, underlying risk estimates are imprecise, and reliable subtype-specific risk estimates are lacking. METHODSWe used a panel of 34 putative susceptibility genes to perform sequencing on samples from 60,466 women with breast cancer and 53,461 controls. In separate analyses for protein-truncating variants and rare missense variants in these genes, we estimated odds ratios for breast cancer overall and tumor subtypes. We evaluated missense-variant associations according to domain and classification of pathogenicity. RESULTSProtein-truncating variants in 5 genes (ATM, BRCA1, BRCA2, CHEK2, and PALB2) were associated with a risk of breast cancer overall with a P value of less than 0.0001. Protein-truncating variants in 4 other genes (BARD1, RAD51C, RAD51D, and TP53) were associated with a risk of breast cancer overall with a P value of less than 0.05 and a Bayesian false-discovery probability of less than 0.05. For protein-truncating variants in 19 of the remaining 25 genes, the upper limit of the 95% confidence interval of the odds ratio for breast cancer overall was less than 2.0. For protein-truncating variants in ATM and CHEK2, odds ratios were higher for estrogen receptor (ER)-positive disease than for ER-negative disease; for protein-truncating variants in BARD1, BRCA1, BRCA2, PALB2, RAD51C, and RAD51D, odds ratios were higher for ER-negative disease than for ER-positive disease. Rare missense variants (in aggregate) in ATM, CHEK2, and TP53 were associated with a risk of breast cancer overall with a P value of less than 0.001. For BRCA1, BRCA2, and TP53, missense variants (in aggregate) that would be classified as pathogenic according to standard criteria were associated with a risk of breast cancer overall, with the risk being similar to that of protein-truncating variants. CONCLUSIONSThe results of this study define the genes that are most clinically useful for inclusion on panels for the prediction of breast cancer risk, as well as provide estimates of the risks associated with protein-truncating variants, to guide genetic counseling. (Funded by European Union Horizon 2020 programs and others.

To identify colorectal cancer (CRC) susceptibility alleles, we conducted a genome-wide association study. In phase 1, we genotyped 550,163 tagSNPs in 940 familial colorectal tumor cases (627 CRC, 313 high-risk adenoma) and 965 controls. In phase 2, we genotyped 42,708 selected SNPs in 2,873 CRC cases and 2,871 controls. In phase 3, we evaluated 11 SNPs showing association at P < 10(-4) in a joint analysis of phases 1 and 2 in 4,287 CRC cases and 3,743 controls. Two SNPs were taken forward to phase 4 genotyping (10,731 CRC cases and 10,961 controls from eight centers). In addition to the previously reported 8q24, 15q13 and 18q21 CRC risk loci, we identified two previously unreported associations: rs10795668, located at 10p14 (P = 2.5 x 10(-13) overall; P = 6.9 x 10(-12) replication), and rs16892766, at 8q23.3 (P = 3.3 x 10(-18) overall; P = 9.6 x 10(-17) replication), which tags a plausible causative gene, EIF3H. These data provide further evidence for the 'common-disease common-variant' model of CRC predisposition.

Importance The causal direction and magnitude of the association between telomere length and incidence of cancer and non-neoplastic diseases is uncertain owing to the susceptibility of observational studies to confounding and reverse causation. Objective To conduct a Mendelian randomization study, using germline genetic variants as instrumental variables, to appraise the causal relevance of telomere length for risk of cancer and non-neoplastic diseases. Data Sources Genomewide association studies (GWAS) published up to January 15, 2015. Study Selection GWAS of noncommunicable diseases that assayed germline genetic variation and did not select cohort or control participants on the basis of preexisting diseases. Of 163 GWAS of noncommunicable diseases identified, summary data from 103 were available. Data Extraction and Synthesis Summary association statistics for single nucleotide polymorphisms (SNPs) that are strongly associated with telomere length in the general population. Main Outcomes and Measures Odds ratios (ORs) and 95% confidence intervals (CIs) for disease per standard deviation (SD) higher telomere length due to germline genetic variation. Results Summary data were available for 35 cancers and 48 non-neoplastic diseases, corresponding to 420 081 cases (median cases, 2526 per disease) and 1 093 105 controls (median, 6789 per disease). Increased telomere length due to germline genetic variation was generally associated with increased risk for site-specific cancers. The strongest associations (ORs [95% CIs] per 1-SD change in genetically increased telomere length) were observed for glioma, 5.27 (3.15-8.81); serous low-malignant-potential ovarian cancer, 4.35 (2.39-7.94); lung adenocarcinoma, 3.19 (2.40-4.22); neuroblastoma, 2.98 (1.92-4.62); bladder cancer, 2.19 (1.32-3.66); melanoma, 1.87 (1.55-2.26); testicular cancer, 1.76 (1.02-3.04); kidney cancer, 1.55 (1.08-2.23); and endometrial cancer, 1.31 (1.07-1.61). Associations were stronger for rarer cancers and at tissue sites with lower rates of stem cell division. There was generally little evidence of association between genetically increased telomere length and risk of psychiatric, autoimmune, inflammatory, diabetic, and other non-neoplastic diseases, except for coronary heart disease (OR, 0.78 [95% CI, 0.67-0.90]), abdominal aortic aneurysm (OR, 0.63 [95% CI, 0.49-0.81]), celiac disease (OR, 0.42 [95% CI, 0.28-0.61]) and interstitial lung disease (OR, 0.09 [95% CI, 0.05-0.15]). Conclusions and Relevance It is likely that longer telomeres increase risk for several cancers but reduce risk for some non-neoplastic diseases, including cardiovascular diseases.

Estrogen receptor (ER)-negative breast cancer shows a higher incidence in women of African ancestry compared to women of European ancestry. In search of common risk alleles for ER-negative breast cancer, we combined genome-wide association study (GWAS) data from women of African ancestry (1,004 ER-negative cases and 2,745 controls) and European ancestry (1,718 ER-negative cases and 3,670 controls), with replication testing conducted in an additional 2,292 ER-negative cases and 16,901 controls of European ancestry. We identified a common risk variant for ER-negative breast cancer at the TERT-CLPTM1L locus on chromosome 5p15 (rs10069690: per-allele odds ratio (OR) = 1.18 per allele, P = 1.0 × 10−10). The variant was also significantly associated with triple-negative (ER-negative, progesterone receptor (PR)-negative and human epidermal growth factor-2 (HER2)-negative) breast cancer (OR = 1.25, P = 1.1 × 10−9), particularly in younger women (<50 years of age) (OR = 1.48, P = 1.9 × 10−9). Our results identify a genetic locus associated with estrogen receptor negative breast cancer subtypes in multiple populations.

Purpose: Angiogenesis is a necessary step in tumor growth and metastasis. Vascular endothelial growth factor (VEGF) is a major mediator of breast cancer angiogenesis. Therefore, we investigated the association of polymorphisms in the VEGF gene with breast cancer risk and prognostic characteristics of the tumors in a large case-control study. Experimental Design: We examined three polymorphisms in the VEGF gene (−2578C/A, −1154G/A, and +936C/T) in 571 familial breast cancer cases from Poland and Germany and −2578C/A, −634G/C, and +936C/T polymorphisms in 974 unselected breast cancer cases from Sweden together with ethnically and geographically selected controls. Results: None of the polymorphisms or any haplotype was significantly associated with either familial or unselected breast cancers. Our study suggests that the +936C/T polymorphism is unlikely to be associated with breast cancer. We also analyzed the unselected cases for genotypes or haplotypes that associated with tumor characteristics. The −634CC genotype and the −2578/−634 CC haplotype were significantly associated with high tumor aggressiveness (large tumor size and high histologic grade, P &lt; 0.01) and the −2578AA genotype and the −2578/−634 AG haplotype with low histologic grade tumors (P = 0.04). The genotypes and haplotypes were not related with other tumor characteristics such as regional or distant metastasis, stage at diagnosis, or estrogen or progesterone receptor status. Conclusions: Although none of the polymorphisms studied in the VEGF gene was found to influence susceptibility to breast cancer significantly, some of the VEGF genotypes and haplotypes may influence tumor growth through an altered expression of VEGF and tumor angiogenesis.

Recent evidence indicate that small non-coding RNA molecules, called micro-RNAs (miRNAs), can bind to the 3' untranslated regions (UTRs) of messenger RNAs and interfere with their translation, thereby regulating cell growth, differentiation, apoptosis and tumorigenesis. Genetic polymorphisms can reside on miRNA-binding sites. Thus, it is conceivable that the miRNA regulation may be affected by polymorphisms on the 3' UTRs. Since gene deregulation is one of the key mechanisms by which cells can progress to cancer, we hypothesize that common polymorphisms within miRNA-target binding sites could play a role in the individual risk of cancer. In the present study, we selected the 3' UTRs of 104 genes candidate for colorectal cancer (CRC) and we identified putative miRNA-binding sites by specialized algorithms (PicTar, DianaMicroT, miRBase, miRanda, TargetScan and microInspector). Fifty-seven single-nucleotide polymorphisms (SNPs) were identified in miRNA-binding sites. We evaluated the SNPs for their ability to affect the binding of the miRNA with its target, by assessing the variation of Gibbs free energy between the two alleles of each SNP. We found eight common polymorphisms that were further investigated by a case-control association studies. The study was carried out on a series of cases and controls from Czech Republic, a population with the highest worldwide incidence of CRC. We found statistically significant associations between risk of CRC and variant alleles of CD86 [odds ratio (OR) = 2.74; 95% confidence interval (CI) = 1.24-6.04, for the variant homozygotes] and INSR genes (OR = 1.94; 95% CI = 1.03-3.66, for the variant homozygotes). These results are the first reporting positive association between miRNA-binding SNPs sequences and cancer risk.