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.

Quantifying the genetic correlation between cancers can provide important insights into the mechanisms driving cancer etiology. Using genome-wide association study summary statistics across six cancer types based on a total of 296,215 cases and 301,319 controls of European ancestry, here we estimate the pair-wise genetic correlations between breast, colorectal, head/neck, lung, ovary and prostate cancer, and between cancers and 38 other diseases. We observed statistically significant genetic correlations between lung and head/neck cancer (r_g = 0.57, p = 4.6 × 10⁻⁸), breast and ovarian cancer (r_g = 0.24, p = 7 × 10⁻⁵), breast and lung cancer (r_g = 0.18, p =1.5 × 10⁻⁶) and breast and colorectal cancer (r_g = 0.15, p = 1.1 × 10⁻⁴). We also found that multiple cancers are genetically correlated with non-cancer traits including smoking, psychiatric diseases and metabolic characteristics. Functional enrichment analysis revealed a significant excess contribution of conserved and regulatory regions to cancer heritability. Our comprehensive analysis of cross-cancer heritability suggests that solid tumors arising across tissues share in part a common germline genetic basis.

The Alpha-Tocopherol, Beta-Carotene Cancer Prevention (ATBC) Study demonstrated that β-carotene supplementation increases lung cancer incidence in smokers. Further, cigarettes with higher tar and nicotine content are associated with a higher risk of lung cancer. However, no studies have examined whether the increased risk associated with β-carotene supplementation in smokers varies by the tar or nicotine content of cigarettes. The ATBC Study was a randomized, double-blind intervention trial conducted in southwest Finland. A total of 29 133 male smokers, aged 50–69 years, were enrolled and randomly assigned to one of four groups (α-tocopherol, β-carotene, both, or placebo). Cox proportional hazards models were used to estimate the hazard ratio (HR) and 95% confidence intervals (CI) of lung cancer risk by β-carotene trial assignment stratified by a priori categories of cigarette tar and nicotine content. The β-carotene supplementation group had significantly higher risk of developing lung cancer in all categories of tar content (yes vs. no β-carotene supplementation—ultralight cigarettes [≤7 mg tar]: HR = 1.31, 95% CI = 0.91 to 1.89; nonfiltered cigarettes [≥21 mg tar]: HR = 1.22, 95% CI = 0.91 to 1.64; p for interaction = .91). Similarly, there was no interaction with nicotine content (yes vs. no β-carotene supplementation—ventilated cigarettes [≤0.8 µg nicotine]: HR = 1.23, 95% CI = 0.98 to 1.54; nonfiltered cigarettes [≥1.3 µg nicotine]: HR = 1.22, 95% CI = 0.91 to 1.64; p for interaction = .83). These findings support the conclusion that supplementation with β-carotene increases the risk of lung cancer in smokers regardless of the tar or nicotine content of cigarettes smoked. Our data suggest that all smokers should continue to avoid β-carotene supplementation. Previous studies demonstrated that β-carotene supplementation increases risk of lung cancer in smokers. This study moves the field forward by examining the potential for modification of risk of lung cancer with different levels of tar and nicotine in cigarettes smoked, as interaction with carcinogens in these components of cigarette smoke is hypothesized to be the mechanism by which β-carotene increases risk. Our study provides evidence that the increased risk of lung cancer in smokers who take β-carotene supplements is not dependent upon the tar or nicotine level of cigarettes smoked and suggests that all smokers should continue to avoid β-carotene supplementation.

Genetic risk scores are a useful tool for examining the cumulative predictive ability of genetic variation on cardiovascular disease. Important considerations for creating genetic risk scores include the choice of genetic variants, weighting, and comparability across ethnicities. Genetic risk scores that use information from genome-wide meta-analyses can successfully predict cardiovascular outcomes and subclinical phenotypes, yet there is limited clinical utility of these scores beyond traditional cardiovascular risk factors in many populations. Novel uses of genetic risk scores include evaluating the genetic contribution of specific intermediate traits or risk factors to cardiovascular disease, risk prediction in high-risk populations, gene-by-environment interaction studies, and Mendelian randomization studies. Though questions remain about the ultimate clinical utility of the genetic risk score, further investigation in high-risk populations and new ways to combine genetic risk scores with traditional risk factors may prove to be fruitful.