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With advances in cancer screening and treatment, there is a growing population of cancer survivors who may develop subsequent primary cancers. While hereditary cancer syndromes account for only a portion of multiple cancer cases, we sought to explore the role of common genetic variation in susceptibility to multiple primary tumors. We conducted a cross-ancestry genome-wide association study (GWAS) and transcriptome-wide association study (TWAS) of 10,983 individuals with multiple primary cancers, 84,475 individuals with single cancer, and 420,944 cancer-free controls from two large-scale studies. Our GWAS identified six lead variants across five genomic regions that were significantly associated (P<5×10 -8 ) with the risk of developing multiple primary tumors (overall and invasive) relative to cancer-free controls (at 3q26, 8q24, 10q24, 11q13.3, and 17p13). We also found one variant significantly associated with multiple cancers when comparing to single cancer cases (at 22q13.1). Multi-tissue TWAS detected associations with genes involved in telomere maintenance in two of these regions ( ACTRT3 in 3q26 and SLK and STN1 in 10q24) and the development of multiple cancers. Additionally, the TWAS also identified several novel genes associated with multiple cancers, including two immune-related genes, IRF4 and TNFRSF6B . Telomere maintenance and immune dysregulation emerge as central, common pathways influencing susceptibility to multiple cancers. These findings underscore the importance of exploring shared mechanisms in carcinogenesis, offering insights for targeted prevention and intervention strategies.

Background: Breast cancer is comprised of distinct molecular subtypes. Studies have reported differences in risk factor associations with breast cancer subtypes, especially by tumor estrogen receptor (ER) status, but their consistency across racial and ethnic populations has not been comprehensively evaluated., Methods: We conducted a qualitative, scoping literature review using the Preferred Reporting Items for Systematic Reviews and Meta-analysis, extension for Scoping Reviews to investigate consistencies in associations between 18 breast cancer risk factors (reproductive, anthropometric, lifestyle, and medical history) and risk of ER-defined subtypes in women who self-identify as Asian, Black or African American, Hispanic or Latina, or White. We reviewed publications between January 1, 1990 and July 1, 2022. Etiologic heterogeneity evidence (convincing, suggestive, none, or inconclusive) was determined by expert consensus., Results: Publications per risk factor ranged from 14 (benign breast disease history) to 66 (parity). Publications were most abundant for White women, followed by Asian, Black or African American, and Hispanic or Latina women. Etiologic heterogeneity evidence was strongest for parity, followed by age at first birth, post-menopausal BMI, oral contraceptive use, and estrogen-only and combined menopausal hormone therapy. Evidence was limited for other risk factors. Findings were consistent across racial and ethnic groups, although the strength of evidence varied., Conclusion: The literature supports etiologic heterogeneity by ER for some established risk factors that are consistent across race and ethnicity groups. However, in non-White populations evidence is limited. Larger, more comparable data in diverse populations is needed to better characterize breast cancer etiologic heterogeneity., (Published by Oxford University Press 2024.)

Introduction: Immune checkpoint inhibitors (ICIs) can yield remarkable clinical responses in subsets of patients with solid tumors but can also often lead to immune-related adverse events (irAEs). Predictive features of clinically severe irAEs leading to cessation of ICIs have yet to be established. Using data from 1,327 patients with lung cancer treated with ICIs between 2009 and 2022 at four academic medical centers, we evaluated the association of a germline polygenic risk score for autoimmune disease and discontinuation of ICIs due to irAEs., Methods: Using Cox proportional hazards model, we assessed the association between a polygenic risk score for autoimmune disease (PRS AD ) and cessation of ICI therapy due to irAEs. All models were adjusted for age at diagnosis, sex, lung cancer histology, type of therapy, recruiting center, and the first 5 principal components. To further understand the differential effects of type of therapy and disease stage on the association between PRS AD and cessation of ICI due to irAEs, we conducted stratified logistic regression analysis by type of ICI therapy and disease stage., Results: We found an association between PRS AD and ICI cessation due to irAEs (HR per SD = 1.18, 95% CI = 1.02 - 1.37, P = 0.03). This association was particularly strong in patients who had ICI cessation due to irAEs within three months of therapy initiation (HR per SD = 1.38, 95% CI = 1.08 - 1.78, P = 0.01). Individuals in the top 20th percentile of PRS AD had 7.2% ICI discontinuation for irAEs by three months, compared to 3.9% discontinuation by three months among patients in the bottom 80th percentile (log-rank P = 0.02). In addition, among patients who received combination PD-1/PD-L1 and CTLA-4 inhibitor therapy, PRS AD had an OR per SD of 1.86 (95% CI = 1.08 - 3.51, P = 0.04)., Conclusions: We demonstrate an association between a polygenic risk score for autoimmune disease and early ICI discontinuation for irAEs, particularly among patients treated with combination ICI therapy. Our results suggest that germline genetics may be used as an adjunctive tool for risk stratification around ICI clinical decision-making in solid tumor oncology.

Immune checkpoint inhibitor-mediated colitis (IMC) is a common adverse event of treatment with immune checkpoint inhibitors (ICI). We hypothesize that genetic susceptibility to Crohn's disease (CD) and ulcerative colitis (UC) predisposes to IMC. In this study, we first develop a polygenic risk scores for CD (PRS CD ) and UC (PRS UC ) in cancer-free individuals and then test these PRSs on IMC in a cohort of 1316 patients with ICI-treated non-small cell lung cancer and perform a replication in 873 ICI-treated pan-cancer patients. In a meta-analysis, the PRS UC predicts all-grade IMC (OR meta =1.35 per standard deviation [SD], 95% CI = 1.12-1.64, P = 2×10 -03 ) and severe IMC (OR meta =1.49 per SD, 95% CI = 1.18-1.88, P = 9×10 -04 ). PRS CD is not associated with IMC. Furthermore, PRS UC predicts severe IMC among patients treated with combination ICIs (OR meta =2.20 per SD, 95% CI = 1.07-4.53, P = 0.03). Overall, PRS UC can identify patients receiving ICI at risk of developing IMC and may be useful to monitor patients and improve patient outcomes., (© 2024. The Author(s).)

Background: Breast cancer is comprised of distinct molecular subtypes. Studies have reported differences in risk factor associations with breast cancer subtypes, especially by tumor estrogen receptor (ER) status, but their consistency across racial and ethnic populations has not been comprehensively evaluated., Methods: We conducted a qualitative, scoping literature review using the Preferred Reporting Items for Systematic Reviews and Meta-analysis, extension for Scoping Reviews to investigate consistencies in associations between 18 breast cancer risk factors (reproductive, anthropometric, lifestyle, and medical history) and risk of ER-defined subtypes in women who self-identify as Asian, Black or African American, Hispanic or Latina, or White. We reviewed publications between January 1, 1990 and July 1, 2022. Etiologic heterogeneity evidence (convincing, suggestive, none, or inconclusive) was determined by expert consensus., Results: Publications per risk factor ranged from 14 (benign breast disease history) to 66 (parity). Publications were most abundant for White women, followed by Asian, Black or African American, and Hispanic or Latina women. Etiologic heterogeneity evidence was strongest for parity, followed by age at first birth, post-menopausal BMI, oral contraceptive use, and estrogen-only and combined menopausal hormone therapy. Evidence was limited for other risk factors. Findings were consistent across racial and ethnic groups, although the strength of evidence varied., Conclusion: The literature supports etiologic heterogeneity by ER for some established risk factors that are consistent across race and ethnicity groups. However, in non-White populations evidence is limited. Larger, more comparable data in diverse populations is needed to better characterize breast cancer etiologic heterogeneity., Competing Interests: CONFLICTS OF INTEREST The authors have no conflicts of interest to disclose.

Background: Genome-wide studies of gene-environment interactions (G×E) may identify variants associated with disease risk in conjunction with lifestyle/environmental exposures. We conducted a genome-wide G×E analysis of ~ 7.6 million common variants and seven lifestyle/environmental risk factors for breast cancer risk overall and for estrogen receptor positive (ER +) breast cancer., Methods: Analyses were conducted using 72,285 breast cancer cases and 80,354 controls of European ancestry from the Breast Cancer Association Consortium. Gene-environment interactions were evaluated using standard unconditional logistic regression models and likelihood ratio tests for breast cancer risk overall and for ER + breast cancer. Bayesian False Discovery Probability was employed to assess the noteworthiness of each SNP-risk factor pairs., Results: Assuming a 1 × 10 -5 prior probability of a true association for each SNP-risk factor pairs and a Bayesian False Discovery Probability < 15%, we identified two independent SNP-risk factor pairs: rs80018847(9p13)-LINGO2 and adult height in association with overall breast cancer risk (OR int  = 0.94, 95% CI 0.92-0.96), and rs4770552(13q12)-SPATA13 and age at menarche for ER + breast cancer risk (OR int  = 0.91, 95% CI 0.88-0.94)., Conclusions: Overall, the contribution of G×E interactions to the heritability of breast cancer is very small. At the population level, multiplicative G×E interactions do not make an important contribution to risk prediction in breast cancer., (© 2023. BioMed Central Ltd., part of Springer Nature.)

Aggressive breast cancers portend a poor prognosis, but current polygenic risk scores (PRSs) for breast cancer do not reliably predict aggressive cancers. Aggressiveness can be effectively recapitulated using tumor gene expression profiling. Thus, we sought to develop a PRS for the risk of recurrence score weighted on proliferation (ROR-P), an established prognostic signature. Using 2363 breast cancers with tumor gene expression data and single nucleotide polymorphism (SNP) genotypes, we examined the associations between ROR-P and known breast cancer susceptibility SNPs using linear regression models. We constructed PRSs based on varying p-value thresholds and selected the optimal PRS based on model r 2 in 5-fold cross-validation. We then used Cox proportional hazards regression to test the ROR-P PRS's association with breast cancer-specific survival in two independent cohorts totaling 10,196 breast cancers and 785 events. In meta-analysis of these cohorts, higher ROR-P PRS was associated with worse survival, HR per SD = 1.13 (95% CI 1.06-1.21, p = 4.0 × 10 -4 ). The ROR-P PRS had a similar magnitude of effect on survival as a comparator PRS for estrogen receptor (ER)-negative versus positive cancer risk (PRS ER-/ER+ ) . Furthermore, its effect was minimally attenuated when adjusted for PRS ER-/ER+ , suggesting that the ROR-P PRS provides additional prognostic information beyond ER status. In summary, we used integrated analysis of germline SNP and tumor gene expression data to construct a PRS associated with aggressive tumor biology and worse survival. These findings could potentially enhance risk stratification for breast cancer screening and prevention., (© 2023. The Author(s).)

Background: Reproductive factors have been shown to be differentially associated with risk of estrogen receptor (ER)-positive and ER-negative breast cancer. However, their associations with intrinsic-like subtypes are less clear., Methods: Analyses included up to 23 353 cases and 71 072 controls pooled from 31 population-based case-control or cohort studies in the Breast Cancer Association Consortium across 16 countries on 4 continents. Polytomous logistic regression was used to estimate the association between reproductive factors and risk of breast cancer by intrinsic-like subtypes (luminal A-like, luminal B-like, luminal B-HER2-like, HER2-enriched-like, and triple-negative breast cancer) and by invasiveness. All statistical tests were 2-sided., Results: Compared with nulliparous women, parous women had a lower risk of luminal A-like, luminal B-like, luminal B-HER2-like, and HER2-enriched-like disease. This association was apparent only after approximately 10 years since last birth and became stronger with increasing time (odds ratio [OR] = 0.59, 95% confidence interval [CI] = 0.49 to 0.71; and OR = 0.36, 95% CI = 0.28 to 0.46 for multiparous women with luminal A-like tumors 20 to less than 25 years after last birth and 45 to less than 50 years after last birth, respectively). In contrast, parous women had a higher risk of triple-negative breast cancer right after their last birth (for multiparous women: OR = 3.12, 95% CI = 2.02 to 4.83) that was attenuated with time but persisted for decades (OR = 1.03, 95% CI = 0.79 to 1.34, for multiparous women 25 to less than 30 years after last birth). Older age at first birth (Pheterogeneity < .001 for triple-negative compared with luminal A-like breast cancer) and breastfeeding (Pheterogeneity < .001 for triple-negative compared with luminal A-like breast cancer) were associated with lower risk of triple-negative breast cancer but not with other disease subtypes. Younger age at menarche was associated with higher risk of all subtypes; older age at menopause was associated with higher risk of luminal A-like but not triple-negative breast cancer. Associations for in situ tumors were similar to luminal A-like., Conclusions: This large and comprehensive study demonstrates a distinct reproductive risk factor profile for triple-negative breast cancer compared with other subtypes, with implications for the understanding of disease etiology and risk prediction., (© The Author(s) 2022. Published by Oxford University Press. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Introduction: 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., Methods: 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., Results: 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)., Conclusion: 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., Implications: 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., (Published by Oxford University Press on behalf of the Society for Research on Nicotine and Tobacco 2018.)

Background: We examined the associations between germline variants and breast cancer mortality using a large meta-analysis of women of European ancestry., Methods: Meta-analyses included summary estimates based on Cox models of twelve datasets using ~10.4 million variants for 96,661 women with breast cancer and 7697 events (breast cancer-specific deaths). Oestrogen receptor (ER)-specific analyses were based on 64,171 ER-positive (4116) and 16,172 ER-negative (2125) patients. We evaluated the probability of a signal to be a true positive using the Bayesian false discovery probability (BFDP)., Results: We did not find any variant associated with breast cancer-specific mortality at P < 5 × 10 -8 . For ER-positive disease, the most significantly associated variant was chr7:rs4717568 (BFDP = 7%, P = 1.28 × 10 -7 , hazard ratio [HR] = 0.88, 95% confidence interval [CI] = 0.84-0.92); the closest gene is AUTS2. For ER-negative disease, the most significant variant was chr7:rs67918676 (BFDP = 11%, P = 1.38 × 10 -7 , HR = 1.27, 95% CI = 1.16-1.39); located within a long intergenic non-coding RNA gene (AC004009.3), close to the HOXA gene cluster., Conclusions: We uncovered germline variants on chromosome 7 at BFDP < 15% close to genes for which there is biological evidence related to breast cancer outcome. However, the paucity of variants associated with mortality at genome-wide significance underpins the challenge in providing genetic-based individualised prognostic information for breast cancer patients.

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.

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., (Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.)

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.

Computational fluid dynamics (CFD) tools are increasingly employed for quantifying incident consequences in quantitative risk analysis (QRA) calculations in the process industry. However, these tools must be validated against representative experimental data, involving combined release and ignition scenarios, in order to have a real predictive capability. Forschungszentrum Karlsruhe (FZK) has recently carried out experiments involving vertically upwards hydrogen releases with different release rates and velocities impinging on a plate in two different geometrical configurations. The dispersed cloud was subsequently ignited and resulting explosion overpressures recorded. Blind CFD simulations were carried out prior to the experiments to predict the results. The simulated gas concentrations are found to correlate reasonably well with observations. The overpressures subsequent to ignition obtained in the blind predictions could not be compared directly as the ignition points chosen in the experiments were somewhat different from those used in the blind simulations, but the pressure levels were similar. Simulations carried out subsequently with the same ignition position as those in the experiments compared reasonably well with the observations. This agreement points to the ability of the CFD tool FLACS to model such complex scenarios even with hydrogen as a fuel. Nevertheless, the experimental set-up can be considered to be small-scale. Future large-scale data of this type will be valuable to confirm ability to predict large-scale accident scenarios., (2010 Elsevier B.V. All rights reserved.)