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Previous studies suggest that polygenic risk scores (PRSs) may improve melanoma risk stratification. However, there has been limited independent validation of PRS-based risk prediction, particularly assessment of calibration (comparing predicted to observed risks).To evaluate PRS-based melanoma risk prediction in prospective UK and Australian cohorts with European ancestry.We analysed invasive melanoma incidence in the UK Biobank (UKB; n = 395 647, 1651 cases) and a case-cohort nested within the Melbourne Collaborative Cohort Study (MCCS, Australia; n = 4765, 303 cases). Three PRSs were evaluated: 68 single-nucleotide polymorphisms (SNPs) at 54 loci from a 2020 meta-analysis (PRS68), 50 SNPs significant in the 2020 meta-analysis excluding UKB (PRS50) and 45 SNPs at 21 loci known in 2018 (PRS45). Ten-year melanoma risks were calculated from population-level cancer registry data by age group and sex, with and without PRS adjustment.Predicted absolute melanoma risks based on age and sex alone underestimated melanoma incidence in the UKB [ratio of expected/observed cases: E/O = 0·65, 95% confidence interval (CI) 0·62-0·68] and MCCS (E/O = 0·63, 95% CI 0·56-0·72). For UKB, calibration was improved by PRS adjustment, with PRS50-adjusted risks E/O = 0·91, 95% CI 0·87-0·95. The discriminative ability for PRS68- and PRS50-adjusted absolute risks was higher than for risks based on age and sex alone (Δ area under the curve 0·07-0·10, P 0·0001), and higher than for PRS45-adjusted risks (Δ area under the curve 0·02-0·04, P 0·001).A PRS derived from a larger, more diverse meta-analysis improves risk prediction compared with an earlier PRS, and might help tailor melanoma prevention and early detection strategies to different risk levels. Recalibration of absolute risks may be necessary for application to specific populations.

We considered whether weight is more informative than body mass index (BMI) = weight/height2 when predicting breast cancer risk for postmenopausal women, and if the weight association differs by underlying familial risk. We studied 6,761 women postmenopausal at baseline with a wide range of familial risk from 2,364 families in the Prospective Family Study Cohort. Participants were followed for on average 11.45 years and there were 416 incident breast cancers. We used Cox regression to estimate risk associations with log-transformed weight and BMI after adjusting for underlying familial risk. We compared model fits using the Akaike information criterion (AIC) and nested models using the likelihood ratio test. The AIC for the weight-only model was 6.22 units lower than for the BMI-only model, and the log risk gradient was 23% greater. Adding BMI or height to weight did not improve fit (ΔAIC = 0.90 and 0.83, respectively; both P = 0.3). Conversely, adding weight to BMI or height gave better fits (ΔAIC = 5.32 and 11.64; P = 0.007 and 0.0002, respectively). Adding height improved only the BMI model (ΔAIC = 5.47; P = 0.006). There was no evidence that the BMI or weight associations differed by underlying familial risk (P > 0.2). Weight is more informative than BMI for predicting breast cancer risk, consistent with nonadipose as well as adipose tissue being etiologically relevant. The independent but multiplicative associations of weight and familial risk suggest that, in terms of absolute breast cancer risk, the association with weight is more important the greater a woman's underlying familial risk.Prevention Relevance:Our results suggest that the relationship between BMI and breast cancer could be due to a relationship between weight and breast cancer, downgraded by inappropriately adjusting for height; potential importance of anthropometric measures other than total body fat; breast cancer risk associations with BMI and weight are across a continuum.

Supplementary Grant Support

Supplementary Figure S1. LocusZoom regional association plots for the seven new cross-cancer loci that were > 1 Mb from known index SNPs. Supplementary Figure S2A-B. Box plots showing eQTL associations between (A) rs9375701 and L3MBTL3 in normal breast and prostate tissues and (B) rs8037137 and RCCD1 in normal breast and ovarian tissues. Supplementary Figure S3. Interactions between BCL2L11 and the 32 Biocarta "Death Pathway" genes. Interactions were identified using the GeneMania server. Circles contain gene names, lines represent interactions, and the color of the line indicates a specific type of interaction as listed in the legend.

Supplementary Table S1. Known risk loci for each cancer. Supplementary Table S2. Regions where known index SNPs for at least two of the three cancers lie within 1 Mb of each other. Supplementary Table S3. Full results from the meta-analysis of breast, ovarian, and prostate cancer showing the 18 independent loci associated at P < 10-8 with the same direction of effect across all three cancers. Supplementary Table S4. Conditional analysis of the rs1469713 (breast-ovarian-prostate cancer) signal, conditioning on the rs4808801 (breast cancer-specific) signal that was < 1 Mb away using GCTA software. Supplementary Table S5. Most significantly-associated genotyped SNP at new loci where index SNPs were imputed. Supplementary Table S6. Expression QTL analysis results from the GTEx Portal for the new cross-cancer risk loci listed in main Table 2. Supplementary Table S7. SNPs with P < 10-8 at the new cross-cancer risk loci listed in main Table 2 that overlap predicted enhancers in at least two cell types. Supplementary Table S8. HaploReg and non-coding RNA annotation of variants with P < 10-8 at the new cross-cancer risk loci listed in main Table 2. Supplementary Table S9. Eight of the 32 genes involved in induction of apoptosis through DR3 and DR4/5 Death Receptors (MSigDB: Biocarta "Death Pathway") a that were < 1 Mb away from an independent P < 10-5 index SNP in the 3-cancer meta-analysis. Supplementary Table S10. INRICH pathway analysis results for pathways with empirical P < 0.05. Apoptosis-related pathways containing BCL2L11 are shaded.

Supplementary Data from Weight is More Informative than Body Mass Index for Predicting Postmenopausal Breast Cancer Risk: Prospective Family Study Cohort (ProF-SC)

Background: We have developed a genome-wide association study analysis method called DEPTH (DEPendency of association on the number of Top Hits) to identify genomic regions potentially associated with disease by considering overlapping groups of contiguous markers (e.g., SNPs) across the genome. DEPTH is a machine learning algorithm for feature ranking of ultra-high dimensional datasets, built from well-established statistical tools such as bootstrapping, penalized regression, and decision trees. Unlike marginal regression, which considers each SNP individually, the key idea behind DEPTH is to rank groups of SNPs in terms of their joint strength of association with the outcome. Our aim was to compare the performance of DEPTH with that of standard logistic regression analysis.Methods: We selected 1,854 prostate cancer cases and 1,894 controls from the UK for whom 541,129 SNPs were measured using the Illumina Infinium HumanHap550 array. Confirmation was sought using 4,152 cases and 2,874 controls, ascertained from the UK and Australia, for whom 211,155 SNPs were measured using the iCOGS Illumina Infinium array.Results: From the DEPTH analysis, we identified 14 regions associated with prostate cancer risk that had been reported previously, five of which would not have been identified by conventional logistic regression. We also identified 112 novel putative susceptibility regions.Conclusions: DEPTH can reveal new risk-associated regions that would not have been identified using a conventional logistic regression analysis of individual SNPs.Impact: This study demonstrates that the DEPTH algorithm could identify additional genetic susceptibility regions that merit further investigation. Cancer Epidemiol Biomarkers Prev; 25(12); 1619–24. ©2016 AACR.

List of the previously identified 100 independent prostate cancer susceptibility SNPs.

Background: Although high-risk mutations in identified major susceptibility genes (DNA mismatch repair genes and MUTYH) account for some familial aggregation of colorectal cancer, their population prevalence and the causes of the remaining familial aggregation are not known.Methods: We studied the families of 5,744 colorectal cancer cases (probands) recruited from population cancer registries in the United States, Canada, and Australia and screened probands for mutations in mismatch repair genes and MUTYH. We conducted modified segregation analyses using the cancer history of first-degree relatives, conditional on the proband's age at diagnosis. We estimated the prevalence of mutations in the identified genes, the prevalence of HR for unidentified major gene mutations, and the variance of the residual polygenic component.Results: We estimated that 1 in 279 of the population carry mutations in mismatch repair genes (MLH1 = 1 in 1,946, MSH2 = 1 in 2,841, MSH6 = 1 in 758, PMS2 = 1 in 714), 1 in 45 carry mutations in MUTYH, and 1 in 504 carry mutations associated with an average 31-fold increased risk of colorectal cancer in unidentified major genes. The estimated polygenic variance was reduced by 30% to 50% after allowing for unidentified major genes and decreased from 3.3 for age Conclusions: Unidentified major genes might explain one third to one half of the missing heritability of colorectal cancer.Impact: Our findings could aid gene discovery and development of better colorectal cancer risk prediction models. Cancer Epidemiol Biomarkers Prev; 26(3); 404–12. ©2016 AACR.

e Table1 contains the minimum number of average metabolic equivalents (METs) per week required to be classified as physically active at baseline and in adolescence for a given age at baseline. eTable 2 presents the association of baseline and adolescent strenuous and moderate recreational physical activity with breast cancer risk in the Prospective Family Study Cohort (N=15,500).

Supplementary Table 1: Results of segregation analyses incorporating mismatch repair gene and MUTYH mutation carrier status. Supplementary Table 2. Estimated population carrier frequency of a mismatch repair gene or MUTYH mutation from previous studies and current study.

We used modified segregation analysis to fit a range of genetic models to the observed colorectal cancer family histories for the proband and their first-degree relatives.

Plot of the first two principal components for the 7,920 participants in the iCOGS dataset.

Technical details of the DEPTH procedure.

Although physical activity is associated with lower breast cancer risk for average-risk women, it is not known if this association applies to women at high familial/genetic risk. We examined the association of recreational physical activity (self-reported by questionnaire) with breast cancer risk using the Prospective Family Study Cohort, which is enriched with women who have a breast cancer family history (N = 15,550). We examined associations of adult and adolescent recreational physical activity (quintiles of age-adjusted total metabolic equivalents per week) with breast cancer risk using multivariable Cox proportional hazards regression, adjusted for demographics, lifestyle factors, and body mass index. We tested for multiplicative interactions of physical activity with predicted absolute breast cancer familial risk based on pedigree data and with BRCA1 and BRCA2 mutation status. Baseline recreational physical activity level in the highest four quintiles compared with the lowest quintile was associated with a 20% lower breast cancer risk (HR, 0.80; 95% confidence interval, 0.68–0.93). The association was not modified by familial risk or BRCA mutation status (P interactions >0.05). No overall association was found for adolescent recreational physical activity. Recreational physical activity in adulthood may lower breast cancer risk for women across the spectrum of familial risk.Significance:These findings suggest that physical activity might reduce breast cancer risk by about 20% for women across the risk continuum, including women at higher-than-average risk due to their family history or genetic susceptibility.See related commentary by Niehoff et al., p. 23

Background Mammographic risk scores based on texture (Cirrus) and density defined by different brightness thresholds Cumulus, Altocumulus, and Cirrocumulus) are associated with breast cancer risk in different ways and could reveal different information about breast cancer risk. Whether there are causal relationships between the mammographic risk measures based on texture and density is unknown. Methods We digitised mammogramsfor 371 monozygotic twin pairs, none diagnosed with breast cancer at the time. We generated normalised, age-adjusted, and standardised risk scores for Cirrus, and for three spatially independent density measures, the light areas (Cumulus minus Altocumulus), the bright areas (Altocumulus minus Cirrocumulus) and the brightest areas (Cirrocumulus). Causal inference was made using the Inference about Causation from Examination of FAmilial CONfounding (ICE FALCON) methodology. Results The risk scores were correlated within twin pairs and with each other (r = 0.22 to 0.81; all P

DNA methylation marks that are inherited from parents to offspring are known to play a role in cancer risk and could explain part of the familial risk for cancer. We therefore conducted a genome-wide search for heritable methylation marks associated with prostate cancer risk. Peripheral blood DNA methylation was measured for 133 of the 469 members of 25 multiple-case prostate cancer families, using the EPIC array. We used these families to systematically search the genome for methylation marks with Mendelian patterns of inheritance, then we tested the 1,000 most heritable marks for association with prostate cancer risk. After correcting for multiple testing, 41 heritable methylation marks were associated with prostate cancer risk. Separate analyses, based on 869 incident cases and 869 controls from a prospective cohort study, showed that 9 of these marks near the metastable epiallele VTRNA2-1 were also nominally associated with aggressive prostate cancer risk in the population.

Little is known regarding the potential relationship between clonal hematopoiesis (CH) of indeterminate potential (CHIP), which is the expansion of hematopoietic stem cells with somatic mutations, and risk of prostate cancer, the fifth leading cause of cancer death of men worldwide. We evaluated the association of age-related CHIP with overall and aggressive prostate cancer risk in two large whole-exome sequencing studies of 75 047 European ancestry men, including 7663 prostate cancer cases, 2770 of which had aggressive disease, and 3266 men carrying CHIP variants. We found that CHIP, defined by over 50 CHIP genes individually and in aggregate, was not significantly associated with overall (aggregate HR = 0.93, 95% CI = 0.76–1.13, P = 0.46) or aggressive (aggregate OR = 1.14, 95% CI = 0.92–1.41, P = 0.22) prostate cancer risk. CHIP was weakly associated with genetic risk of overall prostate cancer, measured using a polygenic risk score (OR = 1.05 per unit increase, 95% CI = 1.01–1.10, P = 0.01). CHIP was not significantly associated with carrying pathogenic/likely pathogenic/deleterious variants in DNA repair genes, which have previously been found to be associated with aggressive prostate cancer. While findings from this study suggest that CHIP is likely not a risk factor for prostate cancer, it will be important to investigate other types of CH in association with prostate cancer risk.

BackgroundGlioma accounts for approximately 80% of malignant adult brain cancer and its most common subtype, glioblastoma, has one of the lowest 5-year cancer survivals. Fifty risk-associated variants within 34 glioma genetic risk regions have been found by genome-wide association studies (GWAS) with a sex difference reported for 8q24.21 region. We conducted an Australian GWAS by glioma subtype and sex.MethodsWe analyzed genome-wide data from the Australian Genomics and Clinical Outcomes of Glioma (AGOG) consortium for 7 573 692 single nucleotide polymorphisms (SNPs) for 560 glioma cases and 2237 controls of European ancestry. Cases were classified as glioblastoma, non-glioblastoma, astrocytoma or oligodendroglioma. Logistic regression analysis was used to assess the associations of SNPs with glioma risk by subtype and by sex.ResultsWe replicated the previously reported glioma risk associations in the regions of 2q33.3 C2orf80, 2q37.3 D2HGDH, 5p15.33 TERT, 7p11.2 EGFR, 8q24.21 CCDC26, 9p21.3 CDKN2BAS, 11q21 MAML2, 11q23.3 PHLDB1, 15q24.2 ETFA, 16p13.3 RHBDF1, 16p13.3 LMF1, 17p13.1 TP53, 20q13.33 RTEL, and 20q13.33 GMEB2 (P < .05). We also replicated the previously reported sex difference at 8q24.21 CCDC26 (P = .0024) with the association being nominally significant for both sexes (P < .05).ConclusionsOur study supports a stronger female risk association for the region 8q24.21 CCDC26 and highlights the importance of analyzing glioma GWAS by sex. A better understanding of sex differences could provide biological insight into the cause of glioma with implications for prevention, risk prediction and treatment.

Multiple myeloma (MM) is the second most common hematological cancer and causes significant mortality and morbidity. Knowledge regarding modifiable risk factors for MM remains limited. This analysis of an Australian population-based case-control family study investigates whether smoking or alcohol consumption is associated with risk of MM and related diseases. Incident cases (

Objective In previous studies using Illumina Infinium methylation arrays, we have identified DNA methylation marks associated with cancer predisposition and progression. In the present study, we have sought to find appropriate technology to both technically validate our data and expand our understanding of DNA methylation in these genomic regions. Here, we aimed to assess the repeatability of methylation measures made using QIAseq targeted methyl panel and to compare them with those obtained from the Illumina HumanMethylation450 (HM450K) assay. We included in the analysis high molecular weight DNA extracted from whole blood (WB) and DNA extracted from formalin-fixed paraffin-embedded tissues (FFPE). Results The repeatability of QIAseq-methylation measures was assessed at 40 CpGs, using the Intraclass Correlation Coefficient (ICC). The mean ICCs and 95% confidence intervals (CI) were 0.72 (0.62–0.81), 0.59 (0.47–0.71) and 0.80 (0.73–0.88) for WB, FFPE and both sample types combined, respectively. For technical replicates measured using QIAseq and HM450K, the mean ICCs (95% CI) were 0.53 (0.39–0.68), 0.43 (0.31–0.56) and 0.70 (0.59–0.80), respectively. Bland–Altman plots indicated good agreement between QIAseq and HM450K measurements. These results demonstrate that the QIAseq targeted methyl panel produces reliable and reproducible methylation measurements across the 40 CpGs that were examined.

Nutritional epidemiology research using self-reported dietary intake is prone to measurement error. Objective methods are being explored to overcome this limitation.We aimed to examine 1) the association between plasma markers related to inflammation and derive marker scores for dietary patterns [Mediterranean dietary score (MDS), energy-adjusted Dietary Inflammatory Index (E-DIITM), Alternative Healthy Eating Index 2010 (AHEI)] and 2) the associations of these marker scores with mortality.Weighted marker scores were derived from the cross-sectional association between 30 plasma markers and each dietary score (assessed using food-frequency questionnaires) using linear regression for 770 participants in the Melbourne Collaborative Cohort Study (aged 50-82 y). Prospective associations between marker scores and mortality (n = 249 deaths) were assessed using Cox regression (median follow-up: 14.4 y).The MDS, E-DII, and AHEI were associated (P 0.05) with 9, 14, and 11 plasma markers, respectively. Healthier diets (higher MDS and AHEI, and lower anti-inflammatory, E-DII) were associated with lower concentrations of kynurenines, neopterin, IFN-γ, cytokines, and C-reactive protein. Five of 6 markers common to the 3 dietary scores were components of the kynurenine pathway. The 3 dietary-based marker scores were highly correlated (Spearman ρ: -0.74, -0.82, and 0.93). Inverse associations (for 1-SD increment) were observed with all-cause mortality for the MDS marker score (HR: 0.84; 95% CI: 0.72-0.98) and the AHEI marker score (HR: 0.76; 95% CI: 0.66-0.89), whereas a positive association was observed with the E-DII marker score (HR: 1.18; 95% CI: 1.01-1.39). The same magnitude of effect was not observed for the respective dietary patterns.Markers involved in inflammation-related processes are associated with dietary quality, including a substantial overlap between markers associated with the MDS, the E-DII, and the AHEI, especially kynurenines. Unfavorable marker scores, reflecting poorer-quality diets, were associated with increased mortality.

Background: Estimates of future burden of cancer attributable to current modifiable causal exposures can guide cancer prevention. We quantified future head and neck cancer burden in Australia attributable to individual and joint causal exposures, and assessed whether these burdens differ between population subgroups. Methods: We estimated the strength of the associations between exposures and head and neck cancer using adjusted proportional hazards models from pooled data from seven Australian cohorts (N = 367,058) linked to national cancer and death registries and estimated exposure prevalence from the 2017 to 2018 Australian National Health Survey. We calculated population attributable fractions (PAF) with 95% confidence intervals (CI), accounting for competing risk of death, and compared PAFs for population subgroups. Results: Contemporary levels of current and former smoking contribute 30.6% (95% CI, 22.7%–37.8%), alcohol consumption exceeding two standard drinks per day 12.9% (95% CI, 7.6%–17.9%), and these exposures jointly 38.5% (95% CI, 31.1%–45.0%) to the future head and neck cancer burden. Alcohol-attributable burden is triple and smoking-attributable burden is double for men compared with women. Smoking-attributable burden is also at least double for those consuming more than two alcoholic drinks daily or doing less than 150 minutes of moderate or 75 minutes of vigorous activity weekly, and for those aged under 65 years, unmarried, with low or intermediate educational attainment or lower socioeconomic status, compared with their counterparts. Conclusions: Two-fifths of head and neck cancers in Australia are preventable by investment in tobacco and alcohol control. Impact: Targeting men and other identified high-burden subgroups can help to reduce head and neck cancer burden disparities.

Background Family history of prostate cancer (PCa) is a well-known risk factor, and both common and rare genetic variants are associated with the disease. Objective To detect new genetic variants associated with PCa, capitalizing on the role of family history and more aggressive PCa. Design, setting, and participants A two-stage design was used. In stage one, whole-exome sequencing was used to identify potential risk alleles among affected men with a strong family history of disease or with more aggressive disease (491 cases and 429 controls). Aggressive disease was based on a sum of scores for Gleason score, node status, metastasis, tumor stage, prostate-specific antigen at diagnosis, systemic recurrence, and time to PCa death. Genes identified in stage one were screened in stage two using a custom-capture design in an independent set of 2917 cases and 1899 controls. Outcome measurements and statistical analysis Frequencies of genetic variants (singly or jointly in a gene) were compared between cases and controls. Results and limitations Eleven genes previously reported to be associated with PCa were detected (ATM, BRCA2, HOXB13, FAM111A, EMSY, HNF1B, KLK3, MSMB, PCAT1, PRSS3, and TERT), as well as an additional 10 novel genes (PABPC1, QK1, FAM114A1, MUC6, MYCBP2, RAPGEF4, RNASEH2B, ULK4, XPO7, and THAP3). Of these 10 novel genes, all but PABPC1 and ULK4 were primarily associated with the risk of aggressive PCa. Conclusions Our approach demonstrates the advantage of gene sequencing in the search for genetic variants associated with PCa and the benefits of sampling patients with a strong family history of disease or an aggressive form of disease. Patient summary Multiple genes are associated with prostate cancer (PCa) among men with a strong family history of this disease or among men with an aggressive form of PCa.

Background: Use of menopausal hormone therapy (MHT) is associated with increased risk for breast cancer. However, the relevant mechanisms and its interaction with genetic variants are not fully understood. Methods: We conducted a genome-wide interaction analysis between MHT use and genetic variants for breast cancer risk in 27,585 cases and 34,785 controls from 26 observational studies. All women were post-menopausal and of European ancestry. Multivariable logistic regression models were used to test for multiplicative interactions between genetic variants and current MHT use. We considered interaction p-values-8 as genome-wide significant, and p-values-5 as suggestive. Linkage disequilibrium (LD)-based clumping was performed to identify independent candidate variants.Results: None of the 9.7 million genetic variants tested for interactions with MHT use reached genome-wide significance. Only 213 variants, representing 18 independent loci, had p-values5. The strongest evidence was found for rs4674019 (p-value=2.27x10-7), which showed genome-wide significant interaction (p-value=3.8x10-8) with current MHT use when analysis was restricted to population-based studies only. Limiting the analyses to combined estrogen-progesterone MHT use only or to estrogen receptor (ER) positive cases did not identify any genome-wide significant evidence of interactions. Conclusions: In this large genome-wide SNP-MHT interaction study of breast cancer, we found no strong support for common genetic variants modifying the effect of MHT on breast cancer risk. These results suggest that common genetic variation has limited impact on the observed MHT–breast cancer risk association.

Mammograms contain information that predicts breast cancer risk. We developed two novel mammogram-based breast cancer risk measures based on image brightness (Cirrocumulus) and texture (Cirrus). Their risk prediction when fitted together, and with an established measure of conventional mammographic density (Cumulus), is not known. We used three studies consisting of: 168 interval cases and 498 matched controls; 422 screen-detected cases and 1197 matched controls; and 354 younger-diagnosis cases and 944 controls frequency-matched for age at mammogram. We conducted conditional and unconditional logistic regression analyses of individually- and frequency-matched studies, respectively. We estimated measure-specific risk gradients as the change in odds per standard deviation of controls after adjusting for age and body mass index (OPERA) and calculated the area under the receiver operating characteristic curve (AUC). For interval, screen-detected and younger-diagnosis cancer risks, the best fitting models (OPERAs [95% confidence intervals]) involved: Cumulus (1.81 [1.41-2.31]) and Cirrus (1.72 [1.38-2.14]); Cirrus (1.49 [1.32-1.67]) and Cirrocumulus (1.16 [1.03 to 1.31]); and Cirrus (1.70 [1.48 to 1.94]) and Cirrocumulus (1.46 [1.27-1.68]), respectively. The AUCs were: 0.73 [0.68-0.77], 0.63 [0.60-0.66], and 0.72 [0.69-0.75], respectively. Combined, our new mammogram-based measures have twice the risk gradient for screen-detected and younger-diagnosis breast cancer (P ≤ 10-12 ), have at least the same discriminatory power as the current polygenic risk score, and are more correlated with causal factors than conventional mammographic density. Discovering more information about breast cancer risk from mammograms could help enable risk-based personalised breast screening.

This literature review provides an up-to-date overview of the evidence relating to the associations of leisure-time physical activity and sedentary behaviour with the risk of colorectal adenoma and cancer. A summary of possible biological mechanisms underpinning these associations is also presented. Leisure-time physical activity and sedentary behaviour are often conceptualised as independent risk factors for colorectal adenoma and cancer. Regular leisure-time physical activity is associated with a 20%–25% reduction in colorectal cancer risk and an estimated 16% reduction in colonic adenoma. The risk estimates are similar for males and females, and for proximal and distal colon cancers. In contrast, the evidence linking physical inactivity to rectal cancer is inconsistent. Epidemiological evidence suggests that television viewing time and occupational sitting are associated with a 54% and 24% increased risk of colon cancer, respectively. Prolonged TV viewing (a commonly measured type of sedentary behaviour) is associated with a 9–16% increased risk of colorectal adenoma, independent of leisure-time physical activity. There is convincing evidence that higher leisure-physical activity is associated with a lower risk of colorectal adenoma and proximal and distal colon cancer; no consistent association emerges for rectal cancer. There is emerging evidence linking sedentary behaviour to risk of colorectal cancer and adenoma. Epidemiological evidence suggests that there may be an interaction between leisure-time physical activity and sedentary behaviour. While it is possible that both exposures contribute to carcinogenesis through similar biological mechanisms, further mechanistic research is required to discern whether there are different biological pathways operating in response to physical activity versus sedentary behaviour.

Rare pathogenic variants in known breast cancer susceptibility genes and known common susceptibility variants do not fully explain the familial aggregation of breast cancer. To investigate plausible genetic models for the residual familial aggregation, we studied 17,425 families ascertained through population-based probands, 86% of whom were screened for pathogenic variants in BRCA1, BRCA2, PALB2, CHEK2, ATM and TP53 using gene-panel sequencing. We conducted complex segregation analyses and fitted genetic models in which breast cancer incidence depended on the effects of pathogenic variants in known susceptibility genes and other unidentified major genes, and a normally distributed polygenic component. The proportion of familial variance explained by BRCA1, BRCA2, PALB2, CHEK2, ATM and TP53 was 46% at age 20-29 years and decreased steadily with age thereafter. After allowing for these genes, the best fitting model for the residual familial variance included a recessively inherited risk component with a combined genotype frequency of 1.7% (95% CI: 0.3-5.4%) and a penetrance to age 80 years of 69% (95% CI: 38-95%) for homozygotes, and a polygenic variance of 1.27 (95% CI: 0.94-1.65) which did not vary with age. The proportion of the residual familial variance explained by the recessive risk component was 40% at age 20-29 years and decreased with age thereafter. The model predicted age-specific familial relative risks consistent with those observed by large epidemiological studies. The findings have implications for strategies to identify new breast cancer susceptibility genes and improve breast cancer risk prediction, especially at a young age.

We quantified the individual and joint contribution of contemporaneous causal behavioural exposures on the future burden of oesophageal and stomach cancers and their subtypes and assessed whether these burdens differ between population groups in Australia, as such estimates are currently lacking.We combined hazard ratios from seven pooled Australian cohorts (N = 367,058) linked to national cancer and death registries with exposure prevalence from the 2017-2018 National Health Survey to estimate Population Attributable Fractions (PAFs) with 95% confidence intervals (CIs), accounting for competing risk of death.Current and past smoking explain 35.2% (95% CI = 11.7-52.4%), current alcohol consumption exceeding three drinks/day 15.7% (95% CI = 0.9-28.4%), and these exposures jointly 41.4% (95% CI = 19.8-57.3%) of oesophageal squamous cell carcinomas in Australia. Current and past smoking contribute 38.2% (95% CI = 9.4-57.9%), obesity 27.0% (95% CI = 0.6-46.4%), and these exposures jointly 54.4% (95% CI = 25.3-72.1%) of oesophageal adenocarcinomas. Overweight and obesity explain 36.1% (95% CI = 9.1-55.1%), current and past smoking 24.2% (95% CI = 4.2-40.0%), and these exposures jointly 51.2% (95% CI = 26.3-67.8%) of stomach cardia cancers. Several population groups had a significantly higher smoking-attributable oesophageal cancer burden, including men and those consuming excessive alcohol.Smoking is the leading preventable behavioural cause of oesophageal cancers and overweight/obesity of stomach cancers.

Background Prostate cancer risk stratification using single-nucleotide polymorphisms (SNPs) demonstrates considerable promise in men of European, Asian, and African genetic ancestries, but there is still need for increased accuracy. We evaluated whether including additional SNPs in a prostate cancer polygenic hazard score (PHS) would improve associations with clinically significant prostate cancer in multi-ancestry datasets. Methods In total, 299 SNPs previously associated with prostate cancer were evaluated for inclusion in a new PHS, using a LASSO-regularized Cox proportional hazards model in a training dataset of 72,181 men from the PRACTICAL Consortium. The PHS model was evaluated in four testing datasets: African ancestry, Asian ancestry, and two of European Ancestry—the Cohort of Swedish Men (COSM) and the ProtecT study. Hazard ratios (HRs) were estimated to compare men with high versus low PHS for association with clinically significant, with any, and with fatal prostate cancer. The impact of genetic risk stratification on the positive predictive value (PPV) of PSA testing for clinically significant prostate cancer was also measured. Results The final model (PHS290) had 290 SNPs with non-zero coefficients. Comparing, for example, the highest and lowest quintiles of PHS290, the hazard ratios (HRs) for clinically significant prostate cancer were 13.73 [95% CI: 12.43–15.16] in ProtecT, 7.07 [6.58–7.60] in African ancestry, 10.31 [9.58–11.11] in Asian ancestry, and 11.18 [10.34–12.09] in COSM. Similar results were seen for association with any and fatal prostate cancer. Without PHS stratification, the PPV of PSA testing for clinically significant prostate cancer in ProtecT was 0.12 (0.11–0.14). For the top 20% and top 5% of PHS290, the PPV of PSA testing was 0.19 (0.15–0.22) and 0.26 (0.19–0.33), respectively. Conclusions We demonstrate better genetic risk stratification for clinically significant prostate cancer than prior versions of PHS in multi-ancestry datasets. This is promising for implementing precision-medicine approaches to prostate cancer screening decisions in diverse populations.

Publisher Copyright: © 2022 The Authors. International Journal of Cancer published by John Wiley & Sons Ltd on behalf of UICC. Previous studies had limited power to assess the associations of testosterone with aggressive disease as a primary endpoint. Further, the association of genetically predicted testosterone with aggressive disease is not known. We investigated the associations of calculated free and measured total testosterone and sex hormone-binding globulin (SHBG) with aggressive, overall and early-onset prostate cancer. In blood-based analyses, odds ratios (OR) and 95% confidence intervals (CI) for prostate cancer were estimated using conditional logistic regression from prospective analysis of biomarker concentrations in the Endogenous Hormones, Nutritional Biomarkers and Prostate Cancer Collaborative Group (up to 25 studies, 14 944 cases and 36 752 controls, including 1870 aggressive prostate cancers). In Mendelian randomisation (MR) analyses, using instruments identified using UK Biobank (up to 194 453 men) and outcome data from PRACTICAL (up to 79 148 cases and 61 106 controls, including 15 167 aggressive cancers), ORs were estimated using the inverse-variance weighted method. Free testosterone was associated with aggressive disease in MR analyses (OR per 1 SD = 1.23, 95% CI = 1.08-1.40). In blood-based analyses there was no association with aggressive disease overall, but there was heterogeneity by age at blood collection (OR for men aged

We examined associations between sex-specific alcohol intake trajectories and alcohol-related cancer risk using data from 22 756 women and 15 701 men aged 40 to 69 years at baseline in the Melbourne Collaborative Cohort Study. Alcohol intake for 10-year periods from age 20 until the decade encompassing recruitment, calculated using recalled beverage-specific frequency and quantity, was used to estimate group-based sex-specific intake trajectories. Hazard ratios (HR) and 95% confidence intervals (CI) were estimated for primary invasive alcohol-related cancer (upper aerodigestive tract, breast, liver and colorectum). Three distinct alcohol intake trajectories for women (lifetime abstention, stable light, increasing moderate) and six for men (lifetime abstention, stable light, stable moderate, increasing heavy, early decreasing heavy, late decreasing heavy) were identified. 2303 incident alcohol-related cancers were diagnosed during 485 525 person-years in women and 789 during 303 218 person-years in men. For men, compared with lifetime abstention, heavy intake (mean ≥ 60 g/day) at age 20 to 39 followed by either an early (from age 40 to 49) (early decreasing heavy; HR = 1.75, 95% CI: 1.25-2.44) or late decrease (from age 60 to 69) (late decreasing heavy; HR = 1.94, 95% CI: 1.28-2.93), and moderate intake (mean60 g/day) at age 20 to 39 increasing to heavy intake in middle-age (increasing heavy; HR = 1.45, 95% CI: 1.06-1.97) were associated with increased risk of alcohol-related cancer. For women, compared with lifetime abstention, increasing intake from age 20 (increasing moderate) was associated with increased alcohol-related cancer risk (HR = 1.25, 95% CI: 1.06-1.48). Similar associations were observed for colorectal (men) and breast cancer. Heavy drinking during early adulthood might increase cancer risk later in life.

Background Higher levels of time spent sitting (sedentary behavior) contribute to adverse health outcomes, including earlier death. This effect may be modified by other lifestyle factors. We examined the association of television viewing (TV), a common leisure-time sedentary behavior, with all-cause mortality, and whether this is modified by body mass index (BMI), physical activity, smoking, alcohol intake, soft drink consumption, or diet-associated inflammation. Methods Using data from participants in the Melbourne Collaborative Cohort Study, flexible parametric survival models assessed the time-dependent association of self-reported TV time (three categories: 3 h/day) with all-cause mortality. Interaction terms were fitted to test whether there was effect modification of TV time by the other risk factors. Results From 19,570 participants, 4,417 deaths were reported over a median follow up of 14.5 years. More TV time was associated with earlier mortality; however, this relationship diminished with increasing age. The hazard ratio (HR) and 95% confidence interval (95% CI) for > 3 h/day compared with 0.16). Conclusions The relationship between TV time and all-cause mortality may change with age. It may also be more pronounced in those who are otherwise inactive or who have a pro-inflammatory diet.

Prospective validation of risk models is needed to assess their clinical utility, particularly over the longer term. We evaluated the performance of six commonly used breast cancer risk models (IBIS, BOADICEA, BRCAPRO, BRCAPRO-BCRAT, BCRAT, and iCARE-lit). 15-year risk scores were estimated using lifestyle factors and family history measures from 7608 women in the Melbourne Collaborative Cohort Study who were aged 50-65 years and unaffected at commencement of follow-up two (conducted in 2003-2007), of whom 351 subsequently developed breast cancer. Risk discrimination was assessed using the C-statistic and calibration using the expected/observed number of incident cases across the spectrum of risk by age group (50-54, 55-59, 60-65 years) and family history of breast cancer. C-statistics were higher for BOADICEA (0.59, 95% confidence interval (CI) 0.56-0.62) and IBIS (0.57, 95% CI 0.54-0.61) than the other models (p-difference ��� 0.04). No model except BOADICEA calibrated well across the spectrum of 15-year risk (p-value < 0.03). The performance of BOADICEA and IBIS was similar across age groups and for women with or without a family history. For middle-aged Australian women, BOADICEA and IBIS had the highest discriminatory accuracy of the six risk models, but apart from BOADICEA, no model was well-calibrated across the risk spectrum., This work was primarily supported by grant 1129136 from the Australian National Health and Medical Research Council (NHMRC) (https://www.nhmrc.gov.au/). MCCS cohort recruitment was funded by Cancer Council Victoria (https://www.cancervic.org.au/) and VicHealth (https://www.vichealth.vic.gov.au/). The MCCS was further supported by Australian NHMRC grants 209057, 396414 and 1074383, and ongoing follow-up and data management has been funded by Cancer Council Victoria since 1995. Cases and their vital status were ascertained through the Victorian Cancer Registry and the Australian Institute of Health and Welfare, including the National Death Index and the Australian Cancer Database.TN-D is a recipient of a Career Development Fellowship from the National Breast Cancer Foundation (Australia). JLH and MCS are Senior Principal and Senior Research Fellows of the National Health and Medical Research Council (Australia), respectively. ACA and AJL are supported by grants from Cancer Research UK (C12292/A20861 and PPRPGM19 Nov20\100002).

Thyroid cancer incidence and the prevalence of overweight and obesity are increasing, but the future thyroid cancer burden attributable to contemporary levels of overweight and obesity has not been evaluated before. We quantified this burden in Australia, and assessed whether the overweight/obesity-attributable burden differed by sex or other population subgroupings. We estimated the strength of the associations of overweight and obesity with thyroid cancer with adjusted proportional hazards models using pooled data from seven Australian cohorts (N=367,058) with 431 thyroid cancer cases ascertained from linked national cancer registry data during a maximum 22-year follow-up. We combined these estimates with nationally representative 2017-2018 estimates of overweight and obesity prevalence to estimate Population Attributable Fractions (PAFs) of future thyroid cancers attributable to overweight and obesity, accounting for competing risk of death, and compared PAFs for population subgroups. Contemporary levels of overweight and obesity explain 18.6% (95%CI=5.2%-30.2%), and obesity alone 13.7% (95%CI=5.2%-21.4%), of the future thyroid cancer burden. The obesity-attributable thyroid cancer burden is 21.4% (95%CI=2.8%-36.5%) for men and 10.1% (95%CI=0.8%-18.6%) for women. Were the currently obese overweight instead, 9.9% (95%CI=1.0%-18.1%) of thyroid cancers could be avoided. The relative overweight/obesity-attributable burden is higher for those consuming on average more than two alcoholic drinks per day (63.4%) and for those who are not married/co-habiting (33.2%). In conclusion, avoiding excess weight, especially obesity, should be a priority for thyroid cancer prevention. Further studies, with findings stratified by tumour size, may reveal the potential role of overdiagnosis in our results. This article is protected by copyright. All rights reserved.

Purpose High-grade disease accounts for ~ 70% of all glioma, and has a high mortality rate. Few modifiable exposures are known to be related to glioma risk or mortality. Methods We examined associations between lifetime physical activity and physical activity at different ages (15–18 years, 19–29 years, 30–39 years, last 10 years) with the risk of glioma diagnosis, using data from a hospital-based family case–control study (495 cases; 371 controls). We followed up cases over a median of 25 months to examine whether physical activity was associated with all-cause mortality. Physical activity and potential confounders were assessed by self-administered questionnaire. We examined associations between physical activity (metabolic equivalent [MET]-h/wk) and glioma risk using unconditional logistic regression and with all-cause mortality in cases using Cox regression. Results We noted a reduced risk of glioma for the highest (≥ 47 MET-h/wk) versus lowest ( Conclusion Our findings are consistent with previous research that suggested physical activity during adolescence might be protective against glioma. Engaging in physical activity during adolescence has many health benefits; this health behavior may also offer protection against glioma.

Background Inflammation is a key feature of aging and a cause of numerous diseases. We investigated the association of 35 blood markers involved in inflammatory processes with age and mortality and developed a signature of ‘inflammaging’. Methods Thirty-five blood markers relating to the kynurenine pathway, vitamin status, and inflammation were measured in 976 participants in the Melbourne Collaborative Cohort Study at baseline (1990-1994, median age 59 years) and follow-up (2003-2007, median age 70 years). Associations of each marker with age and all-cause mortality were assessed using linear and Cox regression, respectively. A signature of inflammaging was obtained via Lasso regression of age on the markers and tested for association with mortality; we compared mortality associations for this signature and two weighted scores across all markers associated with age and mortality, respectively. Results Most markers (29/35) were associated with age, with strongest associations observed for cystatin C, neopterin, quinolinic acid, and the kynurenine/tryptophan ratio, PAr index, and 3-hydroxykynurenine/xanthurenic acid ratio. Many markers (14/35) showed strong associations with mortality in particular neopterin, quinolinic acid, HK/XA, PAr index, CRP, IL-6 and KTr. The inflammaging signature included six markers and showed strong association with mortality (HR = 1.5, 95%CI: 1.3-1.7), almost as strong as the association of weighted scores combining all measured markers. Conclusions Our study highlights the key role played by markers of the kynurenine pathway and vitamin B6 catabolism in aging, along with other well-established inflammation-related markers. Key messages A signature of ‘inflammaging’ based on 6 markers may be useful to better predict mortality.

Background Alcohol consumption is causally linked to several cancer sites but the evidence for stomach cancer is still inconclusive. We aimed to quantify the association between alcohol intake and risk of stomach cancer, including subtypes. Methods We pooled data from two cohort studies including 452,958 individuals enrolled in the European Prospective Investigation into Cancer in 1992-98 and 38,756 Australians enrolled in the Melbourne Collaborative Cohort Study in 1990-94. Adjusted hazard ratios (HR) and 95% confidence intervals (CI) for incident stomach cancer were estimated using Cox regression. Results 1,225 incident stomach cancers were diagnosed over 7,094,637 person-years. Alcohol intake was not associated with overall stomach cancer risk. We observed a weak positive dose-response association for lifetime intake with non-cardia stomach cancer (HR = 1.03, 95% CI: 1.00-1.06/per 10 g/day increment), which is the more common type (77.6% of cases), and a weak inverse association with cardia cancer (HR = 0.93, 95% CI: 0.87-1.00) (phomogeneity=0.006). These associations did not differ appreciably by smoking or Helicobacter pylori infection status. Differences in HRs between diffuse-type and intestinal-type cancer were minimal (phomogeneity=0.97). HRs of 1.50 (95% CI: 1.12-2.01) for non-cardia and 0.53 (95% CI: 0.27-1.02) for cardia cancer were observed for a life course trajectory characterised by sustained heavy drinking compared with light drinking (phomogeneity=0.01). Conclusions Lifetime alcohol intake was associated with increased risk of non-cardia stomach cancer. The inverse association for cardia cancer indicates aetiologic differences between subsites. Key messages Limiting long-term alcohol consumption, and avoiding heavy use in particular, might be beneficial in preventing non-cardia stomach cancer.

Background To improve melanoma early detection, tools to predict personal risk based on genetic information (polygenic risk scores, PRS) have been developed, but require external validation. Methods We analysed invasive melanoma incidence in UK Biobank (UKB; n = 395,647; 1,651 cases) and the Melbourne Collaborative Cohort Study (MCCS, Australia; n = 4,765; 303 cases). Three PRS were evaluated: 68 genetic variants (SNPs) at 54 loci from a 2020 meta-analysis (PRS68); 50 SNPs significant in the 2020 meta-analysis excluding UKB (PRS50); 45 SNPs at 21 loci known pre-2020 (PRS45). 10-year melanoma risks were calculated from population-level cancer registry data by age group and sex, with and without PRS adjustment. Results All PRS were strongly associated with melanoma incidence, including after adjustment for age, sex, ethnicity, and ease of tanning. Predicted absolute melanoma risks based on age and sex alone underestimated melanoma incidence in UKB (ratio expected/observed cases E/O=0.65, 95% confidence interval 0.62-0.68) and MCCS (E/O=0.65, 0.57-0.73). For UKB, this was reduced by PRS-adjustment, e.g. PRS50-adjusted risks E/O=0.91 (0.87-0.95). Discriminative ability for PRS68- and PRS50-adjusted absolute risks was higher than for risks based on age and sex alone (deltaAUC 0.07-0.1, p Conclusions A PRS derived from a larger, more diverse meta-analysis improves melanoma risk prediction compared to an earlier PRS. Re-calibration of absolute risks may be necessary for application to specific populations. Key messages A genetic score can improve prediction of melanoma risk and might help tailor melanoma prevention and early detection strategies to different risk levels.

Background: Advanced prostate cancer etiology is poorly understood. Few studies have examined associations of anthropometric factors (e.g. early adulthood obesity) with advanced prostate cancer risk.Patients and methods: We carried out pooled analyses to examine associations between body fatness, height, and prostate cancer risk. Among 830 772 men, 51 734 incident prostate cancer cases were identified, including 4762 advanced (T4/N1/M1 or prostate cancer deaths) cases, 2915 advanced restricted (same as advanced, but excluding localized cancers that resulted in death) cases, 9489 high-grade cases, and 3027 prostate cancer deaths. Cox proportional hazards models were used to calculate study-specific hazard ratios (HR) and 95% confidence intervals (CI); results were pooled using random effects models.Results: No statistically significant associations were observed for body mass index (BMI) in early adulthood for advanced, advanced restricted, and high-grade prostate cancer, and prostate cancer mortality. Positive associations were shown for BMI at baseline with advanced prostate cancer (HR = 1.30, 95% CI = 0.95-1.78) and prostate cancer mortality (HR = 1.52, 95% CI = 1.12-2.07) comparing BMI >= 35.0 kg/m(2) with 21-22.9 kg/m(2). When considering early adulthood and baseline BMI together, a 27% higher prostate cancer mortality risk (95% CI = 9% to 49%) was observed for men with BMI = 30.0 kg/m(2) at baseline compared with BMI = 110 cm with = 1.00 with = 1.90 m withConclusion: Our findings suggest that height and total and central adiposity in mid-to-later adulthood, but not early adulthood adiposity, are associated with risk of advanced forms of prostate cancer. Thus, maintenance of healthy weight may help prevent advanced prostate cancer.

Accurate colorectal cancer (CRC) risk prediction models are critical for identifying individuals at low and high risk of developing CRC, as they can then be offered targeted screening and interventions to address their risks of developing disease (if they are in a high-risk group) and avoid unnecessary screening and interventions (if they are in a low-risk group). As it is likely that thousands of genetic variants contribute to CRC risk, it is clinically important to investigate whether these genetic variants can be used jointly for CRC risk prediction. In this paper, we derived and compared different approaches to generating predictive polygenic risk scores (PRS) from genome-wide association studies (GWASs) including 55,105 CRC-affected case subjects and 65,079 control subjects of European ancestry. We built the PRS in three ways, using (1) 140 previously identified and validated CRC loci; (2) SNP selection based on linkage disequilibrium (LD) clumping followed by machine-learning approaches; and (3) LDpred, a Bayesian approach for genome-wide risk prediction. We tested the PRS in an independent cohort of 101,987 individuals with 1,699 CRC-affected case subjects. The discriminatory accuracy, calculated by the age- and sex-adjusted area under the receiver operating characteristics curve (AUC), was highest for the LDpred-derived PRS (AUC = 0.654) including nearly 1.2 M genetic variants (the proportion of causal genetic variants for CRC assumed to be 0.003), whereas the PRS of the 140 known variants identified from GWASs had the lowest AUC (AUC = 0.629). Based on the LDpred-derived PRS, we are able to identify 30% of individuals without a family history as having risk for CRC similar to those with a family history of CRC, whereas the PRS based on known GWAS variants identified only top 10% as having a similar relative risk. About 90% of these individuals have no family history and would have been considered average risk under current screening guidelines, but might benefit from earlier screening. The developed PRS offers a way for risk-stratified CRC screening and other targeted interventions.