- Melbourne School of Population and Global Health - Research Publications
Melbourne School of Population and Global Health - Research Publications
Permanent URI for this collection
496 results
Filters
Settings
Statistics
Citations
Search Results
Now showing
1 - 10 of 496
-
ItemIncorporating progesterone receptor expression into the PREDICT breast prognostic modelGrootes, I ; Keeman, R ; Blows, FM ; Milne, RL ; Giles, GG ; Swerdlow, AJ ; Fasching, PA ; Abubakar, M ; Andrulis, IL ; Anton-Culver, H ; Beckmann, MW ; Blomqvist, C ; Bojesen, SE ; Bolla, MK ; Bonanni, B ; Briceno, I ; Burwinkel, B ; Camp, NJ ; Castelao, JE ; Choi, J-Y ; Clarke, CL ; Couch, FJ ; Cox, A ; Cross, SS ; Czene, K ; Devilee, P ; Dork, T ; Dunning, AM ; Dwek, M ; Easton, DF ; Eccles, DM ; Eriksson, M ; Ernst, K ; Evans, DG ; Figueroa, JD ; Fink, V ; Floris, G ; Fox, S ; Gabrielson, M ; Gago-Dominguez, M ; Garcia-Saenz, JA ; Gonzalez-Neira, A ; Haeberle, L ; Haiman, CA ; Hall, P ; Hamann, U ; Harkness, EF ; Hartman, M ; Hein, A ; Hooning, MJ ; Hou, M-F ; Howell, SJ ; Ito, H ; Jakubowska, A ; Janni, W ; John, EM ; Jung, A ; Kang, D ; Kristensen, VN ; Kwong, A ; Lambrechts, D ; Li, J ; Manoochehri, M ; Margolin, S ; Matsuo, K ; Taib, NAM ; Mulligan, AM ; Nevanlinna, H ; Newman, WG ; Offit, K ; Osorio, A ; Park, SK ; Park-Simon, T-W ; Patel, A ; Presneau, N ; Pylkas, K ; Rack, B ; Radice, P ; Rennert, G ; Romero, A ; Saloustros, E ; Sawyer, EJ ; Schneeweiss, A ; Schochter, F ; Schoemaker, MJ ; Shen, C-Y ; Shibli, R ; Sinn, P ; Tapper, WJ ; Tawfiq, E ; Teo, SH ; Teras, LR ; Torres, D ; Vachon, CM ; van Deurzen, CHM ; Wendt, C ; Williams, JA ; Winqvist, R ; Elwood, M ; Schmidt, MK ; Pharoah, PDP (ELSEVIER SCI LTD, 2022-08-04)BACKGROUND: Predict Breast (www.predict.nhs.uk) is an online prognostication and treatment benefit tool for early invasive breast cancer. The aim of this study was to incorporate the prognostic effect of progesterone receptor (PR) status into a new version of PREDICT and to compare its performance to the current version (2.2). METHOD: The prognostic effect of PR status was based on the analysis of data from 45,088 European patients with breast cancer from 49 studies in the Breast Cancer Association Consortium. Cox proportional hazard models were used to estimate the hazard ratio for PR status. Data from a New Zealand study of 11,365 patients with early invasive breast cancer were used for external validation. Model calibration and discrimination were used to test the model performance. RESULTS: Having a PR-positive tumour was associated with a 23% and 28% lower risk of dying from breast cancer for women with oestrogen receptor (ER)-negative and ER-positive breast cancer, respectively. The area under the ROC curve increased with the addition of PR status from 0.807 to 0.809 for patients with ER-negative tumours (p = 0.023) and from 0.898 to 0.902 for patients with ER-positive tumours (p = 2.3 × 10-6) in the New Zealand cohort. Model calibration was modest with 940 observed deaths compared to 1151 predicted. CONCLUSION: The inclusion of the prognostic effect of PR status to PREDICT Breast has led to an improvement of model performance and more accurate absolute treatment benefit predictions for individual patients. Further studies should determine whether the baseline hazard function requires recalibration.
-
ItemAbsolute Risk of Oropharyngeal Cancer After an HPV16-E6 Serology Test and Potential Implications for Screening: Results From the Human Papillomavirus Cancer Cohort Consortium.Robbins, HA ; Ferreiro-Iglesias, A ; Waterboer, T ; Brenner, N ; Nygard, M ; Bender, N ; Schroeder, L ; Hildesheim, A ; Pawlita, M ; D'Souza, G ; Visvanathan, K ; Langseth, H ; Schlecht, NF ; Tinker, LF ; Agalliu, I ; Wassertheil-Smoller, S ; Ness-Jensen, E ; Hveem, K ; Grioni, S ; Kaaks, R ; Sánchez, M-J ; Weiderpass, E ; Giles, GG ; Milne, RL ; Cai, Q ; Blot, WJ ; Zheng, W ; Weinstein, SJ ; Albanes, D ; Huang, W-Y ; Freedman, ND ; Kreimer, AR ; Johansson, M ; Brennan, P (American Society of Clinical Oncology (ASCO), 2022-11-01)PURPOSE: Seropositivity for the HPV16-E6 oncoprotein is a promising marker for early detection of oropharyngeal cancer (OPC), but the absolute risk of OPC after a positive or negative test is unknown. METHODS: We constructed an OPC risk prediction model that integrates (1) relative odds of OPC for HPV16-E6 serostatus and cigarette smoking from the human papillomavirus (HPV) Cancer Cohort Consortium (HPVC3), (2) US population risk factor data from the National Health Interview Survey, and (3) US sex-specific population rates of OPC and mortality. RESULTS: The nine HPVC3 cohorts included 365 participants with OPC with up to 10 years between blood draw and diagnosis and 5,794 controls. The estimated 10-year OPC risk for HPV16-E6 seropositive males at age 50 years was 17.4% (95% CI, 12.4 to 28.6) and at age 60 years was 27.1% (95% CI, 19.2 to 45.4). Corresponding 5-year risk estimates were 7.3% and 14.4%, respectively. For HPV16-E6 seropositive females, 10-year risk estimates were 3.6% (95% CI, 2.5 to 5.9) at age 50 years and 5.5% (95% CI, 3.8 to 9.2) at age 60 years and 5-year risk estimates were 1.5% and 2.7%, respectively. Over 30 years, after a seropositive result at age 50 years, an estimated 49.9% of males and 13.3% of females would develop OPC. By contrast, 10-year risks among HPV16-E6 seronegative people were very low, ranging from 0.01% to 0.25% depending on age, sex, and smoking status. CONCLUSION: We estimate that a substantial proportion of HPV16-E6 seropositive individuals will develop OPC, with 10-year risks of 17%-27% for males and 4%-6% for females age 50-60 years in the United States. This high level of risk may warrant periodic, minimally invasive surveillance after a positive HPV16-E6 serology test, particularly for males in high-incidence regions. However, an appropriate clinical protocol for surveillance remains to be established.
-
ItemCirculating free testosterone and risk of aggressive prostate cancer: Prospective and Mendelian randomisation analyses in international consortiaWatts, EL ; Perez-Cornago, A ; Fensom, GK ; Smith-Byrne, K ; Noor, U ; Andrews, CD ; Gunter, MJ ; Holmes, M ; Martin, RM ; Tsilidis, KK ; Albanes, D ; Barricarte, A ; Bueno-de-Mesquita, B ; Chen, C ; Cohn, BA ; Dimou, NL ; Ferrucci, L ; Flicker, L ; Freedman, ND ; Giles, GG ; Giovannucci, EL ; Goodman, GE ; Haiman, CA ; Hankey, GJ ; Huang, J ; Huang, W-Y ; Hurwitz, LM ; Kaaks, R ; Knekt, P ; Kubo, T ; Langseth, H ; Laughlin, G ; Le Marchand, L ; Luostarinen, T ; MacInnis, RJ ; Maenpaa, HO ; Mannisto, S ; Metter, JE ; Mikami, K ; Mucci, LA ; Olsen, AW ; Ozasa, K ; Palli, D ; Penney, KL ; Platz, EA ; Rissanen, H ; Sawada, N ; Schenk, JM ; Stattin, P ; Tamakoshi, A ; Thysell, E ; Tsai, CJ ; Tsugane, S ; Vatten, L ; Weiderpass, E ; Weinstein, SJ ; Wilkens, LR ; Yeap, BB ; Allen, NE ; Key, TJ ; Travis, RC (WILEY, 2022-06-07)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 <60 years 1.14, CI = 1.02-1.28; Phet = .0003: inverse association for older ages). Associations for free testosterone were positive for overall prostate cancer (MR: 1.20, 1.08-1.34; blood-based: 1.03, 1.01-1.05) and early-onset prostate cancer (MR: 1.37, 1.09-1.73; blood-based: 1.08, 0.98-1.19). SHBG and total testosterone were inversely associated with overall prostate cancer in blood-based analyses, with null associations in MR analysis. Our results support free testosterone, rather than total testosterone, in the development of prostate cancer, including aggressive subgroups.
-
ItemWeight is More Informative than Body Mass Index for Predicting Postmenopausal Breast Cancer Risk: Prospective Family Study Cohort (ProF-SC)Ye, Z ; Li, S ; Dite, GS ; Nguyen, TL ; MacInnis, RJ ; Andrulis, IL ; Buys, SS ; Daly, MB ; John, EM ; Kurian, AW ; Genkinger, JM ; Chung, WK ; Phillips, K-A ; Thorne, H ; Winship, IM ; Milne, RL ; Dugue, P-A ; Southey, MC ; Giles, GG ; Terry, MB ; Hopper, JL (AMER ASSOC CANCER RESEARCH, 2022-03-01)UNLABELLED: 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.
-
ItemCirculating insulin-like growth factors and risks of overall, aggressive and early-onset prostate cancer: a collaborative analysis of 20 prospective studies and Mendelian randomization analysisWatts, EL ; Perez-Cornago, A ; Fensom, GK ; Smith-Byrne, K ; Noor, U ; Andrews, CD ; Gunter, MJ ; Holmes, M ; Martin, RM ; Tsilidis, KK ; Albanes, D ; Barricarte, A ; Bueno-de-Mesquita, HB ; Cohn, BA ; Deschasaux-Tanguy, M ; Dimou, NL ; Ferrucci, L ; Flicker, L ; Freedman, ND ; Giles, GG ; Giovannucci, EL ; Haiman, CA ; Hankey, GJ ; Holly, JMP ; Huang, J ; Huang, W-Y ; Hurwitz, LM ; Kaaks, R ; Kubo, T ; Le Marchand, L ; MacInnis, RJ ; Mannisto, S ; Metter, EJ ; Mikami, K ; Mucci, LA ; Olsen, AW ; Ozasa, K ; Palli, D ; Penney, KL ; Platz, EA ; Pollak, MN ; Roobol, MJ ; Schaefer, CA ; Schenk, JM ; Stattin, P ; Tamakoshi, A ; Thysell, E ; Tsai, CJ ; Touvier, M ; Van Den Eeden, SK ; Weiderpass, E ; Weinstein, SJ ; Wilkens, LR ; Yeap, BB ; Allen, NE ; Key, TJ ; Travis, RC (OXFORD UNIV PRESS, 2022-06-21)BACKGROUND: Previous studies had limited power to assess the associations of circulating insulin-like growth factors (IGFs) and IGF-binding proteins (IGFBPs) with clinically relevant prostate cancer as a primary endpoint, and the association of genetically predicted IGF-I with aggressive prostate cancer is not known. We aimed to investigate the associations of IGF-I, IGF-II, IGFBP-1, IGFBP-2 and IGFBP-3 concentrations with overall, aggressive and early-onset prostate cancer. METHODS: Prospective analysis of biomarkers using the Endogenous Hormones, Nutritional Biomarkers and Prostate Cancer Collaborative Group dataset (up to 20 studies, 17 009 prostate cancer cases, including 2332 aggressive cases). Odds ratios (OR) and 95% confidence intervals (CI) for prostate cancer were estimated using conditional logistic regression. For IGF-I, two-sample Mendelian randomization (MR) analysis was undertaken using instruments identified using UK Biobank (158 444 men) and outcome data from PRACTICAL (up to 85 554 cases, including 15 167 aggressive cases). Additionally, we used colocalization to rule out confounding by linkage disequilibrium. RESULTS: In observational analyses, IGF-I was positively associated with risks of overall (OR per 1 SD = 1.09: 95% CI 1.07, 1.11), aggressive (1.09: 1.03, 1.16) and possibly early-onset disease (1.11: 1.00, 1.24); associations were similar in MR analyses (OR per 1 SD = 1.07: 1.00, 1.15; 1.10: 1.01, 1.20; and 1.13; 0.98, 1.30, respectively). Colocalization also indicated a shared signal for IGF-I and prostate cancer (PP4: 99%). Men with higher IGF-II (1.06: 1.02, 1.11) and IGFBP-3 (1.08: 1.04, 1.11) had higher risks of overall prostate cancer, whereas higher IGFBP-1 was associated with a lower risk (0.95: 0.91, 0.99); these associations were attenuated following adjustment for IGF-I. CONCLUSIONS: These findings support the role of IGF-I in the development of prostate cancer, including for aggressive disease.
-
ItemInflammation and Epigenetic Aging Are Largely Independent Markers of Biological Aging and MortalityCribb, L ; Hodge, AM ; Yu, C ; Li, SX ; English, DR ; Makalic, E ; Southey, MC ; Milne, RL ; Giles, GG ; Dugue, P-A ; Le Couteur, D (OXFORD UNIV PRESS INC, 2022-08-04)Limited evidence exists on the link between inflammation and epigenetic aging. We aimed to (a) assess the cross-sectional and prospective associations of 22 inflammation-related plasma markers and a signature of inflammaging with epigenetic aging and (b) determine whether epigenetic aging and inflammaging are independently associated with mortality. Blood samples from 940 participants in the Melbourne Collaborative Cohort Study collected at baseline (1990-1994) and follow-up (2003-2007) were assayed for DNA methylation and 22 inflammation-related markers, including well-established markers (eg, interleukins and C-reactive protein) and metabolites of the tryptophan-kynurenine pathway. Four measures of epigenetic aging (PhenoAge, GrimAge, DunedinPoAm, and Zhang) and a signature of inflammaging were considered, adjusted for age, and transformed to Z scores. Associations were assessed using linear regression, and mortality hazard ratios (HR) and 95% confidence intervals (95% CI) were estimated using Cox regression. Cross-sectionally, most inflammation-related markers were associated with epigenetic aging measures, although with generally modest effect sizes (regression coefficients per SD ≤ 0.26) and explaining altogether between 1% and 11% of their variation. Prospectively, baseline inflammation-related markers were not, or only weakly, associated with epigenetic aging after 11 years of follow-up. Epigenetic aging and inflammaging were strongly and independently associated with mortality, for example, inflammaging: HR = 1.41, 95% CI = 1.27-1.56, p = 2 × 10-10, which was only slightly attenuated after adjustment for 4 epigenetic aging measures: HR = 1.35, 95% CI = 1.22-1.51, p = 7 × 10-9). Although cross-sectionally associated with epigenetic aging, inflammation-related markers accounted for a modest proportion of its variation. Inflammaging and epigenetic aging are essentially nonoverlapping markers of biological aging and may be used jointly to predict mortality.
-
ItemSegregation analysis of 17,425 population-based breast cancer families: Evidence for genetic susceptibility and risk predictionLi, S ; MacInnis, RJ ; Lee, A ; Nguyen-Dumont, T ; Dorling, L ; Carvalho, S ; Dite, GS ; Shah, M ; Luccarini, C ; Wang, Q ; Milne, RL ; Jenkins, MA ; Giles, GG ; Dunning, AM ; Pharoah, PDP ; Southey, MC ; Easton, DF ; Hopper, JL ; Antoniou, AC (CELL PRESS, 2022-10-06)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 via gene-panel sequencing. We conducted complex segregation analyses and fitted genetic models in which breast cancer incidence depended on the effects of known susceptibility genes and other unidentified major genes and a normally distributed polygenic component. The proportion of familial variance explained by the six genes 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 recessive 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, which may reflect the combined effects of multiple variants acting in a recessive manner, 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 disease-risk prediction, especially at a young age.
-
ItemMethylation-based markers of aging and lifestyle-related factors and risk of breast cancer: a pooled analysis of four prospective studiesDugue, P-A ; Bodelon, C ; Chung, FF ; Brewer, HR ; Ambatipudi, S ; Sampson, JN ; Cuenin, C ; Chajes, V ; Romieu, I ; Fiorito, G ; Sacerdote, C ; Krogh, V ; Panico, S ; Tumino, R ; Vineis, P ; Polidoro, S ; Baglietto, L ; English, D ; Severi, G ; Giles, GG ; Milne, RL ; Herceg, Z ; Garcia-Closas, M ; Flanagan, JM ; Southey, MC (BMC, 2022-09-06)BACKGROUND: DNA methylation in blood may reflect adverse exposures accumulated over the lifetime and could therefore provide potential improvements in the prediction of cancer risk. A substantial body of research has shown associations between epigenetic aging and risk of disease, including cancer. Here we aimed to study epigenetic measures of aging and lifestyle-related factors in association with risk of breast cancer. METHODS: Using data from four prospective case-control studies nested in three cohorts of European ancestry participants, including a total of 1,655 breast cancer cases, we calculated three methylation-based measures of lifestyle factors (body mass index [BMI], tobacco smoking and alcohol consumption) and seven measures of epigenetic aging (Horvath-based, Hannum-based, PhenoAge and GrimAge). All measures were regression-adjusted for their respective risk factors and expressed per standard deviation (SD). Odds ratios (OR) and 95% confidence intervals (CI) were calculated using conditional or unconditional logistic regression and pooled using fixed-effects meta-analysis. Subgroup analyses were conducted by age at blood draw, time from blood sample to diagnosis, oestrogen receptor-positivity status and tumour stage. RESULTS: None of the measures of epigenetic aging were associated with risk of breast cancer in the pooled analysis: Horvath 'age acceleration' (AA): OR per SD = 1.02, 95%CI: 0.95-1.10; AA-Hannum: OR = 1.03, 95%CI:0.95-1.12; PhenoAge: OR = 1.01, 95%CI: 0.94-1.09 and GrimAge: OR = 1.03, 95%CI: 0.94-1.12, in models adjusting for white blood cell proportions, body mass index, smoking and alcohol consumption. The BMI-adjusted predictor of BMI was associated with breast cancer risk, OR per SD = 1.09, 95%CI: 1.01-1.17. The results for the alcohol and smoking methylation-based predictors were consistent with a null association. Risk did not appear to substantially vary by age at blood draw, time to diagnosis or tumour characteristics. CONCLUSION: We found no evidence that methylation-based measures of aging, smoking or alcohol consumption were associated with risk of breast cancer. A methylation-based marker of BMI was associated with risk and may provide insights into the underlying associations between BMI and breast cancer.
-
ItemThe association of age at menarche and adult height with mammographic density in the International Consortium of Mammographic DensityWard, S ; Burton, A ; Tamimi, RM ; Pereira, A ; Garmendia, ML ; Pollan, M ; Boyd, N ; dos-Santos-Silva, I ; Maskarinec, G ; Perez-Gomez, B ; Vachon, C ; Miao, H ; Lajous, M ; Lopez-Ridaura, R ; Bertrand, K ; Kwong, A ; Ursin, G ; Lee, E ; Ma, H ; Vinnicombe, S ; Moss, S ; Allen, S ; Ndumia, R ; Vinayak, S ; Teo, S-H ; Mariapun, S ; Peplonska, B ; Bukowska-Damska, A ; Nagata, C ; Hopper, J ; Giles, G ; Ozmen, V ; Aribal, ME ; Schuez, J ; Van Gils, CH ; Wanders, JOP ; Sirous, R ; Sirous, M ; Hipwell, J ; Kim, J ; Lee, JW ; Dickens, C ; Hartman, M ; Chia, K-S ; Scott, C ; Chiarelli, AM ; Linton, L ; Flugelman, AA ; Salem, D ; Kamal, R ; McCormack, V ; Stone, J (BMC, 2022-07-14)BACKGROUND: Early age at menarche and tall stature are associated with increased breast cancer risk. We examined whether these associations were also positively associated with mammographic density, a strong marker of breast cancer risk. METHODS: Participants were 10,681 breast-cancer-free women from 22 countries in the International Consortium of Mammographic Density, each with centrally assessed mammographic density and a common set of epidemiologic data. Study periods for the 27 studies ranged from 1987 to 2014. Multi-level linear regression models estimated changes in square-root per cent density (√PD) and dense area (√DA) associated with age at menarche and adult height in pooled analyses and population-specific meta-analyses. Models were adjusted for age at mammogram, body mass index, menopausal status, hormone therapy use, mammography view and type, mammographic density assessor, parity and height/age at menarche. RESULTS: In pooled analyses, later age at menarche was associated with higher per cent density (β√PD = 0.023 SE = 0.008, P = 0.003) and larger dense area (β√DA = 0.032 SE = 0.010, P = 0.002). Taller women had larger dense area (β√DA = 0.069 SE = 0.028, P = 0.012) and higher per cent density (β√PD = 0.044, SE = 0.023, P = 0.054), although the observed effect on per cent density depended upon the adjustment used for body size. Similar overall effect estimates were observed in meta-analyses across population groups. CONCLUSIONS: In one of the largest international studies to date, later age at menarche was positively associated with mammographic density. This is in contrast to its association with breast cancer risk, providing little evidence of mediation. Increased height was also positively associated with mammographic density, particularly dense area. These results suggest a complex relationship between growth and development, mammographic density and breast cancer risk. Future studies should evaluate the potential mediation of the breast cancer effects of taller stature through absolute breast density.
-
ItemIdentifying colorectal cancer caused by biallelic MUTYH pathogenic variants using tumor mutational signaturesGeorgeson, P ; Harrison, TA ; Pope, BJ ; Zaidi, SH ; Qu, C ; Steinfelder, RS ; Lin, Y ; Joo, JE ; Mahmood, K ; Clendenning, M ; Walker, R ; Amitay, EL ; Berndt, S ; Brenner, H ; Campbell, PT ; Cao, Y ; Chan, AT ; Chang-Claude, J ; Doheny, KF ; Drew, DA ; Figueiredo, JC ; French, AJ ; Gallinger, S ; Giannakis, M ; Giles, GG ; Gsur, A ; Gunter, MJ ; Hoffmeister, M ; Hsu, L ; Huang, W-Y ; Limburg, P ; Manson, JE ; Moreno, V ; Nassir, R ; Nowak, JA ; Obon-Santacana, M ; Ogino, S ; Phipps, A ; Potter, JD ; Schoen, RE ; Sun, W ; Toland, AE ; Trinh, QM ; Ugai, T ; Macrae, FA ; Rosty, C ; Hudson, TJ ; Jenkins, MA ; Thibodeau, SN ; Winship, IM ; Peters, U ; Buchanan, DD (NATURE PORTFOLIO, 2022-06-06)Carriers of germline biallelic pathogenic variants in the MUTYH gene have a high risk of colorectal cancer. We test 5649 colorectal cancers to evaluate the discriminatory potential of a tumor mutational signature specific to MUTYH for identifying biallelic carriers and classifying variants of uncertain clinical significance (VUS). Using a tumor and matched germline targeted multi-gene panel approach, our classifier identifies all biallelic MUTYH carriers and all known non-carriers in an independent test set of 3019 colorectal cancers (accuracy = 100% (95% confidence interval 99.87-100%)). All monoallelic MUTYH carriers are classified with the non-MUTYH carriers. The classifier provides evidence for a pathogenic classification for two VUS and a benign classification for five VUS. Somatic hotspot mutations KRAS p.G12C and PIK3CA p.Q546K are associated with colorectal cancers from biallelic MUTYH carriers compared with non-carriers (p = 2 × 10-23 and p = 6 × 10-11, respectively). Here, we demonstrate the potential application of mutational signatures to tumor sequencing workflows to improve the identification of biallelic MUTYH carriers.