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Moore A, Machiela MJ, Machado M, Wang SS, Kane E, Slager SL, Zhou W, Carrington M, Lan Q, Milne RL, Birmann BM, Adami HO, Albanes D, Arslan AA, Becker N, Benavente Y, Bisanzi S, Boffetta P, Bracci PM, Brennan P, Brooks-Wilson AR, Canzian F, Caporaso N, Clavel J, Cocco P, Conde L, Cox DG, Cozen W, Curtin K, De Vivo I, de Sanjose S, Foretova L, Gapstur SM, Ghesquières H, Giles GG, Glenn M, Glimelius B, Gao C, Habermann TM, Hjalgrim H, Jackson RD, Liebow M, Link BK, Maynadie M, McKay J, Melbye M, Miligi L, Molina TJ, Monnereau A, Nieters A, North KE, Offit K, Patel AV, Piro S, Ravichandran V, Riboli E, Salles G, Severson RK, Skibola CF, Smedby KE, Southey MC, Spinelli JJ, Staines A, Stewart C, Teras LR, Tinker LF, Travis RC, Vajdic CM, Vermeulen RCH, Vijai J, Weiderpass E, Weinstein S, Doo NW, Zhang Y, Zheng T, Chanock SJ, Rothman N, Cerhan JR, Dean M, Camp NJ, Yeager M, Berndt SI. Genome-wide homozygosity and risk of four non-Hodgkin lymphoma subtypes. JOURNAL OF TRANSLATIONAL GENETICS AND GENOMICS 2021; 5:200-217. [PMID: 34622145 PMCID: PMC8494431 DOI: 10.20517/jtgg.2021.08] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
AIM Recessive genetic variation is thought to play a role in non-Hodgkin lymphoma (NHL) etiology. Runs of homozygosity (ROH), defined based on long, continuous segments of homozygous SNPs, can be used to estimate both measured and unmeasured recessive genetic variation. We sought to examine genome-wide homozygosity and NHL risk. METHODS We used data from eight genome-wide association studies of four common NHL subtypes: 3061 chronic lymphocytic leukemia (CLL), 3814 diffuse large B-cell lymphoma (DLBCL), 2784 follicular lymphoma (FL), and 808 marginal zone lymphoma (MZL) cases, as well as 9374 controls. We examined the effect of homozygous variation on risk by: (1) estimating the fraction of the autosome containing runs of homozygosity (FROH); (2) calculating an inbreeding coefficient derived from the correlation among uniting gametes (F3); and (3) examining specific autosomal regions containing ROH. For each, we calculated beta coefficients and standard errors using logistic regression and combined estimates across studies using random-effects meta-analysis. RESULTS We discovered positive associations between FROH and CLL (β = 21.1, SE = 4.41, P = 1.6 × 10-6) and FL (β = 11.4, SE = 5.82, P = 0.02) but not DLBCL (P = 1.0) or MZL (P = 0.91). For F3, we observed an association with CLL (β = 27.5, SE = 6.51, P = 2.4 × 10-5). We did not find evidence of associations with specific ROH, suggesting that the associations observed with FROH and F3 for CLL and FL risk were not driven by a single region of homozygosity. CONCLUSION Our findings support the role of recessive genetic variation in the etiology of CLL and FL; additional research is needed to identify the specific loci associated with NHL risk.
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Dugué PA, Hodge AM, Wong EM, Joo JE, Jung CH, Hopper JL, English DR, Giles GG, Milne RL, Southey MC. Methylation marks of prenatal exposure to maternal smoking and risk of cancer in adulthood. Int J Epidemiol 2021; 50:105-115. [PMID: 33169152 DOI: 10.1093/ije/dyaa210] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/28/2020] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Prenatal exposure to maternal smoking is detrimental to child health but its association with risk of cancer has seldom been investigated. Maternal smoking induces widespread and long-lasting DNA methylation changes, which we study here for association with risk of cancer in adulthood. METHODS Eight prospective case-control studies nested within the Melbourne Collaborative Cohort Study were used to assess associations between maternal-smoking-associated methylation marks in blood and risk of several cancers: breast (n = 406 cases), colorectal (n = 814), gastric (n = 166), kidney (n = 139), lung (n = 327), prostate (n = 847) and urothelial (n = 404) cancer and B-cell lymphoma (n = 426). We used conditional logistic regression models to estimate odds ratios (ORs) and 95% confidence intervals (CIs) for associations between cancer and five methylation scores calculated as weighted averages for 568, 19, 15, 28 and 17 CpG sites. Models were adjusted for confounders, including personal smoking history (smoking status, pack-years, age at starting and quitting) and methylation scores for personal smoking. RESULTS All methylation scores for maternal smoking were strongly positively associated with risk of urothelial cancer. Risk estimates were only slightly attenuated after adjustment for smoking history, other potential confounders and methylation scores for personal smoking. Potential negative associations were observed with risk of lung cancer and B-cell lymphoma. No associations were observed for other cancers. CONCLUSIONS We found that methylation marks of prenatal exposure to maternal smoking are associated with increased risk of urothelial cancer. Our study demonstrates the potential for using DNA methylation to investigate the impact of early-life, unmeasured exposures on later-life cancer risk.
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Li SX, Milne RL, Nguyen-Dumont T, Wang X, English DR, Giles GG, Southey MC, Antoniou AC, Lee A, Li S, Winship I, Hopper JL, Terry MB, MacInnis RJ. Prospective Evaluation of the Addition of Polygenic Risk Scores to Breast Cancer Risk Models. JNCI Cancer Spectr 2021; 5:pkab021. [PMID: 33977228 PMCID: PMC8099999 DOI: 10.1093/jncics/pkab021] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Revised: 12/13/2020] [Accepted: 02/19/2021] [Indexed: 11/13/2022] Open
Abstract
Background The Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm and the International Breast Cancer Intervention Study breast cancer risk models are used to provide advice on screening intervals and chemoprevention. We evaluated the performance of these models, which now incorporate polygenic risk scores (PRSs), using a prospective cohort study. Methods We used a case-cohort design, involving women in the Melbourne Collaborative Cohort Study aged 50-75 years when surveyed in 2003-2007, of whom 408 had a first primary breast cancer diagnosed within 10 years (cases), and 2783 were from the subcohort. Ten-year risks were calculated based on lifestyle factors, family history data, and a 313-variant PRS. Discrimination was assessed using a C-statistic compared with 0.50 and calibration using the ratio of expected to observed number of cases (E/O). Results When the PRS was added to models with lifestyle factors and family history, the C-statistic (95% confidence interval [CI]) increased from 0.57 (0.54 to 0.60) to 0.62 (0.60 to 0.65) using IBIS and from 0.56 (0.53 to 0.59) to 0.62 (0.59 to 0.64) using BOADICEA. IBIS underpredicted risk (E/O = 0.62, 95% CI = 0.48 to 0.80) for women in the lowest risk category (<1.7%) and overpredicted risk (E/O = 1.40, 95% CI = 1.18 to 1.67) in the highest risk category (≥5%), using the Hosmer-Lemeshow test for calibration in quantiles of risk and a 2-sided P value less than .001. BOADICEA underpredicted risk (E/O = 0.82, 95% CI = 0.67 to 0.99) in the second highest risk category (3.4%-5%); the Hosmer-Lemeshow test and a 2-sided P value was equal to .02. Conclusions Although the inclusion of a 313 genetic variant PRS doubles discriminatory accuracy (relative to reference 0.50), models with and without this PRS have relatively modest discrimination and might require recalibration before their clinical and wider use are promoted.
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Joo JE, Clendenning M, Wong EM, Rosty C, Mahmood K, Georgeson P, Winship IM, Preston SG, Win AK, Dugué PA, Jayasekara H, English D, Macrae FA, Hopper JL, Jenkins MA, Milne RL, Giles GG, Southey MC, Buchanan DD. DNA Methylation Signatures and the Contribution of Age-Associated Methylomic Drift to Carcinogenesis in Early-Onset Colorectal Cancer. Cancers (Basel) 2021; 13:cancers13112589. [PMID: 34070516 PMCID: PMC8199056 DOI: 10.3390/cancers13112589] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Revised: 05/14/2021] [Accepted: 05/19/2021] [Indexed: 12/12/2022] Open
Abstract
Simple Summary The role of DNA methylation in the carcinogenesis of colorectal cancer (CRC) diagnosed <50 years of age (early-onset CRC or EOCRC) is currently unknown. In this study, we investigated the genome-wide DNA methylation of 97 tumour and 54 normal colonic mucosa samples from people with EOCRC with the aim of identifying unique DNA methylation signatures and determining the role of ageing-related DNA methylation drift and age-acceleration in EOCRC aetiology. We found extensive DNA methylation alterations associated with EOCRC carcinogenesis, including a unique signature comprising 234 loci compared with CRCs from people >50 years of age. CpGs that undergo ageing-related methylation drift were significantly altered in EOCRC, and accelerated ageing was also evident in normal mucosa from people with EOCRC. Our study is the first study to identify unique DNA methylation changes in EOCRC, contributing novel information that may aid future efforts towards EOCRC prevention. Abstract We investigated aberrant DNA methylation (DNAm) changes and the contribution of ageing-associated methylomic drift and age acceleration to early-onset colorectal cancer (EOCRC) carcinogenesis. Genome-wide DNAm profiling using the Infinium HM450K on 97 EOCRC tumour and 54 normal colonic mucosa samples was compared with: (1) intermediate-onset CRC (IOCRC; diagnosed between 50–70 years; 343 tumour and 35 normal); and (2) late-onset CRC (LOCRC; >70 years; 318 tumour and 40 normal). CpGs associated with age-related methylation drift were identified using a public dataset of 231 normal mucosa samples from people without CRC. DNAm-age was estimated using epiTOC2. Common to all three age-of-onset groups, 88,385 (20% of all CpGs) CpGs were differentially methylated between tumour and normal mucosa. We identified 234 differentially methylated genes that were unique to the EOCRC group; 13 of these DMRs/genes were replicated in EOCRC compared with LOCRCs from TCGA. In normal mucosa from people without CRC, we identified 28,154 CpGs that undergo ageing-related DNAm drift, and of those, 65% were aberrantly methylated in EOCRC tumours. Based on the mitotic-based DNAm clock epiTOC2, we identified age acceleration in normal mucosa of people with EOCRC compared with normal mucosa from the IOCRC, LOCRC groups (p = 3.7 × 10−16) and young people without CRC (p = 5.8 × 10−6). EOCRC acquires unique DNAm alterations at 234 loci. CpGs associated with ageing-associated drift were widely affected in EOCRC without needing the decades-long accrual of DNAm drift as commonly seen in intermediate- and late-onset CRCs. Accelerated ageing in normal mucosa from people with EOCRC potentially underlies the earlier age of diagnosis in CRC carcinogenesis.
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Nguyen-Dumont T, Dowty J, Steen JA, Renault AL, Hammet F, Mahmoodi M, Theys D, Rewse A, Tsimiklis H, Winship I, Giles GG, Milne RL, Hopper JL, Southey MC. Population-based estimates of the age-specific cumulative risk of breast cancer for pathogenic variants in CHEK2: Findings from the Australian Breast Cancer Family Registry. J Clin Oncol 2021. [DOI: 10.1200/jco.2021.39.15_suppl.551] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
551 Background: Case-control studies of breast cancer have consistently shown that pathogenic variants in CHEK2 are associated with about a 3-fold increased risk of breast cancer. Information about the recurrent protein truncating variant CHEK2c.1100delC dominates this estimate. There have been no formal estimates of age-specific cumulative risk of breast cancer for all CHEK2 pathogenic (including likely pathogenic) variants combined. Methods: We conducted a genetic screen of CHEK2 in an Australian population-based case-control-family study of breast cancer. This study is focused on disease at an early age and participants were unselected for family history. The age-specific cumulative risk (penetrance) of breast cancer was estimated using segregation analysis. Results: The estimated hazard ratio for carriers of pathogenic CHEK2 variants (combined) was 4.9 (95% CI 2.5-9.5; p < 0.0001) relative to non-carriers. The HR for carriers of the CHEK2 c.1100delC variant was estimated to be 3.5 (95% CI 1.02-11.6) and the HR for carriers of all other CHEK2 variants combined was estimated to be 5.7 (95% CI 2.5-12.9). The age-specific cumulative risk of breast cancer was estimated to be 18% (95% CI 11-30%) and 33% (95% CI 21-48%) to age 60 and 80 years, respectively. Conclusions: These findings provide important information for the clinical management of breast cancer risk for women carrying pathogenic variants in CHEK2.
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Coignard J, Lush M, Beesley J, O'Mara TA, Dennis J, Tyrer JP, Barnes DR, McGuffog L, Leslie G, Bolla MK, Adank MA, Agata S, Ahearn T, Aittomäki K, Andrulis IL, Anton-Culver H, Arndt V, Arnold N, Aronson KJ, Arun BK, Augustinsson A, Azzollini J, Barrowdale D, Baynes C, Becher H, Bermisheva M, Bernstein L, Białkowska K, Blomqvist C, Bojesen SE, Bonanni B, Borg A, Brauch H, Brenner H, Burwinkel B, Buys SS, Caldés T, Caligo MA, Campa D, Carter BD, Castelao JE, Chang-Claude J, Chanock SJ, Chung WK, Claes KBM, Clarke CL, Collée JM, Conroy DM, Czene K, Daly MB, Devilee P, Diez O, Ding YC, Domchek SM, Dörk T, Dos-Santos-Silva I, Dunning AM, Dwek M, Eccles DM, Eliassen AH, Engel C, Eriksson M, Evans DG, Fasching PA, Flyger H, Fostira F, Friedman E, Fritschi L, Frost D, Gago-Dominguez M, Gapstur SM, Garber J, Garcia-Barberan V, García-Closas M, García-Sáenz JA, Gaudet MM, Gayther SA, Gehrig A, Georgoulias V, Giles GG, Godwin AK, Goldberg MS, Goldgar DE, González-Neira A, Greene MH, Guénel P, Haeberle L, Hahnen E, Haiman CA, Håkansson N, Hall P, Hamann U, Harrington PA, Hart SN, He W, Hogervorst FBL, Hollestelle A, Hopper JL, Horcasitas DJ, Hulick PJ, Hunter DJ, Imyanitov EN, Jager A, Jakubowska A, James PA, Jensen UB, John EM, Jones ME, Kaaks R, Kapoor PM, Karlan BY, Keeman R, Khusnutdinova E, Kiiski JI, Ko YD, Kosma VM, Kraft P, Kurian AW, Laitman Y, Lambrechts D, Le Marchand L, Lester J, Lesueur F, Lindstrom T, Lopez-Fernández A, Loud JT, Luccarini C, Mannermaa A, Manoukian S, Margolin S, Martens JWM, Mebirouk N, Meindl A, Miller A, Milne RL, Montagna M, Nathanson KL, Neuhausen SL, Nevanlinna H, Nielsen FC, O'Brien KM, Olopade OI, Olson JE, Olsson H, Osorio A, Ottini L, Park-Simon TW, Parsons MT, Pedersen IS, Peshkin B, Peterlongo P, Peto J, Pharoah PDP, Phillips KA, Polley EC, Poppe B, Presneau N, Pujana MA, Punie K, Radice P, Rantala J, Rashid MU, Rennert G, Rennert HS, Robson M, Romero A, Rossing M, Saloustros E, Sandler DP, Santella R, Scheuner MT, Schmidt MK, Schmidt G, Scott C, Sharma P, Soucy P, Southey MC, Spinelli JJ, Steinsnyder Z, Stone J, Stoppa-Lyonnet D, Swerdlow A, Tamimi RM, Tapper WJ, Taylor JA, Terry MB, Teulé A, Thull DL, Tischkowitz M, Toland AE, Torres D, Trainer AH, Truong T, Tung N, Vachon CM, Vega A, Vijai J, Wang Q, Wappenschmidt B, Weinberg CR, Weitzel JN, Wendt C, Wolk A, Yadav S, Yang XR, Yannoukakos D, Zheng W, Ziogas A, Zorn KK, Park SK, Thomassen M, Offit K, Schmutzler RK, Couch FJ, Simard J, Chenevix-Trench G, Easton DF, Andrieu N, Antoniou AC. Author Correction: A case-only study to identify genetic modifiers of breast cancer risk for BRCA1/BRCA2 mutation carriers. Nat Commun 2021; 12:2986. [PMID: 33990587 PMCID: PMC8121813 DOI: 10.1038/s41467-021-23162-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
A Correction to this paper has been published: https://doi.org/10.1038/s41467-021-23162-4
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Park J, Choi JY, Choi J, Chung S, Song N, Park SK, Han W, Noh DY, Ahn SH, Lee JW, Kim MK, Jee SH, Wen W, Bolla MK, Wang Q, Dennis J, Michailidou K, Shah M, Conroy DM, Harrington PA, Mayes R, Czene K, Hall P, Teras LR, Patel AV, Couch FJ, Olson JE, Sawyer EJ, Roylance R, Bojesen SE, Flyger H, Lambrechts D, Baten A, Matsuo K, Ito H, Guénel P, Truong T, Keeman R, Schmidt MK, Wu AH, Tseng CC, Cox A, Cross SS, Andrulis IL, Hopper JL, Southey MC, Wu PE, Shen CY, Fasching PA, Ekici AB, Muir K, Lophatananon A, Brenner H, Arndt V, Jones ME, Swerdlow AJ, Hoppe R, Ko YD, Hartman M, Li J, Mannermaa A, Hartikainen JM, Benitez J, González-Neira A, Haiman CA, Dörk T, Bogdanova NV, Teo SH, Mohd Taib NA, Fletcher O, Johnson N, Grip M, Winqvist R, Blomqvist C, Nevanlinna H, Lindblom A, Wendt C, Kristensen VN, Tollenaar RAEM, Heemskerk-Gerritsen BAM, Radice P, Bonanni B, Hamann U, Manoochehri M, Lacey JV, Martinez ME, Dunning AM, Pharoah PDP, Easton DF, Yoo KY, Kang D. Gene-Environment Interactions Relevant to Estrogen and Risk of Breast Cancer: Can Gene-Environment Interactions Be Detected Only among Candidate SNPs from Genome-Wide Association Studies? Cancers (Basel) 2021; 13:2370. [PMID: 34069208 PMCID: PMC8156547 DOI: 10.3390/cancers13102370] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2021] [Revised: 04/29/2021] [Accepted: 04/30/2021] [Indexed: 12/24/2022] Open
Abstract
In this study we aim to examine gene-environment interactions (GxEs) between genes involved with estrogen metabolism and environmental factors related to estrogen exposure. GxE analyses were conducted with 1970 Korean breast cancer cases and 2052 controls in the case-control study, the Seoul Breast Cancer Study (SEBCS). A total of 11,555 SNPs from the 137 candidate genes were included in the GxE analyses with eight established environmental factors. A replication test was conducted by using an independent population from the Breast Cancer Association Consortium (BCAC), with 62,485 Europeans and 9047 Asians. The GxE tests were performed by using two-step methods in GxEScan software. Two interactions were found in the SEBCS. The first interaction was shown between rs13035764 of NCOA1 and age at menarche in the GE|2df model (p-2df = 1.2 × 10-3). The age at menarche before 14 years old was associated with the high risk of breast cancer, and the risk was higher when subjects had homozygous minor allele G. The second GxE was shown between rs851998 near ESR1 and height in the GE|2df model (p-2df = 1.1 × 10-4). Height taller than 160 cm was associated with a high risk of breast cancer, and the risk increased when the minor allele was added. The findings were not replicated in the BCAC. These results would suggest specificity in Koreans for breast cancer risk.
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Li N, Zethoven M, McInerny S, Devereux L, Huang YK, Thio N, Cheasley D, Gutiérrez-Enríquez S, Moles-Fernández A, Diez O, Nguyen-Dumont T, Southey MC, Hopper JL, Simard J, Dumont M, Soucy P, Meindl A, Schmutzler R, Schmidt MK, Adank MA, Andrulis IL, Hahnen E, Engel C, Lesueur F, Girard E, Neuhausen SL, Ziv E, Allen J, Easton DF, Scott RJ, Gorringe KL, James PA, Campbell IG. Evaluation of the association of heterozygous germline variants in NTHL1 with breast cancer predisposition: an international multi-center study of 47,180 subjects. NPJ Breast Cancer 2021; 7:52. [PMID: 33980861 PMCID: PMC8115524 DOI: 10.1038/s41523-021-00255-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2020] [Accepted: 03/24/2021] [Indexed: 12/14/2022] Open
Abstract
Bi-allelic loss-of-function (LoF) variants in the base excision repair (BER) gene NTHL1 cause a high-risk hereditary multi-tumor syndrome that includes breast cancer, but the contribution of heterozygous variants to hereditary breast cancer is unknown. An analysis of 4985 women with breast cancer, enriched for familial features, and 4786 cancer-free women revealed significant enrichment for NTHL1 LoF variants. Immunohistochemistry confirmed reduced NTHL1 expression in tumors from heterozygous carriers but the NTHL1 bi-allelic loss characteristic mutational signature (SBS 30) was not present. The analysis was extended to 27,421 breast cancer cases and 19,759 controls from 10 international studies revealing 138 cases and 93 controls with a heterozygous LoF variant (OR 1.06, 95% CI: 0.82-1.39) and 316 cases and 179 controls with a missense variant (OR 1.31, 95% CI: 1.09-1.57). Missense variants selected for deleterious features by a number of in silico bioinformatic prediction tools or located within the endonuclease III functional domain showed a stronger association with breast cancer. Somatic sequencing of breast cancers from carriers indicated that the risk associated with NTHL1 appears to operate through haploinsufficiency, consistent with other described low-penetrance breast cancer genes. Data from this very large international multicenter study suggests that heterozygous pathogenic germline coding variants in NTHL1 may be associated with low- to moderate- increased risk of breast cancer.
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Nguyen-Dumont T, Stewart J, Winship I, Southey MC. Rare genetic variants: making the connection with breast cancer susceptibility. AIMS GENETICS 2021. [DOI: 10.3934/genet.2015.4.281] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
AbstractThe practice of clinical genetics in the context of breast cancer predisposition has reached another critical point in its evolution. For the past two decades, genetic testing offered to women attending clinics has been limited to BRCA1 and BRCA2 unless other syndromic indicators have been evident (e.g. PTEN and TP53 for Cowden and Li-Fraumeni syndrome, respectively). Women (and their families) who are concerned about their personal and/or family history of breast and ovarian cancer have enthusiastically engaged with clinical genetics services, anticipating a genetic cause for their cancer predisposition will be identified and to receive clinical guidance for their risk management and treatment options. Genetic testing laboratories have demonstrated similar enthusiasm for transitioning from single gene to gene panel testing that now provide opportunities for the large number of women found not to carry mutations in BRCA1 and BRCA2, enabling them to undergo additional genetic testing. However, these panel tests have limited clinical utility until more is understood about the cancer risks (if any) associated with the genetic variation observed in the genes included on these panels. New data is urgently needed to improve the interpretation of the genetic variation data that is already reported from these panels and to inform the selection of genes included in gene panel tests in the future. To address this issue, large internationally coordinated research studies are required to provide the evidence-base from which clinical genetics for breast cancer susceptibility can be practiced in the era of gene panel testing and oncogenetic practice.Two significant steps associated with this process include i) validating the genes on these panels (and those likely to be added in the future) as bona fide1
breast cancer predisposition genes and ii) interpreting the variation, on a variant-by-variant basis in terms of their likely “pathogenicity”—a process commonly referred to as “variant classification” that will enable this new genetic information to be used at an individual level in clinical genetics services. Neither of these fundamental steps have been achieved for the majority of genes included on the panels.We are thus at a critical point for translational research in breast cancer clinical genetics—how can rare genetic variants be interpreted such that they can be used in clinical genetics services and oncogenetic practice to identify and to inform the management of families that carry these variants?
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Yu C, Wong EM, Joo JE, Hodge AM, Makalic E, Schmidt D, Buchanan DD, Severi G, Hopper JL, English DR, Giles GG, Southey MC, Dugué PA. Epigenetic Drift Association with Cancer Risk and Survival, and Modification by Sex. Cancers (Basel) 2021; 13:cancers13081881. [PMID: 33919912 PMCID: PMC8070898 DOI: 10.3390/cancers13081881] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Revised: 04/09/2021] [Accepted: 04/10/2021] [Indexed: 01/13/2023] Open
Abstract
Simple Summary Ageing is the strongest cancer risk factor, and men and women exhibit different risk profiles in terms of incidence and survival. DNA methylation is known to strongly vary by age and sex. Epigenetic drift refers to age-related DNA methylation changes and the tendency for increasing discordance between epigenomes over time, but it remains unknown to what extent the epigenetic drift contributes to cancer risk and survival. The aims of this study were to identify age-associated, sex-associated and sexually dimorphic age-associated (age-by-sex-associated) DNA methylation markers and investigate whether age- and age-by-sex-associated markers are associated with cancer risk and survival. Our study, which used a total of 2754 matched case–control pairs with DNA methylation in pre-diagnostic blood, is the first large study to examine the association between sex-specific epigenetic drift and cancer development and progression. The results may be useful for cancer early diagnosis and prediction of prognosis. Abstract To investigate age- and sex-specific DNA methylation alterations related to cancer risk and survival, we used matched case–control studies of colorectal (n = 835), gastric (n = 170), kidney (n = 143), lung (n = 332), prostate (n = 869) and urothelial (n = 428) cancers, and mature B-cell lymphoma (n = 438). Linear mixed-effects models were conducted to identify age-, sex- and age-by-sex-associated methylation markers using a discovery (controls)-replication (cases) strategy. Replication was further examined using summary statistics from Generation Scotland (GS). Associations between replicated markers and risk of and survival from cancer were assessed using conditional logistic regression and Cox models (hazard ratios (HR)), respectively. We found 32,659, 23,141 and 48 CpGs with replicated associations for age, sex and age-by-sex, respectively. The replication rates for these CpGs using GS summary data were 94%, 86% and 91%, respectively. Significant associations for cancer risk and survival were identified at some individual age-related CpGs. Opposite to previous findings using epigenetic clocks, there was a strong negative trend in the association between epigenetic drift and risk of colorectal cancer. Methylation at two CpGs overlapping TMEM49 and ARX genes was associated with survival of overall (HR = 0.91, p = 7.7 × 10−4) and colorectal (HR = 1.52, p = 1.8 × 10−4) cancer, respectively, with significant age-by-sex interaction. Our results may provide markers for cancer early detection and prognosis prediction.
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Morra A, Jung AY, Behrens S, Keeman R, Ahearn TU, Anton-Culver H, Arndt V, Augustinsson A, Auvinen PK, Beane Freeman LE, Becher H, Beckmann MW, Blomqvist C, Bojesen SE, Bolla MK, Brenner H, Briceno I, Brucker SY, Camp NJ, Campa D, Canzian F, Castelao JE, Chanock SJ, Choi JY, Clarke CL, Couch FJ, Cox A, Cross SS, Czene K, Dörk T, Dunning AM, Dwek M, Easton DF, Eccles DM, Egan KM, Evans DG, Fasching PA, Flyger H, Gago-Dominguez M, Gapstur SM, García-Sáenz JA, Gaudet MM, Giles GG, Grip M, Guénel P, Haiman CA, Håkansson N, Hall P, Hamann U, Han SN, Hart SN, Hartman M, Heyworth JS, Hoppe R, Hopper JL, Hunter DJ, Ito H, Jager A, Jakimovska M, Jakubowska A, Janni W, Kaaks R, Kang D, Kapoor PM, Kitahara CM, Koutros S, Kraft P, Kristensen VN, Lacey JV, Lambrechts D, Le Marchand L, Li J, Lindblom A, Lubiński J, Lush M, Mannermaa A, Manoochehri M, Margolin S, Mariapun S, Matsuo K, Mavroudis D, Milne RL, Muranen TA, Newman WG, Noh DY, Nordestgaard BG, Obi N, Olshan AF, Olsson H, Park-Simon TW, Petridis C, Pharoah PDP, Plaseska-Karanfilska D, Presneau N, Rashid MU, Rennert G, Rennert HS, Rhenius V, Romero A, Saloustros E, Sawyer EJ, Schneeweiss A, Schwentner L, Scott C, Shah M, Shen CY, Shu XO, Southey MC, Stram DO, Tamimi RM, Tapper W, Tollenaar RAEM, Tomlinson I, Torres D, Troester MA, Truong T, Vachon CM, Wang Q, Wang SS, Williams JA, Winqvist R, Wolk A, Wu AH, Yoo KY, Yu JC, Zheng W, Ziogas A, Yang XR, Eliassen AH, Holmes MD, García-Closas M, Teo SH, Schmidt MK, Chang-Claude J. Breast Cancer Risk Factors and Survival by Tumor Subtype: Pooled Analyses from the Breast Cancer Association Consortium. Cancer Epidemiol Biomarkers Prev 2021; 30:623-642. [PMID: 33500318 PMCID: PMC8026532 DOI: 10.1158/1055-9965.epi-20-0924] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2020] [Revised: 08/31/2020] [Accepted: 01/08/2021] [Indexed: 12/09/2022] Open
Abstract
BACKGROUND It is not known whether modifiable lifestyle factors that predict survival after invasive breast cancer differ by subtype. METHODS We analyzed data for 121,435 women diagnosed with breast cancer from 67 studies in the Breast Cancer Association Consortium with 16,890 deaths (8,554 breast cancer specific) over 10 years. Cox regression was used to estimate associations between risk factors and 10-year all-cause mortality and breast cancer-specific mortality overall, by estrogen receptor (ER) status, and by intrinsic-like subtype. RESULTS There was no evidence of heterogeneous associations between risk factors and mortality by subtype (P adj > 0.30). The strongest associations were between all-cause mortality and BMI ≥30 versus 18.5-25 kg/m2 [HR (95% confidence interval (CI), 1.19 (1.06-1.34)]; current versus never smoking [1.37 (1.27-1.47)], high versus low physical activity [0.43 (0.21-0.86)], age ≥30 years versus <20 years at first pregnancy [0.79 (0.72-0.86)]; >0-<5 years versus ≥10 years since last full-term birth [1.31 (1.11-1.55)]; ever versus never use of oral contraceptives [0.91 (0.87-0.96)]; ever versus never use of menopausal hormone therapy, including current estrogen-progestin therapy [0.61 (0.54-0.69)]. Similar associations with breast cancer mortality were weaker; for example, 1.11 (1.02-1.21) for current versus never smoking. CONCLUSIONS We confirm associations between modifiable lifestyle factors and 10-year all-cause mortality. There was no strong evidence that associations differed by ER status or intrinsic-like subtype. IMPACT Given the large dataset and lack of evidence that associations between modifiable risk factors and 10-year mortality differed by subtype, these associations could be cautiously used in prognostication models to inform patient-centered care.
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Russell H, Kedzierska K, Buchanan DD, Thomas R, Tham E, Mints M, Keränen A, Giles GG, Southey MC, Milne RL, Tomlinson I, Church D, Spurdle AB, O'Mara TA, Lewis A. Correction to: The MLH1 polymorphism rs1800734 and risk of endometrial cancer with microsatellite instability. Clin Epigenetics 2021; 13:69. [PMID: 33794988 PMCID: PMC8015168 DOI: 10.1186/s13148-021-01058-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
An amendment to this paper has been published and can be accessed via the original article.
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Conti DV, Darst BF, Moss LC, Saunders EJ, Sheng X, Chou A, Schumacher FR, Olama AAA, Benlloch S, Dadaev T, Brook MN, Sahimi A, Hoffmann TJ, Takahashi A, Matsuda K, Momozawa Y, Fujita M, Muir K, Lophatananon A, Wan P, Le Marchand L, Wilkens LR, Stevens VL, Gapstur SM, Carter BD, Schleutker J, Tammela TLJ, Sipeky C, Auvinen A, Giles GG, Southey MC, MacInnis RJ, Cybulski C, Wokołorczyk D, Lubiński J, Neal DE, Donovan JL, Hamdy FC, Martin RM, Nordestgaard BG, Nielsen SF, Weischer M, Bojesen SE, Røder MA, Iversen P, Batra J, Chambers S, Moya L, Horvath L, Clements JA, Tilley W, Risbridger GP, Gronberg H, Aly M, Szulkin R, Eklund M, Nordström T, Pashayan N, Dunning AM, Ghoussaini M, Travis RC, Key TJ, Riboli E, Park JY, Sellers TA, Lin HY, Albanes D, Weinstein SJ, Mucci LA, Giovannucci E, Lindstrom S, Kraft P, Hunter DJ, Penney KL, Turman C, Tangen CM, Goodman PJ, Thompson IM, Hamilton RJ, Fleshner NE, Finelli A, Parent MÉ, Stanford JL, Ostrander EA, Geybels MS, Koutros S, Freeman LEB, Stampfer M, Wolk A, Håkansson N, Andriole GL, Hoover RN, Machiela MJ, Sørensen KD, Borre M, Blot WJ, Zheng W, Yeboah ED, Mensah JE, Lu YJ, Zhang HW, Feng N, Mao X, Wu Y, Zhao SC, Sun Z, Thibodeau SN, McDonnell SK, Schaid DJ, West CML, Burnet N, Barnett G, Maier C, Schnoeller T, Luedeke M, Kibel AS, Drake BF, Cussenot O, Cancel-Tassin G, Menegaux F, Truong T, Koudou YA, John EM, Grindedal EM, Maehle L, Khaw KT, Ingles SA, Stern MC, Vega A, Gómez-Caamaño A, Fachal L, Rosenstein BS, Kerns SL, Ostrer H, Teixeira MR, Paulo P, Brandão A, Watya S, Lubwama A, Bensen JT, Fontham ETH, Mohler J, Taylor JA, Kogevinas M, Llorca J, Castaño-Vinyals G, Cannon-Albright L, Teerlink CC, Huff CD, Strom SS, Multigner L, Blanchet P, Brureau L, Kaneva R, Slavov C, Mitev V, Leach RJ, Weaver B, Brenner H, Cuk K, Holleczek B, Saum KU, Klein EA, Hsing AW, Kittles RA, Murphy AB, Logothetis CJ, Kim J, Neuhausen SL, Steele L, Ding YC, Isaacs WB, Nemesure B, Hennis AJM, Carpten J, Pandha H, Michael A, De Ruyck K, De Meerleer G, Ost P, Xu J, Razack A, Lim J, Teo SH, Newcomb LF, Lin DW, Fowke JH, Neslund-Dudas C, Rybicki BA, Gamulin M, Lessel D, Kulis T, Usmani N, Singhal S, Parliament M, Claessens F, Joniau S, Van den Broeck T, Gago-Dominguez M, Castelao JE, Martinez ME, Larkin S, Townsend PA, Aukim-Hastie C, Bush WS, Aldrich MC, Crawford DC, Srivastava S, Cullen JC, Petrovics G, Casey G, Roobol MJ, Jenster G, van Schaik RHN, Hu JJ, Sanderson M, Varma R, McKean-Cowdin R, Torres M, Mancuso N, Berndt SI, Van Den Eeden SK, Easton DF, Chanock SJ, Cook MB, Wiklund F, Nakagawa H, Witte JS, Eeles RA, Kote-Jarai Z, Haiman CA. Publisher Correction: Trans-ancestry genome-wide association meta-analysis of prostate cancer identifies new susceptibility loci and informs genetic risk prediction. Nat Genet 2021; 53:413. [PMID: 33473200 DOI: 10.1038/s41588-021-00786-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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Kapoor PM, Mavaddat N, Choudhury PP, Wilcox AN, Lindström S, Behrens S, Michailidou K, Dennis J, Bolla MK, Wang Q, Jung A, Abu-Ful Z, Ahearn T, Andrulis IL, Anton-Culver H, Arndt V, Aronson KJ, Auer PL, Freeman LEB, Becher H, Beckmann MW, Beeghly-Fadiel A, Benitez J, Bernstein L, Bojesen SE, Brauch H, Brenner H, Brüning T, Cai Q, Campa D, Canzian F, Carracedo A, Carter BD, Castelao JE, Chanock SJ, Chatterjee N, Chenevix-Trench G, Clarke CL, Couch FJ, Cox A, Cross SS, Czene K, Dai JY, Earp HS, Ekici AB, Eliassen AH, Eriksson M, Evans DG, Fasching PA, Figueroa J, Fritschi L, Gabrielson M, Gago-Dominguez M, Gao C, Gapstur SM, Gaudet MM, Giles GG, González-Neira A, Guénel P, Haeberle L, Haiman CA, Håkansson N, Hall P, Hamann U, Hatse S, Heyworth J, Holleczek B, Hoover RN, Hopper JL, Howell A, Hunter DJ, John EM, Jones ME, Kaaks R, Keeman R, Kitahara CM, Ko YD, Koutros S, Kurian AW, Lambrechts D, Le Marchand L, Lee E, Lejbkowicz F, Linet M, Lissowska J, Llaneza A, MacInnis RJ, Martinez ME, Maurer T, McLean C, Neuhausen SL, Newman WG, Norman A, O’Brien KM, Olshan AF, Olson JE, Olsson H, Orr N, Perou CM, Pita G, Polley EC, Prentice RL, Rennert G, Rennert HS, Ruddy KJ, Sandler DP, Saunders C, Schoemaker MJ, Schöttker B, Schumacher F, Scott C, Scott RJ, Shu XO, Smeets A, Southey MC, Spinelli JJ, Stone J, Swerdlow AJ, Tamimi RM, Taylor JA, Troester MA, Vachon CM, van Veen EM, Wang X, Weinberg CR, Weltens C, Willett W, Winham SJ, Wolk A, Yang XR, Zheng W, Ziogas A, Dunning AM, Pharoah PDP, Schmidt MK, Kraft P, Easton DF, Milne RL, García-Closas M, Chang-Claude J. Combined Associations of a Polygenic Risk Score and Classical Risk Factors With Breast Cancer Risk. J Natl Cancer Inst 2021; 113:329-337. [PMID: 32359158 PMCID: PMC7936056 DOI: 10.1093/jnci/djaa056] [Citation(s) in RCA: 39] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2019] [Revised: 03/30/2020] [Accepted: 04/23/2020] [Indexed: 01/01/2023] Open
Abstract
We evaluated the joint associations between a new 313-variant PRS (PRS313) and questionnaire-based breast cancer risk factors for women of European ancestry, using 72 284 cases and 80 354 controls from the Breast Cancer Association Consortium. Interactions were evaluated using standard logistic regression and a newly developed case-only method for breast cancer risk overall and by estrogen receptor status. After accounting for multiple testing, we did not find evidence that per-standard deviation PRS313 odds ratio differed across strata defined by individual risk factors. Goodness-of-fit tests did not reject the assumption of a multiplicative model between PRS313 and each risk factor. Variation in projected absolute lifetime risk of breast cancer associated with classical risk factors was greater for women with higher genetic risk (PRS313 and family history) and, on average, 17.5% higher in the highest vs lowest deciles of genetic risk. These findings have implications for risk prevention for women at increased risk of breast cancer.
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Coignard J, Lush M, Beesley J, O'Mara TA, Dennis J, Tyrer JP, Barnes DR, McGuffog L, Leslie G, Bolla MK, Adank MA, Agata S, Ahearn T, Aittomäki K, Andrulis IL, Anton-Culver H, Arndt V, Arnold N, Aronson KJ, Arun BK, Augustinsson A, Azzollini J, Barrowdale D, Baynes C, Becher H, Bermisheva M, Bernstein L, Białkowska K, Blomqvist C, Bojesen SE, Bonanni B, Borg A, Brauch H, Brenner H, Burwinkel B, Buys SS, Caldés T, Caligo MA, Campa D, Carter BD, Castelao JE, Chang-Claude J, Chanock SJ, Chung WK, Claes KBM, Clarke CL, Collée JM, Conroy DM, Czene K, Daly MB, Devilee P, Diez O, Ding YC, Domchek SM, Dörk T, Dos-Santos-Silva I, Dunning AM, Dwek M, Eccles DM, Eliassen AH, Engel C, Eriksson M, Evans DG, Fasching PA, Flyger H, Fostira F, Friedman E, Fritschi L, Frost D, Gago-Dominguez M, Gapstur SM, Garber J, Garcia-Barberan V, García-Closas M, García-Sáenz JA, Gaudet MM, Gayther SA, Gehrig A, Georgoulias V, Giles GG, Godwin AK, Goldberg MS, Goldgar DE, González-Neira A, Greene MH, Guénel P, Haeberle L, Hahnen E, Haiman CA, Håkansson N, Hall P, Hamann U, Harrington PA, Hart SN, He W, Hogervorst FBL, Hollestelle A, Hopper JL, Horcasitas DJ, Hulick PJ, Hunter DJ, Imyanitov EN, Jager A, Jakubowska A, James PA, Jensen UB, John EM, Jones ME, Kaaks R, Kapoor PM, Karlan BY, Keeman R, Khusnutdinova E, Kiiski JI, Ko YD, Kosma VM, Kraft P, Kurian AW, Laitman Y, Lambrechts D, Le Marchand L, Lester J, Lesueur F, Lindstrom T, Lopez-Fernández A, Loud JT, Luccarini C, Mannermaa A, Manoukian S, Margolin S, Martens JWM, Mebirouk N, Meindl A, Miller A, Milne RL, Montagna M, Nathanson KL, Neuhausen SL, Nevanlinna H, Nielsen FC, O'Brien KM, Olopade OI, Olson JE, Olsson H, Osorio A, Ottini L, Park-Simon TW, Parsons MT, Pedersen IS, Peshkin B, Peterlongo P, Peto J, Pharoah PDP, Phillips KA, Polley EC, Poppe B, Presneau N, Pujana MA, Punie K, Radice P, Rantala J, Rashid MU, Rennert G, Rennert HS, Robson M, Romero A, Rossing M, Saloustros E, Sandler DP, Santella R, Scheuner MT, Schmidt MK, Schmidt G, Scott C, Sharma P, Soucy P, Southey MC, Spinelli JJ, Steinsnyder Z, Stone J, Stoppa-Lyonnet D, Swerdlow A, Tamimi RM, Tapper WJ, Taylor JA, Terry MB, Teulé A, Thull DL, Tischkowitz M, Toland AE, Torres D, Trainer AH, Truong T, Tung N, Vachon CM, Vega A, Vijai J, Wang Q, Wappenschmidt B, Weinberg CR, Weitzel JN, Wendt C, Wolk A, Yadav S, Yang XR, Yannoukakos D, Zheng W, Ziogas A, Zorn KK, Park SK, Thomassen M, Offit K, Schmutzler RK, Couch FJ, Simard J, Chenevix-Trench G, Easton DF, Andrieu N, Antoniou AC. A case-only study to identify genetic modifiers of breast cancer risk for BRCA1/BRCA2 mutation carriers. Nat Commun 2021; 12:1078. [PMID: 33597508 PMCID: PMC7890067 DOI: 10.1038/s41467-020-20496-3] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2019] [Accepted: 11/19/2020] [Indexed: 02/02/2023] Open
Abstract
Breast cancer (BC) risk for BRCA1 and BRCA2 mutation carriers varies by genetic and familial factors. About 50 common variants have been shown to modify BC risk for mutation carriers. All but three, were identified in general population studies. Other mutation carrier-specific susceptibility variants may exist but studies of mutation carriers have so far been underpowered. We conduct a novel case-only genome-wide association study comparing genotype frequencies between 60,212 general population BC cases and 13,007 cases with BRCA1 or BRCA2 mutations. We identify robust novel associations for 2 variants with BC for BRCA1 and 3 for BRCA2 mutation carriers, P < 10-8, at 5 loci, which are not associated with risk in the general population. They include rs60882887 at 11p11.2 where MADD, SP11 and EIF1, genes previously implicated in BC biology, are predicted as potential targets. These findings will contribute towards customising BC polygenic risk scores for BRCA1 and BRCA2 mutation carriers.
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Nguyen-Dumont T, Dowty J, Tucker K, Kirk J, James P, Trainer A, Winship I, Pachter N, Poplawski N, Grist S, Park DJ, Renault AL, Hammet F, Mahmoodi M, Tsimiklis H, Steen JA, Theys D, Rewse A, Willis A, Morrow A, Speechly C, Harris R, Riaz M, Sebra R, Schadt E, Lacaze P, McNeil J, Hopper JL, Southey MC. Abstract PS7-04: Population-based estimates of breast cancer risk for germline pathogenic variants identified by gene-panel testing: An Australian perspective. Cancer Res 2021. [DOI: 10.1158/1538-7445.sabcs20-ps7-04] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Abstract
BRA-STRAP is an Australia-wide study of breast cancer predisposition that brings together gene-panel data from 30,000 adult Australian women of all ages, across the breast cancer risk spectrum, with and without a diagnosis of breast cancer. The “BRA-STRAP panel” includes 24 genes* that are involved in, or putatively associated with, predisposition to breast and/or ovarian cancer. Despite insufficient evidence for clinical translation for some of these genes, all 24 are commonly included on panel tests for breast cancer predisposition.
We present findings from the population-based case-control sub-study of BRA-STRAP, which involved 1451 women diagnosed with breast cancer and 857 age-matched controls participating in the Australian Breast Cancer Family Registry (ABCFR), and 6101 healthy, elderly Australian women enrolled in the ASPREE study. These analyses focus on rare genetic variants predicted to lead to loss of function and/or classified as pathogenic/likely pathogenic (P/LP) in ClinVar. Odds ratios (ORs) for their associations with breast cancer were estimated by aggregating genetic variants for each gene.
For the women diagnosed with breast cancer, the median age at diagnosis (inter-quartile range, IQR) was 40.0 (14.0) years and the overall frequency of P/LP variant carriers across all genes was 156/1451 (10.8%). The median age (IQR) of the ABCFR and ASPREE controls were 39.4 (14.9) and 73.9 (5.8) years, respectively. The frequencies of P/LP variant carriers were 33/857 (3.9%) and 268/6101 (4.4%) in the ABCFR and ASPREE controls, respectively. We combined both control datasets and, after adjusting for age and other potential confounders, the ORs associated with P/LP variants in BRCA1 and BRCA2 were 4.1 [95% confidence interval (CI): 1.8-10.2] and 2.9 [95% CI: 1.5-6], respectively. We also found that the OR for P/LP variants in ATM was 4.0 [95% CI: 1.5-10.4] and the OR for P/LP variants in PALB2 was 2.2 [95% CI: 0.75-5.7] although this did not reach statistical significance.
These results contribute to international efforts to refine the breast cancer risk estimates for genetic variants identified from population-based screening of unselected women using genes that are included on panel tests and thought to be potentially breast cancer predisposition genes.The case-control-family design of the ABCFR will also allow us to estimate the age specific cumulative risk (penetrance) of these genetic variants, which is important for genetic counselling and the clinical management of carrier families.
*ATM, BARD1, BRCA1, BRCA2, BRIP1, CDH1, CHEK2, FANCM, MLH1, MRE11A, MSH2, MSH6, MUTYH, NBN, NF1, PALB2, PMS2, PTEN, RAD50, RAD51C, RAD51D, RECQL, STK11 and TP53
Citation Format: Tu Nguyen-Dumont, James Dowty, Katherine Tucker, Judy Kirk, Paul James, Alison Trainer, Ingrid Winship, Nicholas Pachter, Nicola Poplawski, Scott Grist, Daniel J Park, Anne-Laure Renault, Fleur Hammet, Maryam Mahmoodi, Helen Tsimiklis, Jason A Steen, Derrick Theys, Amanda Rewse, Amanda Willis, April Morrow, Catherine Speechly, Rebecca Harris, Moeen Riaz, Robert Sebra, Eric Schadt, Paul Lacaze, John McNeil, John L Hopper, Melissa C Southey. Population-based estimates of breast cancer risk for germline pathogenic variants identified by gene-panel testing: An Australian perspective [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PS7-04.
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Dorling L, Carvalho S, Allen J, González-Neira A, Luccarini C, Wahlström C, Pooley KA, Parsons MT, Fortuno C, Wang Q, Bolla MK, Dennis J, Keeman R, Alonso MR, Álvarez N, Herraez B, Fernandez V, Núñez-Torres R, Osorio A, Valcich J, Li M, Törngren T, Harrington PA, Baynes C, Conroy DM, Decker B, Fachal L, Mavaddat N, Ahearn T, Aittomäki K, Antonenkova NN, Arnold N, Arveux P, Ausems MGEM, Auvinen P, Becher H, Beckmann MW, Behrens S, Bermisheva M, Białkowska K, Blomqvist C, Bogdanova NV, Bogdanova-Markov N, Bojesen SE, Bonanni B, Børresen-Dale AL, Brauch H, Bremer M, Briceno I, Brüning T, Burwinkel B, Cameron DA, Camp NJ, Campbell A, Carracedo A, Castelao JE, Cessna MH, Chanock SJ, Christiansen H, Collée JM, Cordina-Duverger E, Cornelissen S, Czene K, Dörk T, Ekici AB, Engel C, Eriksson M, Fasching PA, Figueroa J, Flyger H, Försti A, Gabrielson M, Gago-Dominguez M, Georgoulias V, Gil F, Giles GG, Glendon G, Garcia EBG, Alnæs GIG, Guénel P, Hadjisavvas A, Haeberle L, Hahnen E, Hall P, Hamann U, Harkness EF, Hartikainen JM, Hartman M, He W, Heemskerk-Gerritsen BAM, Hillemanns P, Hogervorst FBL, Hollestelle A, Ho WK, Hooning MJ, Howell A, Humphreys K, Idris F, Jakubowska A, Jung A, Kapoor PM, Kerin MJ, Khusnutdinova E, Kim SW, Ko YD, Kosma VM, Kristensen VN, Kyriacou K, Lakeman IMM, Lee JW, Lee MH, Li J, Lindblom A, Lo WY, Loizidou MA, Lophatananon A, Lubiński J, MacInnis RJ, Madsen MJ, Mannermaa A, Manoochehri M, Manoukian S, Margolin S, Martinez ME, Maurer T, Mavroudis D, McLean C, Meindl A, Mensenkamp AR, Michailidou K, Miller N, Mohd Taib NA, Muir K, Mulligan AM, Nevanlinna H, Newman WG, Nordestgaard BG, Ng PS, Oosterwijk JC, Park SK, Park-Simon TW, Perez JIA, Peterlongo P, Porteous DJ, Prajzendanc K, Prokofyeva D, Radice P, Rashid MU, Rhenius V, Rookus MA, Rüdiger T, Saloustros E, Sawyer EJ, Schmutzler RK, Schneeweiss A, Schürmann P, Shah M, Sohn C, Southey MC, Surowy H, Suvanto M, Thanasitthichai S, Tomlinson I, Torres D, Truong T, Tzardi M, Valova Y, van Asperen CJ, Van Dam RM, van den Ouweland AMW, van der Kolk LE, van Veen EM, Wendt C, Williams JA, Yang XR, Yoon SY, Zamora MP, Evans DG, de la Hoya M, Simard J, Antoniou AC, Borg Å, Andrulis IL, Chang-Claude J, García-Closas M, Chenevix-Trench G, Milne RL, Pharoah PDP, Schmidt MK, Spurdle AB, Vreeswijk MPG, Benitez J, Dunning AM, Kvist A, Teo SH, Devilee P, Easton DF. Breast Cancer Risk Genes - Association Analysis in More than 113,000 Women. N Engl J Med 2021; 384:428-439. [PMID: 33471991 PMCID: PMC7611105 DOI: 10.1056/nejmoa1913948] [Citation(s) in RCA: 532] [Impact Index Per Article: 177.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
BACKGROUND Genetic testing for breast cancer susceptibility is widely used, but for many genes, evidence of an association with breast cancer is weak, underlying risk estimates are imprecise, and reliable subtype-specific risk estimates are lacking. METHODS We used a panel of 34 putative susceptibility genes to perform sequencing on samples from 60,466 women with breast cancer and 53,461 controls. In separate analyses for protein-truncating variants and rare missense variants in these genes, we estimated odds ratios for breast cancer overall and tumor subtypes. We evaluated missense-variant associations according to domain and classification of pathogenicity. RESULTS Protein-truncating variants in 5 genes (ATM, BRCA1, BRCA2, CHEK2, and PALB2) were associated with a risk of breast cancer overall with a P value of less than 0.0001. Protein-truncating variants in 4 other genes (BARD1, RAD51C, RAD51D, and TP53) were associated with a risk of breast cancer overall with a P value of less than 0.05 and a Bayesian false-discovery probability of less than 0.05. For protein-truncating variants in 19 of the remaining 25 genes, the upper limit of the 95% confidence interval of the odds ratio for breast cancer overall was less than 2.0. For protein-truncating variants in ATM and CHEK2, odds ratios were higher for estrogen receptor (ER)-positive disease than for ER-negative disease; for protein-truncating variants in BARD1, BRCA1, BRCA2, PALB2, RAD51C, and RAD51D, odds ratios were higher for ER-negative disease than for ER-positive disease. Rare missense variants (in aggregate) in ATM, CHEK2, and TP53 were associated with a risk of breast cancer overall with a P value of less than 0.001. For BRCA1, BRCA2, and TP53, missense variants (in aggregate) that would be classified as pathogenic according to standard criteria were associated with a risk of breast cancer overall, with the risk being similar to that of protein-truncating variants. CONCLUSIONS The results of this study define the genes that are most clinically useful for inclusion on panels for the prediction of breast cancer risk, as well as provide estimates of the risks associated with protein-truncating variants, to guide genetic counseling. (Funded by European Union Horizon 2020 programs and others.).
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Johnson N, Maguire S, Morra A, Kapoor PM, Tomczyk K, Jones ME, Schoemaker MJ, Gilham C, Bolla MK, Wang Q, Dennis J, Ahearn TU, Andrulis IL, Anton-Culver H, Antonenkova NN, Arndt V, Aronson KJ, Augustinsson A, Baynes C, Freeman LEB, Beckmann MW, Benitez J, Bermisheva M, Blomqvist C, Boeckx B, Bogdanova NV, Bojesen SE, Brauch H, Brenner H, Burwinkel B, Campa D, Canzian F, Castelao JE, Chanock SJ, Chenevix-Trench G, Clarke CL, Conroy DM, Couch FJ, Cox A, Cross SS, Czene K, Dörk T, Eliassen AH, Engel C, Evans DG, Fasching PA, Figueroa J, Floris G, Flyger H, Gago-Dominguez M, Gapstur SM, García-Closas M, Gaudet MM, Giles GG, Goldberg MS, González-Neira A, Guénel P, Hahnen E, Haiman CA, Håkansson N, Hall P, Hamann U, Harrington PA, Hart SN, Hooning MJ, Hopper JL, Howell A, Hunter DJ, Jager A, Jakubowska A, John EM, Kaaks R, Keeman R, Khusnutdinova E, Kitahara CM, Kosma VM, Koutros S, Kraft P, Kristensen VN, Kurian AW, Lambrechts D, Le Marchand L, Linet M, Lubiński J, Mannermaa A, Manoukian S, Margolin S, Martens JWM, Mavroudis D, Mayes R, Meindl A, Milne RL, Neuhausen SL, Nevanlinna H, Newman WG, Nielsen SF, Nordestgaard BG, Obi N, Olshan AF, Olson JE, Olsson H, Orban E, Park-Simon TW, Peterlongo P, Plaseska-Karanfilska D, Pylkäs K, Rennert G, Rennert HS, Ruddy KJ, Saloustros E, Sandler DP, Sawyer EJ, Schmutzler RK, Scott C, Shu XO, Simard J, Smichkoska S, Sohn C, Southey MC, Spinelli JJ, Stone J, Tamimi RM, Taylor JA, Tollenaar RAEM, Tomlinson I, Troester MA, Truong T, Vachon CM, van Veen EM, Wang SS, Weinberg CR, Wendt C, Wildiers H, Winqvist R, Wolk A, Zheng W, Ziogas A, Dunning AM, Pharoah PDP, Easton DF, Howie AF, Peto J, Dos-Santos-Silva I, Swerdlow AJ, Chang-Claude J, Schmidt MK, Orr N, Fletcher O. CYP3A7*1C allele: linking premenopausal oestrone and progesterone levels with risk of hormone receptor-positive breast cancers. Br J Cancer 2021; 124:842-854. [PMID: 33495599 PMCID: PMC7884683 DOI: 10.1038/s41416-020-01185-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2020] [Revised: 10/21/2020] [Accepted: 11/05/2020] [Indexed: 01/02/2023] Open
Abstract
BACKGROUND Epidemiological studies provide strong evidence for a role of endogenous sex hormones in the aetiology of breast cancer. The aim of this analysis was to identify genetic variants that are associated with urinary sex-hormone levels and breast cancer risk. METHODS We carried out a genome-wide association study of urinary oestrone-3-glucuronide and pregnanediol-3-glucuronide levels in 560 premenopausal women, with additional analysis of progesterone levels in 298 premenopausal women. To test for the association with breast cancer risk, we carried out follow-up genotyping in 90,916 cases and 89,893 controls from the Breast Cancer Association Consortium. All women were of European ancestry. RESULTS For pregnanediol-3-glucuronide, there were no genome-wide significant associations; for oestrone-3-glucuronide, we identified a single peak mapping to the CYP3A locus, annotated by rs45446698. The minor rs45446698-C allele was associated with lower oestrone-3-glucuronide (-49.2%, 95% CI -56.1% to -41.1%, P = 3.1 × 10-18); in follow-up analyses, rs45446698-C was also associated with lower progesterone (-26.7%, 95% CI -39.4% to -11.6%, P = 0.001) and reduced risk of oestrogen and progesterone receptor-positive breast cancer (OR = 0.86, 95% CI 0.82-0.91, P = 6.9 × 10-8). CONCLUSIONS The CYP3A7*1C allele is associated with reduced risk of hormone receptor-positive breast cancer possibly mediated via an effect on the metabolism of endogenous sex hormones in premenopausal women.
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Dugué PA, Bassett JK, Wong EM, Joo JE, Li S, Yu C, Schmidt DF, Makalic E, Doo NW, Buchanan DD, Hodge AM, English DR, Hopper JL, Giles GG, Southey MC, Milne RL. Biological Aging Measures Based on Blood DNA Methylation and Risk of Cancer: A Prospective Study. JNCI Cancer Spectr 2021; 5:pkaa109. [PMID: 33442664 PMCID: PMC7791618 DOI: 10.1093/jncics/pkaa109] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2020] [Revised: 09/16/2020] [Accepted: 10/29/2020] [Indexed: 02/07/2023] Open
Abstract
Background We previously investigated the association between 5 "first-generation" measures of epigenetic aging and cancer risk in the Melbourne Collaborative Cohort Study. This study assessed cancer risk associations for 3 recently developed methylation-based biomarkers of aging: PhenoAge, GrimAge, and predicted telomere length. Methods We estimated rate ratios (RRs) for the association between these 3 age-adjusted measures and risk of colorectal (N = 813), gastric (N = 165), kidney (N = 139), lung (N = 327), mature B-cell (N = 423), prostate (N = 846), and urothelial (N = 404) cancer using conditional logistic regression models. We also assessed associations by time since blood draw and by cancer subtype, and we investigated potential nonlinearity. Results We observed relatively strong associations of age-adjusted PhenoAge with risk of colorectal, kidney, lung, mature B-cell, and urothelial cancers (RR per SD was approximately 1.2-1.3). Similar findings were obtained for age-adjusted GrimAge, but the association with lung cancer risk was much larger (RR per SD = 1.82, 95% confidence interval [CI] = 1.44 to 2.30), after adjustment for smoking status, pack-years, starting age, time since quitting, and other cancer risk factors. Most associations appeared linear, larger than for the first-generation measures, and were virtually unchanged after adjustment for a large set of sociodemographic, lifestyle, and anthropometric variables. For cancer overall, the comprehensively adjusted rate ratio per SD was 1.13 (95% CI = 1.07 to 1.19) for PhenoAge and 1.12 (95% CI = 1.05 to 1.20) for GrimAge and appeared larger within 5 years of blood draw (RR = 1.29, 95% CI = 1.15 to 1.44 and 1.19, 95% CI = 1.06 to 1.33, respectively). Conclusions The methylation-based measures PhenoAge and GrimAge may provide insights into the relationship between biological aging and cancer and be useful to predict cancer risk, particularly for lung cancer.
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Suman M, Dugué PA, Wong EM, Joo JE, Hopper JL, Nguyen-Dumont T, Giles GG, Milne RL, McLean C, Southey MC. Association of variably methylated tumour DNA regions with overall survival for invasive lobular breast cancer. Clin Epigenetics 2021; 13:11. [PMID: 33461604 PMCID: PMC7814464 DOI: 10.1186/s13148-020-00975-6] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2020] [Accepted: 11/10/2020] [Indexed: 12/12/2022] Open
Abstract
Background Tumour DNA methylation profiling has shown potential to refine disease subtyping and improve the diagnosis and prognosis prediction of breast cancer. However, limited data exist regarding invasive lobular breast cancer (ILBC). Here, we investigated the genome-wide variability of DNA methylation levels across ILBC tumours and assessed the association between methylation levels at the variably methylated regions and overall survival in women with ILBC. Methods Tumour-enriched DNA was prepared by macrodissecting formalin-fixed paraffin embedded (FFPE) tumour tissue from 130 ILBCs diagnosed in the participants of the Melbourne Collaborative Cohort Study (MCCS). Genome-wide tumour DNA methylation was measured using the HumanMethylation 450K (HM450K) BeadChip array. Variably methylated regions (VMRs) were identified using the DMRcate package in R. Cox proportional hazards regression models were used to assess the association between methylation levels at the ten most significant VMRs and overall survival. Gene set enrichment analyses were undertaken using the web-based tool Metaspace. Replication of the VMR and survival analysis findings was examined using data retrieved from The Cancer Genome Atlas (TCGA) for 168 ILBC cases. We also examined the correlation between methylation and gene expression for the ten VMRs of interest using TCGA data. Results We identified 2771 VMRs (P < 10−8) in ILBC tumours. The ten most variably methylated clusters were predominantly located in the promoter region of the genes: ISM1, APC, TMEM101, ASCL2, NKX6, HIST3H2A/HIST3H2BB, HCG4P3, HES5, CELF2 and EFCAB4B. Higher methylation level at several of these VMRs showed an association with reduced overall survival in the MCCS. In TCGA, all associations were in the same direction, however stronger than in the MCCS. The pooled analysis of the MCCS and TCGA data showed that methylation at four of the ten genes was associated with reduced overall survival, independently of age and tumour stage; APC: Hazard Ratio (95% Confidence interval) per one-unit M-value increase: 1.18 (1.02–1.36), TMEM101: 1.23 (1.02–1.48), HCG4P3: 1.37 (1.05–1.79) and CELF2: 1.21 (1.02–1.43). A negative correlation was observed between methylation and gene expression for CELF2 (R = − 0.25, P = 0.001), but not for TMEM101 and APC. Conclusions Our study identified regions showing greatest variability across the ILBC tumour genome and found methylation at several genes to potentially serve as a biomarker of survival for women with ILBC.
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Kho PF, Amant F, Annibali D, Ashton K, Attia J, Auer PL, Beckmann MW, Black A, Brinton L, Buchanan DD, Chanock SJ, Chen C, Chen MM, Cheng THT, Cook LS, Crous-Bous M, Czene K, De Vivo I, Dennis J, Dörk T, Dowdy SC, Dunning AM, Dürst M, Easton DF, Ekici AB, Fasching PA, Fridley BL, Friedenreich CM, García-Closas M, Gaudet MM, Giles GG, Goode EL, Gorman M, Haiman CA, Hall P, Hankinson SE, Hein A, Hillemanns P, Hodgson S, Hoivik EA, Holliday EG, Hunter DJ, Jones A, Kraft P, Krakstad C, Lambrechts D, Le Marchand L, Liang X, Lindblom A, Lissowska J, Long J, Lu L, Magliocco AM, Martin L, McEvoy M, Milne RL, Mints M, Nassir R, Otton G, Palles C, Pooler L, Proietto T, Rebbeck TR, Renner SP, Risch HA, Rübner M, Runnebaum I, Sacerdote C, Sarto GE, Schumacher F, Scott RJ, Setiawan VW, Shah M, Sheng X, Shu XO, Southey MC, Tham E, Tomlinson I, Trovik J, Turman C, Tyrer JP, Van Den Berg D, Wang Z, Wentzensen N, Xia L, Xiang YB, Yang HP, Yu H, Zheng W, Webb PM, Thompson DJ, Spurdle AB, Glubb DM, O'Mara TA. Mendelian randomization analyses suggest a role for cholesterol in the development of endometrial cancer. Int J Cancer 2021; 148:307-319. [PMID: 32851660 PMCID: PMC7757859 DOI: 10.1002/ijc.33206] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2019] [Revised: 05/08/2020] [Accepted: 05/26/2020] [Indexed: 01/14/2023]
Abstract
Blood lipids have been associated with the development of a range of cancers, including breast, lung and colorectal cancer. For endometrial cancer, observational studies have reported inconsistent associations between blood lipids and cancer risk. To reduce biases from unmeasured confounding, we performed a bidirectional, two-sample Mendelian randomization analysis to investigate the relationship between levels of three blood lipids (low-density lipoprotein [LDL] and high-density lipoprotein [HDL] cholesterol, and triglycerides) and endometrial cancer risk. Genetic variants associated with each of these blood lipid levels (P < 5 × 10-8 ) were identified as instrumental variables, and assessed using genome-wide association study data from the Endometrial Cancer Association Consortium (12 906 cases and 108 979 controls) and the Global Lipids Genetic Consortium (n = 188 578). Mendelian randomization analyses found genetically raised LDL cholesterol levels to be associated with lower risks of endometrial cancer of all histologies combined, and of endometrioid and non-endometrioid subtypes. Conversely, higher genetically predicted HDL cholesterol levels were associated with increased risk of non-endometrioid endometrial cancer. After accounting for the potential confounding role of obesity (as measured by genetic variants associated with body mass index), the association between genetically predicted increased LDL cholesterol levels and lower endometrial cancer risk remained significant, especially for non-endometrioid endometrial cancer. There was no evidence to support a role for triglycerides in endometrial cancer development. Our study supports a role for LDL and HDL cholesterol in the development of non-endometrioid endometrial cancer. Further studies are required to understand the mechanisms underlying these findings.
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Karlsson Q, Brook MN, Dadaev T, Wakerell S, Saunders EJ, Muir K, Neal DE, Giles GG, MacInnis RJ, Thibodeau SN, McDonnell SK, Cannon-Albright L, Teixeira MR, Paulo P, Cardoso M, Huff C, Li D, Yao Y, Scheet P, Permuth JB, Stanford JL, Dai JY, Ostrander EA, Cussenot O, Cancel-Tassin G, Hoegel J, Herkommer K, Schleutker J, Tammela TLJ, Rathinakannan V, Sipeky C, Wiklund F, Grönberg H, Aly M, Isaacs WB, Dickinson JL, FitzGerald LM, Chua MLK, Nguyen-Dumont T, Schaid DJ, Southey MC, Eeles RA, Kote-Jarai Z. Rare Germline Variants in ATM Predispose to Prostate Cancer: A PRACTICAL Consortium Study. Eur Urol Oncol 2021; 4:570-579. [PMID: 33436325 PMCID: PMC8381233 DOI: 10.1016/j.euo.2020.12.001] [Citation(s) in RCA: 38] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2020] [Revised: 11/23/2020] [Accepted: 12/01/2020] [Indexed: 12/21/2022]
Abstract
BACKGROUND Germline ATM mutations are suggested to contribute to predisposition to prostate cancer (PrCa). Previous studies have had inadequate power to estimate variant effect sizes. OBJECTIVE To precisely estimate the contribution of germline ATM mutations to PrCa risk. DESIGN, SETTING, AND PARTICIPANTS We analysed next-generation sequencing data from 13 PRACTICAL study groups comprising 5560 cases and 3353 controls of European ancestry. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS Variant Call Format files were harmonised, annotated for rare ATM variants, and classified as tier 1 (likely pathogenic) or tier 2 (potentially deleterious). Associations with overall PrCa risk and clinical subtypes were estimated. RESULTS AND LIMITATIONS PrCa risk was higher in carriers of a tier 1 germline ATM variant, with an overall odds ratio (OR) of 4.4 (95% confidence interval [CI]: 2.0-9.5). There was also evidence that PrCa cases with younger age at diagnosis (<65 yr) had elevated tier 1 variant frequencies (pdifference = 0.04). Tier 2 variants were also associated with PrCa risk, with an OR of 1.4 (95% CI: 1.1-1.7). CONCLUSIONS Carriers of pathogenic ATM variants have an elevated risk of developing PrCa and are at an increased risk for earlier-onset disease presentation. These results provide information for counselling of men and their families. PATIENT SUMMARY In this study, we estimated that men who inherit a likely pathogenic mutation in the ATM gene had an approximately a fourfold risk of developing prostate cancer. In addition, they are likely to develop the disease earlier.
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Conti DV, Darst BF, Moss LC, Saunders EJ, Sheng X, Chou A, Schumacher FR, Olama AAA, Benlloch S, Dadaev T, Brook MN, Sahimi A, Hoffmann TJ, Takahashi A, Matsuda K, Momozawa Y, Fujita M, Muir K, Lophatananon A, Wan P, Le Marchand L, Wilkens LR, Stevens VL, Gapstur SM, Carter BD, Schleutker J, Tammela TLJ, Sipeky C, Auvinen A, Giles GG, Southey MC, MacInnis RJ, Cybulski C, Wokołorczyk D, Lubiński J, Neal DE, Donovan JL, Hamdy FC, Martin RM, Nordestgaard BG, Nielsen SF, Weischer M, Bojesen SE, Røder MA, Iversen P, Batra J, Chambers S, Moya L, Horvath L, Clements JA, Tilley W, Risbridger GP, Gronberg H, Aly M, Szulkin R, Eklund M, Nordström T, Pashayan N, Dunning AM, Ghoussaini M, Travis RC, Key TJ, Riboli E, Park JY, Sellers TA, Lin HY, Albanes D, Weinstein SJ, Mucci LA, Giovannucci E, Lindstrom S, Kraft P, Hunter DJ, Penney KL, Turman C, Tangen CM, Goodman PJ, Thompson IM, Hamilton RJ, Fleshner NE, Finelli A, Parent MÉ, Stanford JL, Ostrander EA, Geybels MS, Koutros S, Freeman LEB, Stampfer M, Wolk A, Håkansson N, Andriole GL, Hoover RN, Machiela MJ, Sørensen KD, Borre M, Blot WJ, Zheng W, Yeboah ED, Mensah JE, Lu YJ, Zhang HW, Feng N, Mao X, Wu Y, Zhao SC, Sun Z, Thibodeau SN, McDonnell SK, Schaid DJ, West CML, Burnet N, Barnett G, Maier C, Schnoeller T, Luedeke M, Kibel AS, Drake BF, Cussenot O, Cancel-Tassin G, Menegaux F, Truong T, Koudou YA, John EM, Grindedal EM, Maehle L, Khaw KT, Ingles SA, Stern MC, Vega A, Gómez-Caamaño A, Fachal L, Rosenstein BS, Kerns SL, Ostrer H, Teixeira MR, Paulo P, Brandão A, Watya S, Lubwama A, Bensen JT, Fontham ETH, Mohler J, Taylor JA, Kogevinas M, Llorca J, Castaño-Vinyals G, Cannon-Albright L, Teerlink CC, Huff CD, Strom SS, Multigner L, Blanchet P, Brureau L, Kaneva R, Slavov C, Mitev V, Leach RJ, Weaver B, Brenner H, Cuk K, Holleczek B, Saum KU, Klein EA, Hsing AW, Kittles RA, Murphy AB, Logothetis CJ, Kim J, Neuhausen SL, Steele L, Ding YC, Isaacs WB, Nemesure B, Hennis AJM, Carpten J, Pandha H, Michael A, De Ruyck K, De Meerleer G, Ost P, Xu J, Razack A, Lim J, Teo SH, Newcomb LF, Lin DW, Fowke JH, Neslund-Dudas C, Rybicki BA, Gamulin M, Lessel D, Kulis T, Usmani N, Singhal S, Parliament M, Claessens F, Joniau S, Van den Broeck T, Gago-Dominguez M, Castelao JE, Martinez ME, Larkin S, Townsend PA, Aukim-Hastie C, Bush WS, Aldrich MC, Crawford DC, Srivastava S, Cullen JC, Petrovics G, Casey G, Roobol MJ, Jenster G, van Schaik RHN, Hu JJ, Sanderson M, Varma R, McKean-Cowdin R, Torres M, Mancuso N, Berndt SI, Van Den Eeden SK, Easton DF, Chanock SJ, Cook MB, Wiklund F, Nakagawa H, Witte JS, Eeles RA, Kote-Jarai Z, Haiman CA. Trans-ancestry genome-wide association meta-analysis of prostate cancer identifies new susceptibility loci and informs genetic risk prediction. Nat Genet 2021; 53:65-75. [PMID: 33398198 PMCID: PMC8148035 DOI: 10.1038/s41588-020-00748-0] [Citation(s) in RCA: 268] [Impact Index Per Article: 89.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2020] [Accepted: 11/05/2020] [Indexed: 01/28/2023]
Abstract
Prostate cancer is a highly heritable disease with large disparities in incidence rates across ancestry populations. We conducted a multiancestry meta-analysis of prostate cancer genome-wide association studies (107,247 cases and 127,006 controls) and identified 86 new genetic risk variants independently associated with prostate cancer risk, bringing the total to 269 known risk variants. The top genetic risk score (GRS) decile was associated with odds ratios that ranged from 5.06 (95% confidence interval (CI), 4.84-5.29) for men of European ancestry to 3.74 (95% CI, 3.36-4.17) for men of African ancestry. Men of African ancestry were estimated to have a mean GRS that was 2.18-times higher (95% CI, 2.14-2.22), and men of East Asian ancestry 0.73-times lower (95% CI, 0.71-0.76), than men of European ancestry. These findings support the role of germline variation contributing to population differences in prostate cancer risk, with the GRS offering an approach for personalized risk prediction.
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Dugué PA, Wilson R, Lehne B, Jayasekara H, Wang X, Jung CH, Joo JE, Makalic E, Schmidt DF, Baglietto L, Severi G, Gieger C, Ladwig KH, Peters A, Kooner JS, Southey MC, English DR, Waldenberger M, Chambers JC, Giles GG, Milne RL. Alcohol consumption is associated with widespread changes in blood DNA methylation: Analysis of cross-sectional and longitudinal data. Addict Biol 2021; 26:e12855. [PMID: 31789449 DOI: 10.1111/adb.12855] [Citation(s) in RCA: 42] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2018] [Revised: 09/29/2019] [Accepted: 11/04/2019] [Indexed: 12/26/2022]
Abstract
DNA methylation may be one of the mechanisms by which alcohol consumption is associated with the risk of disease. We conducted a large-scale, cross-sectional, genome-wide DNA methylation association study of alcohol consumption and a longitudinal analysis of repeated measurements taken several years apart. Using the Illumina HumanMethylation450 BeadChip, DNA methylation was measured in blood samples from 5606 Melbourne Collaborative Cohort Study (MCCS) participants. For 1088 of them, these measures were repeated using blood samples collected a median of 11 years later. Associations between alcohol intake and blood DNA methylation were assessed using linear mixed-effects regression models. Independent data from the London Life Sciences Prospective Population (LOLIPOP) (N = 4042) and Cooperative Health Research in the Augsburg Region (KORA) (N = 1662) cohorts were used to replicate associations discovered in the MCCS. Cross-sectional analyses identified 1414 CpGs associated with alcohol intake at P < 10-7 , 1243 of which had not been reported previously. Of these novel associations, 1078 were replicated (P < .05) using LOLIPOP and KORA data. Using the MCCS data, we also replicated 403 of 518 previously reported associations. Interaction analyses suggested that associations were stronger for women, non-smokers, and participants genetically predisposed to consume less alcohol. Of the 1414 CpGs, 530 were differentially methylated (P < .05) in former compared with current drinkers. Longitudinal associations between the change in alcohol intake and the change in methylation were observed for 513 of the 1414 cross-sectional associations. Our study indicates that alcohol intake is associated with widespread changes in DNA methylation across the genome. Longitudinal analyses showed that the methylation status of alcohol-associated CpGs may change with alcohol consumption changes in adulthood.
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MacInnis RJ, Knight JA, Chung WK, Milne RL, Whittemore AS, Buchsbaum R, Liao Y, Zeinomar N, Dite GS, Southey MC, Goldgar D, Giles GG, Kurian AW, Andrulis IL, John EM, Daly MB, Buys SS, Phillips KA, Hopper JL, Terry MB. Comparing 5-Year and Lifetime Risks of Breast Cancer using the Prospective Family Study Cohort. J Natl Cancer Inst 2020; 113:785-791. [PMID: 33301022 DOI: 10.1093/jnci/djaa178] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2020] [Revised: 08/06/2020] [Accepted: 10/13/2020] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Clinical guidelines often use predicted lifetime risk from birth to define criteria for making decisions regarding breast cancer screening rather than thresholds based on absolute 5-year risk from current age. METHODS We used the Prospective Family Cohort Study of 14 657 women without breast cancer at baseline in which, during a median follow-up of 10 years, 482 women were diagnosed with invasive breast cancer. We examined the performances of the International Breast Cancer Intervention Study (IBIS) and Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA) risk models when using the alternative thresholds by comparing predictions based on 5-year risk with those based on lifetime risk from birth and remaining lifetime risk. All statistical tests were 2-sided. RESULTS Using IBIS, the areas under the receiver-operating characteristic curves were 0.66 (95% confidence interval = 0.63 to 0.68) and 0.56 (95% confidence interval = 0.54 to 0.59) for 5-year and lifetime risks, respectively (Pdiff < .001). For equivalent sensitivities, the 5-year incidence almost always had higher specificities than lifetime risk from birth. For women aged 20-39 years, 5-year risk performed better than lifetime risk from birth. For women aged 40 years or older, receiver-operating characteristic curves were similar for 5-year and lifetime IBIS risk from birth. Classifications based on remaining lifetime risk were inferior to 5-year risk estimates. Results were similar using BOADICEA. CONCLUSIONS Our analysis shows that risk stratification using clinical models will likely be more accurate when based on predicted 5-year risk compared with risks based on predicted lifetime and remaining lifetime, particularly for women aged 20-39 years.
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