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Clyde MA, Palmieri Weber R, Iversen ES, Poole EM, Doherty JA, Goodman MT, Ness RB, Risch HA, Rossing MA, Terry KL, Wentzensen N, Whittemore AS, Anton-Culver H, Bandera EV, Berchuck A, Carney ME, Cramer DW, Cunningham JM, Cushing-Haugen KL, Edwards RP, Fridley BL, Goode EL, Lurie G, McGuire V, Modugno F, Moysich KB, Olson SH, Pearce CL, Pike MC, Rothstein JH, Sellers TA, Sieh W, Stram D, Thompson PJ, Vierkant RA, Wicklund KG, Wu AH, Ziogas A, Tworoger SS, Schildkraut JM. Risk Prediction for Epithelial Ovarian Cancer in 11 United States-Based Case-Control Studies: Incorporation of Epidemiologic Risk Factors and 17 Confirmed Genetic Loci. Am J Epidemiol 2016; 184:579-589. [PMID: 27698005 PMCID: PMC5065620 DOI: 10.1093/aje/kww091] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2015] [Accepted: 03/22/2016] [Indexed: 12/14/2022] Open
Abstract
Previously developed models for predicting absolute risk of invasive epithelial ovarian cancer have included a limited number of risk factors and have had low discriminatory power (area under the receiver operating characteristic curve (AUC) < 0.60). Because of this, we developed and internally validated a relative risk prediction model that incorporates 17 established epidemiologic risk factors and 17 genome-wide significant single nucleotide polymorphisms (SNPs) using data from 11 case-control studies in the United States (5,793 cases; 9,512 controls) from the Ovarian Cancer Association Consortium (data accrued from 1992 to 2010). We developed a hierarchical logistic regression model for predicting case-control status that included imputation of missing data. We randomly divided the data into an 80% training sample and used the remaining 20% for model evaluation. The AUC for the full model was 0.664. A reduced model without SNPs performed similarly (AUC = 0.649). Both models performed better than a baseline model that included age and study site only (AUC = 0.563). The best predictive power was obtained in the full model among women younger than 50 years of age (AUC = 0.714); however, the addition of SNPs increased the AUC the most for women older than 50 years of age (AUC = 0.638 vs. 0.616). Adapting this improved model to estimate absolute risk and evaluating it in prospective data sets is warranted.
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Ioannidis NM, Rothstein JH, Pejaver V, Middha S, McDonnell SK, Baheti S, Musolf A, Li Q, Holzinger E, Karyadi D, Cannon-Albright LA, Teerlink CC, Stanford JL, Isaacs WB, Xu J, Cooney KA, Lange EM, Schleutker J, Carpten JD, Powell IJ, Cussenot O, Cancel-Tassin G, Giles GG, MacInnis RJ, Maier C, Hsieh CL, Wiklund F, Catalona WJ, Foulkes WD, Mandal D, Eeles RA, Kote-Jarai Z, Bustamante CD, Schaid DJ, Hastie T, Ostrander EA, Bailey-Wilson JE, Radivojac P, Thibodeau SN, Whittemore AS, Sieh W. REVEL: An Ensemble Method for Predicting the Pathogenicity of Rare Missense Variants. Am J Hum Genet 2016; 99:877-885. [PMID: 27666373 DOI: 10.1016/j.ajhg.2016.08.016] [Citation(s) in RCA: 1299] [Impact Index Per Article: 162.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2016] [Accepted: 08/23/2016] [Indexed: 02/08/2023] Open
Abstract
The vast majority of coding variants are rare, and assessment of the contribution of rare variants to complex traits is hampered by low statistical power and limited functional data. Improved methods for predicting the pathogenicity of rare coding variants are needed to facilitate the discovery of disease variants from exome sequencing studies. We developed REVEL (rare exome variant ensemble learner), an ensemble method for predicting the pathogenicity of missense variants on the basis of individual tools: MutPred, FATHMM, VEST, PolyPhen, SIFT, PROVEAN, MutationAssessor, MutationTaster, LRT, GERP, SiPhy, phyloP, and phastCons. REVEL was trained with recently discovered pathogenic and rare neutral missense variants, excluding those previously used to train its constituent tools. When applied to two independent test sets, REVEL had the best overall performance (p < 10-12) as compared to any individual tool and seven ensemble methods: MetaSVM, MetaLR, KGGSeq, Condel, CADD, DANN, and Eigen. Importantly, REVEL also had the best performance for distinguishing pathogenic from rare neutral variants with allele frequencies <0.5%. The area under the receiver operating characteristic curve (AUC) for REVEL was 0.046-0.182 higher in an independent test set of 935 recent SwissVar disease variants and 123,935 putatively neutral exome sequencing variants and 0.027-0.143 higher in an independent test set of 1,953 pathogenic and 2,406 benign variants recently reported in ClinVar than the AUCs for other ensemble methods. We provide pre-computed REVEL scores for all possible human missense variants to facilitate the identification of pathogenic variants in the sea of rare variants discovered as sequencing studies expand in scale.
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Ong JS, Cuellar-Partida G, Lu Y, Fasching PA, Hein A, Burghaus S, Beckmann MW, Lambrechts D, Van Nieuwenhuysen E, Vergote I, Vanderstichele A, Anne Doherty J, Anne Rossing M, Chang-Claude J, Eilber U, Rudolph A, Wang-Gohrke S, Goodman MT, Bogdanova N, Dörk T, Dürst M, Hillemanns P, Runnebaum IB, Antonenkova N, Butzow R, Leminen A, Nevanlinna H, Pelttari LM, Edwards RP, Kelley JL, Modugno F, Moysich KB, Ness RB, Cannioto R, Høgdall E, Høgdall CK, Jensen A, Giles GG, Bruinsma F, Kjaer SK, Hildebrandt MA, Liang D, Lu KH, Wu X, Bisogna M, Dao F, Levine DA, Cramer DW, Terry KL, Tworoger SS, Stampfer M, Missmer S, Bjorge L, Salvesen HB, Kopperud RK, Bischof K, Aben KK, Kiemeney LA, Massuger LF, Brooks-Wilson A, Olson SH, McGuire V, Rothstein JH, Sieh W, Whittemore AS, Cook LS, Le ND, Gilks CB, Gronwald J, Jakubowska A, Lubiński J, Kluz T, Song H, Tyrer JP, Wentzensen N, Brinton L, Trabert B, Lissowska J, McLaughlin JR, Narod SA, Phelan C, Anton-Culver H, Ziogas A, Eccles D, Campbell I, Gayther SA, Gentry-Maharaj A, Menon U, Ramus SJ, Wu AH, Dansonka-Mieszkowska A, Kupryjanczyk J, Timorek A, Szafron L, Cunningham JM, Fridley BL, Winham SJ, Bandera EV, Poole EM, Morgan TK, Risch HA, Goode EL, Schildkraut JM, Pearce CL, Berchuck A, Pharoah PD, Chenevix-Trench G, Gharahkhani P, Neale RE, Webb PM, MacGregor S. Association of vitamin D levels and risk of ovarian cancer: a Mendelian randomization study. Int J Epidemiol 2016; 45:1619-1630. [PMID: 27594614 PMCID: PMC5100621 DOI: 10.1093/ije/dyw207] [Citation(s) in RCA: 87] [Impact Index Per Article: 10.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/24/2016] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND In vitro and observational epidemiological studies suggest that vitamin D may play a role in cancer prevention. However, the relationship between vitamin D and ovarian cancer is uncertain, with observational studies generating conflicting findings. A potential limitation of observational studies is inadequate control of confounding. To overcome this problem, we used Mendelian randomization (MR) to evaluate the association between single nucleotide polymorphisms (SNPs) associated with circulating 25-hydroxyvitamin D [25(OH)D] concentration and risk of ovarian cancer. METHODS We employed SNPs with well-established associations with 25(OH)D concentration as instrumental variables for MR: rs7944926 (DHCR7), rs12794714 (CYP2R1) and rs2282679 (GC). We included 31 719 women of European ancestry (10 065 cases, 21 654 controls) from the Ovarian Cancer Association Consortium, who were genotyped using customized Illumina Infinium iSelect (iCOGS) arrays. A two-sample (summary data) MR approach was used and analyses were performed separately for all ovarian cancer (10 065 cases) and for high-grade serous ovarian cancer (4121 cases). RESULTS The odds ratio for epithelial ovarian cancer risk (10 065 cases) estimated by combining the individual SNP associations using inverse variance weighting was 1.27 (95% confidence interval: 1.06 to 1.51) per 20 nmol/L decrease in 25(OH)D concentration. The estimated odds ratio for high-grade serous epithelial ovarian cancer (4121 cases) was 1.54 (1.19, 2.01). CONCLUSIONS Genetically lowered 25-hydroxyvitamin D concentrations were associated with higher ovarian cancer susceptibility in Europeans. These findings suggest that increasing plasma vitamin D levels may reduce risk of ovarian cancer.
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Lawrenson K, Kar S, McCue K, Kuchenbaeker K, Michailidou K, Tyrer J, Beesley J, Ramus SJ, Li Q, Delgado MK, Lee JM, Aittomäki K, Andrulis IL, Anton-Culver H, Arndt V, Arun BK, Arver B, Bandera EV, Barile M, Barkardottir RB, Barrowdale D, Beckmann MW, Benitez J, Berchuck A, Bisogna M, Bjorge L, Blomqvist C, Blot W, Bogdanova N, Bojesen A, Bojesen SE, Bolla MK, Bonanni B, Børresen-Dale AL, Brauch H, Brennan P, Brenner H, Bruinsma F, Brunet J, Buhari SA, Burwinkel B, Butzow R, Buys SS, Cai Q, Caldes T, Campbell I, Canniotto R, Chang-Claude J, Chiquette J, Choi JY, Claes KBM, Cook LS, Cox A, Cramer DW, Cross SS, Cybulski C, Czene K, Daly MB, Damiola F, Dansonka-Mieszkowska A, Darabi H, Dennis J, Devilee P, Diez O, Doherty JA, Domchek SM, Dorfling CM, Dörk T, Dumont M, Ehrencrona H, Ejlertsen B, Ellis S, Engel C, Lee E, Evans DG, Fasching PA, Feliubadalo L, Figueroa J, Flesch-Janys D, Fletcher O, Flyger H, Foretova L, Fostira F, Foulkes WD, Fridley BL, Friedman E, Frost D, Gambino G, Ganz PA, Garber J, García-Closas M, Gentry-Maharaj A, Ghoussaini M, Giles GG, Glasspool R, Godwin AK, Goldberg MS, Goldgar DE, González-Neira A, Goode EL, Goodman MT, Greene MH, Gronwald J, Guénel P, Haiman CA, Hall P, Hallberg E, Hamann U, Hansen TVO, Harrington PA, Hartman M, Hassan N, Healey S, Heitz F, Herzog J, Høgdall E, Høgdall CK, Hogervorst FBL, Hollestelle A, Hopper JL, Hulick PJ, Huzarski T, Imyanitov EN, Isaacs C, Ito H, Jakubowska A, Janavicius R, Jensen A, John EM, Johnson N, Kabisch M, Kang D, Kapuscinski M, Karlan BY, Khan S, Kiemeney LA, Kjaer SK, Knight JA, Konstantopoulou I, Kosma VM, Kristensen V, Kupryjanczyk J, Kwong A, de la Hoya M, Laitman Y, Lambrechts D, Le N, De Leeneer K, Lester J, Levine DA, Li J, Lindblom A, Long J, Lophatananon A, Loud JT, Lu K, Lubinski J, Mannermaa A, Manoukian S, Le Marchand L, Margolin S, Marme F, Massuger LFAG, Matsuo K, Mazoyer S, McGuffog L, McLean C, McNeish I, Meindl A, Menon U, Mensenkamp AR, Milne RL, Montagna M, Moysich KB, Muir K, Mulligan AM, Nathanson KL, Ness RB, Neuhausen SL, Nevanlinna H, Nord S, Nussbaum RL, Odunsi K, Offit K, Olah E, Olopade OI, Olson JE, Olswold C, O'Malley D, Orlow I, Orr N, Osorio A, Park SK, Pearce CL, Pejovic T, Peterlongo P, Pfeiler G, Phelan CM, Poole EM, Pylkäs K, Radice P, Rantala J, Rashid MU, Rennert G, Rhenius V, Rhiem K, Risch HA, Rodriguez G, Rossing MA, Rudolph A, Salvesen HB, Sangrajrang S, Sawyer EJ, Schildkraut JM, Schmidt MK, Schmutzler RK, Sellers TA, Seynaeve C, Shah M, Shen CY, Shu XO, Sieh W, Singer CF, Sinilnikova OM, Slager S, Song H, Soucy P, Southey MC, Stenmark-Askmalm M, Stoppa-Lyonnet D, Sutter C, Swerdlow A, Tchatchou S, Teixeira MR, Teo SH, Terry KL, Terry MB, Thomassen M, Tibiletti MG, Tihomirova L, Tognazzo S, Toland AE, Tomlinson I, Torres D, Truong T, Tseng CC, Tung N, Tworoger SS, Vachon C, van den Ouweland AMW, van Doorn HC, van Rensburg EJ, Van't Veer LJ, Vanderstichele A, Vergote I, Vijai J, Wang Q, Wang-Gohrke S, Weitzel JN, Wentzensen N, Whittemore AS, Wildiers H, Winqvist R, Wu AH, Yannoukakos D, Yoon SY, Yu JC, Zheng W, Zheng Y, Khanna KK, Simard J, Monteiro AN, French JD, Couch FJ, Freedman ML, Easton DF, Dunning AM, Pharoah PD, Edwards SL, Chenevix-Trench G, Antoniou AC, Gayther SA. Functional mechanisms underlying pleiotropic risk alleles at the 19p13.1 breast-ovarian cancer susceptibility locus. Nat Commun 2016; 7:12675. [PMID: 27601076 PMCID: PMC5023955 DOI: 10.1038/ncomms12675] [Citation(s) in RCA: 68] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2015] [Accepted: 07/20/2016] [Indexed: 02/02/2023] Open
Abstract
A locus at 19p13 is associated with breast cancer (BC) and ovarian cancer (OC) risk. Here we analyse 438 SNPs in this region in 46,451 BC and 15,438 OC cases, 15,252 BRCA1 mutation carriers and 73,444 controls and identify 13 candidate causal SNPs associated with serous OC (P=9.2 × 10(-20)), ER-negative BC (P=1.1 × 10(-13)), BRCA1-associated BC (P=7.7 × 10(-16)) and triple negative BC (P-diff=2 × 10(-5)). Genotype-gene expression associations are identified for candidate target genes ANKLE1 (P=2 × 10(-3)) and ABHD8 (P<2 × 10(-3)). Chromosome conformation capture identifies interactions between four candidate SNPs and ABHD8, and luciferase assays indicate six risk alleles increased transactivation of the ADHD8 promoter. Targeted deletion of a region containing risk SNP rs56069439 in a putative enhancer induces ANKLE1 downregulation; and mRNA stability assays indicate functional effects for an ANKLE1 3'-UTR SNP. Altogether, these data suggest that multiple SNPs at 19p13 regulate ABHD8 and perhaps ANKLE1 expression, and indicate common mechanisms underlying breast and ovarian cancer risk.
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Jeffers AM, Sieh W, Lipson JA, Rothstein JH, McGuire V, Whittemore AS, Rubin DL. Breast Cancer Risk and Mammographic Density Assessed with Semiautomated and Fully Automated Methods and BI-RADS. Radiology 2016; 282:348-355. [PMID: 27598536 DOI: 10.1148/radiol.2016152062] [Citation(s) in RCA: 59] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Purpose To compare three metrics of breast density on full-field digital mammographic (FFDM) images as predictors of future breast cancer risk. Materials and Methods This institutional review board-approved study included 125 women with invasive breast cancer and 274 age- and race-matched control subjects who underwent screening FFDM during 2004-2013 and provided informed consent. The percentage of density and dense area were assessed semiautomatically with software (Cumulus 4.0; University of Toronto, Toronto, Canada), and volumetric percentage of density and dense volume were assessed automatically with software (Volpara; Volpara Solutions, Wellington, New Zealand). Clinical Breast Imaging Reporting and Data System (BI-RADS) classifications of breast density were extracted from mammography reports. Odds ratios and 95% confidence intervals (CIs) were estimated by using conditional logistic regression stratified according to age and race and adjusted for body mass index, parity, and menopausal status, and the area under the receiver operating characteristic curve (AUC) was computed. Results The adjusted odds ratios and 95% CIs for each standard deviation increment of the percentage of density, dense area, volumetric percentage of density, and dense volume were 1.61 (95% CI: 1.19, 2.19), 1.49 (95% CI: 1.15, 1.92), 1.54 (95% CI: 1.12, 2.10), and 1.41 (95% CI: 1.11, 1.80), respectively. Odds ratios for women with extremely dense breasts compared with those with scattered areas of fibroglandular density were 2.06 (95% CI: 0.85, 4.97) and 2.05 (95% CI: 0.90, 4.64) for BI-RADS and Volpara density classifications, respectively. Clinical BI-RADS was more accurate (AUC, 0.68; 95% CI: 0.63, 0.74) than Volpara (AUC, 0.64; 95% CI: 0.58, 0.70) and continuous measures of percentage of density (AUC, 0.66; 95% CI: 0.60, 0.72), dense area (AUC, 0.66; 95% CI: 0.60, 0.72), volumetric percentage of density (AUC, 0.64; 95% CI: 0.58, 0.70), and density volume (AUC, 0.65; 95% CI: 0.59, 0.71), although the AUC differences were not statistically significant. Conclusion Mammographic density on FFDM images was positively associated with breast cancer risk by using the computer assisted methods and BI-RADS. BI-RADS classification was as accurate as computer-assisted methods for discrimination of patients from control subjects. © RSNA, 2016.
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Kar SP, Beesley J, Amin Al Olama A, Michailidou K, Tyrer J, Kote-Jarai ZS, Lawrenson K, Lindstrom S, Ramus SJ, Thompson DJ, Kibel AS, Dansonka-Mieszkowska A, Michael A, Dieffenbach AK, Gentry-Maharaj A, Whittemore AS, Wolk A, Monteiro A, Peixoto A, Kierzek A, Cox A, Rudolph A, Gonzalez-Neira A, Wu AH, Lindblom A, Swerdlow A, Ziogas A, Ekici AB, Burwinkel B, Karlan BY, Nordestgaard BG, Blomqvist C, Phelan C, McLean C, Pearce CL, Vachon C, Cybulski C, Slavov C, Stegmaier C, Maier C, Ambrosone CB, Høgdall CK, Teerlink CC, Kang D, Tessier DC, Schaid DJ, Stram DO, Cramer DW, Neal DE, Eccles D, Flesch-Janys D, Edwards DRV, Wokozorczyk D, Levine DA, Yannoukakos D, Sawyer EJ, Bandera EV, Poole EM, Goode EL, Khusnutdinova E, Høgdall E, Song F, Bruinsma F, Heitz F, Modugno F, Hamdy FC, Wiklund F, Giles GG, Olsson H, Wildiers H, Ulmer HU, Pandha H, Risch HA, Darabi H, Salvesen HB, Nevanlinna H, Gronberg H, Brenner H, Brauch H, Anton-Culver H, Song H, Lim HY, McNeish I, Campbell I, Vergote I, Gronwald J, Lubiński J, Stanford JL, Benítez J, Doherty JA, Permuth JB, Chang-Claude J, Donovan JL, Dennis J, Schildkraut JM, Schleutker J, Hopper JL, Kupryjanczyk J, Park JY, Figueroa J, Clements JA, Knight JA, Peto J, Cunningham JM, Pow-Sang J, Batra J, Czene K, Lu KH, Herkommer K, Khaw KT, Matsuo K, Muir K, Offitt K, Chen K, Moysich KB, Aittomäki K, Odunsi K, Kiemeney LA, Massuger LFAG, Fitzgerald LM, Cook LS, Cannon-Albright L, Hooning MJ, Pike MC, Bolla MK, Luedeke M, Teixeira MR, Goodman MT, Schmidt MK, Riggan M, Aly M, Rossing MA, Beckmann MW, Moisse M, Sanderson M, Southey MC, Jones M, Lush M, Hildebrandt MAT, Hou MF, Schoemaker MJ, Garcia-Closas M, Bogdanova N, Rahman N, Le ND, Orr N, Wentzensen N, Pashayan N, Peterlongo P, Guénel P, Brennan P, Paulo P, Webb PM, Broberg P, Fasching PA, Devilee P, Wang Q, Cai Q, Li Q, Kaneva R, Butzow R, Kopperud RK, Schmutzler RK, Stephenson RA, MacInnis RJ, Hoover RN, Winqvist R, Ness R, Milne RL, Travis RC, Benlloch S, Olson SH, McDonnell SK, Tworoger SS, Maia S, Berndt S, Lee SC, Teo SH, Thibodeau SN, Bojesen SE, Gapstur SM, Kjær SK, Pejovic T, Tammela TLJ, Dörk T, Brüning T, Wahlfors T, Key TJ, Edwards TL, Menon U, Hamann U, Mitev V, Kosma VM, Setiawan VW, Kristensen V, Arndt V, Vogel W, Zheng W, Sieh W, Blot WJ, Kluzniak W, Shu XO, Gao YT, Schumacher F, Freedman ML, Berchuck A, Dunning AM, Simard J, Haiman CA, Spurdle A, Sellers TA, Hunter DJ, Henderson BE, Kraft P, Chanock SJ, Couch FJ, Hall P, Gayther SA, Easton DF, Chenevix-Trench G, Eeles R, Pharoah PDP, Lambrechts D. Genome-Wide Meta-Analyses of Breast, Ovarian, and Prostate Cancer Association Studies Identify Multiple New Susceptibility Loci Shared by at Least Two Cancer Types. Cancer Discov 2016; 6:1052-67. [PMID: 27432226 PMCID: PMC5010513 DOI: 10.1158/2159-8290.cd-15-1227] [Citation(s) in RCA: 139] [Impact Index Per Article: 17.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2015] [Accepted: 06/07/2016] [Indexed: 02/02/2023]
Abstract
UNLABELLED Breast, ovarian, and prostate cancers are hormone-related and may have a shared genetic basis, but this has not been investigated systematically by genome-wide association (GWA) studies. Meta-analyses combining the largest GWA meta-analysis data sets for these cancers totaling 112,349 cases and 116,421 controls of European ancestry, all together and in pairs, identified at P < 10(-8) seven new cross-cancer loci: three associated with susceptibility to all three cancers (rs17041869/2q13/BCL2L11; rs7937840/11q12/INCENP; rs1469713/19p13/GATAD2A), two breast and ovarian cancer risk loci (rs200182588/9q31/SMC2; rs8037137/15q26/RCCD1), and two breast and prostate cancer risk loci (rs5013329/1p34/NSUN4; rs9375701/6q23/L3MBTL3). Index variants in five additional regions previously associated with only one cancer also showed clear association with a second cancer type. Cell-type-specific expression quantitative trait locus and enhancer-gene interaction annotations suggested target genes with potential cross-cancer roles at the new loci. Pathway analysis revealed significant enrichment of death receptor signaling genes near loci with P < 10(-5) in the three-cancer meta-analysis. SIGNIFICANCE We demonstrate that combining large-scale GWA meta-analysis findings across cancer types can identify completely new risk loci common to breast, ovarian, and prostate cancers. We show that the identification of such cross-cancer risk loci has the potential to shed new light on the shared biology underlying these hormone-related cancers. Cancer Discov; 6(9); 1052-67. ©2016 AACR.This article is highlighted in the In This Issue feature, p. 932.
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Fehringer G, Kraft P, Pharoah PD, Eeles RA, Chatterjee N, Schumacher FR, Schildkraut JM, Lindström S, Brennan P, Bickeböller H, Houlston RS, Landi MT, Caporaso N, Risch A, Amin Al Olama A, Berndt SI, Giovannucci EL, Grönberg H, Kote-Jarai Z, Ma J, Muir K, Stampfer MJ, Stevens VL, Wiklund F, Willett WC, Goode EL, Permuth JB, Risch HA, Reid BM, Bezieau S, Brenner H, Chan AT, Chang-Claude J, Hudson TJ, Kocarnik JK, Newcomb PA, Schoen RE, Slattery ML, White E, Adank MA, Ahsan H, Aittomäki K, Baglietto L, Blomquist C, Canzian F, Czene K, Dos-Santos-Silva I, Eliassen AH, Figueroa JD, Flesch-Janys D, Fletcher O, Garcia-Closas M, Gaudet MM, Johnson N, Hall P, Hazra A, Hein R, Hofman A, Hopper JL, Irwanto A, Johansson M, Kaaks R, Kibriya MG, Lichtner P, Liu J, Lund E, Makalic E, Meindl A, Müller-Myhsok B, Muranen TA, Nevanlinna H, Peeters PH, Peto J, Prentice RL, Rahman N, Sanchez MJ, Schmidt DF, Schmutzler RK, Southey MC, Tamimi R, Travis RC, Turnbull C, Uitterlinden AG, Wang Z, Whittemore AS, Yang XR, Zheng W, Buchanan DD, Casey G, Conti DV, Edlund CK, Gallinger S, Haile RW, Jenkins M, Le Marchand L, Li L, Lindor NM, Schmit SL, Thibodeau SN, Woods MO, Rafnar T, Gudmundsson J, Stacey SN, Stefansson K, Sulem P, Chen YA, Tyrer JP, Christiani DC, Wei Y, Shen H, Hu Z, Shu XO, Shiraishi K, Takahashi A, Bossé Y, Obeidat M, Nickle D, Timens W, Freedman ML, Li Q, Seminara D, Chanock SJ, Gong J, Peters U, Gruber SB, Amos CI, Sellers TA, Easton DF, Hunter DJ, Haiman CA, Henderson BE, Hung RJ. Cross-Cancer Genome-Wide Analysis of Lung, Ovary, Breast, Prostate, and Colorectal Cancer Reveals Novel Pleiotropic Associations. Cancer Res 2016; 76:5103-14. [PMID: 27197191 PMCID: PMC5010493 DOI: 10.1158/0008-5472.can-15-2980] [Citation(s) in RCA: 77] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2015] [Accepted: 04/05/2016] [Indexed: 01/26/2023]
Abstract
Identifying genetic variants with pleiotropic associations can uncover common pathways influencing multiple cancers. We took a two-stage approach to conduct genome-wide association studies for lung, ovary, breast, prostate, and colorectal cancer from the GAME-ON/GECCO Network (61,851 cases, 61,820 controls) to identify pleiotropic loci. Findings were replicated in independent association studies (55,789 cases, 330,490 controls). We identified a novel pleiotropic association at 1q22 involving breast and lung squamous cell carcinoma, with eQTL analysis showing an association with ADAM15/THBS3 gene expression in lung. We also identified a known breast cancer locus CASP8/ALS2CR12 associated with prostate cancer, a known cancer locus at CDKN2B-AS1 with different variants associated with lung adenocarcinoma and prostate cancer, and confirmed the associations of a breast BRCA2 locus with lung and serous ovarian cancer. This is the largest study to date examining pleiotropy across multiple cancer-associated loci, identifying common mechanisms of cancer development and progression. Cancer Res; 76(17); 5103-14. ©2016 AACR.
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Larson NB, McDonnell S, Albright LC, Teerlink C, Stanford J, Ostrander EA, Isaacs WB, Xu J, Cooney KA, Lange E, Schleutker J, Carpten JD, Powell I, Bailey-Wilson J, Cussenot O, Cancel-Tassin G, Giles G, MacInnis R, Maier C, Whittemore AS, Hsieh CL, Wiklund F, Catolona WJ, Foulkes W, Mandal D, Eeles R, Kote-Jarai Z, Ackerman MJ, Olson TM, Klein CJ, Thibodeau SN, Schaid DJ. Post hoc Analysis for Detecting Individual Rare Variant Risk Associations Using Probit Regression Bayesian Variable Selection Methods in Case-Control Sequencing Studies. Genet Epidemiol 2016; 40:461-9. [PMID: 27312771 PMCID: PMC5063501 DOI: 10.1002/gepi.21983] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2015] [Revised: 04/22/2016] [Accepted: 04/27/2016] [Indexed: 12/27/2022]
Abstract
Rare variants (RVs) have been shown to be significant contributors to complex disease risk. By definition, these variants have very low minor allele frequencies and traditional single-marker methods for statistical analysis are underpowered for typical sequencing study sample sizes. Multimarker burden-type approaches attempt to identify aggregation of RVs across case-control status by analyzing relatively small partitions of the genome, such as genes. However, it is generally the case that the aggregative measure would be a mixture of causal and neutral variants, and these omnibus tests do not directly provide any indication of which RVs may be driving a given association. Recently, Bayesian variable selection approaches have been proposed to identify RV associations from a large set of RVs under consideration. Although these approaches have been shown to be powerful at detecting associations at the RV level, there are often computational limitations on the total quantity of RVs under consideration and compromises are necessary for large-scale application. Here, we propose a computationally efficient alternative formulation of this method using a probit regression approach specifically capable of simultaneously analyzing hundreds to thousands of RVs. We evaluate our approach to detect causal variation on simulated data and examine sensitivity and specificity in instances of high RV dimensionality as well as apply it to pathway-level RV analysis results from a prostate cancer (PC) risk case-control sequencing study. Finally, we discuss potential extensions and future directions of this work.
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Schmidt MK, Hogervorst F, van Hien R, Cornelissen S, Broeks A, Adank MA, Meijers H, Waisfisz Q, Hollestelle A, Schutte M, van den Ouweland A, Hooning M, Andrulis IL, Anton-Culver H, Antonenkova NN, Antoniou AC, Arndt V, Bermisheva M, Bogdanova NV, Bolla MK, Brauch H, Brenner H, Brüning T, Burwinkel B, Chang-Claude J, Chenevix-Trench G, Couch FJ, Cox A, Cross SS, Czene K, Dunning AM, Fasching PA, Figueroa J, Fletcher O, Flyger H, Galle E, García-Closas M, Giles GG, Haeberle L, Hall P, Hillemanns P, Hopper JL, Jakubowska A, John EM, Jones M, Khusnutdinova E, Knight JA, Kosma VM, Kristensen V, Lee A, Lindblom A, Lubinski J, Mannermaa A, Margolin S, Meindl A, Milne RL, Muranen TA, Newcomb PA, Offit K, Park-Simon TW, Peto J, Pharoah PD, Robson M, Rudolph A, Sawyer EJ, Schmutzler RK, Seynaeve C, Soens J, Southey MC, Spurdle AB, Surowy H, Swerdlow A, Tollenaar RA, Tomlinson I, Trentham-Dietz A, Vachon C, Wang Q, Whittemore AS, Ziogas A, van der Kolk L, Nevanlinna H, Dörk T, Bojesen S, Easton DF. Age- and Tumor Subtype-Specific Breast Cancer Risk Estimates for CHEK2*1100delC Carriers. J Clin Oncol 2016; 34:2750-60. [PMID: 27269948 PMCID: PMC5019754 DOI: 10.1200/jco.2016.66.5844] [Citation(s) in RCA: 118] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
PURPOSE CHEK2*1100delC is a well-established breast cancer risk variant that is most prevalent in European populations; however, there are limited data on risk of breast cancer by age and tumor subtype, which limits its usefulness in breast cancer risk prediction. We aimed to generate tumor subtype- and age-specific risk estimates by using data from the Breast Cancer Association Consortium, including 44,777 patients with breast cancer and 42,997 controls from 33 studies genotyped for CHEK2*1100delC. PATIENTS AND METHODS CHEK2*1100delC genotyping was mostly done by a custom Taqman assay. Breast cancer odds ratios (ORs) for CHEK2*1100delC carriers versus noncarriers were estimated by using logistic regression and adjusted for study (categorical) and age. Main analyses included patients with invasive breast cancer from population- and hospital-based studies. RESULTS Proportions of heterozygous CHEK2*1100delC carriers in controls, in patients with breast cancer from population- and hospital-based studies, and in patients with breast cancer from familial- and clinical genetics center-based studies were 0.5%, 1.3%, and 3.0%, respectively. The estimated OR for invasive breast cancer was 2.26 (95%CI, 1.90 to 2.69; P = 2.3 × 10(-20)). The OR was higher for estrogen receptor (ER)-positive disease (2.55 [95%CI, 2.10 to 3.10; P = 4.9 × 10(-21)]) than it was for ER-negative disease (1.32 [95%CI, 0.93 to 1.88; P = .12]; P interaction = 9.9 × 10(-4)). The OR significantly declined with attained age for breast cancer overall (P = .001) and for ER-positive tumors (P = .001). Estimated cumulative risks for development of ER-positive and ER-negative tumors by age 80 in CHEK2*1100delC carriers were 20% and 3%, respectively, compared with 9% and 2%, respectively, in the general population of the United Kingdom. CONCLUSION These CHEK2*1100delC breast cancer risk estimates provide a basis for incorporating CHEK2*1100delC into breast cancer risk prediction models and into guidelines for intensified screening and follow-up.
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Guo Y, Warren Andersen S, Shu XO, Michailidou K, Bolla MK, Wang Q, Garcia-Closas M, Milne RL, Schmidt MK, Chang-Claude J, Dunning A, Bojesen SE, Ahsan H, Aittomäki K, Andrulis IL, Anton-Culver H, Arndt V, Beckmann MW, Beeghly-Fadiel A, Benitez J, Bogdanova NV, Bonanni B, Børresen-Dale AL, Brand J, Brauch H, Brenner H, Brüning T, Burwinkel B, Casey G, Chenevix-Trench G, Couch FJ, Cox A, Cross SS, Czene K, Devilee P, Dörk T, Dumont M, Fasching PA, Figueroa J, Flesch-Janys D, Fletcher O, Flyger H, Fostira F, Gammon M, Giles GG, Guénel P, Haiman CA, Hamann U, Hooning MJ, Hopper JL, Jakubowska A, Jasmine F, Jenkins M, John EM, Johnson N, Jones ME, Kabisch M, Kibriya M, Knight JA, Koppert LB, Kosma VM, Kristensen V, Le Marchand L, Lee E, Li J, Lindblom A, Luben R, Lubinski J, Malone KE, Mannermaa A, Margolin S, Marme F, McLean C, Meijers-Heijboer H, Meindl A, Neuhausen SL, Nevanlinna H, Neven P, Olson JE, Perez JIA, Perkins B, Peterlongo P, Phillips KA, Pylkäs K, Rudolph A, Santella R, Sawyer EJ, Schmutzler RK, Seynaeve C, Shah M, Shrubsole MJ, Southey MC, Swerdlow AJ, Toland AE, Tomlinson I, Torres D, Truong T, Ursin G, Van Der Luijt RB, Verhoef S, Whittemore AS, Winqvist R, Zhao H, Zhao S, Hall P, Simard J, Kraft P, Pharoah P, Hunter D, Easton DF, Zheng W. Genetically Predicted Body Mass Index and Breast Cancer Risk: Mendelian Randomization Analyses of Data from 145,000 Women of European Descent. PLoS Med 2016; 13:e1002105. [PMID: 27551723 PMCID: PMC4995025 DOI: 10.1371/journal.pmed.1002105] [Citation(s) in RCA: 93] [Impact Index Per Article: 11.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/03/2015] [Accepted: 06/24/2016] [Indexed: 02/02/2023] Open
Abstract
BACKGROUND Observational epidemiological studies have shown that high body mass index (BMI) is associated with a reduced risk of breast cancer in premenopausal women but an increased risk in postmenopausal women. It is unclear whether this association is mediated through shared genetic or environmental factors. METHODS We applied Mendelian randomization to evaluate the association between BMI and risk of breast cancer occurrence using data from two large breast cancer consortia. We created a weighted BMI genetic score comprising 84 BMI-associated genetic variants to predicted BMI. We evaluated genetically predicted BMI in association with breast cancer risk using individual-level data from the Breast Cancer Association Consortium (BCAC) (cases = 46,325, controls = 42,482). We further evaluated the association between genetically predicted BMI and breast cancer risk using summary statistics from 16,003 cases and 41,335 controls from the Discovery, Biology, and Risk of Inherited Variants in Breast Cancer (DRIVE) Project. Because most studies measured BMI after cancer diagnosis, we could not conduct a parallel analysis to adequately evaluate the association of measured BMI with breast cancer risk prospectively. RESULTS In the BCAC data, genetically predicted BMI was found to be inversely associated with breast cancer risk (odds ratio [OR] = 0.65 per 5 kg/m2 increase, 95% confidence interval [CI]: 0.56-0.75, p = 3.32 × 10-10). The associations were similar for both premenopausal (OR = 0.44, 95% CI:0.31-0.62, p = 9.91 × 10-8) and postmenopausal breast cancer (OR = 0.57, 95% CI: 0.46-0.71, p = 1.88 × 10-8). This association was replicated in the data from the DRIVE consortium (OR = 0.72, 95% CI: 0.60-0.84, p = 1.64 × 10-7). Single marker analyses identified 17 of the 84 BMI-associated single nucleotide polymorphisms (SNPs) in association with breast cancer risk at p < 0.05; for 16 of them, the allele associated with elevated BMI was associated with reduced breast cancer risk. CONCLUSIONS BMI predicted by genome-wide association studies (GWAS)-identified variants is inversely associated with the risk of both pre- and postmenopausal breast cancer. The reduced risk of postmenopausal breast cancer associated with genetically predicted BMI observed in this study differs from the positive association reported from studies using measured adult BMI. Understanding the reasons for this discrepancy may reveal insights into the complex relationship of genetic determinants of body weight in the etiology of breast cancer.
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86
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Cuellar-Partida G, Lu Y, Dixon SC, Fasching PA, Hein A, Burghaus S, Beckmann MW, Lambrechts D, Van Nieuwenhuysen E, Vergote I, Vanderstichele A, Doherty JA, Rossing MA, Chang-Claude J, Rudolph A, Wang-Gohrke S, Goodman MT, Bogdanova N, Dörk T, Dürst M, Hillemanns P, Runnebaum IB, Antonenkova N, Butzow R, Leminen A, Nevanlinna H, Pelttari LM, Edwards RP, Kelley JL, Modugno F, Moysich KB, Ness RB, Cannioto R, Høgdall E, Høgdall C, Jensen A, Giles GG, Bruinsma F, Kjaer SK, Hildebrandt MAT, Liang D, Lu KH, Wu X, Bisogna M, Dao F, Levine DA, Cramer DW, Terry KL, Tworoger SS, Stampfer M, Missmer S, Bjorge L, Salvesen HB, Kopperud RK, Bischof K, Aben KKH, Kiemeney LA, Massuger LFAG, Brooks-Wilson A, Olson SH, McGuire V, Rothstein JH, Sieh W, Whittemore AS, Cook LS, Le ND, Blake Gilks C, Gronwald J, Jakubowska A, Lubiński J, Kluz T, Song H, Tyrer JP, Wentzensen N, Brinton L, Trabert B, Lissowska J, McLaughlin JR, Narod SA, Phelan C, Anton-Culver H, Ziogas A, Eccles D, Campbell I, Gayther SA, Gentry-Maharaj A, Menon U, Ramus SJ, Wu AH, Dansonka-Mieszkowska A, Kupryjanczyk J, Timorek A, Szafron L, Cunningham JM, Fridley BL, Winham SJ, Bandera EV, Poole EM, Morgan TK, Goode EL, Schildkraut JM, Pearce CL, Berchuck A, Pharoah PDP, Webb PM, Chenevix-Trench G, Risch HA, MacGregor S. Assessing the genetic architecture of epithelial ovarian cancer histological subtypes. Hum Genet 2016; 135:741-56. [PMID: 27075448 PMCID: PMC4976079 DOI: 10.1007/s00439-016-1663-9] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2016] [Accepted: 03/24/2016] [Indexed: 10/22/2022]
Abstract
Epithelial ovarian cancer (EOC) is one of the deadliest common cancers. The five most common types of disease are high-grade and low-grade serous, endometrioid, mucinous and clear cell carcinoma. Each of these subtypes present distinct molecular pathogeneses and sensitivities to treatments. Recent studies show that certain genetic variants confer susceptibility to all subtypes while other variants are subtype-specific. Here, we perform an extensive analysis of the genetic architecture of EOC subtypes. To this end, we used data of 10,014 invasive EOC patients and 21,233 controls from the Ovarian Cancer Association Consortium genotyped in the iCOGS array (211,155 SNPs). We estimate the array heritability (attributable to variants tagged on arrays) of each subtype and their genetic correlations. We also look for genetic overlaps with factors such as obesity, smoking behaviors, diabetes, age at menarche and height. We estimated the array heritabilities of high-grade serous disease ([Formula: see text] = 8.8 ± 1.1 %), endometrioid ([Formula: see text] = 3.2 ± 1.6 %), clear cell ([Formula: see text] = 6.7 ± 3.3 %) and all EOC ([Formula: see text] = 5.6 ± 0.6 %). Known associated loci contributed approximately 40 % of the total array heritability for each subtype. The contribution of each chromosome to the total heritability was not proportional to chromosome size. Through bivariate and cross-trait LD score regression, we found evidence of shared genetic backgrounds between the three high-grade subtypes: serous, endometrioid and undifferentiated. Finally, we found significant genetic correlations of all EOC with diabetes and obesity using a polygenic prediction approach.
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87
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McGuire V, Hartge P, Liao LM, Sinha R, Bernstein L, Canchola AJ, Anderson GL, Stefanick ML, Whittemore AS. Parity and Oral Contraceptive Use in Relation to Ovarian Cancer Risk in Older Women. Cancer Epidemiol Biomarkers Prev 2016; 25:1059-63. [PMID: 27197274 PMCID: PMC4930714 DOI: 10.1158/1055-9965.epi-16-0011] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2016] [Accepted: 03/23/2016] [Indexed: 01/26/2023] Open
Abstract
BACKGROUND Several studies have suggested that the ovarian cancer risk reductions associated with parity and oral contraceptive use are weaker in postmenopausal than premenopausal women, yet little is known about the persistence of these reductions as women age. This question gains importance with the increasing numbers of older women in the population. METHODS We addressed the question using data from three large U.S. cohort studies involving 310,290 white women aged 50+ years at recruitment, of whom 1,815 developed subsequent incident invasive epithelial ovarian cancer. We used Cox regression, stratified by cohort, to examine age-related trends in the HRs per full-term pregnancy and per year of oral contraceptive use. RESULTS The parity-associated risk reductions waned with age (Ptrend < 0.001 in HR with increasing age), particularly among women aged 75 years or more, for whom we observed no association with parity. However, we observed no such attenuation in the oral contraceptive-associated risk reductions (P = 0.79 for trend in HR with increasing age). CONCLUSION These findings suggest that prior oral contraceptive use is important for ovarian cancer risk assessment among women of all ages, while the benefits of parity wane as women age. IMPACT This information, if duplicated in other studies, will be useful to preventive counseling and risk prediction, particularly for women at increased ovarian cancer risk due to a personal history of breast cancer or a family history of ovarian cancer. Cancer Epidemiol Biomarkers Prev; 25(7); 1059-63. ©2016 AACR.
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88
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Zeng C, Guo X, Long J, Kuchenbaecker KB, Droit A, Michailidou K, Ghoussaini M, Kar S, Freeman A, Hopper JL, Milne RL, Bolla MK, Wang Q, Dennis J, Agata S, Ahmed S, Aittomäki K, Andrulis IL, Anton-Culver H, Antonenkova NN, Arason A, Arndt V, Arun BK, Arver B, Bacot F, Barrowdale D, Baynes C, Beeghly-Fadiel A, Benitez J, Bermisheva M, Blomqvist C, Blot WJ, Bogdanova NV, Bojesen SE, Bonanni B, Borresen-Dale AL, Brand JS, Brauch H, Brennan P, Brenner H, Broeks A, Brüning T, Burwinkel B, Buys SS, Cai Q, Caldes T, Campbell I, Carpenter J, Chang-Claude J, Choi JY, Claes KBM, Clarke C, Cox A, Cross SS, Czene K, Daly MB, de la Hoya M, De Leeneer K, Devilee P, Diez O, Domchek SM, Doody M, Dorfling CM, Dörk T, Dos-Santos-Silva I, Dumont M, Dwek M, Dworniczak B, Egan K, Eilber U, Einbeigi Z, Ejlertsen B, Ellis S, Frost D, Lalloo F, Fasching PA, Figueroa J, Flyger H, Friedlander M, Friedman E, Gambino G, Gao YT, Garber J, García-Closas M, Gehrig A, Damiola F, Lesueur F, Mazoyer S, Stoppa-Lyonnet D, Giles GG, Godwin AK, Goldgar DE, González-Neira A, Greene MH, Guénel P, Haeberle L, Haiman CA, Hallberg E, Hamann U, Hansen TVO, Hart S, Hartikainen JM, Hartman M, Hassan N, Healey S, Hogervorst FBL, Verhoef S, Hendricks CB, Hillemanns P, Hollestelle A, Hulick PJ, Hunter DJ, Imyanitov EN, Isaacs C, Ito H, Jakubowska A, Janavicius R, Jaworska-Bieniek K, Jensen UB, John EM, Joly Beauparlant C, Jones M, Kabisch M, Kang D, Karlan BY, Kauppila S, Kerin MJ, Khan S, Khusnutdinova E, Knight JA, Konstantopoulou I, Kraft P, Kwong A, Laitman Y, Lambrechts D, Lazaro C, Le Marchand L, Lee CN, Lee MH, Lester J, Li J, Liljegren A, Lindblom A, Lophatananon A, Lubinski J, Mai PL, Mannermaa A, Manoukian S, Margolin S, Marme F, Matsuo K, McGuffog L, Meindl A, Menegaux F, Montagna M, Muir K, Mulligan AM, Nathanson KL, Neuhausen SL, Nevanlinna H, Newcomb PA, Nord S, Nussbaum RL, Offit K, Olah E, Olopade OI, Olswold C, Osorio A, Papi L, Park-Simon TW, Paulsson-Karlsson Y, Peeters S, Peissel B, Peterlongo P, Peto J, Pfeiler G, Phelan CM, Presneau N, Radice P, Rahman N, Ramus SJ, Rashid MU, Rennert G, Rhiem K, Rudolph A, Salani R, Sangrajrang S, Sawyer EJ, Schmidt MK, Schmutzler RK, Schoemaker MJ, Schürmann P, Seynaeve C, Shen CY, Shrubsole MJ, Shu XO, Sigurdson A, Singer CF, Slager S, Soucy P, Southey M, Steinemann D, Swerdlow A, Szabo CI, Tchatchou S, Teixeira MR, Teo SH, Terry MB, Tessier DC, Teulé A, Thomassen M, Tihomirova L, Tischkowitz M, Toland AE, Tung N, Turnbull C, van den Ouweland AMW, van Rensburg EJ, Ven den Berg D, Vijai J, Wang-Gohrke S, Weitzel JN, Whittemore AS, Winqvist R, Wong TY, Wu AH, Yannoukakos D, Yu JC, Pharoah PDP, Hall P, Chenevix-Trench G, Dunning AM, Simard J, Couch FJ, Antoniou AC, Easton DF, Zheng W. Identification of independent association signals and putative functional variants for breast cancer risk through fine-scale mapping of the 12p11 locus. Breast Cancer Res 2016; 18:64. [PMID: 27459855 PMCID: PMC4962376 DOI: 10.1186/s13058-016-0718-0] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2015] [Accepted: 05/18/2016] [Indexed: 02/08/2023] Open
Abstract
BACKGROUND Multiple recent genome-wide association studies (GWAS) have identified a single nucleotide polymorphism (SNP), rs10771399, at 12p11 that is associated with breast cancer risk. METHOD We performed a fine-scale mapping study of a 700 kb region including 441 genotyped and more than 1300 imputed genetic variants in 48,155 cases and 43,612 controls of European descent, 6269 cases and 6624 controls of East Asian descent and 1116 cases and 932 controls of African descent in the Breast Cancer Association Consortium (BCAC; http://bcac.ccge.medschl.cam.ac.uk/ ), and in 15,252 BRCA1 mutation carriers in the Consortium of Investigators of Modifiers of BRCA1/2 (CIMBA). Stepwise regression analyses were performed to identify independent association signals. Data from the Encyclopedia of DNA Elements project (ENCODE) and the Cancer Genome Atlas (TCGA) were used for functional annotation. RESULTS Analysis of data from European descendants found evidence for four independent association signals at 12p11, represented by rs7297051 (odds ratio (OR) = 1.09, 95 % confidence interval (CI) = 1.06-1.12; P = 3 × 10(-9)), rs805510 (OR = 1.08, 95 % CI = 1.04-1.12, P = 2 × 10(-5)), and rs1871152 (OR = 1.04, 95 % CI = 1.02-1.06; P = 2 × 10(-4)) identified in the general populations, and rs113824616 (P = 7 × 10(-5)) identified in the meta-analysis of BCAC ER-negative cases and BRCA1 mutation carriers. SNPs rs7297051, rs805510 and rs113824616 were also associated with breast cancer risk at P < 0.05 in East Asians, but none of the associations were statistically significant in African descendants. Multiple candidate functional variants are located in putative enhancer sequences. Chromatin interaction data suggested that PTHLH was the likely target gene of these enhancers. Of the six variants with the strongest evidence of potential functionality, rs11049453 was statistically significantly associated with the expression of PTHLH and its nearby gene CCDC91 at P < 0.05. CONCLUSION This study identified four independent association signals at 12p11 and revealed potentially functional variants, providing additional insights into the underlying biological mechanism(s) for the association observed between variants at 12p11 and breast cancer risk.
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Grants
- U10 CA180868 NCI NIH HHS
- R01 CA140323 NCI NIH HHS
- R37 CA070867 NCI NIH HHS
- U10 CA027469 NCI NIH HHS
- U01 CA116167 NCI NIH HHS
- 16561 Cancer Research UK
- R03 CA173531 NCI NIH HHS
- G0700491 Medical Research Council
- N02CP11019 NCI NIH HHS
- 10124 Cancer Research UK
- UG1 CA189867 NCI NIH HHS
- N01 CN025403 NCI NIH HHS
- R01 CA176785 NCI NIH HHS
- RC4 CA153828 NCI NIH HHS
- U10 CA101165 NCI NIH HHS
- R01 CA142996 NCI NIH HHS
- P50 CA125183 NCI NIH HHS
- P01 CA087969 NCI NIH HHS
- UM1 CA164920 NCI NIH HHS
- P30 CA168524 NCI NIH HHS
- U01 CA161032 NCI NIH HHS
- R01 CA092447 NCI NIH HHS
- R01 CA058860 NCI NIH HHS
- 20861 Cancer Research UK
- K07 CA092044 NCI NIH HHS
- UL1 TR000124 NCATS NIH HHS
- 11174 Cancer Research UK
- R01 CA100374 NCI NIH HHS
- P30 CA008748 NCI NIH HHS
- R01 CA128978 NCI NIH HHS
- R01 CA064277 NCI NIH HHS
- R01 CA083855 NCI NIH HHS
- R01 CA047147 NCI NIH HHS
- P30 CA014089 NCI NIH HHS
- U19 CA148537 NCI NIH HHS
- P30 CA051008 NCI NIH HHS
- R01 CA116167 NCI NIH HHS
- R01 CA148667 NCI NIH HHS
- P50 CA116201 NCI NIH HHS
- 16565 Cancer Research UK
- 15106 Cancer Research UK
- U01 CA113916 NCI NIH HHS
- R01 CA063464 NCI NIH HHS
- U10 CA037517 NCI NIH HHS
- N02CP65504 NCI NIH HHS
- U01 CA063464 NCI NIH HHS
- R01 CA077398 NCI NIH HHS
- R01 CA054281 NCI NIH HHS
- R01 CA132839 NCI NIH HHS
- P30 CA068485 NCI NIH HHS
- R01 CA102776 NCI NIH HHS
- U01 CA058860 NCI NIH HHS
- 10118 Cancer Research UK
- U19 CA148112 NCI NIH HHS
- R01 CA149429 NCI NIH HHS
- U01 CA098758 NCI NIH HHS
- U19 CA148065 NCI NIH HHS
- R01 CA069664 NCI NIH HHS
- 001 World Health Organization
- UM1 CA182910 NCI NIH HHS
- U10 CA180822 NCI NIH HHS
- P30 CA006927 NCI NIH HHS
- R37 CA054281 NCI NIH HHS
- R01 CA047305 NCI NIH HHS
- 10119 Cancer Research UK
- National Institutes of Health
- Seventh Framework Programme
- National Cancer Institute
- U.S. Department of Defense
- Canadian Institutes of Health Research
- Susan G. Komen for the Cure
- Breast Cancer Research Foundation
- Ovarian Cancer Research Fund
- National Health and Medical Research Council
- New South Wales Cancer Council
- Victorian Health Promotion Foundation
- Victorian Breast Cancer Research Consortium
- Dutch Cancer Society
- Cancer Institute NSW
- National Breast Cancer Foundation
- Breast Cancer Research Trust
- Breakthrough Breast Cancer
- NIHR Comprehensive Biomedical Research Centre
- Guy's and St Thomas' NHS Foundation Trust
- Oxford Biomedical Research Centre
- Dietmar-Hopp Foundation
- Helmholtz Society
- Fondation de France
- Institut National Du Cancer
- Ligue Contre le Cancer
- Agence Nationale de la Recherche
- Danish Medical Research Council
- Instituto de Salud Carlos III
- Red Temática de Investigacióm Cooperativa en Cáncer
- Asociación Española Contra el Cáncer
- Fondo de Investigación Sanitario
- California Breast Cancer Research Fund
- Lon V Smith Foundation
- Baden-Württemberg Ministry of Science, Research and Arts
- Deutsche Krebshilfe
- Federal Ministry of Education and Research
- Deutsches Krebsforschungszentrum
- Institute for Prevention and Occupational Medicine of the German Social Accident Insurance
- Academy of Finland
- Finnish Cancer Society
- Ministry of Education, Culture, Sports, Science, and Technology
- Ministry of Health, Labour and Welfare
- Takeda Health Foundation
- German Federal Ministry of Research and Education
- Swedish Cancer Society
- Gustav V Jubilee Foundation
- Berth von Kantzows Stiftelse
- Cancer Fund of North Savo
- Finnish Cancer Organizations
- Queensland Cancer Fund
- Prostate Cancer Foundation of Australia (AU)
- Cancer Council of New South Wales
- Cancer Council of Victoria
- Cancer Council of Tasmania
- Cancer Council of South Australia
- U.S. Army Medical Research and Materiel Command
- National Health and Medical Research Council (AU)
- California Breast Cancer Research Program
- Stichting Tegen Kanker
- Hamburg Cancer Society
- Italian Associatin for Cancer Research
- David F and Margaret T Grohne Family Foundation
- Ting Tsung and Wei Fong Chao Foundation
- Robert and Kate Niehaus Clinical Cancer Genetics Initiative
- Quebec Breast Cancer Foundation
- Ministry of Economic Development, Innovation and Export Trade
- Malaysian Ministry of Science, Technology and Innovation
- Malaysian Ministry of Higher Education
- Cancer Resarch Initiatives Foundation
- Biomedical Research Council
- National Medical Research Council
- K G Jebsen Centre for Breast Cancer Research
- Research Council of Norway
- Researhc Council of Norway
- South Eastern Norway Health Authority
- Norwegian Cancer Socieety
- Finnish Cancer Foundation
- Sigrid Juselius Foundation
- Biobanking and Biomolecular Resources Research Infrastructure
- Marit and Hans Rausings Initiative Against Breast Cancer
- Yorkshire Cancer Research
- Sheffield Experimental Cancer Medicine Centre
- Ministry of Education, Science and Technology
- National Cancer Institute Thailand
- Stefanie Spielman Breast Cancer Fund
- Hellenic Cooperative Oncology Group
- Research Council of Lithuania
- Cancer Association of South Africa
- NEYE Foundation
- Spanish Association Against Cancer
- German Cancer Aid
- Ligue Nationale Contre le Cancer
- Jess and Mildred Fisher Center for Familial Cancer Research
- Swing Fore the Cure
- Netherlands Organization of Scientific Research
- Pink Ribbons Project
- Hungarian Research Grants
- Norwegian EEA Financial Mechanism
- Instituto de Salud Carlos III (ES)
- Canadian Breast Cancer Research Alliance
- Ministry for Health, Welfare and Family Affairs
- Andrew Sabin Research Fund
- Russian Federation for Basic Research
- Istituto Toscano Tumori
- Ministry of Higher Education
- Dr. Ralph and Marian Falk Medical Research Trust
- Entertainment Industry Fund National Women's Cancer Research Alliance
- Frieda G and Saul F Shapira BRCA-Associated Cancer Research Program
- American Cancer Society
- National Center for Advancing Translational Sciences
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Dixon SC, Nagle CM, Thrift AP, Pharoah PD, Pearce CL, Zheng W, Painter JN, Chenevix-Trench G, Fasching PA, Beckmann MW, Lambrechts D, Vergote I, Lambrechts S, Van Nieuwenhuysen E, Rossing MA, Doherty JA, Wicklund KG, Chang-Claude J, Rudolph A, Moysich KB, Odunsi K, Goodman MT, Wilkens LR, Thompson PJ, Shvetsov YB, Dörk T, Park-Simon TW, Hillemanns P, Bogdanova N, Butzow R, Nevanlinna H, Pelttari LM, Leminen A, Modugno F, Ness RB, Edwards RP, Kelley JL, Heitz F, Karlan BY, Kjær SK, Høgdall E, Jensen A, Goode EL, Fridley BL, Cunningham JM, Winham SJ, Giles GG, Bruinsma F, Milne RL, Southey MC, Hildebrandt MAT, Wu X, Lu KH, Liang D, Levine DA, Bisogna M, Schildkraut JM, Berchuck A, Cramer DW, Terry KL, Bandera EV, Olson SH, Salvesen HB, Thomsen LC, Kopperud RK, Bjorge L, Kiemeney LA, Massuger LFAG, Pejovic T, Cook LS, Le ND, Swenerton KD, Brooks-Wilson A, Kelemen LE, Lubiński J, Huzarski T, Gronwald J, Menkiszak J, Wentzensen N, Brinton L, Yang H, Lissowska J, Høgdall CK, Lundvall L, Song H, Tyrer JP, Campbell I, Eccles D, Paul J, Glasspool R, Siddiqui N, Whittemore AS, Sieh W, McGuire V, Rothstein JH, Narod SA, Phelan C, Risch HA, McLaughlin JR, Anton-Culver H, Ziogas A, Menon U, Gayther SA, Ramus SJ, Gentry-Maharaj A, Wu AH, Pike MC, Tseng CC, Kupryjanczyk J, Dansonka-Mieszkowska A, Budzilowska A, Spiewankiewicz B, Webb PM. Adult body mass index and risk of ovarian cancer by subtype: a Mendelian randomization study. Int J Epidemiol 2016; 45:884-95. [PMID: 27401727 PMCID: PMC5644573 DOI: 10.1093/ije/dyw158] [Citation(s) in RCA: 54] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/07/2016] [Indexed: 02/02/2023] Open
Abstract
BACKGROUND Observational studies have reported a positive association between body mass index (BMI) and ovarian cancer risk. However, questions remain as to whether this represents a causal effect, or holds for all histological subtypes. The lack of association observed for serous cancers may, for instance, be due to disease-associated weight loss. Mendelian randomization (MR) uses genetic markers as proxies for risk factors to overcome limitations of observational studies. We used MR to elucidate the relationship between BMI and ovarian cancer, hypothesizing that genetically predicted BMI would be associated with increased risk of non-high grade serous ovarian cancers (non-HGSC) but not HGSC. METHODS We pooled data from 39 studies (14 047 cases, 23 003 controls) in the Ovarian Cancer Association Consortium. We constructed a weighted genetic risk score (GRS, partial F-statistic = 172), summing alleles at 87 single nucleotide polymorphisms previously associated with BMI, weighting by their published strength of association with BMI. Applying two-stage predictor-substitution MR, we used logistic regression to estimate study-specific odds ratios (OR) and 95% confidence intervals (CI) for the association between genetically predicted BMI and risk, and pooled these using random-effects meta-analysis. RESULTS Higher genetically predicted BMI was associated with increased risk of non-HGSC (pooled OR = 1.29, 95% CI 1.03-1.61 per 5 units BMI) but not HGSC (pooled OR = 1.06, 95% CI 0.88-1.27). Secondary analyses stratified by behaviour/subtype suggested that, consistent with observational data, the association was strongest for low-grade/borderline serous cancers (OR = 1.93, 95% CI 1.33-2.81). CONCLUSIONS Our data suggest that higher BMI increases risk of non-HGSC, but not the more common and aggressive HGSC subtype, confirming the observational evidence.
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Terry MB, Phillips KA, Daly MB, John EM, Andrulis IL, Buys SS, Goldgar DE, Knight JA, Whittemore AS, Chung WK, Apicella C, Hopper JL. Cohort Profile: The Breast Cancer Prospective Family Study Cohort (ProF-SC). Int J Epidemiol 2016; 45:683-92. [PMID: 26174520 PMCID: PMC5005937 DOI: 10.1093/ije/dyv118] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
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Habel LA, Lipson JA, Achacoso N, Rothstein JH, Yaffe MJ, Liang RY, Acton L, McGuire V, Whittemore AS, Rubin DL, Sieh W. Case-control study of mammographic density and breast cancer risk using processed digital mammograms. Breast Cancer Res 2016; 18:53. [PMID: 27209070 PMCID: PMC4875652 DOI: 10.1186/s13058-016-0715-3] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2015] [Accepted: 05/04/2016] [Indexed: 11/10/2022] Open
Abstract
Background Full-field digital mammography (FFDM) has largely replaced film-screen mammography in the US. Breast density assessed from film mammograms is strongly associated with breast cancer risk, but data are limited for processed FFDM images used for clinical care. Methods We conducted a case-control study nested among non-Hispanic white female participants of the Research Program in Genes, Environment and Health of Kaiser Permanente Northern California who were aged 40 to 74 years and had screening mammograms acquired on Hologic FFDM machines. Cases (n = 297) were women with a first invasive breast cancer diagnosed after a screening FFDM. For each case, up to five controls (n = 1149) were selected, matched on age and year of FFDM and image batch number, and who were still under follow-up and without a history of breast cancer at the age of diagnosis of the matched case. Percent density (PD) and dense area (DA) were assessed by a radiological technologist using Cumulus. Conditional logistic regression was used to estimate odds ratios (ORs) for breast cancer associated with PD and DA, modeled continuously in standard deviation (SD) increments and categorically in quintiles, after adjusting for body mass index, parity, first-degree family history of breast cancer, breast area, and menopausal hormone use. Results Median intra-reader reproducibility was high with a Pearson’s r of 0.956 (range 0.902 to 0.983) for replicate PD measurements across 23 image batches. The overall mean was 20.02 (SD, 14.61) for PD and 27.63 cm2 (18.22 cm2) for DA. The adjusted ORs for breast cancer associated with each SD increment were 1.70 (95 % confidence interval, 1.41–2.04) for PD, and 1.54 (1.34–1.77) for DA. The adjusted ORs for each quintile were: 1.00 (ref.), 1.49 (0.91–2.45), 2.57 (1.54–4.30), 3.22 (1.91–5.43), 4.88 (2.78–8.55) for PD, and 1.00 (ref.), 1.43 (0.85–2.40), 2.53 (1.53–4.19), 2.85 (1.73–4.69), 3.48 (2.14–5.65) for DA. Conclusions PD and DA measured using Cumulus on processed FFDM images are positively associated with breast cancer risk, with similar magnitudes of association as previously reported for film-screen mammograms. Processed digital mammograms acquired for routine clinical care in a general practice setting are suitable for breast density and cancer research.
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Zhao Z, Wen W, Michailidou K, Bolla MK, Wang Q, Zhang B, Long J, Shu XO, Schmidt MK, Milne RL, García-Closas M, Chang-Claude J, Lindstrom S, Bojesen SE, Ahsan H, Aittomäki K, Andrulis IL, Anton-Culver H, Arndt V, Beckmann MW, Beeghly-Fadiel A, Benitez J, Blomqvist C, Bogdanova NV, Børresen-Dale AL, Brand J, Brauch H, Brenner H, Burwinkel B, Cai Q, Casey G, Chenevix-Trench G, Couch FJ, Cox A, Cross SS, Czene K, Dörk T, Dumont M, Fasching PA, Figueroa J, Flesch-Janys D, Fletcher O, Flyger H, Fostira F, Gammon M, Giles GG, Guénel P, Haiman CA, Hamann U, Harrington P, Hartman M, Hooning MJ, Hopper JL, Jakubowska A, Jasmine F, John EM, Johnson N, Kabisch M, Khan S, Kibriya M, Knight JA, Kosma VM, Kriege M, Kristensen V, Le Marchand L, Lee E, Li J, Lindblom A, Lophatananon A, Luben R, Lubinski J, Malone KE, Mannermaa A, Manoukian S, Margolin S, Marme F, McLean C, Meijers-Heijboer H, Meindl A, Miao H, Muir K, Neuhausen SL, Nevanlinna H, Neven P, Olson JE, Perkins B, Peterlongo P, Phillips KA, Pylkäs K, Rudolph A, Santella R, Sawyer EJ, Schmutzler RK, Schoemaker M, Shah M, Shrubsole M, Southey MC, Swerdlow AJ, Toland AE, Tomlinson I, Torres D, Truong T, Ursin G, Van Der Luijt RB, Verhoef S, Wang-Gohrke S, Whittemore AS, Winqvist R, Pilar Zamora M, Zhao H, Dunning AM, Simard J, Hall P, Kraft P, Pharoah P, Hunter D, Easton DF, Zheng W. Association of genetic susceptibility variants for type 2 diabetes with breast cancer risk in women of European ancestry. Cancer Causes Control 2016; 27:679-93. [PMID: 27053251 PMCID: PMC5029371 DOI: 10.1007/s10552-016-0741-6] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2015] [Accepted: 03/25/2016] [Indexed: 12/29/2022]
Abstract
PURPOSE Type 2 diabetes (T2D) has been reported to be associated with an elevated risk of breast cancer. It is unclear, however, whether this association is due to shared genetic factors. METHODS We constructed a genetic risk score (GRS) using risk variants from 33 known independent T2D susceptibility loci and evaluated its relation to breast cancer risk using the data from two consortia, including 62,328 breast cancer patients and 83,817 controls of European ancestry. Unconditional logistic regression models were used to derive adjusted odds ratios (ORs) and 95 % confidence intervals (CIs) to measure the association of breast cancer risk with T2D GRS or T2D-associated genetic risk variants. Meta-analyses were conducted to obtain summary ORs across all studies. RESULTS The T2D GRS was not found to be associated with breast cancer risk, overall, by menopausal status, or for estrogen receptor positive or negative breast cancer. Three T2D associated risk variants were individually associated with breast cancer risk after adjustment for multiple comparisons using the Bonferroni method (at p < 0.001), rs9939609 (FTO) (OR 0.94, 95 % CI = 0.92-0.95, p = 4.13E-13), rs7903146 (TCF7L2) (OR 1.04, 95 % CI = 1.02-1.06, p = 1.26E-05), and rs8042680 (PRC1) (OR 0.97, 95 % CI = 0.95-0.99, p = 8.05E-04). CONCLUSIONS We have shown that several genetic risk variants were associated with the risk of both T2D and breast cancer. However, overall genetic susceptibility to T2D may not be related to breast cancer risk.
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Hollestelle A, van der Baan FH, Berchuck A, Johnatty SE, Aben KK, Agnarsson BA, Aittomäki K, Alducci E, Andrulis IL, Anton-Culver H, Antonenkova NN, Antoniou AC, Apicella C, Arndt V, Arnold N, Arun BK, Arver B, Ashworth A, Baglietto L, Balleine R, Bandera EV, Barrowdale D, Bean YT, Beckmann L, Beckmann MW, Benitez J, Berger A, Berger R, Beuselinck B, Bisogna M, Bjorge L, Blomqvist C, Bogdanova NV, Bojesen A, Bojesen SE, Bolla MK, Bonanni B, Brand JS, Brauch H, Brenner H, Brinton L, Brooks-Wilson A, Bruinsma F, Brunet J, Brüning T, Budzilowska A, Bunker CH, Burwinkel B, Butzow R, Buys SS, Caligo MA, Campbell I, Carter J, Chang-Claude J, Chanock SJ, Claes KBM, Collée JM, Cook LS, Couch FJ, Cox A, Cramer D, Cross SS, Cunningham JM, Cybulski C, Czene K, Damiola F, Dansonka-Mieszkowska A, Darabi H, de la Hoya M, deFazio A, Dennis J, Devilee P, Dicks EM, Diez O, Doherty JA, Domchek SM, Dorfling CM, Dörk T, Silva IDS, du Bois A, Dumont M, Dunning AM, Duran M, Easton DF, Eccles D, Edwards RP, Ehrencrona H, Ejlertsen B, Ekici AB, Ellis SD, Engel C, Eriksson M, Fasching PA, Feliubadalo L, Figueroa J, Flesch-Janys D, Fletcher O, Fontaine A, Fortuzzi S, Fostira F, Fridley BL, Friebel T, Friedman E, Friel G, Frost D, Garber J, García-Closas M, Gayther SA, Gentry-Maharaj A, Gerdes AM, Giles GG, Glasspool R, Glendon G, Godwin AK, Goodman MT, Gore M, Greene MH, Grip M, Gronwald J, Gschwantler Kaulich D, Guénel P, Guzman SR, Haeberle L, Haiman CA, Hall P, Halverson SL, Hamann U, Hansen TVO, Harter P, Hartikainen JM, Healey S, Hein A, Heitz F, Henderson BE, Herzog J, T Hildebrandt MA, Høgdall CK, Høgdall E, Hogervorst FBL, Hopper JL, Humphreys K, Huzarski T, Imyanitov EN, Isaacs C, Jakubowska A, Janavicius R, Jaworska K, Jensen A, Jensen UB, Johnson N, Jukkola-Vuorinen A, Kabisch M, Karlan BY, Kataja V, Kauff N, Kelemen LE, Kerin MJ, Kiemeney LA, Kjaer SK, Knight JA, Knol-Bout JP, Konstantopoulou I, Kosma VM, Krakstad C, Kristensen V, Kuchenbaecker KB, Kupryjanczyk J, Laitman Y, Lambrechts D, Lambrechts S, Larson MC, Lasa A, Laurent-Puig P, Lazaro C, Le ND, Le Marchand L, Leminen A, Lester J, Levine DA, Li J, Liang D, Lindblom A, Lindor N, Lissowska J, Long J, Lu KH, Lubinski J, Lundvall L, Lurie G, Mai PL, Mannermaa A, Margolin S, Mariette F, Marme F, Martens JWM, Massuger LFAG, Maugard C, Mazoyer S, McGuffog L, McGuire V, McLean C, McNeish I, Meindl A, Menegaux F, Menéndez P, Menkiszak J, Menon U, Mensenkamp AR, Miller N, Milne RL, Modugno F, Montagna M, Moysich KB, Müller H, Mulligan AM, Muranen TA, Narod SA, Nathanson KL, Ness RB, Neuhausen SL, Nevanlinna H, Neven P, Nielsen FC, Nielsen SF, Nordestgaard BG, Nussbaum RL, Odunsi K, Offit K, Olah E, Olopade OI, Olson JE, Olson SH, Oosterwijk JC, Orlow I, Orr N, Orsulic S, Osorio A, Ottini L, Paul J, Pearce CL, Pedersen IS, Peissel B, Pejovic T, Pelttari LM, Perkins J, Permuth-Wey J, Peterlongo P, Peto J, Phelan CM, Phillips KA, Piedmonte M, Pike MC, Platte R, Plisiecka-Halasa J, Poole EM, Poppe B, Pylkäs K, Radice P, Ramus SJ, Rebbeck TR, Reed MWR, Rennert G, Risch HA, Robson M, Rodriguez GC, Romero A, Rossing MA, Rothstein JH, Rudolph A, Runnebaum I, Salani R, Salvesen HB, Sawyer EJ, Schildkraut JM, Schmidt MK, Schmutzler RK, Schneeweiss A, Schoemaker MJ, Schrauder MG, Schumacher F, Schwaab I, Scuvera G, Sellers TA, Severi G, Seynaeve CM, Shah M, Shrubsole M, Siddiqui N, Sieh W, Simard J, Singer CF, Sinilnikova OM, Smeets D, Sohn C, Soller M, Song H, Soucy P, Southey MC, Stegmaier C, Stoppa-Lyonnet D, Sucheston L, Swerdlow A, Tangen IL, Tea MK, Teixeira MR, Terry KL, Terry MB, Thomassen M, Thompson PJ, Tihomirova L, Tischkowitz M, Toland AE, Tollenaar RAEM, Tomlinson I, Torres D, Truong T, Tsimiklis H, Tung N, Tworoger SS, Tyrer JP, Vachon CM, Van 't Veer LJ, van Altena AM, Van Asperen CJ, van den Berg D, van den Ouweland AMW, van Doorn HC, Van Nieuwenhuysen E, van Rensburg EJ, Vergote I, Verhoef S, Vierkant RA, Vijai J, Vitonis AF, von Wachenfeldt A, Walsh C, Wang Q, Wang-Gohrke S, Wappenschmidt B, Weischer M, Weitzel JN, Weltens C, Wentzensen N, Whittemore AS, Wilkens LR, Winqvist R, Wu AH, Wu X, Yang HP, Zaffaroni D, Pilar Zamora M, Zheng W, Ziogas A, Chenevix-Trench G, Pharoah PDP, Rookus MA, Hooning MJ, Goode EL. No clinical utility of KRAS variant rs61764370 for ovarian or breast cancer. Gynecol Oncol 2016; 141:386-401. [PMID: 25940428 PMCID: PMC4630206 DOI: 10.1016/j.ygyno.2015.04.034] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2015] [Accepted: 04/19/2015] [Indexed: 12/24/2022]
Abstract
OBJECTIVE Clinical genetic testing is commercially available for rs61764370, an inherited variant residing in a KRAS 3' UTR microRNA binding site, based on suggested associations with increased ovarian and breast cancer risk as well as with survival time. However, prior studies, emphasizing particular subgroups, were relatively small. Therefore, we comprehensively evaluated ovarian and breast cancer risks as well as clinical outcome associated with rs61764370. METHODS Centralized genotyping and analysis were performed for 140,012 women enrolled in the Ovarian Cancer Association Consortium (15,357 ovarian cancer patients; 30,816 controls), the Breast Cancer Association Consortium (33,530 breast cancer patients; 37,640 controls), and the Consortium of Modifiers of BRCA1 and BRCA2 (14,765 BRCA1 and 7904 BRCA2 mutation carriers). RESULTS We found no association with risk of ovarian cancer (OR=0.99, 95% CI 0.94-1.04, p=0.74) or breast cancer (OR=0.98, 95% CI 0.94-1.01, p=0.19) and results were consistent among mutation carriers (BRCA1, ovarian cancer HR=1.09, 95% CI 0.97-1.23, p=0.14, breast cancer HR=1.04, 95% CI 0.97-1.12, p=0.27; BRCA2, ovarian cancer HR=0.89, 95% CI 0.71-1.13, p=0.34, breast cancer HR=1.06, 95% CI 0.94-1.19, p=0.35). Null results were also obtained for associations with overall survival following ovarian cancer (HR=0.94, 95% CI 0.83-1.07, p=0.38), breast cancer (HR=0.96, 95% CI 0.87-1.06, p=0.38), and all other previously-reported associations. CONCLUSIONS rs61764370 is not associated with risk of ovarian or breast cancer nor with clinical outcome for patients with these cancers. Therefore, genotyping this variant has no clinical utility related to the prediction or management of these cancers.
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John EM, Terry MB, Keegan TH, Bradbury AR, Knight JA, Chung WK, Frost CJ, Lilge L, Patrick-Miller L, Schwartz LA, Whittemore AS, Buys SS, Daly MB, Andrulis IL. The LEGACY Girls Study: Growth and Development in the Context of Breast Cancer Family History. Epidemiology 2016; 27:438-48. [PMID: 26829160 PMCID: PMC5341739 DOI: 10.1097/ede.0000000000000456] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
BACKGROUND Although the timing of pubertal milestones has been associated with breast cancer risk, few studies of girls' development include girls at increased breast cancer risk due to their family history. METHODS The Lessons in Epidemiology and Genetics of Adult Cancer from Youth (LEGACY) Girls Study was initiated in 2011 in the USA and Canada to assess the relation between early life exposures and intermediate markers of breast cancer risk (e.g., pubertal development, breast tissue characteristics) and to investigate psychosocial well being and health behaviors in the context of family history. We describe the methods used to establish and follow a cohort of 1,040 girls ages 6-13 years at baseline, half with a breast cancer family history, and the collection of questionnaire data (family history, early life exposures, growth and development, psychosocial and behavioral), anthropometry, biospecimens, and breast tissue characteristics using optical spectroscopy. RESULTS During this initial 5-year phase of the study, follow-up visits are conducted every 6 months for repeated data and biospecimen collection. Participation in baseline components was high (98% for urine, 97.5% for blood or saliva, and 98% for anthropometry). At enrollment, 77% of girls were premenarcheal and 49% were at breast Tanner stage T1. CONCLUSIONS This study design allows thorough examination of events affecting girls' growth and development and how they differ across the spectrum of breast cancer risk. A better understanding of early life breast cancer risk factors will be essential to enhance prevention across the lifespan for those with and without a family history of the disease.
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Usset JL, Raghavan R, Tyrer JP, McGuire V, Sieh W, Webb P, Chang-Claude J, Rudolph A, Anton-Culver H, Berchuck A, Brinton L, Cunningham JM, DeFazio A, Doherty JA, Edwards RP, Gayther SA, Gentry-Maharaj A, Goodman MT, Høgdall E, Jensen A, Johnatty SE, Kiemeney LA, Kjaer SK, Larson MC, Lurie G, Massuger L, Menon U, Modugno F, Moysich KB, Ness RB, Pike MC, Ramus SJ, Rossing MA, Rothstein J, Song H, Thompson PJ, van den Berg DJ, Vierkant RA, Wang-Gohrke S, Wentzensen N, Whittemore AS, Wilkens LR, Wu AH, Yang H, Pearce CL, Schildkraut JM, Pharoah P, Goode EL, Fridley BL. Assessment of Multifactor Gene-Environment Interactions and Ovarian Cancer Risk: Candidate Genes, Obesity, and Hormone-Related Risk Factors. Cancer Epidemiol Biomarkers Prev 2016; 25:780-90. [PMID: 26976855 PMCID: PMC4873330 DOI: 10.1158/1055-9965.epi-15-1039] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2015] [Accepted: 01/21/2016] [Indexed: 11/16/2022] Open
Abstract
BACKGROUND Many epithelial ovarian cancer (EOC) risk factors relate to hormone exposure and elevated estrogen levels are associated with obesity in postmenopausal women. Therefore, we hypothesized that gene-environment interactions related to hormone-related risk factors could differ between obese and non-obese women. METHODS We considered interactions between 11,441 SNPs within 80 candidate genes related to hormone biosynthesis and metabolism and insulin-like growth factors with six hormone-related factors (oral contraceptive use, parity, endometriosis, tubal ligation, hormone replacement therapy, and estrogen use) and assessed whether these interactions differed between obese and non-obese women. Interactions were assessed using logistic regression models and data from 14 case-control studies (6,247 cases; 10,379 controls). Histotype-specific analyses were also completed. RESULTS SNPs in the following candidate genes showed notable interaction: IGF1R (rs41497346, estrogen plus progesterone hormone therapy, histology = all, P = 4.9 × 10(-6)) and ESR1 (rs12661437, endometriosis, histology = all, P = 1.5 × 10(-5)). The most notable obesity-gene-hormone risk factor interaction was within INSR (rs113759408, parity, histology = endometrioid, P = 8.8 × 10(-6)). CONCLUSIONS We have demonstrated the feasibility of assessing multifactor interactions in large genetic epidemiology studies. Follow-up studies are necessary to assess the robustness of our findings for ESR1, CYP11A1, IGF1R, CYP11B1, INSR, and IGFBP2 Future work is needed to develop powerful statistical methods able to detect these complex interactions. IMPACT Assessment of multifactor interaction is feasible, and, here, suggests that the relationship between genetic variants within candidate genes and hormone-related risk factors may vary EOC susceptibility. Cancer Epidemiol Biomarkers Prev; 25(5); 780-90. ©2016 AACR.
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Bolton KL, Tyrer J, Song H, Ramus SJ, Notaridou M, Jones C, Sher T, Gentry-Maharaj A, Wozniak E, Tsai YY, Weidhaas J, Paik D, Van Den Berg DJ, Stram DO, Pearce CL, Wu AH, Brewster W, Anton-Culver H, Ziogas A, Narod SA, Levine DA, Kaye SB, Brown R, Paul J, Flanagan J, Sieh W, McGuire V, Whittemore AS, Campbell I, Gore ME, Lissowska J, Yang HP, Medrek K, Gronwald J, Lubinski J, Jakubowska A, Le ND, Cook LS, Kelemen LE, Brooks-Wilson A, Massuger LFAG, Kiemeney LA, Aben KKH, van Altena AM, Houlston R, Tomlinson I, Palmieri RT, Moorman PG, Schildkraut J, Iversen ES, Phelan C, Vierkant RA, Cunningham JM, Goode EL, Fridley BL, Kruger-Kjaer S, Blaeker J, Hogdall E, Hogdall C, Gross J, Karlan BY, Ness RB, Edwards RP, Odunsi K, Moyisch KB, Baker JA, Modugno F, Heikkinenen T, Butzow R, Nevanlinna H, Leminen A, Bogdanova N, Antonenkova N, Doerk T, Hillemanns P, Dürst M, Runnebaum I, Thompson PJ, Carney ME, Goodman MT, Lurie G, Wang-Gohrke S, Hein R, Chang-Claude J, Rossing MA, Cushing-Haugen KL, Doherty J, Chen C, Rafnar T, Besenbacher S, Sulem P, Stefansson K, Birrer MJ, Terry KL, Hernandez D, Cramer DW, Vergote I, Amant F, Lambrechts D, Despierre E, Fasching PA, Beckmann MW, Thiel FC, Ekici AB, Chen X, Johnatty SE, Webb PM, Beesley J, Chanock S, Garcia-Closas M, Sellers T, Easton DF, Berchuck A, Chenevix-Trench G, Pharoah PDP, Gayther SA. Corrigendum: Common variants at 19p13 are associated with susceptibility to ovarian cancer. Nat Genet 2016; 48:101. [PMID: 26711112 DOI: 10.1038/ng0116-101b] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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Præstegaard C, Kjaer SK, Nielsen TSS, Jensen SM, Webb PM, Nagle CM, Høgdall E, Risch HA, Rossing MA, Doherty JA, Wicklund KG, Goodman MT, Modugno F, Moysich K, Ness RB, Edwards RP, Goode EL, Winham SJ, Fridley BL, Cramer DW, Terry KL, Schildkraut JM, Berchuck A, Bandera EV, Paddock L, Kiemeney LA, Massuger LF, Wentzensen N, Pharoah P, Song H, Whittemore AS, McGuire V, Sieh W, Rothstein J, Anton-Culver H, Ziogas A, Menon U, Gayther SA, Ramus SJ, Gentry-Maharaj A, Wu AH, Pearce CL, Pike MC, Lee AW, Chang-Claude J, Jensen A. The association between socioeconomic status and tumour stage at diagnosis of ovarian cancer: A pooled analysis of 18 case-control studies. Cancer Epidemiol 2016; 41:71-9. [PMID: 26851750 PMCID: PMC4993452 DOI: 10.1016/j.canep.2016.01.012] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2015] [Revised: 01/15/2016] [Accepted: 01/20/2016] [Indexed: 11/17/2022]
Abstract
PURPOSE Socioeconomic status (SES) is a known predictor of survival for several cancers and it has been suggested that SES differences affecting tumour stage at diagnosis may be the most important explanatory factor for this. However, only a limited number of studies have investigated SES differences in tumour stage at diagnosis of ovarian cancer. In a pooled analysis, we investigated whether SES as represented by level of education is predictive for advanced tumour stage at diagnosis of ovarian cancer, overall and by histotype. The effect of cigarette smoking and body mass index (BMI) on the association was also evaluated. METHODS From 18 case-control studies, we obtained information on 10,601 women diagnosed with epithelial ovarian cancer. Study specific odds ratios (ORs) with corresponding 95% confidence intervals (CI) were obtained from logistic regression models and combined into a pooled odds ratio (pOR) using a random effects model. RESULTS Overall, women who completed ≤high school had an increased risk of advanced tumour stage at diagnosis compared with women who completed >high school (pOR 1.15; 95% CI 1.03-1.28). The risk estimates for the different histotypes of ovarian cancer resembled that observed for ovarian cancers combined but did not reach statistical significance. Our results were unchanged when we included BMI and cigarette smoking. CONCLUSION Lower level of education was associated with an increased risk of advanced tumour stage at diagnosis of ovarian cancer. The observed socioeconomic difference in stage at diagnosis of ovarian cancer calls for further studies on how to reduce this diagnostic delay.
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Pharoah PDP, Song H, Dicks E, Intermaggio MP, Harrington P, Baynes C, Alsop K, Bogdanova N, Cicek MS, Cunningham JM, Fridley BL, Gentry-Maharaj A, Hillemanns P, Lele S, Lester J, McGuire V, Moysich KB, Poblete S, Sieh W, Sucheston-Campbell L, Widschwendter M, Whittemore AS, Dörk T, Menon U, Odunsi K, Goode EL, Karlan BY, Bowtell DD, Gayther SA, Ramus SJ. PPM1D Mosaic Truncating Variants in Ovarian Cancer Cases May Be Treatment-Related Somatic Mutations. J Natl Cancer Inst 2016; 108:djv347. [PMID: 26823519 PMCID: PMC5072371 DOI: 10.1093/jnci/djv347] [Citation(s) in RCA: 35] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2015] [Revised: 06/30/2015] [Accepted: 10/20/2015] [Indexed: 11/14/2022] Open
Abstract
Mosaic truncating mutations in the protein phosphatase, Mg(2+)/Mn(2+)-dependent, 1D (PPM1D) gene have recently been reported with a statistically significantly greater frequency in lymphocyte DNA from ovarian cancer case patients compared with unaffected control patients. Using massively parallel sequencing (MPS) we identified truncating PPM1D mutations in 12 of 3236 epithelial ovarian cancer (EOC) case patients (0.37%) but in only one of 3431 unaffected control patients (0.03%) (P = .001). All statistical tests were two-sided. A combination of Sanger sequencing, pyrosequencing, and MPS data suggested that 12 of the 13 mutations were mosaic. All mutations were identified in post-chemotherapy treatment blood samples from case patients (n = 1827) (average 1234 days post-treatment in carriers) rather than from cases collected pretreatment (less than 14 days after diagnosis, n = 1384) (P = .002). These data suggest that PPM1D variants in EOC cases are primarily somatic mosaic mutations caused by treatment and are not associated with germline predisposition to EOC.
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99
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Asgari MM, Wang W, Ioannidis NM, Itnyre J, Hoffmann T, Jorgenson E, Whittemore AS. Identification of Susceptibility Loci for Cutaneous Squamous Cell Carcinoma. J Invest Dermatol 2016; 136:930-937. [PMID: 26829030 DOI: 10.1016/j.jid.2016.01.013] [Citation(s) in RCA: 78] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2015] [Revised: 01/04/2016] [Accepted: 01/06/2016] [Indexed: 12/20/2022]
Abstract
We report a genome-wide association study of cutaneous squamous cell carcinoma conducted among non-Hispanic white members of the Kaiser Permanente Northern California health care system. The study includes a genome-wide screen of 61,457 members (6,891 cases and 54,566 controls) genotyped on the Affymetrix Axiom European array and a replication phase involving an independent set of 6,410 additional members (810 cases and 5,600 controls). Combined analysis of screening and replication phases identified 10 loci containing single-nucleotide polymorphisms (SNPs) with P-values < 5 × 10(-8). Six loci contain genes in the pigmentation pathway; SNPs at these loci appear to modulate squamous cell carcinoma risk independently of the pigmentation phenotypes. Another locus contains HLA class II genes studied in relation to elevated squamous cell carcinoma risk following immunosuppression. SNPs at the remaining three loci include an intronic SNP in FOXP1 at locus 3p13, an intergenic SNP at 3q28 near TP63, and an intergenic SNP at 9p22 near BNC2. These findings provide insights into the genetic factors accounting for inherited squamous cell carcinoma susceptibility.
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Amankwah EK, Lin HY, Tyrer JP, Lawrenson K, Dennis J, Chornokur G, Aben KKH, Anton-Culver H, Antonenkova N, Bruinsma F, Bandera EV, Bean YT, Beckmann MW, Bisogna M, Bjorge L, Bogdanova N, Brinton LA, Brooks-Wilson A, Bunker CH, Butzow R, Campbell IG, Carty K, Chen Z, Chen YA, Chang-Claude J, Cook LS, Cramer DW, Cunningham JM, Cybulski C, Dansonka-Mieszkowska A, du Bois A, Despierre E, Dicks E, Doherty JA, Dörk T, Dürst M, Easton DF, Eccles DM, Edwards RP, Ekici AB, Fasching PA, Fridley BL, Gao YT, Gentry-Maharaj A, Giles GG, Glasspool R, Goodman MT, Gronwald J, Harrington P, Harter P, Hasmad HN, Hein A, Heitz F, Hildebrandt MA, Hillemanns P, Hogdall CK, Hogdall E, Hosono S, Iversen ES, Jakubowska A, Jensen A, Ji BT, Karlan BY, Jim H, Kellar M, Kiemeney LA, Krakstad C, Kjaer SK, Kupryjanczyk J, Lambrechts D, Lambrechts S, Le ND, Lee AW, Lele S, Leminen A, Lester J, Levine DA, Liang D, Lim BK, Lissowska J, Lu K, Lubinski J, Lundvall L, Massuger LF, Matsuo K, McGuire V, McLaughlin JR, McNeish I, Menon U, Milne RL, Modugno F, Moysich KB, Ness RB, Nevanlinna H, Eilber U, Odunsi K, Olson SH, Orlow I, Orsulic S, Weber RP, Paul J, Pearce CL, Pejovic T, Pelttari LM, Permuth-Wey J, Pike MC, Poole EM, Risch HA, Rosen B, Rossing MA, Rothstein JH, Rudolph A, Runnebaum IB, Rzepecka IK, Salvesen HB, Schernhammer E, Schwaab I, Shu XO, Shvetsov YB, Siddiqui N, Sieh W, Song H, Southey MC, Spiewankiewicz B, Sucheston-Campbell L, Teo SH, Terry KL, Thompson PJ, Thomsen L, Tangen IL, Tworoger SS, van Altena AM, Vierkant RA, Vergote I, Walsh CS, Wang-Gohrke S, Wentzensen N, Whittemore AS, Wicklund KG, Wilkens LR, Wu AH, Wu X, Woo YL, Yang H, Zheng W, Ziogas A, Kelemen LE, Berchuck A, Schildkraut JM, Ramus SJ, Goode EL, Monteiro AN, Gayther SA, Narod SA, Pharoah PDP, Sellers TA, Phelan CM. Epithelial-Mesenchymal Transition (EMT) Gene Variants and Epithelial Ovarian Cancer (EOC) Risk. Genet Epidemiol 2015; 39:689-97. [PMID: 26399219 PMCID: PMC4721602 DOI: 10.1002/gepi.21921] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2015] [Revised: 07/17/2015] [Accepted: 07/20/2015] [Indexed: 01/24/2023]
Abstract
Epithelial-mesenchymal transition (EMT) is a process whereby epithelial cells assume mesenchymal characteristics to facilitate cancer metastasis. However, EMT also contributes to the initiation and development of primary tumors. Prior studies that explored the hypothesis that EMT gene variants contribute to epithelial ovarian carcinoma (EOC) risk have been based on small sample sizes and none have sought replication in an independent population. We screened 15,816 single-nucleotide polymorphisms (SNPs) in 296 genes in a discovery phase using data from a genome-wide association study of EOC among women of European ancestry (1,947 cases and 2,009 controls) and identified 793 variants in 278 EMT-related genes that were nominally (P < 0.05) associated with invasive EOC. These SNPs were then genotyped in a larger study of 14,525 invasive-cancer patients and 23,447 controls. A P-value <0.05 and a false discovery rate (FDR) <0.2 were considered statistically significant. In the larger dataset, GPC6/GPC5 rs17702471 was associated with the endometrioid subtype among Caucasians (odds ratio (OR) = 1.16, 95% CI = 1.07-1.25, P = 0.0003, FDR = 0.19), whereas F8 rs7053448 (OR = 1.69, 95% CI = 1.27-2.24, P = 0.0003, FDR = 0.12), F8 rs7058826 (OR = 1.69, 95% CI = 1.27-2.24, P = 0.0003, FDR = 0.12), and CAPN13 rs1983383 (OR = 0.79, 95% CI = 0.69-0.90, P = 0.0005, FDR = 0.12) were associated with combined invasive EOC among Asians. In silico functional analyses revealed that GPC6/GPC5 rs17702471 coincided with DNA regulatory elements. These results suggest that EMT gene variants do not appear to play a significant role in the susceptibility to EOC.
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