Choosing Variant Interpretation Tools for Clinical Applications: Context Matters

Clinical variant interpretation; Healthcare costs; Pathogenicity predictionInterpretación de variantes clínicas; Costes sanitarios; Predicción de patogenicidadInterpretació de variants clíniques; Despeses sanitàries; Predicció de patogenicitatPathogenicity predictors are computational tools that classify genetic variants as benign or pathogenic; this is currently a major challenge in genomic medicine. With more than fifty such predictors available, selecting the most suitable tool for clinical applications like genetic screening, molecular diagnostics, and companion diagnostics has become increasingly challenging. To address this issue, we have developed a cost-based framework that naturally considers the various components of the problem. This framework encodes clinical scenarios using a minimal set of parameters and treats pathogenicity predictors as rejection classifiers, a common practice in clinical applications where low-confidence predictions are routinely rejected. We illustrate our approach in four examples where we compare different numbers of pathogenicity predictors for missense variants. Our results show that no single predictor is optimal for all clinical scenarios and that considering rejection yields a different perspective on classifiers.This work was supported by research grants SAF2016-80255-R from the Spanish Ministerio de Economía y Competitividad (MINECO), PID2019-111217RB-I00 and TED2021-130342B-I00 from the Spanish Ministerio de Ciencia e Innovación, and by the European Regional Development Fund (ERDF) through the Interreg program POCTEFA (Pirepred, EFA086/15)

Recommendations for the classification of germline variants in the exonuclease domain of POLE and POLD1

BackgroundGermline variants affecting the proofreading activity of polymerases epsilon and delta cause a hereditary cancer and adenomatous polyposis syndrome characterized by tumors with a high mutational burden and a specific mutational spectrum. In addition to the implementation of multiple pieces of evidence for the classification of gene variants, POLE and POLD1 variant classification is particularly challenging given that non-disruptive variants affecting the proofreading activity of the corresponding polymerase are the ones associated with cancer. In response to an evident need in the field, we have developed gene-specific variant classification recommendations, based on the ACMG/AMP (American College of Medical Genetics and Genomics/Association for Molecular Pathology) criteria, for the assessment of non-disruptive variants located in the sequence coding for the exonuclease domain of the polymerases.MethodsA training set of 23 variants considered pathogenic or benign was used to define the usability and strength of the ACMG/AMP criteria. Population frequencies, computational predictions, co-segregation data, phenotypic and tumor data, and functional results, among other features, were considered.ResultsGene-specific variant classification recommendations for non-disruptive variants located in the exonuclease domain of POLE and POLD1 were defined. The resulting recommendations were applied to 128 exonuclease domain variants reported in the literature and/or public databases. A total of 17 variants were classified as pathogenic or likely pathogenic, and 17 as benign or likely benign.ConclusionsOur recommendations, with room for improvement in the coming years as more information become available on carrier families, tumor molecular characteristics and functional assays, are intended to serve the clinical and scientific communities and help improve diagnostic performance, avoiding variant misclassifications

A New Set of in Silico Tools to Support the Interpretation of ATM Missense Variants Using Graphical Analysis

Establishing the pathogenic nature of variants in ATM, a gene associated with breast cancer and other hereditary cancers, is crucial for providing patients with adequate care. Unfortunately, achieving good variant classification is still difficult. To address this challenge, we extended the range of in silico tools with a series of graphical tools devised for the analysis of computational evidence by health care professionals. We propose a family of fast and easy-to-use graphical representations in which the impact of a variant is considered relative to other pathogenic and benign variants. To illustrate their value, the representations are applied to three problems in variant interpretation. The assessment of computational pathogenicity predictions showed that the graphics provide an intuitive view of pre-diction reliability, complementing and extending conventional numerical reliability indexes. When applied to variant of unknown significance populations, the representations shed light on the nature of these variants and can be used to prioritize variants of unknown significance for further studies. In a third application, the graphics were used to compare the two versions of the ATM-adapted American College of Medical Genetics and Genomics and Association for Molecular Pathology guidelines, obtaining valuable information on their relative virtues and weaknesses. Finally, a server [ATMision (ATM missense in silico interpretation online)] was generated for users to apply these representations in their variant interpretation problems, to check the ATM-adapted guidelines' criteria for computational evidence on their variant(s) and access different sources of information. (J Mol Diagn 2024, 26: 17-28; https://doi.org/10.1016/j.jmoldx.2023.09.009

Incorporating Alternative Polygenic Risk Scores into the BOADICEA Breast Cancer Risk Prediction Model

Background: The multifactorial risk prediction model BOADI-CEA enables identification of women at higher or lower risk of developing breast cancer. BOADICEA models genetic susceptibility in terms of the effects of rare variants in breast cancer susceptibility genes and a polygenic component, decomposed into an unmeasured and a measured component -the polygenic risk score (PRS). The current version was developed using a 313 SNP PRS. Here, we evaluated approaches to incorporating this PRS and alternative PRS in BOADICEA.Methods: The mean, SD, and proportion of the overall polygenic component explained by the PRS (a2) need to be estimated. a was estimated using logistic regression, where the age-specific log-OR is constrained to be a function of the age-dependent polygenic relative risk in BOADICEA; and using a retrospective likelihood (RL) approach that models, in addition, the unmeasured polygenic component.Results: Parameters were computed for 11 PRS, including 6 variations of the 313 SNP PRS used in clinical trials and imple-mentation studies. The logistic regression approach underestimates a, as compared with the RL estimates. The RL a estimates were very close to those obtained by assuming proportionality to the OR per 1 SD, with the constant of proportionality estimated using the 313 SNP PRS. Small variations in the SNPs included in the PRS can lead to large differences in the mean.Conclusions: BOADICEA can be readily adapted to different PRS in a manner that maintains consistency of the model.Impact : The methods described facilitate comprehensive breast cancer risk assessment

BRCA1 and BRCA2 mutations in males with familial breast and ovarian cancer syndrome. Results of a Spanish multicenter study

Producción CientíficaMale breast cancer (MBC) is a rare disease that represents <1 % of all breast cancers (BCs). We analyze the results of a multicenter study performed in Spanish familial MBC including family history of hereditary breast and ovarian cancer syndrome (HBOCS) and clinicopathological features. We also study the relationship between BRCA1/BRCA2 mutational status in male relatives affected with cancer (MAC) and, family history and tumor types. The study included 312 men index cases with family history of HBOCS and 61 MAC BRCA1/2 mutation-carriers. Family history, histological grade (HG), clinicopathological and immunohistochemistry data were collected. BRCA1/2 mutation analyses were performed by direct sequencing or screening methods and the large rearrangements by multiplex ligation dependent probe amplification. We found 49 mutation-carriers (15.7 %), 95.9 % with BRCA2 mutations. BRCA2 mutation-carriers were associated with families with at least one MBC and one BC in female (type II; p = 0.05). Strong association were found between the presence of pathogenic mutations in MBCs and the advanced HG (p = 0.003). c.658_659delTG, c.2808_2811delACAA, c.6275_6276delTT and c.9026_9030delATCAT were the most prevalent mutations. In 61 MAC we found 20 mutations in BRCA1 and 41 in BRCA2. For MAC we show that mutational status was differentially associated with family history (p = 0.018) and tumor type, being BRCA2 mutations linked with BC and prostatic cancer (p = 0.018). MBC caused by BRCA1/2 mutations define two types of MBCs. The most frequent caused by BRCA2 mutation linked to type II families and the rarest one attributed to BRCA1 mutation. Tumor associated with MAC suggest that only BRCA2 mutations have to do with a specific type of cancer (BC and prostatic cancer); but the linkage to tumors is questionable for BRCA1 mutations

A comprehensive custom panel design for routine hereditary cancer testing: preserving control, improving diagnostics and revealing a complex variation landscape

Altres ajuts: Asociación Española Contra el Cáncer; Asociación de Afectados de Neurofibromatosis; ACNefiWe wanted to implement an NGS strategy to globally analyze hereditary cancer with diagnostic quality while retaining the same degree of understanding and control we had in pre-NGS strategies. To do this, we developed the I2HCP panel, a custom bait library covering 122 hereditary cancer genes. We improved bait design, tested different NGS platforms and created a clinically driven custom data analysis pipeline. The I2HCP panel was developed using a training set of hereditary colorectal cancer, hereditary breast and ovarian cancer and neurofibromatosis patients and reached an accuracy, analytical sensitivity and specificity greater than 99%, which was maintained in a validation set. I2HCP changed our diagnostic approach, involving clinicians and a genetic diagnostics team from panel design to reporting. The new strategy improved diagnostic sensitivity, solved uncertain clinical diagnoses and identified mutations in new genes. We assessed the genetic variation in the complete set of hereditary cancer genes, revealing a complex variation landscape that coexists with the disease-causing mutation. We developed, validated and implemented a custom NGS-based strategy for hereditary cancer diagnostics that improved our previous workflows. Additionally, the existence of a rich genetic variation in hereditary cancer genes favors the use of this panel to investigate their role in cancer risk

Benchmarking of Whole Exome Sequencing and Ad Hoc Designed Panels for Genetic Testing of Hereditary Cancer

Acknowledgements: We thank all patients who contributed to this study. The work was supported by grants from the Instituto de Salud Carlos III (ISCIII, MINECO) (operating grants: PI13/00285 and RD12/0036/0008 awarded to C.L. and PIE13/00022 and RD12/0036/0031 awarded to G.C.) and confunded by FEDER funds/European Regional Development Fund (ERDF) - a way to Build Europe-"// FONDOS FEDER "una manera de hacer Europa", the Generalitat de Catalunya (Government of Catalonia) (operating grant 2014SGR338, awarded to G.C.) and the Asociación Española Contra el Cáncer (operating grants, 2010 Grupos Estables, awarded to G.C.). J.B. received a Spanish Society of Medical Oncology grant. This activity is sponsored by the ISCIII Ministerio de Economía y Competitividad (PT13/0001/0044).Next generation sequencing panels have been developed for hereditary cancer, although there is some debate about their cost-effectiveness compared to exome sequencing. The performance of two panels is compared to exome sequencing. Twenty-four patients were selected: ten with identified mutations (control set) and fourteen suspicious of hereditary cancer but with no mutation (discovery set). TruSight Cancer (94 genes) and a custom panel (122 genes) were assessed alongside exome sequencing. Eightythree genes were targeted by the two panels and exome sequencing. More than 99% of bases had a read depth of over 30x in the panels, whereas exome sequencing covered 94%. Variant calling with standard settings identified the 10 mutations in the control set, with the exception of MSH6 c.255dupC using TruSight Cancer. In the discovery set, 240 unique non-silent coding and canonic splice-site variants were identified in the panel genes, 7 of them putatively pathogenic (in ATM, BARD1, CHEK2, ERCC3, FANCL, FANCM, MSH2). The three approaches identified a similar number of variants in the shared genes. Exomes were more expensive than panels but provided additional data. In terms of cost and depth, panels are a suitable option for genetic diagnostics, although exomes also identify variants in non-targeted genes

Assessing Associations between the AURKA-HMMR-TPX2-TUBG1 Functional Module and Breast Cancer Risk in BRCA1/2 Mutation Carriers

While interplay between BRCA1 and AURKA-RHAMM-TPX2-TUBG1 regulates mammary epithelial polarization, common genetic variation in HMMR (gene product RHAMM) may be associated with risk of breast cancer in BRCA1 mutation carriers. Following on these observations, we further assessed the link between the AURKA-HMMR-TPX2-TUBG1 functional module and risk of breast cancer in BRCA1 or BRCA2 mutation carriers. Forty-one single nucleotide polymorphisms (SNPs) were genotyped in 15,252 BRCA1 and 8,211 BRCA2 mutation carriers and subsequently analyzed using a retrospective likelihood approach. The association of HMMR rs299290 with breast cancer risk in BRCA1 mutation carriers was confirmed: per-allele hazard ratio (HR) = 1.10, 95% confidence interval (CI) 1.04 – 1.15, p = 1.9 x 10−4 (false discovery rate (FDR)-adjusted p = 0.043). Variation in CSTF1, located next to AURKA, was also found to be associated with breast cancer risk in BRCA2 mutation carriers: rs2426618 per-allele HR = 1.10, 95% CI 1.03 – 1.16, p = 0.005 (FDR-adjusted p = 0.045). Assessment of pairwise interactions provided suggestions (FDR-adjusted pinteraction values > 0.05) for deviations from the multiplicative model for rs299290 and CSTF1 rs6064391, and rs299290 and TUBG1 rs11649877 in both BRCA1 and BRCA2 mutation carriers. Following these suggestions, the expression of HMMR and AURKA or TUBG1 in sporadic breast tumors was found to potentially interact, influencing patients’ survival. Together, the results of this study support the hypothesis of a causative link between altered function of AURKA-HMMR-TPX2-TUBG1 and breast carcinogenesis in BRCA1/2 mutation carriers

Copy number variants as modifiers of breast cancer risk for BRCA1/BRCA2 pathogenic variant carriers

The risk of germline copy number variants (CNVs) in BRCA1 and BRCA2 pathogenic variant carriers in breast cancer is assessed, with CNVs overlapping SULT1A1 decreasing breast cancer risk in BRCA1 carriers.The contribution of germline copy number variants (CNVs) to risk of developing cancer in individuals with pathogenic BRCA1 or BRCA2 variants remains relatively unknown. We conducted the largest genome-wide analysis of CNVs in 15,342 BRCA1 and 10,740 BRCA2 pathogenic variant carriers. We used these results to prioritise a candidate breast cancer risk-modifier gene for laboratory analysis and biological validation. Notably, the HR for deletions in BRCA1 suggested an elevated breast cancer risk estimate (hazard ratio (HR) = 1.21), 95% confidence interval (95% CI = 1.09-1.35) compared with non-CNV pathogenic variants. In contrast, deletions overlapping SULT1A1 suggested a decreased breast cancer risk (HR = 0.73, 95% CI 0.59-0.91) in BRCA1 pathogenic variant carriers. Functional analyses of SULT1A1 showed that reduced mRNA expression in pathogenic BRCA1 variant cells was associated with reduced cellular proliferation and reduced DNA damage after treatment with DNA damaging agents. These data provide evidence that deleterious variants in BRCA1 plus SULT1A1 deletions contribute to variable breast cancer risk in BRCA1 carriers.Peer reviewe

Mendelian randomisation study of height and body mass index as modifiers of ovarian cancer risk in 22,588 BRCA1 and BRCA2 mutation carriers

Funder: CIMBA: The CIMBA data management and data analysis were supported by Cancer Research – UK grants C12292/A20861, C12292/A11174. ACA is a Cancer Research -UK Senior Cancer Research Fellow. GCT and ABS are NHMRC Research Fellows. iCOGS: the European Community's Seventh Framework Programme under grant agreement No. 223175 (HEALTH-F2-2009-223175) (COGS), Cancer Research UK (C1287/A10118, C1287/A 10710, C12292/A11174, C1281/A12014, C5047/A8384, C5047/A15007, C5047/A10692, C8197/A16565), the National Institutes of Health (CA128978) and Post-Cancer GWAS initiative (1U19 CA148537, 1U19 CA148065 and 1U19 CA148112 - the GAME-ON initiative), the Department of Defence (W81XWH-10-1-0341), the Canadian Institutes of Health Research (CIHR) for the CIHR Team in Familial Risks of Breast Cancer (CRN-87521), and the Ministry of Economic Development, Innovation and Export Trade (PSR-SIIRI-701), Komen Foundation for the Cure, the Breast Cancer Research Foundation, and the Ovarian Cancer Research Fund. The PERSPECTIVE project was supported by the Government of Canada through Genome Canada and the Canadian Institutes of Health Research, the Ministry of Economy, Science and Innovation through Genome Québec, and The Quebec Breast Cancer Foundation. BCFR: UM1 CA164920 from the National Cancer Institute. The content of this manuscript does not necessarily reflect the views or policies of the National Cancer Institute or any of the collaborating centers in the Breast Cancer Family Registry (BCFR), nor does mention of trade names, commercial products, or organizations imply endorsement by the US Government or the BCFR. BFBOCC: Lithuania (BFBOCC-LT): Research Council of Lithuania grant SEN-18/2015. BIDMC: Breast Cancer Research Foundation. BMBSA: Cancer Association of South Africa (PI Elizabeth J. van Rensburg). CNIO: Spanish Ministry of Health PI16/00440 supported by FEDER funds, the Spanish Ministry of Economy and Competitiveness (MINECO) SAF2014-57680-R and the Spanish Research Network on Rare diseases (CIBERER). COH-CCGCRN: Research reported in this publication was supported by the National Cancer Institute of the National Institutes of Health under grant number R25CA112486, and RC4CA153828 (PI: J. Weitzel) from the National Cancer Institute and the Office of the Director, National Institutes of Health. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. CONSIT: Associazione Italiana Ricerca sul Cancro (AIRC; IG2014 no.15547) to P. Radice. Italian Association for Cancer Research (AIRC; grant no.16933) to L. Ottini. Associazione Italiana Ricerca sul Cancro (AIRC; IG2015 no.16732) to P. Peterlongo. Jacopo Azzollini is supported by funds from Italian citizens who allocated the 5x1000 share of their tax payment in support of the Fondazione IRCCS Istituto Nazionale Tumori, according to Italian laws (INT-Institutional strategic projects ‘5x1000’). DEMOKRITOS: European Union (European Social Fund – ESF) and Greek national funds through the Operational Program "Education and Lifelong Learning" of the National Strategic Reference Framework (NSRF) - Research Funding Program of the General Secretariat for Research & Technology: SYN11_10_19 NBCA. Investing in knowledge society through the European Social Fund. DFKZ: German Cancer Research Center. EMBRACE: Cancer Research UK Grants C1287/A10118 and C1287/A11990. D. Gareth Evans and Fiona Lalloo are supported by an NIHR grant to the Biomedical Research Centre, Manchester. The Investigators at The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust are supported by an NIHR grant to the Biomedical Research Centre at The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust. Ros Eeles and Elizabeth Bancroft are supported by Cancer Research UK Grant C5047/A8385. Ros Eeles is also supported by NIHR support to the Biomedical Research Centre at The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust. FCCC: The University of Kansas Cancer Center (P30 CA168524) and the Kansas Bioscience Authority Eminent Scholar Program. A.K.G. was funded by R0 1CA140323, R01 CA214545, and by the Chancellors Distinguished Chair in Biomedical Sciences Professorship. FPGMX: FISPI05/2275 and Mutua Madrileña Foundation (FMMA). GC-HBOC: German Cancer Aid (grant no 110837, Rita K. Schmutzler) and the European Regional Development Fund and Free State of Saxony, Germany (LIFE - Leipzig Research Centre for Civilization Diseases, project numbers 713-241202, 713-241202, 14505/2470, 14575/2470). GEMO: Ligue Nationale Contre le Cancer; the Association “Le cancer du sein, parlons-en!” Award, the Canadian Institutes of Health Research for the "CIHR Team in Familial Risks of Breast Cancer" program and the French National Institute of Cancer (INCa grants 2013-1-BCB-01-ICH-1 and SHS-E-SP 18-015). GEORGETOWN: the Non-Therapeutic Subject Registry Shared Resource at Georgetown University (NIH/NCI grant P30-CA051008), the Fisher Center for Hereditary Cancer and Clinical Genomics Research, and Swing Fore the Cure. G-FAST: Bruce Poppe is a senior clinical investigator of FWO. Mattias Van Heetvelde obtained funding from IWT. HCSC: Spanish Ministry of Health PI15/00059, PI16/01292, and CB-161200301 CIBERONC from ISCIII (Spain), partially supported by European Regional Development FEDER funds. HEBCS: Helsinki University Hospital Research Fund, Academy of Finland (266528), the Finnish Cancer Society and the Sigrid Juselius Foundation. HEBON: the Dutch Cancer Society grants NKI1998-1854, NKI2004-3088, NKI2007-3756, the Netherlands Organisation of Scientific Research grant NWO 91109024, the Pink Ribbon grants 110005 and 2014-187.WO76, the BBMRI grant NWO 184.021.007/CP46 and the Transcan grant JTC 2012 Cancer 12-054. HRBCP: Hong Kong Sanatorium and Hospital, Dr Ellen Li Charitable Foundation, The Kerry Group Kuok Foundation, National Institute of Health1R 03CA130065, and North California Cancer Center. HUNBOCS: Hungarian Research Grants KTIA-OTKA CK-80745 and OTKA K-112228. ICO: The authors would like to particularly acknowledge the support of the Asociación Española Contra el Cáncer (AECC), the Instituto de Salud Carlos III (organismo adscrito al Ministerio de Economía y Competitividad) and “Fondo Europeo de Desarrollo Regional (FEDER), una manera de hacer Europa” (PI10/01422, PI13/00285, PIE13/00022, PI15/00854, PI16/00563 and CIBERONC) and the Institut Català de la Salut and Autonomous Government of Catalonia (2009SGR290, 2014SGR338 and PERIS Project MedPerCan). IHCC: PBZ_KBN_122/P05/2004. ILUH: Icelandic Association “Walking for Breast Cancer Research” and by the Landspitali University Hospital Research Fund. INHERIT: Canadian Institutes of Health Research for the “CIHR Team in Familial Risks of Breast Cancer” program – grant # CRN-87521 and the Ministry of Economic Development, Innovation and Export Trade – grant # PSR-SIIRI-701. IOVHBOCS: Ministero della Salute and “5x1000” Istituto Oncologico Veneto grant. IPOBCS: Liga Portuguesa Contra o Cancro. kConFab: The National Breast Cancer Foundation, and previously by the National Health and Medical Research Council (NHMRC), the Queensland Cancer Fund, the Cancer Councils of New South Wales, Victoria, Tasmania and South Australia, and the Cancer Foundation of Western Australia. MAYO: NIH grants CA116167, CA192393 and CA176785, an NCI Specialized Program of Research Excellence (SPORE) in Breast Cancer (CA116201),and a grant from the Breast Cancer Research Foundation. MCGILL: Jewish General Hospital Weekend to End Breast Cancer, Quebec Ministry of Economic Development, Innovation and Export Trade. Marc Tischkowitz is supported by the funded by the European Union Seventh Framework Program (2007Y2013)/European Research Council (Grant No. 310018). MODSQUAD: MH CZ - DRO (MMCI, 00209805), MEYS - NPS I - LO1413 to LF and by the European Regional Development Fund and the State Budget of the Czech Republic (RECAMO, CZ.1.05/2.1.00/03.0101) to LF, and by Charles University in Prague project UNCE204024 (MZ). MSKCC: the Breast Cancer Research Foundation, the Robert and Kate Niehaus Clinical Cancer Genetics Initiative, the Andrew Sabin Research Fund and a Cancer Center Support Grant/Core Grant (P30 CA008748). NAROD: 1R01 CA149429-01. NCI: the Intramural Research Program of the US National Cancer Institute, NIH, and by support services contracts NO2-CP-11019-50, N02-CP-21013-63 and N02-CP-65504 with Westat, Inc, Rockville, MD. NICCC: Clalit Health Services in Israel, the Israel Cancer Association and the Breast Cancer Research Foundation (BCRF), NY. NNPIO: the Russian Foundation for Basic Research (grants 17-54-12007, 17-00-00171 and 18-515-12007). NRG Oncology: U10 CA180868, NRG SDMC grant U10 CA180822, NRG Administrative Office and the NRG Tissue Bank (CA 27469), the NRG Statistical and Data Center (CA 37517) and the Intramural Research Program, NCI. OSUCCG: Ohio State University Comprehensive Cancer Center. PBCS: Italian Association of Cancer Research (AIRC) [IG 2013 N.14477] and Tuscany Institute for Tumors (ITT) grant 2014-2015-2016. SEABASS: Ministry of Science, Technology and Innovation, Ministry of Higher Education (UM.C/HlR/MOHE/06) and Cancer Research Initiatives Foundation. SMC: the Israeli Cancer Association. SWE-BRCA: the Swedish Cancer Society. UCHICAGO: NCI Specialized Program of Research Excellence (SPORE) in Breast Cancer (CA125183), R01 CA142996, 1U01CA161032, P20CA233307, American Cancer Society (MRSG-13-063-01-TBG, CRP-10-119-01-CCE), Breast Cancer Research Foundation, Susan G. Komen Foundation (SAC110026), and Ralph and Marion Falk Medical Research Trust, the Entertainment Industry Fund National Women's Cancer Research Alliance. Mr. Qian was supported by the Alpha Omega Alpha Carolyn L. Cuckein Student Research Fellowship. UCLA: Jonsson Comprehensive Cancer Center Foundation; Breast Cancer Research Foundation. UCSF: UCSF Cancer Risk Program and Helen Diller Family Comprehensive Cancer Center. UKFOCR: Cancer Research UK. UPENN: Breast Cancer Research Foundation; Susan G. Komen Foundation for the cure, Basser Center for BRCA. UPITT/MWH: Hackers for Hope Pittsburgh. VFCTG: Victorian Cancer Agency, Cancer Australia, National Breast Cancer Foundation. WCP: Dr Karlan is funded by the American Cancer Society Early Detection Professorship (SIOP-06-258-01-COUN) and the National Center for Advancing Translational Sciences (NCATS), Grant UL1TR000124.Abstract: Background: Height and body mass index (BMI) are associated with higher ovarian cancer risk in the general population, but whether such associations exist among BRCA1/2 mutation carriers is unknown. Methods: We applied a Mendelian randomisation approach to examine height/BMI with ovarian cancer risk using the Consortium of Investigators for the Modifiers of BRCA1/2 (CIMBA) data set, comprising 14,676 BRCA1 and 7912 BRCA2 mutation carriers, with 2923 ovarian cancer cases. We created a height genetic score (height-GS) using 586 height-associated variants and a BMI genetic score (BMI-GS) using 93 BMI-associated variants. Associations were assessed using weighted Cox models. Results: Observed height was not associated with ovarian cancer risk (hazard ratio [HR]: 1.07 per 10-cm increase in height, 95% confidence interval [CI]: 0.94–1.23). Height-GS showed similar results (HR = 1.02, 95% CI: 0.85–1.23). Higher BMI was significantly associated with increased risk in premenopausal women with HR = 1.25 (95% CI: 1.06–1.48) and HR = 1.59 (95% CI: 1.08–2.33) per 5-kg/m2 increase in observed and genetically determined BMI, respectively. No association was found for postmenopausal women. Interaction between menopausal status and BMI was significant (Pinteraction < 0.05). Conclusion: Our observation of a positive association between BMI and ovarian cancer risk in premenopausal BRCA1/2 mutation carriers is consistent with findings in the general population