Polygenic Risk Modelling for Prediction of Epithelial Ovarian Cancer Risk

Polygenic risk scores (PRS) for epithelial ovarian cancer (EOC) have the potential to improve risk stratification. Joint estimation of Single Nucleotide Polymorphism (SNP) effects in models could improve predictive performance over standard approaches of PRS construction. Here, we implemented computationally-efficient, penalized, logistic regression models (lasso, elastic net, stepwise) to individual level genotype data and a Bayesian framework with continuous shrinkage, "select and shrink for summary statistics" (S4), to summary level data for epithelial non-mucinous ovarian cancer risk prediction. We developed the models in a dataset consisting of 23,564 non-mucinous EOC cases and 40,138 controls participating in the Ovarian Cancer Association Consortium (OCAC) and validated the best models in three populations of different ancestries: prospective data from 198,101 women of European ancestry; 7,669 women of East Asian ancestry; 1,072 women of African ancestry, and in 18,915 BRCA1 and 12,337 BRCA2 pathogenic variant carriers of European ancestry. In the external validation data, the model with the strongest association for non-mucinous EOC risk derived from the OCAC model development data was the S4 model (27,240 SNPs) with odds ratios (OR) of 1.38(95%CI:1.28-1.48,AUC:0.588) per unit standard deviation, in women of European ancestry; 1.14(95%CI:1.08-1.19,AUC:0.538) in women of East Asian ancestry; 1.38(95%CI:1.21-1.58,AUC:0.593) in women of African ancestry; hazard ratios of 1.37(95%CI:1.30-1.44,AUC:0.592) in BRCA1 pathogenic variant carriers and 1.51(95%CI:1.36-1.67,AUC:0.624) in BRCA2 pathogenic variant carriers. Incorporation of the S4 PRS in risk prediction models for ovarian cancer may have clinical utility in ovarian cancer prevention programs.

A. Whittemore | W. Chung | L. Kiemeney | J. Marks | M. Beckmann | P. Fasching | C. Weinberg | R. Vierkant | T. Sellers | Jingmei Li | F. Couch | J. Chang-Claude | S. Chanock | E. Goode | B. Bonanni | O. Olopade | Y. Chiew | A. deFazio | B. Karlan | A. Wolk | J. Benítez | N. Le | A. Berchuck | G. Giles | J. Hopper | C. Haiman | E. John | T. Dörk | M. Southey | D. Easton | D. Huntsman | D. Lambrechts | E. Khusnutdinova | M. Greene | K. Offit | A. Antoniou | J. Brenton | Å. Borg | D. Levine | S. Buys | W. Zheng | A. Ziogas | H. Anton-Culver | U. Menon | K. Aben | R. Barkardottir | D. Eccles | D. Evans | G. Chenevix-Trench | H. Nevanlinna | D. Kang | N. Bogdanova | P. Devilee | R. Milne | U. Hamann | C. Lázaro | K. Nathanson | J. Cunningham | M. Goodman | J. Garber | C. Isaacs | J. Dennis | M. Adank | R. Schmutzler | I. Andrulis | G. Glendon | A. Swerdlow | P. Radice | P. Peterlongo | S. Manoukian | A. Jakubowska | J. Lubiński | N. Antonenkova | A. Toland | K. Matsuo | A. Wu | H. Cai | S. Teo | M. Hartman | J. Simard | P. Pharoah | J. Tyrer | L. Titus | S. Neuhausen | M. Bermisheva | D. Prokofyeva | D. Torres | D. Yannoukakos | A. Monteiro | S. Gayther | L. Forétova | L. McGuffog | F. Dao | A. Godwin | B. Peshkin | E. Friedman | N. Tung | A. Sokolenko | E. Imyanitov | C. Huff | P. Ganz | N. Wentzensen | A. Piskorz | M. Bernardini | A. Osorio | R. Sutphen | Michelle R Jones | P. Hulick | E. White | B. Wappenschmidt | I. McNeish | A. Kurian | S. Domchek | D. Stoppa-Lyonnet | D. V. Edwards | Sue-Kyung Park | S. Olson | H. Risch | C. Engel | C. Singer | K. Claes | L. Kelemen | O. Johannsson | J. Rantala | B. Arun | K. Odunsi | I. Campbell | R. Matsuno | I. Runnebaum | O. Díez | Byoung-Gie Kim | G. Aravantinos | J. Doherty | J. Schildkraut | K. Moysich | F. Modugno | Austin Miller | E. Hahnen | F. Nielsen | K. Lu | J. McLaughlin | P. Pérez-Segura | P. James | M. Daly | Y. Woo | A. V. van Altena | Kexin Chen | Eric Ross | R. Fortner | E. Dareng | E. Bandera | M. Hildebrandt | C. Pearce | J. Flanagan | F. Heitz | D. Barnes | M. Thomassen | R. Butzow | C. Rodríguez-Antona | K. Lawrenson | D. Sandler | L. Nikitina-Zake | J. Lester | A. Karnezis | R. Travis | M. Teixeira | J. Balmaña | J. Weitzel | M. Tischkowitz | H. Harris | W. Sieh | M. Terry | M. Rossing | V. Setiawan | Michael E. Jones | S. Winham | Honglin Song | P. Webb | A. Jensen | N. Håkansson | L. Cook | J. Gronwald | F. Lesueur | S. Tworoger | I. Komenaka | E. Oláh | E. Høgdall | C. Høgdall | P. Soucy | D. Barrowdale | T. V. Hansen | M. Montagna | E. J. van Rensburg | S. Ramus | M. Caligo | R. Janavicius | A. Kwong | L. Papi | I. Pedersen | Y. Ding | P. Mai | J. Loud | S. Agata | M. de la Hoya | L. Bjørge | H. Steed | Xin Yang | A. Beeghly-Fadiel | N. Mebirouk | A. H. van der Hout | G. Leslie | M. Parsons | M. Santamariña | Y. Tan | D. Thull | A. Black | T. Pejović | J. Kupryjańczyk | P. Thompson | M. Dürst | K. Terry | M. Larson | E. Van Nieuwenhuysen | E. Macháčková | Marjorie J. Riggan | R. Cannioto | Ruea-Yea Huang | T. May | A. Peixoto | J. Permuth | B. Trabert | K. Zorn | E. Davies | Harshad Pathak | F. Gensini | S. K. Kjaer | Hampus Olsson | A. Bois | K. Zavaglia | Frances F Wang | Lian Li | A. Augustinsson | A. Budziłowska | H. Cassingham | Sarah Colanna | Robin de Putter | A. DePersia | H. Eliassen | Albina N. Minlikeeva | E. Munro | Joanne Ngeow Yuen Yie | H. R. Nielsen | S. Olbrecht | K. Shan | Liv Cecilie Vestrheim Thomsen | Elena Valen | Ana Vega | Li Yan | Michael E. Jones | J. Marks | Goska Leslie | H. Nielsen | R. Huang | Marjorie J Riggan | T. Pejovic | A. Vega | L. Foretova | M. Riggan | K. Lu | A. Monteiro | D. Evans | H. Cai | Ana Peixoto | E. Friedman | K. Lu | Kexin Chen | M. Teixeira | A. van Altena | C. Rodríguez‐Antona | M. Larson | A. Wu | Noura Mebirouk | Johanna Rantala | Diana Torres | J. McLaughlin | D. Evans

[1]  D. Steinemann,et al.  Polygenic risk scores and breast and epithelial ovarian cancer risks for carriers of BRCA1 and BRCA2 pathogenic variants , 2020, Genetics in Medicine.

[2]  J. Denny,et al.  Evaluating the Utility of Polygenic Risk Scores in Identifying High-Risk Individuals for Eight Common Cancers , 2020, JNCI cancer spectrum.

[3]  L. B. Rangel,et al.  Functional Landscape of Common Variants Associated with Susceptibility to Epithelial Ovarian Cancer , 2020, Current Epidemiology Reports.

[4]  R. Vierkant,et al.  Population-based targeted sequencing of 54 candidate genes identifies PALB2 as a susceptibility gene for high-grade serous ovarian cancer , 2019, Journal of Medical Genetics.

[5]  A. Whittemore,et al.  Identification of novel epithelial ovarian cancer loci in women of African ancestry , 2020, International journal of cancer.

[6]  Julie O. Culver,et al.  Cancer Risks Associated With Germline PALB2 Pathogenic Variants: An International Study of 524 Families. , 2019, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[7]  Kylie L. Gorringe,et al.  The molecular origin and taxonomy of mucinous ovarian carcinoma , 2019, Nature Communications.

[8]  Kylie L. Gorringe,et al.  A combination of the immunohistochemical markers CK7 and SATB2 is highly sensitive and specific for distinguishing primary ovarian mucinous tumors from colorectal and appendiceal metastases , 2019, Modern Pathology.

[9]  Jennifer A. Doherty,et al.  Genome-wide association studies identify susceptibility loci for epithelial ovarian cancer in east Asian women. , 2019, Gynecologic oncology.

[10]  M. García-Closas,et al.  BOADICEA: a comprehensive breast cancer risk prediction model incorporating genetic and nongenetic risk factors , 2019, Genetics in Medicine.

[11]  Kristen S Purrington,et al.  Polygenic Risk Scores for Prediction of Breast Cancer and Breast Cancer Subtypes , 2018, American Journal of Human Genetics.

[12]  M. Blum,et al.  Efficient Implementation of Penalized Regression for Genetic Risk Prediction , 2018, Genetics.

[13]  P. Donnelly,et al.  The UK Biobank resource with deep phenotyping and genomic data , 2018, Nature.

[14]  Yang Ni,et al.  Polygenic prediction via Bayesian regression and continuous shrinkage priors , 2018, Nature Communications.

[15]  K. D. Sørensen,et al.  Association analyses of more than 140,000 men identify 63 new prostate cancer susceptibility loci , 2018, Nature Genetics.

[16]  P. Pharoah,et al.  Evaluation of polygenic risk scores for ovarian cancer risk prediction in a prospective cohort study , 2018, Journal of Medical Genetics.

[17]  B. Karlan,et al.  Genetic epidemiology of ovarian cancer and prospects for polygenic risk prediction. , 2017, Gynecologic oncology.

[18]  W. Chung,et al.  Risks of Breast, Ovarian, and Contralateral Breast Cancer for BRCA1 and BRCA2 Mutation Carriers , 2017, JAMA.

[19]  T. Sellers,et al.  Common Genetic Variation and Susceptibility to Ovarian Cancer: Current Insights and Future Directions , 2017, Cancer Epidemiology, Biomarkers & Prevention.

[20]  Pak Chung Sham,et al.  Polygenic scores via penalized regression on summary statistics , 2016, bioRxiv.

[21]  W. Chung,et al.  Evaluation of Polygenic Risk Scores for Breast and Ovarian Cancer Risk Prediction in BRCA1 and BRCA2 Mutation Carriers , 2017, Journal of the National Cancer Institute.

[22]  Lara E Sucheston-Campbell,et al.  Identification of 12 new susceptibility loci for different histotypes of epithelial ovarian cancer , 2017, Nature Genetics.

[23]  Hongyu Zhao,et al.  Leveraging functional annotations in genetic risk prediction for human complex diseases , 2016, bioRxiv.

[24]  T. Perren Mucinous epithelial ovarian carcinoma. , 2016, Annals of oncology : official journal of the European Society for Medical Oncology.

[25]  P. Visscher,et al.  Modeling Linkage Disequilibrium Increases Accuracy of Polygenic Risk Scores , 2015, bioRxiv.

[26]  M. Pike,et al.  Population Distribution of Lifetime Risk of Ovarian Cancer in the United States , 2015, Cancer Epidemiology, Biomarkers & Prevention.

[27]  Hongbing Shen,et al.  Genome-wide association study identifies new susceptibility loci for epithelial ovarian cancer in Han Chinese women , 2022 .

[28]  Justin Zobel,et al.  Performance and Robustness of Penalized and Unpenalized Methods for Genetic Prediction of Complex Human Disease , 2013, Genetic epidemiology.

[29]  P. Visscher,et al.  Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits , 2012, Nature Genetics.

[30]  Rohan L. Fernando,et al.  Extension of the bayesian alphabet for genomic selection , 2011, BMC Bioinformatics.

[31]  P. Visscher,et al.  Common polygenic variation contributes to risk of schizophrenia and bipolar disorder , 2009, Nature.

[32]  Yan V. Sun,et al.  Machine learning in genome‐wide association studies , 2009, Genetic epidemiology.

[33]  Peter M Visscher,et al.  Prediction of individual genetic risk to disease from genome-wide association studies. , 2007, Genome research.

[34]  Jerilyn A. Walker,et al.  Genetic variation among world populations: inferences from 100 Alu insertion polymorphisms. , 2003, Genome research.