Functional Landscape of Common Variants Associated with Susceptibility to Epithelial Ovarian Cancer

Lyra, Paulo C.; Rangel, L.B.A.; Monteiro, Alvaro N.A.

doi:10.1007/s40471-020-00227-4

Cited by 6 publications

(4 citation statements)

References 94 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We used genotype data from 23,564 invasive nonmucinous EOC cases and 40,138 controls with >80% European ancestries from 63 case-control studies included in the Ovarian Cancer Association Consortium (OCAC) for model development. The distribution of cases by histotype was high-grade serous (13,609), low-grade serous (2,749), endometrioid (2,877), clear cell (1,427), and others (2,902). Sample collection, genotyping, and quality control have been previously described [12].…”

Section: Materials (Subjects) and Methods Model Development Study Pop...mentioning

confidence: 97%

“…Rare variants in known high and moderate penetrance susceptibility genes ( BRCA1 , BRCA2 , BRIP1 , PALB2 , RAD51C , RAD51D and the mis-match repair genes) account for about 40% of the inherited component of EOC disease risk [ 1 , 2 ]. Common susceptibility variants, reviewed in Kar et al and Jones et al, explain about 6% of the heritability of EOC [ 1 , 3 ].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Polygenic risk modeling for prediction of epithelial ovarian cancer risk

et al. 2022

View full text Add to dashboard Cite

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 ancestries; 7,669 women of East Asian ancestries; 1,072 women of African ancestries, and in 18,915 BRCA1 and 12,337 BRCA2 pathogenic variant carriers of European ancestries. 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 ancestries; 1.14 (95% CI: 1.08–1.19, AUC: 0.538) in women of East Asian ancestries; 1.38 (95% CI: 1.21–1.58, AUC: 0.593) in women of African ancestries; hazard ratios of 1.36 (95% CI: 1.29–1.43, AUC: 0.592) in BRCA1 pathogenic variant carriers and 1.49 (95% CI: 1.35–1.64, 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.

show abstract

Section: Materials (Subjects) and Methods Model Development Study Pop...mentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

Polygenic risk modeling for prediction of epithelial ovarian cancer risk

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Rare mutations in known high and moderate penetrance susceptibility genes ( BRCA1, BRCA2, BRIP1, PALB2, RAD51C, RAD51D and the mis-match repair genes) account for about 40 percent of the inherited component of EOC disease risk (1,2). Genome wide association studies (GWAS), reviewed in Kar et.…”

Section: Introductionmentioning

confidence: 99%

Polygenic Risk Modelling for Prediction of Epithelial Ovarian Cancer Risk

Dareng¹,

Tyrer²,

Barnes³

et al. 2020

Preprint

View full text Add to dashboard Cite

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.

show abstract

“…Germline SNPs and indels (SNVs) were called with GATK 4.0.0.0 HaplotypeCaller. Germline SNVs reported in ClinVar 26 to disrupt the function of 12 HGSOC risk genes [27][28][29] were recorded (Supp Table S2), and BRCA1/2 variants were found to be enriched in patient samples relative to comparable populations in gnomAD 30 . Somatic SNVs and indels were called as a majority vote between Mutect2 1.…”

mentioning

confidence: 99%

Divergent trajectories to structural diversity impact patient survival in high grade serous ovarian cancer

Ewing,

Meynert,

Silk

et al. 2024

Preprint

View full text Add to dashboard Cite

Deciphering the structural variation across tumour genomes is crucial to determine the events driving tumour progression and better understand tumour adaptation and evolution. High grade serous ovarian cancer (HGSOC) is an exemplar tumour type showing extreme, but poorly characterised structural diversity. We comprehensively describe the mutational landscape driving HGSOC, exploiting a large (N=324), deeply whole genome sequenced dataset. We reveal two divergent evolutionary trajectories, affecting patient survival and involving differing genomic environments. One involves homologous recombination repair deficiency (HRD) while the other is dominated by whole genome duplication (WGD) with frequent chromothripsis, breakage-fusion-bridges and extra-chromosomal DNA. These trajectories contribute to structural variation hotspots, containing novel candidate driver genes with significantly altered expression. While structural variation predominantly drives tumorigenesis, we also find high mtDNA mutation loads associated with shorter patient survival, and acting in combination with alterations in the nuclear genome to impact prognosis and suggesting new strategies for patient stratification.

show abstract

Functional Landscape of Common Variants Associated with Susceptibility to Epithelial Ovarian Cancer

Cited by 6 publications

References 94 publications

Polygenic risk modeling for prediction of epithelial ovarian cancer risk

Polygenic risk modeling for prediction of epithelial ovarian cancer risk

Polygenic Risk Modelling for Prediction of Epithelial Ovarian Cancer Risk

Divergent trajectories to structural diversity impact patient survival in high grade serous ovarian cancer

Contact Info

Product

Resources

About