Clinical Cancer ResearchGenomicsPeer-reviewed journal article
Rare, pathogenic germline variants in Fanconi Anemia genes increase risk for squamous lung cancer
Myvizhi Esai SelvaniD, Robert J. KleiniD, Zeynep Hulya GumusiD
Abstract
PurposeLung cancer is the leading cause of cancer deaths worldwide, with substantially better prognosis in early stage as opposed to late stage disease. Identifying genetic factors for lung squamous carcinoma (SqCC) risk will enable their use in risk stratification, and personalized intensive surveillance, early detection, and prevention strategies for high-risk individuals.\n\nStudy Design and ParticipantsWe analyzed whole-exome sequencing datasets of 318 cases and 814 controls (discovery cohort) and then validated our findings in an independent cohort of 444 patients and 3,479 controls (validation cohort), all of European descent, totaling a combined cohort of 765 cases and 4,344 controls. We focused on rare pathogenic variants found in the ClinVar database and used penalized logistic regression to identify genes in which such variants are enriched in cases. All statistical tests were two-sided.\n\nResultsWe observed an overall enrichment of rare, deleterious germline variants in Fanconi Anemia genes in cases with SqCC (joint analysis OR=3.08, p=1.4e-09, 95% confidence interval [CI]=2.2-4.3). Consistent with previous studies, BRCA2 in particular exhibited an increased overall burden of rare, deleterious variants (joint OR=3.2, p=8.7e-08, 95% CI=2.1-4.7). More importantly, rare deleterious germline variants were enriched in Fanconi Anemia genes even without the BRCA2 rs11571833 variant that is strongly enriched in lung SqCC cases (joint OR=2.76, p=7.0e-04, 95% CI=1.6-4.7).\n\nConclusionsThese findings can be used towards the development of a genetic diagnostic test in the clinic to identify SqCC high-risk individuals, who can benefit from personalized programs, improving prognosis.
- organism: human
- assay: whole-exome sequencing
- research focus: variant effect prediction
- approach: statistical model
- Citations
- 30
- FWCI
- 1.84
- Field percentile
- top 14%
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