Document type

Article

Version

Published version

Publication date

Publication license

cc-by (c) Díaz Gay, Marcos et al., 2019
Please use this identifier to cite or link to this item: https://hdl.handle.net/2445/176253

Integrated Analysis of Germline and Tumor DNA Identifies New Candidate Genes Involved in Familial Colorectal Cancer

Journal Title

Director/Tutor

Additional contribution

Journal ISSN

Volume Title

Abstract

Colorectal cancer (CRC) shows aggregation in some families but no alterations in the known hereditary CRC genes. We aimed to identify new candidate genes which are potentially involved in germline predisposition to familial CRC. An integrated analysis of germline and tumor whole-exome sequencing data was performed in 18 unrelated CRC families. Deleterious single nucleotide variants (SNV), short insertions and deletions (indels), copy number variants (CNVs) and loss of heterozygosity (LOH) were assessed as candidates for first germline or second somatic hits. Candidate tumor suppressor genes were selected when alterations were detected in both germline and somatic DNA, fulfilling Knudson's two-hit hypothesis. Somatic mutational profiling and signature analysis were also performed. A series of germline-somatic variant pairs were detected. In all cases, the first hit was presented as a rare SNV/indel, whereas the second hit was either a different SNV (3 genes) or LOH affecting the same gene (141 genes). BRCA2, BLM, ERCC2, RECQL, REV3L and RIF1 were among the most promising candidate genes for germline CRC predisposition. The identification of new candidate genes involved in familial CRC could be achieved by our integrated analysis. Further functional studies and replication in additional cohorts are required to confirm the selected candidates.

Subject (English)

Citation

Citation

DÍAZ GAY, Marcos, et al. Integrated Analysis of Germline and Tumor DNA Identifies New Candidate Genes Involved in Familial Colorectal Cancer. Cancers. 2019. Vol. 11, num. 3, pags. 362. ISSN 2072-6694. [consulted: 10 of October of 2026]. Available at: https://hdl.handle.net/2445/176253

Export metadata

JSON - METS

Share record