Detection of germline variants with pathogenic potential in 48 patients with familial colorectal cancer by using whole exome sequencing
Singh, Ashish Kumar; Talseth-Palmer, Bente Anita; Xavier, Alexandre; Scott, Rodney J.; Drabløs, Finn Sverre; Sjursen, Wenche
Peer reviewed, Journal article
Published version
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Date
2023Metadata
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- Institutt for klinisk og molekylær medisin [3619]
- Publikasjoner fra CRIStin - NTNU [39142]
- St. Olavs hospital [2620]
Abstract
Background
Hereditary genetic mutations causing predisposition to colorectal cancer are accountable for approximately 30% of all colorectal cancer cases. However, only a small fraction of these are high penetrant mutations occurring in DNA mismatch repair genes, causing one of several types of familial colorectal cancer (CRC) syndromes. Most of the mutations are low-penetrant variants, contributing to an increased risk of familial colorectal cancer, and they are often found in additional genes and pathways not previously associated with CRC. The aim of this study was to identify such variants, both high-penetrant and low-penetrant ones.
Methods
We performed whole exome sequencing on constitutional DNA extracted from blood of 48 patients suspected of familial colorectal cancer and used multiple in silico prediction tools and available literature-based evidence to detect and investigate genetic variants.
Results
We identified several causative and some potentially causative germline variants in genes known for their association with colorectal cancer. In addition, we identified several variants in genes not typically included in relevant gene panels for colorectal cancer, including CFTR, PABPC1 and TYRO3, which may be associated with an increased risk for cancer.
Conclusions
Identification of variants in additional genes that potentially can be associated with familial colorectal cancer indicates a larger genetic spectrum of this disease, not limited only to mismatch repair genes. Usage of multiple in silico tools based on different methods and combined through a consensus approach increases the sensitivity of predictions and narrows down a large list of variants to the ones that are most likely to be significant.