Identification of novel sarcoma risk genes using a two-stage genome wide DNA sequencing strategy in cancer cluster families and population case and control cohorts | BMC Medical Genetics | Full Text

Identification of novel sarcoma risk genes using a two-stage genome wide DNA sequencing strategy in cancer cluster families and population case and control cohorts | BMC Medical Genetics | Full Text

BMC Medical Genetics

Identification of novel sarcoma risk genes using a two-stage genome wide DNA sequencing strategy in cancer cluster families and population case and control cohorts

• Rachel M. Jones,
• Phillip E. Melton,
• Mark Pinese,
• Alexander J. Rea,
• Evan Ingley,
• Mandy L. Ballinger,
• International Sarcoma Kindred Study,
• David J. Wood,
• David M. Thomas and
• Eric K. MosesEmail authorView ORCID ID profile

BMC Medical Genetics201920:69

https://doi.org/10.1186/s12881-019-0808-9

©  The Author(s). 2019

• Received: 31 October 2018
• Accepted: 16 April 2019
• Published: 3 May 2019

Open Peer Review reports

Abstract

Background

Although familial clustering of cancers is relatively common, only a small proportion of familial cancer risk can be explained by known cancer predisposition genes.

Methods

In this study we employed a two-stage approach to identify candidate sarcoma risk genes. First, we conducted whole exome sequencing in three multigenerational cancer families ascertained through a sarcoma proband (n = 19) in order to prioritize candidate genes for validation in an independent case-control cohort of sarcoma patients using family-based association and segregation analysis. The second stage employed a burden analysis of rare variants within prioritized candidate genes identified from stage one in 560 sarcoma cases and 1144 healthy ageing controls, for which whole genome sequence was available.

Results

Variants from eight genes were identified in stage one. Following gene-based burden testing and after correction for multiple testing, two of these genes, ABCB5 and C16orf96, were determined to show statistically significant association with cancer. The ABCB5 gene was found to have a higher burden of putative regulatory variants (OR = 4.9, p-value = 0.007, q-value = 0.04) based on allele counts in sarcoma cases compared to controls. C16orf96, was found to have a significantly lower burden (OR = 0.58, p-value = 0.0004, q-value = 0.003) of regulatory variants in controls compared to sarcoma cases.

Conclusions

Based on these genetic association data we propose that ABCB5 and C16orf96 are novel candidate risk genes for sarcoma. Although neither of these two genes have been previously associated with sarcoma, ABCB5 has been shown to share clinical drug resistance associations with melanoma and leukaemia and C16orf96 shares regulatory elements with genes that are involved with TNF-alpha mediated apoptosis in a p53/TP53-dependent manner. Future genetic studies in other family and population cohorts will be required for further validation of these novel findings.

Keywords

• Sarcoma
• Whole exome sequencing
• Cancer cluster families
• Genetic risk variants
• Whole genome sequencing