Background: Hereditary non-polyposis colorectal cancer (HNPCC)/Lynch syndrome (LS) is a cancer syndrome characterised by early-onset epithelial cancers, especially colorectal cancer (CRC) and endometrial cancer. The aim of the current study was to use SNP-array technology to identify genomic aberrations which can contribute to the increased risk of cancer in HNPCC/LS patients. Individuals diagnosed with HNPCC/LS (100) and healthy controls (384) were genotyped using the Illumina Human610-Quad SNP-arrays. Copy number variation (CNV) calling and association analyses were performed using Nexus software, with significant results validated using QuantiSNP. TaqMan Copy-Number assay was used for biological validation of CNVs showing significant association with HNPCC/LS via both analyses software’s.
Results: We detected copy number (CN) gains associated with HNPCC/LS status on chromosome 7q11.21 (28% cases and 0% controls, p=3.60E-20) and 16p11.2 (46% in cases, while a CN loss was observed in 23% of controls, p=4.93E-21) via in silico analyses. In addition, CNV burden (total CNV length, average CNV length and number of observed CNV events) was significantly greater in cases compared to controls. TaqMan Copy-Number assay was used for validation of CNVs showing significant association with HNPCC/LS.
Conclusion: A greater CNV burden was identified in LS/HNPCC cases compared to controls supporting the notion of higher genomic instability in these patients due to an inadequate DNA repair process. One intergenic locus on chromosome 7q11.21 is possibly associated with disease risk in patients diagnosed with HNPCC/LS as the CN gain was still evident when the dataset was re-analysed on Nexus v6.1 and should therefore not be dismissed as a false positive without further investigation. The results from this study highlight the complexities of fluorescent based CNV analyses. The inefficiency of both CNV detection methods to reproducibly detect observed CNVs demonstrates the need for sequence data to be considered alongside intensity data to avoid false positive results.