Human Leukocyte Antigen-Allelic Variations May Influence the Age at Cancer Diagnosis in Lynch Syndrome

Abstract

Lynch syndrome (LS) is an inherited cancer predisposition disorder associated with an elevated risk of developing various solid cancers, but mostly colorectal cancer (CRC). Despite having the same germline pathogenic variant (PV) in one of the mis-match repair genes or the EPCAM gene, Lynch syndrome variant heterozygotes (LSVH) exhibit a remarkable phenotypic variability in the risk of developing cancer. The role of human leukocyte antigen (HLA) in modifying cancer development risk prompted our hypothesis into whether HLA variations act as potential genetic modifiers influencing the age at cancer diagnosis in LSVH. To investigate this, we studied a unique cohort of 426 LSVH carrying the same germline PV in the hMLH1 gene (MLH1:c.1528C > T) in South Africa. We intuitively selected 100 LSVH with the greatest diversity in age at cancer diagnosis (N = 80) and the oldest cancer unaffected LSVH (N = 20) for a high-throughput HLA genotyping of 11 HLA class I and class II loci using the shotgun next-generation sequencing (NGS) technique on the Illumina MiSeq platform. Statistical analyses employed Kaplan–Meier survival analyses with log-rank tests, and Cox proportional hazards using binned HLA data to minimize type I error. Significant associations were observed between young age at cancer diagnosis and HLA-DPB1*04:02 (mean age: 37 y (25–50); hazard ratio (HR) = 3.37; corrected p-value (q) = 0.043) as well as HLA-DPB1 binned alleles (including HLA-DPB1*09:01, HLA-DPB1*10:01, HLA-DPB1*106:01, HLA-DPB1*18:01, HLA-DPB1*20:01, HLA-DPB1*26:01, HLA-DPB1*28:01, HLA-DPB1*296:01, and HLA-DPB1*55:01) (mean age: 37 y (17–63); HR = 2.30, q = 0.045). The involvement of HLA-DPB1 alleles in the age at cancer diagnosis may highlight the potential role of HLA class II in the immune response against cancer development in LSVH. When validated in a larger cohort, these high-risk HLA-DPB1 alleles could be factored into cancer risk prediction models for personalized cancer screening in LSVH.