Analysis of HLA Region Polymorphisms Associated with Cancer

1. Analysis of HLA Region Polymorphisms Associated with Cancer Amy E. KENNEDY, Sandeep K. SINGH, Karina VILLALBA, M. Tevfik DORAK Department of Epidemiology Robert Stempel College of Public Health and Social Work Florida International University Miami, Florida U.S.A.

2. BACKGROUND • Earlier animal studies suggested a strong influence of HLA polymorphisms on cancer susceptibility • Candidate gene studies reported a number of associations, but most have not been replicated • Genome-wide association studies have identified numerous risk markers even in non-virus-related cancers • Whether these markers are proxies for HLA alleles or likely to be causal markers are unknown

3. BACKGROUND Unique Features of the HLA region • Most gene-dense, most polymorphic region in the genome with the highest deleterious variant proportion, and strong linkage disequilibrium • Strongest cis-eQTL in the genome • Strongest trans-eQTL in the genome • Gene content is enriched in embryo-expressed genes, and genes related to transcriptional/ translational machinery, stress response and genome surveillance

4. ASHI 2013: 79-P ASHI 2012: 148-P Correlations of Complex Disease-associated HLA Region SNPs with HLA Alleles Amy E. Kennedy, Sandeep K. Singh, Malaroviyam Samikkannu, M. Tevfik Dorak Department of Environmental and Occupational Health, Robert Stempel College of Public Health and Social Work, Florida International University, Miami 33199, USA ASHI 2011: 184-P

5. AIM • To gain mechanistic insight into the reported cancer associations in GWAS and selected candidate gene studies • To delineate correlations between these markers and HLA alleles

6. MATERIAL andMETHODS • NHGRI-GWAS catalog to compile cancer associations • CGEMS results from dbGAP (HLA region only) • and WTCCC results for HLA and breast cancer associations from supplementary data file • 103 HLA-typed IHWG reference cell lines • Bioinformatics suits for functional analysis (RegulomeDB, F-SNP, GTeX, GWAS Central, GWASdb)

7. RESULTS Correlations with HLA Most SNPs were not exclusive to HLA alleles/haplotypes/lineages, but the SNPs associated with lymphoid malignancies, nasopharyngeal cancer, lung cancer, and prostate cancer showed some correlations. HLA-DRA rs2395185 ~ HLA-DRB4 lineage (Hodgkin lymphoma, lung cancer) BAG6 rs3117582 ~ HLA-A1-B8-DR3 (lung cancer) BTNL2 rs28362675 ~ HLA-B52-DR15 (prostate cancer) * * * HLA-DPB1 rs2281389 ~ HLA-DPB1*0301 (Hodgkin lymphoma)

8. RESULTS Bioinformatics: RegulomeDB

9. RESULTS Bioinformatics: RegulomeDB Five of the eight European-origin samples homozygous for the variant allele of this SNP are DPB1*0301 homozygous in the IHWG panel. Two Asian samples are DPB1*0901 homozygous in the IHWG panel.

10. RESULTS Bioinformatics: F-SNP BTNL2 SNP associated with prostate cancer risk (Fitzgerald, 2013).

11. RESULTS Bioinformatics: F-SNP This SNP creates a stop codon in BTNL2 Another BTNL2 SNP is associated with lung cancer risk A splice site variant in BTNL2 is associated with sarcoidosis susceptibility

12. RESULTS Bioinformatics: F-SNP EHMT2 SNP associated with breast cancer risk (Cebrian, 2006). No HLA correlation data.

13. CONCLUSIONS By using one of the most underutilized resources in immunogenetics, we have generated useful information for HLA and cancer connection HLA complex harbors genetic variants that modify cancer susceptibility If individual SNP analyses have revealed associations in GWAS, more associations can be unmasked by considering the special features of the HLA complex

14. FUTURE PROSPECTS False negatives in GWAS Unfaithfullness in the HLA region Lack of consideration of effect modification