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.
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
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
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
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
MATERIAL and METHODS
 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)
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)
RESULTS
Bioinformatics: RegulomeDB
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.
RESULTS
Bioinformatics: F-SNP
BTNL2 SNP associated with prostate cancer risk (Fitzgerald, 2013).
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
RESULTS
Bioinformatics: F-SNP
EHMT2 SNP associated with breast cancer risk (Cebrian, 2006). No HLA correlation data.
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
FUTURE PROSPECTS
False negatives in GWAS
Unfaithfullness in the HLA region
Lack of consideration of effect modification