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Common minor histocompatibility antigen discovery based upon patient clinical outcomes and genomic data
Authors:Armistead Paul M  Liang Shoudan  Li Hua  Lu Sijie  Van Bergen Cornelis A M  Alatrash Gheath  St John Lisa  Hunsucker Sally A  Sarantopoulos Stefanie  Falkenburg J H Frederik  Molldrem Jeffrey J
Institution:Section of Transplantation Immunology, Department of Stem Cell Transplant and Cellular Therapy, M.D. Anderson Cancer Center, Houston, Texas, United States of America. paul_armistead@med.unc.edu
Abstract:

Background

Minor histocompatibility antigens (mHA) mediate much of the graft vs. leukemia (GvL) effect and graft vs. host disease (GvHD) in patients who undergo allogeneic stem cell transplantation (SCT) 1], 2], 3], 4]. Therapeutic decision making and treatments 5] based upon mHAs will require the evaluation of multiple candidate mHAs and the selection of those with the potential to have the greatest impact on clinical outcomes. We hypothesized that common, immunodominant mHAs, which are presented by HLA-A, B, and C molecules, can mediate clinically significant GvL and/or GvHD, and that these mHAs can be identified through association of genomic data with clinical outcomes.

Methodology/Principal Findings

Because most mHAs result from donor/recipient cSNP disparities, we genotyped 57 myeloid leukemia patients and their donors at 13,917 cSNPs 6]. We correlated the frequency of genetically predicted mHA disparities with clinical evidence of an immune response and then computationally screened all peptides mapping to the highly associated cSNPs for their ability to bind to HLA molecules. As proof-of-concept, we analyzed one predicted antigen, T4A, whose mHA mismatch trended towards improved overall and disease free survival in our cohort. T4A mHA mismatches occurred at the maximum theoretical frequency for any given SCT. T4A-specific CD8+ T lymphocytes (CTLs) were detected in 3 of 4 evaluable post-transplant patients predicted to have a T4A mismatch.

Conclusions/Significance

Our method is the first to combine clinical outcomes data with genomics and bioinformatics methods to predict and confirm a mHA. Refinement of this method should enable the discovery of clinically relevant mHAs in the majority of transplant patients and possibly lead to novel immunotherapeutics 5].
Keywords:
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