An Approach to Predicting Hematopoietic Stem Cell Transplantation Outcome Using HLA-Mismatch Information Mapped on Protein Structure Data

Article Outline

• Abstract
• Introduction
  • Data Sources
• Methods
• Results and Discussion
• Acknowledgments
• References
• Copyright

In hematopoietic stem cell transplantation (HSCT), the outcome is predicted using HLA-matching procedures, which are very time-consuming. There exists substantial evidence of the importance of early donor acceptance in HSCT outcome. In cases when the donor cannot be perfectly matched, it often is unclear which mismatch is less harmful and thus has a greater likelihood of acceptance. We modeled and analyzed interactions between the protein products of different HLA alleles of the transplant recipient and natural killer and T lymphocyte cell receptors of the donor's immune system. Reactions between these 2 systems often lead to graft-versus-host disease (GVHD). Sequence polymorphisms that define HLA I and II alleles predict not only GVHD, but also host-versus-graft and graft-versus-leukemia effects, all of which influence the overall transplantation outcome. Although complete high-resolution HLA matching of the donor–recipient pair seems to be associated with optimal post-HSCT survival, recent reports suggest that not every HLA disparity is functionally relevant. We performed interaction energy calculations for selected pairs of donor-recipient HLA alleles. Based on the results, we conclude that the energy of contact between the T lymphocyte cell receptor (TCR) and HLA residues can help predict the future development of an immune reaction and, consequently, the outcome of allogeneic HSCT.

Key Words: Hematopoietic stem cell transplantation, HLA mismatch, Donor–recipient matching, HLAp–T cell receptor complex, Protein structure analysis, Conformational search

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Introduction 

T cells use T lymphocyte cell receptors (TCRs) to recognize short antigenic peptides presented by class I and class II major histocompatibility complex (MHC) molecules expressed by antigen-presenting cells (APCs). As suggested by numerous laboratory studies 1, 2, the potency of the ligand (presented peptide) to activate T cells correlates with the half-life of the TCR–peptide MHC (pMHC) complex 3, 4 and likely also with heat-capacity changes during the interaction [1]. The binding affinity of TCRs to ligands also has been reported to correlate with T cell response in vivo [5].

The binding of TCR to its pMHC ligand is a low-affinity interaction that depends very strongly on the presented peptide. Identifying universal key residues that can determine the effects of binding in all known TCR–pMHC complexes is difficult; however, CDR1 and CDR2 loops of the TCR alpha and beta chains are known to preferably contact MHC alpha helices from the binding groove, and CDR3 loops are involved in peptide binding. Thermodynamic studies of TCR–pMHC interactions have shown that many TCRs bind to pMHCs by an induced fit mechanism, meaning that the binding surface is flexible and stabilizes only on ligand engagement 6, 7, 8, 9, whereas the pMHC part of the complex is assumed to be relatively rigid [10]. Numerous approaches for determining the energy of the TCR–pMHC complexes in laboratory conditions are available, most of them based on the assumption that the loss of entropy in the system is consistent with the stabilization of the binding site produced by ligand binding. Experimental results show a wide range of possible thermodynamic values in the case of native complexes (from about -127.5 kJ/mol to 1.6 kJ/mol) 2, 4. In almost all TCR–pMHC I complexes characterized so far, the pMHC part is relatively rigid on TCR engagement. These studies were conducted with TCR interactions involving small peptides (8 to 9 residues long), however. Recently, it has been reported that peptides longer than 10 amino acids can bulge centrally from the MHC binding groove and exhibit some degree of mobility [11].

According to Shlomchik et al. [12], the crucial point in graft-versus-host disease (GVHD) initiation is presentation of the antigen by host APCs. It has been shown experimentally that depleting host APCs before the conditioning regimen for the donor T cell transplantation into the murine hematopoietic stem cell transplantation (HSCT) model abrogates GVHD. These results suggest that replacement of host APCs with donor cells reduces the likelihood that donor CD8⁺ T cells interacting with a host APC will induce GVHD. Both class I and II participate in the presentation of intracellular peptides to donor T cell receptors, and both are expressed by APCs. According to Petersdorf and coworkers 13, 14, mismatching HLA class II increases the risk of acute GVHD (aGVHD) more than mismatching HLA class I.

One of the most critical first steps in HSCT is ensuring that the donor and recipient are a good match in terms of the 5 main transplant antigens: HLA-A, -B, -C, -DR, and -DQ. This is of particular concern for those receiving an allogeneic transplant, which involves the donation of stem cells by a family member, an unrelated individual, or a banked cord blood unit.

HLA matching is a key factor in the overall success of HSCT, as well as in preventing possible complications, such as GVHD or relapse. GVHD can be a major complication after allogeneic HSCT, especially when the donor and recipient are unrelated. Activated donor cytotoxic T lymphocytes (CTLs) and natural killer cells (NKCs) produce granzymes (Gr) that are involved in the pathogenesis of GVHD. In contrast, activated donor T cells can eliminate leukemic host cells and facilitate engraftment. All 3 of these factors influence the general HSCT outcome, which is determined by patient survival after allogeneic HSCT [15].

The minimum acceptable match for a donor–recipient pair initially was defined by matching at the serologic level at 3 loci (HLA-A, -B, and -DR). Over the years, however, the required level of typing resolution has evolved to the present state, in which all 10 antigens considered are determined at the genetic allele level. Flomberg et al. [16] investigated the statistical associations between survival and mismatches, both allele-level and serologic-level, and found that serologic-level mismatches at MHC class I and HLA-DR had a significant effect on survival. According to the analysis of Lee et al. [17], using data on 3857 transplantations performed between 1988 and 2003 in the United States, a single mismatch detected by low- or high-resolution DNA testing at HLA-A, -B, -C, or -DRB1 was associated with higher mortality and lower 1-year survival rates compared with a perfect match. Double mismatches were correlated with even higher risk, whereas single mismatches at HLA-DQB1 or -DP and donor factors other than HLA were not associated with survival [17]. Considering these results, the US National Marrow Donor Program has suggested that a single allele-level donor–recipient mismatch is preferable to an antigen-level donor–recipient mismatch [18]. This rule remains in force in clinical practice. There are still no data allowing for the discrimination of the effects of different mismatches within the same serologic group. Table 1 summarizes the operational rules and guidelines currently in place at state-of-the-art medical practices.

Table 1. Donor matching guidelines according to medical practice 16, 18, 19

As an alternative to the typical statistical studies of post-HSCT survival and its correlation with donor–recipient matching level, an approach involving protein structural data has been proposed. In 2001, Ferrara et al. [20] reported that mismatches with substitutions at the specific positions in the amino acid sequences of the HLA class I heavy chain can strongly influence the outcome of bone marrow transplantation (BMT) from an unrelated donor. Their results indicated a crucial role of the differences in amino acid position 116 of the HLA class I heavy chain. That study was the first attempt to explain the effects of HLA mismatch using structural data on MHC proteins. Another approach, presented by Petersdorf et al. [21], suggests that typing of the 5 HLA loci is only a surrogate for haplotype matching between the donor and recipient, which is of critical importance to HSCT outcome. Haplotype is defined by a series of markers (both known and undetected HLA alleles) that may be involved in the mediation of GVHD, GVL, and engraftment processes. Apart from HLA factors, many non-MHC signals, including interleukins and cytokines, play important roles in the second phase of graft-versus-host (GVH) reaction development, particularly with respect to the proliferation and expansion of T cells.

Data Sources 

Our analysis was based on 3 x-ray structures from Protein Data Bank (PDB) sources: 2NX5, 1MI5, and 2NW3 11, 22 (Table 2). The 3-dimensional structural models of B∗2702, B∗2705, and B∗3503 HLA class I alleles were constructed using Swiss-PdbViewer software and the SWISS-MODEL automated homology modeling server (Swiss Institute of Bioinformatics, Basel, Switzerland), at the amino acid sequences of these alleles, available at the National Center for Biotechnology Information's dbMHC database. Different conformations of presented peptides, all candidates for docking studies, were obtained using the large-scale low-mode (LLMOD) algorithm of MacroModel version 9.5 (Schrödinger, New York, NY) and original structures of peptides bound to MHC in PDB structures 2NX5 (an 11-residue peptide of the BZLF antigen of Epstein-Barr virus [EBV], designated EPLP) and 1MI5 (a 9-residue antigen of EBV). As proven experimentally by in vitro lymphocyte cytotoxicity testing [11], HLA-B∗3501 elicits a strong immune response against the EPLP determinant, but B∗3508 does not elicit a noticeable response to EPLP, even though HLA-B∗3508 can bind to EPLP, and the only difference between these 2 alleles is the single residue polymorphism Arg256 (B∗3508)-Leu256 (B∗3501). This was an important finding on which to base further analysis.

Table 2. PDB structures and tested allele sequences

PDB indicates Protein Data Bank; TCR, T lymphocyte cell receptor.

To analyze our results and verify the matching guidelines presented in Table 1, we collected data on donor matching level and post-HST survival in 1757 cases (Table 3). We used the published data of the HSCT component of the International Histocompatibility Working Group (IHWG) (available at http://www.ncbi.nlm.nih.gov/gv/mhc/ihwg), as well as data collected by the Central Polish Bone Marrow Donor & Cord Blood Registry, which contains data from more than 700 HSCTs performed at Polish transplantation centers between 2001 and 2008. Table 3 summarizes these data and patient group descriptors.

Table 3. Summary of HSCT data (A), diagnoses and age distributions (B), and comparison of overall post-HSCT survival in 2 analyzed groups of patients (C)

HSCT indicates hematopoietic stem cell transplantation; GVHD, graft-versus-host disease.

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Methods 

We began by performing a statistical analysis of the HLA data and post-HSCT survival data collected by the Polish Central Bone Marrow Donor Registry and the IHWG. This analysis allowed us to identify the most frequent HLA mismatches between unrelated donors and recipients. For selected groups, we performed a Kaplan-Meier survival analysis, then compared survival curves for different groups using the log-rank test, in which the result for a given observation is calculated as a logarithm of survival function 17, 23. For the IHWG data, no details about the cause of death or GVHD were available.

Figure 1 shows plots comparing the survival rate between the group of Polish patients diagnosed with GVHD and patients without GVHD symptoms. The influence of serologic-level and allele-level mismatches on overall post-HSCT survival in the entire cohort studied can be estimated using the data given in Figure 2. We categorized the observed mismatches according to the mismatch type and level. The most common B mismatch in our data set was B∗3501(recipient)/B∗3503(donor); the second most common was in donor–recipient pairs with B∗2702/B∗2705 incompatibilities.

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• Figure 1 

  Survival curves for patients with GVHD (gray) and without GVHD (black) receiving transplants from unrelated donors with 2 kinds of HLA-B mismatch, B∗2702/2705 and B∗3501/3503.

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• Figure 2 

  Comparison of survival probability for patients from groups distinguished on the basis of donor matching level: Lr match, low-resolution match (allele mismatch); Lr MM, low-resolution mismatch (antigen mismatch); DBMM, double mismatch (in 2 loci); FM, full-match–high-resolution match.

To investigate the influence of such mismatches on the stability of the TCR–pMHC complex, we constructed a 3-dimensional structural model for each of the 4 aforementioned HLA∗B alleles. We generated candidate peptide structures for docking using an LLMOD conformational searching routine of MacroModel 9.5 with constraints (Figure 3). The conformations obtained (> 800) were ranked according to the energy and root mean square deviation (RMSD) of alpha carbons from the original peptide conformations. Subsequently, the 75 conformations with the lowest values of energy and the 75 conformations with the lowest RMSD were selected for further analysis.

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• Figure 3 

  A, Structure substitution scheme: several candidate conformations of EBV peptide–ball-and-stick representation (1) presented by the original MHC B∗3501 ribbon representation (2) and the HLA-B alleles listed in Table 3. B, TCR–MHC contact region. Residues in the ball-and-stick representation were considered during contact energy calculations. Red, presented peptide; yellow, MHC; blue, TCR.

TCR–peptide and TCR–MHC contact energies were calculated using the MacroModel 9.5 multiple minimization routine (force field: OPLS 2001; conjugate gradient minimization method; convergence threshold: 1.0).

In the first step of the analysis, the structure of the whole complex of peptide, MHC, and TCR was minimized. In the second step, the energy of the TCR–MHC contact zone (MHC residues within 5 Å of the TCR and TCR residues within 5 Å of the HLA antigen) was calculated in the OPLS 2001 force field.

The third step used the following energy subtraction formula:

(1)

Current energies for whole TCR and pMHC parts of the complex were calculated as well. Then we obtained TCR-MHC and TCR-p contact energies by subtraction according to

(2)

In our analysis, we focused only on a single peptide (EBV peptides from experimentally resolved human pMHC/TCR structures). We did not analyze different peptides in the context of the same MHC–TCR pair, because the change of many factors at the same time in such a complicated system could make the interpretation of results obtained much more difficult or even impossible. In this article, we discuss only some of the results we obtained, focusing on the HLA-B mismatch because it represents the greatest number of mismatched donor–recipient pairs, and because of the evident difference between patient survival curves. We attempted to investigate DRB1 and DQB1 mismatches, but, in the case of HLA-DR, the number of donor–recipient pairs with the same kind of allelic difference was insufficient to perform statistical analysis. In the case of HLA-DQ mismatch, we did not find a statistically significant influence of this type of donor–recipient difference on post-HSCT survival. We obtained promising results from analyzing HLA-C mismatches and HLA-Cp–KIR (killer cell immunoglobulin-like receptor) complexes, but the specificity of KIRs as receptors for APCs and their exceptional role in the immune system makes this a subject for separate analysis.

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Results and Discussion 

We replaced the HLA allele in the selected TCR–pMHC complexes (based on the X-ray structures deposited in the PDB database) to evaluate the influence of its substitution on the total energy of the immunologic synapse. We noted that the conformation of the presented peptide is one of the most significant factors determining the estimated recognition energy.

A second important observation is the dependence of the calculated TCR–MHC and TCR–peptide interaction energy on the number and localization of amino acid substitutions between alleles. The difference in contact energy between alleles with a higher number of substitutions (eg, B∗2702/2705, B∗3501/3508) or between separate HLA-B groups (eg, B27/B35) exceeds the differences between tested complexes containing similar alleles varying at a single amino acid position (eg, B∗3501/3503) (Figure 4 and Table 4). This rule remains valid for a second analyzed structure, 1MI5, in which the original HLA belongs to a different HLA-B group (B∗0801). Comparing the interaction energy calculated for a complex that does not activate T cells in vivo (PDB code 2NW3 [11]) with energies obtained through substitution of B∗3501 by B∗2705 and B∗2702, we can conclude that the latter complexes should not trigger a TCR ELS4 reaction. This can provide insight into the possible consequences of B∗2702 or B∗2705/B∗3501 mismatches.

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• Figure 4 

  Results of energy minimization for constructed TCR–pMHC complexes for the 5 alleles evaluated. The y-axis represents the calculated energy of TCR–MHC contacts and TCR–-peptide contacts, according to formula (1). The NX5 structure was used as a template. 2702, 2705, 3501 and 3503 are TCR–pMHC complex models discussed in the text. 3508-ELS is a TCR–pMHC complex (B∗3508/EBVp) that does not activate T cells. 3508-NX5pep is a TCR–pMHC complex (B∗3508/EBVp) with peptide presented in conformation specific for B∗3501. X5ORG is a TCR–pMHC complex (B∗3501/EBVp) triggering T cell reaction.

Table 4. Comparison of interaction energy differences calculated according to formulas (1) and (2) for 3 pairs of alleles and mean survival time observed in 2 groups of graft recipients with B∗2702/2705 mismatches and B∗3501/3503 mismatches

Our analysis of TCR–MHC interactions alone demonstrated quite different relationships between the same alleles, however (Figure 5). The differences in the interaction energies calculated for B∗3501 and B∗3503 were much higher than those calculated for B∗2702 and B∗2705, despite the greater number of overall amino acid differences between the alleles from the second pair. There were no differences between B∗3501 and B∗3503 in the contact zone, as defined based on molecular distances and literature data (Figure 6, Figure 7). The only amino acid substitution between B∗3501 and B∗3503 was located in the α-helix of a binding groove, quite far from the contact surface of the antigen (Figure 7, Figure 8). Perhaps substitutions localized in the binding groove can influence TCR–MHC energy indirectly.

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• Figure 5 

  Contact energies for the 5 Å zone without ligand and with only TCR and MHC residues, according to formula (2). Complexes were reconstructed on the template of the 1NX5.pdb file.

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• Figure 6 

  The molecular surface of the TCR–MHC contact zone with the presented EBV peptide depicted using a ball-and-stick model. TCR chain A, orange; chain B, blue; HLA chain A, grey

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• Figure 7 

  Residues of MHC class I making contact on TCR (sorted by MHC residues) based on the known structures of TCR–pMHC complexes (according to Rudolph et al. [9]) and a proximity parameter of 5 Å. SNP B∗3501/3503 and B∗27-2/2705 also are shown (A105, 104, 101, and A141). The following MHC residues made the most contacts on TCR residues: A58, A59, A62, A65, A66, A69, A69, A70, A72, A73, A75, A76, A79, A146, A 147, A149, A150, A151, A152, A154, A 155, A158, A159, A162, A163, A166, A167, and A170.

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• Figure 8 

  Aligned sequences of 4 analyzed alleles of HLA-B. A, Amino acid differences between alleles of the same group (B2702-B2705 [black] and B3501-B3503 [blue]). B, Amino acid differences between alleles belonging to the separate HLA groups (B27 vs B35).

After measuring the interaction energies for the TCR–MHC and TCR–peptide interactions, we attempted to relate them to HSCT outcomes. We chose the 2 most numerous groups of donor–recipient pairs from the database. In the first group, the only donor–recipient difference was the HLAB∗3501–HLAB∗3503 mismatch; in the second group, the mismatch involved HLAB∗2702 and HLAB∗2705.

We decided to consider post-HSCT survival time as a measure of transplantation success, because we did not have sufficient data on GVHD occurrence in the 2 patient groups. Figure 9 compares the survival curves obtained for the 2 groups selected from all available data.

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• Figure 9 

  Comparison of survival curves for 2 groups of HSCT recipients who received transplants from B∗3501/3503 mismatched donors (MM 3501/3503; dotted line) and from B∗2702/2705 mismatched donors (MM 2702/2705; solid line) (n = 18).

We observed that preliminarily, the patients undergoing transplantation with B∗2702/2705 mismatches seem to have better survival than those receiving B∗3501/3503 mismatches; however, this observation needs to be confirmed using a larger sample size. Our results should be considered preliminary because of the modest sample size, but nonetheless, the difference in survival is striking. Inferring cause-and-effect relationships using clinical data often is difficult because of the many factors that can possibly influence a medical outcome. TCR–MHC interaction and binding is the first step in pMHC complex recognition and has a potentially far-reaching influence on the later stages of the immune response and, consequently, on post-HSCT survival. When the TCR–MHC interaction energy is too low, the TCR may not be engaged. If this occurs, then an immune reaction is not triggered, making the patient susceptible to infections, a major cause of death in post-HSCT patients.

When the TCR recognizes an MHC molecule, the initial complex aggregates, and the TCR has an opportunity to interact with the presented peptide. The results of recognition and T cell activation then depend on the origin of the presented antigen. If the antigen is host-derived, then T cell activation leads to development of GVHD symptoms. But, if the recognized peptide originates from a pathogen, then the immune response thus initiated is beneficial to the host organism. If the TCR–p interaction energy is too low to trigger T cell activation, immunodeficiency can result. Thus, we assume that the observed post-HSCT mortality caused by GVHD and infections can be influenced by initial interactions between TCRs and MHC molecules.

We also attempted to analyze in detail a possible relationship between the cause of death and the type of donor–recipient mismatch. The results, presented in Table 5, show that the differences in fractions between distinguished groups were not statistically significant. According to the data, higher GVHD risk was associated with MHC class I mismatch, but not with MHC class II mismatch (Table 6), just the opposite of what has been reported previously 13, 24. This discrepancy possibly could result from the fact that in our cohort, causes of donor–recipient mismatch at HLA-DRB1 were very poorly represented (presumably as a consequence of the matching rule to “avoid class II mismatches”).

Table 5. Comparison of the relationship between donor–recipient HLA matching level [A, mismatched, full-matched, and double-mismatched pairs; B, low-resolution matches and low-resolution mismatches] and causes of post-HSCT mortality

(A)	FM	MM	P
Deceased	166	116
Alive	293	143
Cause of death
GVHD	38 (36%)	36 (46%)	.16923
Graft rejection/relapse	32 (30%)	17 (22%)	.222273
Infection	19 (21%)	11 (16%)	.389036
Organ failure	10 (9%)	6 (8%)	.809733
Other	8 (7%)	9 (11%)	.338648
Total	107	79
No data available	59 (36%)	37 (32%)

(B)	LrMM	LrMatch	P
Deceased	69	29
Alive	82	38
Cause of death
GVHD	20 (44%)	9 (41%)	.815817
Graft rejection/relapse	8 (18%)	7 (32%)	.198171
Infection	7 (16%)	3 (14%)	.831077
Organ failure	5 (11%)	0
Other	5 (11%)	3 (14%)	.72254
Total	45	22
No data available	24 (35%)	7 (24%)

HSCT indicates hematopoietic stem cell transplantation; GVHD, graft-versus-host disease.

Table 6. Affect of the type of donor–recipient mismatch of GVHD occurrence

Mismatch	MHC Class I	MHC Class II	DBMM	FM
Number of patients	181	47	32	461
Percentage of patients with GVHD symptoms	22%	11%	19%	13%
P value of binomial proportion
difference test (between a given mismatch group and the FM control group)	.004621	.695966	.335145	–

GVHD indicates graft-versus-host disease; FM, full match, high resolution match.

Our data do not support the assumption that mismatches with overall fewer substitutions (when mismatched alleles belong to the same HLA serologic group) are less harmful. The survival for B∗3501/3503 donor–recipient pairs (1 Single Nucleotide Polymorphism [SNP]) is worse than that for B∗2702/2705 (3 SNPs) mismatched group. This may result from the fact that, in the former case, the SNP occurs in the binding groove, whereas the differences between B∗2702 and B∗2705 are found outside of the antigen-presenting region.

To verify the remaining general guidelines for donor matching, we analyzed the full data set in terms of the different types and levels of donor–recipient mismatches. The first rule, “always search for an optimal match” [19], seems justified based on the survival curves (Figure 2, Figure 10, Figure 11); the difference between the survival curve for patients who received transplants from fully matched donors and the curve for patients with suboptimal matches is statistically significant at a confidence level of 99% (P = .00017). The statement to the effect that allele mismatch is more often accepted and less harmful than antigen mismatch remains unconfirmed, however (P = .93).

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• Figure 10 

  Comparison of survival probability for patients from groups distinguished on the basis of the mismatched HLA locus: MMA, patients receiving transplants from HLA-A–mismatched donors; FM, patients receiving transplants from fully matched donors; DBMM, patients receiving transplants from donors mismatched at more than 1 locus. A, Curves plotted based on the whole data set. B, Curves plotted based on only the IHWG data. C, Curves plotted for only the Polish patients.

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• Figure 11 

  Survival curves for patients receiving transplants from donors mismatched in MHC class I or class II and for the fully matched control group.

The recommendation to choose a donor with a mismatch in HLA-A rather than in HLA-B seems invalid. The survival curve for patients receiving transplants from HLA-A–mismatched donors is worse than that for those receiving transplants from HLA-B–mismatched donors (Figure 10A and B). This difference is statistically significant (P = .00161). A separate analysis of the data set from Polish transplantation centers does not confirm this rule, however (Figure 10C). It is possible that survival in recipients of mismatched grafts depends on many other factors associated with clinical conditions and treatment policies.

The conjecture that GVHD may increase the risk of poor HSCT outcomes, and thus mismatches that possibly could cause GVHD should be avoided, could be verified based only on the Polish data set, because there was no appropriate annotation in the IHWG files. A diagnosis of GVHD strongly influenced the overall survival (OS) of the Polish HSCT recipients; the survival probability of recipients with GVHD symptoms was half that of recipients without GVHD.

In light of our results, we propose that the TCR–MHC interaction energies [formula (2)] in the first step of the TCR recognition of the MHC-p complex can aid the prediction of the final immune response and thus the survival of HSCT recipients. Small differences between the estimated energies of interaction (E_MHC-TCR + E_TCR-p) between the donor TCR and MHC compared with the recipient TCR and MHC seem to correlate with better survival. Furthermore, the difference in binding energy between HLA alleles is not directly related to the number of amino acid substitutions between them. Even substitutions in residues distant from the contact zone can change the TCR–MHC surface binding energy, particularly if the substitution occurs in the peptide-binding groove. Contact energy differences between alleles belonging to the same serologic group of HLA-B are not always smaller than the difference values calculated for alleles from different antigen groups.

Verification of the relationships proposed herein requires further study and analysis of more HLA loci. The lack of appropriate data is a significant limitation, because transplantation from mismatched, unrelated donors is generally a last resort, making it difficult to collect a sample of adequate size. HSCT is a relatively new medical procedure, and despite the many international projects and studies conducted to date, many unexplained phenomena related to this procedure remain.

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Acknowledgments 

Financial disclosure: This work was supported by Polish Ministry of Science Grant NN301 2385333. We are grateful to the Central Bone Marrow Donor and CB Registry Poltransplant; Department of Hematology–BMT Unit, Medical University of Gdansk; Department of Hematology and BMT, Medical University of Silesia; Children's University Hospital Hematology–Oncology Lublin, Department of Pediatric Oncology, Hematology & HSCT, Poznan University of Medical Sciences; Department of Hematology, Institute of Haematology and Blood Transfusion, Central Clinical Hospital; Department of Hematology & Oncology, Medical University of Warsaw; the Lower Silesian Center for Cellular Transplantation with the National Bone Marrow Donor Registry; and the Department of Children's Hematology and Oncology, Wroclaw Medical University for their disclosure of post-HSCT data.

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