Apostolos P Georgopoulos,Lisa M James,Spyros A Charonis,Matthew Sanders
Melanoma and Human Leukocyte Antigen (HLA): Immunogenicity of 69 HLA Class I Alleles With 11 Antigens Expressed in Melanoma Tumors
Melanoma,neoantigens,human leukocyte antigen,major histocompatibility complex,immunogenicity
Host immunogenetics play a critical role in the human immune response to melanoma, influencing both melanoma prevalence and immunotherapy outcomes. Beneficial outcomes that stimulate T cell response hinge on binding affinity and immunogenicity of human leukocyte antigen (HLA) with melanoma antigen epitopes. Here, we use an in silico approach to characterize binding affinity and immunogenicity of 69 HLA Class I human leukocyte antigen alleles to epitopes of 11 known melanoma antigens. The findings document a significant proportion of positively immunogenic epitope-allele combinations, with the highest proportions of positive immunogenicity found for the Q13072/BAGE1 melanoma antigen and alleles of the HLA B and C genes. The findings are discussed in terms of a personalized precision HLA-mediated adjunct to immune checkpoint blockade immunotherapy to maximize tumor elimination.
Melanoma and Human Leukocyte Antigen (HLA): Immunogenicity of 69 HLA Class I Alleles With 11 Antigens Expressed in Melanoma Tumors

Host immunogenetics play a critical role in the human immune response to melanoma, influencing both melanoma prevalence and immunotherapy outcomes. Beneficial outcomes that stimulate T cell response hinge on binding affinity and immunogenicity of human leukocyte antigen (HLA) with melanoma antigen epitopes. Here, we use an in silico approach to characterize binding affinity and immunogenicity of 69 HLA Class I human leukocyte antigen alleles to epitopes of 11 known melanoma antigens. The findings document a significant proportion of positively immunogenic epitope-allele combinations, with the highest proportions of positive immunogenicity found for the Q13072/BAGE1 melanoma antigen and alleles of the HLA B and C genes. The findings are discussed in terms of a personalized precision HLA-mediated adjunct to immune checkpoint blockade immunotherapy to maximize tumor elimination.

Melanoma incidence has been increasing for decades and, despite being the least common form of skin cancer, melanoma accounts for a disproportionately high number of skin cancer related deaths. Therapeutic advances such as checkpoint blockade immunotherapy have improved melanoma survival rates; however, checkpoint blockade immunotherapy outcomes are partially dependent on host immunogenetics. To that end, a recent study documented extended survival in patients who possessed certain human leukocyte antigen (HLA) Class I B alleles (B*18:01, B*44:02, B*44:03, B*44:05, B*50:01) and poor outcomes in those with another HLA allele (B*15:01). Remarkably, immunogenetic epidemiological findings demonstrated congruence between the population frequency of those HLA alleles that influence checkpoint blockade immunotherapy outcomes and the population prevalence of melanoma, suggesting that certain HLA alleles may similarly influence overall melanoma risk and protection as well as treatment outcomes. In addition, that study evaluated the influence of a large number of HLA alleles on the population prevalence of melanoma and identified robust protective effects associated primarily with alleles of the HLA Class I B gene, extending HLA-melanoma associations beyond those that are already known to influence melanoma checkpoint blockade immunotherapy outcomes. Taken together, these and other studies highlight the influence of HLA on melanoma population prevalence and individual survival and treatment response and suggest broad HLA-melanoma associations. Here we evaluate those associations in the context of the role of HLA in antigen elimination. HLA plays a critical role in immune surveillance and host protection by facilitating identification and elimination of foreign antigens including cancer neoantigens. Optimally, melanoma antigen-derived peptides bound by HLA class I are exported to the cell surface for presentation to CD8+ T lymphocytes signaling destruction of tumor cells, a process that depends on both binding affinity and immunogenicity. However, the highly polymorphic nature of HLA contributes to variation in the binding groove; consequently, HLA alleles vary tremendously with respect to peptide binding capability, immunogenicity, and subsequent antigen elimination. This variability also has implications for checkpoint blockade immunotherapy outcomes. Various immune-escape mechanisms exploited by tumors may disrupt the host immune response, permitting unchecked tumor cell proliferation. Immune checkpoint inhibitors are aimed at improving the ability of the host’s immune system to detect and eliminate tumor antigens; however, that rests on the assumption that host HLA composition complements that of the tumor antigen to permit antigen binding with sufficient affinity and immunogenicity to support tumor antigen elimination. Indeed, differential immunotherapy outcomes have been attributed to variability in melanoma neoantigen binding. Thus, both host immune response to cancer neoantigens and success of immunotherapy depends on binding affinity and immunogenicity of a given HLA allele-melanoma antigen combination; yet, relatively little is known about binding affinity and immunogenicity of specific HLA alleles with melanoma antigens. We have previously characterized the immunogenetic profile of melanoma with regard to 69 Class I HLA alleles. In that study, we determined the association between allele prevalence and melanoma prevalence at the population level and documented negative (protective) and positive (susceptibility) scores. Here, to test the hypothesis that those protective and susceptibility effects are related to melanoma antigen elimination, we use an in silico approach to characterize binding affinity and immunogenicity of those 69 Class I HLA alleles to 11 known melanoma antigens.

Eleven melanoma antigens (Table 1) were used based on their known occurrence in melanoma tumors ; their amino acid (AA) sequences were retrieved from the Uniprot Database and are given in Supplemental Appendix A.

We used 69 HLA Class I alleles (Table 2), consisting of 20 gene A, 36 gene C, and 13 gene C alleles. Alleles of A and B genes were assigned to supertypes. More specifically, of a total of 56 alleles of A and B genes, 53 alleles could be assigned to supertypes, namely all 20 A gene alleles and 33/36 B gene alleles; B*13:02, B*47:01 and B*49:01 were unassigned (Figure 2 in Sidney et al ). The assignment of alleles to supertypes is given in Table 2 and their distribution in Table 3.

The P/S scores are the Pearson correlations, , between melanoma prevalence and HLA allele frequency across 14 Westerns European Countries, after Fisher z-transformation to normalize their distribution: A negative score stems from a negative correlation between melanoma prevalence and allele frequency and, hence, indicated a protective role of that allele; conversely, a positive score stems from a positive correlation between melanoma prevalence and allele frequency, indicating a susceptibility role. Given the continuous nature of , its absolute value could serve as a measure of the strength of the melanoma protection or susceptibility effect of a particular allele. The 69 alleles evaluated here and their Melanoma-HLA P/S scores protective/susceptibility (PS) HLA scores are given in Table 4.

The INeo-Epp method was used for T-cell receptor (TCR) epitope prediction using the INeo-Epp web tool via the INeo-Epp web form interface. For that purpose, we split a given melanoma antigen to all possible 9-mer AA residue epitopes using a sliding window approach (Figure 1) and submitted each epitope to the web-application together with a specific HLA allele (Table 2). More specifically, we paired all epitopes with all alleles and obtained for each pair its percentile rank, a measure of binding affinity of the epitope-HLA allele complex; smaller percentile ranks indicate higher binding affinity. The web-application also gave a TCR predictive score for pairs with high binding affinities (percentile rank <2); scores >0.4 (called s) indicated positive immunogenicity and were analyzed further.

Since every individual carries 6 classical HLA alleles (2 of each 3 HLA Class I genes), an average Melanoma-HLA P/S score can be calculated: where A, B, C denote HLA Class I genes. Similarly, we computed an average positive immunogenicity score: We obtained expected estimates of ξ and θ using a bootstrap procedure, as follows. For each gene, 2 and θ scores were drawn randomly (with replacement) from the pool of available alleles of that gene and averaged to yield bootstrap values of ξ* and θ*for a simulated “individual” The procedure was repeated 1 million times for a total of 1 000 000 ξ* and θ* values. The association between P/S and immunogenicity scores was evaluated in the following 3 ways. (i) We computed the Pearson correlation between ξ* and θ*, as an overall measure of association. (ii) For each bootstrap iteration, we computed and retained the covariance between ξ* and θ* (COVξ*θ*, N = 6 allele-values per iteration), as a finer-grain measure of association. And (iii) we compared immunogenicity ξ* between protective (θ* < 0) and susceptibility (θ* > 0) Melanoma-HLA P/S groups, to find out whether it was higher in the protective group. Standard parametric (mean, standard deviation, etc.) and nonparametric statistics (median, interquartile range, etc.) were used to evaluate the distribution of ξ* and θ*. We used Tukey’s fences to identify outlier and extreme values, as follows. where Q1, Q3 are the 25th and 75th percentiles, respectively. These fences demarcated outlier values, that is, values lying outside the fences.

Standard statistical methods were used to analyze the data, including descriptive statistics, analysis of variance (ANOVA), Pearson correlation, and the Wilson score and associated 95% confidence intervals (CI) for testing a proportion. These analyses were done using the IBM-SPSS statistical package (version 27). All reported statistical significance P-values are 2-sided. The bootstrap was implemented using FORTRAN (Geany, version 1.38, built on or after 2021-10-09). A 64-bit Mersenne Twister random number generator with a large random double-precision odd seed was used for the random sampling.

Overall, there was a total of10 806 epitope-allele combinations with high estimated binding affinity (percentile rank < 2). Of those, 5817 (53.8%) had a positive immunogenicity score; this proportion (0.538 ± 0.005 SE, 95% CI 0.529-0.548) was statistically highly significant (Z = 7.96, P < .001, Wilson score test for single proportion).

Detailed results are given in Tables 5 to 7 and are plotted in Figures 2 to 6. There was a total of 11 antigens ×´ 69 alleles = 759 antigen-allele combinations, of which 15 had no cases of positive immunogenicity (PI, “hits”). We analyzed the remaining 744 combinations using a univariate ANOVA where the proportion of PI per count of high binding affinity (for each antigen-allele combination) was the dependent variable and the Antigen (N = 11) and Gene (N = 3) were fixed factors. We found the following.

Antigen had a highly significant effect on PI (F[10,711] = 36.7, P < .001). It can be seen in Table 5 and Figure 2 that antigen BAGE had the highest PI score (proportion of hits), followed by PMEL and CTA. The remaining antigens had smaller effects, with SSX having the lowest.

Gene had also a highly significant effect (F[2,711] = 44.0, P < .001), with gene B having the highest PI score (Table 6 and Figure 3), followed by genes C and A.

There was a highly significant Antigen × Gene interaction (F[20,711] = 4.5, P < .001), indicating that the effect of gene differed among antigens, as can be seen in Figure 4.

This was assessed by a univariate ANOVA where the PI score (immunogenicity proportion) was the dependent variable and the allele was a fixed factor. We a highly significant allele effect (F[68,675] = 2.58, P < .001). The immunogenicity scores for each allele are given in Table 7 and are plotted ranked in Figure 5. Allele B*56:01 had the highest score of 0.743, and allele A*03:01 had the lowest (0.257). The frequency distribution of the immunogenicity scores and the corresponding box plot are shown in the left and right panels of Figure 6, respectively. The median was at 0.566, and there were 2 high (2.9%) and 6 low (8.7%) outliers.

This was assessed by a univariate ANOVA where the PI score (immunogenicity proportion) was the dependent variable and the supertype was a fixed factor. We found a highly significant effect (F[12,589] = 8.29, P < .001). The average scores for individual supertypes are plotted in Figure 7. It can be seen that supertypes B44 and A03 A24 had the highest and lowest scores, respectively. Of special interest is the contrast between B44 and B62, since B44 has been found to be beneficial to the outcome of checkpoint blockade immunotherapy of melanoma, in contrast to B62 which had the opposite effect. Indeed, the average scores of B44 and B62 were ranked first and ninth, respectively (Figure 7), reflecting a significantly higher immunogenicity score of B44 than B62 (Figure 8). For B44, the mean ±SEM was 0.650 ± 0.02, N = 121 antigen-allele combinations, whereas for B62 it was 0.496 ± 0.047, N = 22 antigen-allele combinations; the differences was highly statistically significant (t[141] = 3.16, P = .002, independent samples t-test).

The results above dealt with HLA allele immunogenicity at the antigen level. In this analysis we focused on the relations between individual immunogenic nonamers and HLA alleles. Detailed information about the counts of single nonamers with positive immunogenicity and alleles of the 3 HLA Class I genes is given in Supplemental Appendix B. There was a total 837 distinct nonamers with positive immunogenicity. Of those, 830 came each from a different antigen (peptide): although a given antigen comprised a number of immunogenic nonamers (Supplemental Appendix C), any specific immunogenic nonamer came from only one antigen. The remaining 7 nonamers were present in both MAGE1 (P43355) and MAGE4 (P43358) antigens, and, hence, are shown twice in Supplemental Appendices B and C.

The bootstrap analysis was aimed to derive estimates of immunogenicity against the 11 melanoma antigens from sets of 6 HLA alleles (2 per each Class I gene), a realistic assessment at the organism level, given that each individual carries 6 such alleles. For the same set of randomly selected alleles, we computed (a) the average positive immunogenicity score ξ*, (b) the average Melanoma-HLA P/S score θ*, and (c) the covariance COVξ*θ* between the 2 scores. We thus obtained 1 million triplets which enabled us to evaluate the distributions of ξ*and θ*, and their association. The frequency distributions of ξ*and θ* are plotted in Figure 9A and B, respectively. With respect to the distribution of ξ*(Figure 9A), the median was at −0.124, and there were 655 (0.06%) high- and 7525 (0.75%) low-immunogenicity outliers (Figure 10A). With respect to the distribution of θ*Figure 9B, the median was at 0.537, and there were 8093 (0.81%) high (susceptibility) and 40 (0.004%) low (protection) P/S score outliers (Figure 10B). These findings indicate a correspondence between high immunogenicity/high protection and low immunogenicity/low protection (susceptibility). Direct support for this association was provided by 3 additional findings. First, the Pearson correlation between ξ*and θ* was negative and highly significant (P = −.095, P < .001, N = 1 million). Second, the average covariance was significantly negative (P < .001, one-sample t-test). And third, the average immunogenicity ξ* was significantly higher in the group with protective (negative) θ* values: for protective ξ*, θ* = 0.537 ± 0.018 (mean ±SEM, N = 700257), and for susceptibility ξ*, θ* = 0.534 ± 0.021 (N = 299743; P < .001, independent samples t-test).

Here we evaluated the immunogenicity of 69 HLA Class I alleles with 11 antigens expressed in melanoma tumors. Overall, a significant proportion of epitope-allele combinations were positively immunogenic, suggesting a propensity toward melanoma antigen elimination conferred by those HLA Class I alleles. The relatively low rates of melanoma may be partially attributable to the relatively high proportion of positively immunogenic epitope-antigen combinations. Further analyses documented positive immunogenicity varied across melanoma antigens and HLA Class I alleles with the highest proportions of positive immunogenicity found for the Q13072/BAGE1 melanoma antigen and alleles of the HLA B gene. Those findings and their implications are discussed further below. Of the 11 melanoma antigens, Q13072 (BAGE1) had the highest proportion of positive immunogenicity (88%) followed by P78358 (CTG1B) and P40967 (PMEL/gp100) (each 66%). A prior study that reported BAGE1 expression in 22% of melanomas suggested BAGE1 could serve as a target for T cells immunotherapy ; however, that has not been evaluated in clinical trials to date. The present findings showing that the vast majority of BAGE epitope-HLA Class I allele combinations were highly immunogenic support the hypothesis that targeting BAGE may be beneficial. Of note, 9 of the 69 HLA alleles did not have any “hits” in terms of positive immunogenicity for BAGE, highlighting the specificity of antigen epitope-allele effects. We are not aware of clinical trials involving antigen P78358 (CTG1B); however, several clinical trials targeting gp100 have shown varying rates of clinical response. Of course, clinical response is partially reliant on host HLA. Here, while 66% of the epitope-allele combinations resulted in positive immunogenicity for P78358 and P40967 antigens, 34% did not. Thus, for all 3 of these melanoma antigens, a majority of antigen epitope–HLA Class I allele combinations investigated here predict T cell response aimed at tumor elimination, although several HLA Class I alleles may be incapable of promoting an immune response to these antigens thereby permitting tumor proliferation. With regard to HLA, the B gene had the highest proportion of positive immunogenicity with melanoma antigens both overall (Figure 3) and for many of the specific melanoma antigens (Figure 4); however, considerable variation in immunogenicity was documented across alleles within each of the Class I genes (Table 4). Remarkably, alleles of the B44 supertype had significantly higher immunogenicity scores than those of supertype B62 (Figures 7 and 8), corresponding to favorable versus poor outcomes associated with those supertypes, respectively, in melanoma checkpoint blockade immunotherapy. This finding lends further support to the hypothesis that HLA-mediated melanoma antigen elimination underlies successful clinical response to the aforementioned immunotherapy. Variable immunogenicity likely partially accounts for varying HLA associations with melanoma prevalence and immunotherapy outcomes, a conclusion supported by the significant congruence documented here between positive immunogenicity and population-derived immunogenetic scores with respect to melanoma. This congruence was further supported by the results of the bootstrap analyses which took into account the fact that each individual carries 6 HLA Class I alleles, thus documenting the positive association between immunogenicity and melanoma-HLA P/S scores at this more realistic level. In summary, melanoma antigen binding and immunogenicity is presumed to be the mechanism through which certain HLA alleles protect against melanoma at the population level and improve treatment outcomes at the individual level. This idea is illustrated in the schematic diagram of Figure 11. To that end, we have proposed that sufficient immunogenic binding of melanoma antigens may eliminate cancerous cells, potentially even before their detection. In contrast, HLA alleles with poor binding affinity and/or weak/negative immunogenicity may result in cancer proliferation, metastasis, poor checkpoint blockade immunotherapy response, and shortened survival. In the latter case that is characterized by the absence of protective HLA, we propose a novel cancer immunotherapy consisting of administering the mRNA blueprint for the synthesis of specific HLA molecules with the highest affinity and immunogenicity to a patient’s tumor antigen(s). Coupled with immune checkpoint blockade immunotherapy, this personalized precision approach has the potential to maximize the effectiveness of HLA-mediated tumor elimination, and ideally, improve melanoma survival.

Click here for additional data file. Supplemental material, sj-docx-1-cix-10.1177_11769351231172604 for Melanoma and Human Leukocyte Antigen (HLA): Immunogenicity of 69 HLA Class I Alleles With 11 Antigens Expressed in Melanoma Tumors by Apostolos P Georgopoulos, Lisa M James, Spyros A Charonis and Matthew Sanders in Cancer Informatics