The promise of pharmacogenomics depends on advancing predictive medicine. To address this need in the area of immunology, we developed the individualized T cell epitope measure (iTEM) tool to estimate an individual's T cell response to a protein antigen based on HLA binding predictions. In this study, we validated prospective iTEM predictions using data from in vitro and in vivo studies. We used a mathematical formula that converts
Peptide binding to HLA (MHC) is the critical first step required for a T cell response. HLA binding enables antigen presenting cells to engage T cells via the T cell receptor to initiate a cascade of events that stimulate proinflammatory responses [
To calculate an iTEM score we first identify putative HLA ligands and T cell epitope clusters using the EpiMatrix system [
T cell epitope clusters are promiscuous but they are not universal, and human APCs present only two DR alleles. We have observed that certain peptides stimulate immune response in some subjects better than others. In order to explain part of this observed variation we have developed the iTEM Score. iTEM scores are a special case of the EpiMatrix Cluster Score. iTEM scores describe the relationship between a particular patient’s HLA haplotype (considering only two HLA-DR alleles) and the amino acid sequence of a given epitope cluster. iTEM scores are used to predict the likelihood that the amino acid sequence of an antigenic peptide will be presented by a given subject’s antigen presenting cells and in turn stimulate that subject’s T cells. To calculate an iTEM score for a given individual we calculate an EpiMatrix Cluster Score for each HLA allele in the haplotype. Allele-specific cluster scores of less than zero are discarded (literally set to zero), and the two allele specific cluster scores are then added together to form an iTEM score. Negative allele specific cluster scores are discarded because the binding relationship between a given peptide and a given allele is independent of the relationship between that peptide and another allele. In other words the failure of one allele to present a given peptide does not negatively affect the relationship between that peptide and any other allele, and therefore we felt it would be wrong to allow negative allele specific cluster scores to detract from accompanying positive scores. Higher iTEM scores indicate an increased likelihood of immunogenicity.
An example of an EpiMatrix report from which an iTEM score can be calculated is shown in Figure
Calculating an iTEM score. Using the EpiMatrix report for a peptide, the iTEM score is equal to the difference between the sum of significant scores for an allele (shown in the black boxes above) and the expected score for a peptide of that length. Assessments outside the top 10% (
The six case studies reported here are based on immunogenicity measurements made in enzyme-linked immunospot (ELISpot) assays that measure interferon-gamma secretion from antigen-stimulated peripheral blood mononuclear cells (PBMCs) from humans or splenocytes from mice. Four involve vaccine candidate studies for
Antigens from each study were grouped into one of four categories based on overall iTEM score and ELISpot result. True positives are peptide-HLA pairs with both positive iTEM scores and ELISpot results. True negatives are peptide-HLA pairs with both negative iTEM scores and ELISpot results. False positives have positive iTEM scores and negative ELISpot results, and false negatives, have negative iTEM scores and positive ELISpot results. The cutoff for a positive iTEM score was initially set at 2.06, as described above.
Analyses were rerun with a higher cutoff that we hoped would adjust for factors of immunogenicity not predicted by EpiMatrix since peptides that are more likely to bind HLA would probably be less likely to be affected by other factors during antigen processing and presentation. We arbitrarily decided on 2.5 as the higher cutoff. Standard chi-squared and linear regression analysis between iTEM scores and ELISpot results were performed by hand or using Microsoft Excel 2003, respectively. Statistical significance was defined as
In the tuberculosis vaccine study, two groups of six HLA A2/DR1 transgenic mice were immunized intranasally once with a pool of 25 peptides at 2
There were 66 peptides tested in the DR1 mice, whose splenocytes were pooled, and therefore there were 66 peptide-HLA pairs for which we calculated iTEM scores. In the peptide immunization study, when the iTEM threshold was 2.06, there were 54 positives and 12 negatives. Of the 54 positives, 38 (70%) were ELISpot positive, and of the 12 negatives, 11 (92%) were ELISpot negative. Using chi-squared analysis, we found the association between iTEM score and ELISpot result to be statistically significant (
Characteristics of this study are summarized in Table
Design of six different studies used to validate the iTEM predictions. In three cases, inbred HLA transgenic mice were used for measurements of vaccine immunogenicity. In the other three, human subjects were recruited for evaluations of immune responses to protein antigens.
Study | Subject | Immunization | Boost |
---|---|---|---|
Tularemia | HLA Transgenic Mice | DNA vector IN | Peptides IN |
Smallpox | HLA Transgenic Mice | DNA vector IM | Peptides SC |
Tuberculosis | HLA Transgenic Mice | Peptide IN | Peptides |
T1D | Humans | Natural Autoimmunity | Peptides ex vivo |
FPX | Humans | Protein IV or SC | None |
Tularemia | Humans | Natural Infection | None |
A summary of the results using the threshold model for iTEM’s association with immunogenicity listed in descending NPV for an iTEM threshold of 2.06.
iTEM Threshold = 2.06 | iTEM Threshold = 2.5 | ||||||
Study | PPV (%) | NPV (%) | PPV (%) | NPV (%) | |||
Human Tularemia | 232 | <.02 | 19 | 95 | <.10 | 19 | 92 |
Tuberculosis | 66 | <.001 | 70 | 92 | <.001 | 71 | 80 |
Smallpox | 73 | <.10 | 36 | 85 | <.05 | 38 | 84 |
FPX | 30 | <.001 | 94 | 71 | <.001 | 94 | 71 |
Mouse Tularemia | 78 | <.10 | 61 | 63 | <.10 | 61 | 63 |
T1D | 56 | <.01 | 84 | 54 | <.05 | 83 | 44 |
In the smallpox vaccine study, HLA-DR1 or DR3 transgenic mice were immunized using a DNA-prime/peptide-boost strategy. Mice were injected intramuscularly, twice, two weeks apart, with 100
DR1 mice were immunized with 32 different peptides while DR3 mice were immunized with 41 different peptides; therefore, there were 73 peptide-HLA pairs for which we calculated iTEM scores. When the iTEM threshold was set to 2.5, 42 positives and 31 negatives were calculated. Of the 42 positives, 16 (38%) had positive ELISpot results and of the 31 negatives, 26 (84%) had negative ELISpot results. Using chi-squared analysis, we found the association between iTEM score and ELISpot result to be statistically significant (
Characteristics of this study are summarized in Table
The Tularemia antigenicity study involved 23
Due to varying cell yields, PMBCs from all 23 subjects were not exposed to all 27 peptides, and only 510 peptide-HLA pairs were tested via ELISpot. Since EpiMatrix only predicts for certain HLA alleles, there were only 232 peptide-HLA pairs for which an iTEM score could be calculated. Because the study consisted of human subjects who had been naturally infected with tularemia, there was no cutoff to generate a statistically significant association between iTEM score and ELISpot result due to a very large number of false positives. Using the 2.5 iTEM cutoff, there were 181 positives and 51 negatives. Of the 181 positives, 34 (19%) had positive ELISpot results and of the 51 negatives, 47 (95%) had negative ELISpot results (
Characteristics of this study are summarized in Table
In the tularemia vaccine study, six HLA-DR1 transgenic mice were immunized three times, at two-week intervals, with 25
The DNA plasmid administered to the DR1 mice contained 78 different peptide sequences; therefore, 78 peptide-HLA pairs were tested via ELISpot. There were 18 positives and 60 negatives using an iTEM score cutoff of 2.5. Of the 18 positives, 11 (61%) had positive ELISpot results and of the 60 negatives, 38 (63%) had negative ELISpot results. The large number of false negatives yielded a statistically insignificant association between iTEM score and ELISpot results (
Characteristics of this study are summarized in Table
We investigated the autoantigenicity of glutamic acid decarboxylase (GAD65) T cell epitopes in diabetic patients. PBMCs, isolated from whole blood samples of six human subjects, were cultured for seven days with a pool of 14 GAD65 peptides, with IL-2 and IL-7 added to the growth medium every two days. Cells were transferred to an IFN-
Due to varying cell yields, PMBC from all six subjects were not exposed to all 14 peptides, and only 67 peptide-HLA pairs were tested via ELISpot; however, since EpiMatrix only predicts for certain HLA alleles, there were only 56 peptide-HLA pairs for which an iTEM score could be calculated. While this may seem different from the immunogenicity studies carried out above, in T1D, which is mechanically similar to a peptide immunization, a single autoantigen is considered to be the target of autoreactive T cells and therefore an iTEM score of 2.06 had the most predictive power, with 43 positives and 13 negatives. Of the 43 positives, 36 (84%) had positive ELISpot results, and of the 13 negatives, 7 (54%) had negative ELISpot results. Using chi-squared analysis, we found the association between iTEM score and ELISpot result was statistically significant (
Characteristics of this study are summarized in Table
In the FPX immunogenicity study, 36 human subjects received FPX peptides intravenously and 40 others received FPX subcutaneously. PBMCs from 15 subjects were isolated from whole blood samples on day 1 before dosing and then again on days 42 and 180. Cells were cultured for 7 days with 10
45 peptide-HLA pairs were tested via ELISpot; however, since EpiMatrix only predicts for certain HLA alleles, there were only 30 peptide-HLA pairs for which an iTEM score could be calculated. Since this was a peptide immunization study, an iTEM threshold of 2.06 was used. There were 16 positives and 14 negatives according to iTEM scores. Of the 16 positives, 15 (94%) had positive ELISpot responses, and of the 14 negatives, 10 (71%) had negative ELISpot results. Using chi-squared analysis, we found the association between iTEM score and ELISpot result was statistically significant (
Characteristics of this study are summarized in Table
In this study, we developed an immunoinformatic tool to predict the outcomes of experimental measurements of vaccination and immunogenicity analysis. Two methods for predicting the association between iTEM and ELISpot response gave significant correlations. These were the linear model and the threshold model.
Of the studies that could be fit to a linear model, the linear slopes derived were rather varied. The average of the five slopes was 64.51 ± 58.34. While the linear model did reach statistical significance in most of the data, its accuracy was moderate at best. The average
iTEM scores correlated best with immunogenicity for studies in which a protein or small peptide was administered, but the correlation did not reach statistical significance with studies of immune responses to peptides following exposure to a pathogen. The strongest correlations were observed for the TB, T1D and FPX studies. In these studies, the average negative predictive value (NPV) was 72% and the average positive predictive value (PPV) was 83%, when the iTEM threshold was set at 2.06. Thus it appears that processing and antigen presentation do not introduce significant variation unaccounted for by EpiMatrix predictions. Since iTEM scores are generated only by examining specific peptide-HLA interactions, when these variables are minimized by using protein or peptide prime and boosts, the iTEM was a more accurate predictor of immune responses. While HLA presentation is necessary for immunogenicity, it is not sufficient. A number of factors related to the expression and processing of the antigen also influence immunogenicity. If the protein is not expressed or secreted by the pathogen during infection or not properly cleaved or transported by the host, it will not be immunogenic. Even with proper presentation, the peptide may be homologous to self, and therefore either the corresponding T cell has been deleted during thymic selection or T cells that respond to the peptide have been anergized in the periphery.
Adding a second step between immunization and exposure to the protein immunogen and antigen presentation lowered the predictive accuracy of iTEM for a data set. For example, in those studies where DNA vaccination was used, such as smallpox, the predictive accuracy of iTEM was not significant if the cutoff of 2.06 was used, but was improved by increasing the threshold to 2.5. This suggests that T cell responses to those peptides that are very likely to bind HLA as indicated by higher iTEM scores were more likely following DNA vaccination. Perhaps the addition of steps beyond HLA binding (such as protein processing following expression of the pseudoprotein from the DNA vaccine) can accidentally remove peptides otherwise capable of activating the immune system. Thus, in the smallpox vaccine study, iTEM only had a PPV of 38%; on the other hand, its NPV was 84%.
The relationship between immune response and T cell epitope is more complicated in studies where exposure of T cells to the antigen (natural infection studies) is affected by many factors including gene expression, protein processing, dose and route. Thus, for subjects exposed to
iTEM’s accuracy is greatest when used as a negative predictor of immune response, a feature which may be very useful for the interpretation of failed vaccine efficacy studies and for clinical trials of protein therapeutics. Using a threshold of 2.06, iTEM had an average NPV across the six case studies of nearly 80%, but a PPV of only 61%. A high NPV may be due to the fact that peptides that are unable to bind to HLA (as described by a low iTEM score) have a very small chance of being immunogenic. In contrast, a low PPV can be explained by factors affecting the protein’s processing and presentation, despite its constituent epitopes, capacity to bind HLA.
iTEM’s ability to predict the absence of a T cell response suggests useful applications in vaccine design. iTEM analysis might be useful for vaccine studies to explain both interpeptide and intersubject variability in T cell assays and clinical trials. In theory, iTEM analysis could be used to predict whether or not a subject would fall into the 5%–20% of the population that does not respond to commonly used vaccines. If a lack of response is predicted, a patient could be exempt, thus sparing him/her from unnecessary vaccinations.
Others have examined the role that an individual’s HLA DR genotype plays in regulating immune responses [
In the context of protein therapeutics, drug developers generally agree that T cell response, which is associated with the development of antidrug antibodies, is undesirable. The ability to predict that an immune response in a particular subject is likely would be useful for identifying individuals at higher risk of developing antidrug antibodies. These individuals could be excluded from clinical trials and/or advised to avoid the use of the protein therapeutic, increasing the safety of protein therapeutics for human use.
For example, in a prospective study, Koren et al identified selected HLA types that were associated with a stronger immune response against a novel antitumor therapeutic. These HLA alleles were associated with higher iTEM scores, higher T cell responses, and higher antibody responses in the Phase I trial [
In summary, the iTEM tool appears to be a useful method for predicting the absence of T cell response to a given vaccine or protein therapeutic. While this study has only examined CD4 epitopes, we expect that a similar, and possibly stronger correlation may exist for CD8 epitopes given that the constraints imposed by the closed-end HLA Class I binding groove [