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Analysis of HLA-A24-restricted peptides of carcinoembryonic antigen using a novel structure-based peptide-HLA docking algorithm

Authors


To whom correspondence should be addressed. E-mail: y.akiyama@scchr.jp

Abstract

Carcinoembryonic antigen (CEA) is a very common tumor marker because many types of solid cancer usually produce a variety of CEA and a highly sensitive measuring kit has been developed. However, immunological responses associated with CEA have not been fully characterized, and specifically a weak immunogenicity of CEA protein as a tumor antigen is reported in human leukocyte antigen (HLA)-A24-restricted CEA peptide-based cancer immunotherapy. These observations demonstrated that immunogenic and potent HLA-A24-restricted CTL epitope peptides derived from CEA protein are seemingly difficult to predict using a conventional bioinformatics approach based on primary amino acid sequence. In the present study, we developed an in silico docking simulation assay system of binding affinity between HLA-A24 protein and A24-restricted peptides using two software packages, AutoDock and MODELLER, and a crystal structure of HLA-A24 protein obtained from the Protein Data Bank. We compared the current assay system with HLA–peptide binding predictions of the bioinformatics and molecular analysis section (BIMAS) in terms of the prediction capability using MHC stabilization and peptide-stimulated CTL induction assays for CEA and other HLA-A24 peptides. The MHC stabilization score was inversely correlated with the affinity calculated in the docking simulation alone (r = −0.589, P = 0.015), not with BIMAS score or the IFN-γ production index. On the other hand, BIMAS was not significantly correlated with any other parameters. These results suggested that our in silico assay system has potential advantages in efficiency of epitope prediction over BIMAS and ease of use for bioinformaticians. (Cancer Sci 2011; 102: 690–696)

The carcinoembryonic antigen (CEA) is a very common tumor marker because many types of solid cancer usually produce a variety of CEA and a highly sensitive measuring kit has been developed.(1–3) However, immunological responses associated CEA have not been fully characterized except the observation that human leukocyte antigen (HLA)-A2 CEA epitope has been identified and contributed moderately to cancer vaccinations,(4,5) because there are several issues to be investigated. Specifically: (i) there are few efficient CEA epitopes with HLA-A24 restriction inducing cytotoxic T-lymphocytes (CTL) reaction in cancer patients; (ii) there have been few attempts to induce CEA-specific CTL using cancer patient-derived peripheral blood mononuclear cells (PBMC); and (iii) there have been few successful clinical trials using HLA-A24 restricted, which is well known as CEA peptide-based cancer vaccines.

Additionally, a weak immunogenicity or immunotolerance of CEA protein as a tumor antigen has been reported in cancer patients with advanced clinical stages.(6–8) Basically, CEA is originally expressed in the thymus and belongs to the CD66 family, which comprises highly homologous molecules expressed on hematopoietic cells, raising concerns about tolerance interfering with the development of anti-CEA immunity. Pickford et al.(6) reported that some effector T cells in PBMC can proliferate and some regulatory T cells can produce interleukin (IL)-10 with the stimulation by CEA protein using PBMC from 50 healthy volunteers. On the other hand, Crosti et al.(7) showed four CEA helper epitopes stimulating the proliferation of CD4+ T-lymphocytes from end-stage lung cancer patients. These results supported that CEA tolerance could be overcome by peptide vaccine recognized specifically by effector T cells.

Nukaya et al.(9) reported CEA652-A24-restricted peptide (TYACFVSNL) using healthy donor PBMC-based CTL induction assay for the first time and demonstrated that peptide-stimulated CTL efficiently killed CEA-positive cancer cell lines. The clinical trial by Matsuda et al.(10) and other researchers(11–13) using CEA652 peptide-treated dendritic cell (DC) vaccine showed that no significant clinical responses were seen, but some of the patients given vaccines developed in vitro CTL response against CEA peptide after vaccination. Considering that any HLA-A24 immunogenic CEA peptides besides CEA652 have not yet been found, it seems difficult to identify and predict immunogenic and potent HLA-A24-restricted CTL epitope peptides derived from CEA using conventional bioinformatics approach like HLA binding predictions of the bioinformatics and molecular analysis section (BIMAS) (http://www-bimas.cit.nih.gov/molbio/hla_bind/).

In the present study, we developed an in silico assay system to assess the affinity between HLA-A24 protein and A24-restricted peptide binder by combining two free software packages, protein 3D structure prediction software, MODELLER,(14) and protein-ligand docking simulation software, AutoDock.(15) In this method, we used a complex structure of HLA-A24 protein and its epitope peptide deposited in the Protein Data Bank (PDB)(16) as a template of 3D structure backbone. We then compared the current simulation assay with BIMAS in terms of the prediction capability and investigated the correlation of simulation results with other biological assays regarding HLA-A24 epitope binding.

Materials and Methods

Reagents and cell lines.  Recombinant human (rh) granulocyte macrophage colony-stimulating factor (GM-CSF), rhIL-2, rhIL-4, rhIL-7 and tumor necrosis factor (TNF)-α were purchased (Pepro Tech Inc., Rocky Hill, NJ, USA). The GM-CSF, IL-4 and TNF-α were used at 10 ng/mL for dendritic cell (DC) cultures. The TISI cell line was purchased (American Type Culture Collection, Manassas, VA, USA). Mouse anti-human HLA-A24 monoclonal antibody (MoAb) and FITC-labeled anti-mouse IgG were purchased (One Lamda, Canoga Park, CA, USA and Pharmingen, San Diego, CA, USA).

Synthetic peptides.  CEA peptide sequences with HLA-A24 binding motif were searched at the website of BIMAS as HLA–peptide binding predictions. The top 10 peptides (Table 1) were selected and synthesized using the method reported by Knorr et al.(17) The sequences of other synthetic peptides used in the present study are CMVpp65341–349 (QYDPVAALF), HIVgp120584–592 (RYLRDQQLL), MAGE-1135–143 (NYKHCFPEI), MAGE-2156–164 (EYLQLVFGI), MAGE-3195–203 (IMPKAGLLI), gp100152–160 (VWKTWGQYW), tyrosinase206–214 (AFLPWHRLF), EBNA3A246–254 (RYSIFFDYM), WT1-1M235–243 (CYTWNQMNL), WT1-2235–243 (CMTWNQMNL), HER2-163–71 (TYLPTNASL) and HER2-2780–788 (PYVSRLLGI).

Table 1.   CEA peptide sequences with HLA-A24 binding motif
Peptide nameAmino acid sequencePositionAmino acid lengthBIMAS score
CEA A24-1TYYRPGVNL425–4339200
CEA A24-2TYACFVSNL652–6609200
CEA A24-3VYAEPPKPF318–3269120
CEA A24-4IYPNASLLI101–109975
CEA A24-5LYGPDAPTI234–242960
CEA A24-6LYGPDTPII590–598960
CEA A24-7QYSWRINGI624–632960
CEA A24-8LYGPDDPTI412–420960
CEA A24-9TFQQSTQEL276–284939.6
CEA-A24-10KTITVSAEL492–500918.5
CMVpp65 A24QYDPVAALF341–3499168

MHC stabilization assay.  The protocol of MHC stabilization assay was described previously.(18) Briefly, 2 × 105 of T2-A24 cells suspended in 200 μL Iscove’s modified Dulbecco’s medium (IMDM; Gibco, Paisley, UK) containing 0.1% FBS were incubated with each peptide at 10 μM at 26°C for 16 h, and at 37°C for 3 h. The cells were stained with mouse anti-HLA-A24 MoAb and FITC-labeled anti-mouse IgG, and analyzed on a flow cytometer. Mean fluorescence intensity (MFI) increase was calculated as follows: MFI increase = (MFI with the given peptide − MFI without peptide)/(MFI without peptide).

CTL induction cultures.  Thirty two cases of HLA-A*2402+ cancer patients who showed more than 10 ng/mL of plasma CEA level, consisting of four lung and 28 colon cancer patients, were enrolled in the present study. Based on the collected cell number, finally PBMC from 29 cases of 32 were used for in vitro CTL inductions. The clinical research using PBMC from patients was approved by the Institutional Review Board of Shizuoka Cancer Center, Shizuoka, Japan. All patients gave written informed consent.

Briefly, PBMC were incubated in 6-well culture plates (Corning Inc., Corning, NY, USA) at 4 × 106 cells/mL in RPMI1640 medium supplemented with l-glutamine (2 mM), penicillin (100 U/mL), streptomycin (100 U/mL) and 5% (v/v) AB human serum (Lonza, Basel, Switzerland), referred to as DC medium, for 90 min. After incubation, the non-adherent cells were removed and the adherent monocyte-enriched population was cultured in the presence of 10 ng/mL of GM-CSF and 10 ng/mL of IL-4. On day 5 of culture, TNF-α was added at the dose of 10 ng/mL. After 7 days culture, most DC were positive for maturation markers like CD83, CD80, CD86, CD11c and HLA-DR in FACS analysis (data not shown). Harvested DC were suspended with Dulbecco’s PBS with calcium and magnesium (referred to as PBS[+]) containing 1% human serum albumin (Kaketsuken, Kumamoto, Japan) and incubated with various HLA-A24 peptides (each final 25 μg/mL) for 2 h at 37°C. The DC were irradiated (30 Gy) and incubated with non-adherent autologous PBMC at a ratio of 1:50~100 in the presence of 10 ng/mL of IL-7. After 7 days of culture, the PBMC were restimulated with irradiated peptide-pulsed DC again and incubated for 1 week. Human IL-2 was added to PBMC cultures every 2–3 days at a final dose of 2.5 ng/mL. After two rounds of in vitro DC stimulation, PBMC were used for negative selection with anti-CD4 and anti-CD56 MoAb (BD Biosciences, Franklin Lakes, NJ, USA) on AutoMACS (Miltenyi, Gradbach, Germany). Finally, 29 cases of enriched CD8+ T cells were used for IFN-γ production assay.

IFN-γ production assay.  The TISI cells were incubated with HLA-A24 peptide overnight at 20 μg/mL suspended in PBS(+) containing 1% human serum albumin and used as target cells. Cultured PBMC (1 × 105) and HLA-A24 peptide-pulsed TISI cells (1 × 105) were co-incubated in a round-bottomed 96-well microculture plate for 24 h. Finally, supernatants were collected and IFN-γ levels were measured using an ELISA kit specific for human IFN-γ (Biosource, Camallilo, CA, USA). The IFN-γ production index was calculated as follows: IFN-γ production index = (IFN-γ level with the given peptide)/(IFN-γ level without peptide).

Establishment of in silico docking simulation assay for epitope peptide binding to HLA-A24 protein.  First, we obtained a crystallized complex structure of HLA-A24 protein and its epitope peptide from PDB. The PDB code is 2BCK, which is currently only available as a 3D structure of HLA-A24 protein. The original epitope is a 9-mer peptide (VYGFVRACL) from human telomerase reverse transcriptase (hTERT). To build an initial 3D structure of any given A24-restricted epitope candidate peptide, we used the homology-modeling software, MODELLER (version 9v5) (University of California, San Francisco, CA, USA) with structure of hTERT epitope as a backbone template. Then, we predicted the affinities between HLA-A24 protein and synthesized peptides (see above) by receptor-ligand docking simulation software based on Lamarckian genetic algorithm, AutoDock (version 4.0) (The Scripps Research Institute, La Jolla, CA, USA). In AutoDock, an affinity between receptor and ligand molecules is calculated as the Gibbs free energy of binding (ΔG). Since the epitope-binding pocket on HLA protein and mode of binding have already been investigated,(19) the conformational search space in the AutoDock simulation can be limited to the vicinity of the HLA pocket by reference to the 2BCK data. Thus, the grid center coordinate defined in the search space was determined according to that of the original hTERT peptide and the size of the grid box was set to the default plus three points on each side of cuboid. For each of epitope candidate peptides, after Kollman charges are added, the docking simulations to HLA-A24 protein were run 50 times in each of which 1 × 106 energy evaluations were performed based on a genetic algorithm. Finally, we obtained the mean of top three affinities (the three lowest ΔGs) among the 50 runs as an affinity value of peptide binding.

Statistical analysis.  The correlation coefficient, r, was calculated and statistical difference was analyzed using the Pearson’s correlation test. Values of < 0.05 were considered statistically significant.

Results

MHC stabilization assay of potential HLA-A24-binding peptides within CEA protein.  The MHC stabilization assay was performed to test CEA and other protein-derived peptide candidates with HLA-A24-binding motifs for HLA-A2402 binding efficiency using T2-A24 cells. Stabilization efficiency was evaluated as high in one (MFI increase ≧ 3), medium in three (2 ≦ MFI increase < 3), low in six (1 ≦ MFI increase < 2) and not binder in one peptide (MFI increase < 1). The CMVpp65 HLA-A24-binding peptide with high BIMAS score showed high efficiency for MHC stabilization, while CEA HLA-A24-binding peptides with a BIMAS score above 100 did not always indicate high stabilization efficiency (Fig. 1).

Figure 1.

 A MHC stabilization assay of potential HLA-A24-binding peptides within CEA protein. The MHC stabilization assay was performed to check the binding affinity of CEA-derived peptides candidates using T2-A24 cells. Stabilization efficiency was rated by mean fluorescence intensity (MFI) increase, which was calculated as MFI increase = (MFI with the given peptide − MFI without peptide)/(MFI without peptide).

CTL induction assay using CEA HLA-A24 peptides and serum CEA-positive cancer patient-derived PBMC.  In Figure 2, six representative cases of positive CTL response are shown. These CTL responded to various CEA HLA-A24-binding peptides including peptides 3, 6, 8 and 9. Especially, CTL from Case015 demonstrated very large amounts of IFN-γ production, 9.7 ng/mL. A summary of CTL induction assays using cancer patient PBMC is shown in Table 2. The CTL derived from 29 cases responded to all CEA HLA-A24 peptides except peptides 2, 7 and 10. The frequency of positive responses was relatively high against CEA peptides 6 and 9. Among 29 cases, 12 patients including two who showed positive responses for more than two peptides, demonstrated significant IFN-γ responses against peptide-pulsed TISI cells (positive rate 41.4%). On the other hand, the positive response rate against CMVpp65 peptide as positive control was 55.2%.

Figure 2.

 The CTL inducing activity of CEA HLA-A24-restricted peptides. CTL induction cultures were successful in 29 of 32 cases of CEA-positive cancer patients. Six representative cases of positive CTL response against CEA peptides 4, 6, 8 and 9 are shown. Measuring IFN-γ levels in supernatant from co-culture of CD8+ T cells and peptide-pulsed TISI cells was performed in triplicate. *< 0.05, **< 0.01.

Table 2.   Frequency of positive CTL-inducing activities in CEA-positive cancer patients
Peptide nameTested casesPositive casesPositive rate (%)
  1. †Two cases showed positive CTL responses for more than 2 peptides.

CMVpp65 A24291655.2
CEA A24-12926.9
CEA A24-22900
CEA A24-32913.4
CEA A24-42926.9
CEA A24-52926.9
CEA A24-625416
CEA A24-72900
CEA A24-82913.4
CEA A24-929310.3
CEA-A24-102900
SUM2912†41.4

Docking simulation assay for CEA HLA-A24-peptides binding to HLA protein.  Eight CEA peptides and CMVpp65 were shown positive for docking simulation to HLA-A24 protein (Fig. 3). The peptide with highest affinity was CEA A24-3 (ΔG = −43.5 kJ/mol), but CEA 7 and 10 were not binding. Meanwhile, CMVpp65 A24 peptide showed high affinity (−32.2 kJ/mol).

Figure 3.

 Characterization of CEA HLA-A24-restricted peptides using peptide-HLA docking simulation assay. For each of 9-mer epitope candidate peptides, the mean and standard deviation of the three lowest binding free energies are shown.

Additionally, we prepared an example figure of our in silico docking simulation assay using CMVpp65341–349 peptide, which showed a high docking affinity (Fig. 4).

Figure 4.

 Docking structure of CMVpp65341–349 peptide and HLA-A24 protein. The two subunits of HLA-A24 protein (shown as ribbons in green and cyan) are identical to chains D and F of 2BCK in the Protein Data Bank, respectively, and the CMVpp65 peptide (shown as a space-filling model of atoms) was docked to the binding pocket of the HLA protein. This model was drawn by PyMOL (http://www.pymol.org/).

Characterization of known HLA-A24-restricted peptides using various peptide-evaluation tools.  Sixteen HLA-A24-restricted peptides including CMVpp65 and CEA HLA-A24 peptides 2, 6, 9 and 10 were investigated in binding activity using BIMAS, MHC-stabilization, docking simulation and IFN-γ production assay (Table 3). The IFN-γ production assay was performed using PBMC from enrolled HLA-A*2402+ cancer patients who were not included in the CEA peptide-CTL induction cultures. BIMAS scores ranged 0.1–720. The MHC stabilization assay demonstrated that peptides with high, medium and low affinity were 5, 5 and 4, respectively. Docking simulation assay revealed that MAGE3, tyrosinase and HER2 peptides showed high affinities (<−14 kJ/mol) besides CEA and CMVpp65 peptides. The IFN-γ production assay indicated that six HLA-A24 peptides exhibited significant CTL induction activity. Taking these observations into consideration, three peptides (MAGE3, HER2-2, CMVpp65) alone showed a high score in all three parameters except BIMAS.

Table 3.   Peptide-HLA docking scores and other immunological factors of various HLA-A24-binding peptides
Peptide nameAmino acid sequencePositionBIMAS scoreMHC stabilization assayDocking (kJ/mol)IFN-γ production index
  1. †Modified peptide from natural WT1235–243 peptide.

HIVgp120RYLRDQQLL584–5927201.6013.51.02
MAGE1NYKHCFPEI135–143663.38−0.71.37
MAGE2EYLQLVFGI156–164902.27−6.11.14
MAGE3IMPKAGLLI195–2031.53.12−14.94.72
TyrosinaseAFLPWHRLF206–214182.50−17.30.96
WT1-1MCYTWNQMNL†235–2432002.87−9.20.83
WT1-2CMTWNQMNL235–24341.07−8.40.97
HER2-1TYLPTNASL63–713603.23−14.50.89
HER2-2PYVSRLLGI780–7887.53.20−15.92.54
EBNA3ARYSIFFDYM246–254601.40−11.41.28
CEA A24-2TYACFVSNL652–6602002.85−22.70.94
CEA A24-6LYGPDTPII590–598602.46−26.41.03
CEA A24-9TFQQSTQEL276–28439.61.00−11.52.25
CEA A24-10KTITVSAEL492–50018.50.010.20.97
CMVpp65QYDPVAALF341–3491683.45−32.02.31
gp100VWKTWGQYW152–1600.10.3413.11.00

Correlation between peptide docking and other immunological parameters.  Correlations between HLA-A24-restricted peptide docking and other immunological parameters including BIMAS, MHC stabilization and IFN-γ production, were evaluated using a correlation analysis. The MHC stabilization score was inversely correlated with the affinity calculated in docking simulation alone (r = −0.589, P = 0.015), not with BIMAS or IFN-γ production index (Fig. 5). On the other hand, BIMAS was not significantly correlated with any other parameters.

Figure 5.

 Correlation of peptide–HLA binding predictions with immunological parameters. (a) Correlation of BIMAS score and peptide–HLA binding affinity predicted in docking simulation assay with MFI increase ratio in MHC stabilization assay and IFN-γ production index. MHC: MHC stabilization assay ratio; docking: peptide-HLA docking simulation assay; IFN-γ: IFN-γ production index. (b) Correlation of binding affinity predicted in docking simulation assay with MHC stabilization assay ratio or IFN-γ production index.

Discussion

HLA molecules are cell surface proteins that bind to antigen peptides and present them as HLA–peptide complexes to T-lymphocytes, thereby inducing a specific immune response. The binding site of HLA-class I molecules is formed by a deep cleft between two α-helices on top of an extended β-sheet structure. Only approximately bound peptides with a high affinity could trigger a potent immune response. Therefore, to identify possible high-affinity peptides derived from tumor antigens is a very important theme for developing cancer vaccines that have therapeutic efficacy.

Recently, a large amount of data for crystal structures of HLA–peptide complexes has been accumulated, allowing bioinformaticians to predict molecular binding simulations.(20,21) However, despite biophysical and biochemical analysis of HLA complexes, accurate prediction of antigen peptide binding to HLA molecule is still difficult.

In the present study, we developed an in silico docking simulation assay between the HLA-A24 molecule and an antigen peptide based on computational structural analysis, aiming to predict optimal docking peptides with high affinity leading to effective CTL activation. Conventional algorithms based on primary amino acid sequence have been successful so far in predicting antigenic peptides binding to the HLA class I pocket, but such algorithms have limited accuracy and provide no structural information.(22,23)

Recently, the development of algorithms to predict the structure of MHC–peptide complex has been tried. Briefly, Logean and Rognan(24) used a binding free energy scoring function in a newly developed EpiDock, and Desmet et al.(25) developed a quantitative structure-based affinity scoring method for prediction of anchoring peptide side chains in peptide–HLA complex.

Unfortunately, these methods have not been applied to the intact HLA-A24 protein because of no such available structure in PDB at that time. We performed, for the first time, the epitope prediction for HLA-A24 protein using the 3D complex structure of the protein and epitope (PDB code 2BCK). In this study, we applied two software packages, AutoDock and MODELLER, to a novel in silico assay system of peptide binding affinity. MODELLER was performed to build the initial conformation of any given peptide, and AutoDock was used to assess the affinity (ΔG) between HLA-A24 protein and the given peptide. In general, AutoDock is originally available for predicting the binding mode of low-molecular compounds in the context of receptor–ligand docking, and is not always suitable for highly flexible ligands such as 9-mer peptides. In the case of HLA–peptide docking, however, the binding pocket and epitope conformation have been well investigated, and the conformation search parameters in simulation can be limited or narrowed down differently from so called “blind docking.” We have shown that a local search simulation around the binding pocket of HLA-A24 protein reproduced the affinities between the protein and A24-restricted peptides. More importantly, all of these procedures are performed by combination of free software (AutoDock and MODELLER), suggesting that our in silico simulation assay is easily available to many bioinformaticians with the same interests. Recently, Fuhrmann et al.(26) reported a receptor–ligand docking simulation method based on a new Lamarckian genetic algorithm which can treat a large number of degrees of freedom.(26) Thus, the further improvement of an in silico binding assay system may be possible for not only HLA class I epitope peptides (approximately 10-mer) but also class II epitope peptides (approximately 20-mer). Additionally, we have performed MHC stabilization and CTL induction assays regarding various CEA and other HLA-A24-restricted peptides and investigated the correlation of docking simulation assay with other immunological parameters, the MHC stabilization score and IFN-γ production index.

Cytotoxic T-lymphocyte induction experiments using PBMC from CEA-positive cancer patients demonstrated that CEA A24-6 and 9 were mainly positive for CTL inducing activity. These peptides were shown to have moderate to high affinity in a docking simulation assay. These observations may support the negative selection theory that intermediate binders can be sometimes good CTL inducers in cancer patients because CTL precursors responding to high affinity peptides have been depleted in the thymus by negative selection, and such CTL may be exhausted after exposure to specific cancer antigen peptides.(27)

Unfortunately, CEA A24-3, which has the highest affinity in a docking assay, did not show strong CTL activation. Similarly, CEA A24-1, with the highest affinity in MHC stabilization, showed marginal binding score in a docking simulation, which indicates the discrepancy of affinity between docking simulations and other immunological binding assays. In our current method, unlike peptide epitopes, we did not permit the flexibility of the epitope-binding pocket structure of the HLA protein because of long computational time. The MHC stability assay suggests that CEA A24-1 is a good binder (Fig. 1), but its docking affinity is not high (Fig. 3). Since CEA A24-1 is the second largest peptide (1082.2 Da) among those in Table 1, it might not stably occupy a rigid binding pocket of HLA-A24 protein in a docking simulation. Actually, CEA A24-7, the largest peptide (1136.2 Da), has shown the lowest docking affinity. Although a strong correlation was not observed between the epitope’s molecular weight and docking affinity (r2 = 0.16), the rigid pocket simulation condition for HLA protein may have affected the docking conformation and affinity. A docking simulation that allows a pocket structure to be flexible is a challenge for the future, and it may be solved by advances in computer technology.

Most interestingly, CEA652 peptide, which is well known for specific CTL epitopes, did not show any positive CTL inducing activity in all patient PBMC. On the other hand, CEA A24-6, which showed the highest frequency of CTL induction, has not been reported as a CTL epitope previously, but few CTL induction studies using CEA-positive cancer patient PBMC have been done. Therefore, there might be some difference in CTL culture environments including immunological regulation between healthy donor and cancer patients, resulting in selection of antigenic peptides. Crosti et al.(7) showed four CEA helper epitopes stimulating the proliferation of CD4+ T-lymphocytes from end-stage lung cancer patients. These results supported positively our results that CTL inducing activity for any CEA peptides with HLA-A24 restriction was seen in 12 of 29 cancer patients.

Nukaya et al.(9) reported CEA652-A24-restricted peptide using healthy donor PBMC-based CTL induction assay for the first time and demonstrated peptide-stimulated CTL efficiently killed CEA-positive cancer cell lines. The CEA652 peptide has been also used for DC-based cancer vaccine against CEA-positive gastrointestinal solid cancers. The clinical trial by Matsuda et al.(10) using CEA-peptide treated DC vaccine showed that no significant clinical responses were seen, but four of seven patients developed in vitro CTL response against CEA peptide after vaccination. In the present study 44% of cancer patients demonstrated CTL activity against various CEA peptides; however, there was no response to CEA652 peptide.

In another CTL induction assay using patient PBMC and 16 well-known HLA-A24 peptides including CEA peptides, six peptides were positive for specific CTL activation. With regard to these 16 peptides, we also obtained other immunological data. In Figure 5, correlations between HLA-A24 peptide docking simulation and other immunological parameters, including BIMAS, MHC stabilization and IFN-γ production, were investigated using a correlation analysis. The binding affinity predicted in docking simulation was inversely correlated with MHC stabilization score (r = −0.589, P = 0.015), whereas BIMAS was not significantly correlated with that or any other parameter. These observations suggested that our docking simulation assay might have potential advantage in efficiency of epitope prediction over BIMAS. However, our docking simulation assay is still not developed enough to compare with other predictive programs precisely, and the accumulation of more simulation data is needed.

Recently, novel conventional algorithms based on primary amino acid sequence like BIMAS have been developed. They show better prediction than BIMAS,(28–30) but such algorithms have limited accuracy. Similarly, our in silico assay system has yet to predict genuine CTL epitope peptides with high affinity. Computational methods for modeling of peptide-HLA binding include threading, molecular dynamics and approaches based on HLA binding pocket recognition. Technical improvements for all methods should be requested to elevate the accuracy of CTL epitope prediction in future.

Acknowledgments

We thank Dr. Mochizuki for supplying several synthetic peptides and for excellent technical assistance. This work was supported in part by a grant from the Cooperation of Innovative Technology and Advanced Research in Evolutional Area (CITY AREA) program from the Ministry of Education, Culture, Sports, Science and Technology, Japan.

Disclosure Statement

The authors have no conflict of interest.

Abbreviations
CEA

carcinoembryonic antigen

HLA

human leukocyte antigen

PDB

The Protein Data Bank

CTL

cytotoxic T cells

BIMAS

bioinformatics and molecular analysis section

PBMC

peripheral blood mononuclear cells

CMV

cytomegalovirus

Ancillary