ARTICLE OPEN The identi ﬁcation of effective tumor-suppressing neoantigens using a tumor-reactive TIL TCR-pMHC ternary complex Sang Hoon Kim1,10, Bo Ryeong Lee1,10, Sung-Min Kim1,10, Sungsik Kim1, Min-seok Kim1, Jaehyun Kim2, Inkyu Lee2,3, Hee-Soo Kim1, Gi-Hoon Nam2,4, In-San Kim3,5, Kyuyoung Song1, Yoonjoo Choi6✉, Dong-Sup Lee7✉and Woong-Yang Park1,8,9✉ © The Author(s) 2024 Neoantigens are ideal targets for cancer immunotherapy because they are expressed de novo in tumor tissue but not in healthy tissue and are therefore recognized as foreign by the immune system. Advances in next-generation sequencing and bioinformatics technologies have enabled the quick identi ﬁcation and prediction of tumor-speci ﬁc neoantigens; however, only a small fraction of predicted neoantigens are immunogenic. To improve the predictability of immunogenic neoantigens, we developed the in silico neoantigen prediction work ﬂows VACINUS pMHC and VACINUS TCR:VACINUS pMHC incorporates physical binding between peptides and MHCs (pMHCs), and VACINUS TCRintegrates T cell reactivity to the pMHC complex through deep learning-based pairing with T cell receptors (TCRs) of putative tumor-reactive CD8 tumor-in ﬁltrating lymphocytes (TILs). We then validated our neoantigen prediction work ﬂows both in vitro and in vivo in patients with hepatocellular carcinoma (HCC) and in a B16F10 mouse melanoma model. The predictive abilities of VACINUS pMHC and VACINUS TCRwere con ﬁrmed in a validation cohort of 8 patients with HCC. Of a total of 118 neoantigen candidates predicted by VACINUS pMHC , 48 peptides were ultimately selected using VACINUS TCR. In vitro validation revealed that among the 48 predicted neoantigen candidates, 13 peptides were immunogenic. Assessment of the antitumor ef ﬁcacy of the candidate neoepitopes using a VACINUS TCRin vivo mouse model suggested that vaccination with the predicted neoepitopes induced neoantigen-speci ﬁc T cell responses and enabled the traf ﬁcking of neoantigen-speci ﬁc CD8+T cell clones into the tumor tissue, leading to tumor suppression. This study showed that the prediction of immunogenic neoantigens can be improved by integrating a tumor-reactive TIL TCR-pMHC ternary complex. Experimental & Molecular Medicine (2024) 56:1461–1471; https://doi.org/10.1038/s12276-024-01259-2 INTRODUCTION Neoantigens have been characterized as promising therapeutic targets for cancer immunotherapy because they are newly expressed peripherally on cancer tissue and, thus, are not subjectto thymic central tolerance1. During the progression of cancer, the tumor immune microenvironment becomes immunosuppressive, which compromises the activation and effector capacity of cancer- speci ﬁc T cells. In these circumstances, vaccines designed to target tumor-speci ﬁc neoepitopes are promising for effective tumor control. Vaccines targeting mutant proteins present in tumor cells have been successful in mouse models and clinical studies. In humans,neoantigen vaccine studies have shown the ability to generate neoantigen-speci ﬁc T cells in melanoma, glioblastoma, and pancrea- tic cancer2–4and are supportive of the ability of these therapies to induce vaccine-related tumor regression in melanoma2and to protect against tumor recurrence in melanoma and pancreatic cancer4,5. Neoantigen-targeting therapies are thus viewed aseffective approaches for generat ing antitumor immune responses6,7, and clinical trials have revealed that personalized neoantigen-based cancer vaccines are feasible, safe, and effective in patients with melanoma, glioblastoma, breast cancer, and pancreatic cancer8. The ef ﬁcacy of cancer vaccines primarily depends on the selection of highly immunogenic neoantigens that can induce the strong cytotoxic CD8 +T cell response required to reject tumors. Foreign antigens or highly expressed tumor-speci ﬁc aberrant proteins are proteolytically cleaved by the proteasome, and theresulting mutated peptides are presented on the membrane of malignant cells via the peptide-MHC class I complex (pMHC). The activation of CD8 +T cells is fundamentally dependent on the precise interaction between T cell receptors (TCRs) and exogenous antigenic peptides presented via MHC class I. Thus, the interaction between the pMHC complex and TCR is of paramount importance. We developed in silico neoantigen prediction work ﬂows, VACINUS pMHC and VACINUS TCR, to predict tumor neoantigens more accurately from raw sequencing data based on peptide-MHC Received: 4 January 2024 Accepted: 14 March 2024 Published online: 12 June 20241Geninus Inc., Seoul 05836, Korea.2Department of Research and Development, SHIFTBIO Inc., Seoul 02751, Korea.3KU-KIST Graduate School of Converging Science and Technology, Korea University, Seoul 02841, Korea.4Department of Biochemistry and Molecular Biology, Korea University College of Medicine, Seoul 02841, Korea.5Chemical & Biological Integrative Research Center, Biomedical Research Division, Korea Institute of Science and Technology (KIST), Seoul 02792, Korea.6Combinatorial Tumor Immunotherapy MRC, Chonnam National University Medical School, Hwasun-gun, Jeollanam-do 58128, Korea.7Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, Korea.8Department of Health Science and Technology, Samsung Advanced Institute of Health Science and Technology, Sungkyunkwan University, Seoul, Korea.9Samsung Genome Institute, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.10These authors contributed equally: Sang Hoon Kim, Bo Ryeong Lee, Sung-Min Kim.✉email: kalikuta@gmail.com; dlee5522@snu.ac.kr; woongyang.park@kr-geninus.comwww.nature.com/emm1234567890();,: binding and a putative tumor-reactive tumor-in ﬁltrating lympho- cyte (TIL) TCR-pMHC ternary complex, respectively. Using a B16F10 mouse tumor model, we showed that these work ﬂows can be used to better select therapeutically relevant cancer vaccines. MATERIALS AND METHODS Patients This study was approved by the institutional review board (IRB) of Asan Medical Center (IRB number: 2022-0263), and informed consent was obtained from all participants. A total of 33 patients were recruited: 6 with colorectal cancer (CRC), 2 with melanoma, 14 with hepatocellularcarcinoma (HCC), and 11 with gastric cancer (GC). Of the 33 patients, 8patients with HCC were assigned to the validation cohort for neoantigenprediction work ﬂows, while the remaining 25 patients composed the training cohort. All procedures were carried out in accordance with relevant guidelines and regulations. Identi ﬁcation of potential neoantigens using next-generation sequencing (NGS) data The neoantigen selection process was based on the following features: NGS data, peptide/MHC-I interactions, and TCR-pMHC interactions. First, weselected highly expressed nonsynonymous or indel mutations using wholetranscriptome sequencing (WTS) data. The selection criteria included VAF > 0,read counts > 10 at the transcript level, and the sum of transcripts per million(TPM) calculated based on genes > log 0.4. Second, an in silico prediction method was employed to assess the binding af ﬁnity of the mutated peptides to the HLA class I molecules of the patient. It is known that there is acorrelation between immunogenicity and high-af ﬁnity binding to HLA class I molecules 9, and in general, in silico prediction tools have high positive predictive value in the identi ﬁcation of strong binders10.O n l yp e p t i d e sw i t h 9–10 amino acids were considered. We used MHC ﬂurry (v2.0.1)11to estimate the binding af ﬁnity of the pMHC-I complex. In the selection process, the second amino acid is an important binding anchor position; thus, any mutations in the second position were excluded. Third, the bindingcharacteristics of the peptides presented on the HLA molecules to the TCRwere considered. The interaction between the TCR and the pMHC complexhas been shown to impact immunogenicity 12,13. We applied PRIME, which combines predictions of binding to MHC-I molecules and propensity for TCR recognition14, and obtained a PRIME score corresponding to alleles based on immunogenicity. Finally, we applied an ensemble model built using variousfeatures, including immune-related parameters such as peptide-MHC af ﬁnity, dissimilarity, and foreignness (see the section “Results ”). Prioritization of immunogenic neoantigens Using scRNA-seq data of CD8 +T cells isolated from TILs, we performed a standard clustering process using the Louvain method to identify putative tumor-reactive CD8 +T cells. Previous studies reported exhausted T cells and proliferative T cells as tumor-reactive CD8 +T cells15–17. To maximize the number of putative tumor-reactive T cells present in the tumor tissue,we additionally included activated and effector T cells. CD8 +T cell subtype markers were obtained mainly from the report by Oliveira et al.;proliferating T cells ( MKI67, TOP2A ), exhausted T cells ( PDCD1, TIGIT, HAVCR2, LAG3, CTLA4 ), effector T cells ( GZMA, GZMK, GZMH, PRF1, NKG7, TNF, GNLY, IFNG ), and activated T cells ( HLA-DR ) 15–18. The remaining T cell types were classi ﬁed as naive/memory T cells ( SELL, CCR7, and IL7R ), NK/ γδ- like T cells ( TRGV9, TRDV2, KLRB1, and KLRC3 ), and MAIT cells ( TRAV1-2 )19. For prioritizing the immunogenic tumor neoantigen candidates, peptides were binary-classi ﬁed as “Tier 1 ”(high priority) or “Nontier 1 ” based on the binding between the TCR of the putative tumor-reactive TILs and pMHC complexes. The binding speci ﬁcities between TCRs and peptide sequences presented on the HLA molecules were predicted using pMTnet(v1.0)20. While the default cutoff value for pMTnet is 0.01, we opted for a more inclusive approach by setting the threshold higher, using 0.05 as thecutoff to expand our pool of candidate peptides. RESULTS Preﬁltration criteria for the selection of candidate neoantigens The work ﬂow of neoantigen identi ﬁcation is shown in Fig. 1, which integrates commonly used bioinformatics tools(Supplementary Table 1). To establish pre ﬁltration criteria for the selection of candidate neoantigens, we used our in-house immunogenicity dataset and the Tumor Neoantigen Selection Alliance (TESLA) dataset21. To determine the pre ﬁltering criteria, we used only the results for immunogenic neoantigens from bothdatasets. Three patients with melanoma (1, 2, 3) and 2 patients with non-small cell lung cancer (NSCLC) (12, 16) in the TESLA dataset provided 25 immunogenic neoantigens. The in-house dataincluded the results of in vitro immunogenicity screening ofneoantigens from a training cohort of 25 patients (CRC: 6, GC: 11, HCC: 6, MN: 2). The neoantigens comprised 27 immunogenic neoantigens (data not shown). Only 9- and 10-mer peptides and aWTS VAF > 0 were considered. Taking the second anchoringposition into account, any peptides that contained mutations at the second position were excluded. We tried to maintain high sensitivity in the selection process of potential neoantigencandidates and thus set cutoff values accordingly. We used log 0.4 as the TPM cutoff for both datasets (Supplementary Fig. 1a). For MHC ﬂurry and PRIME, 140 nM and 0.847 (PRIME %rank score) were used since the cutoff values maintained 90% sensitivity inboth datasets (Supplementary Fig. 1b, c). Applying these cutoff criteria, 2 immunogenic neoantigens were ﬁltered out from the in- house dataset, resulting in a total of 25 immunogenic neoantigensavailable for VACINUS pMHC modeling. Development of an in silico neoantigen prediction work ﬂow, VACINUS pMHC The TESLA dataset produced 199 neoantigens that passed the preﬁltration criteria for selecting candidate neoantigens, of which 25 were positive for immunogenicity. In the in-house dataset,there were 99 neoantigens consisting of 25 immunogenicity-positive and 74 immunogenicity-negative neoantigens that met the pre ﬁltering criteria (data not shown). Our computational pipeline, VACINUS, is mainly composed of peptide-MHC bindingprediction and immunogenicity prediction using TCR information.The peptide-MHC binding prediction protocol VACINUS pMHC is an ensemble model that utilizes 13 features (Supplementary Table 2). We initially considered 20 features and reduced them to 13 afterrecursive feature elimination while optimizing PRAUC (0.347 to0.383 after feature selection). Considering both the F1 score and immunogenicity-positive neoantigen sensitivity, we selected the point with the highest sensitivity while maintaining the highestF1 score as our criterion for selection. This threshold, 0.14, represents the suitable agreement between the sensitivity and PPV (0.76 and 0.25, respectively). Subsequent validation of the VACINUS pMHC work ﬂow in a validation cohort of 8 patients with HCC identi ﬁed a total of 118 neoantigens. The synthesized neoantigens, crafted as short peptides, underwent in vitro immunogenicity screening utilizingautologous PBMCs. Of the 118 neoantigens, 30 were con ﬁrmed to be immunogenic by in vitro immunogenicity screening (25.4%, Supplementary Table 3, Fig. 2a, b), which is relatively better than previously reported results2,15. The evaluation of VACINUS pMHC on the TESLA dataset demonstrated a neoantigen selection sensitivity of 81.5% (Supplementary Fig. 2), surpassing the leading team ’s achievement of 73.1% and outperforming the top 5 teams ’ average sensitivity of 59%21. Development of VACINUS TCRand in vitro validation In pursuit of a comprehensive neoantigen prediction modelgrounded in a tumor-reactive TIL TCR-pMHC ternary complex, weconducted scRNA-seq on a collective pool of 202,575 CD8 +TILs obtained from the training cohort of 25 cancer patients for neoantigen prediction work ﬂows (CRC: 6, GC: 11, HCC: 6, MN: 2) (Supplementary Fig. 3a). Further details can be found in the supplementary materials and methods. After quality control, a subset of 70,817 cells met the established criteria, with furtherselection narrowing to 25,411 CD8 +T cells expressing CD8 andS.H. Kim et al. 1462 Experimental & Molecular Medicine (2024) 56:1461 – 1471 excluding those expressing CD4. Through cluster-based T cell annotation of the aforementioned 25,411 cells, a subset compris- ing 14,592 cells was identi ﬁed and designated putative tumor- reactive T cells. This subset constituted 57.4% of the total CD8 +T cell population. Notably, putative tumor-reactive CD8 +T cells encompass the following functional phenotypes: exhausted T cells, proliferative T cells, activated T cells, and effectorT cells15–18(Supplementary Fig. 3b). As previously shown17, the expansion indices of putative tumor-reactive T cells and the remaining T cell populations revealed a signi ﬁcant increase in clonality among the tumor-reactive T cells ( p< 0.01, Supplemen- tary Fig. 3c). The TCR βchain repertoire of these tumor-reactive T cells was characterized by sequencing highly variable CDR3 regions. pMTnet (v1.0)20was utilized to predict the binding speci ﬁcity between the CDR3 βsequences of putative tumor- reactive T cells and pMHC complexes to establish VACINUS TCR. Neoantigens predicted to bind the TCR of putative tumor-reactive T cells are designated tier 1 neoantigens, and the remainingcandidates are designated nontier 1.We then tried to validate this work ﬂow using a distinct dataset from 8 patients with HCC (Supplementary Fig. 4). Among the 99,331 CD8 +sorted TILs sequenced, 24,091 cells successfully met the quality control threshold. Within this re ﬁned subset, 21,961 cells were identi ﬁed as CD8 +T cells. Notably, among the CD8+T cell population, 12,650 cells were categorized as putative tumor-reactive T cells, constituting 57.6% of the total T cellpopulation. The marker genes used to identify the CD8 +T cell subtypes were similar to those used in other studies 16,22,23 (Supplementary Fig. 4a –c), and the gene expression levels for each subtype are listed in Supplementary Table 4. The putativetumor-reactive CD8 +T cells included exhausted T cells, prolif- erative T cells, activated T cells, and effector T cells (Supplemen- tary Fig. 4d). The clonality observed in putative tumor-reactiveT cells surpassed that in the other T cells, demonstrating statisticalsigni ﬁcance ( p< 0.05) (Supplementary Fig. 4e). The 12,650 putative tumor-reactive T cells exhibited a repertoire of 2526 unique TCR clones. These unique TCR clone sequences were then leveragedfor the selection of tier 1 neoantigens. From the VACINUS pMHC Fig. 1 VACINUS work ﬂow for neoantigen prediction and tier selection.S.H. Kim et al. 1463 Experimental & Molecular Medicine (2024) 56:1461 – 1471 work ﬂow, a comprehensive total of 118 neoantigens were identi ﬁed from a distinct cohort of 8 patients with HCC. Out of this pool, 48 neoantigens were predicted to bind with the TCRs of putative tumor-reactive T cells using pMTnet (v1.0)20. These neoantigens were subsequently designated tier 1. Of the 48 tier 1 neoantigens, 13 demonstrated immunogenicity (27.1%, in vitro validation for VACINUS pMHC ), whereas 17 out of the 70 nontier 1 neoantigens showed immunogenicity (24.3%) (Fig. 2b, Supple- mentary Table 5). Among the 8 patients, 7 showed positive responses to one or more neoantigens, resulting in a response rate of 87.5% (Fig. 2c). The variability in neoantigen immunogeni- city across patients is evident in these data. The number of tier 1neoantigens ranged from 2 to 10 per patient, while the number of nontier 1 neoantigens ranged from 3 to 15. The positivity rates for immunogenicity showed considerable variability among the neoantigen candidates, ranging from 12.5% to 41.2% acrossdifferent patients (Supplementary Table 5). Identi ﬁcation of tumor neoepitopes expressed by B16F10 tumors in vivoWhole-exome sequencing (WES) and WTS were performed on three distinct B16F10 tumor tissues. Highly expressed nonsynon- ymous and indel mutations were identi ﬁed through tumor –normal DNA analysis from splenocytes harvested from Fig. 2 In vitro validation of the predicted neoantigens. a Representative images of IFN- γELISpots generated from PBMCs stimulated with peptide antigens. bImmunogenic and nonimmunogenic neoantigens from 8 patients with HCC. cOf the 8 patients with HCC, 7 (87.5%) exhibited immunogenicity for at least one neoantigen.S.H. Kim et al. 1464 Experimental & Molecular Medicine (2024) 56:1461 – 1471 the same animals. Mutations observed in all three mice were considered neoantigen candidates (Supplementary Fig. 5a). A total of 510 nonsynonymous mutations expressing mutated alleles were identi ﬁed. Should neoantigens be presented by MHC-I molecules on tumor cells to drive CD8 +T cell responses, the binding af ﬁnity of a peptide to MHC-I is the major criterion for identifying neoantigens. In total, we identi ﬁed 186 potential neoepitopes from B16F10, representing 140 unique nonsynon-ymous/InDel mutations. Using VACINUS pMHC , a total of 30 candidate neoantigens were predicted from the B16F10 tumor model (Supplementary Fig. 5b, Supplementary Table 6). To further enrich immunogenic neoepitopes, the TCR/pMHC interaction was considered based on the assumption that tumor-speci ﬁc pMHC complexes are recognized by T cells and trigger an anticancer immune response in patients. We characterized the TCR βchain repertoire in putative tumor-reactive TILs by sequencing highly variable CDR3 regions. scRNA and TCR sequencing were performed on CD8 +TILs obtained from the B16F10 tumor model. We categorized a total of 6677 CD8 +T cells into ﬁve distinct clusters based on their gene expression pro ﬁles. These clusters included activated T cells, exhausted T cells, non- exhausted memory-like T cells, proliferative T cells, and NK-like T cells. Among the analyzed CD8 +T cells, three subtypes — exhausted T cells, proliferative T cells, and activated T cells —were designated putative target TILs, collectively constituting 70.4% (4,701 cells) of the total population (Supplementary Fig. 5c, d, Supplementary Table 7). Within the CD8 +TIL subset, the highest proportion was attributed to activated T cells, accounting for ~30%, followed by exhausted T cells, non-exhausted T cells, and proliferative T cells, each contributing ~20%. NK-like T cellscomprised ~10% of the CD8 +TIL population (data not shown). The 30 candidate neoantigens were further divided into 2 categories, tier 1 and nontier 1, based on the TCR of the tumor- reactive TIL/pMHC interaction. Ten neoantigens were selected astier 1 antigens in the B16F10 tumor model (SupplementaryTable 6). Synthetic long peptides (SLPs, 27 amino acids) targeting the 30 candidate MHC class I neoantigens were synthesized. Theimmunogenicity of each peptide was assessed by vaccinatingnon–tumor-bearing mice with 2 neoepitope peptides dissolved in sterile water with poly (I:C) as an adjuvant. Following the experimental plan, the mice were sacri ﬁced, and splenocytes were extracted for further analysis. Splenocytes were then stimulated with 10 µg/ml short peptide neoantigen, and IFN- γ- producing CD8 +T cells were analyzed by ﬂow cytometry (Fig. 3a). Neoantigens were considered immunogenic based on the meanﬂuorescence intensity (MFI) of IFN- γ+CD8+T cells compared to that of the background control without peptide stimulation (Fig. 3b). Immunogenicity was con ﬁrmed in three (B16-1-3-E1, B16-1-4-E1, and B16-1-7-E1) tier 1 neoantigens and in six (B16-3-1-E1, B16-3-5-E1, B16-3-6-E1, B16-15-E1, B16-3-16-E1, and B16-3-18- E1) nontier 1 neoantigens. Of the 10 tier 1 neoantigens predicted using our pipeline in the B16F10 tumor model, 3 (30%)demonstrated in vivo immunogenicity, which exceeded the predictive power reported in other studies (Fig. 3c) 24. The genes associated with these 9 in vivo immunogenic neoantigens were Pcmtd1 ,D230025D16Rik ,Nfrkb ,Rpl13a ,Pnp, Ctsd,Pask,Tbc1d5 , and Hsf2. Of the 9 neoantigens, 3 neoantigens overlapped with previously reported neoantigens with known immunogenicity in B16F10 cells, suggesting that these neoanti-gens exhibited the same characteristics as previously reported.The remaining 6 neoantigens with in vivo immunogenicity were novel predictions by the VACINUS platform (Supplementary Table 8). Based on these observations, we chose to vaccinate tumor- bearing mice using immunogenic neoepitopes. Of the 9 neoanti- gens derived from B16F10 cells con ﬁrmed to have in vivo immunogenicity, 6 neoantigens, including 3 tier 1 (1 –3, 1–4, 1–7)and 3 nontier 1 (3-1, 3 –5, 3–6), were selected for in vivo antitumor efﬁcacy testing. Neoantigen vaccination combined with anti-PD-1 therapy inhibited tumor growthWeﬁrst investigated the ef ﬁcacy of selected neoantigens in regulating tumor growth using the B16F10 tumor model. C57BL/6 mice were subcutaneously injected with 2.5 × 10 5B16F10 cancer cells, and neoantigen vaccination started 3 days later in thetherapeutic setting shown in Fig. 4a. All 7 treatments were well tolerated, with no weight loss throughout the entire experimental period (Fig. 4b). Compared with the poly(I:C) treatment, mono- therapy with tier 1 neoantigens did not signi ﬁcantly improve tumor growth ( p=0.239), and monotherapy with nontier 1 neoantigens showed no improvement in tumor growth (p=0.968) (Fig. 4c, Supplementary Fig. 6a, b). However, combina- tion treatment with tier 1 neoantigen and anti-PD-1 signi ﬁcantly reduced tumor growth (tier 1: p=0.019), whereas combined treatment with nontier 1 neoantigen and anti-PD-1 was notequally effective (nontier 1: p=0.986). Survival analysis using Kaplan ‒Meier curves revealed a signi ﬁcant increase in survival in the combined treatment group with anti-PD-1 +tier 1 neoantigen compared to the poly(I:C) +anti-PD-1 group ( p=0.027) (Fig. 4d). The mice in the anti-PD-1 +nontier 1 neoantigen combination group had longer survival; however, the difference was not statistically signi ﬁcant ( p=0.235). No signi ﬁcant differences in survival were found between tier 1 ( p=0.161) or nontier 1 neoantigen ( p=0.497) compared to the poly(I:C) monotherapy. The combination of tier 1 neoantigen with anti-PD-1 therapy increased the percentage of mice rendered tumor-free(i.e., complete remission at 25 days after treatment). In the grouptreated with anti-PD-1 alone, 4 out of 11 mice were tumor-free, whereas, in the group treated with both anti-PD-1 and tier 1 neoantigen vaccination, 10 out of 11 mice were tumor-free(Fig. 4e). Tier 1 neoantigen-induced tumor growth inhibition is associated with increased numbers of neoepitope-speci ﬁc CD8+T cells To further investigate the immune responses to tier 1 neoantigen peptides, C57BL/6 mice were injected subcutaneously with B16F10 melanoma cells (2.5 × 10 5cells per mouse) on Day −3 following the experimental schedule (Supplementary Fig. 7a). After the tumors were established, the mice were administered tier 1 neoantigen (SLP of 27-mer) +poly (I:C) adjuvant on Days 3 and 10. In addition, anti-PD-1 was injected intraperitoneally every3 days for 2 weeks (100 µg per mouse each time). To assess the antitumor ef ﬁcacy and toxicity of the tier 1 neoantigen vaccine, tumor size and body weight were measured on Day 16 aftertumor inoculation. No changes in weight were observed acrossthe 5 treatment groups throughout the entire experimental period (Supplementary Fig. 7b). Compared with PBS, the tier 1 neoanti- gen vaccine alone and anti-PD-1 combination therapy signi ﬁcantly reduced tumor growth (tier 1: p< 0.001, tier 1 +anti-PD-1: p< 0.001) (Supplementary Fig. 7c). To examine the tier 1 neoantigen-speci ﬁc T cell response, mice were sacri ﬁced on Day 13, and splenocytes were isolated. We ﬁrst assessed the antigen speci ﬁcity of splenocytes against overlapping 9-mer peptides corresponding to the immunizing peptides. Splenocytes were subjected to restimulation with 10 µg/ml tier 1neoepitopes, which were 9 amino acids in length. The percentage ofCD8+T cells secreting IFN- γwas quanti ﬁed through FACS analysis. As illustrated in Supplementary Fig. 7d, discernible interferon responses were observed exclusively in the vaccinated groups, incontrast to the unvaccinated control groups. These data indicated that the tumor-reducing effect of control poly(I:C) treatment was not related to speci ﬁc T cell activation but rather to the nonspeci ﬁc innate immune response induced by the adjuvant.S.H. Kim et al. 1465 Experimental & Molecular Medicine (2024) 56:1461 – 1471 To elucidate the speci ﬁc T cell subtypes and states contributing to the immune responses triggered by our vaccine, we conducted scRNA-seq and TCR-seq analyses on splenic CD8 +T cells that bind to the tier 1 epitope-dextramer, as well as on the entire tumorpopulation. To explore the subtypes of T cells in greater detail,clustering of the obtained T cells was performed on a total of 43,670 scRNA-seq datasets derived from 4367 single-cell averages in each experimental group from the spleen and tumor.Comprehensive analysis of CD8 +T cells obtained from the spleen and tumor revealed both immune and nonimmune cells delineated by the expression of Ptprc (CD45+) (Fig. 5a). The proportion of CD45 +cells in the tumor was much greater in thevaccinated group than in the unvaccinated group (Fig. 5b). Both lymphoid and myeloid cell numbers were greater in the vaccinated group than in the unvaccinated group (Supplementary Fig. 8a). Subsequent to the initial analysis, a more comprehensiveinvestigation was undertaken, resulting in the identi ﬁcation of 12 distinct immune subsets within the CD45 +cell population of the tumor. These subsets were characterized based on the expression proﬁles of established marker genes (Supplementary Table 9). Comparison of the proportions of immune cell subsets in thetumors between the vaccinated and unvaccinated groups revealed that both CD4 +and CD8 +T cells were increased in the vaccinated group (Fig. 5c). Additionally, natural killer (NK) Fig. 3 In vivo immunogenicity of neoantigen peptides. a Naive mice were vaccinated subcutaneously 2 times a week with synthetic long peptide (SLP) bearing tier 1 or nontier 1 neoantigen and adjuvant poly(I:C) and were sacri ﬁced on Day 13. The experimental groups included those receiving poly(I:C) only or poly(I:C) combined with SLP . bMean ﬂuorescence intensity (MFI) of IFN- γin CD8+T cells determined by FACS. Splenocytes isolated from immunized mice were restimulated in vitro with the tested antigens and stained with CD8 and IFN- γantibodies. cThe hit rate of ‘VACINUS ’was con ﬁrmed by the estimation of immunogenicity after vaccination. The results were pooled, with n=3 mice per group. pvalues are shown; statistical comparisons were performed using two-way ANOVA. * p< 0.05, ** p< 0.01.S.H. Kim et al. 1466 Experimental & Molecular Medicine (2024) 56:1461 – 1471 cells, macrophages and monocytes accumulated in the tumors of the vaccinated groups (Supplementary Fig. 8b). Thirteen clusters of T cells were identi ﬁed and annotated based on the expression of marker genes (Supplementary Fig. 8c, d, SupplementaryTable 10). The compositions of the 13 subtypes of T cells areshown in a stacked bar graph (Fig. 5d). Analyses of T cell subtypes in the tumor showed that, except for terminally exhausted CD8+T cells, T cells with proper functions, including pre- exhausted CD8 +T, ISG+(interferon-stimulated genes)25CD8+T, and effector CD8 +T cells, were increased in the vaccinated group Fig. 4 Antitumor effect of the tier 1 vaccine in a B16F10 mouse melanoma model. All mice were monitored for tumor growth and survival. aB16F10 tumor cells (2.5 × 105cells) were injected into the right ﬂank subcutaneously. The synthetic long peptide (27-mer) vaccine was composed of a pool of 3 SLPs from tier 1 or nontier 1 epitopes. The anti-PD-1 was injected intraperitoneally 5 times every 3 days, and thevaccine was injected subcutaneously into the right ﬂank 2 times a week 3 days after cancer cell inoculation. Seven groups of mice were used (n=11 mice per treatment group): control, poly(I:C) alone, poly(I:C) +tier1 vaccine, poly(I:C) +nontier1 vaccine, poly(I:C) +anti-PD-1, poly(I:C) +aPD-1+tier 1 vaccine, and poly(I:C) +aPD-1+nontier1 vaccine combination. bEffects of various treatments on body weight. There were no signi ﬁcant differences in body weight throughout the entire experimental period. cGrowth curves of subcutaneous tumors in B16F10 melanoma-bearing mice receiving the respective therapies. The tumor volume was measured on Days 13, 16, 19, and 22. dSurvival curves of mice bearing B16F10 tumors after various treatments. The survival of the mice was measured until Day 45. eThe percentage of mice that were tumor-free after various treatments.S.H. Kim et al. 1467 Experimental & Molecular Medicine (2024) 56:1461 – 1471 compared to the unvaccinated group (Fig. 5d, e). A substantial reduction in the percentage of terminally exhausted CD8 +T cells within the vaccinated group provides compelling evidence thateither tier 1 vaccination speci ﬁcally enhanced proliferation, thusincreasing the percentage of precursor exhausted CD8 +T cells, or, more interestingly, tier 1 vaccination effectively hindered the progression of CD8 +T cells from a lightly exhausted state to a fully dysfunctional state (Fig. 5d, e). In the spleen, a greater Fig. 5 Induction of T cell immune responses by the tier 1 vaccine. The tumors from each treatment group were pooled ( n=5), dissociated and subjected to scRNA-seq. The spleens from each treatment group were pooled ( n=5) and sorted for antigen-speci ﬁc CD8+T cells using dextramer and anti-CD8 antibodies and subjected to scRNA-seq. ScRNA-seq data were pooled from 5 mice per group ( n=5).aClustering and UMAP visualization of cells isolated from the spleen and tumor. UMAP visualization of CD45 +populations, colored. CD45 +cells were immune cells and CD45 −cells were nonimmune cells. bThe proportions of CD45 +cells among total tumor cells are shown according to treatment. cProportion of immune cell subtypes based on gene markers. The ﬁve stacked bar graphs on the left show data from the spleen, and the ﬁve stacked bar graphs on the right show data from the tumor. dProportions of T cell subsets among CD3 +T cells in the spleen and tumor are shown according to treatment. eProportion of T cell subtypes according to treatment.S.H. Kim et al. 1468 Experimental & Molecular Medicine (2024) 56:1461 – 1471 percentage of effector CD8 +T cells was observed in the vaccinated group (Supplementary Fig. 8e). To further investigate the complexities of T cell subsets in tumors following vaccination, T cell clonal expansion, migrationdynamics, and T cell subset transitions were investigated using STARTRAC (Fig. 6a–c)26. The clonal expansion index ranged from 0 to 1, where the number indicates the degree of clonal expansion.The T cell expansion index was greater in the tumors than in the Fig. 6 Antigen-speci ﬁc CD8 T cell responses to the tier 1 vaccine. All single-cell data were analyzed with T cells pooled from the splenocytes and tumor cells of 5 mice. The proportions of the CD3 +T cell subsets were determined. The expansion, migration, and transition of T cell subsets were analyzed by STARTRAC tools. aClonal expansion of T cells in the spleen or tumor was compared among groups. The proportions of T cell subtypes in the spleen and tumor are distinguished with different color bars. bThe migration of T cells from the spleen to the tumor was calculated by STARTRAC in each treatment group. cPaired transitions between each subset of T cells are shown. dThe TCR sequence from single-cell sequencing data was compared between splenocytes and tumor cells, and the matched TCR sequence is indicated in red. An exactly matched TCR sequence between splenocytes and tumor cells was considered an overlapping clone. TCR sequencing was performedon CD8 +dextramer +antigen-speci ﬁc T cells sorted from the spleen and CD8 +T cells from the whole tumor. The data are not shown for the groups treated with PBS or poly(I:C) plus an anti-PD-1 antibody due to a lack of T cells. Clone size 1 was ﬁltered out in the spleen, and a clone size >2 was used for TCR clone matching. eThe frequency of overlapping clones in the spleen or tumor is shown in bars from the vaccinated group. fThe proportion and distribution of overlapping clones in the spleen and tumor are shown in 2D UMAP , and T cell subtypes are indicated with colors.S.H. Kim et al. 1469 Experimental & Molecular Medicine (2024) 56:1461 – 1471 spleens of the vaccinated group (Fig. 6a). Along with the expansion index, the migration index in the vaccinated group was greater than that in the unvaccinated group (Fig. 6b). To assess whether the observed migration correlated with T cellfunctionality, we examined the expression of CXCR3, a chemokinereceptor that is known to play a pivotal role in T cell traf ﬁcking and function 27,28. CXCR3 expression was observed only in the vaccinated group, supporting the prevalence of functionally activemigrating T cells (Supplementary Fig. 9a). When T cell plasticitywas examined using the transition index, the most frequent transition was found between effector CD8 +T cells and pre- exhausted CD8 +T cells, followed by pre-exhausted CD8 +T cells and terminally exhausted CD8 +T cells (Fig. 6c). The expression of IFN-γor Granzyme B was greater in the vaccinated group than in the unvaccinated group, verifying the effector function of T cells in tumors (Supplementary Fig. 9b, c). We compared the extent of TCR repertoire overlap between CD8+TILs and splenic T cells using single-cell TCR sequencing. The hypervariable CDR3 βis unique to individual T cell clones and can, therefore, be used to monitor the dynamics of T cell clonality.Due to the high clonal variability of the splenic TCR, 2> clones were selected. Clones overlapping with TILs were identi ﬁed only in the vaccinated group (Fig. 6d, Supplementary Fig. 10a). In the vaccinated group, more than 25% of the T cells that in ﬁltrated the tumor overlapped with those in the spleen (Fig. 6e, Supplemen- tary Fig. 10b). These overlapping clones were mainly present in effector/exhausted CD8 +T cells (Fig. 6f). DISCUSSION In this study, we developed neoantigen prediction work ﬂows, VACINUS pMHC and VACINUS TCR, based on the neoepitope pre- sentation on the patient ’s MHC class I molecules and the interaction between the patient ’s putative tumor-reactive CD8 + TIL TCR and neoepitope-MHC recognition, respectively, andvalidated the predicted neoantigens in vitro and in vivo. VACINUS pMHC was effective at predicting neoantigens, showing a neoantigen selection sensitivity of 81.5% using the TESLAdataset, which was higher than the 73.1% sensitivity achieved bythe leading TESLA team 21. Neoepitope prediction algorithms have predominantly been based on mutation selection and binding to patient MHC molecules2,4,5,29. Since T cells recognize neoantigen peptides presented on MHC class I molecules via TCRs, the interaction between TCRs and pMHCs is critical. Therefore, we prioritized immunogenic neoantigens by assessing the bindingspeci ﬁcity between TCRs (CDR3 βsequences) and HLA based on peptide sequences using pMTnet (v1.0)20. Neoantigens were classi ﬁed into either tier 1 or nontier 1 according to the calculated binding af ﬁnity of the TCR of tumor-reactive TILs and pMHC complexes. Tier 1 vaccination reduced tumor growth, and whencombined with anti-PD-1 therapy, it led to complete remission of the tumor. Tier 1 monotherapy and the combination treatment were superior to the nontier 1 vaccination (Fig. 4). The tier 1 vaccine was immunogenic and elicited antitumor effects, whereas the activity of the nontier 1 vaccine was not comparable to that of the control, suggesting that TCR-pMHCbinding is essential for eliminating cancer cells and forneoantigen-based immunotherapies. Previous studies have shown favorable antitumor immune responses in mouse models and in melanoma and glioblastoma patients without the use of immunecheckpoint inhibitors 2,3,5. In our study, monotherapy with tier 1 neoantigens improved tumor growth compared to that in the poly(I:C) group, although the difference was not statistically signi ﬁcant. In several previous reports in which neoantigen peptide alone was not effective30, poly(I:C) was also used as a vaccine adjuvant to enhance innate immunity to trigger adaptive immune responses. Treatment with poly(I:C) alone stronglyinhibited tumor growth and increased survival. Additionally,treatment with the adjuvant poly(I:C) without the addition of peptides resulted in a fairly good antitumor response, which is a typical characteristic of B16F10 melanoma cells, and increased variability in responsiveness. Previous studies used up to 20peptides in 4 –5 pools 3,5compared to the 3 peptides used in our study. All these parameters might have underestimated the efﬁcacy of our VACINUS platform. Moreover, the number of CD8+T cells producing effector cytokines increased only in the vaccinated group and not in the control poly(I:C)-only group.These data indicated that the tumor-reducing effect of poly(I:C) treatment alone was not related to speci ﬁc anticancer T cell activation but rather to nonspeci ﬁc innate immune activation. The prediction of highly cancer-speci ﬁc TCRs and agonistic neoantigens re ﬂects the way our body selects the self-MHC- restricted T cell repertoire in the thymus during development (i.e., the characteristic nature of thymic positive selection). After thymicpositive selection, the repertoire of high-af ﬁnity antigen-speci ﬁc T cells is reduced compared to that of low- to intermediate-af ﬁnity T cells. Thus, the precise selection of neoantigens through analgorithm based on the interaction of tumor-reactive TIL TCRs withpMHC molecules will not only stimulate a speci ﬁc anticancer immune response but also reduce adverse immune-related events. The limitations of our study include the following: (1) Additional studies are warranted to elucidate why nontier 1 vaccination failed to control tumor growth. (2) Only the publicly available pMTnet tool was utilized for predicting TCR-pMHC binding. A more precisemodel is needed to improve TIL TCR-pMHC binding prediction. Numerous challenges must be overcome for personalized neoantigen-based cancer vaccines to progress and becomeestablished as mainstream therapeutic approaches. Of all thechallenges to be confronted, the swift and accurate prediction of immunogenic neoantigens is imperative because in vivo evalua- tion of neoantigens derived from in silico analyses is excessivelytime-consuming. Our study con ﬁrmed that the prediction of neoantigens based on a ternary complex of tumor-reactive TIL and the pMHC can enhance the identi ﬁcation of immunogenic neoantigens. DATA AVAILABILITY All data relevant to the study are included in the article or uploaded as supplementary information. The data are available upon reasonable request (woongyang.park@kr-geninus.com). REFERENCES 1. Lang, F., Schrörs, B., Löwer, M., Türeci, Ö. & Sahin, U. Identi ﬁcation of neoantigens for individualized cancer immunotherapy. Nat. Rev. Drug Discov. 21, 261 –282 (2022). 2. Sahin, U. et al. Personalized RNA mutanome vaccines mobilize poly-speci ﬁc therapeutic immunity against cancer. Nature 547, 222 –226 (2017).