The heavy burden imposed by the COVID-19 pandemic on our society triggered the race toward the development of therapies or preventive strategies. Among these, antibodies and vaccines are particularly attractive because of their high specificity, low probability of drug-drug interaction, and potentially long-standing protective effects. While the threat at hand justifies the pace of research, the implementation of therapeutic strategies cannot be exempted from safety considerations. There are several potential adverse events reported after the vaccination or antibody therapy, but two are of utmost importance: antibody-dependent enhancement (ADE) and cytokine storm syndrome (CSS). On the other hand, the depletion or exhaustion of T-cells has been reported to be associated with worse prognosis in COVID-19 patients. This observation suggests a potential role of vaccines eliciting cellular immunity, which might simultaneously limit the risk of ADE and CSS. Such risk was proposed to be associated with FcR-induced activation of proinflammatory macrophages (M1) by Fu et al. (2020) and Iwasaki and Yang (2020). All aspects of the newly developed vaccine (including the route of administration, delivery system, and adjuvant selection) may affect its effectiveness and safety. In this work we use a novel in silico approach (based on AI and bioinformatics methods) developed to support the design of epitope-based vaccines. We evaluated the capabilities of our method for predicting the immunogenicity of epitopes. Next, the results of our approach were compared with other vaccine-design strategies reported in the literature. The risk of immuno-toxicity was also assessed. The analysis of epitope conservation among other Coronaviridae was carried out in order to facilitate the selection of peptides shared across different SARS-CoV-2 strains and which might be conserved in emerging zootic coronavirus strains. Finally, the potential applicability of the selected epitopes for the development of a vaccine eliciting cellular immunity for COVID-19 was discussed, highlighting the benefits and challenges of such an approach.

The heavy burden imposed by the COVID-19 pandemic on our society triggered the race towards the development of therapies or preventive strategies. Among these, antibodies and vaccines are particularly attractive because of their high specificity, low probability of drug-drug interaction, and potentially long-standing protective effects. While the threat at hand justifies the pace of research, the implementation of therapeutic strategies cannot be exempted from safety considerations. There are several potential adverse events reported after the vaccination or antibody therapy, but two are of utmost importance: antibody-dependent enhancement (ADE) and cytokine storm syndrome (CSS). On the other hand, the depletion or exhaustion of T-cells has been reported to be associated with worse prognosis in COVID-19 patients. This observation suggests a potential role of vaccines eliciting cellular immunity, which might simultaneously limit the risk of ADE and CSS. Such risk was proposed to be associated with FcR-induced activation of proinflammatory macrophages (M1) by Fu et al. 2020 and Iwasaki et al. 2020. All aspects of the newly developed vaccine (including the route of administration, delivery system, and adjuvant selection) may affect its effectiveness and safety. In this work we use a novel in silico approach (based on AI and bioinformatics methods) developed to support the design of epitope-based vaccines. We evaluated the capabilities of our method for predicting the immunogenicity of epitopes. Next, the results of our approach were compared with other vaccine-design strategies reported in the literature. The risk of immuno-toxicity was also assessed. The analysis of epitope conservation among other Coronaviridae was carried out in order to facilitate the selection of peptides shared across different SARS-CoV-2 strains and which might be conserved in emerging zootic coronavirus strains. Finally, the potential applicability of the selected epitopes for the development of a vaccine eliciting cellular immunity for COVID-19 was discussed, highlighting the benefits and challenges of such an approach.

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Introduction: The purpose of this study is to present a novel method for neoepitope prediction, along with extensive benchmarks to existing solutions and identification of the most predictive constituent biological features. The accurate prediction of neoepitope immunogenicity represents an invaluable tool for the design of personalized cancer vaccines with effective treatment outcomes. The effectiveness of the host’s adaptive immune response against cancer relies on the correct HLA-mediated neoepitope presentation and the recognition by specific CD8+ clones. Cancer immunotherapies act by boosting the activity of these effector T-cells. Methods: The proposed AI-driven bioinformatics solution allows to perform an accurate prediction of HLA I-restricted neoepitope immunogenicity by including several analytical modules simulating biological processes leading to the activation of CD8+ T-cells. These modules can be flexibly composed on the basis of the data available and include: (i) selection of potential neoepitope based on cancer NGS data, (ii) expression of neoepitope-associated genes, (iii) similarity to self, (iv) prediction of neoepitope-HLA binding affinity and stability, (v) effect of post-translational modification on neoepitope presentation and TCR recognition, (vi) prediction of neoepitope:TCR recognition probability based on neoepitope-HLA:TCR structural-derived features and TCR CDR3 sequence similarities. The model was trained on a curated dataset joining records of multiple studies containing neoepitopes experimentally validated for their ability to elicit adaptive immune response. Results: We present the results of a benchmarking study where we compare the performance of widely used methods for the assessment of neoepitope immunogenicity along with our solution. We investigate the predictive power of each analytical module to determine the individual information gain and determine the most informative ones. All the methods included in the benchmarking study were tested using the dataset provided by Chowell et al. 2015. As the metric for performance evaluation we use ROC AUC and the overall percentage of correctly detected immunogenic neoepitopes (precision). Conclusion: The results present the utility of the compared methods for personalized cancer vaccine design and the importance of modules related to biological processes underlying the neoepitope presentation and immunogenicity. Citation Format: Giovanni Mazzocco, Iga Niemiec, Oleksandr Myronov, Edyta Kowalczyk, Jan Kaczmarczyk, Anna Sanecka-Duin, Piotr Stepniak. Predicting immunogenic neoepitopes with biology-aware machine learning [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 1447.

Background: The purpose of our study is to aid the development of adoptive T-cell receptor (TCR) -based cancer therapies with an AI platform that models binding between peptide-human leukocyte antigen (pHLA) complexes and TCRs. One of the main challenges in the field is finding the right pHLA target and the TCR that binds it. The development of in silico methods that can reduce the cost and time of laboratory research is the ‘holy grail’ of novel TCR therapies design. Molecular Dynamics (MD) simulation is a computational method allowing to gain a direct insight into the movements of protein complexes and giving the possibility to study the dynamics of the pHLA:TCR interaction. The combination of the MD and AI models helps us to increase the accuracy of the pHLA:TCR binding prediction. Methods: We conducted large-scale MD simulations of the crystal structures of 130 pHLA:TCR complexes available in the Protein Data Bank (PDB). Next, we analyzed the obtained trajectories in terms of formed bonds and other physico-chemical properties. This led us to postulate an MD model of interaction further used as an inductive bias for the AI binding prediction model, in which we applied deep learning on public single-cell pHLA:TCR datasets to estimate the probability of binding.In order to validate and improve our platform, we are building a pHLA:TCR interaction database with paired alpha and beta chain TCR sequences from a cohort of 100 colorectal cancer patients (NCT04994093). Results: We established the agretopicity and epitopicity of peptide residues within the pHLA:TCR systems based on the characteristics of the bonds formed between peptides and HLAs or TCRs in MD simulations. Additionally, the incorporation of other molecular properties, e.g. solvent accessible surface area, improved our AI pHLA:TCR model. In order to refine our results, we also account for the HLA type and physico-chemical properties of peptides. The introduction of the detailed TCR-peptide interaction matrix allowed the model to generalize to previously unseen pHLA targets. Conclusions: Rational design of effective TCR therapies can benefit from novel pHLA:TCR models for which the predictive accuracy is driven by the synergistic AI-MD approach. Citation Format: Alexander Myronov, Sławomir Stachura, Anna Sanecka-Duin, Oskar Gniewek, Łukasz Grochowalski, Maciej Jasiński, Paulina Król, Joanna Marczyńska-Grzelak, Piotr Skoczylas, Daniel Wojciechowski, Giovanni Mazzocco, Mikołaj Mizera, Jan Kaczmarczyk, Agnieszka Blum. Modeling pHLA:TCR interactions for effective TCR therapies: Leveraging AI and molecular dynamics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2810.

BackgroundAdoptive cell therapies with T lymphocytes expressing engineered T cell receptors (TCRs) are one of the most promising approaches to cancer therapy.1 However, the experimentally driven development of novel TCR therapies is limited by the enormous biological variability of peptide:Human Leukocyte Antigen:TCR (pHLA:TCR) complexes. The in silico methods hold the promise to streamline the discovery of novel TCR therapies by reducing costs and time of laboratory research. In particular, the prediction of TCR binding to a target antigen, as well as the prediction of TCR off-target toxicity2 can provide useful insights supporting the development of safe therapies. We aimed at the development of an experimentally validated AI model of pHLA:TCR binding that will help to prioritize and reduce the number of in vitro assays necessary to discover novel TCRs for cancer therapies.MethodsThe limiting factor of successful pHLA:TCR binding modeling is data availability and completeness of TCR characterization. To address this issue, we are building an oncological pHLA:TCR database with paired alpha and beta chain TCR sequences. We are collecting and sequencing tumor and normal samples from 100 cancer patients, as part of an observational clinical trial. Those data are then screened with the Ardigen's ArdImmune Vax platform3 4 to select immunogenic epitopes. T cells that bind those epitopes are subsequently sorted and used to generate TCR sequencing data at single-cell resolution. We use data-driven and simulation-based models to extract insights about the dynamics of a pHLA:TCR system to predict the binding probability and explain the inference made by the model.ResultsWe optimized our data collection pipeline for the cost-efficient acquisition of a large oncological pHLA:TCR dataset. These data will enable us to build efficient models to streamline the development of TCR therapies against cancer.We benchmarked our modeling approach for pHLA:TCR binding against existing solutions5–7 on publicly available data. We also show how focus on model explainability facilitates the detection of model inconsistency of uncertain predictions by expert inspection. Our toxicity assessment solution2 extends the applicability of our system to the prediction of TCR safety profile.ConclusionsThe presented work shows perspectives and limitations of AI-aided TCR therapy development. We present results for our pHLA:TCR binding model, a TCR-toxicity-screening solution, and the study design of our observational clinical trial. Our growing database of pHLA:TCR interactions will enable us to develop highly predictive pHLA:TCR binding models, in particular for oncological targets.AcknowledgementsWe acknowledge funding through the project “Creating an innovative AI-based (Artificial Intelligence) IN SILICO TECHNOLOGY TCRact to launch a NEW SERVICE for designing and optimizing T-cell receptors (TCR) for use in cancer immunotherapies” cofunded by European Regional Development Fund (ERDF) as part of Smart Growth Operational Programme 2014–2020.ReferencesFarkona S, Diamandis EP, Blasutig IM. Cancer immunotherapy: the beginning of the end of cancer? BMC Med 2016;14:73. PMCID: PMC4858828.Murcia Pienkowski VA, Mazzocco G, Niemiec I, Sanecka-Duin A, Krol P, Myronov O, Skoczylas P, Kaczmarczyk J, Blum A. Off-target toxicity prediction in cellular cancer immunotherapies [Internet]. Cytotherapy. 2021;S96. Available from: http://dx.doi.org/10.1016/s1465324921004229.Stepniak P, Mazzocco G, Myronov A, Niemiec I, Gruba K, Skoczylas P, Sanecka-Duin A, Drwal M, Kaczmarczyk J. AI-augmented design of effective therapeutic cancer vaccines and adoptive cell therapies. Journal For Immunotherapy Of Cancer. Bmc Campus, 4 Crinan St, London N1 9xw, England; 2019.Mazzocco G, Niemiec I, Myronov A, Skoczylas P, Kaczmarczyk J, Sanecka-Duin A, Gruba K, Król P, Drwal M, Szczepanik M, Pyrc K, Stȩpniak P. AI aided design of epitope-based vaccine for the induction of cellular immune responses against SARS-CoV-2. Front Genet. 2021;12:602196. PMCID: PMC8027494.Weber A, Born J, Rodriguez Martínez M. TITAN: T-cell receptor specificity prediction with bimodal attention networks. Bioinformatics. 2021;37(Suppl_1):i237–i244. PMCID: PMC8275323.Springer I, Besser H, Tickotsky-Moskovitz N, Dvorkin S, Louzoun Y. Prediction of specific TCR-peptide binding from large dictionaries of TCR-Peptide Pairs. Front Immunol 2020;11:1803. PMCID: PMC7477042.Jurtz VI, Jessen LE, Bentzen AK, Jespersen MC, Mahajan S, Vita R, Jensen KK, Marcatili P, Hadrup SR, Peters B, Nielsen M. NetTCR: sequence-based prediction of TCR binding to peptide-MHC complexes using convolutional neural networks [Internet]. Available from: http://dx.doi.org/10.1101/433706

The purpose of this study is to evaluate the importance of immune escape mechanisms (IEMs) when designing cancer immunotherapeutic strategies. Cancer IEMs induced by negative selective pressure on tumor cells can have dramatic effects on immunotherapy treatment outcome. IEMs occurring during cancer evolution have been shown to facilitate loss of mutation-associated neoantigens (Anagnostou et al. Cancer Discov 2017). These mechanisms include: Loss of Heterozygosity (LOH), underexpression of HLA genes and mutations in the neoantigen presentation machinery. During cancer evolution, the initially arising clonal neoantigens may be shared across many patients, followed by predominantly subclonal private neoantigens. The propensity of shared neoantigens to be clonal makes them promising candidates for the development of off-the-shelf cancer vaccines. In order to optimize patient treatment strategies and qualify patients for either shared or private neoantigen-directed therapy, it is crucial to correctly assess the indication-specific IEMs and neoantigen landscape in both hot and cold tumors. We carry out an extensive analysis of tumor neoantigen landscape from different indications based on TCGA, on Parkhurst et al. Cancer Discov. 2019 and other available data for selected indications (including MEL, NSCLC, CRC, and GBM). Using bioinformatics methods we investigate IEMs and their potential effects on the plasticity of neoantigen repertoires including enrichment of immunogenic neoantigens within the predicted vaccine composition, computed per patient. Immunogenicity is assessed using advanced deep learning models for neoantigen presentation and functional T-cell response prediction (Mazzocco et al., poster presented at SITC 2019). We present the results of the comparison between different cancer indications including CRC, MEL, NSCLC and GBM using TCGA patient data and GI-tract cancer patients with validated T-cell responses. We present the frequency of particular immune evasion events, pointing out the differences in the number and quality of immunogenic neoepitopes (both shared and private) under those conditions and provide summary statistics. As a result, we draw conclusions for vaccine design strategies, which are informed by the investigation of immune evasion events and are tested on selected datasets including experimentally-validated neoantigen responses. Citation Format: Alexander Myronov, Iga Niemiec, Katarzyna Gruba, Giovanni Mazzocco, Anna Sanecka-Duin, Piotr Skoczylas, Michał Drwal, Jan Kaczmarczyk, Piotr Stepniak. Accounting for immune escape mechanisms in personalized and shared neoantigen cancer vaccine design [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 6541.

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