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.

2567 Background: No biomarker satisfactorily predict response to anti-PD-L1 therapies. Biomarker studies suffer from small sample size, presence of disease subtypes, and lack of simultaneous measurement of multiple biomarkers. The IMvigor210 dataset (Mariathasan et al., Nature 2018) provides baseline measurements for multiple biomarkers of response to atezolizumab (n range: 105-298) coupled with genomewide RNAseq profiles. We examined predictive performance of individual biomarkers and combined information from multiple biomarkers to measure changes in predictive performance. Methods: We built classification models (PR/CR vs. PD/SD) using genes and gene sets that provide information on pathways (mSigDB), immune components (xCell, Cibersort), and predictors of response (IMPRES, Immunophenoscore, and TIDE). Prognostic features were removed based on survival association in TCGA. All experiments were done with repeated five-fold double cross validation. Predictions from the gene sets model were used as a single biomarker. PD-L1 expression by IHC in tumor core and immune cells, tumor mutation burden(TMB), neo-antigen burden (NB), location of metastatic disease, immune phenotype and genomic subtypes were then systematically merged with the gene set based model. Results: NB was the best predictor of response (AUC 0.77), while a model combining NB, TMB, ECOG and expression signatures was marginally better (AUC 0.81) with a chance of over fitting. Chi-square tests for independence suggested that examined biomarkers do not provide independent information explaining lack of increase in AUC. Signatures for TP53 mutations, M1 macrophages, CD8+ T effector cell and DNA repair, among others, were present frequently in classification using gene expression information (AUC 0.71), suggesting their independent contributions to response. Adding gene expression information to NB didn’t improve AUC for response but provided better survival stratification. Conclusions: Integration of examined biomarkers with machine learning did not improve response prediction significantly. We are now examining sizes of subgroups defined by combination of low NB/TMB with these biomarkers.

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.