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Summary Increasing evidence indicates CD4+ T cells can recognize cancer-specific antigens and control tumor growth. However, it remains difficult to predict the antigens that will be presented by human leukocyte antigen class II molecules (HLA-II), hindering efforts to optimally target them therapeutically. Obstacles include inaccurate peptide-binding prediction and unsolved complexities of the HLA-II pathway. To address these challenges, we developed an improved technology for discovering HLA-II binding motifs and conducted a comprehensive analysis of tumor ligandomes to learn processing rules relevant in the tumor microenvironment. We profiled >40 HLA-II alleles and showed that binding motifs were highly sensitive to HLA-DM, a peptide-loading chaperone. We also revealed that intratumoral HLA-II presentation was dominated by professional antigen-presenting cells (APCs) rather than cancer cells. Integrating these observations, we developed algorithms that accurately predicted APC ligandomes, including peptides from phagocytosed cancer cells. These tools and biological insights will enable improved HLA-II-directed cancer therapies.

While tumor-reactive CD4+ T cells have been associated with effective immunotherapy responses, accurate prediction of MHC class II-restricted ligands remains a challenge, limiting our ability to harness CD4+ immunity for cancer therapy. To this end, we have developed a state-of-the-art MHC class II binding predictor, neonmhc2, trained on HLA-II ligands identified from mass spectrometry (MS) of cell lines engineered to express a single affinity-tagged HLA-II allele. Over 60 HLA-II alleles have been characterized to date. We demonstrate that neonmhc2 outperforms NetMHCIIpan, the current benchmark for class II prediction, in distinguishing 1) HLA-II ligands presented in cell lines and tissues, and critically, 2) immunogenic CD4+ epitopes identified with tetramer-guided epitope mapping. Furthermore, we show that numerous neoantigen peptides that were ranked highly by neonmhc2 but poorly by NetMHCIIpan were immunogenic in an ex vivo induction. We next studied HLA-II antigen processing in order to further boost our ability to predict CD4+ epitopes. We determined a gene-level bias by comparing transcript expression and gene length to the frequency of observations in MS, finding that secreted genes are over-represented in HLA-II ligandomes from tissues. We built numerous primary sequence-based processability predictors trained on MS data, but only achieved significant prediction improvement when using the simple feature of determining if a candidate peptide contained sequence that overlapped a previously observed HLA-II ligand. By combining neonmhc2 binding prediction, transcript expression, gene bias, and the overlap feature in an integrated presentation predictor, we were able to achieve up to a 61-fold increase in ability to predict HLA-II peptides presented from tissue MS data over NetMHCIIpan alone. We also sought to understand which cells are presenting HLA-II ligands in the tumor microenvironment to elucidate the presentation pathway most relevant to immunotherapy. By leveraging publicly available RNA-seq (bulk and single cell), we found that professional antigen-presenting cells rather than tumor cells are primarily responsible for HLA-II presentation. We developed a novel SILAC-based MS workflow to directly interrogate peptides derived from phagocytosed tumor cells that are presented by dendritic cells. The experiment revealed that mitochondrial genes are preferentially presented from phagocytosed cells. In conclusion, by integrating proteomics and genomics data at large scale, we have defined new rules for understanding HLA-II processing and presentation, particularly in the context of the tumor microenvironment. This work should enhance our ability to predict CD4+ epitopes for immunotherapy. Citation Format: Dewi Harjanto, Jennifer G. Abelin, Matthew Malloy, Prerna Suri, Tyler Colson, Scott P. Goulding, Amanda L. Creech, Lia R. Serrano, Gibbs Nasir, Yusuf Nasrullah, Christopher D. McGann, Diana Velez, Ying S. Ting, Asaf Poran, Daniel A. Rothenberg, Sagar Chhangawala, Alex Rubinsteyn, Jeff Hammerbacher, Richard B. Gaynor, Edward F. Fritsch, Rob C. Oslund, Dominik Barthelme, Terri A. Addona, Christina M. Arieta, Michael S. Rooney. Enhanced HLA-II epitope prediction for immunotherapy with novel proteomics and genomics approaches [abstract]. In: Proceedings of the AACR Special Conference on Tumor Immunology and Immunotherapy; 2019 Nov 17-20; Boston, MA. Philadelphia (PA): AACR; Cancer Immunol Res 2020;8(3 Suppl):Abstract nr B23.

Background: Neoantigens are tumor-specific antigens that have been shown to be critical in the anti-tumor immune response. These antigens are not subject to central immune tolerance and are therefore potentially more immunogenic than tumor-associated antigens. The goal of our studies is to assess the immunogenic potential of high-quality neoantigen targets, as defined through our bioinformatic engine RECON®, as well as perform detailed characterization of the induced T cell responses towards these neoantigen targets. Methods: Patient-specific neoantigens were predicted using our bioinformatic engine RECON. The predicted high quality neoantigens were utilized in our proprietary ex-vivo stimulation protocol NEO-STIM to assess immunogenicity. NEO-STIM is used to prime, activate and expand memory and de novo T cell responses from both the CD4+ as well as the CD8+ compartment. In-depth analysis was performed to characterize the specificity, functionality (cytokine production and cytolytic capacity) and diversity of the induced T cell responses. Results: Here we present the successful induction of memory and de novo CD8+ T cell responses in PBMCs isolated by leukapheresis from three patients using NEO-STIM (2 with melanoma & 1 with NSCLC). We then deeply characterize the induced CD8+ T cell responses of one of the melanoma patients and show that these responses are functional, specific and have cytolytic capacity. Furthermore, we use paired single cell RNA and TCR sequencing to assess the diversity and gene expression profile of these responses. Conclusions: NEO-STIM is a novel platform to understand the immunogenic potential of high-quality neoantigen-targets in detail. Moreover, this platform can be utilized to generate T cell products for adoptive cell therapy for patients with a variety of solid and liquid tumors. Citation Format: Divya Lenkala, Jessica Kohler, Brian McCarthy, Michael Nelson, Rachel DeBarge, Yvonne Ware, Yuting Huang, Janani Sridar, Yusuf Nasrullah, Dewi Hartjano, Asaf Poran, Sejuti Sengupta, Joost H. van den Berg, Matt Goldstein, Richard B. Gaynor, Marit M. van Buuren. Detailed analysis of immune responses induced against patient-specific neoantigens using the ex-vivo induction protocol, NEO-STIM™ [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 580.