Project Summary The broader use of T-cell-based therapies is still hindered by challenges related to the identification of peptide- targets that are both immunogenic (capable of activating T-cells) and safe (do not trigger on-target/off-tumor or off-target toxicities). This is in part due to persistent dependency on biased sequence-based methods, despite recent breakthroughs in structural modeling and machine learning that could be leveraged to support new workflows for the identification of tumor-associated antigens (TAAs). To address this issue, and foster the design of better T-cell-based immunotherapies, we propose a new computational environment (HLA-arena 2.0) that will integrate existing ITCR resources, with new bioinformatics methods for structural modeling and analysis of key cellular immunity receptors; namely T-cell receptors (TCRs) and Human Leukocyte Antigen (HLA) receptors. Our working hypothesis is that the combination of multi-omics data with large-scale structure-based analysis can overcome most of the limitations of existing pipelines for TAA discovery, therefore enabling the design of better and safer T-cell-based immunotherapies. To test this hypothesis, we will implement a new workflow for structure-guided TAA discovery, integrating HLA-Arena with pVACtools (ITCR-funded package for sequence-based neoantigen discovery) and CrossDome (an R package for off-target toxicity prediction). In collaboration with researchers from MD Anderson Cancer Center, the PI will develop and test workflows to address existing needs in T-cell- based immunotherapy. We will focus on two different cancer types, that represent different challenges for cancer immunotherapy. In collaboration with Dr. Lizée, we will benchmark our structure-guided TAA discovery workflow using immunopeptidomics data on melanoma. We will also run off-target toxicity predictions to identify the safest among 10 potentially therapeutic T-cell clones targeting two melanoma-derived TAAs from SLC45A2. Melanoma is a type of solid tumor for which greater success has been observed with immunotherapy treatments. On the other hand, acute myeloid leukemia (AML) is a type of blood cancer in which severe reactions to immunotherapy have been observed. In this context, we will work with Dr. Abbas to examine transcriptomic datasets (bulk and single-cell data) from AML patients, aiming at uncovering TAAs and TCRs that are associated with effective immune response to AML. Finally, we will use CrossDome and existing data on known TAAs to develop The Cancer off-target Toxicity Atlas (TCTA). For each known TAA, this new database will contain a list of potential off-targets that should be tested when targeting these TAAs with immunotherapies. Predicted off-targets will be annotated with additional data (e.g., tissue expression, HLA-binding, immunogenicity, etc). All methods will be made available to the community through user-friendly workflows, facilitating the design of better and safer T-cell-based immunotherapies for numerous types of cancer. The proposed methods will be deeply integrated into the ITCR network, creating many opportunities for future collaborations. In addition, the long-term goals of the proposed research are well aligned with NCI’s mission to achieve more effective and less toxic cancer treatments, therefore helping people live longer and healthier lives.

Project Narrative T-cell-based immunotherapies are providing new opportunities for personalized treatment of different types of cancer, but computational methods supporting these efforts are still missing key structural information. Our research aims at bridging this gap through the development of a new computational environment (HLA-Arena 2.0), which integrates new structural bioinformatics methods with existing ITCR resources. This will enable (i) structure-guided discovery of tumor-associated antigens, (ii) identification of safer therapeutic T-cells for cancer immunotherapy, and (iii) the implementation of the first database for T-cell off-target toxicity risk assessment.