Gregory A. Lizee, Ph.D. - US grants

DESCRIPTION (provided by applicant): Immunotherapies that utilize cytotoxic T lymphocytes (CTLs) have proven effective at eradicating large tumor burdens in both animal models and human cancer patients. Since dendritic cells (DCs) are the most potent antigen-presenting cells for priming naive CD8+ T-cells to become activated CTLs that efficiently kill target cells in an MHC class I-restricted fashion, there has been widespread interest in developing DC-based vaccines for use in cancer therapy. The specific objective of this project is to generate an improved DC vaccine by exploiting the natural mechanisms that control MHC-I trafficking and DC surface expression to improve the quality and duration of tumor antigen presentation to CD8+ T cells. It is our central hypothesis that conserved motifs within the MHC-I cytoplasmic tail control not only the duration of presentation of MHC-I/peptide complexes at the cell surface, but also MHC-I trafficking through specialized, endocytic cross-presentation compartments. We have formulated this hypothesis on the basis of our Preliminary Results identifying two functionally distinct MHC-I tail motifs that directly control DC endocytic trafficking and cross-presentation function of murine MHC-I molecules, and which play a crucial role in the generation of antiviral CTL responses in vivo (Lizee et al, Nature Immunology). The rationale for this proposal is that utilizing our knowledge of how these conserved motifs operate in DCs will allow for the ability to improve tumor antigen loading and extend duration of antigen presentation in human DC-based cancer vaccines, thus improving CTL priming outcomes. We plan to test our central hypothesis and accomplish our overall objective of improving DC-based cancer vaccines by focusing on the following three specific aims: (1) Determine how modifications to the MHC-I cytoplasmic tail impact the priming of antigen-specific CTLs and alter the dynamics of DC antigen presentation. (2) Using established murine models, assess the efficacy of MHC-I tail-modified DC vaccines in priming antigen-specific CTLs and in the induction of antitumor responses. (3) Analyze how inflammatory mediators and tumor-derived factors affect MHC-I tail phosphorylation, intracellular trafficking, and antigen presentation in DCs. The proposed work is innovative, because it will uncover the molecular mechanisms utilized by DCs to prime optimally effective antitumor CTL responses. It will also fill in gaps in the current knowledge base with regard to the dynamic changes in MHC-I trafficking and antigen presentation that occur during DC activation by toll-like receptor (TLR)-ligands or innate immune signals. Such results will have an important positive impact, because they will pave the way towards the next generation of improved, DC-based vaccines. They will enable the design of novel therapeutics capable of modifying the MHC-I tail, thus potentially allowing for manipulation of immune responses at the level of MHC-I antigen presentation. Successful completion of these studies is likely to have an impact in other areas of human disease treatment, including autoimmunity, transplant immunology, and pathogen infections. PUBLIC HEALTH RELEVANCE: Cancer vaccines work by eliciting specific cytotoxic ('killer') T lymphocytes (CTL) in the blood of cancer patients, which in turn destroy the tumor. Dendritic cell (DC)-based vaccines have shown much promise clinically, but our lack of basic understanding of DC biology has limited the development of these vaccines. This grant proposes to utilize our basic knowledge of MHC class I biology to improve the potency of DC-based based cancer vaccines, with the ultimate goal of improving patient responses to CTL-based immunotherapy.

Project Summary Developing effective cancer treatments remains one of the most important challenges for healthcare, and T-cell based immunotherapy has provided some very positive recent advances in cancer treatment. Cytotoxic T lym- phocytes (CTLs) can circulate through the body and are capable of identifying and eliminating tumorigenic cells. The recognition of tumor depends on the specific interaction between the T-cell receptor of CTLs and Human Leucocyte Antigen (HLA) class I molecules at the tumor cell surface, which binds and displays peptides derived from intracellular proteins. Peptide-HLA complexes are presented by all nucleated cells, constituting an efficient surveillance mechanism by which the immune system can recognize aberrant changes within cells of the body. Al- though CTL surveillance likely evolved to eliminate virally-infected cells, this system also provides very promising opportunities for cancer treatment and specifically the development of immune-based therapies. However, such therapies must be highly personalized since most of these tumor-associated peptides are patient-specific. This is due mainly to the high level of HLA diversity within the human population, combined with the fact that each person?s tumor acquires unique genetic aberrations. A further challenge is the identification of tumor-specific pep- tides that are not also expressed by normal cells, which will likely ensure less off-target effects during therapy. Our long-term goal is to perform structure-guided selection of tumor-derived peptides with potential for immunother- apy, which will also allow structural analysis of different peptide-HLA complexes recognized by a given T-cell; this knowledge will help to prevent dangerous off-target toxicities. The objective of this project is to develop computa- tional tools to enable docking-based modeling of peptide-HLA complexes, starting with HLA variants (allotypes) that are highly prevalent within human population and moving toward others that are less prevalent (for person- alized treatment). Our Preliminary Data supports the need for a structural framework to improve the selection of targets for immunotherapy, since current methods have important limitations, particularly with regard to less prevalent HLAs. The central hypothesis is that structure-based analysis can be used to improve peptide target selection for individual HLA allotypes and thus facilitate the development of personalized immunotherapies for all cancer patients. Two specific aims were designed to test this hypothesis. In Specific Aim 1, a docking method will be specifically tailored to make binding predictions of tumor-derived peptides to two highly frequent and well- studied HLAs, HLA-A*2402 and HLA*A1101, collectively expressed by >55% of the world population. In Specific Aim 2, the HLA-A3 superfamily, collectively expressed by >40% of the human population, will be used as a model for extending the methods towards less well-studied HLAs. Innovative computational methods will be applied in this project and cutting-edge experimental resources will be used to train and validate computational methods. The underlying rationale is that developing a computational framework for these prevalent HLA allotypes will facilitate the development of personalized, antigen-specific immunotherapies, which would benefit a much larger number of cancer patients.

Project Summary Understanding protein?ligand molecular interactions is fundamental to understanding the role of proteins in complex diseases such as cancer. For instance, there is growing interest in predicting the binding modes of peptide-based ligands (e.g., cyclic and phosphorylated peptides) to inhibit or induce targeted degradation of high-pro?le cancer targets. Another promising example is the identi?cation of tumor-associated antigens for cancer immunotherapy applications. Both examples involve very speci?c molecular interactions, provide opportunities for computer-aided design of better cancer treatments, and highlight the need for structural analyses in cancer research. They also require new methods that account for the ?exibility and variability of the protein receptors involved in these molecular interactions. The objective of this project is to develop an integrated approach to the structural modeling and analysis of protein?ligand interactions in cancer research that will be implemented in the proteomics toolkit PROTEAN-CR. The proposed toolkit will adopt a data-science approach to the problem by introducing approaches for data acquisition and aggregation, as well as algorithmic advances for handling receptor ?exibility and for modeling driver mutations, drug-resistance polymorphisms, and post-translational modi?cations. PROTEAN-CR will streamline running structural analyses at scale while providing meaningful data analytics. The long-term goal of our research is to fully integrate three-dimensional structural information about proteins and ligands and structural analysis into cancer research. The PIs will work with collaborators to target a wide range of users, from experimentalists with little to no programming experience, to advanced users who are comfortable scripting large-scale analyses and integrating the toolkit with their own computational pipeline. The central hypothesis is that a uni?ed data-science-inspired approach can be used to address major challenges in structural analysis of protein?ligand interactions in cancer research at scale. The ?rst aim will incorporate protein ?exibility in docking studies for cancer research. Speci?c work?ows will be used to generate ensembles of protein conformations (receptor ?exibility) and innovative machine learning methods will be implemented aiming at a better scoring of protein?ligand complexes. The second aim will focus on including cancer variability into structural analysis. We aim to ?ll the gap that exists between available data on cancer variants and the structural analysis of ensembles of tumor-associated mutations and protein modi?cations. Finally, the third aim will focus on customization, interpretability and scalability, where user-friendly methods will be deployed to manage ensembles of protein-ligand complexes. PROTEAN-CR will be developed focusing on speci?c cancer-related projects, and with a broad network of collaborators, enabling the design, implementation and evolution of the tool according to the needs of the cancer research community.