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Principal Investigator: Bo Li
Organization: CHILDREN'S HOSP OF PHILADELPHIA
Fiscal Year: 2024
Award: $386,817
Funding agency: National Cancer Institute
Project Summary/Abstract
Cancer immunotherapy has achieved remarkable clinical success treating late-stage tumors, yet the response
rates remain low and the side effects are often severe. Designing effective immunotherapies relies on accurate
identification of tumor-reactive T cells. This is an extremely difficult task because 1) most of the cancer
antigens are unknown; 2) the majority of the tumor-infiltrating T cells (TIL) does not recognize cancer cells; and
3) without known antigens, the only approach to acquire such T cells is to perform ex vivo expansion of TILs
stimulated by autologous cancer cells, which generates non-specific T cells and is infeasible to many patients.
Nonetheless, this strategy is widely adopted in current clinical trials for anti-cancer treatment, despite its
reduced therapeutic efficacy and unpredictable side effects of autoimmunity. Therefore, unbiased, antigen-
independent identification of tumor-reactive T cells, if possible, will be a major clinical priority as it will
significantly increase the efficiency and safety of T cell based immunotherapies. Here we propose to achieve
this goal through the development of novel machine learning methods. Such approach has not yet been
explored because the fundamental difference between cancer and non-cancer T cells lies in their receptor
sequences (TCR), and training data of cancer-specific TCRs is currently unavailable. To prepare for this task,
we have developed the software TRUST, to extract the T cell antigen-binding CDR3 regions from bulk tumor
RNA-seq data, and the software iSMART to group these CDR3s into antigen-specific clusters. These tools
allowed us to develop a new rationale for producing large training sets of tumor-reactive TCRs, even without
knowing cancer antigens. In our preliminary analysis, we observed that TCRs from the training data can be
matched to tumor antigens that bind to HLA-A*02:01 and elicit immune response in vivo. The cancer-specific
CDR3 amino acid sequences also show significantly different biochemical features from non-cancer ones,
based on which we further developed software DeepCAT to demonstrate the feasibility of de novo prediction of
cancer TCRs. These exciting results highlighted the importance to develop better computational method to
track the tumor-reactive T cells for clinical applications. Accordingly, we propose the following Specific Aims: In
Aim 1, we will deliver a new machine learning method for accurate classification of tumor-reactive T cells using
the CDR3 sequences. In Aim 2, we will derive a set of biomarkers for the cancer-specific T cells for fast and
accurate flow sorting of these T cells from TILs. In Aim 3, we will perform single cell sequencing and functional
validation of cancer-specific T cells using humanized animal model to validate the predicted genes, and to
produce a prioritized list of promising targets for cancer diagnosis, prognosis and therapy development. These
Aims will be accomplished with the great support from the excellent collaborators specialized in cancer
immunology at UTSW. Successful completion of this proposal will provide an exciting new paradigm to identify
tumor-reactive T cells for precision cancer immunotherapies.
Terms: <Adaptive Immune System><Adopted><Amino Acid Sequence><Animal Model><Animal Models and Related Studies><Antigens><Assay><Autoimmune><Autoimmune Status><Autoimmunity><Autologous><B-raf-1><BRAF><BRAF gene><Binding><Bioassay><Biochemical><Biological Assay><Biological Markers><Blood Precursor Cell><Body Tissues><CD25><CD28><CD28 gene><CDR3-region><Cancer Genes><Cancer Patient><Cancer cell line><Cancer-Promoting Gene><Cancers><Cell Isolation><Cell Line><Cell Segregation><Cell Separation><Cell Separation Technology><CellLine><Cellular immunotherapy><Classification><Clinical><Clinical Trials><Complementarity Determining Region 3><Complementarity Determining Region III><Computer software><Computing Methodologies><Cord Blood Hematopoietic progenitor><Cord Blood Hematopoietic stem cells><Data><Data Set><Development><Diagnosis><Differential Gene Expression><Future><Genes><Goals><HLA-A><HLA-A gene><HLAA><Hematopoietic Progenitor Cells><Hematopoietic stem cells><Heterograft><Heterologous Transplantation><Human><IL2R><IL2RA><IL2RA gene><Immune><Immune mediated therapy><Immune response><Immunes><Immunodeficient Mouse><Immunological response><Immunologically Directed Therapy><Immunotherapy><Individual><Infiltration><Innate Immune System><MHC Receptor><Machine Learning><Major Histocompatibility Complex Receptor><Malignant Cell><Malignant Neoplasms><Malignant Tumor><Melanoma Cell><Methods><Modern Man><Molecular Interaction><ORFs><Oncogenes><Open Reading Frames><Outcome><Patients><Post-Translational Modification Protein/Amino Acid Biochemistry><Post-Translational Modifications><Post-Translational Protein Modification><Post-Translational Protein Processing><Posttranslational Modifications><Posttranslational Protein Processing><Primary Protein Structure><Prognosis><Protein Coding Region><Protein Modification><RAFB1><RNA Seq><RNA sequencing><RNAseq><Receptor Protein><Retroviral Antigen gag Protein><Safety><Sampling><Single cell seq><Software><Sorting><Source><Strains Cell Lines><Systematics><T cell infiltration><T-Cell Antigen Receptors><T-Cell Receptor><T-Cells><T-Lymphocyte><T-Stage><T44><TCGFR><Testing><Tissue-Specific Differential Gene Expression><Tissue-Specific Gene Expression><Tissues><Training><Transforming Genes><Treatment Efficacy><Tumor Antigens><Tumor Cell><Tumor Expansion><Tumor stage><Tumor-Associated Antigen><Validation><Viral gag Proteins><Xenograft><Xenograft procedure><Xenotransplantation><acquired immune system><anti-cancer><anti-cancer immunotherapy><anti-cancer treatment><anticancer immunotherapy><antigen binding><antigen bound><antigen-specific T cells><bio-markers><biologic marker><biomarker><biomarker identification><blood cell progenitor><blood progenitor><blood stem cell><blood-forming stem cell><cancer antigens><cancer biomarkers><cancer cell><cancer diagnosis><cancer genomics><cancer immunology><cancer immunotherapy><cancer infiltrating T cells><cancer markers><cancer microenvironment><cell sorting><cell-based immunotherapy><clinical applicability><clinical application><complementarity-determining region 3><computational methodology><computational methods><computer based method><computer methods><computing method><cultured cell line><data to train><dataset to train><deep learning><deep learning method><deep learning strategy><design><designing><develop software><develop therapy><developing computer software><developmental><gag Antigens><gag Gene Products><gag Polyproteins><gag Protein><gene signatures><genetic signature><genomic data><genomic data-set><genomic dataset><global gene expression><global transcription profile><group specific antigen><hematopoietic progenitor><hematopoietic stem progenitor cell><hemopoietic progenitor><hemopoietic stem cell><host response><humanized mice><humanized mouse><identification of biomarkers><identification of new biomarkers><immune cell therapy><immune system response><immune therapeutic approach><immune therapeutic interventions><immune therapeutic regimens><immune therapeutic strategy><immune therapy><immune-based cancer therapies><immune-based therapies><immune-based treatments><immuno therapy><immunogen><immunoresponse><immunotherapy for cancer><immunotherapy of cancer><improved><in vivo><intervention development><intervention efficacy><learning activity><learning method><learning strategies><learning strategy><machine based learning><machine learning based method><machine learning method><machine learning methodologies><malignancy><marker identification><model of animal><neo-antigen><neo-epitopes><neoantigens><neoepitopes><neoplasm immunology><neoplasm/cancer><neoplastic cell><novel><oncogenomics><peripheral blood><predictive biomarkers><predictive marker><predictive molecular biomarker><protein sequence><receptor><reconstitute><reconstitution><response><scRNA-seq><side effect><single cell RNA-seq><single cell RNAseq><single cell expression profiling><single cell next generation sequencing><single cell sequencing><single cell transcriptomic profiling><single-cell RNA sequencing><software development><success><therapeutic efficacy><therapy development><therapy efficacy><third complementarity-determining region><thymus derived lymphocyte><tool><training data><transcriptome><transcriptome sequencing><transcriptomic sequencing><treatment development><tumor><tumor immunology><tumor infiltrating T cells><tumor microenvironment><tumor-specific antigen><unsupervised learning><unsupervised machine learning><v-raf Murine Sarcoma Viral Oncogene Homolog B1><validations><xeno-transplant><xeno-transplantation>