At the heart of most cancer immunotherapies are the specific interactions between the principal cancer cell killers, T cells, and antigens presented by the tumor cells. Those interactions may be exposed through the use of checkpoint inhibitors, or they can be amplified through the use of engineered cell-based therapies. A class of antigens that has emerged as being particularly important are neoantigens, which are small fragments of mutated proteins that contain the mutation, and, for CD8+ T cell recognition, are presented by MHC Class I. In principle, neoantigens that draw T cells into a tumor can comprise personalized vaccines, and the T cell receptors (TCRs) that recognize those neoantigens can be engineered as personalized cellular therapies. However, identifying which neoantigens are drawing which T cells into the tumor and promoting cell killing has been a challenge. In this talk I will describe an approach, called nanoparticle-barcoded nucleic acid cell sorting (NP-barcoded NACS), designed for this purpose. The approach begins with a list of putative neoantigens generated from in silico analysis of the tumor genome and transcriptome. Those neoantigens are prepared as a NP-barcoded NACS library so that each neoantigen is paired with a DNA barcode. The entire library is then used to precipitate neoantigen specific CD8+ T cell populations from the patient's tumor or blood. Each cell is then individually decoded using sequential fluorescent reads to identify its cognate antigen, and the T cells may be further separated for single cell TCR sequencing. We have used this method to analyze tumor materials and blood from melanoma cancer patients responding to anti-PD-1 therapy, mouse models of immunotherapy, and tumor materials collected from various other cancer patients. The method, which is shown to be at least an order of magnitude more sensitive than alternative approaches, requires very little material, and so may be used for the analysis of non-expanded tumor infiltrating lymphocytes (TILs) or peripheral blood mononuclear cells (PBMCs). I will discuss some of the analytical details of this method, and prospects for harnessing it to develop personalized immunotherapies. I will also provide results of kinetic studies, via blood analysis, that appear to provide insights into patient responses to checkpoint inhibitor therapy.
Citation Format: James R. Heath, Songming Peng, Alice Hsu, Shannon Esswein, John Heath, Won Jun Noh, Jesse Zaretsky, Toni Ribas. Technologies for personalizing cancer immunotherapies [abstract]. In: Proceedings of the Second CRI-CIMT-EATI-AACR International Cancer Immunotherapy Conference: Translating Science into Survival; 2016 Sept 25-28; New York, NY. Philadelphia (PA): AACR; Cancer Immunol Res 2016;4(11 Suppl):Abstract nr IA30.
- ©2016 American Association for Cancer Research.