Designing T Cell Liquide Biopsies From Single-Cell Resolution Data
Tumor immunotherapy has revolutionized the clinical course of cancer treatment, but the majority of patients still do not respond. Identification of biomarkers to predict a patient’s response to therapy and establishment of a better understanding of the molecular mechanisms at play during a successful response are paramount for improving patient care. Tracking anti-tumor T cell responses in the blood is appealing due to the ability to easily and repeatedly sample blood from patients. However, tracking tumor antigen-specific T cells using conventional methods (like peptide MHC tetramers or PD-1 expression) has been challenging due to e.g. a limited number of defined tumor antigens. Previous pioneering work has revealed that subsets of T cells within blood and T cell clonality in circulation could have predictive values on anti-tumor response. Here, we propose to conduct a global, unrestricted characterization of the tumor-directed T cell population within blood, followed by development of techniques by which this population can be isolated and utilized in clinic. By utilizing paired transcriptional and TCR profiling at single-cell resolution (10X Chromium), we will study the tumor-directed T cell component in blood by identifying the subset of T cells in blood that have TCRs that match with TCRs observed in the tumor. In this work we will (1) expand our developed COMET tool for identification of marker panels from single-cell transcriptomics data, (2) utilize COMET for the identification of marker panels for the isolation of the tumor-directed T cell component in blood of treatment-naïve melanoma patients and (3) use our annotated marker panels to enrich for tumor-directed T cells before and after PD-1 therapy and search for biomarkers of response. Our work will provide a framework for identification of marker panels from single-cell RNA-seq patient data, and will characterize the potential of using the tumor-directed T cell component in blood to track the anti-tumor response.