Tumor-specific cytolytic CD4 T cells mediate immunity against human cancer.

CD4 T cells have been implicated in cancer immunity for their helper functions. Moreover, their direct cytotoxic potential has been shown in some patients with cancer. Here, by mining single-cell RNA-seq datasets, we identified CD4 T cell clusters displaying cytotoxic phenotypes in different human cancers, resembling CD8 T cell profiles. Using the peptide-MHCII-multimer technology, we confirmed ex vivo the presence of cytolytic tumor-specific CD4 T cells. We performed an integrated phenotypic and functional characterization of these cells, down to the single-cell level, through a high-throughput nanobiochip consisting of massive arrays of picowells and machine learning. We demonstrated a direct, contact-, and granzyme-dependent cytotoxic activity against tumors, with delayed kinetics compared to classical cytotoxic lymphocytes. Last, we found that this cytotoxic activity was in part dependent on SLAMF7. Agonistic engagement of SLAMF7 enhanced cytotoxicity of tumor-specific CD4 T cells, suggesting that targeting these cells might prove synergistic with other cancer immunotherapies.

Optimized combinatorial pMHC class II multimer labeling for precision immune monitoring of tumor-specific CD4 T cells in patients.

BACKGROUND: With immunotherapy gaining increasing approval for treatment of different tumor types, scientists rely on cutting edge methods for the monitoring of immune responses and biomarker development in patients. Due to the lack of tools to efficiently detect rare circulating human tumor-specific CD4 T cells, their characterization in patients still remains very limited. METHODS: We have used combinatorial staining strategies with peptide major histocompatibility complex class II (pMHCII) multimer constructs of different alleles to establish an optimized staining procedure for in vitro and direct ex-vivo visualization of tumor-specific CD4 T cells, in patient samples. Furthermore, we have generated reversible multimers to achieve optimal cell staining and yet disassemble prior to in vitro cell expansion, thus preventing activation induced cell death. RESULTS: We observed a vastly improved detection of tumor-specific, viral-specific and bacterial-specific cells with our optimization methods compared with the non-optimized staining procedure. By increasing the variety of fluorochromes used to label the pMHCII multimers, we were also able to increase the parallel detection of different specificities within one sample, including antigen-specific CD8 T cells. A decrease in cell viability was observed when using the full optimization method, but this was mitigated by the removal of neuraminidase and the use of reversible multimers. CONCLUSION: This new optimized staining procedure represents an advance toward better detection and analysis of antigen-specific CD4 T cells. It should facilitate state-of-the art precision monitoring of tumor-specific CD4 T cells and contribute to accelerate the use and the targeting of these cells in cancer immunotherapy.

Robust prediction of HLA class II epitopes by deep motif deconvolution of immunopeptidomes.

Predictions of epitopes presented by class II human leukocyte antigen molecules (HLA-II) have limited accuracy, restricting vaccine and therapy design. Here we combined unbiased mass spectrometry with a motif deconvolution algorithm to profile and analyze a total of 99,265 unique peptides eluted from HLA-II molecules. We then trained an epitope prediction algorithm with these data and improved prediction of pathogen and tumor-associated class II neoepitopes.