Anti-metabolite chemotherapy increases LAG-3 expressing tumor-infiltrating lymphocytes which can be targeted by combination immune checkpoint blockade.

BACKGROUND: Antibodies that target immune checkpoints such as cytotoxic T lymphocyte antigen 4 (CTLA-4), programmed cell death protein/ligand 1 (PD-1/PD-L1) are approved for treatment of multiple cancer types. Chemotherapy is often administered with immune checkpoint blockade (ICB) therapies that target CTLA-4 and/or PD-(L)1. ICB targeting other immune checkpoints such as lymphocyte activating gene-3 (LAG-3) has the potential to improve antitumor responses when combined with chemotherapy. Response to anti-PD-1 ICB is dependent on progenitor exhausted CD8+ T cells (TPEX) in the tumor, but it is unclear how chemotherapy alters TPEX proportions and phenotype. METHODS: Here we investigated whether sequential chemotherapy altered TPEX frequency and immune checkpoint expression in multiple murine tumor models. RESULTS: Two doses of two different anti-metabolite chemotherapies increased tumor infiltrating CD4+, and CD8+ TPEX expressing LAG-3 in multiple mouse models, which was not restricted to tumor antigen specific CD8+ T cells. To determine if LAG-3+tumor infiltrating lymphocytes (TILs) could be targeted to improve tumor control, we administered anti-LAG-3 and anti-PD-1 ICB after two doses of chemotherapy and found combination therapy generated robust antitumor responses compared with each agent alone. Both anti-LAG-3 and anti-PD-1 ICB with chemotherapy were required for the complete tumor regression observed. CONCLUSIONS: Changes in immune checkpoint expression on TILs during chemotherapy administration informs selection of ICB therapies to combine with.

Immune checkpoint therapy responders display early clonal expansion of tumor infiltrating lymphocytes.

Immune checkpoint therapy (ICT) causes durable tumour responses in a subgroup of patients, but it is not well known how T cell receptor beta (TCRß) repertoire dynamics contribute to the therapeutic response. Using murine models that exclude variation in host genetics, environmental factors and tumour mutation burden, limiting variation between animals to naturally diverse TCRß repertoires, we applied TCRseq, single cell RNAseq and flow cytometry to study TCRß repertoire dynamics in ICT responders and non-responders. Increased oligoclonal expansion of TCRß clonotypes was observed in responding tumours. Machine learning identified TCRß CDR3 signatures unique to each tumour model, and signatures associated with ICT response at various timepoints before or during ICT. Clonally expanded CD8+ T cells in responding tumours post ICT displayed effector T cell gene signatures and phenotype. An early burst of clonal expansion during ICT is associated with response, and we report unique dynamics in TCRß signatures associated with ICT response.