2018) Combination immune checkpoint blockade as an effective therapy for mesothelioma, OncoImmunology, 7:10, e1494111, ABSTRACT Mesothelioma is an aggressive asbestos induced cancer with extremely poor prognosis and limited treatment options. Immune checkpoint blockade (ICPB) has demonstrated effective therapy in melanoma and is now being applied to other cancers, including mesothelioma. However, the efficacy of ICPB and which immune checkpoint combinations constitute the best therapeutic option for mesothelioma have yet to be fully elucidated. Here, we used our well characterised mesothelioma tumour model to investigate the efficacy of different ICBP treatments to generate effective therapy for mesothelioma. We show that tumour resident regulatory T cell co-express high levels of CTLA-4, OX40 and GITR relative to T effector subsets and that these receptors are co-expressed on a large proportion of cells. Targeting any of CTLA-4, OX40 or GITR individually generated effective responses against mesothelioma. Furthermore, the combination of αCTLA-4 and αOX40 was synergistic, with an increase in complete tumour regressions from 20% to 80%. Other combinations did not synergise to enhance treatment outcomes. Finally, an early pattern in T cell response was predictive of response, with activation status and ICP receptor expression profile of T effector cells harvested from tumour and dLN correlating with response to immunotherapy. Taken together, these data demonstrate that combination ICPB can work synergistically to induce strong, durable immunity against mesothelioma in an animal model. ARTICLE HISTORY
Immune checkpoint therapy (ICT) results in durable responses in individuals with some cancers, but not all patients respond to treatment. ICT improves CD8 + cytotoxic T lymphocyte (CTL) function, but changes in tumor antigen-specific CTLs post-ICT that correlate with successful responses have not been well characterized. Here, we studied murine tumor models with dichotomous responses to ICT. We tracked tumor antigen-specific CTL frequencies and phenotype before and after ICT in responding and non-responding animals. Tumor antigen-specific CTLs increased within tumor and draining lymph nodes after ICT, and exhibited an effector memory-like phenotype, expressing IL-7R (CD127), KLRG1, T-bet, and granzyme B. Responding tumors exhibited higher infiltration of effector memory tumor antigen-specific CTLs, but lower frequencies of regulatory T cells compared to non-responders. Tumor antigen-specific CTLs persisted in responding animals and formed memory responses against tumor antigens. Our results suggest that increased effector memory tumor antigen-specific CTLs, in the presence of reduced immunosuppression within tumors is part of a successful ICT response. Temporal and nuanced analysis of T cell subsets provides a potential new source of immune based biomarkers for response to ICT.
Chemotherapy has historically been the mainstay of cancer treatment, but our understanding of what drives a successful therapeutic response remains limited. The diverse response of cancer patients to chemotherapy has been attributed principally to differences in the proliferation rate of the tumor cells, but there is actually very little experimental data supporting this hypothesis. Instead, other mechanisms at the cellular level and the composition of the tumor microenvironment appear to drive chemotherapy sensitivity. In particular, the immune system is a critical determinant of chemotherapy response with the depletion or knock-out of key immune cell populations or immunological mediators completely abrogating the benefits of chemotherapy in pre-clinical models. In this perspective, we review the literature regarding the known mechanisms of action of cytotoxic chemotherapy agents and the determinants of response to chemotherapy from the level of individual cells to the composition of the tumor microenvironment. We then summarize current work toward the development of dynamic biomarkers for response and propose a model for a chemotherapy sensitive tumor microenvironment.
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