Tripartite target-agnostic combination immunotherapy cures established poorly immunogenic tumors
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ABSTRACT: Single-agent immunotherapy, such as immune checkpoint inhibition, has shown remarkable efficacy in selected cancer entities and individual patients. However, most patients fail to respond. This is likely due to diverse immunosuppressive mechanisms acting in a concerted way to suppress the host anti-tumor immune response reducing the efficacy of single-agent immunotherapy. Combination immunotherapy approaches that are effective in such poorly immunogenic tumors mostly rely on precise knowledge of immunological targets on tumor cells by vaccinations or antibodies engineered to directly target those. Thus, creating a target-agnostic combination immunotherapy that is effective in poorly immunogenic tumors for which an immunological target is not known is a major challenge. We show that combined adoptive cellular therapy (ACT) with lymphokine-activated killer cells (LAKs), cytokine-induced killer cells (CIKs), Vγ9Vδ2-T-cells (γδ-T-cells) and adaptive, tumor-specific T-cells (CTLs) display synergistic anti-tumor treatment effects, which is further enhanced by co-treatment with anti-PD1 antibodies. Most strikingly, combination of this ACT with anti-PD1 antibodies, local immunotherapy of agonists against Toll-like receptor 3, 7 and 9 and pre-ACT lymphodepletion, a protocol we named TRI-IT, eradicates established, poorly immunogenic tumors and induces durable anti-tumor immunity in a variety of poorly immunogenic syngeneic, autochthonous, as well as autologous humanized patient-derived models. Mechanistically, we show that TRI-IT co-activates adaptive cellular and humoral, as well as innate anti-tumor immune responses to mediate its anti-tumor effect without inducing off-target toxicity. Our data demonstrate the efficacy of a target-agnostic combination immunotherapy that eradicates and potentially cures established poorly immunogenic tumors.
ORGANISM(S): Mus musculus
PROVIDER: GSE173107 | GEO | 2022/10/28
REPOSITORIES: GEO
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