Tripartite antigen-agnostic combination immunotherapy cures established poorly immunogenic tumors

Background Single-agent immunotherapy 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. Combination immunotherapy approaches that are effective in such poorly immunogenic tumors mostly rely on precise knowledge of antigenic determinants on tumor cells. Creating an antigen-agnostic combination immunotherapy that is effective in poorly immunogenic tumors for which an antigenic determinant is not known is a major challenge. Methods We use multiple cell line and poorly immunogenic syngeneic, autochthonous, and autologous mouse models to evaluate the efficacy of a novel combination immunotherapy named tripartite immunotherapy (TRI-IT). To elucidate TRI-ITs mechanism of action we use immune cell depletions and comprehensive tumor and immune infiltrate characterization by flow cytometry, RNA sequencing and diverse functional assays. Results We show that combined adoptive cellular therapy (ACT) with lymphokine-activated killer cells, cytokine-induced killer cells, V gamma 9V delta 2-T-cells (gamma delta-T-cells) and T-cells enriched for tumor recognition (CTLs) display synergistic antitumor effects, which are further enhanced by cotreatment with anti-PD1 antibodies. Most strikingly, the full TRI-IT protocol, a combination of this ACT with anti-PD1 antibodies, local immunotherapy of agonists against toll-like receptor 3, 7 and 9 and pre-ACT lymphodepletion, eradicates 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 coactivates adaptive cellular and humoral, as well as innate antitumor immune responses to mediate its antitumor effect without inducing off-target toxicity. Conclusions Overall, TRI-IT is a novel, highly effective, antigen-agnostic, non-toxic combination immunotherapy. In this study, comprehensive insights into its preclinical efficacy, even in poorly immunogenic tumors, and mode of action are given, so that translation into clinical trials is the next step

In-depth cell-free DNA sequencing reveals genomic landscape of Hodgkin's lymphoma and facilitates ultrasensitive residual disease detection

Background: Individualization of treatment in Hodgkin's lymphoma is necessary to improve cure rates and reduce treatment side effects. Currently, it is hindered by a lack of genomic characterization and sensitive molecular response assessment. Sequencing of cell-free DNA is a powerful strategy to understand the cancer genome and can be used for extremely sensitive disease monitoring. In Hodgkin's lymphoma, a high proportion of cell-free DNA is tumor-derived, whereas traditional tumor biopsies only contain a little tumor-derived DNA. Methods: We comprehensively genotype and assess minimal residual disease in 121 patients with baseline plasma as well as 77 follow-up samples from a subset of patients with our targeted cell-free DNA sequencing platform. Findings: We present an integrated landscape of mutations and copy number variations in Hodgkin's lymphoma. In addition, we perform a deep analysis of mutational processes driving Hodgkin's lymphoma, investigate the clonal structure of Hodgkin's lymphoma, and link several genotypes to Hodgkin's lymphoma phenotypes and outcome. Finally, we show that minimal residual disease assessment by repeat cell-free DNA sequencing, as early as a week after treatment initiation, predicts treatment response and progression-free survival, allowing highly improved treatment guidance and relapse prediction. Conclusions: Our targeted cell-free DNA sequencing platform reveals the genomic landscape of Hodgkin's lymphoma and facilitates ultrasensitive detection of minimal residual disease. Funding: Mildred Scheel School of Oncology Aachen-Bonn-Cologne-Düsseldorf MD Research Stipend, Next Generation Sequencing Competence Network grant 423957469, Deutsche Krebshilfe grant 70112502, Deutsche Forschungsgemeinschaft (DFG) grant EN 179/13-1, the HL MRD consortium, and the Frau-Weiskam und Christel Ruranski-Stiftung