Abstract A068: Determinants of efficacy in cancer immunovirotherapy
Bibliographic record
Abstract
Abstract Replicating oncolytic viruses (OVs) are emerging as a promising therapeutic approach for different tumor entities. Positive data from numerous clinical studies are accumulating and as a first-in-class oncolytic herpes virus encoding GM-CSF, Talimogene Laherparepvec (T-VEC), has been approved for treatment of unresectable melanoma by the U.S. Food and Drug Administration at the end of 2015. Although it has been recognized that combinations of OVs with different immunomodulators can significantly improve therapeutic outcome, understanding of immune effector mechanisms determining success of a particular approach remains limited, restricting the development of rational combination strategies. In this study we aimed to determine the most beneficial immunomodulation strategies for combination with oncolytic measles virus (MeV). Furthermore, we performed a comprehensive analysis of anti-tumor immune effector mechanisms associated with response to pinpoint mechanisms crucial for therapeutic efficacy. We developed MeV Schwarz vaccine strain vectors (MeVac) encoding different immunomodulators to target the main phases in establishment of an anti-tumor immune response: GM-CSF - to enhance maturation of antigen-presenting cells, an IL-12 fusion protein (FmIL-12) and IP-10 - to enhance immune effector cell responses, antibodies against CTLA-4 and PD-L1 and a soluble form of CD80 - to counteract immunosuppression in the tumor microenvironment. Therapeutic efficacy of the novel vectors was evaluated in the fully immunocompetent murine colon adenocarcinoma model MC38cea. MeVac encoding anti-PD-L1 and FmIL-12, respectively, were identified as the most effective therapeutics. Notably, MeVac encoding FmIL-12 showed a superior therapeutic efficacy, achieving 90% complete remissions of established tumors. Animals treated with the immunomodulatory MeVac vectors rejected secondary tumor engraftments, indicating establishment of protective anti-tumor immunity. Higher IFN-γ production upon restimulation of splenocytes with tumor cells in vitro was observed for animals treated with MeVac FmIL-12 in comparison to treatment with MeVac anti-PD-L1 and a control vector encoding constant region of antibody (MeVac IgG1-Fc), indicating establishment of a more potent memory response. Intratumoral cytokine profiling with cytokine bead arrays revealed upregulation of the effector cytokines IFN-γ and TNF-α after treatment with MeVac FmIL-12. A massive decrease in intratumoral natural killer (NK) cell counts and an increase of activation marker CD69 expression on NK cells as well as an increase in T cell amount was observed in flow cytometry analysis one day after treatment with MeVac FmIL-12. This indicates early direct activation of these lymphocyte subsets through direct IL-12 signaling. An increase in intratumoral T cells and a decrease in NK cells and activated cytotoxic T cells (CD8+CD69+) was observed four days after the last treatment with both MeVac anti-PD-L1 and MeVac IgG1-Fc, with slightly more pronounced effects in the MeVac anti-PD-L1 group. This suggests that MeVac vectors per se activate T and NK cell responses, which can further be supported with PD-L1 blockade. This study establishes MeVac encoding FmIL-12 as a potent immunomodulatory oncolytic vector and provides insights into its mechanisms of action, thereby creating a basis for further rational vector modifications to translate immunomodulatory MeV into clinical application. Citation Format: Rūta Veinalde, Christian Grossardt, Marie-Claude Bourgeois-Daigneault, Christof von Kalle, Dirk Jaeger, Guy Ungerechts, Christine E. Engeland. Determinants of efficacy in cancer immunovirotherapy [abstract]. In: Proceedings of the Second CRI-CIMT-EATI-AACR International Cancer Immunotherapy Conference: Translating Science into Survival; 2016 Sept 25-28; New York, NY. Philadelphia (PA): AACR; Cancer Immunol Res 2016;4(11 Suppl):Abstract nr A068.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".