Abstract 623: NK cells are involved in promoting anti-tumor responses to DPX-peptide immunotherapy
Bibliographic record
Abstract
Abstract DPX™ technology is a non-aqueous, immune-educating therapeutic platform that delivers specific instruction to the immune system. Antigenic peptides can be packaged within DPX to elicit a robust and persistent tumor antigen-specific T cell response. Maveropepimut-S (MVP-S), formerly DPX-Survivac, contains 5 peptides derived from the tumor antigen survivin, as well as poly dIdC and a T helper peptide. In clinical trials, MVP-S consistently incites a robust and persistent, survivin-specific immune response and promotes T and B cell infiltration into tumor tissues. Importantly, MVP-S based therapy has provided compelling evidence for clinical benefit in multiple cancer indications, notably DLBCL and ovarian cancer. Herein, we provide the first evidence from clinical and preclinical studies that Natural Killer (NK) cells are involved in the anti-cancer efficacy of DPX-based therapy. We evaluated clinical samples from the DeCidE1 trial in advanced, recurrent ovarian cancer (NCT02785250). RNA-profiling of immune cells within tumor tissue revealed the presence of both activated and resting NK cells. Prior to treatment, activated NK cells were more evident in samples from patients achieving partial response (by RECISTv1.1). A higher percentage of subjects within the partial response group were more likely to maintain or increase resting NK cell infiltration on-treatment. PBMCs obtained prior to and on treatment showed no difference in CD56high and low NK profiles related to clinical benefit, highlighting that these changes in NK cell involvement are specific within tumor tissue. We sought to explore whether NK cells may be involved in DPX-based therapeutic efficacy using the HPV E7 model, C3, implanted in Rag1-/- mice, deficient in T and B cells. Mice were pre-treated with DPX packaged with the HPV E7 49-57 T cell peptide antigen (DPX-R9F) and 16 days later implanted with C3 tumor cells. At 40 days post treatment, 100% of control mice showed C3 tumor growth. By contrast, in the DPX-R9F immunized group, 60% of the mice were tumor free. Moreover, in the remaining 40% of the immunized group, the C3 tumors grew significantly slower than C3 tumors of the control group. Together, these data indicate that immunization with DPX-R9F enabled tumor control even in the absence of T or B cell function. To assess whether tumor control involved NK cells/perforin function, we repeated the experiment in Rag1-/-/Perforin-/- mice. In these mice, DPX-R9F immunization was far less effective than in the Rag1-/- mice, showing that only 20% of immunized mice were tumor free and suggesting that DPX-R9F mediated tumor control was partially dependent upon NK cell/perforin function. Taken together, these results from both clinical/translational studies and preclinical models suggest a distinct role for NK cells, in addition to the previously recognized role for T and B cells, in DPX-mediated immunotherapeutic efficacy. Citation Format: Moamen Bydoun, Daniel Medina-Luna, Brennan Dirk, Jeremy Graff, Olga Hrytsenko, Andrew Makrigiannis. NK cells are involved in promoting anti-tumor responses to DPX-peptide immunotherapy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 623.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".