Abstract 2111: Mobilization of memory natural killer cells in cancer immunotherapy
Bibliographic record
Abstract
Abstract Introduction: Immunological memory has long been exclusively attributed to T and B lymphocytes; however, we now know that natural killer (NK) cells also possess an analogous function. Studies of Rag-1-deficient mice (Rag1-/-), which lack T and B cells, provided evidence of NK cell-mediated immunological memory. As NK cells also possess a natural capacity to eliminate tumor cells, we set out to define the role of NK cell memory in anti-cancer immune responses using NK cell-targeted cancer immunotherapy through immunization. Methods: Rag1-/- mice (n=15) were immunized with the E749-57 RAHNIVYTIF (R9F) peptide from Human Papillomavirus 16, using the proprietary DepoVax (DPX) vaccine formulation, (IMV, Inc., NS Canada). Age-matched Rag1-/- mice (n=15) treated with the DPX vehicle alone served as controls. Sixteen days after immunization, all mice were flank-injected with C3 tumor cells, which express the R9F antigen. Tumor appearance and tumor growth rates were recorded. To test the requirement for Perforin, Rag1/Prf1/- mice (n=15) or age-matched Rag1-/- (n=15) were similarly immunized and tumor challenge. To determine the antigen specificity of NK cell memory, Rag1-/- mice (n=15) were immunized with the chicken-ovalbumin model antigen OVA257-265 SIINFEKL (DPX-SIINFEKL) or DPX vehicle (n=15). Mice for these experiments were implanted with C3 cells that express the ovalbumin gene and present the SIINFEKL antigen (C3-OVA cells). For all groups, tumor appearance and tumor growth rates were recorded. Results: Rag1-/- mice vaccinated with DPX-R9F had better protection against C3 tumor development, with 60% remaining tumor-free in comparison to the DPX vehicle control cohort, in which all mice developed tumors. Additionally, the tumor growth rate in DPX-R9F immunized mice was significantly slower than in the control cohort. Perforin has a key role in mediating the observed anti-tumor responses as Rag1/Prf1/- mice vaccinated with DPX-R9F had less tumor protection compared to the Rag1-/- mice, of which only 20% remained tumor-free. Finally, we found 60% of mice immunized with DPX-SIINFEKL and challenged with C3-OVA remained tumor-free, indicating that tumor protection could be induced by distinct antigens. Conclusions: Our preliminary results suggest that DPX-R9F or DPX-SIINFEKL immunization induces antigen-specific protection against tumor development in mice in a T cell- and B cell-independent manner. These immunizations appear to induce memory NK cells to mount antigen-specific antitumor responses that rely on Perforin as an effector molecule. By demonstrating that memory NK cells have a role in protection against tumor development, future cancer immunotherapies could be improved by priming not only T cells but also NK cells, proposing a therapeutic approach with better patient outcomes and improving, at the same time, the safety profile of these immunotherapies. Citation Format: Daniel Medina-Luna, Gayani Gamage, Michal Scur, Haggah Zein, Brendon Parsons, Andrew P. Makrigiannis. Mobilization of memory natural killer cells in cancer 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 2111.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".