Abstract A021: Chimeric antigen receptor armored natural killer cell immunotherapy for osteosarcoma
Bibliographic record
Abstract
Abstract Over the last decade, Chimeric Antigen Receptor based T cell (CAR-T) therapy has developed into an effective immunotherapy for several cancers, primarily limited to those of hematological origin. In other cancers, especially solid tumors, CAR-T cell therapies have several shortcomings and clinical success has been underwhelming. Challenges of CAR-T cell therapy include tumor immune evasion through loss of target antigen expression by tumor cells and inhibition of CAR-T cell function by tumor expressed inhibitory molecules. Natural killer (NK) cells present an alternative to T cells that could be more effective due to their ability to perform both antigen dependent and independent killing. NK cells can mediate the direct killing of transformed cells with reduced or absent MHC expression in addition to carrying out antibody dependent cell mediated cytotoxicity (ADCC) of cells bound by antibodies via the NK cell CD16A receptor. Engineering NK cells to express CARs will effectively enable them with additional antigen specific killing. Due to the multiple modalities for cancer cell killing, there is an increased interest in NK cells for cancer immunotherapy. As NK cells are not associated with graft versus host disease, neurotoxicity, long-term autoimmunity, nor cytokine release syndrome, they are more suited for use in allogeneic settings than T cells and have significant clinical potential for use as off-the-shelf products. However, previous publications and clinical trials have demonstrated that the use of unmanipulated NK cells to treat cancer is minimally effective, likely due to limited engraftment, little in vivo expansion or persistence, and suppression by the tumor microenvironment. NK cells activated and expanded with engineered feeder cells expressing membrane bound interleukin-21 (mbIL-21) and 4-1BBL have shown promising results clinically with high-risk myeloid malignancies and preclinically in several solid tumor models. Therefore, we hypothesize that activated/expanded CAR-NK cells that have been genetically edited can be used to successfully treat osteosarcoma, a disease for which patient outcome has not improved in over forty years. Our proposed objective is to evaluate the non-viral KI of several CARs, either alone or in combination, that optimally activate NK cell antigen-specific killing. Genetically engineered CAR-NK cells will be evaluated for enhanced therapeutic efficacy and safety in osteosarcoma models. Our preliminary data strongly supports the hypothesis that CAR-NK cell-based cancer immunotherapy can be fully realized using activated, genome engineered CAR-NK cells. Citation Format: Gabrielle Robbins, Kenta Yamomoto, Walker Lahr, Joseph Skeate. Chimeric antigen receptor armored natural killer cell immunotherapy for osteosarcoma [abstract]. In: Proceedings of the AACR Special Conference: Sarcomas; 2022 May 9-12; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2022;28(18_Suppl):Abstract nr A021.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".