Abstract 559: Targeting hematological malignancies and solid tumors with switchable chimeric antigen receptor-engineered iPSC-derived natural killer cells
Bibliographic record
Abstract
Abstract Switchable chimeric antigen receptors (sCAR) provide an important new strategy to precisely regulate CAR-mediated anti-tumor activity. The sCAR system combines a CAR that recognizes peptide neoepitope (PNE) with an injectable “switch” molecule that consists of an anti-tumor Fab linked to PNE. Configurating PNE at different location or chain of the Fab enables us to identify best switches optimal for tumor targeting. Previous studies demonstrate that this sCAR system expressed in T cells provides tight control over anti-tumor activity. Here, we translated this approach to engineer natural killer (NK) cells with the sCAR to provide a universal, targeted cell-therapy approach in a patient-nonspecific manner. First, we engineered human induced pluripotent stem cells (iPSCs) with the sCAR combining the PNE-specific CAR scFv with our previously described NK cell optimized CAR4 signaling motifs consisting of the NKG2D transmembrane domain, 2B4 co-stimulatory domain and the CD3ζ chain. We selected clones that maintained highest level of pluripotency and most stable expression of the sCAR4 on the surface. Next, we generated mature sCAR4-expressing iPSC-derived NK cells that expressed common surface receptors similar to that of donor peripheral blood mononuclear cell-derived NK cells. We then used three panels of switches with specificity to CD19 (consisting of 9 different configurations), to CD33, CD123 and CLL1 (1 configuration each), and to FRIZZLED7 (FZD7; 6 configurations) to target CD19+ B cell lymphoma, acute myeloid leukemia (AML), and ovarian cancer, respectively. All three targets demonstrated switch-specific dose response in killing tumor cell lines. Different configurations conferred variable efficiencies in switch-mediating tumor cell killings and the optimal switch concentrations were found to be different from what was observed previously with sCAR-expressing T cells. In a comparison with the “conventional” (non-switchable) CARs (cCARs), iPSC-NK cells expressing either sCAR4 and treated with an anti-CD19 switch or iPSC-NK cells with an anti-CD19-cCAR4 demonstrated similar level of cytotoxicity against B cell leukemia cells. Finally, we have built a mouse model and are currently testing the iPSC-NK-sCAR system in vivo. Together, this iPSC-NK-sCAR strategy enables close control over CAR-mediated activity with flexibility to target multiple tumor types and a potential to become a novel off-the-shelf therapy. Citation Format: Xiao-Hua Li, Benjamin Goldenson, Jaya Lakshmi Thangaraj, Matthew Gynn, Diana Gumber, Myan Do, Karl Willert, Dan S. Kaufman. Targeting hematological malignancies and solid tumors with switchable chimeric antigen receptor-engineered iPSC-derived natural killer cells [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 559.
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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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".