Engineering Intracellular Signaling Domains to Drive CAR-T Cell Differentiation from Pluripotent Stem Cell 2865
Bibliographic record
Abstract
Abstract Description Immunotherapy has revolutionized cancer treatment by harnessing the immune system to target malignant cells, with chimeric antigen receptor (CAR) T cell therapy showing notable efficacy against B cell lymphomas. While conventional CAR-T therapy relies on patient-derived cells, induced pluripotent stem cell (iPSC)-derived CAR T cells offer a scalable, off-the-shelf alternative, mitigating issues of cost and patient variability. However, differentiating iPSCs into CAR T cells is challenged by a tendency of cells to skew toward innate-like phenotypes due to tonic signaling from the CAR during early development. To overcome this, we are engineering CAR intracellular signaling domains (ICDs) to modulate tonic signaling and direct iPSC differentiation toward functional CD4+ and CD8+ CAR T cells. Our CAR signaling domain library screen revealed that a novel ICD combination demonstrated a 2.5-fold reduction in tonic signaling index compared to the conventional CD28 ICD. Additionally, these CAR-transduced iPSC-derived hematopoietic stem progenitor cells (HSPCs) showed a fivefold increase in CD4+CD8+ T cells, which are precursors to mature CD4+ and CD8+ T cells, suggesting reduced innate skewing. This work enhances our understanding of lymphoid cell development with CAR signaling, supporting the identification of optimal ICD combinations to promote iPSC differentiation into CAR T cells under feeder- and serum-free conditions, and advancing standardized CAR-T cell therapy options. Funding Sources Four Year Fellowship (University of British Columbia) Topic Categories Tumor Immunology: Checkpoints, Prevention, and Treatment (TIPT)
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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".