Peptide-Specific CARs Recognize WT1 Promiscuously Presented by Diverse HLA Class II Alleles
Bibliographic record
Abstract
Abstract Chimeric antigen receptor (CAR) technology has revolutionized B-cell malignancy treatment by enabling T cells to effectively recognize and target cancer-specific surface antigens. However, CAR T cells show limited efficacy against other blood cancers and solid tumors due to challenges in identifying suitable surface targets. Here, we present a novel approach to CAR development, targeting the intracellular Wilms’ tumor 1 (WT1) oncoprotein, cross-presented by surface HLA-class II (HLA-II) alleles. WT1-CAR T cells, derived from an antibody raised solely against a WT1 peptide, recognized the WT1330-348 peptide promiscuously presented by 18 out of 20 tested HLA-II alleles, overcoming traditional HLA restrictions. WT1-CAR T cells specifically recognized leukemic cells in a WT1 and HLA-II-dependent manner and mediated an antitumor response in vitro and in vivo. This innovative approach to CAR T cell development transcends traditional HLA restrictions and offers a promising therapeutic option to a wide and genetically diverse patient population. Statement of significance This study describes a novel CAR T therapy approach leveraging the distinctive and shared characteristic of HLA-II-peptide binding promiscuity, enabling targeting of the intracellular oncoprotein WT1 presented across diverse HLA-II families. Our study demonstrates a viable framework for designing CAR T therapies that benefit genetically diverse patient populations.
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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) | 0.003 | 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".