HLA molecules: Another challenge for CAR T cell therapy
Bibliographic record
Abstract
CAR-T cell therapy marks a groundbreaking advancement in the treatment of hematological malignancies, demonstrating significant potential to induce durable remissions. However, several limitations hinder its effectiveness, including antigen loss, immune evasion, and hostile tumor microenvironment. The interplay between CAR-T cell therapy and HLA molecules, particularly HLA-DR and HLA-G, emphasises critical challenges to achieving sustained therapeutic success. Monocytes with low or negative HLA-DR expression and high HLA-G presence contribute to immune evasion and reduced CAR-T cell effectiveness. Additionally, genetic variations in HLA influence susceptibility to hematological malignancies and disease progression. Therapeutic strategies to regulate HLA expression and function are crucial to overcome these obstacles. Approaches such as blocking HLA-G, enhancing HLA-DR expression, and optimising HLA profiles in patients by gene editing can improve outcomes of CAR-T cell therapy. Continued research on HLA-mediated immune modulation will improve these strategies and advance CAR-T cell therapy.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".