Mechanisms of antigen-dependent resistance to chimeric antigen receptor (CAR)-T cell therapies
Bibliographic record
Abstract
Cancer immunotherapy has reshaped the landscape of cancer treatment over the past decades. Genetic manipulation of T cells to express synthetic receptors, known as chimeric antigen receptors (CAR), has led to the creation of tremendous commercial and therapeutic success for the treatment of certain hematologic malignancies. However, since the engagement of CAR-T cells with their respective antigens is solely what triggers their cytotoxic reactions against target cells, the slightest changes to the availability and/or structure of the target antigen often result in the incapacitation of CAR-T cells to enforce tumoricidal responses. This results in the resistance of tumor cells to a particular CAR-T cell therapy that requires meticulous heeding to sustain remissions in cancer patients. In this review, we highlight the antigen-dependent resistance mechanisms by which tumor cells dodge being recognized and targeted by CAR-T cells. Moreover, since substituting the target antigen is the most potent strategy for overcoming antigen-dependent disease relapse, we tend to highlight the current status of some target antigens that might be considered suitable alternatives to the currently available antigens in various cancers. We also propose target antigens whose targeting might reduce the off-tumor adverse events of CAR-T cells in certain malignancies.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".