Immunotherapy for B-Cell Neoplasms Using T-Cells Expressing Chimeric Antigen Receptors : From Antigen Choice to Clinical Implementation
Bibliographic record
Abstract
Immunotherapy with T cells expressing chimeric antigen receptors (CAR) is being evaluated as a potential treatment for B-cell neoplasms. In recent clinical trials it has shown promising results. As the number of potential candidate antigens expands, the choice of suitable target antigens becomes more challenging to design studies and to assess optimal efficacy of CAR. Careful evaluation of candidate target antigens is required to ensure that T cells expressing CAR will preferentially kill malignant cells with a minimal toxicity against normal tissues. B cells express specific surface antigens that can theoretically act as targets for CAR design. Although many of these antigens can stimulate effective cellular immune responses in vivo, their implementation in clinical settings remains a challenge. Only targeted B-cell antigens CD19 and CD20 have been tested in clinical trials. This article reviews exploitable B cell surface antigens for CAR design and examines obstacles that could interfere with the identification of potentially useful cellular targets.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".