CD28: A roadblock to designing long-lasting CARs?
Bibliographic record
Abstract
Abstract Multiple myeloma (MM) remains an incurable hematological malignancy by conventional chemotherapeutic approaches. Chimeric antigen receptor T (CAR-T) cells targeting B cell maturation antigen (BCMA) on MM have shown remarkable efficacy in relapsed/refractory patients, leading to their recent FDA approval. However, many MM patients fail to achieve durable remissions leading to relapse in the months following treatment, highlighting the need to better understand mechanisms of CAR-T cell failure. Recent studies demonstrate that reducing CD28 co-stimulatory signals delivered by CARs promote CAR-T cell persistence and reduce exhaustion. Whether endogenous CD28 co-stimulation contributes to in vivo CAR-T cell transience or dysfunction is unknown. Our preliminary data indicate that genetic deletion of the endogenous CD28 receptor does not impair CAR-T cell cytotoxicity but promotes in vivo persistence while targeted mutation of CD28 reduces exhaustion marker expression. These findings suggest that targeting endogenous CD28 on CAR-T cells with CTLA4-Ig (Abatacept) may represent a viable clinical strategy to increase the durability of a potent CAR-T cell design. Importantly, in the context of MM, we have previously shown that CD28 is expressed on and delivers pro-survival signals to MM, contributing to therapy resistance. Moreover, given the abundance of CD28 ligands, CD80 and CD86, within the bone marrow, we hypothesize that CD28 signaling reduces the efficacy of BCMA CAR-T therapy by providing a survival signal to MM cells while simultaneously limiting the durability of CAR-T cells. The combination therapy of BCMA CAR-T cells and Abatacept is expected to enhance the efficacy of adoptive CAR-T cell therapy for MM patients. This work is supported by grants from NIH (R03 CA256122, R01 AI155499).
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".