MARC, a novel modular chimeric antigen receptor, improves T cell-based cancer immunotherapies by preventing early T cell exhaustion and enhancing persistence
Bibliographic record
Abstract
BACKGROUND: Chimeric antigen receptor T cell (CAR-T)-based immunotherapies have reshaped the therapeutic landscape of cancer treatment, in particular for patients afflicted with leukemia. However, defects in CAR behaviors and clinical complications have hindered their widespread application across diverse cancer types. Chief among these defects is high tonic signaling, absent in native activating immune receptors, which accelerates T cell exhaustion and undermines treatment efficacy. We hypothesized that these limitations arise because current CAR architectures fail to replicate the modular design of native activating immune receptors, which integrate distinct receptor and signaling modules. This modular assembly is crucial for maintaining proper receptor regulation and function. METHODS: Therefore, we set forth to develop a modular chimeric antigen receptor leveraging the same assembly principles found in native activating immune receptors to reestablish the intrinsic safeguards in receptor expression and signaling. RESULTS: The resulting Modular Actuation Receptor Complex (MARC) displayed surface expression levels akin to its native immune receptor counterpart, the NK cell receptor KIR2DS3, while eliminating tonic signaling. In a clinically relevant mouse leukemia model, MARC-T cells exhibited remarkable long-term persistence and a less exhausted phenotype compared with conventional CAR-T cells. CONCLUSIONS: With its modular architecture, the MARC offers unparalleled opportunities for optimization and broad applicability across different cell types, paving the way for transformative advancements in cell-based therapies. This innovation holds immense promise as a next-generation therapeutic tool in clinical settings.
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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) | 0.001 | 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".