Identifying Distinct Molecular Response of CAR-T cells to Solid Tumors by Synthetic Single-Cell Transcriptomic Analyses
Bibliographic record
Abstract
Abstract Chimeric Antigen Receptor (CAR)-T cell therapy is a novel personalized treatment that engineers patient immune cells to fight against cancer cells. Recently, CAR-T cell therapy has demonstrated remarkable success in the treatment of hematopoietic cancers, whereas the treatment of solid tumors is more challenging, likely in part due to their severely immunosuppressive tumor microenvironment. To address distinct molecular responses of CAR-T cells between blood and solid tumors, we performed synthetic analysis of single-cell transcriptomics of CAR-T cells and identified unique immunosuppressive subpopulations and aberrant signalling inductions in CD4 + and CD8 + CAR-T cells in the context of solid tumors. Furthermore, we also found that PD-1-independent exhaustion-like CD8 + CAR-T cells, characterized by high expression of TNFRSF9 and CCL3 , were preferentially generated under solid tumor stimulation. Collectively, our comprehensive analyses provide essential molecular insights into solid tumor-stimulated CAR-T cells and assists in overcoming the limited efficacy of CAR-T cell therapy against solid tumors.
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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".