Tumor-Associated Extracellular Matrix Obstacles for CAR-T Cell Therapy: Approaches to Overcoming
Bibliographic record
Abstract
Chimeric antigen receptor (CAR)-T cell therapy yields good results in the treatment of various hematologic malignancies. However, the efficacy of CAR-T cell therapy against solid tumors has proven to be limited, primarily because the tumor-associated extracellular matrix (ECM) creates an intractable barrier for the cytotoxic CAR-T cells that are supposed to kill cancer cells. This review unravels the multifaceted role of the tumor-associated ECM in impeding CAR-T cell infiltration, survival, and functions within solid tumors. We analyze the situations when intratumoral ECM limits the efficacy of CAR-T cell therapy by being a purely physical barrier that complicates lymphocyte penetration/migration and also acts as an immunosuppressive factor that impairs the antitumor activities of CAR-T cells. In addition, we highlight promising approaches such as engineering CAR-T cells with improved capabilities to penetrate and migrate into/through the intratumoral ECM, combination therapies aimed at attenuating the high density and immunosuppressive potential of the intratumoral ECM, and others that enable overcoming ECM-related obstacles. A detailed overview of the data of relevant studies not only helps to better understand the interactions between CAR-T cells and the intratumoral ECM but also outlines potential ways to more effectively use 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".