Optimizing Chimeric Artificial T Cell Receptor Therapy for Solid Tumour Targeting by Manipulating the Tumour Microenvironment: A Literature Review
Bibliographic record
Abstract
Introduction: Chimeric antigen receptor (CAR)-T cell therapy represents a breakthrough in cancer treatment by harnessing the power of genetically engineered T cells to target and eliminate tumour cells. This approach involves modifying a patient’s T cells to express CARs that recognize and bind to specific cancer antigens, bypassing conventional antigen presentation mechanisms. While CAR-T therapy has achieved notable success in treating ‘blood,’ or ‘liquid’ cancers, its application to solid tumours remains problematic due to the immunosuppressive tumour microenvironment (TME), which impairs T cell function and promotes tumour escape. Methods: A comprehensive review was conducted using PubMed and Google Scholar, focusing on publications from March 2002 to March 2024. The search criteria included terms related to CAR-T therapy, solid tumours and TME modulation. Studies were selected based on their relevance to CAR-T therapy challenges, advancements in TME modulation and clinical applications. Research focusing exclusively on liquid tumours was excluded to ensure the review’s focus on solid tumour contexts. Results: Analysis of lymphocyte densities revealed that hot tumours, characterized by high densities of CD3+ and CD8+ lymphocytes at both the tumour periphery and core, demonstrated the most favorable prognostic outcomes. Checkpoint blockades targeting CTLA-4 and PD-1/PD-L1 have been shown to prevent tumour escape. Additionally, engineering CAR-T cells to express immune checkpoint (IC) inhibitors counteracts the expression of IC ligands by immunosuppressive cells. T cells engineered to express IL-12 or IL-18 cytokines also allowed for better infiltration into the TME. Discussion: Targeting inhibitory immune checkpoints and suppressive cells, as well as manipulating CAR-T cytokine expression, have all shown to be promising ways in which the TME can be modulated to improve patient outcomes. Future research may include looking into the development of strategies to ensure long-term immunological memory in CAR-T cells, particularly for chronic or recurring solid tumours. Conclusion: Modulating the TME through various molecular and cellular avenues is crucial in improving the effectiveness of CAR-T cell therapy and therefore, optimizing antitumour immunity.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".