Reprogramming the tumor microenvironment to boost adoptive T cell therapy
Bibliographic record
Abstract
Adoptive T cell therapies (ACT) have revolutionized the management of hematologic malignancies; however, their efficacy in solid tumors remains limited. Accumulating evidence implicates the tumor microenvironment (TME) - a highly complex and immunosuppressive niche as a major barrier to their effectiveness. In this review, we propose that the next generation of ACT will require a fundamental shift from a reductionist focus on T cell engineering alone to an integrated approach that considers the interactions between immune cells and the TME. A comprehensive literature review identified several emerging strategies to enhance the efficacy of ACT, including reprogramming tumor vasculature, repolarizing immunosuppressive myeloid and stromal cells, leveraging oncolytic viruses to remodel antigen presentation, inducing acute sterile inflammation, and targeting the physical properties of the extracellular matrix. While many of these approaches remain in early-stage development, some have already progressed to clinical trials, indicating their potential for clinical translation. Additionally, we found that conventional therapies, such as surgery, chemotherapy, and radiotherapy, can be strategically integrated with ACT to improve therapeutic outcomes. These findings highlight a shift in the field toward more integrative approaches. Future advances will likely depend on reprogramming the TME to support T cell persistence and functions. Addressing these interconnected challenges will require closer collaboration between immunology, oncology, and bioengineering disciplines.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".