Reprogramming T-cell metabolism to enhance adoptive cell therapies
Bibliographic record
Abstract
Adoptive cell therapy (ACT) is an immunotherapeutic approach that involves isolating T cells from a patient, culturing them ex vivo, then reinfusing the cells back into the patient. Although this strategy has shown remarkable efficacy in hematological malignancies, the solid-tumour microenvironment (TME) has presented serious challenges for therapy efficacy. Particularly, the TME has immunosuppressive signalling and presents a metabolically challenging environment that leads to T-cell suppression. T-cell metabolism is an expanding field of research with a focus on understanding its inherent link to T-cell function. Here, we review the current model of T-cell metabolism from naïve cells through effector and memory life stages, as well as updates to the model from recent literature. These models of metabolism have provided us with the tools and understanding to explore T-cell metabolic and mitochondrial insufficiency in the TME. We discuss manipulations that can be made to these mitochondrial and metabolic pathways to enhance the persistence of infused T cells, overcome the metabolically challenging TME and improve the efficacy of therapy in ACT models. Further understanding and investigation of the impact of metabolic pathways on T-cell performance could contribute to improving therapy efficacy for patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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; both teacher heads agree on what is shown here.
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".