Metabolic optimization of adoptive T cell transfer cancer immunotherapy: A historical overview
Bibliographic record
Abstract
Abstract After prolonged extracorporeal multiplication in physiological culture media, there can be curative infusions of a cancer patient's own cytotoxic T cells (adoptive T cell transfer; ACT), which must achieve efficient activation in potentially adverse tumour microenvironments. With spectacular, yet irregular, success, improvements are needed. Developing lymphoid cells are biologically selected, not only for ‘near‐self’ reactivity (positive selection), but also to avoid self‐reactivity (negative selection). Thus, success requires harnessing near‐self cells while avoiding extreme autoimmune phenomena. Abrupt metabolic changes accompanying T cell activation to leave the G0 stage and enter the G1 stage of the cell cycle (eg enhanced glycolysis) are accompanied by increased transcription of the G0S9 gene that mediates salvage synthesis of NAD+ from nicotinamide; the latter has recently been shown to increase the efficiency of ACT. Despite theoretical and experimental advances, there has not been parallel progress in simulating in vivo conditions with culture media that were initially formulated for their positive benefits for tumour cell lines (cell survival and proliferation). Yet for lymphoid cells, inhibition or death (ie immunological tolerance) is as important as their activation and proliferation (immunological response). Thus, use of media optimized for the latter may mask the former. The resilience of established culture protocols may have been partly politically driven. However, unphysiological conditions have sometimes yielded fortuitous insights. Optimization of culture media for specific tissues must consider the nature of problems addressed in research settings and the need to avoid mishaps in clinical settings.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".