Genetic Regulation of Fate Decisions in Therapeutic T Cells to Enhance Tumor Protection and Memory Formation
Bibliographic record
Abstract
A key challenge in the field of T-cell immunotherapy for cancer is creating a suitable platform for promoting differentiation of effector cells while at the same time enabling self-renewal needed for long-term memory. Although transfer of less differentiated memory T cells increases efficacy through greater expansion and persistence in vivo, the capacity of such cells to sustain effector functions within immunosuppressive tumor microenvironments may still be limiting. We have therefore directly compared the impact of effector versus memory differentiation of therapeutic T cells in tumor-bearing mice by introducing molecular switches that regulate cell fate decisions via mTOR. Ectopic expression of RAS homolog enriched in brain (RHEB) increased mTORC1 signaling, promoted a switch to aerobic glycolysis, and increased expansion of effector T cells. By rapidly infiltrating tumors, RHEB-transduced T cells significantly reduced the emergence of immunoedited escape variants. In contrast, expression of proline-rich Akt substrate of 40 kDa (PRAS40) inhibited mTORC1, promoted quiescence, and blocked tumor infiltration. Fate mapping studies following transient expression of PRAS40 demonstrated that mTORC1(low) T cells made no contribution to initial tumor control but instead survived to become memory cells proficient in generating recall immunity. Our data support the design of translational strategies for generating heterogeneous T-cell immunity against cancer, with the appropriate balance between promoting effector differentiation and self-renewal. Unlike pharmacologic inhibitors, the genetic approach described here allows for upregulation as well as inhibition of the mTORC1 pathway and is highly selective for the therapeutic T cells without affecting systemic mTORC1 functions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".