Improving CAR T-Cell Persistence
Bibliographic record
Abstract
Over the last decade remarkable progress has been made in enhancing the efficacy of CAR T therapies. However, the clinical benefits are still limited, especially in solid tumors. Even in hematological settings, patients that respond to CAR T therapies remain at risk of relapsing due to several factors including poor T-cell expansion and lack of long-term persistence after adoptive transfer. This issue is even more evident in solid tumors, as the tumor microenvironment negatively influences the survival, infiltration, and activity of T-cells. Limited persistence remains a significant hindrance to the development of effective CAR T therapies due to several determinants, which are encountered from the cell manufacturing step and onwards. CAR design and ex vivo manipulation, including culture conditions, may play a pivotal role. Moreover, previous chemotherapy and lymphodepleting treatments may play a relevant role. In this review, the main causes for decreased persistence of CAR T-cells in patients will be discussed, focusing on the molecular mechanisms underlying T-cell exhaustion. The approaches taken so far to overcome these limitations and to create exhaustion-resistant T-cells will be described. We will also examine the knowledge gained from several key clinical trials and highlight the molecular mechanisms determining T-cell stemness, as promoting stemness may represent an attractive approach to improve T-cell therapies.
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) | 0.001 | 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 | 0.000 | 0.000 |
| 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".