Facing the future: challenges and opportunities in adoptive T cell therapy in cancer
Bibliographic record
Abstract
INTRODUCTION: In recent years, immunotherapy for the treatment of solid cancer has emerged as a promising therapeutic alternative. Adoptive cell therapy (ACT), especially T cell-based, has been found to cause tumor regression and even cure in a percentage of treated patients. Checkpoint inhibitors further underscore the potential of the T cell compartment in the treatment of cancer. Not all patients respond to these treatments; however, many challenges remain. AREAS COVERED: This review covers the challenges and progress in tumor antigen target identification and selection, and cell product manufacturing for T cell ACT. Tumor immune escape mechanisms and strategies to overcome those in the context of T cell ACT are also discussed. EXPERT OPINION: The immunotherapy toolbox is rapidly expanding and improving, and the future promises further breakthroughs in the T cell ACT field. The heterogeneity of the tumor microenvironment and the multiplicity of tumor immune escape mechanisms pose formidable challenges to successful T cell immunotherapy in solid tumors, however. Individualized approaches and strategies combining treatments targeting different immunotherapeutic aspects will be needed in order to expand the applicability and improve the response rates in future.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".