Immunotherapy of sarcomas with modified T cells
Bibliographic record
Abstract
PURPOSE OF REVIEW: To summarize the development of modified T-cell therapies in sarcomas and discuss relevant published and ongoing clinical trials to date. RECENT FINDINGS: Numerous clinical trials are underway evaluating tumor-specific chimeric antigen receptor T cells and high affinity T-cell receptor (TCR)-transduced T cells in sarcomas. Notably, translocation-dependent synovial sarcoma and myxoid/round cell liposarcoma are the subject of several phase II trials evaluating TCRs targeting cancer testis antigens New York esophageal squamous cell carcinoma-1 (NY-ESO-1) and melanoma antigen-A4 (MAGE A4), and response rates of up to 60% have been observed for NY-ESO-1 directed, modified T cells in synovial sarcoma. Challenges posed by modified T-cell therapy include limitations conferred by HLA-restriction, non-immunogenic tumor microenvironments (TME), aggressive lymphodepletion and immune-mediated toxicities restricting coinfusion of cytokines. SUMMARY: Cellular therapy to augment the adaptive immune response through delivery of modified T cells is an area of novel therapeutic development in sarcomas where a reliably expressed, ubiquitous target antigen can be identified. Therapeutic tools to improve the specificity, signaling, proliferation and persistence of modified TCRs and augment clinical responses through safe manipulation of the sarcoma TME will be necessary to harness the full potential of this approach.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".