Trabectedin for inoperable or recurrent soft tissue sarcoma in adult patients: a retrospective cohort study
Bibliographic record
Abstract
BACKGROUND: Trabectedin is an antineoplastic agent used for patients with soft tissue sarcoma (STS) who fail standard-of-care treatment. Real-world data of its performance is scarce. This study evaluates the safety and effectiveness of trabectedin for patients with advanced STS who were treated at a high-volume sarcoma center. METHODS: A retrospective chart review was performed on 77 patients treated with trabectedin (24 h infusion q3w) between 01/2005 and 05/2014. Data regarding safety, objective radiological response, progression-free and overall survival were analyzed. RESULTS: Median age at treatment onset was 52y [interquartile range (IQR): 45-61y]. Tumors included leiomyosarcoma (41.6%), liposarcoma (18.2%), and synovial sarcoma (13%). Trabectedin was provided as ≥ third-line chemotherapy in 71.4%. Median number of cycles was 2 (range: 1-17). Dose reduction and treatment delays occurred in 19.5 and 40.3%, respectively. Toxicities occurred in 78%, primarily for neutropenia or elevated liver enzymes. Two patients died secondary to trabectedin-induced rhabdomyolysis. Treatment was discontinued because of disease progression (84.7%), toxicity (10%), and patient preference (5%). Partial response or stable disease occurred in 14.1 and 33.8%, respectively, while 52.1% developed progressive disease. Median progression-free survival was 1.3 m (IQR: 0.7-3.5 m) and was significantly higher in patients lacking severe toxicities or progressive disease. Median overall survival was 6.7 m (IQR: 2.3-12.7 m) and was significantly higher in patients with leiomyosarcoma or liposarcoma relative to other histologies. CONCLUSIONS: Trabectedin has an acceptable safety profile as an anti-tumor agent. Our data further suggest there may be some benefit in using trabectedin particularly in patients with leiomyo- or liposarcoma who failed standard-of-care agents.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| 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 teacher head, 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".