TRAbectedin in adVanced rEtroperitoneal well differentiated/dedifferentiated Liposarcoma and Leiomyosarcoma (TRAVELL): results of a phase II study from the Italian Sarcoma Group
Bibliographic record
Abstract
BACKGROUND: This is a multicentre, single-arm, phase II study aimed at further exploring the activity of trabectedin as second-/further-line treatment in retroperitoneal leiomyosarcoma (LMS) and well-differentiated/dedifferentiated liposarcoma (LPS). MATERIALS AND METHODS: The primary endpoint was the growth modulation index (GMI) defined as the ratio between PFS under trabectedin (PFS) and during previous chemotherapy treatment: time to progression (TTP-1). Secondary endpoints were objective response rate (ORR) and PFS. As per protocol, patients were considered responders if the GMI was >1.33, non-responders if <0.75 and neither if 0.76-1.32. RESULTS: Overall 91 patients were assessable for the primary endpoint (32 patients with LMS and 59 patients with LPS): the median number of cycles received was 6.0 (Q1-Q3 3.0-12.0), and the main reason for treatment discontinuation was disease progression in 72% of patients. The median PFS was 6.0 months, while the median TTP1 was 7.5 months (8.1 and 6.4 months for LMS and LPS, respectively). Thirty-three patients [52%, 95% confidence interval (CI) 36% to 58%, P = 0.674, odds of response 1.1] had a GMI >1.33 (LMS 46%, 95% CI 26% to 67%, odds of response 0.85; LPS 56%, 95% CI 40% to 72%, odds of response 1.3). Overall, in LPS we observed 15/47 patients with a GMI <0.5 and 15/47 patients with a GMI >2. Among LMS patients, 9/26 had a GMI <0.5 and 10/26 had a GMI >2. Overall, ORR (complete response + partial response) was 16% (24% for LMS and 12% for LPS). CONCLUSIONS: While the primary endpoint of the study was not met, we noticed a subgroup of patients with a markedly discrepant TTP with trabectedin in comparison to previous therapy (GMI <0.5 or >2, the latter including some patients with a long TTP with trabectedin). A mismatch between PFS and overall survival was observed, possibly due to the natural history of the two different histologies and the availability of further lines in LMS.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".