Sacituzumab govitecan in Chinese patients with recurrent/metastatic cervical cancer: Results from the phase 2 EVER-132-003 basket study (NCT05119907)
Bibliographic record
Abstract
BACKGROUND: Standard treatment for metastatic cervical cancer is chemotherapy plus immunotherapy in first line and antibody-drug conjugates in second line. Limited treatment options exist after progression on first-line therapy, particularly after immunotherapy. METHODS: In the cervical cancer cohort of open-label, phase 2 EVER-132-003 (NCT05119907) study, Chinese patients with previously treated recurrent/metastatic cervical cancer received sacituzumab govitecan (SG) 10 mg/kg intravenously on days 1 and 8 of 21-day cycles. Primary endpoint was investigator-assessed objective response rate (ORR). Secondary endpoints included investigator-assessed duration of response (DOR) and progression-free survival (PFS) as well as overall survival and safety. RESULTS: For the 40 patients enrolled, median age was 54 years. Patients received median two (range, 1-5) prior systemic treatments for recurrent/metastatic disease; 68 % received prior immunotherapy. In the full analysis set, at 9.6 months' median follow-up, ORR was 43 % (95 % CI, 27-59) and median DOR 9.2 (95 % CI, 4.6-11.7) months. Median PFS was 7.1 (95 % CI, 4.2-8.4) months. Similar efficacy was observed in patients previously treated with immunotherapy (ORR 48 %, median DOR 9.5 months). Exploratory analysis of Trop-2 expression showed limited correlation with SG efficacy. Grade ≥3 treatment-emergent adverse events (TEAEs) occurred in 25 (63 %) patients, most frequently neutropenia (17 [43 %]), leukopenia (15 [38 %]), and anemia (8 [20 %]). Overall, 3 (8 %) patients discontinued SG due to TEAEs. No TEAEs led to death. CONCLUSIONS: Single-agent SG demonstrated promising antitumor activity in pretreated Chinese patients with recurrent/metastatic cervical cancer and in patients previously treated with immunotherapy. AEs were manageable and consistent with known SG safety profile.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".