Molecular Results and Potential Biomarkers Identified from the Phase 3 MILO/ENGOT-ov11 Study of Binimetinib versus Physician Choice of Chemotherapy in Recurrent Low-Grade Serous Ovarian Cancer
Bibliographic record
Abstract
PURPOSE: We present the results of a post hoc tumor tissue analysis from the phase 3 MILO/ENGOT-ov11 study (NCT01849874). PATIENTS AND METHODS: Mutation/copy-number analysis was performed on tissue obtained pre-randomization. The Kaplan-Meier method was used to estimate progression-free survival (PFS). Unbiased univariate analysis, Cox regression, and binary logistic regression were used to test associations between mutation status and outcomes, including PFS and binary response by local RECIST 1.1. RESULTS: MILO/ENGOT-ov11 enrolled 341 patients, ranging in age from 22 to 79, from June, 2013 to April, 2016. Patients were randomized 2:1 to binimetinib or physician's choice of chemotherapy (PCC). The most commonly altered gene was KRAS (33%). In 135 patients treated with binimetinib with response rate (RR) data, other detected MAPK pathway alterations included: NRAS (n = 11, 8.1%), BRAF V600E (n = 8, 5.9%), RAF1 (n = 2, 1.5%), and NF1 (n = 7, 5.2%). In those with and without MAPK pathway alterations, the RRs with binimetinib were 41% and 13%, respectively. PFS was significantly longer in patients with, compared with those without, MAPK pathway alterations treated with binimetinib [HR, 0.5; 95% confidence interval (CI) 0.31-0.79]. There was a nonsignificant trend toward PFS improvement in PCC-treated patients with MAPK pathway alterations compared with those without (HR, 0.82; 95% CI, 0.43-1.59). CONCLUSIONS: Although this hypothesis-generating analysis is limited by multiple testing, higher RRs and longer PFS were seen in patients with low-grade serous ovarian cancer (LGSOC) treated with binimetinib, and to a lesser extent in those treated with PCC, who harbored MAPK pathway alterations. Somatic tumor testing should be routinely considered in patients with LGSOC and used as a future stratification factor.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".