Molecular results and potential biomarkers identified from MILO/ENGOT-ov11 phase 3 study of binimetinib versus physicians choice of chemotherapy (PCC) in recurrent low-grade serous ovarian cancer (LGSOC).
Bibliographic record
Abstract
5519 Background: Lower responses to chemotherapy and the unique molecular profile of LGSO led to the adoption of MEK-inhibitors for this disease. Updated analysis from the MILO/ENGOT-ov11 phase III study of binimetinib vs PCC in recurrent LGSOC showed response rate of 24% in those treated with binimetinib (JCO, 2020; NCT01849874). Here we present results of the post-hoc tumor tissue biomarker analysis performed with MILO/ENGOT-ov11. Methods: Mutational/copy number analysis was performed via Foundation Medicine on archival tissue obtained prior to randomization. Unbiased univariate analysis was used to test association between mutation status and outcomes in binimetinib and PCC treated patients. Outcomes examined were progression free survival (PFS), binary response by local RECIST 1.1 (complete or partial response [CR/PR] vs. stable [SD] or progressive disease [PD]), and ordinal response. Kaplan-Meier was used to estimate PFS. Cox regression, binary logistic regression, and ordinal logistic regression were used to examine relationship between mutation status and outcomes. Results: MILO/ENGOT-OV11 enrolled 341 patients from June, 2013 to April, 2016. Patients were randomized 2:1 to binimetinib or PCC. Based on January 1, 2020 data cut-off the data is as-is, amongst those patients treated with binimetinib with molecular results available, PFS data is available for 144 and response rate (RR) data for 135. There were 47 mutations detected in ≥5% of patients, most commonly KRAS (33%). Patients harboring a KRAS mutation had 3.4 times the odds of responding to treatment with binimetinib as patients without KRAS mutation (95% CI 1.57,7.67; p-value 0.002). There was no difference in effect of KRAS G12V mutation vs other KRAS mutation on PFS (PFS HR 1.06; 95% CI:0.53, 2.12; p value 0.9). In the 135 patients with binimetinib RR data, other MAPK mutations were identified as follows; NRAS in 11(8.1%), BRAFV600E in 8(5.9%), RAF1 in 2 (1.5%), NF1 in 7 (5.2%). In patients with MAPK mutation (as defined above) the RR was 41% vs 13% in those without MAPK mutation. PFS was significantly better in patients treated with binimetinib harboring MAPK mutation vs those without (HR 0.5; 95% CI 0.31, 0.79; p = 0.003). In patients treated with PCC there was a nonsignificant trend towards improved PFS in those with MAPK mutation vs those without (HR 0.82; 95% CI 0.43,1.59; p = 0.6). A test for interaction between treatment and MAPK pathway was not significant by Cox regression model (p = 0.32). Conclusions: While this hypothesis generating analysis is limited by multiple testing, higher response rates and longer PFS were seen in those patients with LGSOC treated with binimetinib who harbored MAPK mutations, most commonly in KRAS. Somatic tumor testing should be routinely performed in patients with recurrent LGSOC to aid in clinical decision making. Clinical trial information: NCT01849874.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".