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).
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».