Biomarkers of treatment benefit with atezolizumab plus vemurafenib plus cobimetinib in BRAFV600 mutation–positive melanoma
Notice bibliographique
Résumé
•The combination of atezolizumab, vemurafenib, and cobimetinib seems most beneficial in patients with elevated LDH and PD-L1– tumors.•LDH remains the primary predictor of outcomes regardless of treatment.•IFN-γ and TMB further differentiate outcomes for patients treated with triplet combination therapy.•Patients with normal LDH and high IFN-γ gene signature have the most favorable outcomes on triplet combination therapy. BackgroundThe phase III IMspire150 study (NCT02908672) demonstrated significantly improved progression-free survival (PFS) with atezolizumab, vemurafenib, and cobimetinib (atezolizumab group) versus placebo, vemurafenib, and cobimetinib (control group) in patients with BRAFV600-mutated advanced melanoma. We report exploratory biomarker analyses to optimize targeting of patients who are more likely to benefit from triplet combination therapy.Patients and methodsFive hundred fourteen patients were randomized to atezolizumab (n = 256) or control (n = 258). Outcomes were evaluated in subgroups defined by key biomarkers, including programmed death-ligand 1 (PD-L1) expression, lactate dehydrogenase (LDH) level, tumor mutational burden (TMB), and interferon-γ (IFN-γ) gene signature. Exploratory recursive partitioning analysis was then used to model associations between PFS and baseline covariates, including key biomarkers.ResultsPFS benefit for atezolizumab versus control was greater in patients with high TMB [≥10 mutations/Mb; hazard ratio (HR) 0.73; 95% confidence interval (CI) 0.52-1.02; P = 0.067] versus low TMB (<10 mutations/Mb; HR 0.92; 95% CI 0.65-1.30; P = 0.64) and similar between patients with strong IFN-γ (≥median; HR 0.76; 95% CI 0.54-1.06) versus weak IFN-γ (<median; HR 0.79; 95% CI 0.58-1.08). In patients with elevated LDH, PFS benefit for atezolizumab versus control was greater in the PD-L1− subgroup (HR 0.53; 95% CI 0.29-0.95; P = 0.032) than in the PD-L1+ subgroup (HR 1.16; 95% CI 0.75-1.80; P = 0.51). Recursive partitioning analysis showed that IFN-γ discriminated PFS outcomes in patients with normal LDH, whereas TMB discriminated outcomes in patients with elevated LDH in the atezolizumab group. Neither IFN-γ nor TMB discriminated PFS outcomes in the control group.ConclusionsTreatment benefits in the atezolizumab group seemed to be most evident in patients with elevated LDH and PD-L1– tumors. LDH remains the primary predictor of outcomes regardless of treatment. IFN-γ and TMB further differentiate outcomes for patients treated with atezolizumab, vemurafenib, and cobimetinib. The phase III IMspire150 study (NCT02908672) demonstrated significantly improved progression-free survival (PFS) with atezolizumab, vemurafenib, and cobimetinib (atezolizumab group) versus placebo, vemurafenib, and cobimetinib (control group) in patients with BRAFV600-mutated advanced melanoma. We report exploratory biomarker analyses to optimize targeting of patients who are more likely to benefit from triplet combination therapy. Five hundred fourteen patients were randomized to atezolizumab (n = 256) or control (n = 258). Outcomes were evaluated in subgroups defined by key biomarkers, including programmed death-ligand 1 (PD-L1) expression, lactate dehydrogenase (LDH) level, tumor mutational burden (TMB), and interferon-γ (IFN-γ) gene signature. Exploratory recursive partitioning analysis was then used to model associations between PFS and baseline covariates, including key biomarkers. PFS benefit for atezolizumab versus control was greater in patients with high TMB [≥10 mutations/Mb; hazard ratio (HR) 0.73; 95% confidence interval (CI) 0.52-1.02; P = 0.067] versus low TMB (<10 mutations/Mb; HR 0.92; 95% CI 0.65-1.30; P = 0.64) and similar between patients with strong IFN-γ (≥median; HR 0.76; 95% CI 0.54-1.06) versus weak IFN-γ (<median; HR 0.79; 95% CI 0.58-1.08). In patients with elevated LDH, PFS benefit for atezolizumab versus control was greater in the PD-L1− subgroup (HR 0.53; 95% CI 0.29-0.95; P = 0.032) than in the PD-L1+ subgroup (HR 1.16; 95% CI 0.75-1.80; P = 0.51). Recursive partitioning analysis showed that IFN-γ discriminated PFS outcomes in patients with normal LDH, whereas TMB discriminated outcomes in patients with elevated LDH in the atezolizumab group. Neither IFN-γ nor TMB discriminated PFS outcomes in the control group. Treatment benefits in the atezolizumab group seemed to be most evident in patients with elevated LDH and PD-L1– tumors. LDH remains the primary predictor of outcomes regardless of treatment. IFN-γ and TMB further differentiate outcomes for patients treated with atezolizumab, vemurafenib, and cobimetinib.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».