A phase II, open-label, randomized trial of durvalumab (D) with olaparib (O) or cediranib (C) in patients (pts) with leiomyosarcoma (LMS).
Notice bibliographique
Résumé
11522 Background: The use of immune checkpoint blockade (ICB) in non-inflamed (cold) tumors is associated with limited clinical efficacy. Combination of ICB with certain molecularly targeted agents (MTA) is hypothesized to increase tumor immunogenicity by recruiting tumor infiltrating lymphocytes in cold tumors, such as LMS. Here, we present the results of LMS cohort treated on the DAPPER study (NCT03851614). Methods: LMS pts with ECOG 0-1 were randomized to either D+O (arm A), or D+C (arm B). In a 28-day cycle, D 1500mg i.v. q4w with either O 300mg bid po qd or C 20mg po qd 5d/week were administered. Overall response rates (ORR) were determined using RECISTv1.1. Evaluation of tumor kinetics (TK) was performed by calculating tumor growth rate (TGR) of target lesions on CT images at baseline and on-treatment, adjusted to account for the time difference between scans. TGR is expressed as % tumor growth/week (Ferte C et al. CCR, 2014). Additionally, paired FFPE samples (from baseline and on-treatment biopsies) were assessed using multispectral fluorescent immunohistochemistry (IHC) panel: CD3, CD8, CD20, CD68, FOXP3 and cytokeratin. Tumor areas were identified by a pathologist and immune cells were quantified using InForm image analysis software. Results: 25 metastatic LMS pts were randomized to arm A (n = 11) or B (n = 14) over 21 months. Median age was 53 years, 96% were females and 60% of pts had ≥3 lines of therapy. In 23 evaluable pts, no responses were seen, 7 pts had stable disease (SD) while 16 has progressive disease (PD). TK analysis was evaluable for 18 pts (arm A = 8, B = 10). 5/8 pts (62.5%) in arm A and 6/10 pts (60%) in arm B showed decreased TK (defined as TGRbaseline > TGRon-treatment). In 4/5 (80%) pts who had deceleration of TK in arm A, SD was maintained for ≥6 months. The reduction in TGR on treatment, compared to baseline was significant in arm A but not in arm B (measured as median % tumor growth/week of 0.5 vs 5.1, 95% CI 0.2-4.3, p = 0.035 in arm A; and 1.3 vs 2.9, 95% CI 0.2-2.7, p = 0.088 in arm B). The median PFS of arm A and B were 9 (95% CI 3-12.8) and 4 (95% CI 2.2-4.6) months respectively. There were no statistically significant differences in tumor-infiltrating immune cells when comparing baseline and on-treatment biopsies from arm A or B. In arm A, one pt with SD > 6 months had a 2.5-fold increase in CD8 (CD3+CD8+) T cells and a 7.6-fold increase in macrophages (CD68+). Conclusions: D+O or D+C resulted in stable disease in 30% of pts, mostly on arm A (D+O). TK analysis may identify pts with prolonged SD on treatment. Although a cold-to-hot immunophenotype change was not generally seen, changes in tumor infiltrating immune cell subsets were observed in one patient with prolonged stable disease. These findings support further molecular and immunophenotype characterization in LMS patients treated with D+O or D+C. Clinical trial information: NCT03851614.
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) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,001 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 0,001 |
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 ».