Evaluating the role of immune-checkpoint inhibitor (ICI) combinations in patients (pts) with unselected “cold” tumors enrolled in early clinical trials (CT).
Notice bibliographique
Résumé
2597 Background: In order to improve the expected response rate (ORR) of less than 10% in cold tumors, several ICI combinations are being evaluated in clinical trials. However, most of these trials don’t require any biomarker and pts are included based solely in histology. We aimed to assess the benefit of ICI combinations in pts with unselected cold tumors included in early CT. Methods: ICI naïve pts with cold tumors treated from 2015 to 2021 with ICI combinations in early CT at VHIO were reviewed. Clinico-pathological data and anti-tumor activity were extracted from a prospective database. ORR was defined as per RECIST v1.1 and clinical benefit rate (CBR) as complete/partial response (CR/PR) + stable disease (SD) for ≥ 4 months (m). Kaplan Meier estimates of progression-free survival (PFS) and overall survival (OS) were calculated and a Cox model according to LIPI (Lung Immune Prognostic Index = baseline LDH and derived neutrophil to lymphocyte ratio) was constructed. Immune-related adverse events (irAE) were classified as per CTCAE v.4.03. Hyperprogressive disease (HPD) was evaluated using RECIST v1.1 (Matos et al, 2020). Results: Out of 97 pts, median age was 62y, 61% had ECOG 0 and 29.8% had LIPI 0 (good prognostic score). Most pts had microsatellite stable (MSS) colorectal cancer (60.8%) or ovarian cancer (14.4%). Regimens included anti-PD1/L1 + another ICI in 69% (most commonly anti-LAG3 [26,8%] and CD40 agonist [20.9%]), anti-PD1/L1 + other molecule in 21.7% (most commonly SHP2 inhibitor [33.3%] and anti p53-HDM2 [28.5%]) and bispecific antibodies in 9.3% (anti-PD1/L1 + anti-LAG3 or CD137 agonist). No patient achieved a response. CBR was 15.3% (11 pts with MSS colorectal cancer, 2 ovarian cancer, 1 olfactory neuroblastoma, 1 paraganglioma). 33 pts (34%) presented irAE, 15 pts (15.5%) had irAE ≥ G2, 4 pts (4.1%) had G3 irAE (dry mouth, hypertransaminasemia, myocarditis and neutrophils count decreased) and 1 patient (1%) had G4 hyperglicemia. 58 pts (59.7%) had progressive disease (PD) as best response, 19 of these pts (32.7%) presented irAE. Overall, 20 pts (20.6%) met definition of HPD, representing 34.4% of pts with PD as best response. Median PFS for overall and CBR population were 1.9 m (CI95% 1.7-2.0) and 5.9 m (5.4-NR), respectively. Median OS for overall population was 7.6 m (5.9-9.5), with a trend for improved OS if LIPI good score vs. others (12.6 m vs. 6.2 m, hazard ratio 1.9, (CI 95% 1.1-3.3), p = 0.02). Among hyperprogressors, median OS was 5.33 m (3.39 - NR) and significantly worse LIPI scores (intermediate [1] or poor [2]) were observed as compared to pts with CBR (75% vs 53.3% p = 0.001). Conclusions: ICI combinations demonstrated very limited activity in pts with unselected cold tumors. However, the risk for irAE and HPD remain substantial. Further drug-biomarker co-development strategies are urgently needed to increase the risk benefit ratio for these pts.
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 | 0,002 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,000 | 0,001 |
| 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) | 0,001 | 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 ».