Vascular endothelial growth factor-targeted therapy in patients with renal cell carcinoma pretreated with immune checkpoint inhibitors: A systematic literature review
Notice bibliographique
Résumé
INTRODUCTION: We conducted a systematic literature review to identify evidence for use of vascular endothelial growth factor (VEGF)-targeted (anti-VEGF) treatment in patients with renal cell carcinoma (RCC) following prior checkpoint inhibitor (CPI)-based therapy. METHODS: This was a PRISMA-standard systematic literature review; registered with PROSPERO (CRD42021255568). Literature searches were conducted in MEDLINE®, Embase, and the Cochrane Library (January 28, 2021; updated September 13, 2022) to identify publications reporting efficacy/effectiveness and safety/tolerability evidence for anti-VEGF treatment in patients with RCC who had received prior CPI therapy. RESULTS: Of 2,639 publications screened, 48 were eligible and featured 2,759 patients treated in trials and 2,209 in real-world studies (RWS). Most patients with available data were treated with anti-VEGF tyrosine kinase inhibitor-based regimens (trials: 93 %; RWS: 100 %), most commonly cabozantinib, which accounted for 46 % of trial and 62 % of RWS patients in publications with available data. Collectively, there was consistent evidence of anti-VEGF treatment activity after prior CPI therapy. Activity was reported for all anti-VEGF regimens and regardless of prior CPI-based regimen. No new safety signals were detected for subsequent anti-VEGF therapy; no studies suggested increased immune-related adverse events associated with prior CPI therapy. The results were limited by data quality; study heterogeneity prohibited meta-analyses. CONCLUSION: Based on the available data (most commonly for cabozantinib), anti-VEGF therapy appears to be a rational treatment choice in patients with RCC who have progressed despite prior CPI-based therapy. Results from ongoing trials of combination anti-VEGF plus CPI regimen post prior CPI therapy trials will contribute more definitive evidence. PLAIN LANGUAGE SUMMARY: Anticancer treatments that work by reducing levels of a substance in the body called Vascular Endothelial Growth Factor are known as anti-VEGF drugs. Reducing VEGF levels helps to reduce blood supply to tumors, which can slow the speed at which the cancer grows. Some other types of anticancer drugs that help the immune system to fight cancer cells are called checkpoint inhibitors. Here, we looked at published studies that investigated how anti-VEGF drugs work, and what side effects they cause, in people who have already been treated with checkpoint inhibitors for a type of kidney cancer called renal cell carcinoma. We aimed to summarize the available evidence to help doctors decide how best to use anti-VEGF drugs in these patients. We found 48 studies that included almost 5,000 patients. The results of the studies showed that anti-VEGF drugs have anticancer effects in people with renal cell carcinoma who had already been treated with checkpoint inhibitors. All of the VEGF-targeting drugs had anticancer effects, irrespective of what checkpoint inhibitor treatment people had received before. There were different amounts of evidence available for the different anti-VEGF drugs. The anti-VEGF cabozantinib had the largest amount of evidence. Importantly, previous checkpoint inhibitor treatment did not seem to affect the number or type of side-effects associated with anti-VEGF drugs. Results from ongoing, well-designed studies will be helpful to confirm these results. Our findings may be useful for doctors considering using anti-VEGF drugs in patients with renal cell carcinoma who have received checkpoint inhibitor treatment.
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) | 0,003 | 0,001 |
| Méta-épidémiologie (sens large) | 0,013 | 0,003 |
| Bibliométrie | 0,001 | 0,003 |
| 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 | 0,001 | 0,001 |
| 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; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».