Strength in numbers: predicting response to checkpoint inhibitors from large clinical datasets.

Détails

ID Serval
serval:BIB_F9D89455E11F
Type
Article: article d'un périodique ou d'un magazine.
Sous-type
Editorial
Collection
Publications
Institution
Titre
Strength in numbers: predicting response to checkpoint inhibitors from large clinical datasets.
Périodique
Cell
Auteur⸱e⸱s
Stenzinger A., Kazdal D., Peters S.
ISSN
1097-4172 (Electronic)
ISSN-L
0092-8674
Statut éditorial
Publié
Date de publication
04/02/2021
Peer-reviewed
Oui
Volume
184
Numéro
3
Pages
571-573
Langue
anglais
Notes
Publication types: Journal Article
Publication Status: ppublish
Résumé
The advent of immune checkpoint blockers for cancer therapy has spawned great interest in identifying molecular features reflecting the complexity of tumor immunity, which can subsequently be leveraged as predictive biomarkers. In a thorough big-data approach analyzing the largest series of homogenized molecular and clinical datasets, Litchfield et al. identified a set of genomic biomarkers that identifies immunotherapy responders across cancer types.
Pubmed
Web of science
Création de la notice
08/02/2021 16:57
Dernière modification de la notice
19/11/2021 7:40
Données d'usage