Predicting response to pembrolizumab in metastatic melanoma by a new personalization algorithm

• Verfasst von: Tsur, Neta [VerfasserIn]
• Verfasst von: Vogler, Nils [VerfasserIn]
• Verfasst von: Utikal, Jochen [VerfasserIn]
• Titel: Predicting response to pembrolizumab in metastatic melanoma by a new personalization algorithm
• Verf.angabe: Neta Tsur, Yuri Kogan, Evgenia Avizov-Khodak, Désirée Vaeth, Nils Vogler, Jochen Utikal, Michal Lotem & Zvia Agur
• E-Jahr: 2019
• Jahr: 07 October 2019
• Umfang: 15 S.
• Fussnoten: Gesehen am 27.01.2020
• Titel Quelle: Enthalten in: Journal of translational medicine
• Ort Quelle: London : BioMed Central, 2003
• Jahr Quelle: 2019
• Band/Heft Quelle: 17(2019), Artikel-ID 338, Seite 1-15
• ISSN Quelle: 1479-5876
• DOI: doi:10.1186/s12967-019-2081-2
• Datenträger: Online-Ressource
• Sprache: eng
• K10plus-PPN: 1688542027

Abstract

At present, immune checkpoint inhibitors, such as pembrolizumab, are widely used in the therapy of advanced non-resectable melanoma, as they induce more durable responses than other available treatments. However, the overall response rate does not exceed 50% and, considering the high costs and low life expectancy of nonresponding patients, there is a need to select potential responders before therapy. Our aim was to develop a new personalization algorithm which could be beneficial in the clinical setting for predicting time to disease progression under pembrolizumab treatment.