Impact of renal cell carcinoma molecular subtypes on immunotherapy and targeted therapy outcomes.

Abstract

Saliby et al. show that a machine learning approach can accurately classify clear cell renal cell carcinoma (RCC) into distinct molecular subtypes using transcriptomic data. When applied to tumors biospecimens from the JAVELIN Renal 101 (JR101) trial, a benefit is observed with immune checkpoint inhibitor (ICI)-based therapy across all molecular subtypes.

• Authors: Renée Maria Saliby, Chris Labaki, Tejas R Jammihal, Wanling Xie, Maxine Sun, Valisha Shah, Eddy Saad, M Harry Kane, Soki Kashima, Katherine Sadak, Talal El Zarif, Deepak Poduval, Robert J Motzer, Thomas Powles, Brian I Rini, Laurence Albiges, Sumanta K Pal, Bradley A McGregor, Rana R McKay, Sabina Signoretti, Eliezer M Van Allen, Sachet A Shukla, Toni K Choueiri, David A Braun
• Journal: Cancer cell (Cancer Cell) Vol. 42 Issue 5 Pg. 732-735 (May 13 2024) ISSN: 1878-3686 [Electronic] United States
• PMID: 38579722 (Publication Type: Journal Article, Letter)
• Copyright: Copyright © 2024 Elsevier Inc. All rights reserved.

Topics

• Carcinoma, Renal Cell (genetics, immunology, therapy, drug therapy)
• Humans
• Kidney Neoplasms (immunology, genetics, therapy, drug therapy)
• Immunotherapy (methods)
• Immune Checkpoint Inhibitors (therapeutic use, pharmacology)
• Molecular Targeted Therapy (methods)
• Treatment Outcome
• Machine Learning