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

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

doi:10.1016/j.ccell.2024.03.002

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

Cancer Cell. 2024 May 13;42(5):732-735. doi: 10.1016/j.ccell.2024.03.002. Epub 2024 Apr 4.

Authors

Renée Maria Saliby¹, Chris Labaki², Tejas R Jammihal³, Wanling Xie⁴, Maxine Sun⁵, Valisha Shah⁵, Eddy Saad⁵, M Harry Kane⁶, Soki Kashima⁷, Katherine Sadak⁶, Talal El Zarif⁸, Deepak Poduval⁶, Robert J Motzer⁹, Thomas Powles¹⁰, Brian I Rini¹¹, Laurence Albiges¹², Sumanta K Pal¹³, Bradley A McGregor⁵, Rana R McKay¹⁴, Sabina Signoretti¹⁵, Eliezer M Van Allen⁵, Sachet A Shukla¹⁶, Toni K Choueiri¹⁷, David A Braun¹⁸

Affiliations

¹ Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA; Center of Molecular and Cellular Oncology (CMCO), Yale University, New Haven, CT, USA.
² Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA; Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA.
³ Children's Hospital of Philadelphia, Philadelphia, PA, USA.
⁴ Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA, USA.
⁵ Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA.
⁶ Center of Molecular and Cellular Oncology (CMCO), Yale University, New Haven, CT, USA.
⁷ Center of Molecular and Cellular Oncology (CMCO), Yale University, New Haven, CT, USA; Department of Urology, Akita University School of Medicine, Akita, Japan.
⁸ Yale School of Medicine, New Haven, CT, USA.
⁹ Memorial Sloan Kettering Cancer Center, New York, NY, USA; Department of Medicine, Weill Cornell Medical Center, New York, NY, USA.
¹⁰ Barts Cancer Institute and the Royal Free Hospital, Queen Mary University of London, London, UK.
¹¹ Division of Hematology and Oncology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.
¹² Department of Cancer Medicine, Gustave Roussy, Villejuif, France.
¹³ Department of Medical Oncology, City of Hope Comprehensive Cancer Center, Duarte, CA, USA.
¹⁴ Moores Cancer Center, University of California San Diego Health, La Jolla, CA, USA.
¹⁵ Department of Pathology, Brigham and Women's Hospital, Boston, MA, USA.
¹⁶ Department of Hematopoietic Biology and Malignancy, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
¹⁷ Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA. Electronic address: toni_choueiri@dfci.harvard.edu.
¹⁸ Center of Molecular and Cellular Oncology (CMCO), Yale University, New Haven, CT, USA. Electronic address: david.braun@yale.edu.

PMID: 38579722
DOI: 10.1016/j.ccell.2024.03.002

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.

Publication types

Letter

MeSH terms

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

Grants and funding

R37 CA279822/CA/NCI NIH HHS/United States