Work

A systems-level machine learning approach uncovers therapeutic targets in clear cell renal cell carcinoma

Year: 2026

Type: article

Abstract: Clear cell renal cell carcinoma (ccRCC) is an aggressive malignancy with limited treatment options and high rates of resistance to first-line kinase inhibitors. Current therapies largely target the tumor microenvironment, leaving intrinsic tumor vulnerabilities underexplored. Here, we introduce a systems-based machine learning pipeline that integrates single-cell RNA sequencing, protein interaction networks, and drug proximity analysis to identify therapeutic targets in ccRCC. Candidate genes were refined using CRISPR screening data and functional relevance and validated across independent transcriptomic datasets. The pipeline recovered several established treatment pathways and uncovered previously underexplored therapeutic mechanisms, including ABL1, CDK4/6, and JAK inhibition. We identified FDA-approved compounds acting through these pathways, three of which, Ribociclib, Ponatinib, and Dasatinib, showed superior efficacy to current therapies across renal cancer cell lines in preclinical screens. By acting through mechanisms distinct from current therapies, they represent promising candidates for combination strategies aimed at overcoming resistance and improving clinical outcomes in ccRCC.

Source: npj Drug Discovery.

DOI: 10.1038/s44386-025-00036-z

Authors: Silas Ruhrberg Estévez, Greta Baltusyte, Gehad Youssef, Namshik Han

Institutions: University of Cambridge, Wellcome/MRC Cambridge Stem Cell Institute, Cambridge Centre for AI in Medicine, Yonsei University, Institute for Basic Science

Language: English

Topic: Renal cell carcinoma treatment

Subfield: Pulmonary and Respiratory Medicine

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $2,990