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Updated: Jul 2, 2026
The Use of Reverse Phase Protein Arrays (RPPA) to Explore Protein Expression Variation within Individual Renal Cell Cancers
Published on: January 22, 2013
A systems-level machine learning approach uncovers therapeutic targets in clear cell renal cell carcinoma
Silas Ruhrberg Estévez1, Greta Baltusyte1,2,3,4, Gehad Youssef5,6,7,8
1Milner Therapeutics Institute, University of Cambridge, Cambridge, UK.
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.
Insights
New machine learning identifies novel targets for clear cell renal cell carcinoma (ccRCC). FDA-approved drugs like Ribociclib, Ponatinib, and Dasatinib show promise in preclinical models, offering new hope against this aggressive cancer.
Area of Science:
- Oncology
- Bioinformatics
- Genomics
Background:
- Clear cell renal cell carcinoma (ccRCC) is an aggressive cancer with limited treatment options.
- Current therapies often target the tumor microenvironment, neglecting intrinsic tumor vulnerabilities.
Purpose of the Study:
- To identify novel therapeutic targets and drugs for ccRCC using a systems-based machine learning approach.
- To uncover new therapeutic mechanisms distinct from current ccRCC treatments.
Main Methods:
- Integrated single-cell RNA sequencing, protein interaction networks, and drug proximity analysis.
- Refined candidate genes using CRISPR screening data and functional relevance.
- Validated findings across independent transcriptomic datasets.
Main Results:
- Identified ABL1, CDK4/6, and JAK inhibition as underexplored therapeutic mechanisms in ccRCC.
- Found FDA-approved compounds targeting these pathways, including Ribociclib, Ponatinib, and Dasatinib.
- These drugs demonstrated superior efficacy compared to current therapies in preclinical renal cancer cell line screens.
Conclusions:
- The identified drugs represent promising candidates for combination therapies to overcome ccRCC resistance.
- These novel therapeutic strategies could improve clinical outcomes for patients with ccRCC.
- The machine learning pipeline effectively identified actionable therapeutic targets in ccRCC.
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