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Stratification system with dual human endogenous retroviruses for predicting immunotherapy efficacy in metastatic clear-cell renal cell carcinoma

  • 0Department of Cancer and Functional Genomics, Institute of Genetics and Molecular and Cellular Biology (IGBMC), CNRS/INSERM/UNISTRA, Illkirch-Graffenstaden, France.
Journal for Immunotherapy of Cancer +

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January 23, 2025

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January 23, 2025

View abstract on PubMed

Summary

This summary is machine-generated.

A novel dual endogenous retrovirus (ERV) risk model predicts immune checkpoint inhibitor (ICI) response in metastatic clear-cell renal cell carcinoma (ccRCC). This ERV stratification system improves upon existing models for precision oncology.

Area Of Science

  • Genomics and Oncology
  • Immunotherapy Research
  • Cancer Biomarkers

Background

  • Endogenous retroviruses (ERVs) are remnants of ancient infections in the human genome.
  • ERV dysregulation is linked to immune infiltration in cancers, but its role in immune checkpoint inhibitor (ICI) response in metastatic clear-cell renal cell carcinoma (ccRCC) is unclear.

Purpose Of The Study

  • To investigate the correlation between ERV expression and ICI treatment outcomes in metastatic ccRCC.
  • To develop a novel predictive model for ICI response based on ERV expression.

Main Methods

  • Analysis of RNA sequencing data from 229 metastatic ccRCC patients across two clinical trials (nivolumab and ipilimumab-nivolumab).
  • Quantification of ERV expression using the ERVmap algorithm.
  • Univariate Cox regression and bootstrap methods to assess prognostic significance of 666 ERVs.

Main Results

  • Two ERVs, E4421_chr17 and E1659_chr4, showed opposing prognostic impacts.
  • A three-tier ERV-based risk model was developed, significantly correlating with ICI treatment outcomes.
  • The model outperformed traditional transcriptomic signatures and improved with combined DNA methylation data.

Conclusions

  • A dual ERV-based stratification system effectively predicts risk and clinical outcomes for ccRCC patients receiving ICI therapy.
  • This system enhances predictive precision and supports personalized medicine approaches in oncology.

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