Breakthrough in Personalized Medicine for Breast Cancer Recurrence Detection

Strategic Insights -

A new study has made a significant advancement in predicting breast cancer recurrence, a persistent challenge in treating the disease. Researchers from the First Affiliated Hospital of Guizhou University of Traditional Chinese Medicine, in collaboration with international partners, have developed an immune-related gene signature (IRGS) that demonstrates outstanding predictive performance in identifying patients at high risk of recurrence. This breakthrough has the potential to enhance personalized treatment planning and improve patient outcomes.

Key Takeaways:

  • The IRGS, comprising 12 genes, was developed by systematically analyzing RNA-seq high-throughput data and combining 10 machine learning algorithms.
  • The optimal algorithm combination, StepCox[both] and ridge regression, was identified, and the IRGS demonstrated superior performance across multiple datasets.
  • The IRGS revealed significant enrichment differences in cellular processes, diseases, and immune-related pathways between high- and low-risk recurrence patients.
  • Low recurrence risk patients exhibited a stronger immune phenotype and better survival prognosis, associated with higher infiltration of CD4 + and CD8 + T cells.
  • High M2 macrophage infiltration in low recurrence risk patients suggests potential immune escape, but combined with immune checkpoint expression levels and TIDE results, it is suggested that low-risk patients may respond positively to immunotherapy.
  • Drug sensitivity analysis identified potential drugs more effective for both high- and low-risk groups.

Statistics:

  • 117 models were constructed using a combination of 10 machine learning algorithms.
  • The IRGS demonstrated outstanding predictive performance across multiple datasets, surpassing 10 previously published signatures.
  • The study analyzed high-throughput RNA-seq data to develop the IRGS.
  • High recurrence risk patients exhibited lower infiltration of CD4 + and CD8 + T cells, while low recurrence risk patients showed higher infiltration.

Sources:

  • Machine learning-based integration develops relapse related signature for predicting prognosis and indicating immune microenvironment infiltration in breast cancer. Scientific Reports, 2025;15(1):19773.
  • Nature Portfolio, Heidelberger Platz 3, Berlin, 14197, Germany.
  • First Affiliated Hospital of Guizhou University of Traditional Chinese Medicine.