DNA methylation-based classifier and gene expression signatures detect BRCAness in osteosarcoma

Area of Science:

• Oncology
• Genetics
• Epigenetics

Background:

• Osteosarcoma (OS) is the most common primary bone cancer in children and adolescents.
• BRCAness, a defect in homologous recombination repair, predicts sensitivity to poly (ADP)-ribose polymerase inhibitors (PARPi).
• Osteosarcoma exhibits molecular features of BRCAness, suggesting potential for PARPi therapy.

Purpose of the Study:

• To develop a DNA methylation-based classifier to identify BRCAness in osteosarcoma.
• To complement existing genomic methods for clinical decision-making regarding PARPi treatment.
• To identify epigenomic and transcriptomic signatures associated with BRCAness in OS.

Main Methods:

• Classified OS samples (n=41) into BRCAness-positive and negative groups based on DNA methylation and genomic instability.
• Trained a random forest algorithm using the top 2000 methylation probes.
• Performed differential gene expression and gene set enrichment analysis (GSEA) on RNA-Seq data.

Main Results:

• The random forest classifier achieved 85% accuracy and an AUC of 0.95 on training data.
• Validated classifier showed 90% accuracy and an AUC of 0.87 on an independent OS set (n=20).
• GSEA identified enriched DNA replication, mismatch repair, and homologous recombination signatures in BRCAness-positive OS.

Conclusions:

• A novel DNA methylation classifier accurately detects BRCAness in osteosarcoma.
• The classifier aids in predicting PARPi sensitivity for OS patients.
• Epigenomic and transcriptomic analyses enhance the evaluation of BRCAness in osteosarcoma and other cancers.