PredictONCO: a web tool supporting decision-making in precision oncology by extending the bioinformatics predictions...

Area of Science:

• Bioinformatics
• Computational Biology
• Precision Oncology

Background:

• Cancer is driven by mutations in key proteins.
• Targeted therapies require understanding mutation effects.
• Drug repurposing offers personalized treatment avenues.

Purpose of the Study:

• To develop and present PredictONCO 1.0, a web server for analyzing mutation impacts in cancer.
• To predict effects of mutations on protein stability and function.
• To identify potential therapeutic inhibitors, including repurposed drugs, for individualized cancer treatment.

Main Methods:

• Integration of predictive algorithms and computational tools.
• Analysis of protein sequential and structural properties.
• Virtual screening for drug-target interactions and binding affinity calculations.
• Validation against 108 clinically validated mutations.

Main Results:

• PredictONCO 1.0 analyzes mutations in 44 common oncological targets.
• It assesses mutation impact on protein stability and function.
• Calculates binding affinities of FDA/EMA-approved drugs for wild-type and mutant proteins.
• Demonstrated utility in three use cases: CDK4, ALK, and EGFR mutations.

Conclusions:

• PredictONCO 1.0 enhances confidence in variant pathogenicity assessment.
• The tool aids in identifying effective inhibitors for personalized cancer therapy.
• Provides rapid, actionable insights for time-sensitive oncological decision-making.