Mutation based treatment recommendations from next generation sequencing data: a comparison of web tools

Area of Science:

• Genomic medicine
• Computational oncology
• Translational oncology

Background:

• Personalized cancer therapy relies on interpreting complex tumor genome data.
• Current methods for deriving therapeutic conclusions from tumor profiling are not standardized.
• Discrepancies exist between commercial and academic cancer genome data interpretation tools.

Purpose of the Study:

• To compare drug and clinical trial recommendations from multiple web-based tools using targeted sequencing data from metastatic breast cancer.
• To assess the concordance of therapeutic recommendations generated by different genomic data interpretation platforms.

Main Methods:

• Targeted sequencing of 315 cancer genes was performed on 75 metastatic breast cancer biopsies.
• Results were analyzed using four distinct web tools: Drug-Gene Interaction Database (DGidb), My Cancer Genome (MCG), Personalized Cancer Therapy (PCT), and cBioPortal.
• Recommendations were compared against each other and the FoundationOne assay's own recommendations.

Main Results:

• The identification of actionable genomic alterations varied significantly across the evaluated recommendation sources.
• Only 33% of cases received concordant drug recommendations from four or more sources for at least one altered gene.
• Substantial heterogeneity in therapeutic recommendations was observed among the different tools.

Conclusions:

• There is a critical need for the development and standardization of robust software tools for interpreting cancer genomic data.
• Current algorithms require enhancement, potentially through artificial intelligence, to improve accuracy and real-time status updates for therapeutic recommendations.
• Standardization is essential for reliable clinical decision-making in precision oncology.