Identifying Actionable Variants in Cancer - The Dual Web and Batch Processing Tool MTB-Report.

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• Next-generation sequencing (NGS) generates vast amounts of genomic variant data in cancer.
• Manual interpretation of these variants for precision oncology is time-consuming and does not scale.
• Clinical decision-making in molecular tumor boards requires efficient data analysis tools.

Purpose of the Study:

• To develop a computational tool for annotating, filtering, and sorting cancer genomic variants.
• To provide evidence for actionable variants to support precision oncology and molecular tumor boards.
• To create a user-friendly application for both clinical and research settings.

Main Methods:

• The MTB-Report tool integrates public genomic databases for variant annotation.
• It offers a web interface for interactive use and a command-line mode for batch processing.
• The tool is implemented in R and provided as a Docker image for easy deployment and reproducibility.

Main Results:

• MTB-Report annotates, filters, and sorts genetic variants, highlighting actionable targets.
• It supports clinicians in molecular tumor boards and researchers analyzing large datasets.
• Containerization ensures stable, reproducible results.

Conclusions:

• MTB-Report addresses the need for automated analysis of large-scale genomic data in cancer.
• The tool enhances efficiency and reproducibility in identifying therapeutic targets.
• It facilitates data-driven decision-making in precision oncology and clinical practice.