Prioritizing Clinically Significant Lung Cancer Somatic Mutations for Targeted Therapy Through Efficient NGS Data Filtering System.

Abstract

In the realm of lung cancer treatment, where genetic heterogeneity presents formidable challenges, precision oncology demands an exacting approach to identify and hierarchically sort clinically significant somatic mutations. Current Next-Generation Sequencing (NGS) data filtering pipelines, while utilizing various external databases for mutation screening, often fall short in comprehensive integration and flexibility needed to keep pace with the evolving landscape of clinical data. Our study introduces a sophisticated NGS data filtering system, which not only aggregates but effectively synergizes diverse data sources, encompassing genetic variants, gene functions, clinical evidence, and an extensive body of literature. This system is distinguished by a unique algorithm that facilitates a rigorous, multi-tiered filtration process. This allows for the efficient prioritization of 420 genes and 1,193 variants from large datasets, with a particular focus on 80 variants demonstrating high clinical actionability. These variants have been aligned with FDA approvals, NCCN guidelines, and thoroughly reviewed literature, thereby equipping oncologists with a refined arsenal for targeted therapy decisions. The innovation of our system lies in its dynamic integration framework and its algorithm, tailored to emphasize clinical utility and actionability-a nuanced approach often lacking in existing methodologies. Our validation on real-world lung adenocarcinoma NGS datasets has shown not only an enhanced efficiency in identifying genetic targets but also the potential to streamline clinical workflows, thus propelling the advancement of precision oncology. Planned future enhancements include expanding the range of integrated data types and developing a user-friendly interface, aiming to facilitate easier access to data and promote collaborative efforts in tailoring cancer treatments.