Combined utility of genomic breakpoints and frame is a reliable predictor of ALK transcript function

Lung cancer is a major cause of cancer-related deaths globally. Targeted therapies, specifically attacking cancer cells based on genetic mutations, offer promising alternatives. ALK (anaplastic lymphoma kinase) fusions result in aberrant proteins that drive cancer growth. Drugs like crizotinib and ceritinib have shown efficacy in treating ALK-positive NSCLC. Accurate detection of ALK fusions is crucial for guiding these therapies. We conducted a retrospective analysis of a Chinese cohort of 131 ALK rearrangement-positive patients detected by DNA NGS between January 2017 and December 2021. Among those 131 ALK fusions, RNA-NGS confirmed positive transcripts in 88% of canonical ALK fusions and 75% of ALK fusions with rare partners in samples sequenced by both DNA NGS and RNA NGS. The secondary classification approach increased transcript prediction accuracy to 95.4% when combining common breakpoints and inframe fusion analysis in canonical ALK fusions. Combining rare breakpoints and inframe fusion could increase transcript prediction accuracy to 100%. For ALK fusions with rare partners, combining common breakpoints and frameshift improved transcript prediction accuracy to 100%. Additionally, combining rare breakpoints with inframe or frameshift could enhance the prediction accuracy to 100%. Combining DNA NGS and RNA NGS with a secondary classification approach significantly enhances the transcript prediction accuracy at the RNA level. This method optimizes clinical diagnostic and therapeutic strategies for ALK-positive NSCLC, highlighting the importance of advanced sequencing techniques in precision oncology.