Advancing Lung Cancer Staging: Integrating IASLC Recommendations and Bioinformatics to Delineate Tumor Origins

INTRODUCTION: Accurate distinction between separate primary lung carcinomas (SPLCs) and intrapulmonary metastases (IPMs) is essential for staging and treatment of multifocal NSCLC. Next-generation sequencing (NGS) enables assessment of clonal relatedness. The proposed International Association for the Study of Lung Cancer (IASLC) algorithm integrates histologic and molecular data, though its clinical utility is yet to be validated. METHODS: We focused on the molecular component of the algorithm and assessed 240 tumor pairs from 120 patients with formalin-fixed, paraffin-embedded tumor samples that underwent small-scale gene-panel NGS testing (31-54 genes) within routine clinical care. Most tumors were adenocarcinomas (n = 222), with 18 tumors other NSCLC subtypes. Inconclusive pairs by molecular classification were subjected to large-scale panel analyses (531 genes). In addition, we developed a bioinformatic method to complement and refine the IASLC method. RESULTS: In total, 22 tumor pairs (18%) remained inconclusive and 16 (13%) were classified ambiguous (probable SPLCs) using the molecular IASLC method. Resequencing classified nine of 22 inconclusive pairs as IPMs. Using a newly developed bioinformatic method for clonality classification incorporating likelihood ratios of mutational prevalence and small-scale sequencing, only three pairs remained inconclusive (2%). Tumors classified as SPLCs had a significantly longer overall survival than IPMs. CONCLUSIONS: Small-scale panel sequencing of biopsy material allows unambiguous clonality determination in three of four cases. Large-scale sequencing resolves approximately half of inconclusive cases. Our bioinformatic method reduces inconclusive pairs to 2% even with small-scale NGS. It is made publicly available as a Shiny App. Clonality is reflected in survival data and therefore pivotal in daily clinical practice.