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

Science and Research

INTRODUCTION: Accurate distinction between separate primary lung carcinomas (SPLCs) and intrapulmonary metastases (IPMs) is essential for staging and treatment of multifocal non-small cell lung carcinoma (NSCLC). Next-generation sequencing (NGS) enables assessment of clonal relatedness. The proposed IASLC algorithm integrates histological 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 (FFPE) tumor samples that underwent small-scale gene panel NGS testing (31-54 genes) within routine clinical care. Most tumors were adenocarcinomas (n=222), 18 tumors other NSCLC subtypes. Inconclusive pairs by molecular classification were subjected to large-scale panel analyses (531 genes). Additionally, 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. Re-sequencing classified 9 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 3 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 3 of 4 cases. Large-scale sequencing resolves about 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.