Optimizing the NGS-based discrimination of multiple lung cancers from the perspective of evolution

Abstract

Background: Next-generation sequencing (NGS) can help differentiate multiple primary lung cancers (MPLC) from intrapulmonary metastasis (IPM), but remains vague in panel choice and clonal relatedness interpretation. Methods: First, cases with definite diagnosis of MPLC or IPM were simulatedusing the whole-exome sequencing (WES)data from 80 single lung cancer, samples from different tumors mimicking MPLC while those from the same tumor simulating IPM. Different panels were modeled by gene subsampling. Two interpretation methods of clonal relatedness were compared: counting the shared mutations (MoleA) versus probability calculation based on all the mutations (MoleB). We drew ROC curves for each panel and interpretation method with reference to the definite diagnosis, and selected the optimal combinations according to area under the ROC curve (AUCs) and inconclusive rate. Results: MoleB outperformed MoleA with all panels. The AUCs plateaued at high levels when applying NCCNplus MoleB (9 driver genes recommended by the National Comprehensive Cancer Network [NCCN] plus TP53) (AUC = 0.950±0.002) or pancancer MoleA (363-genes) (AUC = 0.792±0.004). Then the superiority of selected strategies was validated in two independent cohorts of multiple lung cancers. All NGS-based methodologies significantly separated the disease-free survival in the WES cohort (N = 42), and NCCNplus MoleB also successfully stratified the prognosis in the non-WES cohort (N = 94). Further phylogenetic analysis and timing of driver alterations revealed the evolutionary differences between MPLC and IPM. Conclusions: These findings have established the first modified panel and corresponding NGS-based procedures to discriminate multiple lung cancers (MLCs).