Purpose: Pulmonary sarcomatoid carcinoma (PSC) is a rare aggressive subtype of non–small cell lung cancer (NSCLC) with limited therapeutic strategies. We attempted to elucidate the evolutionary trajectories of PSC using multiregional and longitudinal tumor samples. Materials and Methods: A total of 31 patients were enrolled in this study and 11 longitudinal samples were available from them. Using whole exome sequencing data, we analyzed the mutational signatures in both carcinomatous and sarcomatous areas in primary tumors of the 31 patients and longitudinal samples obtained from 11 patients. Furthermore, digital droplet polymerase chain reaction (ddPCR), and programmed death-ligand 1 (PD-L1) immunohistochemistry using the Ventana SP263 assay were performed. Results: TP53 was identified as the most frequently altered gene in the primary (74%) and metastatic (73%) samples. MET exon 14 skipping mutations, confirmed by ddPCR, and TP53 mutations were mutually exclusive; whereas, MET exon 14 skipping mutations frequently co-occurred with MDM2 amplification. Metastatic tumors showed dissimilar genetic profiles from either primary component. During metastasis, the signatures of APOBEC decreased in metastatic lesions compared with that in primary lesions. PSC showed higher MET and KEAP1 mutations and stronger PD-L1 protein expression compared with that recorded in other NSCLCs. Conclusion Decreased APOBEC signatures and subclonal diversity were detected during malignant progression in PSC. Frequent MET mutations and strong PD-L1 expression distinguished PSC from other NSCLCs. The aggressiveness and therapeutic difficulties of PSC were possibly attributable to profound intratumoral and intertumoral genetic diversity. Next-generation sequencing could suggest the appropriate treatment strategy for PSC.

Purpose: Since tumor mutational burden (TMB) and gene expression profiling (GEP) have complementary effects, they may have improved predictive power when used in combination. Here, we investigated the ability of TMB and GEP to predict the immunotherapy response in patients with non–small cell lung cancer (NSCLC) and assessed if this combination can improve predictive power compared to that when used individually. Materials and Methods: This retrospective cohort study included 30 patients with NSCLC who received immune checkpoint inhibitors (ICI) therapy at the Seoul National University Bundang Hospital. programmed cell death-ligand-1 (PD-L1) protein expression was assessed using immunohistochemistry, and TMB was measured by targeted deep sequencing. Gene expression was determined using NanoString nCounter analysis for the PanCancer IO360 panel, and enrichment analysis were performed. Results: Eleven patients (36.7%) showed a durable clinical benefit (DCB), whereas 19 (63.3%) showed no durable benefit (NDB). TMB and enrichment scores (ES) showed significant differences between the DCB and NDB groups (p=0.044 and p=0.017, respectively); however, no significant correlations were observed among TMB, ES, and PD-L1. ES was the best single biomarker for predicting DCB (area under the curve [AUC], 0.794), followed by TMB (AUC, 0.679) and PD-L1 (AUC, 0.622). TMB and ES showed the highest AUC (0.837) among other combinations (AUC [TMB and PD-L1], 0.777; AUC [PD-L1 and ES], 0.763) and was similar to that of all biomarkers used together (0.832). Conclusion The combination of TMB and ES may be an effective predictive tool to identify patients with NSCLC patients who would possibly benefit from ICI therapies.