Real‐world comprehensive diagnosis and “Surgery + X” treatment strategy of early‐stage synchronous multiple primary lung cancer

Abstract Background Diagnosing and treating synchronous multiple primary lung cancers (sMPLC) are complex and challenging. This study aimed to report real‐world data on the comprehensive diagnosis and treatment of patients with early‐stage sMPLC. Materials and Methods A single‐center cohort study was carried out and a large number of patients with early‐stage sMPLC were included. A single‐ or two‐stage surgery was performed to remove the primary and co‐existing lesions. The “X” strategies, including ablation, SBRT, and EGFR‐TKIs treatment, were applied to treat the high‐risk residual lesions. Wide panel‐genomic sequencing was performed to assess the genetic heterogeneity of the co‐existing lesions. Results A total of 465 early‐stage sMPLC patients with 1198 resected lesions were included. Despite most patients being histologically different or harboring different genetic alternations, about 7.5% of the patients had the same histological type and driver gene mutation changes, comprehensive re‐evaluation is thus needed. The “Surgery + X” strategy showed remarkable efficacy and safety in treating multiple lesions. Follow‐up data revealed that the T2 stage (p = 0.014) and the solid presence of a primary lesion (p < 0.001) were significantly related to tumor recurrence. And a T2‐stage primary tumor had a significantly higher rate of developing new lesions after the initial surgery (p < 0.001). Conclusions In real‐world practice, histopathological and radiological evaluation combined with genetic analyses could be a robust diagnostic approach for sMPLC. The “Surgery + X” treatment strategy showed remarkable efficacy, superiority, and safety in the clinical treatment of early‐stage sMPLC

Whole-genome DNA methylation and DNA methylation-based biomarkers in lung squamous cell carcinoma

Summary: Exploring early detection methods through comprehensive evaluation of DNA methylation for lung squamous cell carcinoma (LUSC) patients is of great significance. By using different machine learning algorithms for feature selection and model construction based on The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, five methylation biomarkers in LUSC (along with mapped genes) were identified including cg14823851 (TBX4), cg02772121 (TRIM15), cg10424681 (C6orf201), cg12910906 (ARHGEF4), and cg20181079 (OR4D11), achieving extremely high sensitivity and specificity in distinguishing LUSC from normal samples in independent cohorts. Pyrosequencing assay verified DNA methylation levels, meanwhile qRT-PCR and immunohistochemistry results presented their accordant methylation-related gene expression statuses in paired LUSC and normal lung tissues. The five methylation-based biomarkers proposed in this study have great potential for the diagnosis of LUSC and could guide studies in methylation-regulated tumor development and progression