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Simple Summary: Monoclonal antibodies targeting the programmed death 1 (PD-1) receptor and its ligand (PD-L1) have demonstrated improved clinical response and survival in non-small cell lung cancer (NSCLC). Although extrinsic immunologic factors play important roles in the regulation of PD-L1 and PD-1, tumor intrinsic factors, including genetic alterations, epigenetic alterations, oncogenic and tumor suppressor signals, and transcription factors also play important roles in PD-L1 expression. There is an urgent need to investigate the intrinsic transcriptional profiles affecting the clinical response to PD-1 inhibitors in patients with non-small cell lung cancer. In our study, PD-1 inhibitor-associated intrinsic gene patterns were very different between lung adenocarcinoma and squamous cell carcinoma. In lung adenocarcinoma, the intrinsic gene signature was a very good predictive or prognostic biomarker. Our findings prove for the first time that an intrinsic gene signature is well predictive of responsiveness to PD-1 inhibitors in lung adenocarcinoma. Using a machine learning method, we investigated the intrinsic and extrinsic transcriptional profiles that affect the clinical response to PD-1 inhibitors in 57 patients with non-small cell lung cancer (NSCLC). Among the top 100 genes associated with the responsiveness to PD-1 inhibitors, the proportion of intrinsic genes in lung adenocarcinoma (LUAD) (69%) was higher than in NSCLC overall (36%) and lung squamous cell carcinoma (LUSC) (33%). The intrinsic gene signature of LUAD (mean area under the ROC curve (AUC) = 0.957 and mean accuracy = 0.9) had higher predictive power than either the intrinsic gene signature of NSCLC or LUSC or the extrinsic gene signature of NSCLC, LUAD, or LUSC. The high intrinsic gene signature group had a high overall survival rate in LUAD (p = 0.034). When we performed a pathway enrichment analysis, the cell cycle and cellular senescence pathways were related to the upregulation of intrinsic genes in LUAD. The intrinsic signature of LUAD also showed a positive correlation with other immune checkpoint targets, including CD274, LAG3, and PDCD1LG2 (Spearman correlation coefficient > 0.25). PD-1 inhibitor-related intrinsic gene patterns differed significantly between LUAD and LUSC and may be a particularly useful biomarker in LUAD. [ABSTRACT FROM AUTHOR]

Background: Textbook outcome (TO) has been introduced as a novel composite measure for lung cancer surgery. We investigated TO after lobectomy for early‐stage non‐small cell lung cancer (NSCLC) in a Korean tertiary hospital and its prognostic implications for overall survival and recurrence. Methods: Between January 2012 and December 2017, 418 consecutive patients who underwent lobectomy for clinical stages I and II NSCLC were identified and retrospectively reviewed. TO was defined as complete resection (negative resection margins and sufficient lymph node dissection), no 30‐day or in‐hospital mortality, no reintervention within 30 days, no readmission to the intensive care unit, no prolonged hospital stay (<14 days), no hospital readmission within 30 days, and no major complications. Propensity score matching analysis was performed to investigate the association between TO, medical costs, and long‐term outcomes. Results: Of 418 patients, 277 (66.3%) achieved TO. The most common events leading to TO failure were prolonged air leakage (n = 54, 12.9%) and prolonged hospital stay (n = 53, 12.7%). Male sex (odds ratio [OR] = 2.148, p = 0.036) and low diffusing capacity for carbon monoxide (OR = 0.986, p = 0.047) were significant risk factors for failed TO in multivariate analysis. In matched cohorts, achieving TO was associated with lower medical costs and better overall survival but not cancer recurrence. Conclusions: TO is associated with low medical cost and favorable overall survival; thus, surgical teams and hospitals should make efforts to improve the quality of care and achieve TO. [ABSTRACT FROM AUTHOR]

Simple Summary: It is important to be able to predict brain metastasis in lung adenocarcinoma patients; however, research in this area is still lacking. Much of the previous work on tumor microenvironments in lung adenocarcinoma with brain metastasis concerns the tumor immune microenvironment. The importance of the tumor nonimmune microenvironment (extracellular matrix (ECM), epithelial–mesenchymal transition (EMT) feature, and angiogenesis) has been overlooked with regard to brain metastasis. We evaluated tumor nonimmune-microenvironment-related gene expression signatures that could predict brain metastasis after the surgical resection of lung adenocarcinoma using a machine learning approach. We identified a tumor nonimmune-microenvironment-related 17-gene expression signature, and this signature showed high brain metastasis predictive power in four machine learning classifiers. The immunohistochemical expression of the top three genes of the 17-gene expression signature yielded similar results to NanoString tests. Our tumor nonimmune-microenvironment-related gene expression signatures are important biological markers that can predict brain metastasis and provide patient-specific treatment options. Using a machine learning approach with a gene expression profile, we discovered a tumor nonimmune-microenvironment-related gene expression signature, including extracellular matrix (ECM) remodeling, epithelial–mesenchymal transition (EMT), and angiogenesis, that could predict brain metastasis (BM) after the surgical resection of 64 lung adenocarcinomas (LUAD). Gene expression profiling identified a tumor nonimmune-microenvironment-related 17-gene expression signature that significantly correlated with BM. Of the 17 genes, 11 were ECM-remodeling-related genes. The 17-gene expression signature showed high BM predictive power in four machine learning classifiers (areas under the receiver operating characteristic curve = 0.845 for naïve Bayes, 0.849 for support vector machine, 0.858 for random forest, and 0.839 for neural network). Subgroup analysis revealed that the BM predictive power of the 17-gene signature was higher in the early-stage LUAD than in the late-stage LUAD. Pathway enrichment analysis showed that the upregulated differentially expressed genes were mainly enriched in the ECM–receptor interaction pathway. The immunohistochemical expression of the top three genes of the 17-gene expression signature yielded similar results to NanoString tests. The tumor nonimmune-microenvironment-related gene expression signatures found in this study are important biological markers that can predict BM and provide patient-specific treatment options. [ABSTRACT FROM AUTHOR]