Advanced metastatic cancer poses utmost clinical challenges and may present molecular and cellular features distinct from an early-stage cancer. Herein, we present single-cell transcriptome profiling of metastatic lung adenocarcinoma, the most prevalent histological lung cancer type diagnosed at stage IV in over 40% of all cases. From 208,506 cells populating the normal tissues or early to metastatic stage cancer in 44 patients, we identify a cancer cell subtype deviating from the normal differentiation trajectory and dominating the metastatic stage. In all stages, the stromal and immune cell dynamics reveal ontological and functional changes that create a pro-tumoral and immunosuppressive microenvironment. Normal resident myeloid cell populations are gradually replaced with monocyte-derived macrophages and dendritic cells, along with T-cell exhaustion. This extensive single-cell analysis enhances our understanding of molecular and cellular dynamics in metastatic lung cancer and reveals potential diagnostic and therapeutic targets in cancer-microenvironment interactions.

Moon SM, et al. Tuberc Respir Dis. 2019 Feb;82:e12. https://doi.org/10.4046/trd.2018.0082

The objective of this study was to discover DNA methylation biomarkers for detecting non-small lung cancer (NSCLC) in bronchial washings and understanding the association between DNA methylation and smoking cessation. DNA methylation was analyzed in bronchial washing samples from 70 NSCLCs and 53 hospital-based controls using Illumina HumanMethylation450K BeadChip. Methylation levels in these bronchial washings were compared to those in 897 primary lung tissues of The Cancer Genome Atlas (TCGA) data. Twenty-four CpGs (p < 1.03E−07) were significantly methylated in bronchial washings from 70 NSCLC patients compared to those from 53 controls. The CpGs also had significant methylation in the TCGA cohort. The 123 participants were divided into a training set (N = 82) and a test set (N = 41) to build a classification model. Logistic regression model showed the best performance for classification of lung cancer in bronchial washing samples: the sensitivity and specificity of a marker panel consisting of seven CpGs in TFAP2A, TBX15, PHF11, TOX2, PRR15, PDGFRA, and HOXA11 genes were 87.0 and 83.3% in the test set, respectively. The area under the curve (AUC) was equal to 0.87 (95% confidence interval = 0.73–0.96, p < 0.001). Methylation levels of two CpGs in RUNX3 and MIR196A1 genes were inversely associated with duration of smoking cessation in the controls, but not in NSCLCs, after adjusting for pack-years of smoking. The present study suggests that NSCLC may be detected by analyzing methylation changes of seven CpGs in bronchial washings. Furthermore, smoking cessation may lead to decreased DNA methylation in nonmalignant bronchial epithelial cells in a gene-specific manner.

Background: Nuclear protein in testis (NUT) carcinoma is a rare tumor associated with NUT rearrangement that can present as poorly differentiated to undifferentiated carcinoma, with or without abrupt squamous differentiation. It is often misdiagnosed as poorly differentiated carcinoma or undifferentiated carcinoma if NUT is not suspected. In this study, we retrospectively analyzed pulmonary NUT carcinoma cases diagnosed with NUT immunohistochemical staining and discuss the differential diagnosis to provide information for this rare and aggressive entity.Methods: Cases, diagnosed as “NUT carcinoma” in lung pleura and “metastatic NUT carcinoma from the lung” in lymph nodes were diagnosed between 2017 and 2019 at the Samsung Medical Center (SMC). Clinical features such as age, sex, treatment and follow‐up period, and pathological reports were obtained by reviewing patients’ electronic medical records.Results: A total of 10 NUT carcinoma cases were found in the SMC pathology database. Seven patients were men and six were non‐smokers. Tumor cells showed various cellular features such as round, squamoid, and spindle. Some cases had initially been misdiagnosed as spindle cell neoplasm, round cell sarcoma, squamous cell carcinoma and small cell carcinoma. All cases showed diffuse strong nuclear expression of NUT immunohistochemical staining, and some were positive for p63 staining and negative for CD56 staining.Conclusions: NUT carcinoma is often misdiagnosed because of its various morphologies. It is important to consider NUT as one of the differential diagnoses when encountering lung biopsy with undifferentiated morphology.Key points: Due to various morphological features, NUT carcinoma can be misdiagnosed It is important to consider NUT carcinoma when diagnosing a poorly differentiated or undifferentiated tumor

Four commonly used clinical models for predicting the probability of malignancy in pulmonary nodules were compared. While three of the models (Mayo Clinic, Veterans Association [VA], and Brock University) are based on clinical and computed tomography (CT) characteristics, one model (Herder) additionally includes the ¹⁸F-fluorodeoxyglucose (FDG) uptake value among the positron emission tomography (PET) characteristics. This study aimed to compare the predictive power of these four models in the context of a population drawn from a single center in an endemic area for tuberculosis in Korea. A retrospective analysis of 242 pathologically confirmed nodules (4–30 mm in diameter) in 242 patients from January 2015 to December 2015 was performed. The area under the receiver operating characteristic curve (AUC) was used to assess the predictive performance with respect to malignancy. Of 242 nodules, 187 (77.2%) were malignant and 55 (22.8%) were benign, with tuberculosis granuloma being the most common type of benign nodule (23/55). PET was performed for 227 nodules (93.8%). The Mayo, VA, and Brock models showed similar predictive performance for malignant nodules (AUC: 0.6145, 0.6042 and 0.6820, respectively). The performance of the Herder model (AUC: 0.5567) was not significantly different from that of the Mayo (vs. Herder, p = 0.576) or VA models (vs. Herder, p = 0.999), and there were no differences among the three models in determining the probability of malignancy of pulmonary nodules. However, compared with the Brock model, the Herder model showed a significantly lower ability to predict malignancy (adjusted p = 0.0132). In our study, the Herder model including the ¹⁸FDG uptake value did not perform better than the other models in predicting malignant nodules, suggesting the limited utility of adding PET/CT data to models predicting malignancy in populations within endemic areas for benign inflammatory nodules, such as tuberculosis.