Subnormothermic ex vivo lung perfusion possibly protects against ischemia-reperfusion injury via the mTORC-HIF-1α pathway.

Background: Ex vivo lung perfusion (EVLP) is a useful technique for evaluating and repairing donor lungs for transplantation. However, studies examining the effects of perfusate temperature on graft function are limited. Thus, this study aimed to examine these effects during EVLP on ischemic-reperfusion injury in the donor lung. Methods: Twenty-four male Sprague-Dawley rats were randomly divided into three groups, as follows: no treatment (sham group, n=5), normothermic EVLP (37 °C, n=5), and subnormothermic EVLP (30 °C, n=5). Lung function analyses, including oxygen capacity (OC), compliance, and pulmonary vascular resistance (PVR), were performed hourly during EVLP. Further, after 4 h of EVLP, histological evaluation of the right lobe was performed using the lung injury severity (LIS) scale. The expression levels of inflammatory cytokines such as tumor necrosis factor (TNF)-α, interleukin (IL)-1ß, IL-6, and IL-18 were evaluated. Metabolomic analysis of left lung tissues was conducted using capillary electrophoresis time-of-flight mass spectrometry (CE-TOFMS) after 4 h of EVLP in the EVLP groups and after 1 h of cold preservation in the sham group. Results: Compared with those in the normothermic group, in the subnormothermic group, functional parameters during EVLP and subsequent histologic results were significantly superior, expression levels of inflammatory cytokines such as TNF-α, IL-1ß, IL-6, and IL-18 were significantly lower, and glycolytic activity was significantly decreased. Furthermore, expression levels of mammalian target of rapamycin complex (mTORC), hypoxia-inducible factor (HIF) 1α, and nucleotide-binding domain, leucine-rich-containing family pyrin domain containing 3 (NLRP3) and its effector caspase-1 were significantly lower in the subnormothermic group than in the normothermic group. Conclusions: EVLP with subnormothermic perfusion improves lung graft function by reducing the expression of pro-inflammatory cytokines and glycolytic activity during EVLP. Additionally, EVLP can be a useful target for the improvement of graft function after transplantation.

Prediction of Responsiveness to PD-L1/PD-1 Inhibitors Using miRNA Profiles Associated With PD-L1 Expression in Lung Adenocarcinoma and Squamous Cell Carcinoma.

BACKGROUND/AIM: MicroRNAs (miRNAs) regulate programmed cell death ligand 1 (PD-L1) and play a crucial role in tumor immune response. However, the relationship between miRNA expression patterns and PD-L1 remains unclear in both lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC). We investigated PD-L1-related miRNAs that can predict treatment response in patients treated with PD-L1/PD-1 inhibitors. PATIENTS AND METHODS: We selected miRNAs that were correlated with PD-L1 expression within the LUAD and LUSC datasets obtained from The Cancer Genome Atlas (TCGA) and Clinical Proteomic Tumor Analysis Consortium (CPTAC). We validated whether the miRNA profile could be used to predict the prognosis of patients treated with PD-L1/PD-1 inhibitors. RESULTS: Based on four public datasets, we selected 66 and 23 miRNAs associated with PD-L1 expression in LUAD and LUSC, respectively. From the above miRNAs, we identified 5 miRNAs in LUSC and 1 miRNA in LUAD that could predict the response to PD-L1/PD-1 inhibitors in a validation set of patients treated with PD-L1/PD-1 inhibitors. In LUSC, the miRNA profile exhibited a high predictive capability for the response to PD-L1/PD-1 treatment [area under the curve (AUC)=0.963] and accurately predicted prognosis (p=0.031). In LUAD, the miRNA profile was relatively less predictive than in LUSC (AUC=0.691 and p=0.213). Additionally, we observed variations in the PD-L1-associated miRNA profiles, as well as in the associated pathways, between LUAD and LUSC. CONCLUSION: The PD-L1-associated miRNA profile may predict treatment response in LUSC patients treated with PD-L1/PD-1 inhibitors and help select the PD-L1/PD-1 inhibitor treatment group.

Machine learning-driven prediction of brain metastasis in lung adenocarcinoma using miRNA profile and target gene pathway analysis of an mRNA dataset.

BACKGROUND: Brain metastasis (BM) is common in lung adenocarcinoma (LUAD) and has a poor prognosis, necessitating predictive biomarkers. MicroRNAs (MiRNAs) promote cancer cell growth, infiltration, and metastasis. However, the relationship between the miRNA expression profiles and BM occurrence in patients with LUAD remains unclear. METHODS: We conducted an analysis to identify miRNAs in tissue samples that exhibited different expression levels between patients with and without BM. Using a machine learning approach, we confirmed whether the miRNA profile could be a predictive tool for BM. We performed pathway analysis of miRNA target genes using a matched mRNA dataset. RESULTS: We selected 25 miRNAs that consistently exhibited differential expression between the two groups of 32 samples. The 25-miRNA profile demonstrated a strong predictive potential for BM in both Group 1 and Group 2 and the entire dataset (area under the curve [AUC] = 0.918, accuracy = 0.875 in Group 1; AUC = 0.867, accuracy = 0.781 in Group 2; and AUC = 0.908, accuracy = 0.875 in the entire group). Patients predicted to have BM, based on the 25-miRNA profile, had lower survival rates. Target gene analysis of miRNAs suggested that BM could be induced through the ErbB signaling pathway, proteoglycans in cancer, and the focal adhesion pathway. Furthermore, patients predicted to have BM based on the 25-miRNA profile exhibited higher expression of the epithelial-mesenchymal transition signature, TWIST, and vimentin than those not predicted to have BM. Specifically, there was a correlation between EGFR mRNA levels and BM. CONCLUSIONS: This 25-miRNA profile may serve as a biomarker for predicting BM in patients with LUAD.

Differential efficacy of tyrosine kinase inhibitors according to the types of EGFR mutations and agents in non-small cell lung cancer: a real-world study.

BACKGROUND: Both first and second-generation EGFR-TKIs are recommended in advanced NSCLC with common EGFR mutations. However, there are few data on the difference in efficacy of EGFR-TKIs based on the type of EGFR mutation and agents. METHODS: This retrospective real-world study evaluated the outcomes and clinicopathologic characteristics, including the type of EGFR mutations, of 237 advanced NSCLC patients treated with first- or second-generation (afatinib) EGFR-TKIs as first-line therapy. RESULTS: The median progression-free survival (PFS) and overall survival (OS) of all patients were 11 months (M) and 25M, respectively. In the univariate analysis, patients with exon 19 deletion (del) (n=130) had significantly longer median OS compared to those with other mutations (L858R: 84, others: 23) (30 vs. 22 M, p=0.047), without a difference in PFS (p=0.138). Patients treated with afatinib (n=60) showed significantly longer median OS compared to those treated with first-generation TKIs (gefitinib: 159, erlotinib: 18) (30 vs. 23 M, p=0.037), without a difference in PFS (p=0.179). In patients with exon 19 del, there was no significant difference in median PFS (p=0.868) or OS (p=0.361) between patients treated with afatinib and those treated with first-generation TKIs, while significantly better PFS (p=0.042) and trend in OS (p=0.069) were observed in patients receiving afatinib in other mutations. Exon 19 del was independently associated with favorable OS (p=0.028), while age >70 years (p=0.017), ECOG performance status ≥2 (p=0.001), primary metastatic disease (p=0.007), and synchronous brain metastasis (p=0.026) were independent prognostic factors of poor OS. CONCLUSIONS: The EGFR exon 19 del was associated with favorable OS in advanced NSCLC patients receiving first-line EGFR-TKIs. Moreover, in patients with exon 19 del, first-generation TKIs seem to be a reasonable treatment option if osimertinib is unavailable.

Characteristics of the immune microenvironment associated with RRM2 expression and its application to PD-L1/PD-1 inhibitors in lung adenocarcinoma.

Recent studies have indicated that RRM2 plays a crucial part in the tumor immune microenvironment. According to the expression of RRM2, we evaluated immune cell infiltration, immunotherapy biomarkers, and the expression of immune checkpoint molecules in four lung adenocarcinoma (LUAD) datasets. We employed the Tumor Immune Dysfunction and Exclusion (TIDE) and CIBERSORTx algorithms to examine the patterns of immune cell distribution and evaluate the responses to anti-programmed death protein-1/programmed death ligand-1 (PD-1/PD-L1) therapy in three publicly available LUAD datasets. These findings were corroborated using a validation group comprising patients who received treatment with PD-1/PD-L1 inhibitors. Additionally, we conducted experiments using LUAD cell lines to investigate how RRM2 affects the expression of PD-L1. In comparison to the low RRM2 group, the high RRM2 group exhibited a high interferon gamma signature, high T-cell-inflamed signature, high CD274 expression, high CD8+ T cell levels, low cancer-associated fibroblasts, and low M2 macrophages, according to TIDE analysis in the three LUAD datasets. Analysis of the three LUAD datasets using CIBERSORTx confirmed a positive correlation between RRM2 and CD8+ T cells, and this finding was validated by immunohistochemistry in a separate validation set. In the three LUAD datasets without PD-1/PD-L1 inhibitor treatment, higher RRM2 expression was associated with a poorer prognosis. However, in the LUAD dataset treated with PD-1/PD-L1 inhibitors, higher RRM2 expression was associated with better prognosis. In the three datasets, the high-RRM2 group exhibited higher expression of inhibitory immune checkpoint molecules. In a LUAD cell line study, we discovered that RRM2 regulates PD-L1 expression through the ANXA1/AKT pathway. The expression of RRM2 shows promise as a predictive biomarker for PD-1/PD-L1 inhibitors in LUAD patients, and it may represent a new target to overcome resistance to PD-L1/PD-1 therapies.

Immune profiles according to EGFR mutant subtypes and correlation with PD-1/PD-L1 inhibitor therapies in lung adenocarcinoma.

Background: We examined the distributions of 22 immune cell types and the responses to PD-1/PD-L1 inhibitors according to EGFR mutation profile, in three independent datasets of lung adenocarcinoma (LUAD). Methods: We used CIBERSORTx to analyze the distributions of immune cells, and tumor immune dysfunction and exclusion (TIDE) or tumor mutation burden (TMB) to analyze responses to anti-PD-1/PD-L1 therapy, in two public LUAD datasets. The results were verified with a validation set that included patients treated with PD-1/PD-L1 inhibitors. Results: Compared to EGFR mutants, EGFR wild-type carcinomas had higher numbers of CD8+ T cells, CD4 memory activated T cells and neutrophils, and lower numbers of resting dendritic cells and resting mast cells, in two of the datasets. In our subgroup analyses, CD8+ T cells and CD4 memory activated T cells were more numerous in EGFR rare variants than in wild-types, L858R mutants, and exon 19 deletion mutants. In our TIDE or TMB analyses, EGFR rare variants were predicted to respond better to PD-1/PD-L1 inhibitors than wild-types, L858R mutants, and exon 19 deletion mutants. In the validation set verified by immunohistochemical staining, levels of CD8+ T cells in the EGFR rare variant or wild-type groups were significantly higher than in the EGFR L858R and exon 19 deletion groups. In patients treated with PD-1/PD-L1 inhibitors, the survival rates of patients with EGFR wild-type and rare mutant carcinomas were higher than those with L858R and exon 19 deletion carcinomas. Conclusion: The EGFR rare mutation form of LUAD shows a higher immune activation state compared to wild-type, L858R, and exon 19 deletion variants, indicating it as a potential target for PD-1/PD-L1 inhibitor therapy.

Prognostic and predictive value of YTHDF1 and YTHDF2 and their correlation with tumor-infiltrating immune cells in non-small cell carcinoma.

Background: YTH domain-containing family protein 1 (YTHDF1) or YTHDF2 play crucial roles in cancer immunotherapy. We examine the expression of YTHDF1, YTHDF2, CD8, CD4, and FOXP3 to identify their prognostic or predictive role for PD-1/PD-L1 inhibitor in non-small cell lung cancer (NSCLC). Methods: Immunohistochemical expression of YTHDF1, YTHDF2, CD8, CD4, and FOXP3 was investigated in 266 patients not receiving PD-1/PD-L1 inhibitors and in 59 patients receiving PD-1/PD-L1 inhibitors. Immunohistochemical results were verified using mRNA dataset obtained from The Cancer Genome Atlas (TCGA) database. Results: Immunohistochemical expression of YTHDF1 or YTHDF2 was negatively associated with CD8- and CD4-positive T cells; however, the same expression was positively associated with FOXP3-positive T cells. YTHDF1 or YTHDF2 mRNA expression was also negatively associated with CD8- and CD4-positive T cells. Gene set enrichment analysis revealed that low YTHDF1 was related to immune hot tumor gene sets. Expression of YTHDF1 or YTHDF2 was negatively associated with expression of most immune checkpoints. YTHDF1 and YTHDF2 were predictive markers of response to PD-1/PD-L1 inhibitors. YTHDF1 or YTHDF2 expression was associated with better prognosis. YTHDF1 has an immune hot profile in both cell types, whereas YTHDF2 is only seen in adenocarcinoma. Conclusion: Low YTHDF1 or YTHDF2 reflects an immune hot tumor signature and may serve as a predictor or prognostic marker.

A Machine Learning Approach Using PET/CT-based Radiomics for Prediction of PD-L1 Expression in Non-small Cell Lung Cancer.

BACKGROUND/AIM: We explored the prediction of programmed cell death ligand 1 (PD-L1) expression level in non-small cell lung cancer using a machine learning approach with positron emission tomography/computed tomography (PET/CT)-based radiomics. PATIENTS AND METHODS: A total of 312 patients (189 adenocarcinomas, 123 squamous cell carcinomas) who underwent F-18 fluorodeoxyglucose PET/CT were retrospectively analysed. Imaging biomarkers with 46 CT and 48 PET radiomic features were extracted from segmented tumours on PET and CT images using the LIFEx package. Radiomic features were ranked, and the top five best feature subsets were selected using the Gini index based on associations with PD-L1 expression in at least 50% of tumour cells. The areas under the receiver operating characteristic curves (AUCs) of binary classifications afforded by several machine learning algorithms (random forest, neural network, Naïve Bayes, logistic regression, adaptive boosting, stochastic gradient descent, support vector machine) were compared. The model performances were tested by 10-fold cross validation. RESULTS: We developed and validated a PET/CT-based radiomic model predicting PD-L1 expression levels in lung cancer. Long run high grey-level emphasis, homogeneity, mean Hounsfield unit, long run emphasis from CT, and maximum standardised uptake value from PET were the five best feature subsets for positive PD-L1 expression. The Naïve Bayes model (AUC=0.712), with a sensitivity of 75.3% and specificity of 58.2%, outperformed all other classifiers. It was followed by the neural network model (AUC=0.711), random forest (AUC=0.700), logistic regression (AUC=0.673) and adaptive boosting (AUC=0.604). CONCLUSION: PET/CT-based radiomic features may help clinicians identify tumours with positive PD-L1 expression in a non-invasive manner using machine learning algorithms.

Ciliated muconodular papillary tumor of the lung with cavitary change: A case report with 11-year preoperative follow-up.

Ciliated muconodular papillary tumor (CMPT) is a rare benign lung tumor characterized by ciliated columnar cells, mucous cells, and basal cells. Herein, we report a case of CMPT with 11 years of preoperative follow-up, depicting the natural course of the tumor and changes in computed tomography (CT) findings. A 39-year-old man had a 5-mm solid pulmonary nodule in the right lower lobe that had slowly grown and transformed into a thin-walled cavitary lesion. Right lower lobe lobectomy was performed and the tumor was confirmed to be a CMPT. Although it is difficult to diagnose CMPT with CT findings alone, CMPT should be considered as a possible diagnosis when a slowly growing nodule undergoes cavitary changes.

Intrinsic and Extrinsic Transcriptional Profiles That Affect the Clinical Response to PD-1 Inhibitors in Patients with Non-Small Cell Lung Cancer.

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.

Tumor Nonimmune-Microenvironment-Related Gene Expression Signature Predicts Brain Metastasis in Lung Adenocarcinoma Patients after Surgery: A Machine Learning Approach Using Gene Expression Profiling.

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.

Consistency of recommendations for the diagnosis and treatment of non-small cell lung cancer: a systematic review.

BACKGROUND: To systematically assess the consistency of recommendations regarding diagnosis and treatment of non-small cell lung cancer (NSCLC) in clinical practice guidelines (CPGs). METHODS: We systematically searched relevant literature databases and websites to identify CPGs related to NSCLC. We extracted the general characteristics of the included guidelines and their recommendations and descriptively compared and analyzed the consistency of recommendations across the guidelines. RESULTS: A total of 28 NSCLC guidelines were retrieved. The recommendations covered mainly diagnosis and treatment. The recommendations in the guidelines differed substantially in various topics, such as the application of positron emission tomography (PET) and the classification of stage III. Fourteen guidelines divided stage III into two types: operable and inoperable; and the remaining 14 guidelines into three sub-stages IIIA, IIIB and IIIC. Recommendations regarding the treatment in stage III were relatively inconsistent. In driver gene (EGFR, ALK, ROS1) positive patients, targeted therapy was the most common recommendation for first-line treatment, but recommendations regarding second-line treatment varied according to the site of the mutation. In driver gene negative patients, immunotherapy was the most frequently recommended option as both first- and second-line treatment, followed by chemotherapy. DISCUSSION: A number of countries are devoting themselves to develop NSCLC guidelines and the process of updating guidelines is accelerating, yet recommendations between guidelines are not consistent. We adopted a systematic review method to systematically search and analyze the NSCLC guidelines worldwide. We objectively reviewed the differences in recommendations for NSCLC diagnosis and treatment between the guidelines. Inconsistency of recommendations across guidelines can result from multiple potential reasons. Such as, the guidelines developed time, different countries and regions and many more. Poor consistency across CPGs can confuse the guideline users, and we therefore advocate paying more attention to examining the controversies and updating guidelines timely to improve the consistency among CPGs. Our study had also several limitations, we limited the search to CPGs published in Chinese or English, the interpretation of recommendations is inherently subjective, we did not evaluate the details of the clinical content of the CPG recommendations. Our research presents the current status of NSCLC guidelines worldwide and give the opportunity to pay more attention to the existing gaps. Further investigations should determine the reasons for inconsistency, the implications for recommendation development, and the role of synthesis across recommendations for optimal guidance of clinical care treatment. With the continuous revision and update of the guidelines, we are confident that future guidelines will be formulated with higher quality to form clear, definite and consistent recommendations for NSCLC diagnosis and treatment.

An immune-related gene expression signature predicts brain metastasis in lung adenocarcinoma patients after surgery: gene expression profile and immunohistochemical analyses.

BACKGROUND: Lung adenocarcinoma (LUAD) with brain metastasis (BM) occurs frequently and has a poor prognosis. In this study, we aimed to assess the correlation between gene expression signatures and the development of BM after surgical resection of LUAD. METHODS: We analyzed the immune-related gene expression profiles of 72 LUADs with and without BM after surgery and verified them using NanoString method and immunohistochemistry (IHC). We matched the Tumor, Node, Metastasis (TNM) stage in the groups with and without BM to minimize the effect of TNM stage. Pathway enrichment studies were also performed. RESULTS: In the NanoString results, we identified 11 upregulated immune-related gene signature that correlated specifically with BM in the discovery and validation sets [area under the curve (AUC) =0.750 and 0.787, respectively]. The discovery set achieved 94% sensitivity and 62% specificity and the validation set displayed 100% sensitivity and 50% specificity. Eight out of the 11 genes were verified by IHC and had profiles similar to the gene expression profile results (AUC =0.844 for the discovery set and AUC =0.795 for the validation set). Subgroup analysis revealed that 11 immune-related gene signature enabled prediction of BM at all TNM stages. There were no differences in the 11 immune-related gene expression signatures between the primary LUAD samples and the matched brain samples. Pathway enrichment analysis revealed that the cytokine-cytokine receptor interaction pathway was closely correlated with BM. CONCLUSIONS: The 11 identified immune-related gene expression signatures may be potentially clinically useful predictors for BM and can provide patient-specific treatment options.

High SLC2A1 expression associated with suppressing CD8 T cells and B cells promoted cancer survival in gastric cancer.

High expression of glucose transporter family members, which augment glucose uptake and glycolytic flux, has been shown to play a pivotal role in the proliferation and survival of tumor cells, contributing to the energy supply, biosynthesis and homeostasis of cancer cells. Among the many members, solute carrier family 2 member 1 (SLC2A1) encodes a glucose transporter, GLUT1, that is critical in the metabolism of glucose, which is an energy source for cell growth that contributes to cancer progression and development. The aim of this study was to analyze the survival and genetic changes/immune profiles in patients with gastric cancer with high SLC2A1 expression and to provide treatment for improving prognosis. This study investigated the clinicopathologic parameters, the proportion of immune cells and gene sets affecting SLC2A1 expression in 279 and 415 patients with gastric cancer from the Eulji Hospital cohort and The Cancer Genome Atlas, respectively. We assessed the response to conventional chemotherapy drugs, including fluorouracil, a compound of fluoropyrimidine S-1, oxaliplatin, and all-trans-retinoic acid (ATRA), in gastric cancer cell lines with high SLC2A1 expression. High SLC2A1 expression was associated with poor prognosis, cancer cell proliferation, decreased immune cells, including CD8 T cells and B cells, and a low prognostic nutrition index, representing body nutrition-related status. In pathway network analysis, SLC2A1 was indirectly linked to the retinoic signaling pathway and negatively regulated immune cells/receptors. In the drug response analysis, the drug ATRA inhibited gastric cancer cell lines with high SLC2A1 expression. Treatment involving the use of SLC2A1 could contribute to better clinical management/research for patients with gastric cancer.

Expression of PD-L1, cancer stem cell and epithelial-mesenchymal transition phenotype in non-small cell lung cancer.

The purpose of this study was to investigate the expression of PD-L1, cancer stem cells (CSC) markers (CD44, NANOG, and ALDH1) and epithelial-mesenchymal transition (EMT) markers and to evaluate their correlation and prognostic significance in non-small cell lung cancer (NSCLC) patients. PD-L1 protein expression was evaluated in resected 277 NSCLC cases and its correlation with CSC and EMT marker expression and survival was determined based on immunohistochemical (IHC) analysis. mRNA expression obtained from The Cancer Genome Atlas and the Kaplan-Meier plotter database were compared with the IHC results. PD-L1 expression was negatively correlated with ALDH1 expression in adenocarcinoma by IHC and mRNA expression. PD-L1 expression was also associated with mesenchymal phenotype (vimentin and TWIST) adenocarcinoma by IHC and mRNA expression. PD-L1 expression was associated with poor prognosis in adenocarcinoma by IHC. However, NANOG or ALDH1 expression measured by IHC and mRNA expression was correlated with a favourable prognosis in adenocarcinoma. Epithelial marker p120-catenin detected by IHC and mRNA expression was associated with a favourable prognosis in adenocarcinoma, however mesenchymal marker TWIST was associated with a worse prognosis. Patients with low PD-L1 and high ALDH1 expression showed more favourable prognoses than adenocarcinoma patients with other expression patterns. In multivariate analysis, ALDH1 detected by IHC and mRNA expression was an independent favourable prognostic marker for adenocarcinoma. Our study results support the hypothesis that PD-L1 interacts with CSC and EMT features and that PD-L1, ALDH1, and the mesenchymal phenotype may serve as tandem markers as prognostic factor in NSCLC.

HIP1R Expression and Its Association with PD-1 Pathway Blockade Response in Refractory Advanced NonSmall Cell Lung Cancer: A Gene Set Enrichment Analysis.

Huntingtin-interacting protein 1-related protein (HIP1R) plays an important role in the regulation of programmed death-ligand 1 (PD-L1). The aim of this study was to investigate the expression of HIP1R and confirm its predictive or prognostic roles in anti-PD-1 therapy in nonsmall cell lung cancer (NSCLC) patients. HIP1R and PD-L1 immunohistochemical expression was examined in 52 refractory advanced NSCLC patients treated with anti-PD-1 inhibitors. We performed gene set enrichment analysis (GSEA) to detect HIP1R-specific gene sets. Patients in the PD-1 inhibitor responder group had lower HIP1R expression by univariate logistic regression analysis (odds ratio (OR) = 0.235, p = 0.015) and multivariate logistic regression analysis (OR = 0.209, p = 0.014). Patients with high HIP1R expression had poorer progression-free survival (PFS) than patients with low HIP1R expression in univariate analysis (p = 0.037) and multivariate Cox analysis (hazard ratio = 2.098, p = 0.019). The web-based mRNA dataset also showed that high HIP1R expression correlated with inferior overall survival in lung adenocarcinoma (p = 0.026). GSEA revealed that HIP1R levels correlate with a set of genes that reflect PD-L1-related immune pathways. HIP1R expression may be a promising predictor for determination of patient responses to anti-PD-1 treatment.

DRG2 supports the growth of primary tumors and metastases of melanoma by enhancing VEGF-A expression.

Malignant metastatic melanoma (MM) is the most lethal of all skin cancers, but detailed mechanisms for regulation of melanoma metastasis are not fully understood. Here, we demonstrated that developmentally regulated GTP-binding protein 2 (DRG2) is required for the growth of primary tumors and for metastasis. DRG2 expression was significantly increased in MM compared with primary melanoma (PM) and dysplastic nevi. A correlation between DRG2 expression and poor disease-specific survival in melanoma patients was also identified. Furthermore, inhibition of DRG2 suppressed the binding of Hypoxia-inducible factor 1α to the VEGF-A promoter region, expression of vascular endothelial growth factor (VEGF)-A, and formation of endothelial cell tubes. In experimental mice, DRG2 depletion inhibited the growth of PM and lung metastases and increased survival. These results identify DRG2 as a critical regulator of VEGF-A expression and of growth of PMs and lung metastases.

A Machine-Learning Approach Using PET-Based Radiomics to Predict the Histological Subtypes of Lung Cancer.

PURPOSE: We sought to distinguish lung adenocarcinoma (ADC) from squamous cell carcinoma using a machine-learning algorithm with PET-based radiomic features. METHODS: A total of 396 patients with 210 ADCs and 186 squamous cell carcinomas who underwent FDG PET/CT prior to treatment were retrospectively analyzed. Four clinical features (age, sex, tumor size, and smoking status) and 40 radiomic features were investigated in terms of lung ADC subtype prediction. Radiomic features were extracted from the PET images of segmented tumors using the LIFEx package. The clinical and radiomic features were ranked, and a subset of useful features was selected based on Gini coefficient scores in terms of associations with histological class. The areas under the receiver operating characteristic curves (AUCs) of classifications afforded by several machine-learning algorithms (random forest, neural network, naive Bayes, logistic regression, and a support vector machine) were compared and validated via random sampling. RESULTS: We developed and validated a PET-based radiomic model predicting the histological subtypes of lung cancer. Sex, SUVmax, gray-level zone length nonuniformity, gray-level nonuniformity for zone, and total lesion glycolysis were the 5 best predictors of lung ADC. The logistic regression model outperformed all other classifiers (AUC = 0.859, accuracy = 0.769, F1 score = 0.774, precision = 0.804, recall = 0.746) followed by the neural network model (AUC = 0.854, accuracy = 0.772, F1 score = 0.777, precision = 0.807, recall = 0.750). CONCLUSIONS: A machine-learning approach successfully identified the histological subtypes of lung cancer. A PET-based radiomic features may help clinicians improve the histopathologic diagnosis in a noninvasive manner.

Increased CMTM6 can predict the clinical response to PD-1 inhibitors in non-small cell lung cancer patients.

CKLF-like MARVEL transmembrane domain containing 6 (CMTM6) plays a crucial role in the stability of the programmed death-ligand 1 (PD-L1). However, there has been no previous study of CMTM6 in non-small cell lung cancer (NSCLC) and its association with PD-L1 has not been confirmed. The aim of this study was to investigate the expression of CMTM6 and PD-L1 and to confirm their predictive roles for anti-PD-1 therapy in non-small cell lung cancer. CMTM6 and PD-L1 immunohistochemical expressions were evaluated in 35 advanced, treatment-refractory NSCLC patients who received PD-1 inhibitor therapy. The correlation between CMTM6 and PD-L1 expression was also determined based on immunohistochemistry and RNA-sequencing data obtained from The Cancer Genome Atlas (TCGA) database. CMTM6 expression was positively correlated with PD-L1 expression in immunohistochemical data (Pearson's r = 0.342 and p = .044). A positive correlation was also identified in the mRNA expression data. Using receiver operating characteristic curves, the levels of CMTM6 and PD-L1 expression which provided the best distinguishing point between responder versus non-responder to PD-1 inhibitors were 70 and 75 H-scores, respectively. The patients in the PD-1 inhibitor responder group had higher CMTM6 expressions in univariate logistic regression analysis (odds ratio (OR) = 5.333, p = .037). However, PD-L1 expression was not associated with response to PD-1 inhibitor (p = .288). In multivariate analysis, CMTM6 was also found to be an independent predictor of the response to PD-1 inhibitors (OR = 6.226, p = .032). CMTM6 expression can be a promising predictor useful for therapeutic decision-making regarding PD-1 inhibitors.

Prognostic significance of IFITM1 expression and correlation with microvessel density and epithelial-mesenchymal transition signature in lung adenocarcinoma.

We evaluated the relationship between interferon-induced transmembrane protein 1 (IFITM1) expression, epithelial-mesenchymal transition (EMT) signature and angiogenesis in lung adenocarcinoma. Additionally, we examined prognostic significance of IFITM1 according to pTNM stage to confirm that IFITM1 can serve as a complement to the pTNM stage. A total of 141 lung adenocarcinoma specimens were evaluated retrospectively by immunohistochemical staining for IFITM1, EMT markers (e-cadherin, ß-catenin, and vimentin), and CD31 to measure microvessel density. IFITM1was expressed in 46.8% of the specimens. IFITM1 expression was significantly correlated with increased microvessel density (P = 0.048). However, IFITM1 expression was not associated with three EMT markers. In a multivariate analysis, IFITM1 was an independent prognostic factor for overall survival in a multivariate analysis (hazard ratio: 2.59, P = 0.01). Online database with data from 720 lung adenocarcinoma patients also revealed a negative prognostic significance of IFITM1 (P < 0.001). Furthermore, high IFITM1 expression was significantly correlated with decreased OS rates in each pTNM stage. IFITM1 is significantly correlated with angiogenesis and it may be used as a useful additional prognostic marker to aid pTNM classification.