Overall Survival with Osimertinib in Resected EGFR-Mutated NSCLC.

BACKGROUND: Among patients with resected, epidermal growth factor receptor (EGFR)-mutated, stage IB to IIIA non-small-cell lung cancer (NSCLC), adjuvant osimertinib therapy, with or without previous adjuvant chemotherapy, resulted in significantly longer disease-free survival than placebo in the ADAURA trial. We report the results of the planned final analysis of overall survival. METHODS: In this phase 3, double-blind trial, we randomly assigned eligible patients in a 1:1 ratio to receive osimertinib (80 mg once daily) or placebo until disease recurrence was observed, the trial regimen was completed (3 years), or a discontinuation criterion was met. The primary end point was investigator-assessed disease-free survival among patients with stage II to IIIA disease. Secondary end points included disease-free survival among patients with stage IB to IIIA disease, overall survival, and safety. RESULTS: Of 682 patients who underwent randomization, 339 received osimertinib and 343 received placebo. Among patients with stage II to IIIA disease, the 5-year overall survival was 85% in the osimertinib group and 73% in the placebo group (overall hazard ratio for death, 0.49; 95.03% confidence interval [CI], 0.33 to 0.73; P<0.001). In the overall population (patients with stage IB to IIIA disease), the 5-year overall survival was 88% in the osimertinib group and 78% in the placebo group (overall hazard ratio for death, 0.49; 95.03% CI, 0.34 to 0.70; P<0.001). One new serious adverse event, pneumonia related to coronavirus disease 2019, was reported after the previously published data-cutoff date (the event was not considered by the investigator to be related to the trial regimen, and the patient fully recovered). Adjuvant osimertinib had a safety profile consistent with that in the primary analysis. CONCLUSIONS: Adjuvant osimertinib provided a significant overall survival benefit among patients with completely resected, EGFR-mutated, stage IB to IIIA NSCLC. (Funded by AstraZeneca; ADAURA ClinicalTrials.gov number, NCT02511106.).

An integrated cell atlas of the lung in health and disease.

Single-cell technologies have transformed our understanding of human tissues. Yet, studies typically capture only a limited number of donors and disagree on cell type definitions. Integrating many single-cell datasets can address these limitations of individual studies and capture the variability present in the population. Here we present the integrated Human Lung Cell Atlas (HLCA), combining 49 datasets of the human respiratory system into a single atlas spanning over 2.4 million cells from 486 individuals. The HLCA presents a consensus cell type re-annotation with matching marker genes, including annotations of rare and previously undescribed cell types. Leveraging the number and diversity of individuals in the HLCA, we identify gene modules that are associated with demographic covariates such as age, sex and body mass index, as well as gene modules changing expression along the proximal-to-distal axis of the bronchial tree. Mapping new data to the HLCA enables rapid data annotation and interpretation. Using the HLCA as a reference for the study of disease, we identify shared cell states across multiple lung diseases, including SPP1+ profibrotic monocyte-derived macrophages in COVID-19, pulmonary fibrosis and lung carcinoma. Overall, the HLCA serves as an example for the development and use of large-scale, cross-dataset organ atlases within the Human Cell Atlas.

Risk factors and actionable molecular signatures in COVID-19-associated lung adenocarcinoma and lung squamous cell carcinoma patients.

The molecular mechanism of the pathological impact of COVID-19 in lung cancer patients remains poorly understood to date. In this study, we used differential gene expression pattern analysis to try to figure out the possible disease mechanism of COVID-19 and its associated risk factors in patients with the two most common types of non-small-cell lung cancer, namely, lung adenocarcinoma and lung squamous cell carcinoma. We also used network-based approaches to identify potential diagnostic and molecular targets for COVID-19-infected lung cancer patients. Our study showed that lung cancer and COVID-19 patients share 36 genes that are expressed differently and in common. Most of these genes are expressed in lung tissues and are mostly involved in the pathogenesis of different respiratory tract diseases. Additionally, we also found that COVID-19 may affect the expression of several cancer-associated genes in lung cancer patients, such as the oncogenes JUN, TNC, and POU2AF1. Moreover, our findings suggest that COVID-19 may predispose lung cancer patients to other diseases like acute liver failure and respiratory distress syndrome. Additionally, our findings, in concert with published literature, suggest that molecular signatures, such as hsa-mir-93-5p, CCNB2, IRF1, CD163, and different immune cell-based approaches could help both diagnose and treat this group of patients. Altogether, the scientific findings of this study will help formulate appropriate management measures and guide the development of diagnostic and therapeutic measures for COVID-19-infected lung cancer patients.

Pan-cancer landscape of CENPO and its underlying mechanism in LUAD.

BACKGROUND: Centromere protein O (CENPO) is a newly discovered constitutive centromeric protein, associated with cell death. However, little is known about how CENPO expression is associated with human cancers or immune infiltration. Here, we assessed the function of CENPO in pan-cancer and further verified the results in lung adenocarcinoma (LUAD) through in vitro and in vivo experiments. METHODS: Sangerbox and TCGA databases were used to evaluate the CENPO expression level in different human cancer types. A subsequent evaluation of the potential role of CENPO as a diagnostic and prognostic biomarker in pancancer was conducted. The CENPO mutations were analyzed using the cBioPortal database and its function was analyzed using the LinkedOmics and CancerSEA databases. The TIMER2 and TISIDB websites were used to find out how CENPO affects immune infiltration. The expression level of CENPO in LUAD was revealed by TCGA database and immunohistochemical (IHC) staining. Targetscan, miRWalk, miRDB, miRabel, LncBase databases, and Cytoscape tool were used to identify microRNAs (miRNAs) and long noncoding RNAs (lncRNAs) that regulate expression and construct ceRNA network. Subsequently, loss-of-function assays were performed to identify the functions of CENPO on the malignant behavior and tumor growth of LUAD in vitro and in vivo experiments. RESULTS: In most cancers, CENPO was upregulated and mutated, which predicted a poorer prognosis. Furthermore, infiltration of CENPO and myeloid-derived suppressor cells (MDSC) showed a significant positive correlation, while T-cell NK infiltration showed a significant negative correlation in most cancers. CENPO was expressed at high levels in LUAD and was correlated with p-TNM stage. Furthermore, CENPO knockdown suppressed the malignant phenotypes of LUAD cells, manifested by slower proliferation, cycle in G2, increased apoptosis, decreased migration, and attenuated tumorigenesis. Furthermore, CENPO knockdown decreased CDK1/6, PIK3CA, and inhibited mTOR phosphorylation, suggesting that the mTOR signaling pathway may be involved in CENPO-mediated regulation of LUAD development. CONCLUSIONS: In pan-cancer, especially LUAD, CENPO may be a potential biomarker and oncogene. Furthermore, CENPO has been implicated in immune cell infiltration in pan-cancer and represents a potential immunotherapeutic target for tumor therapy.

The emerging diagnostic and therapeutic roles of small nucleolar RNAs in lung diseases.

Small nucleolar RNAs (snoRNAs) are non-coding RNA molecules that range from 60 to 300 nucleotides in length and are primarily located in the nucleoli of cells. They play a critical role in modifying ribosomal RNA and can also regulate alternative splicing and posttranscriptional modification of mRNA. Alterations in snoRNA expression can affect numerous cellular processes, including cell proliferation, apoptosis, angiogenesis, fibrosis, and inflammation, making them a promising target for diagnostics and treatment of various human pathologies. Recent evidence suggests that abnormal snoRNA expression is strongly associated with the development and progression of several lung diseases, such as lung cancer, asthma, chronic obstructive pulmonary disease, and pulmonary hypertension, as well as COVID-19. While few studies have shown a causal relationship between snoRNA expression and disease onset, this research field presents exciting opportunities for identifying new biomarkers and therapeutic targets in lung disease. This review discusses the emerging role and molecular mechanisms of snoRNAs in the pathogenesis of lung diseases, focusing on research opportunities, clinical studies, biomarkers, and therapeutic potential.

Identification of Potential Key Genes and Prognostic Biomarkers of Lung Cancer Based on Bioinformatics.

Objective: To analyze and identify the core genes related to the expression and prognosis of lung cancer including lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) by bioinformatics technology, with the aim of providing a reference for clinical treatment. Methods: Five sets of gene chips, GSE7670, GSE151102, GSE33532, GSE43458, and GSE19804, were obtained from the Gene Expression Omnibus (GEO) database. After using GEO2R to analyze the differentially expressed genes (DEGs) between lung cancer and normal tissues online, the common DEGs of the five sets of chips were obtained using a Venn online tool and imported into the Database for Annotation, Visualization, and Integrated Discovery (DAVID) database for Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. The protein-protein interaction (PPI) network was constructed by STRING online software for further study, and the core genes were determined by Cytoscape software and KEGG pathway enrichment analysis. The clustering heat map was drawn by Excel software to verify its accuracy. In addition, we used the University of Alabama at Birmingham Cancer (UALCAN) website to analyze the expression of core genes in P53 mutation status, confirmed the expression of crucial core genes in lung cancer tissues with Gene Expression Profiling Interactive Analysis (GEPIA) and GEPIA2 online software, and evaluated their prognostic value in lung cancer patients with the Kaplan-Meier online plotter tool. Results: CHEK1, CCNB1, CCNB2, and CDK1 were selected. The expression levels of these four genes in lung cancer tissues were significantly higher than those in normal tissues. Their increased expression was negatively correlated with lung cancer patients (including LUAD and LUSC) prognosis and survival rate. Conclusion: CHEK1, CCNB1, CCNB2, and CDK1 are the critical core genes of lung cancer and are highly expressed in lung cancer. They are negatively correlated with the prognosis of lung cancer patients (including LUAD and LUSC) and closely related to the formation and prediction of lung cancer. They are valuable predictors and may be predictive biomarkers of lung cancer.

Artificial Intelligence-Powered Prediction of ALK Gene Rearrangement in Patients With Non-Small-Cell Lung Cancer.

PURPOSE: Several studies reported the possibility of predicting genetic abnormalities in non-small-cell lung cancer by deep learning (DL). However, there are no data of predicting ALK gene rearrangement (ALKr) using DL. We evaluated the ALKr predictability using the DL platform. MATERIALS AND METHODS: We selected 66 ALKr-positive cases and 142 ALKr-negative cases, which were diagnosed by ALKr immunohistochemical staining in our institution from January 2009 to March 2019. We generated virtual slide of 300 slides (150 ALKr-positive slides and 150 ALKr-negative slides) using NanoZoomer. HALO-AI was used to analyze the whole-slide imaging data, and the DenseNet network was used to build the learning model. Of the 300 slides, we randomly assigned 172 slides to the training cohort and 128 slides to the test cohort to ensure no duplication of cases. In four resolutions (16.0/4.0/1.0/0.25 µm/pix), ALKr prediction models were built in the training cohort and ALKr prediction performance was evaluated in the test cohort. We evaluated the diagnostic probability of ALKr by receiver operating characteristic analysis in each ALKr probability threshold (50%, 60%, 70%, 80%, 90%, and 95%). We expected the area under the curve to be 0.64-0.85 in the model of a previous study. Furthermore, in the test cohort data, an expert pathologist also evaluated the presence of ALKr by hematoxylin and eosin staining on whole-slide imaging. RESULTS: The maximum area under the curve was 0.73 (50% threshold: 95% CI, 0.65 to 0.82) in the resolution of 1.0 µm/pix. In this resolution, with an ALKr probability of 50% threshold, the sensitivity and specificity were 73% and 73%, respectively. The expert pathologist's sensitivity and specificity in the same test cohort were 13% and 94%. CONCLUSION: The ALKr prediction by DL was feasible. Further study should be addressed to improve accuracy of ALKr prediction.

Identification of the effects of COVID-19 on patients with pulmonary fibrosis and lung cancer: a bioinformatics analysis and literature review.

Coronavirus disease 2019 (COVID-19) poses a serious threat to human health and life. The effective prevention and treatment of COVID-19 complications have become crucial to saving patients' lives. During the phase of mass spread of the epidemic, a large number of patients with pulmonary fibrosis and lung cancers were inevitably infected with the SARS-CoV-2 virus. Lung cancers have the highest tumor morbidity and mortality rates worldwide, and pulmonary fibrosis itself is one of the complications of COVID-19. Idiopathic lung fibrosis (IPF) and various lung cancers (primary and metastatic) become risk factors for complications of COVID-19 and significantly increase mortality in patients. Therefore, we applied bioinformatics and systems biology approaches to identify molecular biomarkers and common pathways in COVID-19, IPF, colorectal cancer (CRC) lung metastasis, SCLC and NSCLC. We identified 79 DEGs between COVID-19, IPF, CRC lung metastasis, SCLC and NSCLC. Meanwhile, based on the transcriptome features of DSigDB and common DEGs, we identified 10 drug candidates. In this study, 79 DEGs are the common core genes of the 5 diseases. The 10 drugs were found to have positive effects in treating COVID-19 and lung cancer, potentially reducing the risk of pulmonary fibrosis.

Systematic comparison of ranking aggregation methods for gene lists in experimental results.

MOTIVATION: A common experimental output in biomedical science is a list of genes implicated in a given biological process or disease. The gene lists resulting from a group of studies answering the same, or similar, questions can be combined by ranking aggregation methods to find a consensus or a more reliable answer. Evaluating a ranking aggregation method on a specific type of data before using it is required to support the reliability since the property of a dataset can influence the performance of an algorithm. Such evaluation on gene lists is usually based on a simulated database because of the lack of a known truth for real data. However, simulated datasets tend to be too small compared to experimental data and neglect key features, including heterogeneity of quality, relevance and the inclusion of unranked lists. RESULTS: In this study, a group of existing methods and their variations that are suitable for meta-analysis of gene lists are compared using simulated and real data. Simulated data were used to explore the performance of the aggregation methods as a function of emulating the common scenarios of real genomic data, with various heterogeneity of quality, noise level and a mix of unranked and ranked data using 20 000 possible entities. In addition to the evaluation with simulated data, a comparison using real genomic data on the SARS-CoV-2 virus, cancer (non-small cell lung cancer) and bacteria (macrophage apoptosis) was performed. We summarize the results of our evaluation in a simple flowchart to select a ranking aggregation method, and in an automated implementation using the meta-analysis by information content algorithm to infer heterogeneity of data quality across input datasets. AVAILABILITY AND IMPLEMENTATION: The code for simulated data generation and running edited version of algorithms: https://github.com/baillielab/comparison_of_RA_methods. Code to perform an optimal selection of methods based on the results of this review, using the MAIC algorithm to infer the characteristics of an input dataset, can be downloaded here: https://github.com/baillielab/maic. An online service for running MAIC: https://baillielab.net/maic. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Optimising tissue acquisition and the molecular testing pathway for patients with non-small cell lung cancer: A UK expert consensus statement.

Targeted therapy against actionable variants has revolutionised the treatment landscape for non-small cell lung cancer (NSCLC). Approximately half of NSCLC adenocarcinomas have an actionable variant, making molecular testing a critical component of the diagnostic process to personalise therapeutic options, optimise clinical outcomes and minimise toxicity. Recently, genomic testing in England has undergone major changes with the introduction of Genomic Laboratory Hubs, designed to consolidate and enhance existing laboratory provision and deliver genomic testing as outlined in the National Genomic Test Directory. Similar changes are ongoing in Scotland, Wales and Northern Ireland. However, multiple challenges exist with current tissue acquisition procedures and the molecular testing pathway in the UK, including quantity and quality of available tissue, adequacy rates, test availability among genomic laboratories, turnaround times, multidisciplinary team communication, and limited guidance and standardisation. The COVID-19 pandemic has added an extra layer of complexity. Herein, we summarise best practice recommendations, based on expert opinion, to overcome existing challenges in the UK. The least invasive biopsy technique should be undertaken with the aim of acquiring the greatest quality and quantity of tissue. Use of sedation should be considered to improve patient experience. Rapid on-site evaluation may also be useful to help guide adequate sampling, and liquid biopsy may be beneficial in some instances. Sample processing should be appropriate to facilitate biomarker testing, in particular, next-generation sequencing for comprehensive genomic information. Steps to optimise tissue utilisation and turnaround times, such as planning of tissue usage, limiting immunohistochemistry, tumour enrichment, and reflex testing at diagnosis, should be implemented. Guidelines for tissue acquisition and sample processing may help to improve sample adequacy to perform downstream testing. Communication among genomic laboratories will help to standardise test availability across England and local auditing could identify further areas for optimisation, including ways to improve turnaround times and adequacy rates.

Molecular Beacon for Detection miRNA-21 as a Biomarker of Lung Cancer.

Lung cancer (LC) is the leading cause of cancer-related death worldwide. Although the diagnosis and treatment of non-small cell lung cancer (NSCLC), which accounts for approximately 80% of LC cases, have greatly improved in the past decade, there is still an urgent need to find more sensitive and specific screening methods. Recently, new molecular biomarkers are emerging as potential non-invasive diagnostic agents to screen NSCLC, including multiple microRNAs (miRNAs) that show an unusual expression profile. Moreover, peripheral blood mononuclear cells' (PBMCs) miRNA profile could be linked with NSCLC and used for diagnosis. We developed a molecular beacon (MB)-based miRNA detection strategy for NSCLC. Following PBMCs isolation and screening of the expression profile of a panel of miRNA by RT-qPCR, we designed a MB targeting of up-regulated miR-21-5p. This MB 21-5p was characterized by FRET-melting, CD, NMR and native PAGE, allowing the optimization of an in-situ approach involving miR-21-5p detection in PBMCs via MB. Data show the developed MB approach potential for miR-21-5p detection in PBMCs from clinical samples towards NSCLC.

Landscape of Molecular Crosstalk Perturbation between Lung Cancer and COVID-19.

BACKGROUND: Lung cancer patients have the worst outcomes when affected by coronavirus disease 2019 (COVID-19). The molecular mechanisms underlying the association between lung cancer and COVID-19 remain unknown. The objective of this investigation was to determine whether there is crosstalk in molecular perturbation between COVID-19 and lung cancer, and to identify a molecular signature, molecular networks and signaling pathways shared by the two diseases. METHODS: We analyzed publicly available gene expression data from 52 severely affected COVID-19 human lung samples, 594 lung tumor samples and 54 normal disease-free lung samples. We performed network and pathways analysis to identify molecular networks and signaling pathways shared by the two diseases. RESULTS: The investigation revealed a signature of genes associated with both diseases and signatures of genes uniquely associated with each disease, confirming crosstalk in molecular perturbation between COVID-19 and lung cancer. In addition, the analysis revealed molecular networks and signaling pathways associated with both diseases. CONCLUSIONS: The investigation revealed crosstalk in molecular perturbation between COVID-19 and lung cancer, and molecular networks and signaling pathways associated with the two diseases. Further research on a population impacted by both diseases is recommended to elucidate molecular drivers of the association between the two diseases.

Designing Novel Compounds for the Treatment and Management of RET-Positive Non-Small Cell Lung Cancer-Fragment Based Drug Design Strategy.

Rearranged during transfection (RET) is an oncogenic driver receptor that is overexpressed in several cancer types, including non-small cell lung cancer. To date, only multiple kinase inhibitors are widely used to treat RET-positive cancer patients. These inhibitors exhibit high toxicity, less efficacy, and specificity against RET. The development of drug-resistant mutations in RET protein further deteriorates this situation. Hence, in the present study, we aimed to design novel drug-like compounds using a fragment-based drug designing strategy to overcome these issues. About 18 known inhibitors from diverse chemical classes were fragmented and bred to form novel compounds against RET proteins. The inhibitory activity of the resultant 115 hybrid molecules was evaluated using molecular docking and RF-Score analysis. The binding free energy and chemical reactivity of the compounds were computed using MM-GBSA and density functional theory analysis, respectively. The results from our study revealed that the developed hybrid molecules except for LF21 and LF27 showed higher reactivity and stability than Pralsetinib. Ultimately, the process resulted in three hybrid molecules namely LF1, LF2, and LF88 having potent inhibitory activity against RET proteins. The scrutinized molecules were then subjected to molecular dynamics simulation for 200 ns and MM-PBSA analysis to eliminate a false positive design. The results from our analysis hypothesized that the designed compounds exhibited significant inhibitory activity against multiple RET variants. Thus, these could be considered as potential leads for further experimental studies.

Decreased TMPRSS2 expression by SARS-CoV-2 predicts the poor prognosis of lung cancer patients through metabolic pathways and immune infiltration.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has rapidly spread around the world and became a global pandemic in 2020. One promising drug target for SARS-CoV-2 is the transmembrane protease serine 2 (TMPRSS2). This study was designed to explore the expression status, prognostic significance and molecular functions of TMPRSS2 in lung cancer. TMPRSS2 expression was investigated using the TIMER, Oncomine, UALCAN, GEO, HPA and TCGA databases. The prognostic value of TMPRSS2 was examined using Cox regression and a nomogram. KEGG, GO and GSEA were performed to investigate the cellular function of TMPRSS2 in lung cancer. The relationship between TMPRSS2 and immune infiltration was determined using the TIMER and CIBERSORT algorithms. TMPRSS2 mRNA and protein expression was significantly reduced in lung cancer. Decreased TMPRSS2 expression and increased DNA methylation of TMPRSS2 were associated with various clinicopathological parameters in patients with lung cancer. Low TMPRSS2 mRNA expression also correlated with poor outcome in lung cancer patients. Moreover, a nomogram was constructed and exhibited good predictive power for the overall survival of lung cancer patients. KEGG and GO analyses and GSEA implied that multiple immune- and metabolism-related pathways were significantly linked with TMPRSS2 expression. Intriguingly, TMPRSS2 expression associated with immune cell infiltration in lung cancer. More importantly, TMPRSS2 expression was markedly decreased in SARS-CoV-infected cells. These findings indicate that TMPRSS2 may be a promising prognostic biomarker and therapeutic target for lung cancer through metabolic pathways and immune cell infiltration.

Cathepsin B is a potential therapeutic target for coronavirus disease 2019 patients with lung adenocarcinoma.

Coronavirus disease 2019 (COVID-19) was declared a serious global public health emergency. Hospitalization and mortality rates of lung cancer patients diagnosed with COVID-19 are higher than those of patients presenting with other cancers. However, the reasons for the outcomes being disproportionately severe in lung adenocarcinoma (LUAD) patients with COVID-19 remain elusive. The present study aimed to identify the possible causes for disproportionately severe COVID-19 outcomes in LUAD patients and determine a therapeutic target for COVID-19 patients with LUAD. We used publicly available data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases and various bioinformatics tools to identify and analyze the genes implicated in SARS-CoV-2 infection in LUAD patients. Upregulation of the SARS-CoV-2 infection-related molecules dipeptidyl peptidase 4, basigin, cathepsin B (CTSB), methylenetetrahydrofolate dehydrogenase, and peptidylprolyl isomerase B rather than angiotensin-converting enzyme 2 may explain the relatively high susceptibility of LUAD patients to SARS-CoV-2 infection. CTSB was highly expressed in the LUAD tissues after SARS-CoV-2 infection, and its expression was positively correlated with immune cell infiltration and proinflammatory cytokine expression. These findings suggest that CTSB plays a vital role in the hyperinflammatory response in COVID-19 patients with LUAD and is a promising target for the development of a novel drug therapy for COVID-19 patients.

Core genes involved in the regulation of acute lung injury and their association with COVID-19 and tumor progression: A bioinformatics and experimental study.

Acute lung injury (ALI) is a specific form of lung damage caused by different infectious and non-infectious agents, including SARS-CoV-2, leading to severe respiratory and systemic inflammation. To gain deeper insight into the molecular mechanisms behind ALI and to identify core elements of the regulatory network associated with this pathology, key genes involved in the regulation of the acute lung inflammatory response (Il6, Ccl2, Cat, Serpine1, Eln, Timp1, Ptx3, Socs3) were revealed using comprehensive bioinformatics analysis of whole-genome microarray datasets, functional annotation of differentially expressed genes (DEGs), reconstruction of protein-protein interaction networks and text mining. The bioinformatics data were validated using a murine model of LPS-induced ALI; changes in the gene expression patterns were assessed during ALI progression and prevention by anti-inflammatory therapy with dexamethasone and the semisynthetic triterpenoid soloxolone methyl (SM), two agents with different mechanisms of action. Analysis showed that 7 of 8 revealed ALI-related genes were susceptible to LPS challenge (up-regulation: Il6, Ccl2, Cat, Serpine1, Eln, Timp1, Socs3; down-regulation: Cat) and their expression was reversed by the pre-treatment of mice with both anti-inflammatory agents. Furthermore, ALI-associated nodal genes were analysed with respect to SARS-CoV-2 infection and lung cancers. The overlap with DEGs identified in postmortem lung tissues from COVID-19 patients revealed genes (Saa1, Rsad2, Ifi44, Rtp4, Mmp8) that (a) showed a high degree centrality in the COVID-19-related regulatory network, (b) were up-regulated in murine lungs after LPS administration, and (c) were susceptible to anti-inflammatory therapy. Analysis of ALI-associated key genes using The Cancer Genome Atlas showed their correlation with poor survival in patients with lung neoplasias (Ptx3, Timp1, Serpine1, Plaur). Taken together, a number of key genes playing a core function in the regulation of lung inflammation were found, which can serve both as promising therapeutic targets and molecular markers to control lung ailments, including COVID-19-associated ALI.

Human Recombinant DNase I (Pulmozyme®) Inhibits Lung Metastases in Murine Metastatic B16 Melanoma Model That Correlates with Restoration of the DNase Activity and the Decrease SINE/LINE and c-Myc Fragments in Blood Cell-Free DNA.

Tumor-associated cell-free DNAs (cfDNA) play an important role in the promotion of metastases. Previous studies proved the high antimetastatic potential of bovine pancreatic DNase I and identified short interspersed nuclear elements (SINEs) and long interspersed nuclear elements (LINEs)and fragments of oncogenes in cfDNA as the main molecular targets of enzyme in the bloodstream. Here, recombinant human DNase I (commercial name Pulmozyme®), which is used for the treatment of cystic fibrosis in humans, was repurposed for the inhibition of lung metastases in the B16 melanoma model in mice. We found that Pulmozyme® strongly reduced migration and induced apoptosis of B16 cells in vitro and effectively inhibited metastases in lungs and liver in vivo. Pulmozyme® was shown to be two times more effective when administered intranasally (i.n.) than bovine DNase I, but intramuscular (i.m.) administration forced it to exhibit as high an antimetastatic activity as bovine DNase I. Both DNases administered to mice either i.m. or i.n. enhanced the DNase activity of blood serum to the level of healthy animals, significantly decreased cfDNA concentrations, efficiently degraded SINE and LINE repeats and c-Myc fragments in the bloodstream and induced apoptosis and disintegration of neutrophil extracellular traps in metastatic foci; as a result, this manifested as the inhibition of metastases spread. Thus, Pulmozyme®, which is already an approved drug, can be recommended for use in the treatment of lung metastases.

Spectacular Progress in Lung Cancer Care.

Against the difficult and trying backdrop of the pandemic, cancer investigators persisted, and for patients with lung cancer, that persistence paid off in spectacular ways. With several new FDA approved treatments, as well as 2 new targetable mutations in non-small cell lung cancer (NSCLC), 2020 was a banner year in the overall lung cancer space. ONCOLOGY® recently sat down with Jennifer W. Carlisle, MD, of Emory University's Winship Cancer Institute, to discuss the many advances made during the last year for patients with lung cancer along with her hopes for further significant milestones in the year to come.