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.

Ado-trastuzumab emtansine in the treatment of lung adenocarcinoma with ERBB2 mutation: a case report and literature review.

The erb-b2 receptor tyrosine kinase 2 (ERBB2), also known as HER2, has long been recognized as an oncogenic driver in some breast and gastroesophageal cancers, and ERBB2-targeted therapies are standard for ERBB2-positive breast and gastric cancer. However, there are currently no standard therapies targeting the ERBB2 pathway in non-small cell lung cancer. Recently, somatic mutations in ERBB2 have been reported in 2-3% of patients with advanced lung adenocarcinoma, these mutations are trans-forming in lung cancer models and result in kinase activation, conferring some in-vitro sensitivity to trastuzumab. The ado-trastuzumab emtansine (T-DM1) is an antibody-drug conjugate composed of trastuzumab joined via a stable linker to DM1. In this report, a 67-year-old male patient was diagnosed with advanced lung adenocarcinoma with multiple lymph node metastases, and multi-chemotherapy and immunotherapy were not effective. The results of genetic testing indicated a non-frameshift insertion mutation in exon 20 of the ERBB2 gene. The patients received T-DM1 at a dose of 3.6 mg/kg by intravenous infusion every 21 days until for 12 cycles. Partial response appeared in the tumor lesions after treatment for four cycles, and PET-computer tomography showed the tumor lesions were effectively controlled, and the efficacy evaluation was complete response after treatment for six cycles. Although the patient experienced second degree of thrombocytopenia during the treatment, the corresponding symptomatic treatment was taken, and the platelets could return to normal before the next cycle of T-DM1. Follow-up review showed the patient is in good health and the tumor has not recurred.

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.

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.

Expression and Clinical Significance of SARS-CoV-2 Human Targets in Neoplastic and Non-Neoplastic Lung Tissues.

BACKGROUND: A higher incidence of COVID-19 infection was demonstrated in cancer patients, including lung cancer patients. This study was conducted to get insights into the enhanced frequency of COVID-19 infection in cancer. METHODS: Using different bioinformatics tools, the expression and methylation patterns of ACE2 and TMPRSS2 were analyzed in healthy and malignant tissues, focusing on lung adenocarcinoma and data were correlated to clinical parameters and smoking history. RESULTS: ACE2 and TMPRSS2 were heterogeneously expressed across 36 healthy tissues with the highest expression levels in digestive, urinary and reproductive organs, while the overall analysis of 72 paired tissues demonstrated significantly lower expression levels of ACE2 in cancer tissues when compared to normal counterparts. In contrast, ACE2, but not TMPRSS2, was overexpressed in LUAD, which inversely correlated to the promoter methylation. This upregulation of ACE2 was age-dependent in LUAD, but not in normal lung tissues. TMPRSS2 expression in non-neoplastic lung tissues was heterogeneous and dependent on sex and smoking history, while it was downregulated in LUAD of smokers. Cancer progression was associated with a decreased TMPRSS2 but unaltered ACE2. In contrast, ACE2 and TMPRSS2 of lung metastases derived from different cancer subtypes was higher than organ metastases of other sites. TMPRSS2, but not ACE2, was associated with LUAD patients' survival. CONCLUSIONS: Comprehensive molecular analyses revealed a heterogeneous and distinct expression and/or methylation profile of ACE2 and TMPRSS2 in healthy lung vs. LUAD tissues across sex, age and smoking history and might have implications for COVID-19 disease.