MiCA: An extended tool for microarray gene expression analysis.

Gene expression microarrays capture a complete image of all the transcriptional activity in a biological sample. Microarrays produce a large amount of data, which becomes a challenge when it comes to exploring and interpreting using modern computational and statistical tools. We propose the Microarray Analysis (MiCA) tool that outperforms other similar tools both in terms of ease of use and statistical features requiring minimal input to conduct an analysis. MiCA is an integrated, interactive, and streamlined desktop software for the analysis of microarray gene expression data. MiCA consists of a complete microarray analysis pipeline including but not limited to fetching data directly from GEO, normalization, interactive quality control, batch-effect correction, regression analysis, surrogate variable analysis and functional annotation methods such as GSVA using known existing R packages. We compare the features offered by MiCA and other similar tools while performing differential expression analysis using previously published datasets. MiCA offers additional statistical and visualization methods to conduct a microarray data analysis compared to other available microarray analysis tools. MiCA minimizes the need for technical knowledge by providing a very intuitive and versatile interface that integrates all necessary tasks and features required for basic microarray data analysis. We analyzed multiple published datasets and showed that the features offered by MiCA not only simplify the analysis pipeline but also provide additional interpretation to the data.

Immune evasion before tumour invasion in early lung squamous carcinogenesis.

Early detection and treatment are critical for improving the outcome of patients with cancer1. Understanding the largely uncharted biology of carcinogenesis requires deciphering molecular processes in premalignant lesions, and revealing the determinants of the intralesional immune reaction during cancer development. The adaptive immune response within tumours has previously been shown to be strongest at the earliest stage of carcinoma2,3. Here we show that immune activation and immune escape occur before tumour invasion, and reveal the relevant immune biomarkers of the pre-invasive stages of carcinogenesis in the lung. We used gene-expression profiling and multispectral imaging to analyse a dataset of 9 morphological stages of the development of lung squamous cell carcinoma, which includes 122 well-annotated biopsies from 77 patients. We identified evolutionary trajectories of cancer and immune pathways that comprise (1) a linear increase in proliferation and DNA repair from normal to cancerous tissue; (2) a transitory increase of metabolism and early immune sensing, through the activation of resident immune cells, in low-grade pre-invasive lesions; (3) the activation of immune responses and immune escape through immune checkpoints and suppressive interleukins from high-grade pre-invasive lesions; and, ultimately, (4) the activation of the epithelial-mesenchymal transition in the invasive stage of cancer. We propose that carcinogenesis in the lung involves a dynamic co-evolution of pre-invasive bronchial cells and the immune response. These findings highlight the need to develop immune biomarkers for early detection as well as immunotherapy-based chemopreventive approaches for individuals who are at high risk of developing lung cancer.

Tobacco-Related Alterations in Airway Gene Expression are Rapidly Reversed Within Weeks Following Smoking-Cessation.

The physiologic response to tobacco smoke can be measured by gene-expression profiling of the airway epithelium. Temporal resolution of kinetics of gene-expression alterations upon smoking-cessation might delineate distinct biological processes that are activated during recovery from tobacco smoke exposure. Using whole genome gene-expression profiling of individuals initiating a smoking-cessation attempt, we sought to characterize the kinetics of gene-expression alterations in response to short-term smoking-cessation in the nasal epithelium. RNA was extracted from the nasal epithelial of active smokers at baseline and at 4, 8, 16, and 24-weeks after smoking-cessation and put onto Gene ST arrays. Gene-expression levels of 119 genes were associated with smoking-cessation (FDR < 0.05, FC ≥1.7) with a majority of the changes occurring by 8-weeks and a subset changing by 4-weeks. Genes down-regulated by 4- and 8-weeks post-smoking-cessation were involved in xenobiotic metabolism and anti-apoptotic functions respectively. These genes were enriched among genes previously found to be induced in smokers and following short-term in vitro exposure of airway epithelial cells to cigarette smoke (FDR < 0.05). Our findings suggest that the nasal epithelium can serve as a minimally-invasive tool to measure the reversible impact of smoking and broadly, may serve to assess the physiological impact of changes in smoking behavior.

Asthma-COPD overlap. Clinical relevance of genomic signatures of type 2 inflammation in chronic obstructive pulmonary disease.

RATIONALE: Chronic obstructive pulmonary disease (COPD) is a heterogeneous disease and likely includes a subgroup that is biologically comparable to asthma. Studying asthma-associated gene expression changes in COPD could add insight into COPD pathogenesis and reveal biomarkers that predict a favorable response to corticosteroids. OBJECTIVES: To determine whether asthma-associated gene signatures are increased in COPD and associated with asthma-related features. METHODS: We compared disease-associated airway epithelial gene expression alterations in an asthma cohort (n = 105) and two COPD cohorts (n = 237, 171). The T helper type 2 (Th2) signature (T2S) score, a gene expression metric induced in Th2-high asthma, was evaluated in these COPD cohorts. The T2S score was correlated with asthma-related features and response to corticosteroids in COPD in a randomized, placebo-controlled trial, the Groningen and Leiden Universities study of Corticosteroids in Obstructive Lung Disease (GLUCOLD; n = 89). MEASUREMENTS AND MAIN RESULTS: The 200 genes most differentially expressed in asthma versus healthy control subjects were enriched among genes associated with more severe airflow obstruction in these COPD cohorts (P < 0.001), suggesting significant gene expression overlap. A higher T2S score was associated with decreased lung function (P < 0.001), but not asthma history, in both COPD cohorts. Higher T2S scores correlated with increased airway wall eosinophil counts (P = 0.003), blood eosinophil percentage (P = 0.03), bronchodilator reversibility (P = 0.01), and improvement in hyperinflation after corticosteroid treatment (P = 0.019) in GLUCOLD. CONCLUSIONS: These data identify airway gene expression alterations that can co-occur in asthma and COPD. The association of the T2S score with increased severity and "asthma-like" features (including a favorable corticosteroid response) in COPD suggests that Th2 inflammation is important in a COPD subset that cannot be identified by clinical history of asthma.

Bridging the clinical gaps: genetic, epigenetic and transcriptomic biomarkers for the early detection of lung cancer in the post-National Lung Screening Trial era.

Lung cancer is the leading cause of cancer death worldwide in part due to our inability to identify which smokers are at highest risk and the lack of effective tools to detect the disease at its earliest and potentially curable stage. Recent results from the National Lung Screening Trial have shown that annual screening of high-risk smokers with low-dose helical computed tomography of the chest can reduce lung cancer mortality. However, molecular biomarkers are needed to identify which current and former smokers would benefit most from annual computed tomography scan screening in order to reduce the costs and morbidity associated with this procedure. Additionally, there is an urgent clinical need to develop biomarkers that can distinguish benign from malignant lesions found on computed tomography of the chest given its very high false positive rate. This review highlights recent genetic, transcriptomic and epigenomic biomarkers that are emerging as tools for the early detection of lung cancer both in the diagnostic and screening setting.

A dynamic bronchial airway gene expression signature of chronic obstructive pulmonary disease and lung function impairment.

RATIONALE: Molecular phenotyping of chronic obstructive pulmonary disease (COPD) has been impeded in part by the difficulty in obtaining lung tissue samples from individuals with impaired lung function. OBJECTIVES: We sought to determine whether COPD-associated processes are reflected in gene expression profiles of bronchial airway epithelial cells obtained by bronchoscopy. METHODS: Gene expression profiling of bronchial brushings obtained from 238 current and former smokers with and without COPD was performed using Affymetrix Human Gene 1.0 ST Arrays. MEASUREMENTS AND MAIN RESULTS: We identified 98 genes whose expression levels were associated with COPD status, FEV1% predicted, and FEV1/FVC. In silico analysis identified activating transcription factor 4 (ATF4) as a potential transcriptional regulator of genes with COPD-associated airway expression, and ATF4 overexpression in airway epithelial cells in vitro recapitulates COPD-associated gene expression changes. Genes with COPD-associated expression in the bronchial airway epithelium had similarly altered expression profiles in prior studies performed on small-airway epithelium and lung parenchyma, suggesting that transcriptomic alterations in the bronchial airway epithelium reflect molecular events found at more distal sites of disease activity. Many of the airway COPD-associated gene expression changes revert toward baseline after therapy with the inhaled corticosteroid fluticasone in independent cohorts. CONCLUSIONS: Our findings demonstrate a molecular field of injury throughout the bronchial airway of active and former smokers with COPD that may be driven in part by ATF4 and is modifiable with therapy. Bronchial airway epithelium may ultimately serve as a relatively accessible tissue in which to measure biomarkers of disease activity for guiding clinical management of COPD.

Modeling the functional consequences of single residue replacements in bacteriophage f1 gene V protein.

A computational mutagenesis methodology utilizing a four-body, knowledge-based, statistical contact potential is applied toward globally quantifying relative environmental perturbations (residual scores) in bacteriophage f1 gene V protein (GVP) due to single amino acid substitutions. We show that residual scores correlate well with experimentally measured relative changes in protein function upon mutation. Residual scores also distinguish between GVP amino acid positions grouped according to protein structural or functional roles or based on similarities in physicochemical characteristics. For each mutant, the in silico mutagenesis additionally yields local measures of environmental change (EC scores) occurring at every residue position (residual profile) relative to the native protein. Implementation of the random forest (RF) algorithm, utilizing experimental GVP mutants whose feature vector components include EC scores at the mutated position and at six structurally nearest neighbors, correctly classifies mutants based on function with up to 77% cross-validation accuracy while achieving 0.82 area under the receiver operating characteristic curve. A control experiment highlights the effectiveness of mutant feature vector signals, and a variety of learning curves are generated to analyze the impact of GVP mutant data set size on performance measures. An optimally trained RF model is subsequently used for inferring function for all the remaining unexplored GVP mutants.