In the last decade, optimized treatment for non-small cell lung cancer had lead to improved prognosis, but the overall survival is still very short. To further understand the molecular basis of the disease we have to identify biomarkers related to survival. Here we present the development of an online tool suitable for the real-time meta-analysis of published lung cancer microarray datasets to identify biomarkers related to survival. We searched the caBIG, GEO and TCGA repositories to identify samples with published gene expression data and survival information. Univariate and multivariate Cox regression analysis, Kaplan-Meier survival plot with hazard ratio and logrank P value are calculated and plotted in R. The complete analysis tool can be accessed online at: www.kmplot.com/lung. All together 1,715 samples of ten independent datasets were integrated into the system. As a demonstration, we used the tool to validate 21 previously published survival associated biomarkers. Of these, survival was best predicted by CDK1 (p<1E-16), CD24 (p<1E-16) and CADM1 (p = 7E-12) in adenocarcinomas and by CCNE1 (p = 2.3E-09) and VEGF (p = 3.3E-10) in all NSCLC patients. Additional genes significantly correlated to survival include RAD51, CDKN2A, OPN, EZH2, ANXA3, ADAM28 and ERCC1. In summary, we established an integrated database and an online tool capable of uni- and multivariate analysis for in silico validation of new biomarker candidates in non-small cell lung cancer.

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Online Survival Analysis Software to Assess the Prognostic Value of Biomarkers Using Transcriptomic Data in Non-Small-Cell Lung Cancer

A decade ago, the Gene Expression Omnibus (GEO) database was established at the National Center for Biotechnology Information (NCBI). The original objective of GEO was to serve as a public repository for high-throughput gene expression data generated mostly by microarray technology. However, the research community quickly applied microarrays to non-gene-expression studies, including examination of genome copy number variation and genome-wide profiling of DNA-binding proteins. Because the GEO database was designed with a flexible structure, it was possible to quickly adapt the repository to store these data types. More recently, as the microarray community switches to next-generation sequencing technologies, GEO has again adapted to host these data sets. Today, GEO stores over 20 000 microarray- and sequence-based functional genomics studies, and continues to handle the majority of direct high-throughput data submissions from the research community. Multiple mechanisms are provided to help users effectively search, browse, download and visualize the data at the level of individual genes or entire studies. This paper describes recent database enhancements, including new search and data representation tools, as well as a brief review of how the community uses GEO data. GEO is freely accessible at http://www.ncbi.nlm.nih.gov/geo/.

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NCBI GEO: archive for functional genomics data sets—10 years on

HSF1 Drives a Transcriptional Program Distinct from Heat Shock to Support Highly Malignant Human Cancers

Validation of multi-gene biomarkers for clinical outcomes is one of the most important issues for cancer prognosis. An important source of information for virtual validation is the high number of available cancer datasets. Nevertheless, assessing the prognostic performance of a gene expression signature along datasets is a difficult task for Biologists and Physicians and also time-consuming for Statisticians and Bioinformaticians. Therefore, to facilitate performance comparisons and validations of survival biomarkers for cancer outcomes, we developed SurvExpress, a cancer-wide gene expression database with clinical outcomes and a web-based tool that provides survival analysis and risk assessment of cancer datasets. The main input of SurvExpress is only the biomarker gene list. We generated a cancer database collecting more than 20,000 samples and 130 datasets with censored clinical information covering tumors over 20 tissues. We implemented a web interface to perform biomarker validation and comparisons in this database, where a multivariate survival analysis can be accomplished in about one minute. We show the utility and simplicity of SurvExpress in two biomarker applications for breast and lung cancer. Compared to other tools, SurvExpress is the largest, most versatile, and quickest free tool available. SurvExpress web can be accessed in http://bioinformatica.mty.itesm.mx/SurvExpress (a tutorial is included). The website was implemented in JSP, JavaScript, MySQL, and R.

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SurvExpress: An Online Biomarker Validation Tool and Database for Cancer Gene Expression Data Using Survival Analysis

Lung cancer is the leading cause of cancer death worldwide, and adenocarcinoma is its most common histological subtype. Clinical and molecular evidence indicates that lung adenocarcinoma is a heterogeneous disease, which has important implications for treatment. Here we performed genome-scale DNA methylation profiling using the Illumina Infinium HumanMethylation27 platform on 59 matched lung adenocarcinoma/non-tumor lung pairs, with genome-scale verification on an independent set of tissues. We identified 766 genes showing altered DNA methylation between tumors and non-tumor lung. By integrating DNA methylation and mRNA expression data, we identified 164 hypermethylated genes showing concurrent down-regulation, and 57 hypomethylated genes showing increased expression. Integrated pathways analysis indicates that these genes are involved in cell differentiation, epithelial to mesenchymal transition, RAS and WNT signaling pathways, and cell cycle regulation, among others. Comparison of DNA methylation profiles between lung adenocarcinomas of current and never-smokers showed modest differences, identifying only LGALS4 as significantly hypermethylated and down-regulated in smokers. LGALS4, encoding a galactoside-binding protein involved in cell-cell and cell-matrix interactions, was recently shown to be a tumor suppressor in colorectal cancer. Unsupervised analysis of the DNA methylation data identified two tumor subgroups, one of which showed increased DNA methylation and was significantly associated with KRAS mutation and to a lesser extent, with smoking. Our analysis lays the groundwork for further molecular studies of lung adenocarcinoma by identifying novel epigenetically deregulated genes potentially involved in lung adenocarcinoma development/progression, and by describing an epigenetic subgroup of lung adenocarcinoma associated with characteristic molecular alterations.

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Genome-scale analysis of DNA methylation in lung adenocarcinoma and integration with mRNA expression

Background: Current clinical therapy of non-small cell lung cancer depends on histo-pathological classification. This approach poorly predicts clinical outcome for individual patients. Gene expression profiling holds promise to improve clinical stratification, thus paving the way for individualized therapy. Methodology and Principal Findings:A genome-wide gene expression analysis was performed on a cohort of 91 patients. We used 91 tumor- and 65 adjacent normal lung tissue samples. We defined sets of predictor genes (probe sets) with the expression profiles. The power of predictor genes was evaluated using an independent cohort of 96 non-small cell lung cancer- and 6 normal lung samples. We identified a tumor signature of 5 genes that aggregates the 156 tumor and normal samples into the expected groups. We also identified a histology signature of 75 genes, which classifies the samples in the major histological subtypes of non-small cell lung cancer. Correlation analysis identified 17 genes which showed the best association with post-surgery survival time. This signature was used for stratification of all patients in two risk groups. Kaplan-Meier survival curves show that the two groups display a significant difference in post-surgery survival time (p = 5.6E-6). The performance of the signatures was validated using a patient cohort of similar size (Duke University, n = 96). Compared to previously published prognostic signatures for NSCLC, the 17 gene signature performed well on these two cohorts. Conclusions:The gene signatures identified are promising tools for histo-pathological classification of non-small cell lung cancer, and may improve the prediction of clinical outcome.

https://repub.eur.nl/pub/82588/journal.pone.0010312.pdf

Gene expression-based classification of non-small cell lung carcinomas and survival prediction.

PurposeErlotinib depends on stomach pH for its bioavailability. When erlotinib is taken concurrently with a proton pump inhibitor (PPI), stomach pH increases, which results in a clinically relevant decrease of erlotinib bioavailability. We hypothesized that this drug-drug interaction is reversed by taking erlotinib with the acidic beverage cola. The effects of cola on erlotinib bioavailability in patients not treated with a PPI were also studied.Patients and MethodsIn this randomized, cross-over, pharmacokinetic study in patients with non–small-cell lung cancer, we studied intrapatient differences in absorption (area under the plasma concentration time curve [AUC0-12h]) after a 7-day period of concomitant treatment with erlotinib, with or without esomeprazole, with either cola or water. At the 7th and 14th day, patients were hospitalized for 1 day for pharmacokinetic sampling.ResultsTwenty-eight evaluable patients were included in the analysis. In patients treated with erlotinib and esomeprazole with cola...

Influence of the Acidic Beverage Cola on the Absorption of Erlotinib in Patients With Non-Small-Cell Lung Cancer

Purpose: There is a lack of robust and clinically utilizable markers for the diagnosis and prognostication of malignant pleural mesothelioma (MPM). This research was aimed at optimizing and exploring novel approaches to improve the diagnosis and prognostication of MPM in pleural effusions and peripheral blood samples. Methods: CellSearch-based and flow cytometry-based assays using melanoma cell adhesion molecule (MCAM) to identify circulating tumor cells (CTCs) in pleural effusions and peripheral blood samples of MPM patients were optimized, validated, explored clinically and, in case of pleural effusions, compared with cytological analyses. Additionally, tumor-associated circulating endothelial cells (CECs) were measured in peripheral blood samples. The assays were performed on a MPM cohort encompassing patients with histology-confirmed MPM (n=27) and in a control cohort of patients with alternative diagnoses (n=22). Exploratory analyses on the prognostic value of all assays were also performed. Results: The malignancy of MCAM-positive cells in pleural effusions from MPM patients was confirmed. The detection of MPM CTCs in pleural effusions by CellSearch showed a poor specificity. The detection of MPM CTCs in pleural effusions by flow cytometry showed a superior sensitivity (48%) to standard cytological analysis (15%) (p = 0.03). In peripheral blood, CTCs were detected in 26% of the MPN patients, whereas in 42% of the MPM patients tumor-associated CECs were detected above the upper limit of normal (ULN). In exploratory analyses the absence of CTCs in pleural effusions, and tumor-associated CECs in peripheral blood samples above the ULN, appeared to be associated with a worse overall survival. Conclusion: MCAM-based flow cytometric analysis of pleural effusions is more sensitive than routine cytological analysis. Flow cytometric analysis of pleural effusions and tumor-associated CECs in peripheral blood may serve as a promising approach for the prognostication of MPM patients and, therefore, warrants further study.

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Improved diagnosis and prognostication of patients with pleural malignant mesothelioma using biomarkers in pleural effusions and peripheral blood samples – a short report

Cor van der Leest

Papers

Gene expression-based classification of non-small cell lung carcinomas and survival prediction.

Influence of the Acidic Beverage Cola on the Absorption of Erlotinib in Patients With Non-Small-Cell Lung Cancer

Improved diagnosis and prognostication of patients with pleural malignant mesothelioma using biomarkers in pleural effusions and peripheral blood samples – a short report