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MO05 - Prognostic and Predictive Biomarkers II (ID 95)
- Event: WCLC 2013
- Type: Mini Oral Abstract Session
- Track: Medical Oncology
- Presentations: 1
MO05.05 - Lung Cancer Explorer (LCE): an open web portal to explore gene expression and clinical associations in lung cancer (ID 2512)
16:15 - 17:45 | Author(s): J.D. Allen
Lung cancer is the leading cause of death from cancer for both men and women in the United States with a 5-year survival rate of approximately 15%. Many gene expression microarray datasets have been collected through different studies, while a single genomics study usually contains no more than 500 microarrays due to the high cost. We collected and manually curated mRNA expression microarrays together with clinical information for 5,218 lung cancer patients from 40 studies. The wealth of these large-scale datasets provides us great opportunities to generate significant scientific findings, while also posing great challenges for data integration.
To facilitate clinicians and researchers to access and use the resource, we developed an open web portal, The Lung Cancer Explorer, to explore gene expression and clinical associations in lung cancer. This database aggregates over 40 public clinically-annotated lung cancer gene expression studies, along with some private data from the University of Texas Southwestern Medical Center, and presents a user-friendly, web-based interface to explore and analyze this data. The database stores various information about patients including demographics, histology, stage classifications, clinical outcomes, and also stores the probe-level genome-wide mRNA expression information, allowing users to perform very rich analysis on the data.
From the user’s perspective, usage is as easy as logging in and clicking a button to perform any of our current analysis functions: · Survival Analysis: Test the association between the gene expression level and patients’ overall survival time in one study. · Meta-Survival Analysis: Summarize the association between the gene expression level and patients’ overall survival time across multiple studies. · Comparative Analysis: Test the association between the gene expression level and patients’ characteristics, such as gender, age, histology types, disease stages, etc. · Tumor vs. Normal: Test whether the gene expression levels different significantly between tumor samples and normal samples. · Co-expression analysis: Calculate the correlations among a list of user-specified genes based on the gene expression levels. The web application is now online and available for usage: http://qbrc.swmed.edu/lce/ . I will talk about the data curation, quality control, database development and the usage of this resource.
The Lung Cancer Explorer is a highly interactive open resource for lung cancer research and it can greatly facilitate the translational lung cancer research.
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