Proteomic characterization of non-small cell lung cancer in a comprehensive translational thoracic oncology database

Mosmi Surati, Matthew Robinson, Suvobroto Nandi, Leonardo Faoro, Carley Demchuk, Cleo E. Rolle, Rajani Kanteti, Benjamin D. Ferguson, Rifat Hasina, Tara C. Gangadhar, April K. Salama, Qudsia Arif, Colin Kirchner, Eneida Mendonca, Nicholas Campbell, Suwicha Limvorasak, Victoria Villaflor, Thomas A. Hensing, Thomas Krausz, Everett E. VokesAliya N. Husain, Mark K. Ferguson, Theodore G. Karrison, Ravi Salgia*

*Corresponding author for this work

Research output: Contribution to journalArticle

10 Scopus citations

Abstract

Background: In recent years, there has been tremendous growth and interest in translational research, particularly in cancer biology. This area of study clearly establishes the connection between laboratory experimentation and practical human application. Though it is common for laboratory and clinical data regarding patient specimens to be maintained separately, the storage of such heterogeneous data in one database offers many benefits as it may facilitate more rapid accession of data and provide researchers access to greater numbers of tissue samples.Description: The Thoracic Oncology Program Database Project was developed to serve as a repository for well-annotated cancer specimen, clinical, genomic, and proteomic data obtained from tumor tissue studies. The TOPDP is not merely a library--it is a dynamic tool that may be used for data mining and exploratory analysis. Using the example of non-small cell lung cancer cases within the database, this study will demonstrate how clinical data may be combined with proteomic analyses of patient tissue samples in determining the functional relevance of protein over and under expression in this disease.Clinical data for 1323 patients with non-small cell lung cancer has been captured to date. Proteomic studies have been performed on tissue samples from 105 of these patients. These tissues have been analyzed for the expression of 33 different protein biomarkers using tissue microarrays. The expression of 15 potential biomarkers was found to be significantly higher in tumor versus matched normal tissue. Proteins belonging to the receptor tyrosine kinase family were particularly likely to be over expressed in tumor tissues. There was no difference in protein expression across various histologies or stages of non-small cell lung cancer. Though not differentially expressed between tumor and non-tumor tissues, the over expression of the glucocorticoid receptor (GR) was associated improved overall survival. However, this finding is preliminary and warrants further investigation.Conclusion: Though the database project is still under development, the application of such a database has the potential to enhance our understanding of cancer biology and will help researchers to identify targets to modify the course of thoracic malignancies.

Original languageEnglish (US)
Article number8
JournalJournal of Clinical Bioinformatics
Volume1
Issue number1
DOIs
StatePublished - Feb 28 2011

ASJC Scopus subject areas

  • Health Informatics

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