Abstract
BackgroundIn 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.DescriptionThe 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.ConclusionThough 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.
Highlights
In recent years, there has been tremendous growth and interest in translational research, in cancer biology
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
It is essential to explore the functional relevance of these alterations as they relate to tumorigenesis in order to progress from an interesting observation to a beneficial therapeutic strategy
Summary
There is considerable interest in understanding the pathophysiology contributing to cancer. Statistics We have used the database to correlate proteomic information with clinical parameters for patients with nonsmall cell lung cancer Within this database, a unique patient often had several TMA punches captured within the TMA table for a particular protein, reflecting the multiple types of tissue obtained for each patient. An averaged protein expression score was calculated for all available normal and tumor samples for each patient (i.e., replicates of the same type of tissue for a given patient were averaged) for each protein studied in the TMA database. These patients were a subset of the 105 patients included in the TMA analysis and were selected because they had protein expression data within each of the protein families These patients are comparable to the TMA analysis group in terms of gender, racial, histologic, and stage characterization, vital status, mean age at diagnosis, and median survival (Table 3). These results should be viewed as hypothesis-generating only, in need of further confirmation in an independent dataset
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