C04747: A Novel Statistical Method to Identify Robust Prostate Cancer Marker GenesNovelty:
The disclosed invention is a novel method for discovering robust, accurate, sensitive and specific gene biomarkers by combing microarray gene expression sets collected from different patient populations sharing the same diagnosis across multiple laboratories.
Value Proposition:
Many current biomarker tests are not reliable. The standard PSA test was found to be inadequate via the proposed new method. The new model uses accumulated microarray data and statistical analysis to determine optimal marker genes. By applying this novel classification algorithm to microarray data sets, we have identified reliable prostate cancer marker genes that could be used to develop an accurate, specific and sensitive diagnostic test for prostate cancer. The invention can be used to find robust, accurate, sensitive, and specific gene biomarkers for disease prediction. Advantages include:
• Can use a smaller patient sample set than other methods
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• No need to normalize samples because the method finds the largest change between normal and diseased states
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• Applicable to other types of microarray data for marker gene identification
.
Technical Details:
Johns Hopkins researchers have developed the TSP (Top Scoring Pair) classifier algorithm, which is a robust method of integrating multiple sets of data from different DNA microarray studies. The method is used to find a set of markers with inverted expression levels of RNA that can differentiate cancerous versus normal patient samples. The statistical methodology central to the invention finds a set of two biomarkers that have the biggest difference in expression between the normal and diseased states (i.e. the biomarker with the largest increase and the largest decrease would yield the biggest difference).
Looking for Partners:
To develop and commercialize the technology as a highly accurate, specific and sensitive biomarker test for prostate cancer.
Stage of Development:
Pre-Clinical
Data Availability:
Under CDA/NDA
Publications/Associated Cases:
Robust prostate cancer marker genes emerge from direct
integration of inter-study microarray data. Bioinformatics.
