UC San Diego

In the past two decades, there has been an exponential growth in methods for biomarker discovery for diseases. Despite all the new technology and data available, there is a disconnect between the biomarker discovery phase and application in a clinical setting. Most methods have not provided actionable biomarkers that allow us to design tests for early diagnosis, pass new therapeutics or prognosticate the risk of disease progression. Our lab leverages the abundance of publicly available data and uses the power of Boolean implication analysis to identify and validate biomarkers in multiple independent datasets. In my dissertation, I aim to showcase that using the Boolean implication approach in the context of diseases can provide more information about disease progression compared to traditional methods. In Chapter 1, I use Boolean implication network analysis to identify a gene signature for gastric cancer. I extensively validate this signature in multiple independent datasets and show that this signature can prognosticate the risk of progression to gastric cancer. In Chapter 2, I explore how a Boolean implication analysis identified gene – CDX2 – can be used as a biomarker for epithelial damage in inflammatory bowel disease. I perform a newly developed protocol for immunohistochemistry staining that shows a loss of CDX2 in certain regions of moderate to severe IBD samples. I also demonstrate how CDX2 expression can predict response to various therapeutics. Finally in Chapter 3, I use Boolean implication network-derived macrophage and fibroblast polarization signatures to identify changes in rheumatoid arthritis (RA). I derive RA-specific macrophage and fibroblast signatures that can accurately predict RA samples. Through this dissertation, I demonstrate that the Boolean approach provides a more comprehensive framework to identify more reliable targets for disease diagnosis, therapeutics and prognostication.