Multivariate studies of receptor tyrosine kinase function in cancer

Abstract

Receptor tyrosine kinases (RTKs) are critical regulators of cellular homeostasis in multicellular organisms. They influence cell proliferation, migration, differentiation, and transcriptional activation, among other processes, and are therefore also relevant to cancer biology. Upon interaction with cognate ligand, RTKs initiate signaling cascades dependent in part on the phosphorylation of proteins. From a computational perspective, this thesis has studied methods for quantifying relationships between measured signals (using Bayesian network inference, correlation, and mutual information-based methods), and between signals and cellular phenotypes (using linear regression, partial least squares regression, and feature selection methods). From a biological perspective, this thesis has studied signaling between RTKs, signaling and cell migration downstream of RTKs in epithelial versus mesenchymal cell states, and comparative signaling across six RTKs. In the latter case, the results show that the six RTKs cluster into three classes based on their inferred signaling networks. Using publicly available transcriptional and pharmacological profiling data from hundreds of cancer cell lines, it was determined that expression of same-class RTK genes or their cognate ligands can correlate with insensitivity to drugs targeting other RTKs in that class. This suggests that resistance to RTK-targeted therapies in cancer may emerge in part because same-class RTKs can compensate for the reduced signaling of the inhibited receptor. The thesis concludes by quantitatively exploring the features of experimental data that improve model accuracy.