Analysis of how quantitative cellular network variation impacts tumor progression

University Of Wisconsin-Madison, Madison WI

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Abstract

DESCRIPTION (provided by applicant): In many tumors, mutations in genes impact protein functions (i.e., loss or gain of function); when these mutations impact proteins in cellular pathways that control key processes such as proliferation and invasion, they provide potential drug targets to slow tumor progression. In contrast, other tumors such as high-grade serous ovarian cancer (HGSOC) do not have a significant number of such mutations, and instead have subtle changes in the relative levels of multiple proteins distributed across the cellular network, setting up cellular networks that are qualitatively the same (i.e., nearly all components are present and have normal functionality) but quantitatively very different. The outlined research program will seek to analyze this new paradigm in order to address the hypothesis that at each stage of progression in HGSOC (i.e., fallopian tube ovary, ovary peritoneum, and platinum-sensitive platinum-resistant), specific quantitative changes in the cell network influence the likelihood of progression. To address this hypothesis, we propose to analyze cells in in vitro culture systems that mimic the in vivo environment of HGSOC. Primary fallopian tube epithelial cells and immortalized HGSOC cell lines derived from patients with metastatic disease, mimicking early and late stages of HGSOC, will be genetically manipulated to develop panels of cells with quantitative variation in proteins across the cell network. With these cell models, we will determine how different network combinations influence likelihood of successful progression in 1) a novel biomimetic surrogate for the ovarian surface epithelium that mimics the initial breach from the fallopian tube, 2) a model to mimic tumor cell dissemination to the peritoneum, and 3) a chemotherapy regimen that induces platinum resistance. Using multivariate analysis techniques, we will identify patterns in the quantitative protein data that together predict or classify cell behavior. These models will be experimentally validated and analyzed to determine combinations of proteins that influence tumor progression, providing potential co-treatment strategies. In addition to significantly improving our understanding of HGSOC progression and identifying potential therapeutic strategies, this innovative approach can be broadly applied to understand progression in other tumor types that have quantitative as well as qualitative variation in cell networks. Our lab is uniquely suited to carry out this innovative research program as we have extensive expertise in biomimetic culture development, analysis of quantitative data by systems biology models, and ovarian cancer biology.