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.

Public Health Relevance

Tumors develop as a result of changes to the tumor cell signaling network;these changes can either drastically change the cell network structure (e.g., a mutation that results in an inactive protein) or be more subtle (e.g., changes in protein expression levels). In this proposal we will examine how these subtle, quantitative changes to cellular networks impact tumor progression in ovarian cancer, which has a survival rate of less than 50%. Using a series of biomimetic environments and systems biology modeling, we will examine how this quantitative variation impacts tumor progression in order to identify new therapeutic strategies that are appropriate for this paradigm.

National Institute of Health (NIH)
National Cancer Institute (NCI)
NIH Director’s New Innovator Awards (DP2)
Project #
Application #
Study Section
Special Emphasis Panel (ZRG1)
Program Officer
Couch, Jennifer A
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
University of Wisconsin Madison
Biomedical Engineering
Biomed Engr/Col Engr/Engr Sta
United States
Zip Code
Fleszar, Andrew J; Walker, Alyssa; Porubsky, Veronica et al. (2018) The Extracellular Matrix of Ovarian Cortical Inclusion Cysts Modulates Invasion of Fallopian Tube Epithelial Cells. APL Bioeng 2:
Carroll, Molly J; Fogg, Kaitlin C; Patel, Harin A et al. (2018) Alternatively-Activated Macrophages Upregulate Mesothelial Expression of P-Selectin to Enhance Adhesion of Ovarian Cancer Cells. Cancer Res 78:3560-3573
Kreeger, Pamela K; Strong, Laura E; Masters, Kristyn S (2018) Engineering Approaches to Study Cellular Decision Making. Annu Rev Biomed Eng 20:49-72
Bourgeois, Danielle L; Kreeger, Pamela K (2017) Partial Least Squares Regression Models for the Analysis of Kinase Signaling. Methods Mol Biol 1636:523-533
Masters, Kristyn S; Kreeger, Pamela K (2017) Ten simple rules for developing a mentor-mentee expectations document. PLoS Comput Biol 13:e1005709
Tian, Dan; Mitchell, Isaiah; Kreeger, Pamela K (2016) Quantitative analysis of insulin-like growth factor 2 receptor and insulin-like growth factor binding proteins to identify control mechanisms for insulin-like growth factor 1 receptor phosphorylation. BMC Syst Biol 10:15
Carroll, Molly J; Kapur, Arvinder; Felder, Mildred et al. (2016) M2 macrophages induce ovarian cancer cell proliferation via a heparin binding epidermal growth factor/matrix metalloproteinase 9 intercellular feedback loop. Oncotarget 7:86608-86620
Bourgeois, Danielle L; Kabarowski, Karl A; Porubsky, Veronica L et al. (2015) High-grade serous ovarian cancer cell lines exhibit heterogeneous responses to growth factor stimulation. Cancer Cell Int 15:112
Tian, Dan; Solodin, Natalia M; Rajbhandari, Prashant et al. (2015) A kinetic model identifies phosphorylated estrogen receptor-? (ER?) as a critical regulator of ER? dynamics in breast cancer. FASEB J 29:2022-31