Our goal is to understand the regulation of ErbB signaling in endometrial and breast cancers, diseases where amplification of ErbB genes (EGFR, ErbB2, ErbB3) is associated with poor outcome. The composition of the UNM team is unique, with expertise in signal transduction, high resolution microscopy, mathematical modeling and animal models, as well as leadership roles in GOG clinical trials. This multiscale project begins with nanoscale evaluation of receptor topography and behavior, measured with innovative electron microscopy, live cell imaging and flow cytometry technologies. Innovations include development of monovalent quantum dot probes for single particle tracking of resting and ligand-bound receptors. These measurements provide quantitative information for mathematical analysis using a mixed stochastic/continumum approach that is aimed at evaluating the contributions of membrane spatial organization to ErbB signaling. The stochastic platform simulates diffusion, clustering and internalization of receptors and signaling molecules using Monte Carlo and agent-based methods. By applying state-of-the-art experimental and modeling approaches, we will specifically consider the impact of combinatorial complexity upon signal propagation. We will also address receptor mutations, receptor conformational state, and the use of clinically-relevant inhibitors. For example, the group discovered a new ErbB3 mutation in the previous funding cycle. We intend to screen for this mutation, as well as evaluate ErbB3 expression levels, in over 200 human breast and endometrial cancers. Critical tools include cell lines bearing mutant receptors or expressing specific combinations of ErbB receptors as VFP-fusion proteins. By combining our unique and comprehensive data sets with stochastic modeling, we have already challenged current estimations of homo- and hetero-dimerization between Erb family members. We will continue this exciting work to fully explore the spatial and temporal aspects of ErbB signaling at the cellular level. By year 3 of the project, the experimentalists will complement their studies at the cell and molecular scales with xenograft models in mice. This work will permit evaluation of oncogenic signaling in vivo and move the project from nanometer and micron length scales to millimeter and centimeter length scales. Time scales also move from millisecond and minutes up to hours or days. Data from the mouse model will provide critical parameters for a new a cell-based solid tumor model. The tumor model will then be used to predict rates of tumor growth, angiogenesis and drug responsiveness in vivo. Cell behavior in the tumor model will be governed by outcomes from the stochastic model, bridging the two mathematical modeling platforms that consider markedly different length and time scales. Public Health Relevance. This project targets two women's cancer, breast and endometrium, and the ErbB family of receptors. ErbB signals are powerful inducers of gene transcription involved in tumor growth and survival, providing a rationale for use of drugs that inhibits ErbB pathways in the treatment of cancer. The mixed response of individual patients indicates better understanding is needed to predict initial patient response and the likely characteristics of recurrent tumors. Hormone-responsive cancers, such breast, ovarian and prostate, develop in a multistep process that starts from a local benign hyperplasia and ends with an invasive tumor able to metastasize to other organs. Tumors are genetically heterogenous by the time their presence is discovered, a property that translates to complexity in selecting the proper therapeutics for specific patients. Both our biology and modeling are ultimately aimed at understanding the heterogeneity of ErbB expression in individual tumors and applying that knowledge for personalized, predictive tumor therapy.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Research Project (R01)
Project #
Application #
Study Section
Special Emphasis Panel (ZRG1-BCMB-B (90))
Program Officer
Couch, Jennifer A
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
University of New Mexico Health Sciences Center
Schools of Medicine
United States
Zip Code
Winner, Kimberly Kanigel; Steinkamp, Mara P; Lee, Rebecca J et al. (2016) Spatial Modeling of Drug Delivery Routes for Treatment of Disseminated Ovarian Cancer. Cancer Res 76:1320-1334
McCabe Pryor, Meghan; Steinkamp, Mara P; Halasz, Adam M et al. (2015) Orchestration of ErbB3 signaling through heterointeractions and homointeractions. Mol Biol Cell 26:4109-23
Steinkamp, Mara P; Low-Nam, Shalini T; Yang, Shujie et al. (2014) erbB3 is an active tyrosine kinase capable of homo- and heterointeractions. Mol Cell Biol 34:965-77
Davies, Suzy; Holmes, Anna; Lomo, Lesley et al. (2014) High incidence of ErbB3, ErbB4, and MET expression in ovarian cancer. Int J Gynecol Pathol 33:402-10
Steinkamp, Mara P; Winner, Kimberly Kanigel; Davies, Suzy et al. (2013) Ovarian tumor attachment, invasion, and vascularization reflect unique microenvironments in the peritoneum: insights from xenograft and mathematical models. Front Oncol 3:97
Pryor, Meghan McCabe; Low-Nam, Shalini T; Halász, Adám M et al. (2013) Dynamic transition states of ErbB1 phosphorylation predicted by spatial stochastic modeling. Biophys J 105:1533-43
Radhakrishnan, Krishnan; Halász, Ádám; McCabe, Meghan M et al. (2012) Mathematical simulation of membrane protein clustering for efficient signal transduction. Ann Biomed Eng 40:2307-18
Low-Nam, Shalini T; Lidke, Keith A; Cutler, Patrick J et al. (2011) ErbB1 dimerization is promoted by domain co-confinement and stabilized by ligand binding. Nat Struct Mol Biol 18:1244-9
Hsieh, Ming-yu; Yang, Shujie; Raymond-Stinz, Mary Ann et al. (2010) Spatio-temporal modeling of signaling protein recruitment to EGFR. BMC Syst Biol 4:57
Lidke, Diane S; Wilson, Bridget S (2009) Caught in the act: quantifying protein behaviour in living cells. Trends Cell Biol 19:566-74

Showing the most recent 10 out of 16 publications