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The Klinke Lab @ West Virginia University

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Summary of Research Program

Schematic diagram of research program.

An antibody is naturally produced by the body to recognize and bind to a specific molecular pattern. An antibody can also be engineered commercially to attach itself to a specific molecular pattern associated with cancer cells. These commercially produced antibodies are called monoclonal antibodies and comprise one of the largest classes of cancer drugs. Herceptin is a commonly known example. The clinical response to these molecularly targeted drugs are thought to occur through two mechanisms - a direct effect on cancer cells and an indirect effect whereby the drugs label the cancer cells so that immune cells can destroy the cancer cells. The relative importance of these two mechanisms in humans is unknown. While these drugs have a remarkable effect in certain groups of patients, de novo (i.e., a patient should respond but they don't) and acquired (i.e., they initially respond but over a period of time the drug loses efficacy) resistance to these drugs is a persistent problem.

The research projects described below focus on different aspects of this problem, as summarized in the graphic. The award from the National Cancer Institute focuses on understanding how cancer cells get around the therapeutic action of the antibody by re-wiring their internal circuitry (i.e., How do malignant cells interpret these biochemical signals?). This internal circuitry governs how a cell processes information and makes decisions (i.e., whether it proliferates, doesn't do anything, or dies). The CAREER award from the National Science Foundation focuses on understanding how cancer cells escape the action of these drugs by interfering with the immune response (i.e., What biochemical signals do cells use to communicate?). The award from the National Institute of Allergy and Infectious Disease focuses on understanding how immune cells interpret all of these biochemical signals. A common theme in these projects is the combination of targeted experiment and model-based inference. Model-based inference is the process of encoding our prior knowledge of how cells interpret biochemical signals in the form of a mathematical model. The model is then used to test whether our prior knowledge is consistent with the experimental data. Model-based inference is a key tool as biological systems exhibit intrinsic uncertainty - due to either ethical constraints or due to technical limitations of the available experimental techniques. By combining experimental study with model-based inference, we hope to obtain greater fidelity in understanding how these systems work than could be obtained using either technique in isolation.

Cell Heterogeneity and Emergent Trastuzumab Resistance in Breast Cancer

Yogesh Kulkarni, Vivian Suarez
Funding source: PhRMA Foundation, National Cancer Institute

Prof. Klinke and Vivian Suarez, a graduate student in ChE, identify differentially expressed proteins using 2-D gel electrophoresis.

Monoclonal antibodies, such as trastuzumab, are one of the largest categories of new drugs that target specifically molecules that differentiate cancer cells from normal cells. Despite the remarkable clinical efficacy and specificity of these molecularly targeted therapies, acquired and de novo resistance to therapy is an important clinical problem. Understanding emergent resistance to trastuzumab is inhibited by the inability to quantify aberrant cell signaling pathways among heterogeneous populations of breast cancer cells. Thus there is urgent need for multidisciplinary approaches to assess and interpret the clinical importance of cellular heterogeneity within breast cancer tumors. Our long-term goal is to improve the clinical management of cancer by establishing the scientific foundation for a prognostic technology that will identify individuals who will develop resistance to molecularly targeted therapies. The overall objective of this project is to identify unique patterns of signaling proteins associated with drug sensitivity and apply computational tools from chemical kinetics and Bayesian statistics to interpret the significance of these patterns of protein expression. Our central hypothesis is that breast cancer cells that overexpress ErbB2 exhibit heterogeneity in response to trastuzumab. Furthermore, this heterogeneity is due to variations in expression of proteins that influence the ErbB2 signaling pathway. Prior studies identify such proteins that individually correlate with trastuzumab resistance. The challenge is inferring how these proteins act in concert to influence trastuzumab resistance. The rationale that underlies the proposed research is that identifying patterns of signaling proteins that are correlated with sensitivity to trastuzumab will enable measuring these protein patterns at the single-cell level in tumor biopsy samples. The proposed research is innovative as it provides a novel approach that combines cutting-edge techniques in computational systems biology and proteomics to address the pressing issue of emergent resistance to trastuzumab in breast cancer patients.

CAREER: Interrogating Antagonistic Mechanisms of Signaling Cross-talk in Natural Killer Cells

Yueting Wu, Kisheon Alexander
Collaborators: Jonathan Bramson, McMaster University, Hamilton, ON
Funding source: National Science Foundation

This NSF award by the Biotechnology, Biochemical and Biomass Engineering program supports work to improve our fundamental understanding of how cancer cells escape the cytotoxic action of monoclonal antibodies. Monoclonal antibodies comprise one of the largest classes of cancer drugs that target molecules unique to cancer cells. However, the emergence of resistance to molecular targeted therapies is an increasing, and poorly understood, problem. Without improved understanding of how cancer cells resist the action of molecular targeted therapies, designing effective treatments will remain limited. To improve ultimately the effectiveness of mAbs as cancer drugs, we propose a conceptually novel approach that combines aspects of cellular engineering, immunology, cancer biology, and computationally intensive model-based inference. The research objectives are integrated with educational objectives that aim to promote cross-disciplinary communication among experts and to improve the ability of scientists and engineers to communicate scientific concepts, like how theory and computation are used in scientific practice, effectively with the lay public. It is expected that these aims will have an impact that ranges from local to international. At the local level, the proposed research will provide interdisciplinary training opportunities for graduate and undergraduate students at the interface between multiple disciplines, including biochemical engineering, cancer biology, molecular biology, immunology, and pharmacology. The proposed education aims will also focus outward to create scientists and engineers that can collaborate more effectively across disciplines and, more importantly, that can convey what they do and it's importance to the lay public. Finally, the fundamental fruits of this research may be applied to improve therapies for cancer, a disease that, in developed countries, kills one in three.

Dendritic Cell Heterogeneity in Toll-like receptor 4 Signaling

Priyanka Dixit, Huanling Liu, Ning Cheng
Collaborators: Chris Cuff, WVU School of Medicine
Funding source: National Institute of Allergy and Infectious Disease

Understanding the basis for individual sensitivity to triggers of innate immunity is inhibited by the inability to interpret multivariate changes in quantitative signaling parameters that are related to Toll-like receptor signaling. Thus there is urgent need for multidisciplinary approaches to assess and interpret variability in Toll-like receptor signaling in terms of its impact on susceptibility to infectious agents. The overall objective of this project is to identify unique patterns of signaling proteins associated with sensitivity to infectious agents and to apply computational tools from chemical kinetics and Bayesian statistics to interpret the significant of these patterns of protein expression.