How do we translate observations obtained in model systems, such as inbred mouse strains and cell lines, to improve patient outcomes? Can we develop predictive models to help tailor treatment strategies to the individual patient? These two questions help frame the research efforts in the Klinke lab. Within this context, we have focused on applied questions related to how tumors use direct and indirect methods to create a favorable environment for tumor growth. To address these challenging questions, we use combine classical engineering tools, such as dimensional analysis and chemical kinetics, with the experimental tools of molecular and cellular biology. In addition, we have created some new tools to minimize the impact of bias on our statements of belief in how we think these systems operate. This synthesis of molecular and cellular biology with computational tools to model and predict system behavior may be classified as systems biology. Particular research projects include:
Cell Heterogeneity and Emergent Trastuzumab Resistance in Breast Cancer
Yogesh Kulkarni, Vivian Suarez
Funding source: PhRMA Foundation, National Cancer Institute
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.
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.