Klinke:Research

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Our lab uses computational modeling and wet-lab experimentation to investigate how cells process information and make decisions. Research projects include: <br> <br>
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=== Cell Heterogeneity and Emergent Trastuzumab Resistance in Breast Cancer ===
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'''Yogesh Kulkarni''' <br>
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'''Funding source: National Cancer Institute''' <br>
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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 the computational tools of reaction pathway analysis 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.
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 the computational tools of reaction pathway analysis 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.

Revision as of 22:42, 12 October 2009

The Klinke Lab @ West Virginia University

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Our lab uses computational modeling and wet-lab experimentation to investigate how cells process information and make decisions. Research projects include:

Cell Heterogeneity and Emergent Trastuzumab Resistance in Breast Cancer

Yogesh Kulkarni
Funding source: 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 the computational tools of reaction pathway analysis 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.

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