High Throughput Analysis of Signaling Pathways in Lung Cancer and Premalignancy

University Of Colorado Denver, Aurora CO

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Abstract

Lung cancers are characterized by changes in signaling pathways that promote tumor growth and survival. Signaling pathways altered in lung cancer not only regulate the properties of the tumor itself but result in cytokine expression that induce a paracrine response of surrounding stroma. A major signaling axis for lung cancer is driven by the EGF receptor (EGFR). The EGFR is now a primary target for therapeutic intervention in the treatment of lung cancer. The hypothesis of this project is that signaling pathways controlled by the EGFR and additional cytokines produced by lung tumors contribute to the lung cancer phenotype. Defining the "nodes" that control the signaling pathways altered in the tumor and surrounding stroma relative to normal lung, will define new targets in addition to the EGFR for therapeutic intervention and treatment of lung cancer. To define the "signaling state" of lung tumors and adjacent stroma tissue arrays from patient biopsies will be used. Phospho-specific antibodies that recognize the activated state of the EGFR, Her2/neu, FAK, Akt, ERK1/2, p38, c-Jun, Smads, IKK, Stat 3 and 6, CREB and Elk-1 will measure the "activation sate" of signal transduction pathways. RNA from tumor biopsies will be used for gene expression profiling using the 39,000 gene Affymetrix gene chips. Computational methods will be used to analyze the relationships between signaling pathway activity and gene expression profile in tumors with patient response to therapy and clinical outcome. The goal is to use the combination of tissue array analysis of signaling pathways and gene profiling to provide statistical predictors of patient response to therapy. Xenograft models in mice will be used to validate signaling "nodes" that control tumor growth. Molecularly-targeted therapeutics will be used in the xenograft model to monitor their action in integrated regulation of signaling, gene expression and tumor growth. RNAi (siRNA) will be used to "knockdown" proteins defined as nodes in the above described experiments using lung cancer cell lines to define their requirement for proliferation versus apoptosis. Cumulatively, the studies will define the dynamic interaction between tumor and adjacent tissue and how these interactions can be targeted for the development of new therapeutic strategies to treat lung cancer.