Abstract LB-132: Critical prognostic genes for stage I lung cancer are identified from normal growth factor-regulated gene network by overcoming cancer heterogeneity
Authors: Noriko Gotoh, Mai Yamauchi, Rui Yamauchi, Masao Nagasaki, Teppei Shimamura, Seiya Imoto, Ayumu Saito, Kazuko Ueno, Yousuke Hatanaka, Ryo Yoshida, Tomoyuki Higuchi, Masaharu Nomura, Takashi Kohno, Jun Yokota, David G. Beer, Satoru Miyano
Journal: Cancer Research (2010)

Abstract There is a great need for reliable markers that can assess the aggressiveness of lung cancer, particularly of stage I tumors, to select high-risk patients who will benefit from additional treatments, such as adjuvant chemotherapy. Potential biomarkers have been proposed by analyzing gene-expression profiles of surgically-obtained lung cancer tissues using microarray technologies. However, the performance and general applicability of these signatures for stage I tumors is difficult to establish, because a specific signature might work well in one dataset but not well in another. It appears the controversy has arisen because the reported signatures derived from analysis of cancer tissues have not been properly validated. In fact, there have been no established signatures that accurately predict the survival of stage I lung cancer to date. Since it has been difficult to identify common key genes reflecting early stage aggressiveness by analyzing cancer tissues because of tremendous heterogeneity, we focused normal lung epithelial cells. Epidermal growth factor (EGF), a major growth factor for lung epithelial cells, stimulates a variety of cellular responses with regulated expression of many genes that are known to be involved in cancer aggressiveness. Although expression levels of many growth factor-signaling genes are thought to be regulated in a gene network in which each gene affects other genes, there remains no way to clarify the connectivity between each gene to overview the whole network that shows dynamic changes over time. Because of this problem, key genes regulated by EGF receptor tyrosine kinase (RTK) have not been identified yet. Here, we applied a State Space Model (SSM), a novel mathematical method, to clarify the EGF-signaling gene network in normal lung epithelial cells. We accurately simulated the time-dependent EGF-signaling gene network in silico. Moreover, we identified the key genes based on the influence of EGF RTK to the network and demonstrated that they accurately predict the survival of patients with stage I lung cancer. The accuracy was adequately validated by using completely independent expression profiling of lung cancer derived from publically available sources. In addition, most molecules encoded by the key genes are known to play important roles in cancer aggressiveness, providing a theoretical basis for prognosis prediction. We thus solved long-standing problems to identify the best prognostic genes for stage I lung cancer, for clinical use, by sidestepping the overwhelming complexities of alterations in cancer tissues in vivo. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr LB-132.