Mai Yamauchi

RACK1 Regulates VEGF/Flt1-mediated Cell Migration via Activation of a PI3K/Akt Pathway

Vascular endothelial growth factor (VEGF) is vital to physiological as well as pathological angiogenesis, and regulates a variety of cellular functions, largely by activating its 2 receptors, fms-like tyrosine kinase (Flt1) and kinase domain receptor (KDR). KDR plays a critical role in the proliferation of endothelial cells by controlling VEGF-induced phospholipase Cγ-protein kinase C (PLCγ-PKC) signaling. The function of Flt1, however, remains to be clarified. Recent evidence has indicated that Flt1 regulates the VEGF-triggered migration of endothelial cells and macrophages. Here, we show that RACK1, a ubiquitously expressed scaffolding protein, functions as an important regulator of this process. We found that RACK1 (receptor for activated protein kinase C 1) binds to Flt1 in vitro. When the endogenous expression of RACK1 was attenuated by RNA interference, the VEGF-driven migration was remarkably suppressed whereas the proliferation was unaffected in a stable Flt1-expressing cell line, AG1-G1-Flt1. Further, we demonstrated that the VEGF/Flt-mediated migration of AG1-G1-Flt1 cells occurred mainly via the activation of the PI3 kinase (PI3K)/Akt and Rac1 pathways, and that RACK1 plays a crucial regulatory role in promoting PI3K/Akt-Rac1 activation.

Epidermal Growth Factor Receptor Tyrosine Kinase Defines Critical Prognostic Genes of Stage I Lung Adenocarcinoma

Purpose To identify stage I lung adenocarcinoma patients with a poor prognosis who will benefit from adjuvant therapy. Patients and Methods Whole gene expression profiles were obtained at 19 time points over a 48-hour time course from human primary lung epithelial cells that were stimulated with epidermal growth factor (EGF) in the presence or absence of a clinically used EGF receptor tyrosine kinase (RTK)-specific inhibitor, gefitinib. The data were subjected to a mathematical simulation using the State Space Model (SSM). “Gefitinib-sensitive” genes, the expressional dynamics of which were altered by addition of gefitinib, were identified. A risk scoring model was constructed to classify high- or low-risk patients based on expression signatures of 139 gefitinib-sensitive genes in lung cancer using a training data set of 253 lung adenocarcinomas of North American cohort. The predictive ability of the risk scoring model was examined in independent cohorts of surgical specimens of lung cancer. Results The risk scoring model enabled the identification of high-risk stage IA and IB cases in another North American cohort for overall survival (OS) with a hazard ratio (HR) of 7.16 (Pu200a=u200a0.029) and 3.26 (Pu200a=u200a0.0072), respectively. It also enabled the identification of high-risk stage I cases without bronchioalveolar carcinoma (BAC) histology in a Japanese cohort for OS and recurrence-free survival (RFS) with HRs of 8.79 (Pu200a=u200a0.001) and 3.72 (Pu200a=u200a0.0049), respectively. Conclusion The set of 139 gefitinib-sensitive genes includes many genes known to be involved in biological aspects of cancer phenotypes, but not known to be involved in EGF signaling. The present result strongly re-emphasizes that EGF signaling status in cancer cells underlies an aggressive phenotype of cancer cells, which is useful for the selection of early-stage lung adenocarcinoma patients with a poor prognosis. Trial Registration The Gene Expression Omnibus (GEO) GSE31210

A STATE SPACE REPRESENTATION OF VAR MODELS WITH SPARSE LEARNING FOR DYNAMIC GENE NETWORKS

We propose a state space representation of vector autoregressive model and its sparse learning based on L1 regularization to achieve efficient estimation of dynamic gene networks based on time course microarray data. The proposed method can overcome drawbacks of the vector autoregressive model and state space model; the assumption of equal time interval and lack of separation ability of observation and systems noises in the former method and the assumption of modularity of network structure in the latter method. However, in a simple implementation the proposed model requires the calculation of large inverse matrices in a large number of times during parameter estimation process based on EM algorithm. This limits the applicability of the proposed method to a relatively small gene set. We thus introduce a new calculation technique for EM algorithm that does not require the calculation of inverse matrices. The proposed method is applied to time course microarray data of lung cells treated by stimulating EGF receptors and dosing an anticancer drug, Gefitinib. By comparing the estimated network with the control network estimated using non-treated lung cells, perturbed genes by the anticancer drug could be found, whose up- and down-stream genes in the estimated networks may be related to side effects of the anticancer drug.

Elevated β-catenin pathway as a novel target for patients with resistance to EGF receptor targeting drugs.

There is a high death rate of lung cancer patients. Epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs) are effective in some lung adenocarcinoma patients with EGFR mutations. However, a significant number of patients show primary and acquire resistance to EGFR-TKIs. Although the Akt kinase is commonly activated due to various resistance mechanisms, the key targets of Akt remain unclear. Here, we show that the Akt-β-catenin pathway may be a common resistance mechanism. We analyzed gene expression profiles of gefitinib-resistant PC9M2 cells that were derived from gefitinib-sensitive lung cancer PC9 cells and do not have known resistance mechanisms including EGFR mutation T790M. We found increased expression of Axin, a β-catenin target gene, increased phosphorylation of Akt and GSK3, accumulation of β-catenin in the cytoplasm/nucleus in PC9M2 cells. Both knockdown of β-catenin and treatment with a β-catenin inhibitor at least partially restored gefitinib sensitivity to PC9M2 cells. Lung adenocarcinoma tissues derived from gefitinib-resistant patients displayed a tendency to accumulate β-catenin in the cytoplasm. We provide a rationale for combination therapy that includes targeting of the Akt-β-catenin pathway to improve the efficacy of EGFR-TKIs.

Identifying regulational alterations in gene regulatory networks by state space representation of vector autoregressive models and variational annealing

BackgroundIn the analysis of effects by cell treatment such as drug dosing, identifying changes on gene network structures between normal and treated cells is a key task. A possible way for identifying the changes is to compare structures of networks estimated from data on normal and treated cells separately. However, this approach usually fails to estimate accurate gene networks due to the limited length of time series data and measurement noise. Thus, approaches that identify changes on regulations by using time series data on both conditions in an efficient manner are demanded.MethodsWe propose a new statistical approach that is based on the state space representation of the vector autoregressive model and estimates gene networks on two different conditions in order to identify changes on regulations between the conditions. In the mathematical model of our approach, hidden binary variables are newly introduced to indicate the presence of regulations on each condition. The use of the hidden binary variables enables an efficient data usage; data on both conditions are used for commonly existing regulations, while for condition specific regulations corresponding data are only applied. Also, the similarity of networks on two conditions is automatically considered from the design of the potential function for the hidden binary variables. For the estimation of the hidden binary variables, we derive a new variational annealing method that searches the configuration of the binary variables maximizing the marginal likelihood.ResultsFor the performance evaluation, we use time series data from two topologically similar synthetic networks, and confirm that our proposed approach estimates commonly existing regulations as well as changes on regulations with higher coverage and precision than other existing approaches in almost all the experimental settings. For a real data application, our proposed approach is applied to time series data from normal Human lung cells and Human lung cells treated by stimulating EGF-receptors and dosing an anticancer drug termed Gefitinib. In the treated lung cells, a cancer cell condition is simulated by the stimulation of EGF-receptors, but the effect would be counteracted due to the selective inhibition of EGF-receptors by Gefitinib. However, gene expression profiles are actually different between the conditions, and the genes related to the identified changes are considered as possible off-targets of Gefitinib.ConclusionsFrom the synthetically generated time series data, our proposed approach can identify changes on regulations more accurately than existing methods. By applying the proposed approach to the time series data on normal and treated Human lung cells, candidates of off-target genes of Gefitinib are found. According to the published clinical information, one of the genes can be related to a factor of interstitial pneumonia, which is known as a side effect of Gefitinib.

Theme: Oncology - Molecular mechanisms determining the efficacy of EGF receptor-specific tyrosine kinase inhibitors help to identify biomarker candidates

Non-small-cell lung cancer is a major subtype of lung cancer, which is the most common and fatal cancer in the world. Gefitinib (Iressa) and later erlotinib (Tarceva), specific tyrosine kinase inhibitors for EGF receptors (EGFRs), have been demonstrated to be effective for some non-small-cell lung cancer patients and used in clinics as pioneers of molecule-based targeted drugs for cancer. There has been an urgent need to develop biomarkers and to select appropriate patients who should benefit from treatment with these drugs because of the high sensitivity of target cancer cells. However, problems of acquired resistance after long-term treatment with these drugs have been recognized. Emerging evidence indicates that the efficacy of these drugs is partly dependent on somatic mutations in the EGFR. In this review, we summarize recent understandings of the molecular mechanisms that determine the efficacy of EGFR-tyrosine kinase inhibitors. Towards the end of this article, we discuss recent ongoing projects validating potential biomarkers and future prospects.

Collocation-based sparse estimation for constructing dynamic gene networks.

One of the open problems in systems biology is to infer dynamic gene networks describing the underlying biological process with mathematical, statistical and computational methods. The first-order difference equation-based models such as dynamic Bayesian networks and vector autoregressive models were used to infer time-lagged relationships between genes from time-series microarray data. However, two primary problems greatly reduce the effectiveness of current approaches. The first problem is the tacit assumption that time lag is stationary. The second is the inseparability between measurement noise and process noise (unmeasured disturbances that pass through time process). To address these problems, we propose a stochastic differential equation model for inferring continuous-time dynamic gene networks under the situation in which both of the process noise and the observation noise exist. We present a collocation-based sparse estimation for simultaneous parameter estimation and model selection in the model. The collocation-based approach requires considerably less computational effort than traditional methods in ordinary stochastic differential equation models. We also incorporate various biological knowledge easily to refine the estimation accuracy with the proposed method. The results using simulated data and real time-series expression data of human primary small airway epithelial cells demonstrate that the proposed approach outperforms competing approaches and can provide significant genes influenced by gefitinib.

Abstract B19: Critical prognostic genes for stage I lung adenocarcinoma are identified from normal growth factor- signaling system by overcoming cancer heterogeneity

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 and identified the key genes based on the influence of EGF RTK to the network. We found that most molecules encoded by the key genes are known to play important roles in cancer aggressiveness, providing a theoretical basis for prognosis prediction. We demonstrated that the EGF-signaling key genes 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. Moreover, we collected a total of 110 stage I lung adenocarcinomas with high-quality gene-expression data and pathological and clinical information of Japanese patients, including relapse-free survival, from National Cancer Center in Japan. We demonstrated the EGF-signaling key genes accurately predict the relapse-free survival of these patients. The accuracy was further validated even we separated the patients into stage IA and IB groups. We thus solved long-standing problems to identify the good prognostic genes for stage I lung cancer, for clinical use, by sidestepping the overwhelming complexities of alterations in cancer tissues in vivo. Citation Information: Clin Cancer Res 2010;16(14 Suppl):B19.

Abstract LB-132: Critical prognostic genes for stage I lung cancer are identified from normal growth factor-regulated gene network by overcoming cancer heterogeneity

Proceedings: AACR 101st Annual Meeting 2010‐‐ Apr 17‐21, 2010; Washington, DCnnThere 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.nnWe 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. nnCitation 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.