Since the mid-1990s, approximately 150,000 Americans have died of lung cancer every year, and the upward trend in total cancer deaths is largely due to the increasing rate of lung cancer mortality. Even if we could prevent cigarette smoking and exposure to other carcinogens today, hundreds of thousands of lung cancer cases would still need to be treated in the next decades. A major current effort in the lung cancer field is to detect and treat lung cancer at earlier stages to improve the survival of patients. In addition, identifying novel drug targets would allow for the development of more efficacious therapeutic strategies against lung tumors. Finally, lung cancer patients are treated following well-established protocols that most often do not take into account the genetic diversity of their tumors, and very little is known about prognostic factors for individual lung cancer patients. A better knowledge of the molecular events in lung cancer development would help to identify diagnostic and prognostic markers in lung cancer patients. Rb, p53 and Kras are among the most frequently mutated genes in human cancer. In particular, combinations of mutations in these three genes are often found in human lung cancer and define important clinical subtypes. Using advanced gene-targeting approaches, we and others have generated genetically engineered mice with mutations in these genes. These mutant mice develop tumors that closely resemble human lung tumors and provide a genetically tractable system to study lung tumorigenesis in vivo. Here, we propose to use comparative gene expression analysis to define genotype-specific oncogenic signatures using these mouse models of lung cancer. Our specific goals are: - To develop gene expression signatures from mouse tumors and compare them to human data to identify new human lung cancer subtypes. The validation of subtype-specific genes from these signatures will be performed using human tissue arrays. - To identify key regulators and "drivers" of these gene expression signatures using conventional bioinformatics approaches as well as a novel "event" centered gene network that we will develop. In particular, we will introduce the notion of ordered, causal events in lung cancer gene networks to identify key nodes in these gene networks. - To functionally analyze potential key regulators of these lung cancer gene expression signatures. To this end, we will first use gene expression-based high throughput screening to identify such regulators and we will then test their functional role in lung cancer development in vivo. The overall goal of our work is to begin to define critical pathways that are required for genotype-specific oncogenesis. Characterization of these pathways may provide a useful approach for identification of new approaches for diagnosis, prognosis, and targeted therapy in lung cancer patients.

Public Health Relevance

We propose to use a novel gene network to identify molecular events downstream of key oncogenic "driver" mutations for lung cancer by comparing gene expression profiles in lung tumors from genetically defined mouse models to gene expression profiles from human lung tumors. We will test the functional role of candidate regulators of lung cancer in mouse models and human tumor cell lines and tissues. Our experiments will lay the foundation needed for the development of novel strategies to detect and treat lung cancer, the number one cancer killer in both men and women in the United States.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Project (R01)
Project #
5R01CA138256-05
Application #
8303011
Study Section
Special Emphasis Panel (ZCA1-GRB-I (O3))
Program Officer
Mechanic, Leah E
Project Start
2008-09-23
Project End
2014-07-31
Budget Start
2012-08-01
Budget End
2014-07-31
Support Year
5
Fiscal Year
2012
Total Cost
$521,052
Indirect Cost
$198,863
Name
Stanford University
Department
Pediatrics
Type
Schools of Medicine
DUNS #
009214214
City
Stanford
State
CA
Country
United States
Zip Code
94305
Chen, Ron; Khatri, Purvesh; Mazur, Pawel K et al. (2014) A meta-analysis of lung cancer gene expression identifies PTK7 as a survival gene in lung adenocarcinoma. Cancer Res 74:2892-902
Khatri, Purvesh; Roedder, Silke; Kimura, Naoyuki et al. (2013) A common rejection module (CRM) for acute rejection across multiple organs identifies novel therapeutics for organ transplantation. J Exp Med 210:2205-21
Jahchan, Nadine S; Dudley, Joel T; Mazur, Pawel K et al. (2013) A drug repositioning approach identifies tricyclic antidepressants as inhibitors of small cell lung cancer and other neuroendocrine tumors. Cancer Discov 3:1364-77
Khatri, Purvesh; Sirota, Marina; Butte, Atul J (2012) Ten years of pathway analysis: current approaches and outstanding challenges. PLoS Comput Biol 8:e1002375
Dudley, Joel T; Sirota, Marina; Shenoy, Mohan et al. (2011) Computational repositioning of the anticonvulsant topiramate for inflammatory bowel disease. Sci Transl Med 3:96ra76
Sirota, Marina; Dudley, Joel T; Kim, Jeewon et al. (2011) Discovery and preclinical validation of drug indications using compendia of public gene expression data. Sci Transl Med 3:96ra77
Chen, Rong; Davydov, Eugene V; Sirota, Marina et al. (2010) Non-synonymous and synonymous coding SNPs show similar likelihood and effect size of human disease association. PLoS One 5:e13574
Schaffer, Bethany E; Park, Kwon-Sik; Yiu, Gloria et al. (2010) Loss of p130 accelerates tumor development in a mouse model for human small-cell lung carcinoma. Cancer Res 70:3877-83
Vicent, Silvestre; Chen, Ron; Sayles, Leanne C et al. (2010) Wilms tumor 1 (WT1) regulates KRAS-driven oncogenesis and senescence in mouse and human models. J Clin Invest 120:3940-52