The development of molecularly targeted drugs, specifically those which modulate the activities of one or several proteins involved in the pathogenesis of a cancer, is the most exciting field for cancer treatment because targeted anticancer drugs have the potential to provide dramatic clinical benefits with little toxicity. In order to develop new molecularly targeted drugs for lung cancer, the leading cause of cancer in the world, we have collected a large amount of data, including genetic/epigenetic (mutations, copy number variation, and methylation), mRNA expression, protein expression and genome-wide RNAi functional screening data on 108 non-small cell lung cancer (NSCLC) cell lines. Integrating these large-scale and complementary datasets from different sources will provide great opportunities to discover new molecular mechanisms of lung cancer.
In Aim 1 of this study, we will develop a powerful computational model to integrate multiple genomic, proteomic and functional datasets to identify new lung cancer driver genes. Only a small subset of tumor driver genes is traditionally """"""""druggable"""""""" targets.
In Aim 2 of this study, we will use a data-drive and unbiased approach to discover and evaluate potential new therapeutic targets in lung cancer. A novel reverse engineering approach will be proposed to construct a lung-cancer-specific gene network.
In Aim 3 of this study, we will develop a publicly available comprehensive lung cancer database with a user-friendly interface and powerful analysis engine. This database will include all genomic, proteomic and functional data together with the de-identified clinical data used in this study. By using the state-of-the-art information technolog, we will integrate these datasets with analytic algorithms and a user-friendly interface in a publicly available database so that researchers worldwide can utilize and test the data and computational tools generated from this study.

Public Health Relevance

Lung cancer is the leading cause of death from cancer for both men and women in the United States with a 5- year survival rate of approximately 15%. The overall goal of this study is to develop novel analytical models and systems biology approaches to identify new potential therapeutic targets of lung cancer.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Project (R01)
Project #
5R01CA172211-02
Application #
8743190
Study Section
Drug Discovery and Molecular Pharmacology Study Section (DMP)
Program Officer
Li, Jerry
Project Start
2013-09-26
Project End
2018-08-31
Budget Start
2014-09-01
Budget End
2015-08-31
Support Year
2
Fiscal Year
2014
Total Cost
Indirect Cost
Name
University of Texas Sw Medical Center Dallas
Department
Other Clinical Sciences
Type
Schools of Medicine
DUNS #
City
Dallas
State
TX
Country
United States
Zip Code
75390
Vega-Rubín-de-Celis, Silvia; Zou, Zhongju; Fernández, Álvaro F et al. (2018) Increased autophagy blocks HER2-mediated breast tumorigenesis. Proc Natl Acad Sci U S A 115:4176-4181
Wang, Shidan; Chen, Alyssa; Yang, Lin et al. (2018) Comprehensive analysis of lung cancer pathology images to discover tumor shape and boundary features that predict survival outcome. Sci Rep 8:10393
Yi, Faliu; Yang, Lin; Wang, Shidan et al. (2018) Microvessel prediction in H&E Stained Pathology Images using fully convolutional neural networks. BMC Bioinformatics 19:64
Cai, Ling; Li, Qiwei; Du, Yi et al. (2017) Genomic regression analysis of coordinated expression. Nat Commun 8:2187
Yang, Lin; Wang, Shidan; Zhou, Yunyun et al. (2017) Evaluation of the 7th and 8th editions of the AJCC/UICC TNM staging systems for lung cancer in a large North American cohort. Oncotarget :
Wang, Tao; Yun, Jonghyun; Xie, Yang et al. (2017) Finding RNA-Protein Interaction Sites Using HMMs. Methods Mol Biol 1552:177-184
Yi, Faliu; Huang, Junzhou; Yang, Lin et al. (2017) Automatic extraction of cell nuclei from H&E-stained histopathological images. J Med Imaging (Bellingham) 4:027502
Lee, Sangin; Liang, Faming; Cai, Ling et al. (2017) Integrative Analysis of Gene Networks and Their Application to Lung Adenocarcinoma Studies. Cancer Inform 16:1176935117690778
Yu, Donghyeon; Lim, Johan; Wang, Xinlei et al. (2017) Enhanced construction of gene regulatory networks using hub gene information. BMC Bioinformatics 18:186
Frankel, Arthur E; Coughlin, Laura A; Kim, Jiwoong et al. (2017) Metagenomic Shotgun Sequencing and Unbiased Metabolomic Profiling Identify Specific Human Gut Microbiota and Metabolites Associated with Immune Checkpoint Therapy Efficacy in Melanoma Patients. Neoplasia 19:848-855

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