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.
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.
|Kim, Jiwoong; Kim, Min Soo; Koh, Andrew Y et al. (2016) FMAP: Functional Mapping and Analysis Pipeline for metagenomics and metatranscriptomics studies. BMC Bioinformatics 17:420|
|Liu, Peiying; Dimitrov, Ivan; Andrews, Trevor et al. (2016) Multisite evaluations of a T2 -relaxation-under-spin-tagging (TRUST) MRI technique to measure brain oxygenation. Magn Reson Med 75:680-7|
|Zang, Chongzhi; Wang, Tao; Deng, Ke et al. (2016) High-dimensional genomic data bias correction and data integration using MANCIE. Nat Commun 7:11305|
|Zang, Xiao; Chen, Min; Zhou, Yunyun et al. (2015) Identifying CDKN3 Gene Expression as a Prognostic Biomarker in Lung Adenocarcinoma via Meta-analysis. Cancer Inform 14:183-91|
|Zhou, Mian; Wang, Tao; Fu, Jingjing et al. (2015) Nonoptimal codon usage influences protein structure in intrinsically disordered regions. Mol Microbiol 97:974-87|
|Wang, Tao; Xiao, Guanghua; Chu, Yongjun et al. (2015) Design and bioinformatics analysis of genome-wide CLIP experiments. Nucleic Acids Res 43:5263-74|
|Eduati, Federica; Mangravite, Lara M; Wang, Tao et al. (2015) Prediction of human population responses to toxic compounds by a collaborative competition. Nat Biotechnol 33:933-40|
|Tang, Hao; Sebti, Salwa; Titone, Rossella et al. (2015) Decreased BECN1 mRNA Expression in Human Breast Cancer is Associated with Estrogen Receptor-Negative Subtypes and Poor Prognosis. EBioMedicine 2:255-263|
|Zhong, Rui; Dong, Xiaonan; Levine, Beth et al. (2015) iScreen: Image-Based High-Content RNAi Screening Analysis Tools. J Biomol Screen 20:998-1002|
|Yu, Donghyeon; Son, Won; Lim, Johan et al. (2015) Statistical completion of a partially identified graph with applications for the estimation of gene regulatory networks. Biostatistics 16:670-85|
Showing the most recent 10 out of 18 publications