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-driven 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 technology, 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.
|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|
|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|
|Tang, H; Wang, S; Xiao, G et al. (2017) Comprehensive evaluation of published gene expression prognostic signatures for biomarker-based lung cancer clinical studies. Ann Oncol 28:733-740|
|Luo, Xin; Zang, Xiao; Yang, Lin et al. (2017) Comprehensive Computational Pathological Image Analysis Predicts Lung Cancer Prognosis. J Thorac Oncol 12:501-509|
|Piper, Hannah G; Fan, Di; Coughlin, Laura A et al. (2017) Severe Gut Microbiota Dysbiosis Is Associated With Poor Growth in Patients With Short Bowel Syndrome. JPEN J Parenter Enteral Nutr 41:1202-1212|
|Gönen, Mehmet; Weir, Barbara A; Cowley, Glenn S et al. (2017) A Community Challenge for Inferring Genetic Predictors of Gene Essentialities through Analysis of a Functional Screen of Cancer Cell Lines. Cell Syst 5:485-497.e3|
|Li, Lie; Wang, Xinlei; Xiao, Guanghua et al. (2017) Integrative gene set enrichment analysis utilizing isoform-specific expression. Genet Epidemiol 41:498-510|
|Cai, Ling; Li, Qiwei; Du, Yi et al. (2017) Genomic regression analysis of coordinated expression. Nat Commun 8:2187|
Showing the most recent 10 out of 33 publications