Integration of imaging and systems biology to study treatment of medulloblastoma

作者: Sean S. Wang , Olga C. Rodriguez , Ye Tian , Shaozhen Ye , Emanual Petricoin

DOI: 10.1109/GENSIPS.2012.6507717

关键词: BioinformaticsMedulloblastomaIn vivoArsenic trioxideTumor growthBiological networkChildhood brain tumorGeneSystems biologyCancer researchBiology

摘要: Medulloblastoma is a highly malignant childhood brain tumor and often characterized by alterations in cell cycle regulatory pathways genes. Using FDA-approved arsenic trioxide (ATO) treated ND2-SmoAl mouse model, we present an integrated imaging systems biology approach to assess responses ATO uncover the complexity of therapeutic molecular biology. Kaplan-Meier survival MRI growth analyses established effectiveness treatment. Differential analysis protein data identified biologically plausible gene markers. dependence network further revealed novel rewiring “hubs” biological networks triggered at level. Functional on statistically significant networked markers confirmed ATO's role as effective anti-proliferative pro-apoptotic drug, vivo.

参考文章(14)
P. Tseng, Convergence of a Block Coordinate Descent Method for Nondifferentiable Minimization Journal of Optimization Theory and Applications. ,vol. 109, pp. 475- 494 ,(2001) , 10.1023/A:1017501703105
Elspeth M. Beauchamp, Lymor Ringer, Gülay Bulut, Kamal P. Sajwan, Michael D. Hall, Yi-Chien Lee, Daniel Peaceman, Metin Özdemirli, Olga Rodriguez, Tobey J. Macdonald, Chris Albanese, Jeffrey A. Toretsky, Aykut Üren, Arsenic trioxide inhibits human cancer cell growth and tumor development in mice by blocking Hedgehog/GLI pathway Journal of Clinical Investigation. ,vol. 121, pp. 148- 160 ,(2011) , 10.1172/JCI42874
Huai Li, Jianhua Xuan, Yue Wang, Ming Zhan, Inferring regulatory networks Frontiers in Bioscience. ,vol. 13, pp. 263- 275 ,(2008) , 10.2741/2677
Albert-László Barabási, Natali Gulbahce, Joseph Loscalzo, Network medicine: a network-based approach to human disease Nature Reviews Genetics. ,vol. 12, pp. 56- 68 ,(2011) , 10.1038/NRG2918
Bai Zhang, Huai Li, Rebecca B. Riggins, Ming Zhan, Jianhua Xuan, Zhen Zhang, Eric P. Hoffman, Robert Clarke, Yue Wang, Differential dependency network analysis to identify condition-specific topological changes in biological networks Bioinformatics. ,vol. 25, pp. 526- 532 ,(2009) , 10.1093/BIOINFORMATICS/BTN660
Yue Wang, Tülay Adali, Sun-Yuan Kung, Zsolt Szabo, Quantification and segmentation of brain tissues from MR images: a probabilistic neural network approach IEEE Transactions on Image Processing. ,vol. 7, pp. 1165- 1181 ,(1998) , 10.1109/83.704309
T. Ideker, O. Ozier, B. Schwikowski, A. F. Siegel, Discovering regulatory and signalling circuits in molecular interaction networks. intelligent systems in molecular biology. ,vol. 18, pp. 233- 240 ,(2002) , 10.1093/BIOINFORMATICS/18.SUPPL_1.S233
William Pao, Pingzhen Guo, Valerie M. Rusch, Marc Ladanyi, Naiyer A. Rizvi, Lawrence H. Schwartz, Binsheng Zhao, Geoffrey R. Oxnard, Chaya S. Moskowitz, Mark G. Kris, A Pilot Study of Volume Measurement as a Method of Tumor Response Evaluation to Aid Biomarker Development Clinical Cancer Research. ,vol. 16, pp. 4647- 4653 ,(2010) , 10.1158/1078-0432.CCR-10-0125
J. Friedman, T. Hastie, R. Tibshirani, Sparse inverse covariance estimation with the graphical lasso Biostatistics. ,vol. 9, pp. 432- 441 ,(2008) , 10.1093/BIOSTATISTICS/KXM045
Robert Tibshirani, Regression Shrinkage and Selection Via the Lasso Journal of the Royal Statistical Society: Series B (Methodological). ,vol. 58, pp. 267- 288 ,(1996) , 10.1111/J.2517-6161.1996.TB02080.X