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Browsing by Author "Radovich, M."
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Item Genetic variant predicts bevacizumab-induced hypertension in ECOG-5103 and ECOG-2100(Nature Publishing Group, 2014-09-09) Schneider, B. P.; Li, L.; Shen, F.; Miller, K. D.; Radovich, M.; O'Neill, A.; Gray, R. J.; Lane, D.; Flockhart, D. A.; Jiang, G.; Wang, Z.; Lai, D.; Koller, D.; Pratt, J. H.; Dang, C. T.; Northfelt, D.; Perez, E. A.; Shenkier, T.; Cobleigh, M.; Smith, M. L.; Railey, E.; Partridge, A.; Gralow, J.; Sparano, J.; Davidson, N. E.; Foroud, T.; Sledge, G. W.; Department of Medicine, IU School of MedicineBackground: Bevacizumab has broad anti-tumour activity, but substantial risk of hypertension. No reliable markers are available for predicting bevacizumab-induced hypertension. Methods: A genome-wide association study (GWAS) was performed in the phase III bevacizumab-based adjuvant breast cancer trial, ECOG-5103, to evaluate for an association between genotypes and hypertension. GWAS was conducted in those who had experienced systolic blood pressure (SBP) >160 mm Hg during therapy using binary analysis and a cumulative dose model for the total exposure of bevacizumab. Common toxicity criteria (CTC) grade 3–5 hypertension was also assessed. Candidate SNP validation was performed in the randomised phase III trial, ECOG-2100. Results: When using the phenotype of SBP>160 mm Hg, the most significant association in SV2C (rs6453204) approached and met genome-wide significance in the binary model (P=6.0 × 10−8Item IODNE: An integrated optimization method for identifying the deregulated subnetwork for precision medicine in cancer(Wiley, 2017-03) Renbarger, J.; Radovich, M.; Vasudevaraja, V.; Kinnebrew, G.H.; Zhang, S.; Cheng, L.; Inavolu Mounika, S.; Department of Biohealth Informatics, School of Informatics and ComputingSubnetwork analysis can explore complex patterns of entire molecular pathways for the purpose of drug target identification. In this article, the gene expression profiles of a cohort of patients with breast cancer are integrated with protein-protein interaction (PPI) networks using, simultaneously, both edge scoring and node scoring. A novel optimization algorithm, integrated optimization method to identify deregulated subnetwork (IODNE), is developed to search for the optimal dysregulated subnetwork of the merged gene and protein network. IODNE is applied to select subnetworks for Luminal-A breast cancer from The Cancer Genome Atlas (TCGA) data. A large fraction of cancer-related genes and the well-known clinical targets, ER1/PR and HER2, are found by IODNE. This validates the utility of IODNE. When applying IODNE to the triple-negative breast cancer (TNBC) subtype data, we identified subnetworks that contain genes such as ERBB2, HRAS, PGR, CAD, POLE, and SLC2A1.