title: A systems biology case study of ovarian cancer drug resistance.
abstract: In ovarian cancer treatment, the chemotherapy drug cisplatin often induce drug resistance after prolonged use, causing cancer relapse and the eventual deaths of patients. Cisplatin-induced drug resistance is known to involve a complex set of cellular changes but its molecular mechanism(s) remain unclear. In this study, we designed a systems biology approach to examine global protein level and network level changes by comparing Proteomics profiles between cisplatin-resistant cell lines and cisplatin-sensitive cell lines. First, we used an experimental proteomics method based on a Label-free Liquid Chromatography / Mass Spectrometry (LC/MS) platform to obtain a list of 119 proteins that are differentially expressed in the samples. Second, we expanded these proteins into a cisplatin-resistant activated sub-network, which consists of 1230 proteins in 1111 protein interactions. An examination of network topology features reveals the activated responses in the network are closely coupled. Third, we examined sub-network proteins using Gene Ontology categories. We found significant enrichment of proton-transporting ATPase and ATP synthase complexes in addition to protein binding proteins. Fourth, we examined sub-network protein interaction function categories using 2-dimensional visualization matrixes. We found that significant cellular physiological responses arise from endogenous, abiotic, and stress-related signals, which correlates well with known facts that internalized cisplatin cause DNA damage and induce cell stress. Fifth and finally, we developed a new visual representation structure for display of activated sub-networks using functional categories as network nodes and their crosstalk as network edges. This type of sub-network further shows that while cell communication and cell growth are generally important to tumor mechanisms, molecular regulation of cell differentiation and development caused by responses to genomic-wide stress seem to be more relevant to the acquisition of drug resistance.
authors: Indiana University School of Informatics, Indiana University/Purdue University Indianapolis, Indianapolis, IN 46202, USA. jakechen@iupui.edu
authors: Jake Y
authors: JY
authors: Chen
authors: Changyu
authors: C
authors: Shen
authors: Zhong
authors: Z
authors: Yan
authors: Dawn P G
authors: DP
authors: Brown
authors: Mu
authors: M
authors: Wang
mesh_terms: D000970:Antineoplastic Agents / Q000494:pharmacology; D002853:Chromatography, Liquid / Q000379:methods; D002945:Cisplatin / Q000494:pharmacology; D019295:Computational Biology / Q000379:methods*; D019008:Drug Resistance, Neoplasm; D005260:Female; D015972:Gene Expression Regulation, Neoplastic; D016678:Genome; D006801:Humans; D013058:Mass Spectrometry / Q000379:methods; D015233:Models, Statistical; D010051:Ovarian Neoplasms / Q000188:drug therapy* / Q000473:pathology*; D040901:Proteomics / Q000379:methods; D012984:Software; D049490:Systems Biology / Q000379:methods*
chemical_list: D000970:Antineoplastic Agents; D002945:Cisplatin