Proteomic identification of carboplatin resistance in ovarian cancer cells

Wrzeszczynski, K. O., Fan, G. F., Fu, C. X., Pappin, D. J., Lucito, R., Tonks, N. K. (April 2013) Proteomic identification of carboplatin resistance in ovarian cancer cells. Cancer Research, 73 (8 Supp). p. 2502. ISSN 0008-5472

DOI: 10.1158/1538-7445.am2013-2502


The analysis of quantitative protein expression is necessary for the determination of actual biochemical protein concentrations that can lead to a more accurate description of cellular function. A knowledgebase of cancer function based on cellular protein levels can more directly implicate potential gene candidates for the detection and treatment of disease. Therefore, we analyzed iTRAQ (isobaric tag for relative and absolute quantification) data from 11 serous ovarian cancer cell lines and 2 normal ovarian surface epithelial cell lines. We profiled cellular protein abundance in over 3000 proteins with 749 proteins common to all 13 cell lines. Of the 749, 52 proteins exhibited elevated expression and 46 proteins had diminished expression in the cancerous cells when compared to the normal cell lines. We identified the 300 most variable iTRAQ expressed proteins based on median absolute deviation within 8 carboplatin sensitive and resistant cell lines. Using iTRAQ protein expression profiles from these 300 we successfully segregated the cell lines based on carboplatin sensitivity and resistance measurements. We further examined correlations among iTRAQ protein expression, individual gene RNA expression and DNA methylation data. There was no substantial correlation between protein abundance and RNA expression or epigenetic data. Furthermore RNA expression and DNA methylation data alone did not show any significant discrimination for chemosensitivity or resistance. We therefore present the potential of proteomics based discovery for carboplatin response in ovarian cancer and also identify putative gene candidates that may be functionally responsible for chemotherapy response which otherwise would not have been predicted using other singular whole genome data sources.

Item Type: Paper
Additional Information: Meeting Abstract
Subjects: diseases & disorders > cancer
bioinformatics > genomics and proteomics
diseases & disorders > cancer > drugs and therapies > chemoresistance
diseases & disorders > cancer > drugs and therapies > chemotherapy
Publication Type > Meeting Abstract
diseases & disorders > cancer > cancer types > ovarian cancer
CSHL Authors:
Communities: CSHL labs > Pappin lab
CSHL labs > Tonks lab
Depositing User: Matt Covey
Date: April 2013
Date Deposited: 11 Apr 2014 16:47
Last Modified: 21 Feb 2018 19:25

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