Applying Meta-Pathway Analyses Across Multiple Phosphoproteomics Data Sets to Identify Common Adaptive Responses to Tyrosine Kinase Inhibitors in Cancer Cells

• Activity Number: 349
• Type: Contributed
• Date/Time: Tuesday, August 5, 2014 : 10:30 AM to 11:15 AM
• Sponsor: Biopharmaceutical Section
• Author(s): Yian Chen*+ and Kate Fisher
• Companies: Moffitt Cancer Center & Research Institute and Moffitt Cancer Center & Research Institute
• Keywords: proteomics ; meta-analysis ; cancer ; resistance ; phosphorylation

Abstract

Although of targeted agents for cancer treatments (e.g., tyrosine kinase inhibitors (TKIs)) were initially promising, patients eventually develop resistance. Cell line experiments are often used to study mechanism of resistance. However, small sample size is often used and therefore under power. The state-of-the-art proteomics technologies, such as LC-MS/MS, are developed to quantify phosphorylation levels. However, the quantification and normalization methods evolve rapidly as datasets are generated. We propose a statistically straightforward meta-pathway analysis to synthesize information across multiple phosphoproteomics datasets to identify the general compensatory response to TKI. This addresses the cross platform issue and increases the strength of evidence. Three datasets with two control samples and two treatement samples were first normalized and analyzed separately using t-tests. We then applied a random-effect meta-analysis to summarize phosphosite-specific differential phosphorylation. Among the 111 phosphosites, 5 were significantly up regulated (q< 0.05). Finally, we identified compensatory pathways using both nonparametric and parametric meta-analyses.