Atypical activation of signaling downstream of inactivated Bcr-Abl mediates chemoresistance in chronic myeloid leukemia.

Chronic myeloid leukemia (CML) epitomises successful targeted therapy, where inhibition of tyrosine kinase activity of oncoprotein Bcr-Abl1 by imatinib, induces remission in 86% patients in initial chronic phase (CP). However, in acute phase of blast crisis, 80% patients show resistance, 40% among them despite inhibition of Bcr-Abl1 activity. This implies activation of either Bcr-Abl1- independent signalling pathways or restoration of signalling downstream of inactive Bcr-Abl1. In the present study, mass spectrometry and subsequent in silico pathway analysis of differentiators in resistant CML-CP cells identified key differentiators, 14-3-3ε and p38 MAPK, which belong to Bcr-Abl1 pathway. Their levels and activity respectively, indicated active Bcr-Abl1 pathway in CML-BC resistant cells, though Bcr-Abl1 is inhibited by imatinib. Further, contribution of these components to resistance was demonstrated by inhibition of Bcr-Abl1 down-stream signalling by knocking-out of 14-3-3ε and inhibition of p38 MAPK activity. The observations merit clinical validation to explore their translational potential.

Clinical biomarker discovery by SWATH-MS based label-free quantitative proteomics: impact of criteria for identification of differentiators and data normalization method.

BACKGROUND: SWATH-MS has emerged as the strategy of choice for biomarker discovery due to the proteome coverage achieved in acquisition and provision to re-interrogate the data. However, in quantitative analysis using SWATH, each sample from the comparison group is run individually in mass spectrometer and the resulting inter-run variation may influence relative quantification and identification of biomarkers. Normalization of data to diminish this variation thereby becomes an essential step in SWATH data processing. In most reported studies, data normalization methods used are those provided in instrument-based data analysis software or those used for microarray data. This study, for the first time provides an experimental evidence for selection of normalization method optimal for biomarker identification. METHODS: The efficiency of 12 normalization methods to normalize SWATH-MS data was evaluated based on statistical criteria in 'Normalyzer'-a tool which provides comparative evaluation of normalization by different methods. Further, the suitability of normalized data for biomarker discovery was assessed by evaluating the clustering efficiency of differentiators, identified from the normalized data based on p-value, fold change and both, by hierarchical clustering in Genesis software v.1.8.1. RESULTS: Conventional statistical criteria identified VSN-G as the optimal method for normalization of SWATH data. However, differentiators identified from VSN-G normalized data failed to segregate test and control groups. We thus assessed data normalized by eleven other methods for their ability to yield differentiators which segregate the study groups. Datasets in our study demonstrated that differentiators identified based on p-value from data normalized with Loess-R stratified the study groups optimally. CONCLUSION: This is the first report of experimentally tested strategy for SWATH-MS data processing with an emphasis on identification of clinically relevant biomarkers. Normalization of SWATH-MS data by Loess-R method and identification of differentiators based on p-value were found to be optimal for biomarker discovery in this study. The study also demonstrates the need to base the choice of normalization method on the application of the data.