It has been shown that a proteomic algorithm based on 8 MALDI TOF MS signals obtained from plasma of NSCLC patients treated with EGFR TKIs, is able to predict patients' clinical outcome. In the current study, we identified the proteins originating 4 out of 8 mass signals in the classification algorithm. Plasma samples collected before the beginning of gefitinib therapy were analyzed by MALDI TOF MS and classified according to the proteomic algorithm in good and poor profiles. Two pools of good and poor classified samples were prepared using MARS and ProteoMiner Protein Enrichment kit before 2DE analysis. Proteins differentially expressed between good and poor 2DE samples were excised from gels and analyzed with MALDI TOF MS and LC MS/MS. The identified proteins were validated by Immunodepletion and Western blot analyses. serum amyloid A protein 1 (SAA1), together with its two truncated forms, was over-expressed in plasma of poor classified patients, and was identified as the protein that generates 4 out of the 8 mass signals composing the proteomic algorithm VeriStrat. SAA levels measured by ELISA in 97 NSCLC patients treated with gefitinib correlated with the clinical outcome of the patients. This article is part of a Special Issue entitled: Integrated omics.
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