Abstract
We aimed to identify candidate proteins for tumor markers to predict the response to gefitinib treatment. We did two-dimensional difference gel electrophoresis to create the protein expression profile of lung adenocarcinoma tissues from patients who showed a different response to gefitinib treatment. We used a support vector machine algorithm to select the proteins that best distinguished 31 responders from 16 nonresponders. The prediction performance of the selected spots was validated by an external sample set, including six responders and eight nonresponders. The results were validated using specific antibodies. We selected nine proteins that distinguish responders from nonresponders. The predictive performance of the nine proteins was validated examining an additional six responders and eight nonresponders, resulting in positive and negative predictive values of 100% (six of six) and 87.5% (seven of eight), respectively. The differential expression of one of the nine proteins, heart-type fatty acid-binding protein, was successfully validated by ELISA. We also identified 12 proteins as a signature to distinguish tumors based on their epidermal growth factor receptor gene mutation status. Study of these proteins may contribute to the development of personalized therapy for lung cancer patients.
Highlights
We aimed to identify candidate proteins for tumor markers to predict the response to gefitinib treatment
Preclinical studies involving mRNA profiling of Non – small cell lung carcinoma (NSCLC) xenografts resulted in the identification of a set of genes that were differentially expressed between tumors that were sensitive and insensitive to gefitinib treatment [22, 23]
To identify the proteomic signature for sensitivity to gefitinib and to use that signature as a tumor marker to predict the response to gefitinib, we analyzed global protein expression levels in lung adenocarcinoma tissues for whom we have detailed information on epidermal growth factor receptor (EGFR) gene status
Summary
We aimed to identify candidate proteins for tumor markers to predict the response to gefitinib treatment. MRNA expression does not necessarily correlate with protein level, and posttranslational modifications, such as phosphorylation, cannot be predicted from the amount of RNA or from the DNA sequence [24] With this background, comprehensive expression studies at the protein level, an approach called proteomics, have been conducted in patients with lung cancer to develop biomarkers that predict clinical outcomes [25]. To identify the proteomic signature for sensitivity to gefitinib and to use that signature as a tumor marker to predict the response to gefitinib, we analyzed global protein expression levels in lung adenocarcinoma tissues for whom we have detailed information on EGFR gene status. The predictive performance of the protein set was validated with an independent data set and compared with that of EGFR mutation
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