Proteomic signature corresponding to the response to gefitinib (Iressa, ZD1839), an epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor, and mutation in EGFR in lung adenocarcinoma

DOI
  • Okano Tetsuya
    Proteome Bioinformatics Project, National Cancer Center Research Institute Respiratory-organs, Infection, and Neoplasm of Internal Medicine, Nippon Medical School
  • Kondo Tadashi
    Proteome Bioinformatics Project, National Cancer Center Research Institute
  • Fujii Kiyonaga
    Proteome Bioinformatics Project, National Cancer Center Research Institute
  • Takano Toshimi
    National Cancer Center Hospital
  • Ohe Yuichiro
    National Cancer Center Hospital
  • Tsuta Koji
    National Cancer Center Hospital
  • Matsuno Yoshihiro
    National Cancer Center Hospital
  • Gemma Akihiko
    Respiratory-organs, Infection, and Neoplasm of Internal Medicine, Nippon Medical School
  • Nishimura Toshihide
    Clinical Proteome Center, Tokyo Medical University
  • Kato Harbumi
    Clinical Proteome Center, Tokyo Medical University Department of Surgery, Tokyo Medical University
  • Kudoh Shoji
    Respiratory-organs, Infection, and Neoplasm of Internal Medicine, Nippon Medical School
  • Hirohashi Setsuo
    Proteome Bioinformatics Project, National Cancer Center Research Institute

Bibliographic Information

Other Title
  • プロテオーム解析を用いた肺腺がんにおけるgefitinibの奏効性やEGFR遺伝子変異に関わるタンパク質の探求

Description

Gefitinib (Iressa, ZD1839), an inhibitor of the epidermal growth factor receptor (EGFR) tyrosine kinase, has been approved for patients with advanced non-small-cell lung cancer (NSCLC). However, gefitinib benefits only a limited proportion of patients and is associated with the potentially lethal adverse effect of interstitial pneumonia. To develop tumor markers able to predict the response to gefitinib treatment, we used two-dimensional difference gel electrophoresis to conduct a proteomic study on NSCLC patients who relapsed after surgery and received gefitinib monotherapy. Protein expression profiles were created from tumor tissues obtained at the time of surgery and a support vector machine algorithm was used to identify nine proteins by which 31 responders could be distinguished from 16 non-responders. The predictive performance of the nine proteins was validated by an additional six responders and eight non-responders, resulting in positive and negative predictive values of 100% (6/6) and 87.5% (7/8), respectively, for the response to gefitinib. Differential expression of one of the nine proteins, H-FABP, was monitored by an ELISA assay and was consistent with the 2D-DIGE results. We also identified 12 proteins able to distinguish tumors on the basis of EGFR gene mutation status. Study of these proteins will contribute to the development of personalized therapy for patients with NSCLC.

Journal

Keywords

Details 詳細情報について

  • CRID
    1390001205674864768
  • NII Article ID
    130007007612
  • DOI
    10.14905/jscp.2006.0.16.0
  • Text Lang
    ja
  • Data Source
    • JaLC
    • CiNii Articles
  • Abstract License Flag
    Disallowed

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