Biomarkers for prediction of sensitivity to EGFR inhibitors in non-small cell lung cancer
| Autor: | Fred R. Hirsch, Samir E. Witta |
|---|---|
| Rok vydání: | 2005 |
| Předmět: |
Oncology
Cancer Research medicine.medical_specialty Lung Neoplasms medicine.medical_treatment Antineoplastic Agents Gene mutation Sensitivity and Specificity Stable Disease Gefitinib Predictive Value of Tests Carcinoma Non-Small-Cell Lung Internal medicine Biomarkers Tumor medicine Humans Epidermal growth factor receptor Lung cancer Protein Kinase Inhibitors EGFR inhibitors Chemotherapy Predictive marker Epidermal Growth Factor biology business.industry medicine.disease ErbB Receptors Drug Resistance Neoplasm biology.protein business medicine.drug |
| Zdroj: | Current Opinion in Oncology. 17:118-122 |
| ISSN: | 1040-8746 |
| DOI: | 10.1097/01.cco.0000155059.39733.9d |
| Popis: | PURPOSE OF REVIEW Epidermal growth factor receptor (EGFR) Inhibitors have shown promising results in patients with advanced non-small cell lung cancers (NSCLC) who previously have failed on chemotherapy. Objective response is achieved in 10 to 28% of the patients, and about 30% will achieve stable disease. A major problem is how to select the patients, who will benefit from treatment, and who will not. RECENT FINDINGS The predictive role of EGFR protein expression assessed by IHC is still debated. Specific EGFR gene mutations have been identified associated with response to gefitinib (Iressa(R)), but seem not to be associated with stable disease. No studies have yet demonstrated any association between EGFR gene mutations and survival. In this review we describe other marker studies, which are associated with sensitivity to EGFR inhibitors. Increased EGFR gene copy number based on FISH analysis is demonstrated to be a good predictive marker for response, stable disease, time to progression, and survival. SUMMARY EGFR/FISH seems today to be the best predictive marker for clinical benefit from EGFR inhibitors in NSCLC. Prospective large scale clinical studies must identify the most optimal paradigm for selection of patients. |
| Databáze: | OpenAIRE |
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