Predictive molecular markers for EGFR-TKI in non-small cell lung cancer patients: New insights and critical aspects

Paola Ulivi, Daniele Calistri, Wainer Zoli, Dino Amadori

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

In recent years, a number of novel agents have been investigated that target specific molecular pathways in non-small cell lung cancer (NSCLC). A great deal of effort has been focused on identifying specific markers that predict treatment response, given that a tailored approach would maximize both the therapeutic index and the cost-effectiveness. The epidermal growth factor receptor (EGFR) pathway has emerged as a key regulator of cancer cell proliferation and invasion, and several specific EGFR inhibitors have been examined. Gefitinib and erlotinib are selective EGFR tyrosine kinase inhibitors (EGFR-TKIs), demonstrating good results in selected cases both in terms of objective response rate and of overall survival. At present, EGFR gene mutations are the best positive predictive factors for TKI therapy, and a number of other potential biomarkers are being investigated as additional positive or negative predictors of response. The correct selection of patients that could benefit from these innovative therapies, based on an accurate molecular characterization, is mandatory to provide the best clinical management. Currently, the main factor limiting the characterization of metastatic NSCLC patients is the small quantity of tumor cells available for molecular analysis. In this paper we provide an overview of the most important molecular predictive markers for EGFR-TKIs therapy in NSCLC patients, and focus attention on biological samples suitable for analysis and alternative sampling approaches such as plasma- or serum-derived DNA.

Original languageEnglish
Pages (from-to)47-54
Number of pages8
JournalJournal of Nucleic Acids Investigation
Volume1
Issue number1
DOIs
Publication statusPublished - 2010

Fingerprint

Epidermal Growth Factor Receptor
Non-Small Cell Lung Carcinoma
Cells
erbB-1 Genes
Investigational Therapies
Therapeutics
Protein-Tyrosine Kinases
Patient Selection
Cost-Benefit Analysis
Cell proliferation
Biomarkers
Neoplasms
Cost effectiveness
Survival Rate
Cell Proliferation
Tumors
Mutation
Genes
DNA
Sampling

Keywords

  • Epidermal growth factor receptor- tyrosine kinase inhibitors
  • Non-small cell lung cancer
  • Plasma/serum
  • Predictive markers

ASJC Scopus subject areas

  • Biochemistry

Cite this

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abstract = "In recent years, a number of novel agents have been investigated that target specific molecular pathways in non-small cell lung cancer (NSCLC). A great deal of effort has been focused on identifying specific markers that predict treatment response, given that a tailored approach would maximize both the therapeutic index and the cost-effectiveness. The epidermal growth factor receptor (EGFR) pathway has emerged as a key regulator of cancer cell proliferation and invasion, and several specific EGFR inhibitors have been examined. Gefitinib and erlotinib are selective EGFR tyrosine kinase inhibitors (EGFR-TKIs), demonstrating good results in selected cases both in terms of objective response rate and of overall survival. At present, EGFR gene mutations are the best positive predictive factors for TKI therapy, and a number of other potential biomarkers are being investigated as additional positive or negative predictors of response. The correct selection of patients that could benefit from these innovative therapies, based on an accurate molecular characterization, is mandatory to provide the best clinical management. Currently, the main factor limiting the characterization of metastatic NSCLC patients is the small quantity of tumor cells available for molecular analysis. In this paper we provide an overview of the most important molecular predictive markers for EGFR-TKIs therapy in NSCLC patients, and focus attention on biological samples suitable for analysis and alternative sampling approaches such as plasma- or serum-derived DNA.",
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AU - Amadori, Dino

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