» Articles » PMID: 20028749

Somatic EGFR Mutation and Gene Copy Gain As Predictive Biomarkers for Response to Tyrosine Kinase Inhibitors in Non-small Cell Lung Cancer

Overview
Journal Clin Cancer Res
Specialty Oncology
Date 2009 Dec 24
PMID 20028749
Citations 65
Authors
Affiliations
Soon will be listed here.
Abstract

Purpose: The aim of this systematic review and meta-analysis was to characterize common EGFR molecular aberrations as potential predictive biomarkers for response to monotherapy with tyrosine kinase inhibitors (TKI) in non-small cell lung cancer (NSCLC).

Experimental Design: We systematically identified articles investigating EGFR status [somatic mutational and gene copy aberrations (copy number)] in patients with NSCLC treated with TKIs. Eligible studies had to report complete and partial response rates stratified by EGFR status. We used random effects models for bivariable meta-analysis of sensitivity and specificity; positive and negative likelihood ratios (+LR and -LR, respectively) were also calculated and were considered as secondary end points.

Results: Among 222 retrieved articles, 59 were considered eligible for the somatic EGFR mutation meta-analysis (1,020 mutations among 3,101 patients) and 21 were considered eligible for the EGFR gene copy number meta-analysis (542 gene gain among 1,539 patients). EGFR mutations were predictive of response to single-agent TKIs [sensitivity, 0.78; 95% confidence interval (95% CI), 0.74-0.82; specificity, 0.86; 95% CI, 0.82-0.89; +LR, 5.6; -LR, 0.25]. EGFR gene gain was also associated with response to TKIs, albeit with lower sensitivity and specificity. In subgroup analysis, the only recognized trend was for a higher predictive value in Whites compared with East Asians for both mutation and gene copy number.

Conclusion: This analysis provides empirical evidence that EGFR mutations are sensitive and specific predictors of response to single-agent epidermal growth factor receptor TKIs in advanced NSCLC. The diagnostic performance of mutations seems better than that of EGFR gene gain.

Citing Articles

D3EGFR: a webserver for deep learning-guided drug sensitivity prediction and drug response information retrieval for EGFR mutation-driven lung cancer.

Shi Y, Li C, Zhang X, Peng C, Sun P, Zhang Q Brief Bioinform. 2024; 25(3).

PMID: 38555474 PMC: 10981678. DOI: 10.1093/bib/bbae121.


NSCLC: from tumorigenesis, immune checkpoint misuse to current and future targeted therapy.

Raskova Kafkova L, Mierzwicka J, Chakraborty P, Jakubec P, Fischer O, Skarda J Front Immunol. 2024; 15:1342086.

PMID: 38384472 PMC: 10879685. DOI: 10.3389/fimmu.2024.1342086.


Mutational Damages in Malignant Lung Tumors.

Yermekova S, Orazgaliyeva M, Goncharova T, Rakhimbekova F, Dushimova Z, Vasilieva T Asian Pac J Cancer Prev. 2023; 24(2):709-716.

PMID: 36853323 PMC: 10162606. DOI: 10.31557/APJCP.2023.24.2.709.


Receptor Tyrosine Kinases as Candidate Prognostic Biomarkers and Therapeutic Targets in Meningioma.

Roesler R, Souza B, Isolan G Int J Mol Sci. 2021; 22(21).

PMID: 34768783 PMC: 8583503. DOI: 10.3390/ijms222111352.


Comprehensive analysis of NGS and ARMS-PCR for detecting EGFR mutations based on 4467 cases of NSCLC patients.

He C, Wei C, Wen J, Chen S, Chen L, Wu Y J Cancer Res Clin Oncol. 2021; 148(2):321-330.

PMID: 34693477 PMC: 8800890. DOI: 10.1007/s00432-021-03818-w.