Mutation patterns of epidermal growth factor receptor gene in non-small cell lung cancer among Egyptian patients. Issue 1 (31st December 2022)

• Record Type: Journal Article
• Authors: Elmetnawy, Wafaa H.; Qenawi, Mona; Sabet, Salwa; Bassiony, Heba
• Is Part Of: Egyptian journal of basic and applied sciences. Volume 9:Issue 1(2022)
• Page Start: 77
• Page End: 90
• Publication Date: 2022-12-31
• Subjects: Non–small cell lung cancer -- epidermal growth factor receptor -- tyrosine kinase receptors -- mutations -- genes -- tumor; Science -- Periodicals; Technology -- Periodicals; Technology; Science; Science; Electronic journals; Periodicals; Periodical; 505
• DOI: 10.1080/2314808X.2021.2022924
• Languages: English
• ISSNs: 2314-808X

Abstract

ABSTRACT: Epidermal growth factor receptor (EGFR) mutations have been reported to be associated with non–small cell lung cancer (NSCLC) and correlated to the responsiveness of tumors to EGFR tyrosine kinase inhibitors (TKIs). EGFR mutations in NSCLC Egyptian patients was investigated in formalin-fixed paraffin-embedded lung tumor tissues of 120 NSCLC patients and 20 control tissues from patients with benign lung tumor using ViennaLab StripAssay. Patients showed higher rates of females (87/120, 72.5%) (P=0.043) and never-smokers (82/120, 68.3%) (P=0.003), than the control group. EGFR mutations were significantly related to NSCLC adenocarcinoma, where 49 (40.8%) of patients were mutant (P=0.013) compared with the control group who have no mutations. EGFR mutations were most found in exons 1821, whereas the most mutated exons were exon 19 (55.1%) and exon 21 (26.5%). While exon 18 (10.2%) and exon 20 (8.2%) were the less mutated exons. Moreover, the most common mutations were; L858R (Leu858Arg) in exon 21 and L747-P753 in exon 19; representing 22.4% and 18.4% of all mutations; respectively. Our findings imply that, somatic EGFR mutations could be helpful for NSCLC diagnosis and can be used in combination with clinical factors to select the patients in Egypt who will respond more effectively to TKIs.