Clinical efficacy and identification of factors confer resistance to afatinib (tyrosine kinase inhibitor) in EGFR-overexpressing esophageal squamous cell carcinoma.
Yanni WangChang LiuHuan ChenXi JiaoYujiao WangYanshuo CaoJian LiXiaotian ZhangYu SunNa ZhuoFengxiao DongMengting GaoFengyuan WangLiyuan DongJifang GongTianqi SunWei ZhuHenghui ZhangLin ShenZhihao LuPublished in: Signal transduction and targeted therapy (2024)
Epidermal growth factor receptor (EGFR) is reportedly overexpressed in most esophageal squamous cell carcinoma (ESCC) patients, but anti-EGFR treatments offer limited survival benefits. Our preclinical data showed the promising antitumor activity of afatinib in EGFR-overexpressing ESCC. This proof-of-concept, phase II trial assessed the efficacy and safety of afatinib in pretreated metastatic ESCC patients (n = 41) with EGFR overexpression (NCT03940976). The study met its primary endpoint, with a confirmed objective response rate (ORR) of 39% in 38 efficacy-evaluable patients and a median overall survival of 7.8 months, with a manageable toxicity profile. Transcriptome analysis of pretreatment tumors revealed that neurotrophic receptor tyrosine kinase 2 (NTRK2) was negatively associated with afatinib sensitivity and might serve as a predictive biomarker, irrespective of EGFR expression. Notably, knocking down or inhibiting NTRK2 sensitized ESCC cells to afatinib treatment. Our study provides novel findings on the molecular factors underlying afatinib resistance and indicates that afatinib has the potential to become an important treatment for metastatic ESCC patients.
Keyphrases
- epidermal growth factor receptor
- tyrosine kinase
- small cell lung cancer
- advanced non small cell lung cancer
- end stage renal disease
- newly diagnosed
- chronic kidney disease
- peritoneal dialysis
- squamous cell carcinoma
- gene expression
- stem cells
- poor prognosis
- patient reported outcomes
- artificial intelligence
- clinical trial
- big data
- electronic health record
- study protocol
- long non coding rna
- induced apoptosis
- machine learning
- open label
- pi k akt
- cell death
- patient reported