Epidermal growth factor receptor inhibitors in advanced cutaneous squamous cell carcinoma: A systematic review and meta-analysis.
James P PhamAnthony RodriguesSimone M GoldingerHao-Wen SimJia Jenny LiuPublished in: Experimental dermatology (2023)
Patients with advanced cutaneous squamous cell carcinoma (cSCC) who are not eligible for or who fail to respond to anti-PD1 immunotherapy have few treatment options. Epidermal growth factor receptor (EGFR) inhibitors have been investigated as a therapeutic option for advanced cSCC; however, data are limited to small single-arm trials or retrospective studies. A systematic review and meta-analysis was conducted to PRISMA guidelines (CRD42023394300). Studies reporting on outcomes of EGFR inhibition in advanced cSCC were identified. Objective response rate (ORR), progression-free survival (PFS), overall survival (OS) and adverse event (AE) rate were pooled using a random effects model and the inverse variance method. Twelve studies (six prospective, six retrospective) were identified, representing 324 patients. Pooled ORR was 26% (95% confidence interval [CI] 18-36), median PFS was 4.8 months (95% CI 3.9-6.6) and median OS was 11.7 months (95% CI 9.2-14.1). Any grade AEs occurred in 93% of patients (95% CI 85-97) while grade 3 and higher AEs occurred in 30% (95% CI 14-54). These results were similar between anti-EGFR monoclonal antibodies (MAbs) and tyrosine kinase inhibitors (TKIs). EGFR inhibitors can be considered in patients with advanced cSCC who are contraindicated for or progress on first-line anti-PD1 immunotherapy. Future studies should evaluate their activity and safety following anti-PD1, identify predictive biomarkers for their efficacy and explore combination approaches.
Keyphrases
- epidermal growth factor receptor
- tyrosine kinase
- advanced non small cell lung cancer
- squamous cell carcinoma
- small cell lung cancer
- end stage renal disease
- ejection fraction
- chronic kidney disease
- newly diagnosed
- prognostic factors
- case control
- peritoneal dialysis
- type diabetes
- cross sectional
- machine learning
- systematic review
- insulin resistance
- electronic health record
- metabolic syndrome
- radiation therapy
- artificial intelligence
- adverse drug
- deep learning
- big data