EGFR cut-off point for prognostic impact in laryngeal squamous cell carcinoma

Abstract

Background: Various molecular biomarkers, including epidermal growth factor receptor (EGFR), have gained importance as predictors of head and neck cancer disease progression.

Aims: This study aimed to investigate the ability of EGFR expression as a prognostic marker for laryngeal squamous cell carcinoma (LSCC).

Materials and methods: A total of 31 patients with LSCC with immunohistochemistry (IHC) staining for EGFR were examined. Digital image processing was applied to analyze EGFR staining intensity and percent distribution, which were calculated as the H-score. We used a receiver operating characteristic (ROC) curve to identify the best cut-off point of EGFR expression, with H-score separated into high- and low-grade for cancer recurrence prediction.

Results: The cut-off point of EGFR expression for high- and low-grades was an H-score of 170 with a sensitivity of 75% and a specificity of 66.7%. Using this cut-off, 14 (45.16%) and 17 (54.84%) patients were categorized as having high- and low-grades EGFR, respectively. The analysis showed a significant reverse correlation between the EGFR grade and LSCC recurrence (RR, 0.4; 95% CI, 0.17–0.98; p = .02).

Conclusions: Our study demonstrated that EGFR grading using H-score with the generated cut-off point by the ROC curve might be further applied as a potential marker for LSCC prognostic prediction.

Chinese abstract

背景：各种分子生物标志物, 包括表皮生长因子受体（EGFR）, 已成为预测头颈癌疾病发展的重要指标。

目的：本研究旨在研究EGFR表达作为喉鳞状细胞癌（LSCC）预后标志物的能力。

材料和方法：共检查31例LSCC患者, 他们接受了免疫组织化学（IHC）染色以检测EGFR。应用数字图像处理来分析EGFR染色强度和百分比分布, 并将其计算为H值。我们使用接收者操作特征（ROC）曲线来确定EGFR表达的最佳临界点, 将H值分为高等级和低等级以进行癌症复发预测。

结果：高、低等级EGFR表达的临界点是H值为170, 敏感性为75％, 特异性为66.7％。使用该临界值, 分别将14位（45.16％）和17位（54.84％）患者归为高、低等级EGFR。分析显示, EGFR等级与LSCC复发之间存在显著的反向相关性（RR, 0.4; 95％CI, 0.17-0.98; p  = .02）。

结论：我们的研究表明, 使用H值和ROC曲线产生的临界点产生的EGFR分级可进一步用作LSCC预后预测的潜在标志。

Keywords:

• EGFR
• laryngeal squamous cell carcinoma
• immunohistochemistry
• H-score
• receiver operating characteristic curve
• prognostic factor

Acknowledgements

The authors would like to thank Assoc. Prof. Nakarin Kitkumthorn DDS., PhD for his assistance in collecting data and literature review.

Disclosure statement

The authors declare no conflict of interest, financial or otherwise in this work.

Additional information

Funding

The research was funded by Chulalongkorn University; Government Budget GB B_62_006_30_03, Chulalongkorn University GB-CU-61-24-30-14, and Ratchadapiseksompotch Fund, Faculty of Medicine, Chulalongkorn University, grant number RA59/039.