Bates Amber M, Lanzel Emily A, Qian Fang, Abbasi Taher, Vali Shireen, Brogden Kim A
Iowa Institute for Oral Health Research, College of Dentistry, University of Iowa, Iowa City, IA, USA.
Department of Oral Pathology, Radiology and Medicine, College of Dentistry, University of Iowa, Iowa City, IA, USA.
Oral Surg Oral Med Oral Pathol Oral Radiol. 2017 Aug;124(2):157-164. doi: 10.1016/j.oooo.2017.05.474. Epub 2017 May 25.
Programmed death-ligand 1 (PD-L1) expression is correlated with objective response rates to PD-1 and PD-L1 immunotherapies. However, both immunotherapies have only demonstrated 12%-24.8% objective response rates in patients with head and neck squamous cell carcinoma (HNSCC), demonstrating a need for a more accurate method to identify those who will respond before their therapy. Immunohistochemistry to detect PD-L1 reactivity in tumors can be challenging, and additional methods are needed to predict and confirm PD-L1 expression. Here, we hypothesized that HNSCC tumor cell genomics influences cell signaling and downstream effects on immunosuppressive biomarkers and that these profiles can predict patient clinical responses.
We identified deleterious gene mutations in SCC4, SCC15, and SCC25 and created cell line-specific predictive computational simulation models. The expression of 24 immunosuppressive biomarkers were then predicted and used to sort cell lines into those that would respond to PD-L1 immunotherapy and those that would not.
SCC15 and SCC25 were identified as cell lines that would respond to PD-L1 immunotherapy treatment and SCC4 was identified as a cell line that would not likely respond to PD-L1 immunotherapy treatment.
This approach, when applied to HNSCC cells, has the ability to predict PD-L1 expression and predict PD-1- or PD-L1-targeted treatment responses in these patients.
程序性死亡配体1(PD-L1)表达与PD-1和PD-L1免疫疗法的客观缓解率相关。然而,这两种免疫疗法在头颈部鳞状细胞癌(HNSCC)患者中的客观缓解率仅为12% - 24.8%,这表明需要一种更准确的方法来在治疗前识别哪些患者会产生反应。通过免疫组织化学检测肿瘤中PD-L1反应性具有挑战性,因此需要额外的方法来预测和确认PD-L1表达。在此,我们假设HNSCC肿瘤细胞基因组学影响细胞信号传导以及对免疫抑制生物标志物的下游效应,并且这些特征可以预测患者的临床反应。
我们在SCC4、SCC15和SCC25中鉴定出有害基因突变,并创建了细胞系特异性预测计算模拟模型。然后预测24种免疫抑制生物标志物的表达,并用于将细胞系分为对PD-L1免疫疗法有反应和无反应的两类。
SCC15和SCC25被鉴定为对PD-L1免疫疗法有反应的细胞系,而SCC4被鉴定为不太可能对PD-L1免疫疗法有反应的细胞系。
这种方法应用于HNSCC细胞时,有能力预测PD-L1表达,并预测这些患者对PD-1或PD-L1靶向治疗的反应。