Babar Laila, Kosovec Juliann E, Jahangiri Vida, Chowdhury Nobel, Zheng Ping, Omstead Ashten N, Salvitti Madison S, Smith Matthew A, Goel Ajay, Kelly Ronan J, Jobe Blair A, Zaidi Ali H
Esophageal and Lung Institute, Allegheny Health Network, Pittsburgh, PA, USA.
Beckman Research Institute, City of Hope Comprehensive Cancer Center, Monrovia, CA, USA.
Oncotarget. 2019 Jul 16;10(44):4546-4555. doi: 10.18632/oncotarget.27052.
Treatment options and risk stratification for esophageal adenocarcinomas (EAC) currently rely on pathological criteria such as tumor staging. However, with advancement in immune modulated treatments, there is a need for accurate predictive biomarkers that will help identify high-risk patients and provide novel therapeutic targets. Hence, we analyzed as prognostic classifiers a host of histopathological parameters in conjunction with novel immune biomarkers. Specifically, gene expression levels for CXCL9, IDO1, LAG3, and TIM3 were established in treatment naïve samples. Additionally, PD-L1 and CD8 positivity was determined by immunohistochemical staining. Based on our finding, a Cox model consisting of pathological complete response (CR), LAG3, and CXCL9 provided improved predictability for disease-free survival (DFS) compared to CR alone, and it demonstrated statistical significance for predictability of recurrence (p=0.0001). Likewise, for overall survival (OS), a Cox model constituted of TIM3, CR, and IDO1 performed better than CR alone, and it demonstrated statistical significance for predictability of survival (p = 0.0004). TIM3 was identified as the best predictor for OS (HR=4.43, p=0.0023). In conclusion, given the paucity of treatment options for EAC, evaluation of these biomarkers early in the disease course will lead to better risk stratification of patients and much needed alternatives for improved therapy.
目前,食管腺癌(EAC)的治疗方案和风险分层依赖于肿瘤分期等病理标准。然而,随着免疫调节治疗的进展,需要准确的预测生物标志物来帮助识别高危患者并提供新的治疗靶点。因此,我们结合新的免疫生物标志物分析了一系列组织病理学参数作为预后分类指标。具体而言,在未经治疗的样本中确定了CXCL9、IDO1、LAG3和TIM3的基因表达水平。此外,通过免疫组织化学染色确定PD-L1和CD8的阳性情况。基于我们的发现,与单独使用病理完全缓解(CR)相比,由病理完全缓解、LAG3和CXCL9组成的Cox模型对无病生存期(DFS)具有更好的预测性,并且在复发预测方面具有统计学意义(p=0.0001)。同样,对于总生存期(OS),由TIM3、CR和IDO1组成的Cox模型比单独使用CR表现更好,并且在生存预测方面具有统计学意义(p = 0.0004)。TIM3被确定为OS的最佳预测指标(HR=4.43,p=0.0023)。总之,鉴于EAC的治疗选择有限,在疾病进程早期评估这些生物标志物将导致更好的患者风险分层,并为改善治疗提供急需的替代方案。