Wei Miaoyan, Xu Jin, Hua Jie, Meng Qingcai, Liang Chen, Liu Jiang, Zhang Bo, Wang Wei, Yu Xianjun, Shi Si
Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, Shanghai, China.
Pancreatic Cancer Multidisciplinary Center, Fudan University Shanghai Cancer Center, Shanghai, China.
Front Immunol. 2021 Apr 23;12:654660. doi: 10.3389/fimmu.2021.654660. eCollection 2021.
Immune infiltration plays an important role in tumor development and progression and shows promising prognostic value in numerous tumors. In this study, we aimed to identify the role of immune infiltration in pancreatic neuroendocrine tumors (Pan-NETs) and to establish an Immunoscore system to improve the prediction of postsurgical recurrence-free survival.
To derive transcriptional signatures and deconvolute specific immune populations, two GEO datasets containing 158 Pan-NET patients were reanalyzed to summarize the immune infiltration landscape and identify immune-related signatures. Using real-time reverse transcription-polymerase chain reaction, immunofluorescence and immunochemistry methods, candidate signatures were further detected. The least absolute shrinkage and selection operator (LASSO) logistic regression model used statistically significant survival predicators in the training cohort (n=125) to build an Immunoscore system. The prognostic and predictive accuracy was validated in an external independent cohort of 77 patients.
The immune infiltration profile in Pan-NETs showed significant heterogeneity, among which accumulated immune cells, T lymphocytes and macrophages were predominant. Fourteen statistically significant immune-related signatures were further identified in the screening cohort. The Immunoscore system for Pan-NETs (ISpnet) consisting of six immune features (CCL19, IL-16, CD163, IRF4, CD8 and CD8) was constructed to classify patients as high and low risk in the training cohort (cutoff value = 2.14). Low-risk patients demonstrated longer 5-year recurrence-free survival (HR, 0.061; 95% CI, 0.026 to 0.14; p < 0.0001), with fewer recurrences and better prognoses. To predict the individual risk of recurrence, a nomogram incorporating both immune signatures and clinicopathological characteristics was developed.
Our model, ISpnet, captures immune feature-associated prognostic indicators in Pan-NETs and represents the first immune feature-based score for the postsurgical prognostic prediction. The nomogram based on the ISpnet and independent clinical risk factors might facilitate decision-making regarding early recurrence risk monitoring, identify high-risk patients in need of adjuvant therapy, and provide auxiliary guidance for patients with Pan-NETs that may benefit from immunotherapy in clinical trials.
免疫浸润在肿瘤的发生发展中起重要作用，并且在众多肿瘤中显示出有前景的预后价值。在本研究中，我们旨在确定免疫浸润在胰腺神经内分泌肿瘤（Pan-NETs）中的作用，并建立一个免疫评分系统以改善对术后无复发生存的预测。
为了推导转录特征并解卷积特定免疫群体，重新分析了两个包含158例Pan-NET患者的GEO数据集，以总结免疫浸润情况并识别免疫相关特征。使用实时逆转录-聚合酶链反应、免疫荧光和免疫化学方法，进一步检测候选特征。最小绝对收缩和选择算子（LASSO）逻辑回归模型在训练队列（n = 125）中使用具有统计学意义的生存预测因子来构建免疫评分系统。在一个由77例患者组成的外部独立队列中验证了其预后和预测准确性。
Pan-NETs中的免疫浸润谱显示出显著的异质性，其中累积的免疫细胞、T淋巴细胞和巨噬细胞占主导。在筛选队列中进一步鉴定出14个具有统计学意义的免疫相关特征。构建了由六个免疫特征（CCL19、IL-16、CD163、IRF4、CD8和CD8）组成的Pan-NETs免疫评分系统（ISpnet），以在训练队列中将患者分为高风险和低风险（临界值 = 2.14）。低风险患者表现出更长的5年无复发生存期（HR，0.061；95% CI，0.026至0.14；p < 0.0001），复发较少且预后较好。为了预测个体复发风险，开发了一个结合免疫特征和临床病理特征的列线图。
我们的模型ISpnet捕捉了Pan-NETs中与免疫特征相关的预后指标，并且代表了首个基于免疫特征的术后预后预测评分。基于ISpnet和独立临床风险因素的列线图可能有助于关于早期复发风险监测的决策制定，识别需要辅助治疗的高风险患者，并为在临床试验中可能从免疫治疗中获益的Pan-NETs患者提供辅助指导。
