Li Zi-Zhuo, Liu Peng-Fei, An Ting-Ting, Yang Hai-Chao, Zhang Wei, Wang Jia-Xu
Department of Abdominal Ultrasound, The First Affiliated Hospital of Harbin Medical University China.
Department of Magnetic Resonance, The First Affiliated Hospital of Harbin Medical University China.
Transl Oncol. 2021 Jun;14(6):101065. doi: 10.1016/j.tranon.2021.101065. Epub 2021 Mar 21.
This study aimed to identify a series of prognostically relevant immune features by immunophenoscore. Immune features were explored using MRI radiomics features to prediction the overall survival (OS) of lower-grade glioma (LGG) patients and their response to immune checkpoints.
LGG data were retrieved from TCGA and categorized into training and internal validation datasets. Patients attending the First Affiliated Hospital of Harbin Medical University were included in an external validation cohort. An immunophenoscore-based signature was built to predict malignant potential and response to immune checkpoint inhibitors in LGG patients. In addition, a deep learning neural network prediction model was built for validation of the immunophenoscore-based signature.
Immunophenotype-associated mRNA signatures (IMriskScore) for outcome prediction and ICB therapeutic effects in LGG patients were constructed. Deep learning of neural networks based on radiomics showed that MRI radiomic features determined IMriskScore. Enrichment analysis and ssGSEA correlation analysis were performed. Mutations in CIC significantly improved the prognosis of patients in the high IMriskScore group. Therefore, CIC is a potential therapeutic target for patients in the high IMriskScore group. Moreover, IMriskScore is an independent risk factor that can be used clinically to predict LGG patient outcomes.
The IMriskScore model consisting of a sets of biomarkers, can independently predict the prognosis of LGG patients and provides a basis for the development of personalized immunotherapy strategies. In addition, IMriskScore features were predicted by MRI radiomics using a deep learning approach using neural networks. Therefore, they can be used for the prognosis of LGG patients.
本研究旨在通过免疫表型评分确定一系列与预后相关的免疫特征。利用MRI影像组学特征探索免疫特征，以预测低级别胶质瘤（LGG）患者的总生存期（OS）及其对免疫检查点的反应。
从TCGA检索LGG数据，并将其分类为训练数据集和内部验证数据集。哈尔滨医科大学附属第一医院的患者被纳入外部验证队列。构建基于免疫表型评分的特征，以预测LGG患者的恶性潜能和对免疫检查点抑制剂的反应。此外，构建深度学习神经网络预测模型，以验证基于免疫表型评分的特征。
构建了用于预测LGG患者预后和ICB治疗效果的免疫表型相关mRNA特征（IMriskScore）。基于影像组学的神经网络深度学习表明，MRI影像组学特征决定IMriskScore。进行了富集分析和ssGSEA相关性分析。CIC突变显著改善了高IMriskScore组患者的预后。因此，CIC是高IMriskScore组患者的潜在治疗靶点。此外，IMriskScore是一个独立的危险因素，可在临床上用于预测LGG患者的预后。
由一组生物标志物组成的IMriskScore模型可独立预测LGG患者的预后，并为制定个性化免疫治疗策略提供依据。此外，IMriskScore特征通过使用神经网络的深度学习方法由MRI影像组学预测。因此，它们可用于LGG患者的预后评估。
