Huang Jiahao, Liu Haizhou, Zhao Yang, Luo Tao, Liu Jungang, Liu Junjie, Pan Xiaoyan, Tang Weizhong
Department of Gastrointestinal Surgery, Affiliated Tumor Hospital, Guangxi Medical University, Nanning, China.
Department of Colorectal and Anal Surgery, The First Affiliated Hospital, Guangxi Medical University, Nanning, China.
Front Oncol. 2021 Feb 9;10:550986. doi: 10.3389/fonc.2020.550986. eCollection 2020.
Tumor mutational burden (TMB) could be a measure of response to immune checkpoint inhibitors therapy for patients with colorectal cancer (CRC). MicroRNAs (miRNAs) participate in anticancer immune responses. In the present study, we determined miRNA expression patterns in patients with CRC and built a signature that predicts TMB.
Next generation sequencing (NGS) on formalin-fixed paraffin-embedded samples from CRC patients was performed to measure TMB levels. We used datasets from The Cancer Genome Atlas to compare miRNA expression patterns in samples with high and low TMB from patients with CRC. We created an miRNA-based signature index using the selection operator (LASSO) and least absolute shrinkage method from the training set. We used an independent test set as internal validation. We used real-time polymerase chain reaction (RT-PCR) to validate the miRNA-based signature classifier.
Twenty-seven samples from CRC patients underwent NGS to determine the TMB level. We identified four miRNA candidates in the training set for predicting TMB (N = 311). We used the test set (N = 204) for internal validation. The four-miRNA-based signature classifier was an accurate predictor of TMB, with accuracy 0.963 in the training set. In the test set, it was 0.902; and it was 0.946 in the total set. The classifier was superior to microsatellite instability (MSI) for predicting TMB in TCGA dataset. In the validation cohort, MSI status more positively correlated with TMB levels than did the classifier. Validation from RT-qPCR showed good target discrimination of the classifier for TMB prediction.
To our knowledge, this is the first miRNA-based signature classifier validated using high quality clinical data to accurately predict TMB level in patients with CRC.
肿瘤突变负荷（TMB）可能是衡量结直肠癌（CRC）患者对免疫检查点抑制剂治疗反应的一个指标。微小RNA（miRNA）参与抗癌免疫反应。在本研究中，我们确定了CRC患者的miRNA表达模式，并构建了一个预测TMB的特征模型。
对CRC患者福尔马林固定石蜡包埋样本进行下一代测序（NGS）以测量TMB水平。我们使用来自癌症基因组图谱的数据集比较CRC患者中高TMB和低TMB样本的miRNA表达模式。我们使用选择算子（LASSO）和最小绝对收缩法从训练集中创建了一个基于miRNA的特征指数。我们使用独立测试集进行内部验证。我们使用实时聚合酶链反应（RT-PCR）验证基于miRNA的特征分类器。
对27例CRC患者的样本进行NGS以确定TMB水平。我们在训练集中鉴定出4个预测TMB的miRNA候选物（N = 311）。我们使用测试集（N = 204）进行内部验证。基于4种miRNA的特征分类器是TMB的准确预测指标，在训练集中准确率为0.963。在测试集中，准确率为0.902；在总样本集中为0.946。在TCGA数据集中，该分类器在预测TMB方面优于微卫星不稳定性（MSI）。在验证队列中，MSI状态与TMB水平的正相关性高于分类器。RT-qPCR验证显示该分类器对TMB预测具有良好的靶点区分能力。
据我们所知，这是首个使用高质量临床数据验证的基于miRNA的特征分类器，可准确预测CRC患者的TMB水平。
