A Pan-Cancer Approach to Predict Responsiveness to Immune Checkpoint Inhibitors by Machine Learning
A Pan-Cancer Approach to Predict Responsiveness to Immune Checkpoint Inhibitors by Machine Learning
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DOI：
10.3390/cancers11101562
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发表时间：
2019-10-01
期刊：
影响因子：
5.2
通讯作者：
Toffoli, Giuseppe
中科院分区：
文献类型：
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作者：
Polano, Maurizio;Chierici, Marco;Toffoli, Giuseppe
使用免疫检查点抑制剂（ICI）的免疫疗法极大地改善了各种癌症的治疗选择，提高了治疗患者的生存率。然而，在不同的癌症类型中，甚至在受特定癌症影响的患者中，对ICI的反应率也存在差异。因此，确定预测免疫治疗方法反应的因素变得至关重要。对肿瘤的突变和免疫学方面进行全面的研究有助于获得可靠的预测。通过对来自癌症基因组图谱（TCGA， 8055例和29种癌症类型）的基因表达数据进行泛癌症分析，我们建立并验证了一种机器学习方法来预测对ICI的积极反应的可能性。支持向量机（SVM）和极端梯度增强（XGboost）模型对80%的TCGA病例进行了10x5倍交叉验证，以预测由肿瘤突变负担和tgf - β信号相结合的评分定义的ICI反应性。在剩余的20%验证子集上，我们的SVM模型的准确率为0.88，马修斯相关系数为0.27。提出的机器学习方法可用于通过原发肿瘤的表达数据预测对ICI治疗的推定反应。
Immunotherapy by using immune checkpoint inhibitors (ICI) has dramatically improved the treatment options in various cancers, increasing survival rates for treated patients. Nevertheless, there are heterogeneous response rates to ICI among different cancer types, and even in the context of patients affected by a specific cancer. Thus, it becomes crucial to identify factors that predict the response to immunotherapeutic approaches. A comprehensive investigation of the mutational and immunological aspects of the tumor can be useful to obtain a robust prediction. By performing a pan-cancer analysis on gene expression data from the Cancer Genome Atlas (TCGA, 8055 cases and 29 cancer types), we set up and validated a machine learning approach to predict the potential for positive response to ICI. Support vector machines (SVM) and extreme gradient boosting (XGboost) models were developed with a 10x5-fold cross-validation schema on 80% of TCGA cases to predict ICI responsiveness defined by a score combining tumor mutational burden and TGF-beta signaling. On the remaining 20% validation subset, our SVM model scored 0.88 accuracy and 0.27 Matthews Correlation Coefficient. The proposed machine learning approach could be useful to predict the putative response to ICI treatment by expression data of primary tumors.