A Machine Learning Model to Predict the Triple Negative Breast Cancer Immune Subtype.
A Machine Learning Model to Predict the Triple Negative Breast Cancer Immune Subtype.
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DOI：
10.3389/fimmu.2021.749459
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发表时间：
2021
影响因子：
7.3
通讯作者：
Shi W
中科院分区：
文献类型：
--
作者：
Chen Z;Wang M;De Wilde RL;Feng R;Su M;Torres-de la Roche LA;Shi W
免疫检查点阻断（ICB）已被批准用于治疗三阴性乳腺癌（TNBC），因为它显著改善了无进展生存期（PFS）。然而，由于ICB的低应答率和潜在的不良反应，只有约10%的TNBC患者可以达到ICB的完全应答（CR）。下载来自癌症基因组图谱（TCGA）和基因表达综合数据库（GEO）的开放数据集，以进行无监督聚类分析，根据表达谱鉴定免疫亚型。比较免疫亚型之间的预后、富集途径和ICB指标。随后，使用来自乳腺癌国际联盟（METABRIC）数据集的样本来验证免疫亚型与预后的相关性。选择接受ICB的患者的数据来验证免疫亚型与ICB应答的相关性。使用机器学习模型来构建可视化网络服务器，以预测需要ICB的TNBC患者的免疫亚型。共有8个开放数据集，包括931个TNBC样本，用于无监督聚类。在TNBC患者中鉴定出两种新的免疫亚型（称为S1和S2）。与S2相比，S1与更高的免疫评分、更高的免疫细胞水平和更好的免疫治疗预后相关。在验证数据集中，亚型1样本的预后优于亚型2样本，无论是总生存期（OS）（p = 0.00036）还是无复发生存期（RFS）（p = 0.0022）。生物信息学分析确定了11个与免疫亚型相关的枢纽基因（LCK、IL 2RG、CD 3G、STAT 1、CD 247、IL 2RB、CD 3D、IRF 1、OAS 2、IRF 4和IFNG）。基于随机森林算法的鲁棒机器学习模型由11个中枢基因建立，并且其表现相当好，受试者操作特征（AUC）值的曲线下面积= 0.76。开发了一个基于随机森林模型的开放和免费的Web服务器，命名为三阴性乳腺癌免疫亚型（TNBCIS），可从。TNBC开放数据集允许我们根据基因表达谱将样本分层为不同的免疫治疗应答亚组。基于两个新的亚型，ICB的候选人具有更高的响应率和更好的预后，可以通过使用免费的可视化在线Web服务器，我们设计的选择。
Immune checkpoint blockade (ICB) has been approved for the treatment of triple-negative breast cancer (TNBC), since it significantly improved the progression-free survival (PFS). However, only about 10% of TNBC patients could achieve the complete response (CR) to ICB because of the low response rate and potential adverse reactions to ICB. Open datasets from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) were downloaded to perform an unsupervised clustering analysis to identify the immune subtype according to the expression profiles. The prognosis, enriched pathways, and the ICB indicators were compared between immune subtypes. Afterward, samples from the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) dataset were used to validate the correlation of immune subtype with prognosis. Data from patients who received ICB were selected to validate the correlation of the immune subtype with ICB response. Machine learning models were used to build a visual web server to predict the immune subtype of TNBC patients requiring ICB. A total of eight open datasets including 931 TNBC samples were used for the unsupervised clustering. Two novel immune subtypes (referred to as S1 and S2) were identified among TNBC patients. Compared with S2, S1 was associated with higher immune scores, higher levels of immune cells, and a better prognosis for immunotherapy. In the validation dataset, subtype 1 samples had a better prognosis than sub type 2 samples, no matter in overall survival (OS) (p = 0.00036) or relapse-free survival (RFS) (p = 0.0022). Bioinformatics analysis identified 11 hub genes (LCK, IL2RG, CD3G, STAT1, CD247, IL2RB, CD3D, IRF1, OAS2, IRF4, and IFNG) related to the immune subtype. A robust machine learning model based on random forest algorithm was established by 11 hub genes, and it performed reasonably well with area Under the Curve of the receiver operating characteristic (AUC) values = 0.76. An open and free web server based on the random forest model, named as triple-negative breast cancer immune subtype (TNBCIS), was developed and is available from . TNBC open datasets allowed us to stratify samples into distinct immunotherapy response subgroups according to gene expression profiles. Based on two novel subtypes, candidates for ICB with a higher response rate and better prognosis could be selected by using the free visual online web server that we designed.