Precise reconstruction of the TME using bulk RNA-seq and a machine learning algorithm trained on artificial transcriptomes
Precise reconstruction of the TME using bulk RNA-seq and a machine learning algorithm trained on artificial transcriptomes
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DOI：
10.1016/j.ccell.2022.07.006
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发表时间：
2022-08-08
期刊：
影响因子：
50.3
通讯作者：
Bagaev, Alexander
中科院分区：
文献类型：
--
作者：
Zaitsev, Aleksandr;Chelushkin, Maksim;Bagaev, Alexander
细胞去卷积算法通过分析复杂组织的基因表达来虚拟重建组织成分。我们提出了决策树机器学习算法Kassandra，该算法在数百万个人工转录本中整合了广泛的组织和血液分类细胞RNA图谱，以准确重建肿瘤微环境(TME)。对技术和生物变异性的生物信息学校正、异常癌细胞表达的包含性以及转录表达的准确量化和标准化增加了Kassandra的稳定性和健壮性。通过与细胞学、免疫组织化学或单细胞RNA序列测量进行比较，在4,000张H&E切片和1,000个组织上验证了性能。卡桑德拉准确地去卷积了TME元素，显示了这些群体在肿瘤发病机制和其他生物过程中的作用。数字化TME重建显示，PD-1阳性CD8(+)T细胞的存在与免疫治疗反应密切相关，并增加了已建立的生物标志物的预测潜力，表明卡桑德拉有可能在未来的临床应用中使用。
Cellular deconvolution algorithms virtually reconstruct tissue composition by analyzing the gene expression of complex tissues. We present the decision tree machine learning algorithm, Kassandra, trained on a broad collection of >9,400 tissue and blood sorted cell RNA profiles incorporated into millions of artificial transcriptomes to accurately reconstruct the tumor microenvironment (TME). Bioinformatics correction for technical and biological variability, aberrant cancer cell expression inclusion, and accurate quantification and normalization of transcript expression increased Kassandra stability and robustness. Performance was validated on 4,000 H&E slides and 1,000 tissues by comparison with cytometric, immunohistochemical, or single-cell RNA-seq measurements. Kassandra accurately deconvolved TME elements, showing the role of these populations in tumor pathogenesis and other biological processes. Digital TME reconstruction revealed that the presence of PD-1-positive CD8(+) T cells strongly correlated with immunotherapy response and increased the predictive potential of established biomarkers, indicating that Kassandra could potentially be utilized in future clinical applications.