一种基于转录组学的模型，可预测大剂量他汀类药物治疗后纤维帽厚度的增加：通过连续冠状动脉 OCT 成像进行验证。

A transcriptomic model to predict increase in fibrous cap thickness in response to high-dose statin treatment: Validation by serial intracoronary OCT imaging.

机构信息

Institute for Next Generation Healthcare, Mount Sinai Health System, New York, NY, United States of America; Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, United States of America.

Bakar Computational Health Sciences Institute, The University of California, San Francisco, San Francisco, CA, United States of America.

出版信息

EBioMedicine. 2019 Jun;44:41-49. doi: 10.1016/j.ebiom.2019.05.007. Epub 2019 May 22.

DOI:10.1016/j.ebiom.2019.05.007

PMID:31126891

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6607084/

Abstract

BACKGROUND

Fibrous cap thickness (FCT), best measured by intravascular optical coherence tomography (OCT), is the most important determinant of plaque rupture in the coronary arteries. Statin treatment increases FCT and thus reduces the likelihood of acute coronary events. However, substantial statin-related FCT increase occurs in only a subset of patients. Currently, there are no methods to predict which patients will benefit. We use transcriptomic data from a clinical trial of rosuvastatin to predict if a patient's FCT will increase in response to statin therapy.

METHODS

FCT was measured using OCT in 69 patients at (1) baseline and (2) after 8-10 weeks of 40  mg rosuvastatin. Peripheral blood mononuclear cells were assayed via microarray. We constructed machine learning models with baseline gene expression data to predict change in FCT. Finally, we ascertained the biological functions of the most predictive transcriptomic markers.

FINDINGS

Machine learning models were able to predict FCT responders using baseline gene expression with high fidelity (Classification AUC = 0.969 and 0.972). The first model (elastic net) using 73 genes had an accuracy of 92.8%, sensitivity of 94.1%, and specificity of 91.4%. The second model (KTSP) using 18 genes has an accuracy of 95.7%, sensitivity of 94.3%, and specificity of 97.1%. We found 58 enriched gene ontology terms, including many involved with immune cell function and cholesterol biometabolism.

INTERPRETATION

In this pilot study, transcriptomic models could predict if FCT increased following 8-10 weeks of rosuvastatin. These findings may have significance for therapy selection and could supplement invasive imaging modalities.

摘要

背景

纤维帽厚度（FCT）是冠状动脉斑块破裂的最重要决定因素，最好通过血管内光学相干断层扫描（OCT）进行测量。他汀类药物治疗可增加 FCT，从而降低急性冠状动脉事件的发生概率。然而，大量他汀类药物相关的 FCT 增加仅发生在一部分患者中。目前，尚无方法预测哪些患者将从中受益。我们使用瑞舒伐他汀临床试验的转录组数据来预测患者的 FCT 是否会因他汀类药物治疗而增加。

方法

在 69 例患者中，使用 OCT 在（1）基线和（2）接受 40mg 瑞舒伐他汀治疗 8-10 周后测量 FCT。通过微阵列检测外周血单核细胞。我们使用基线基因表达数据构建机器学习模型，以预测 FCT 的变化。最后，我们确定了最具预测性转录组标志物的生物学功能。

结果

机器学习模型能够使用基线基因表达以高精度预测 FCT 应答者（分类 AUC=0.969 和 0.972）。使用 73 个基因的第一个模型（弹性网络）的准确率为 92.8%，灵敏度为 94.1%，特异性为 91.4%。使用 18 个基因的第二个模型（KTSP）的准确率为 95.7%，灵敏度为 94.3%，特异性为 97.1%。我们发现了 58 个富集的基因本体论术语，包括许多与免疫细胞功能和胆固醇生物代谢有关的术语。