Center for Molecular Medicine, University Medical Center Utrecht, Utrecht, the Netherlands.
Oncode Institute, Utrecht, the Netherlands.
Clin Cancer Res. 2020 Nov 15;26(22):5952-5961. doi: 10.1158/1078-0432.CCR-20-0742. Epub 2020 Sep 10.
Proteasome inhibitors are widely used in treating multiple myeloma, but can cause serious side effects and response varies among patients. It is, therefore, important to gain more insight into which patients will benefit from proteasome inhibitors.
We introduce simulated treatment learned signatures (STLsig), a machine learning method to identify predictive gene expression signatures. STLsig uses genetically similar patients who have received an alternative treatment to model which patients will benefit more from proteasome inhibitors than from an alternative treatment. STLsig constructs gene networks by linking genes that are synergistic in their ability to predict benefit.
In a dataset of 910 patients with multiple myeloma, STLsig identified two gene networks that together can predict benefit to the proteasome inhibitor, bortezomib. In class "benefit," we found an HR of 0.47 ( = 0.04) in favor of bortezomib, while in class "no benefit," the HR was 0.91 ( = 0.68). Importantly, we observed a similar performance (HR class benefit, 0.46; = 0.04) in an independent patient cohort. Moreover, this signature also predicts benefit for the proteasome inhibitor, carfilzomib, indicating it is not specific to bortezomib. No equivalent signature can be found when the genes in the signature are excluded from the analysis, indicating that they are essential. Multiple genes in the signature are linked to working mechanisms of proteasome inhibitors or multiple myeloma disease progression.
STLsig can identify gene signatures that could aid in treatment decisions for patients with multiple myeloma and provide insight into the biological mechanism behind treatment benefit.
蛋白酶体抑制剂被广泛用于治疗多发性骨髓瘤，但会引起严重的副作用，且患者之间的反应存在差异。因此，深入了解哪些患者将从蛋白酶体抑制剂中获益非常重要。
我们引入了模拟治疗学习特征（STLsig），这是一种用于识别预测性基因表达特征的机器学习方法。STLsig 使用接受替代治疗的遗传上相似的患者来构建模型，以预测哪些患者将从蛋白酶体抑制剂中获益更多，而不是从替代治疗中获益。STLsig 通过链接在预测获益方面具有协同作用的基因来构建基因网络。
在一个包含 910 名多发性骨髓瘤患者的数据集，STLsig 确定了两个基因网络，这两个网络共同可以预测蛋白酶体抑制剂硼替佐米的获益。在“获益”类中，我们发现有利于硼替佐米的 HR 为 0.47（=0.04），而在“无获益”类中，HR 为 0.91（=0.68）。重要的是，我们在一个独立的患者队列中观察到了类似的表现（获益类 HR，0.46；=0.04）。此外，该特征还预测了蛋白酶体抑制剂卡非佐米的获益，表明它不仅针对硼替佐米。当特征中的基因从分析中排除时，无法找到等效的特征，表明它们是必不可少的。特征中的多个基因与蛋白酶体抑制剂或多发性骨髓瘤疾病进展的作用机制有关。
STLsig 可以识别出有助于多发性骨髓瘤患者治疗决策的基因特征，并深入了解治疗获益背后的生物学机制。
