Model ensembling as a tool to form interpretable multi-omic predictors of cancer pharmacosensitivity.

IF 6.8 2区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS
Sébastien De Landtsheer, Apurva Badkas, Dagmar Kulms, Thomas Sauter
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引用次数: 0

Abstract

Stratification of patients diagnosed with cancer has become a major goal in personalized oncology. One important aspect is the accurate prediction of the response to various drugs. It is expected that the molecular characteristics of the cancer cells contain enough information to retrieve specific signatures, allowing for accurate predictions based solely on these multi-omic data. Ideally, these predictions should be explainable to clinicians, in order to be integrated in the patients care. We propose a machine-learning framework based on ensemble learning to integrate multi-omic data and predict sensitivity to an array of commonly used and experimental compounds, including chemotoxic compounds and targeted kinase inhibitors. We trained a set of classifiers on the different parts of our dataset to produce omic-specific signatures, then trained a random forest classifier on these signatures to predict drug responsiveness. We used the Cancer Cell Line Encyclopedia dataset, comprising multi-omic and drug sensitivity measurements for hundreds of cell lines, to build the predictive models, and validated the results using nested cross-validation. Our results show good performance for several compounds (Area under the Receiver-Operating Curve >79%) across the most frequent cancer types. Furthermore, the simplicity of our approach allows to examine which omic layers have a greater importance in the models and identify new putative markers of drug responsiveness. We propose several models based on small subsets of transcriptional markers with the potential to become useful tools in personalized oncology, paving the way for clinicians to use the molecular characteristics of the tumors to predict sensitivity to therapeutic compounds.

以模型组合为工具,形成可解释的癌症药敏性多组学预测指标。
对确诊为癌症的患者进行分层已成为个性化肿瘤学的一个主要目标。其中一个重要方面是准确预测对各种药物的反应。预计癌细胞的分子特征包含足够的信息来检索特定特征,从而可以仅根据这些多原子数据进行准确预测。理想情况下,这些预测结果应能向临床医生解释,以便纳入患者护理中。我们提出了一种基于集合学习的机器学习框架,以整合多组学数据并预测对一系列常用和实验化合物(包括化学毒性化合物和靶向激酶抑制剂)的敏感性。我们在数据集的不同部分训练了一组分类器,以生成omic特异性特征,然后在这些特征上训练了一个随机森林分类器,以预测药物反应性。我们使用《癌症细胞系百科全书》数据集来建立预测模型,该数据集包含数百种细胞系的多组学和药物敏感性测量结果,并使用嵌套交叉验证对结果进行了验证。我们的结果表明,在最常见的癌症类型中,有几种化合物具有良好的性能(接收曲线下面积大于 79%)。此外,我们的方法非常简单,因此可以检查模型中哪些指标层更重要,并确定药物反应性的新假定标记。我们提出了几个基于小型转录标记子集的模型,它们有可能成为个性化肿瘤学的有用工具,为临床医生利用肿瘤的分子特征预测对治疗化合物的敏感性铺平道路。
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来源期刊
Briefings in bioinformatics
Briefings in bioinformatics 生物-生化研究方法
CiteScore
13.20
自引率
13.70%
发文量
549
审稿时长
6 months
期刊介绍: Briefings in Bioinformatics is an international journal serving as a platform for researchers and educators in the life sciences. It also appeals to mathematicians, statisticians, and computer scientists applying their expertise to biological challenges. The journal focuses on reviews tailored for users of databases and analytical tools in contemporary genetics, molecular and systems biology. It stands out by offering practical assistance and guidance to non-specialists in computerized methodologies. Covering a wide range from introductory concepts to specific protocols and analyses, the papers address bacterial, plant, fungal, animal, and human data. The journal's detailed subject areas include genetic studies of phenotypes and genotypes, mapping, DNA sequencing, expression profiling, gene expression studies, microarrays, alignment methods, protein profiles and HMMs, lipids, metabolic and signaling pathways, structure determination and function prediction, phylogenetic studies, and education and training.
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