Pathway-structured predictive modeling for multi-level drug response in multiple myeloma.
Pathway-structured predictive modeling for multi-level drug response in multiple myeloma.
复制标题
多发性骨髓瘤多水平药物反应的通路结构预测模型。
DOI：
10.1093/bioinformatics/bty436
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发表时间：
2018
期刊：
影响因子：
5.8
通讯作者：
Yi Nengjun
中科院分区：
文献类型：
--
作者：
Zhang Xinyan;Li Bingzong;Han Huiying;Song Sha;Xu Hongxia;Yi Zixuan;Hong Yating;Zhuang Wenzhuo;Yi Nengjun
分子分析表明骨髓瘤由不同的亚型组成，这些亚型具有不同的分子病理学和对某些治疗的不同反应率。多发性骨髓瘤（MM）的药物应答通常记录为多水平有序结局。药物反应研究的目标之一是根据患者的临床和分子特征预测其高概率属于哪种反应类别。然而，由于大多数基因的影响很小，基于基因的模型可能提供有限的预测准确性。在这种情况下，通过将生物途径预测多层次的有序药物反应的方法是理想的，但还没有被developedyou.ResultsWe提出了一个路径结构的方法，用于预测多层次的有序反应，使用两个阶段的方法。我们首先开发了层次有序逻辑模型和一个有效的拟牛顿算法，共同分析众多的相关变量。我们的两阶段方法首先通过使用分层有序逻辑方法拟合每个通路内的所有预测因子来获得每个通路的线性预测因子（称为通路评分），然后将通路评分作为新的预测因子组合以构建预测模型。我们将所提出的方法应用于两个公开可用的数据集，用于使用大规模基因表达数据和途径信息预测MM中的多水平有序药物反应。我们的结果表明，与相应的基于基因的模型相比，我们的方法不仅显着提高了预测性能，而且还使我们能够识别生物相关的途径。可用性和实现所提出的方法已在我们的R包BhGLM中实现，该包可从公共GitHub存储库https://github.com/abbyyan3/BhGLM免费获取。
MotivationMolecular analyses suggest that myeloma is composed of distinct sub-types that have different molecular pathologies and various response rates to certain treatments. Drug responses in multiple myeloma (MM) are usually recorded as a multi-level ordinal outcome. One of the goals of drug response studies is to predict which response category any patients belong to with high probability based on their clinical and molecular features. However, as most of genes have small effects, gene-based models may provide limited predictive accuracy. In that case, methods for predicting multi-level ordinal drug responses by incorporating biological pathways are desired but have not been developed yet.ResultsWe propose a pathway-structured method for predicting multi-level ordinal responses using a two-stage approach. We first develop hierarchical ordinal logistic models and an efficient quasi-Newton algorithm for jointly analyzing numerous correlated variables. Our two-stage approach first obtains the linear predictor (called the pathway score) for each pathway by fitting all predictors within each pathway using the hierarchical ordinal logistic approach, and then combines the pathway scores as new predictors to build a predictive model. We applied the proposed method to two publicly available datasets for predicting multi-level ordinal drug responses in MM using large-scale gene expression data and pathway information. Our results show that our approach not only significantly improved the predictive performance compared with the corresponding gene-based model but also allowed us to identify biologically relevant pathways.Availability and implementationThe proposed approach has been implemented in our R package BhGLM, which is freely available from the public GitHub repository https://github.com/abbyyan3/BhGLM.