Genomic data integration by WON-PARAFAC identifies interpretable factors for predicting drug-sensitivity in vivo
Genomic data integration by WON-PARAFAC identifies interpretable factors for predicting drug-sensitivity in vivo
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DOI：
10.1038/s41467-019-13027-2
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发表时间：
2019-11-06
影响因子：
16.6
通讯作者：
Vis, Daniel J.
中科院分区：
文献类型：
--
作者：
Kim, Yongsoo;Bismeijer, Tycho;Vis, Daniel J.
总结并将分子数据与治疗敏感性联系起来的综合分析对于捕获生物学复杂性至关重要，这对于进一步的精准医学至关重要。我们介绍加权正交非负并行因子分析（WON-PARAFAC），一种数据集成方法，识别稀疏和可解释的因素。WON-PARAFAC将GDSC 1000细胞系纲要总结为130个因子。我们根据这些因素与复发性分子改变、途径富集、癌症类型和药物反应的相关性来解释这些因素。至关重要的是，细胞系衍生因子捕获了患者衍生异种移植物（PDX）模型中的大多数相关生物学变异，这强烈表明我们的因子捕获了癌症生物学的不变和可推广的方面。此外，与基于原始特征的预测因子相比，使用基于因子的预测因子，细胞系中的药物应答更好，并且更一致地转化为PDX。WON-PARAFAC有效地总结和整合了多路高维基因组数据，并增强了从细胞系到患者来源的异种移植物的药物反应预测的可翻译性。
Integrative analyses that summarize and link molecular data to treatment sensitivity are crucial to capture the biological complexity which is essential to further precision medicine. We introduce Weighted Orthogonal Nonnegative parallel factor analysis (WON-PARAFAC), a data integration method that identifies sparse and interpretable factors. WON-PARAFAC summarizes the GDSC1000 cell line compendium in 130 factors. We interpret the factors based on their association with recurrent molecular alterations, pathway enrichment, cancer type, and drug-response. Crucially, the cell line derived factors capture the majority of the relevant biological variation in Patient-Derived Xenograft (PDX) models, strongly suggesting our factors capture invariant and generalizable aspects of cancer biology. Furthermore, drug response in cell lines is better and more consistently translated to PDXs using factor-based predictors as compared to raw feature-based predictors. WON-PARAFAC efficiently summarizes and integrates multiway high-dimensional genomic data and enhances translatability of drug response prediction from cell lines to patient-derived xenografts.