PDF(Pubmed)
Abstract：
The inherent heterogeneity of cancer contributes to highly variable responses to any anticancer treatments. This underscores the need to first identify precise biomarkers through complex multi-omics datasets that are now available. Although much research has focused on this aspect, identifying biomarkers associated with distinct drug responders still remains a major challenge. Here, we develop MOMLIN, a multi-modal and -omics machine learning integration framework, to enhance drug-response prediction. MOMLIN jointly utilizes sparse correlation algorithms and class-specific feature selection algorithms, which identifies multi-modal and -omics-associated interpretable components. MOMLIN was applied to 147 patients\' breast cancer datasets (clinical, mutation, gene expression, tumor microenvironment cells and molecular pathways) to analyze drug-response class predictions for non-responders and variable responders. Notably, MOMLIN achieves an average AUC of 0.989, which is at least 10% greater when compared with current state-of-the-art (data integration analysis for biomarker discovery using latent components, multi-omics factor analysis, sparse canonical correlation analysis). Moreover, MOMLIN not only detects known individual biomarkers such as genes at mutation/expression level, most importantly, it correlates multi-modal and -omics network biomarkers for each response class. For example, an interaction between ER-negative-HMCN1-COL5A1 mutations-FBXO2-CSF3R expression-CD8 emerge as a multimodal biomarker for responders, potentially affecting antimicrobial peptides and FLT3 signaling pathways. In contrast, for resistance cases, a distinct combination of lymph node-TP53 mutation-PON3-ENSG00000261116 lncRNA expression-HLA-E-T-cell exclusions emerged as multimodal biomarkers, possibly impacting neurotransmitter release cycle pathway. MOMLIN, therefore, is expected advance precision medicine, such as to detect context-specific multi-omics network biomarkers and better predict drug-response classifications.
摘要：
癌症的固有异质性导致对任何抗癌治疗的高度可变的反应。这强调了首先需要通过现在可用的复杂的多组学数据集识别精确的生物标志物。虽然很多研究都集中在这方面，识别与不同药物应答者相关的生物标志物仍然是一个主要挑战。这里,我们开发MOMLIN，多模态和组学机器学习集成框架，增强药物反应预测。MOMLIN联合利用稀疏相关算法和特定类别的特征选择算法，它识别多模态和组学相关的可解释组件。MOMLIN应用于147例乳腺癌患者数据集(临床，突变,基因表达，肿瘤微环境细胞和分子途径），以分析无应答者和可变应答者的药物应答类别预测。值得注意的是,MOMLIN的平均AUC为0.989，与当前最新技术相比至少高出10%（使用潜在成分发现生物标志物的数据整合分析，多组学因子分析，稀疏典型相关分析)。此外，MOMLIN不仅检测已知的个体生物标志物，如突变/表达水平的基因，最重要的是,它关联了每个反应类别的多模态和组学网络生物标志物。例如,ER阴性-HMCN1-COL5A1突变-FBXO2-CSF3R表达-CD8之间的相互作用成为应答者的多模态生物标志物，潜在影响抗菌肽和FLT3信号通路。相比之下,对于抵抗案例，淋巴结-TP53突变-PON3-ENSG00000261116lncRNA表达-HLA-E-T细胞排除的独特组合作为多模态生物标志物出现，可能影响神经递质释放周期通路。MOMLIN,因此,有望推进精准医学，例如检测特定环境的多组学网络生物标志物并更好地预测药物反应分类。
