Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, 08826, South Korea.
Department of Internal Medicine and Liver Research Institute, Seoul National University College of Medicine, Seoul, 03080, South Korea.
BMC Bioinformatics. 2018 Aug 13;19(Suppl 9):288. doi: 10.1186/s12859-018-2270-7.
Component-based structural equation modeling methods are now widely used in science, business, education, and other fields. This method uses unobservable variables, i.e., "latent" variables, and structural equation model relationships between observable variables. Here, we applied this structural equation modeling method to biologically structured data. To identify candidate drug-response biomarkers, we first used proteomic peptide-level data, as measured by multiple reaction monitoring mass spectrometry (MRM-MS), for liver cancer patients. MRM-MS is a highly sensitive and selective method for proteomic targeted quantitation of peptide abundances in complex biological samples.
We developed a component-based drug response prediction model, having the advantage that it first combines collapsed peptide-level data into protein-level information, facilitating subsequent biological interpretation. Our model also uses an alternating least squares algorithm, to efficiently estimate both coefficients of peptides and proteins. This approach also considers correlations between variables, without constraint, by a multiple testing problem. Using estimated peptide and protein coefficients, we selected significant protein biomarkers by permutation testing, resulting in our model for predicting liver cancer response to the tyrosine kinase inhibitor sorafenib.
Using data from a cohort of liver cancer patients, we then "fine-tuned" our model to successfully predict drug responses, as demonstrated by a high area under the curve (AUC) score. Such drug response prediction models may eventually find clinical translation in identifying individual patients likely to respond to specific therapies.
基于组件的结构方程建模方法现在广泛应用于科学、商业、教育等领域。这种方法使用不可观测变量，即“潜在”变量，以及可观测变量之间的结构方程模型关系。在这里，我们将这种结构方程建模方法应用于具有生物学结构的数据。为了鉴定候选药物反应生物标志物，我们首先使用了由多重反应监测质谱（MRM-MS）测量的肝癌患者的蛋白质组肽水平数据。MRM-MS 是一种高度敏感和选择性的方法，用于对复杂生物样本中的肽丰度进行蛋白质组靶向定量。
我们开发了一种基于组件的药物反应预测模型，其优点是它首先将合并的肽水平数据组合成蛋白质水平信息，便于随后的生物学解释。我们的模型还使用交替最小二乘法算法，有效地估计肽和蛋白质的系数。这种方法还考虑了变量之间的相关性，而不受约束，通过多重检验问题。使用估计的肽和蛋白质系数，我们通过置换检验选择了显著的蛋白质生物标志物，从而建立了预测肝癌对酪氨酸激酶抑制剂索拉非尼反应的模型。
我们使用肝癌患者队列的数据对我们的模型进行了“微调”，成功地预测了药物反应，这表现为高曲线下面积（AUC）评分。这种药物反应预测模型最终可能会在识别可能对特定治疗有反应的个体患者方面找到临床转化。
