通过网络控制策略发现癌症中单样本的个性化驱动突变特征。

Discovering personalized driver mutation profiles of single samples in cancer by network control strategy.

机构信息

Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, Xi'an 710072, China.

Key Laboratory of Systems Biology, CAS Center for Excellence in Molecular Cell Science, Innovation Center for Cell Signaling Network, Institute of Biochemistry and Cell Biology, Shanghai Institutes for Biological Science, University of Chinese Academy of Sciences, Shanghai 200031, China.

出版信息

Bioinformatics. 2018 Jun 1;34(11):1893-1903. doi: 10.1093/bioinformatics/bty006.

DOI:10.1093/bioinformatics/bty006

PMID:29329368

Abstract

MOTIVATION

It is a challenging task to discover personalized driver genes that provide crucial information on disease risk and drug sensitivity for individual patients. However, few methods have been proposed to identify the personalized-sample driver genes from the cancer omics data due to the lack of samples for each individual. To circumvent this problem, here we present a novel single-sample controller strategy (SCS) to identify personalized driver mutation profiles from network controllability perspective.

RESULTS

SCS integrates mutation data and expression data into a reference molecular network for each patient to obtain the driver mutation profiles in a personalized-sample manner. This is the first such a computational framework, to bridge the personalized driver mutation discovery problem and the structural network controllability problem. The key idea of SCS is to detect those mutated genes which can achieve the transition from the normal state to the disease state based on each individual omics data from network controllability perspective. We widely validate the driver mutation profiles of our SCS from three aspects: (i) the improved precision for the predicted driver genes in the population compared with other driver-focus methods; (ii) the effectiveness for discovering the personalized driver genes and (iii) the application to the risk assessment through the integration of the driver mutation signature and expression data, respectively, across the five distinct benchmarks from The Cancer Genome Atlas. In conclusion, our SCS makes efficient and robust personalized driver mutation profiles predictions, opening new avenues in personalized medicine and targeted cancer therapy.

AVAILABILITY AND IMPLEMENTATION

The MATLAB-package for our SCS is freely available from http://sysbio.sibcb.ac.cn/cb/chenlab/software.htm.

CONTACT

zhangsw@nwpu.edu.cn or zengtao@sibs.ac.cn or lnchen@sibs.ac.cn.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

摘要

动机

发现提供个体患者疾病风险和药物敏感性关键信息的个性化驱动基因是一项具有挑战性的任务。然而，由于每个个体的样本数量较少，因此很少有方法能够从癌症组学数据中识别个性化样本的驱动基因。为了解决这个问题，我们从网络可控性的角度提出了一种新的单样本控制器策略（SCS）来识别个性化的驱动突变谱。

结果

SCS 将突变数据和表达数据整合到每个患者的参考分子网络中，以个性化的方式获得驱动突变谱。这是第一个从计算角度将个性化驱动突变发现问题与结构网络可控性问题联系起来的计算框架。SCS 的关键思想是从网络可控性的角度，基于每个个体的组学数据，检测那些能够使个体从正常状态转变为疾病状态的突变基因。我们从三个方面广泛验证了我们的 SCS 的驱动突变谱：（i）与其他关注驱动基因的方法相比，在人群中预测驱动基因的精度提高；（ii）发现个性化驱动基因的有效性；（iii）通过整合驱动突变特征和表达数据，分别应用于风险评估，横跨五个来自癌症基因组图谱的不同基准。总之，我们的 SCS 实现了高效、稳健的个性化驱动突变谱预测，为个性化医学和靶向癌症治疗开辟了新途径。