Department of Applied Mathematics & Statistics, Stony Brook University, Stony Brook, 11794, USA.
Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, 10064, USA.
Sci Rep. 2018 Apr 23;8(1):6402. doi: 10.1038/s41598-018-24679-3.
In the present work, we apply a geometric network approach to study common biological features of anticancer drug response. We use for this purpose the panel of 60 human cell lines (NCI-60) provided by the National Cancer Institute. Our study suggests that mathematical tools for network-based analysis can provide novel insights into drug response and cancer biology. We adopted a discrete notion of Ricci curvature to measure, via a link between Ricci curvature and network robustness established by the theory of optimal mass transport, the robustness of biological networks constructed with a pre-treatment gene expression dataset and coupled the results with the GI50 response of the cell lines to the drugs. Based on the resulting drug response ranking, we assessed the impact of genes that are likely associated with individual drug response. For genes identified as important, we performed a gene ontology enrichment analysis using a curated bioinformatics database which resulted in biological processes associated with drug response across cell lines and tissue types which are plausible from the point of view of the biological literature. These results demonstrate the potential of using the mathematical network analysis in assessing drug response and in identifying relevant genomic biomarkers and biological processes for precision medicine.
在本工作中，我们应用几何网络方法研究抗癌药物反应的常见生物学特征。为此，我们使用了美国国立癌症研究所提供的 60 个人类细胞系（NCI-60）的面板。我们的研究表明，基于网络的分析数学工具可以为药物反应和癌症生物学提供新的见解。我们采用离散的 Ricci 曲率概念，通过 Ricci 曲率和最优物质输运理论建立的网络稳健性之间的联系，来衡量基于预处理基因表达数据集构建的生物网络的稳健性，并将结果与细胞系对药物的 GI50 反应相关联。基于得到的药物反应排序，我们评估了与个体药物反应相关的基因的影响。对于被确定为重要的基因，我们使用了一个经过精心整理的生物信息学数据库进行了基因本体论富集分析，结果得到了与细胞系和组织类型的药物反应相关的生物学过程，这从生物学文献的角度来看是合理的。这些结果证明了在评估药物反应和识别相关基因组生物标志物和生物学过程以实现精准医学方面，使用数学网络分析的潜力。
