Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), Via F.Cervi 93, 20090 Segrate, Milan, Italy.
Gene. 2019 May 25;698:100-106. doi: 10.1016/j.gene.2019.02.064. Epub 2019 Mar 3.
The knowledge of cancer cell response to conventional therapies is crucial in order to choose the correct therapy of patients affected by cancer. The major problem is generally attributed to the lack of specific biological processes able to predict the therapy efficacy. Here, we optimized a computational method for the analysis of gene networks able to detect and quantify the effects of a drug in a pan-cancer study. Overall, our method, using several network topological measures has identified a cancer gene network with a key role in biological processes. The gene network, able to classify with a good performance cancer vs normal samples, was modulated in silico to evaluate the effects of new or approved drugs. This computational model could offer an interesting hint to decipher molecular mechanisms contributing to resistance or inefficacy of drugs.
为了为癌症患者选择正确的治疗方法，了解癌细胞对常规疗法的反应至关重要。主要问题通常归因于缺乏能够预测治疗效果的特定生物过程。在这里，我们优化了一种用于分析基因网络的计算方法，该方法能够在泛癌研究中检测和量化药物的作用。总的来说，我们的方法使用了几种网络拓扑度量，确定了一个在生物过程中起关键作用的癌症基因网络。该基因网络能够很好地区分癌症和正常样本，我们对其进行了计算机模拟，以评估新药或已批准药物的作用。这种计算模型可以为揭示导致药物耐药或无效的分子机制提供一个有趣的线索。
