Castagnino Nicoletta, Maffei Massimo, Tortolina Lorenzo, Zoppoli Gabriele, Piras Daniela, Nencioni Alessio, Moran Eva, Ballestrero Alberto, Patrone Franco, Parodi Silvio
Department of Internal Medicine and Medical Specializations (DIMI), University of Genoa, Genoa, Italy.
IRCCS Azienda Ospedaliera Universitaria San Martino-IST, Genoa, Italy.
Wiley Interdiscip Rev Syst Biol Med. 2016 Jul;8(4):314-36. doi: 10.1002/wsbm.1342. Epub 2016 May 30.
Current colorectal cancer (CRC) treatment guidelines are primarily based on clinical features, such as cancer stage and grade. However, outcomes may be improved using molecular treatment guidelines. Potentially useful biomarkers include driver mutations and somatically inherited alterations, signaling proteins (their expression levels and (post) translational modifications), mRNAs, micro-RNAs and long noncoding RNAs. Moving to an integrated system is potentially very relevant. To implement such an integrated system: we focus on an important region of the signaling network, immediately above the G1-S restriction point, and discuss the reconstruction of a Molecular Interaction Map and interrogating it with a dynamic mathematical model. Extensive model pretraining achieved satisfactory, validated, performance. The model helps to propose future target combination priorities, and restricts drastically the number of drugs to be finally tested at a cellular, in vivo, and clinical-trial level. Our model allows for the inclusion of the unique molecular profiles of each individual patient's tumor. While existing clinical guidelines are well established, dynamic modeling may be used for future targeted combination therapies, which may progressively become part of clinical practice within the near future. WIREs Syst Biol Med 2016, 8:314-336. doi: 10.1002/wsbm.1342 For further resources related to this article, please visit the WIREs website.
当前的结直肠癌(CRC)治疗指南主要基于临床特征,如癌症分期和分级。然而,使用分子治疗指南可能会改善治疗效果。潜在有用的生物标志物包括驱动突变和体细胞遗传改变、信号蛋白(其表达水平和(后)翻译修饰)、信使核糖核酸(mRNA)、微小核糖核酸(micro-RNA)和长链非编码核糖核酸(lncRNA)。转向一个综合系统可能非常重要。为了实施这样一个综合系统:我们聚焦于信号网络中紧接在G1-S限制点上方的一个重要区域,并讨论分子相互作用图谱的重建以及用动态数学模型对其进行研究。广泛的模型预训练取得了令人满意的、经过验证的性能。该模型有助于提出未来的靶点组合优先级,并大幅减少最终在细胞、体内和临床试验水平进行测试的药物数量。我们的模型允许纳入每个患者肿瘤的独特分子特征。虽然现有的临床指南已经确立,但动态建模可用于未来的靶向联合治疗,在不久的将来可能会逐渐成为临床实践的一部分。《WIREs系统生物学与医学》2016年,8:314 - 336。doi:10.1002/wsbm.1342 有关本文的更多资源,请访问WIREs网站。