Institute for Medical Research and Occupational Health, Ksaverska c 2, 10000, Zagreb, Croatia.
Institute of Medicine, Peoples' Friendship University of Russia, Moscow, Russian Federation.
Pathol Oncol Res. 2019 Oct;25(4):1269-1277. doi: 10.1007/s12253-018-0467-8. Epub 2018 Sep 16.
Large investments by pharmaceutical companies in the development of new antineoplastic drugs have not been resulting in adequate advances of new therapies. Despite the introduction of new methods, technologies, translational medicine and bioinformatics, the usage of collected knowledge is unsatisfactory. In this paper, using examples of pancreatic ductal adenocarcinoma (PaC) and castrate-resistant prostate cancer (CRPC), we proposed a concept showing that, in order to improve applicability of current knowledge in oncology, the re-clustering of clinical and scientific data is crucial. Such an approach, based on systems oncology, would include bridging of data on biomarkers and pathways between different cancer types. Proposed concept would introduce a new matrix, which enables combining of already approved therapies between cancer types. Paper provides a (a) detailed analysis of similarities in mechanisms of etiology and progression between PaC and CRPC, (b) diabetes as common hallmark of both cancer types and (c) knowledge gaps and directions of future investigations. Proposed horizontal and vertical matrix in cancer profiling has potency to improve current antineoplastic therapy efficacy. Systems biology map using Systems Biology Graphical Notation Language is used for summarizing complex interactions and similarities of mechanisms in biology of PaC and CRPC.
制药公司在开发新型抗肿瘤药物方面的大量投资并未带来新疗法的充分进展。尽管引入了新方法、技术、转化医学和生物信息学,但对已收集知识的应用仍不尽如人意。在本文中,我们以胰腺导管腺癌 (PaC) 和去势抵抗性前列腺癌 (CRPC) 为例,提出了一个概念,表明为了提高肿瘤学中当前知识的适用性,对临床和科学数据进行重新聚类至关重要。这种基于系统肿瘤学的方法将包括在不同癌症类型之间桥接生物标志物和途径的数据。所提出的概念将引入一个新的矩阵,使已经批准的不同癌症类型之间的治疗方法相结合成为可能。本文提供了:(a)PaC 和 CRPC 在病因学和进展机制方面的相似性的详细分析,(b) 糖尿病是这两种癌症类型的共同标志,以及 (c) 知识空白和未来研究的方向。在癌症分析中提出的水平和垂直矩阵有可能提高当前的抗肿瘤治疗效果。使用系统生物学图形表示法的系统生物学图谱用于总结 PaC 和 CRPC 生物学中机制的复杂相互作用和相似性。