Yalcin Gizem Damla, Danisik Nurseda, Baygin Rana Can, Acar Ahmet
Department of Biological Sciences, Middle East Technical University, Universiteler Mah. Dumlupınar Bulvarı 1, Çankaya, Ankara 06800, Turkey.
J Pers Med. 2020 Oct 19;10(4):180. doi: 10.3390/jpm10040180.
Over the past decade, we have witnessed an increasing number of large-scale studies that have provided multi-omics data by high-throughput sequencing approaches. This has particularly helped with identifying key (epi)genetic alterations in cancers. Importantly, aberrations that lead to the activation of signaling networks through the disruption of normal cellular homeostasis is seen both in cancer cells and also in the neighboring tumor microenvironment. Cancer systems biology approaches have enabled the efficient integration of experimental data with computational algorithms and the implementation of actionable targeted therapies, as the exceptions, for the treatment of cancer. Comprehensive multi-omics data obtained through the sequencing of tumor samples and experimental model systems will be important in implementing novel cancer systems biology approaches and increasing their efficacy for tailoring novel personalized treatment modalities in cancer. In this review, we discuss emerging cancer systems biology approaches based on multi-omics data derived from bulk and single-cell genomics studies in addition to existing experimental model systems that play a critical role in understanding (epi)genetic heterogeneity and therapy resistance in cancer.
在过去十年中，我们目睹了越来越多的大规模研究，这些研究通过高通量测序方法提供了多组学数据。这尤其有助于识别癌症中的关键（表观）遗传改变。重要的是，通过破坏正常细胞稳态导致信号网络激活的畸变在癌细胞以及邻近的肿瘤微环境中均可见。癌症系统生物学方法能够将实验数据与计算算法有效整合，并实施可操作的靶向治疗，作为治疗癌症的例外情况。通过对肿瘤样本和实验模型系统进行测序获得的全面多组学数据，对于实施新型癌症系统生物学方法以及提高其在定制癌症新型个性化治疗模式方面的疗效至关重要。在本综述中，我们除了讨论在理解癌症中的（表观）遗传异质性和治疗抗性方面起关键作用的现有实验模型系统外，还将探讨基于来自大量和单细胞基因组学研究的多组学数据的新兴癌症系统生物学方法。
