Systems Biology and Experimental Model Systems of Cancer.

Gizem Damla Yalcin,Ahmet Acar,Nurseda Danisik,Rana Can Baygin

doi:10.3390/jpm10040180

Gizem Damla Yalcin, Ahmet Acar + Show 2 more

Open Access

https://doi.org/10.3390/jpm10040180

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Abstract

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.

Highlights

Introduction to Cancer Systems BiologyCancer is an extremely complex disease with heterotypic interactions between cancer cells and neighboring stromal cells that support the proliferation, invasion, and the metastatic cascade of tumor cells [1,2]
Of the review, we sought to discuss current preclinical model systems used in cancer research and their role in predicting how cancer will progress and respond to the therapy when these model systems are integrated with system biology approaches
In this study, HCT-116 cells grown in both 2D and 3D model systems and their sensitivities against four commonly used anticancer agents were tested. The efficacy of these inhibitors was higher in the 2D than the 3D culture system, suggesting that phenotypic differences and distinct cell-to-cell interactions between these model systems might be responsible for observing the differences in drug sensitivities

Summary

Introduction to Cancer Systems Biology

Cancer is an extremely complex disease with heterotypic interactions between cancer cells and neighboring stromal cells that support the proliferation, invasion, and the metastatic cascade of tumor cells [1,2]. Multi-omics approaches empowered by next-generation technologies have enabled genomic characterization and evolutionary histories of both primary and metastatic cancer progression [3,4,5,6]. These technologies that shed light on the genome, transcriptome, metabolome, and proteome of cancer cells corroborate our understanding about systems biology-level approaches in cancer (Figure 1) [7]. The combination of information derived from the genomic architecture and various gene networks from a single or a group of cells determines the fate of these cells during development and a progression to cancer occurs as a result of the deregulation of these interactions.

Cancer Systems Biology for Precision Medicine

Experimental Model Systems of Cancer

Cell Line-Based Model Systems

Patient Sample-Based Model Systems

Conclusions