Department of Systems and Computational Biology, Albert Einstein College of Medicine, Bronx, NY, USA.
Department of Systems and Computational Biology, Albert Einstein College of Medicine, Bronx, NY, USA; Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA; Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, Brisbane, Queensland, Australia.
Methods. 2017 Dec 1;131:74-82. doi: 10.1016/j.ymeth.2017.07.021. Epub 2017 Jul 25.
Synthetic lethal interactions (SLIs) are robust mechanisms that provide cells with the ability to remain viable despite having mutations in genes critical to the DNA damage response, a core cellular process. Studies in model organisms such as S. cerevisiae showed that thousands of genes important in maintaining DNA integrity cooperated in a SLI network. Two genes participate in a SLI when a mutation in one gene has no effect on the cell, but mutations in both interacting genes are lethal. Furthermore in C. elegans, a mutation in a critical gene that is important for development induced a change in expression variability in the synthetic lethal interactor. In cancer, targeting SLIs shows promise in selectively killing cancer cells. For example, targeting PARP1 is an effective treatment for BRCA1/2- breast and ovarian cancers. Although PARP1 is already identified as having a SLI with BRCA1/2-, computationally searching for other genes that cooperate in the SLI network could highlight genes that may have promise for being a cancer-specific drug target. Using RNA sequencing data for ovarian cancer patients with BRCA2 mutations and the R Bioconductor package pathVar, we showed that genes whose expression changes to an invariant, stable expression state are likely candidates for SLIs with BRCA2. Our results highlight the interactions between the genes with predicted SLIs and protein-coding genes that are functionally important in the DNA damage response. The method of analyzing expression variability to computationally identify genes with SLIs can be applied to query SLIs in other tumor types.
合成致死相互作用（SLI）是一种强大的机制，使细胞能够在 DNA 损伤反应的关键基因发生突变的情况下保持存活能力，这是一种核心细胞过程。在模式生物（如酿酒酵母）中的研究表明，成千上万对维持 DNA 完整性重要的基因在 SLI 网络中协同作用。当一个基因的突变对细胞没有影响，但两个相互作用的基因的突变都是致命的时，两个基因就参与了 SLI。此外，在秀丽隐杆线虫中，一个对发育至关重要的关键基因的突变会导致合成致死相互作用体的表达变异性发生变化。在癌症中，靶向 SLI 有望选择性地杀死癌细胞。例如，靶向 PARP1 是治疗 BRCA1/2-乳腺癌和卵巢癌的有效方法。尽管 PARP1 已经被确定与 BRCA1/2-具有 SLI，但通过计算搜索在 SLI 网络中协作的其他基因可以突出可能具有成为癌症特异性药物靶点潜力的基因。我们使用具有 BRCA2 突变的卵巢癌患者的 RNA 测序数据和 R Bioconductor 包 pathVar，表明表达变化为不变、稳定表达状态的基因可能是与 BRCA2 具有 SLI 的候选基因。我们的结果突出了具有预测 SLI 的基因与在 DNA 损伤反应中具有功能重要性的蛋白质编码基因之间的相互作用。分析表达变异性以计算识别具有 SLI 的基因的方法可以应用于查询其他肿瘤类型的 SLI。
