Gillman Rhys, Field Matt A, Schmitz Ulf, Karamatic Rozemary, Hebbard Lionel
Department of Biomedical Sciences and Molecular and Cell Biology, College of Public Health, Medical, and Veterinary Sciences, James Cook University, Townsville, Queensland, Australia.
Centre for Tropical Bioinformatics and Molecular Biology, James Cook University, Cairns, Queensland, Australia.
Comput Struct Biotechnol J. 2023 Oct 13;21:5028-5038. doi: 10.1016/j.csbj.2023.10.019. eCollection 2023.
Cancer is a heterogeneous disease with a strong genetic component making it suitable for precision medicine approaches aimed at identifying the underlying molecular drivers within a tumour. Large scale population-level cancer sequencing consortia have identified many actionable mutations common across both cancer types and sub-types, resulting in an increasing number of successful precision medicine programs. Nonetheless, such approaches fail to consider the effects of mutations unique to an individual patient and may miss rare driver mutations, necessitating personalised approaches to driver-gene prioritisation. One approach is to quantify the functional importance of individual mutations in a single tumour based on how they affect the expression of genes in a gene interaction network (GIN). These GIN-based approaches can be broadly divided into those that utilise an existing reference GIN and those that construct patient-specific GINs. These single-tumour approaches have several limitations that likely influence their results, such as use of reference cohort data, network choice, and approaches to mathematical approximation, and more research is required to evaluate the and applicability of their predictions. This review examines the current state of the art methods that identify driver genes in single tumours with a focus on GIN-based driver prioritisation.
癌症是一种具有强烈遗传成分的异质性疾病，这使其适合采用精准医学方法，旨在识别肿瘤内潜在的分子驱动因素。大规模人群水平的癌症测序联盟已经确定了许多在癌症类型和亚型中常见的可操作突变，从而催生了越来越多成功的精准医学项目。尽管如此，这些方法未能考虑个体患者特有的突变影响，可能会遗漏罕见的驱动突变，因此需要个性化的驱动基因优先级排序方法。一种方法是根据单个肿瘤中个体突变对基因相互作用网络（GIN）中基因表达的影响来量化其功能重要性。这些基于GIN的方法大致可分为利用现有参考GIN的方法和构建患者特异性GIN的方法。这些单肿瘤方法有几个可能影响其结果的局限性，例如参考队列数据的使用、网络选择和数学近似方法，需要更多研究来评估其预测的准确性和适用性。本综述考察了目前在单肿瘤中识别驱动基因的先进方法，重点是基于GIN的驱动基因优先级排序。
