Huang Yu-E, Zhou Shunheng, Liu Haizhou, Zhou Xu, Yuan Mengqin, Hou Fei, Chen Sina, Chen Jiahao, Wang Lihong, Jiang Wei
Department of Biomedical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China.
Department of Pathophysiology, School of Medicine, Southeast University, Nanjing 210009, China.
Brief Bioinform. 2023 Mar 19;24(2). doi: 10.1093/bib/bbad066.
Drug resistance is one of principal limiting factors for cancer treatment. Several mechanisms, especially mutation, have been validated to implicate in drug resistance. In addition, drug resistance is heterogeneous, which makes an urgent need to explore the personalized driver genes of drug resistance. Here, we proposed an approach DRdriver to identify drug resistance driver genes in individual-specific network of resistant patients. First, we identified the differential mutations for each resistant patient. Next, the individual-specific network, which included the genes with differential mutations and their targets, was constructed. Then, the genetic algorithm was utilized to identify the drug resistance driver genes, which regulated the most differentially expressed genes and the least non-differentially expressed genes. In total, we identified 1202 drug resistance driver genes for 8 cancer types and 10 drugs. We also demonstrated that the identified driver genes were mutated more frequently than other genes and tended to be associated with the development of cancer and drug resistance. Based on the mutational signatures of all driver genes and enriched pathways of driver genes in brain lower grade glioma treated by temozolomide, the drug resistance subtypes were identified. Additionally, the subtypes showed great diversity in epithelial-mesenchyme transition, DNA damage repair and tumor mutation burden. In summary, this study developed a method DRdriver for identifying personalized drug resistance driver genes, which provides a framework for unlocking the molecular mechanism and heterogeneity of drug resistance.
耐药性是癌症治疗的主要限制因素之一。多种机制，尤其是突变，已被证实与耐药性有关。此外，耐药性具有异质性，这使得迫切需要探索耐药性的个性化驱动基因。在此，我们提出了一种方法DRdriver，用于在耐药患者的个体特异性网络中识别耐药驱动基因。首先，我们确定了每位耐药患者的差异突变。接下来，构建了个体特异性网络，其中包括具有差异突变的基因及其靶点。然后，利用遗传算法识别耐药驱动基因，这些基因调控差异表达最多的基因和差异表达最少的基因。我们总共为8种癌症类型和10种药物确定了1202个耐药驱动基因。我们还证明，所确定的驱动基因比其他基因的突变频率更高，并且往往与癌症的发展和耐药性相关。基于所有驱动基因的突变特征以及替莫唑胺治疗的脑低级别胶质瘤中驱动基因的富集途径，确定了耐药亚型。此外，这些亚型在上皮-间质转化、DNA损伤修复和肿瘤突变负担方面表现出极大的多样性。总之，本研究开发了一种识别个性化耐药驱动基因的方法DRdriver，为揭示耐药性的分子机制和异质性提供了一个框架。
