Yu Jie, Chang Xinzhong
Foreign Languages College, Tianjin Normal University, Tianjin, China.
Department of Breast Surgery, Cancer Institute and Hospital, Tianjin Medical University, Tianjin, China.
Asia Pac J Oncol Nurs. 2021 Jan 29;8(2):112-114. doi: 10.4103/2347-5625.308301. eCollection 2021 Mar-Apr.
Cancer is the largest health problem worldwide. A number of targeted therapies are currently employed for the treatment of different cancers. Determining the molecular mechanisms that are necessary for cancer development and progression is the most critical step in targeted therapies. Currently, many studies have identified a large number of frequently mutated cancer-associated genes using recurrence-based methods. However, only the cancer-associated mutations with a mutation frequency >15% can be identified by these methods. In other words, they cannot be used to identify driver genes that have low mutation frequency but play a major role in tumorigenesis and development. Thus, there is an urgent need for a method for identifying cancer-associated genes that are not based on recurrence. In a study, recently published in Nature Communications, research team led by Prof. Raúl Rabadán from the Columbia University successfully devised a novel topological data analysis approach to identify low-prevalence cancer-associated gene mutations using expression data from multiple cancers.
癌症是全球最大的健康问题。目前有多种靶向疗法用于治疗不同类型的癌症。确定癌症发生和发展所必需的分子机制是靶向治疗中最关键的一步。目前，许多研究使用基于复发的方法鉴定出了大量频繁突变的癌症相关基因。然而，这些方法只能鉴定出突变频率>15%的癌症相关突变。换句话说，它们无法用于鉴定那些突变频率低但在肿瘤发生和发展中起主要作用的驱动基因。因此，迫切需要一种不基于复发的鉴定癌症相关基因的方法。在最近发表于《自然·通讯》的一项研究中，哥伦比亚大学的劳尔·拉巴丹教授领导的研究团队成功设计出一种新颖的拓扑数据分析方法，利用多种癌症的表达数据来鉴定低发生率的癌症相关基因突变。
