Ding Ruo-Fan, Zhang Yun, Wu Lv-Ying, You Pan, Fang Zan-Xi, Li Zhi-Yuan, Zhang Zhong-Ying, Ji Zhi-Liang
State Key Laboratory of Cellular Stress Biology, National Institute for Data Science in Health and Medicine, School of Life Sciences, Xiamen University, Xiamen, China.
Department of Clinical Laboratory, Xiamen Xianyue Hospital, Xiamen, China.
Front Oncol. 2022 Jun 20;12:898117. doi: 10.3389/fonc.2022.898117. eCollection 2022.
Metastasis is the main fatal cause of colorectal cancer (CRC). Although enormous efforts have been made to date to identify biomarkers associated with metastasis, there is still a huge gap to translate these efforts into effective clinical applications due to the poor consistency of biomarkers in dealing with the genetic heterogeneity of CRCs. In this study, a small cohort of eight CRC patients was recruited, from whom we collected cancer, paracancer, and normal tissues simultaneously and performed whole-exome sequencing. Given the exomes, a novel statistical parameter LIP was introduced to quantitatively measure the local invasion power for every somatic and germline mutation, whereby we affirmed that the innate germline mutations instead of somatic mutations might serve as the major driving force in promoting local invasion. Furthermore, bioinformatic analyses of big data derived from the public zone, we identified ten potential driver variants that likely urged the local invasion of tumor cells into nearby tissue. Of them, six corresponding genes were new to CRC metastasis. In addition, a metastasis resister variant was also identified. Based on these eleven variants, we constructed a logistic regression model for rapid risk assessment of early metastasis, which was also deployed as an online server, AmetaRisk (http://www.bio-add.org/AmetaRisk). In summary, we made a valuable attempt in this study to exome-wide explore the genetic driving force to local invasion, which provides new insights into the mechanistic understanding of metastasis. Furthermore, the risk assessment model can assist in prioritizing therapeutic regimens in clinics and discovering new drug targets, and thus substantially increase the survival rate of CRC patients.
转移是结直肠癌（CRC）的主要致死原因。尽管迄今为止已经付出了巨大努力来识别与转移相关的生物标志物，但由于生物标志物在应对CRC基因异质性方面的一致性较差，将这些努力转化为有效的临床应用仍存在巨大差距。在本研究中，招募了一小群8名CRC患者，我们同时从他们身上收集了癌组织、癌旁组织和正常组织，并进行了全外显子组测序。基于外显子组，引入了一个新的统计参数LIP来定量测量每个体细胞和种系突变的局部侵袭能力，据此我们确认先天的种系突变而非体细胞突变可能是促进局部侵袭的主要驱动力。此外，通过对来自公共领域的大数据进行生物信息学分析，我们鉴定出10个可能促使肿瘤细胞向附近组织局部侵袭的潜在驱动变异。其中，6个相应基因在CRC转移方面是新发现的。此外，还鉴定出一个转移抗性变异。基于这11个变异，我们构建了一个用于早期转移快速风险评估的逻辑回归模型，该模型还被部署为一个在线服务器AmetaRisk（http://www.bio-add.org/AmetaRisk）。总之，我们在本研究中进行了一次有价值的尝试，在外显子组范围内探索局部侵袭的遗传驱动力，这为转移的机制理解提供了新的见解。此外，该风险评估模型可以协助临床确定治疗方案的优先级并发现新的药物靶点，从而大幅提高CRC患者的生存率。
