探索克隆进化自动重建的当前挑战和观点。
Exploring Current Challenges and Perspectives for Automatic Reconstruction of Clonal Evolution.
机构信息
Institute of Medical Informatics, University of Münster, Münster, Germany;
Institute of Medical Informatics, University of Münster, Münster, Germany.
出版信息
Cancer Genomics Proteomics. 2022 Mar-Apr;19(2):194-204. doi: 10.21873/cgp.20314.
BACKGROUND/AIM: In the field of cancer research, reconstructing clonal evolution is of major interest. The technique provides new insights for analysis and prediction of tumor development. However, reconstruction based on mutational data is characterized by several challenges.
MATERIALS AND METHODS
By performing extensive literature research, we identified 51 currently available tools for reconstructing clonal evolution. By analyzing two cancer data sets (n=21), we investigated the applicability and performance of each tool.
RESULTS
Seventeen out of 51 tools could be applied to our data. Correct clustering of variants can be observed for 4 patients in the presence of ≤3 clusters and ≥5 time points. Correct phylogenetic trees are determined for 10 patients. Accurate visualization is possible, by applying adjustments to the original algorithms.
CONCLUSION
Despite bearing considerable potential, automatic reconstruction of clonal evolution remains challenging. To replace tedious manual reconstruction, further research including systematic error analyses using simulation tools needs to be conducted.
背景/目的：在癌症研究领域，重建克隆进化是一个主要关注点。该技术为肿瘤发展的分析和预测提供了新的视角。然而，基于突变数据的重建具有若干挑战。
材料和方法
通过广泛的文献研究，我们确定了 51 种目前可用于重建克隆进化的工具。通过分析两个癌症数据集（n=21），我们研究了每个工具的适用性和性能。
结果
在存在≤3 个聚类和≥5 个时间点的情况下，51 个工具中的 17 个可应用于我们的数据。在 4 名患者中可以观察到变异的正确聚类。对于 10 名患者，可以确定正确的系统发生树。通过对原始算法进行调整，可以实现准确的可视化。
结论
尽管具有相当大的潜力，但自动重建克隆进化仍然具有挑战性。为了替代繁琐的手动重建，需要进行包括使用模拟工具进行系统误差分析在内的进一步研究。
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