重构肿瘤克隆单体型的改进生成树算法

目的 基于三代测序数据重构肿瘤克隆单体型,有效识别肿瘤异质性。方法 该算法提取混合肿瘤数据中的变异位点数据,通过概率函数求解各体细胞突变位点的连接权值;设计了一种基于最大生成树的单体型重构算法,遵循肿瘤克隆间继承原则逐级扩展最大生成树,以确定克隆中各变异位点的连接模式;采用厚度剥离方法估计求得子克隆个数、配比及演化关系。结果 在仿真实验中,分别对测序覆盖度、读段长度、亚克隆数目及体细胞变异率四个指标进行了准确率分析,充分说明了该算法具有良好的鲁棒性;该算法对肿瘤克隆单体型重构精度均值可达到97%以上,与其它工具进行性能比较具有显著优势。结论 所提方法可以较为精确的重构肿瘤亚克隆单体型,明晰肿瘤克隆演化过程,为肿瘤异质性研究和临床决策提供理论依据。

Reconstruction of tumor clonal haplotypes based on an improved spanning algorithm

Objective To reconstruct tumor clonal haplotypes based on the third- generation sequencing data to effectively identify tumor heterogeneity. Methods We developed an algorithm for extracting somatic mutational event from the mixed tumor data and determining the connection weight of each somatic cell mutation site through the probability function. A reconstruction algorithm of the haplotype was designed based on the maximum spanning tree, and following the principle of inheritance between tumor clones, the connection pattern was determined at each mutation site in the clonal maximum spanning tree in a stepwise manner. The number, ratio and evolution of the sub-clones were estimated using the depth stripping method. Results In the simulation experiments, we analyzed the accuracy of the algorithm based on 4 indexes, namely the coverage, read length, subclone number and somatic variant rate, and the results demonstrated a good robustness of the algorithm. The results of the experiments showed that the mean sub-clone haplotypes accuracy exceeded 97%, suggesting that this algorithm significantly outperformed the previous methods. Conclusion The proposed method can accurately reconstruct tumor subclonal haplotypes and clarify the process of tumor clonal evolution, and can thus provide a theoretical basis for tumor heterogeneity research and assist in clinical decision-making.