基于肿瘤DNA测序数据的肿瘤克隆异质性与进化关系重建

Reconstructing tumor clonal heterogeneity and evolutionary relationships based on tumor DNA sequencing data

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BRIEFINGS IN BIOINFORMATICS

影响因子:7.7

分区:生物学2区 / 数学与计算生物学1区生化研究方法2区

发表日期:2024 Sep 23

作者: Zhen Wang, Yanhua Fang, Ruoyu Wang, Liwen Kong, Shanshan Liang, Shuai Tao

DOI: 10.1093/bib/bbae516

摘要

肿瘤克隆的异质性驱动着不同肿瘤细胞群体的选择和演化，形成复杂动态的肿瘤进化过程。虽然肿瘤整体DNA测序有助于揭示肿瘤内异质性，但由于拷贝数变异导致的突变多重性误判以及重建过程中的不确定性，限制了肿瘤演化的准确推断。本研究提出了一种新方法——REconstructing Tumor Clonal Heterogeneity and Evolutionary Relationships（RETCHER），它通过准确识别突变多重性并考虑不确定性，刻画更为真实的癌细胞比例，同时评估亚克隆聚类的可信度和合理性。该方法能够全面、准确地推断多种形式的肿瘤克隆异质性及系统发育关系。在模拟数据和五种肿瘤类型的多样本测序真实数据中，RETCHER均优于现有方法，能更清晰地揭示亚克隆结构和进化关系。通过精确分析肿瘤内复杂的克隆异质性，RETCHER为肿瘤演化研究提供了新途径，也为精准个性化治疗提供科学依据。该工具免费开源，网址为https://github.com/zlsys3/RETCHER。

Abstract

The heterogeneity of tumor clones drives the selection and evolution of distinct tumor cell populations, resulting in an intricate and dynamic tumor evolution process. While tumor bulk DNA sequencing helps elucidate intratumor heterogeneity, challenges such as the misidentification of mutation multiplicity due to copy number variations and uncertainties in the reconstruction process hinder the accurate inference of tumor evolution. In this study, we introduce a novel approach, REconstructing Tumor Clonal Heterogeneity and Evolutionary Relationships (RETCHER), which characterizes more realistic cancer cell fractions by accurately identifying mutation multiplicity while considering uncertainty during the reconstruction process and the credibility and reasonableness of subclone clustering. This method comprehensively and accurately infers multiple forms of tumor clonal heterogeneity and phylogenetic relationships. RETCHER outperforms existing methods on simulated data and infers clearer subclone structures and evolutionary relationships in real multisample sequencing data from five tumor types. By precisely analysing the complex clonal heterogeneity within tumors, RETCHER provides a new approach to tumor evolution research and offers scientific evidence for developing precise and personalized treatment strategies. This approach is expected to play a significant role in tumor evolution research, clinical diagnosis, and treatment. RETCHER is available for free at https://github.com/zlsys3/RETCHER.