基于肿瘤DNA测序数据，重建肿瘤克隆异质性和进化关系
Reconstructing tumor clonal heterogeneity and evolutionary relationships based on tumor DNA sequencing data
影响因子:7.70000
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
摘要
肿瘤克隆的异质性驱动了不同肿瘤细胞种群的选择和演变，从而导致了复杂而动态的肿瘤演化过程。虽然肿瘤散装DNA测序有助于阐明肿瘤内异质性，但诸如由于拷贝数变化而导致的突变多样性的挑战和重建过程中的不确定性造成的挑战阻碍了肿瘤进化的准确推断。在这项研究中，我们介绍了一种新颖的方法，重建了肿瘤克隆异质性和进化关系（Retcher），该方法通过准确识别突变多样性来表征更现实的癌细胞分数，同时考虑重建过程中的不确定性以及亚克隆聚类的可信度和合理性。该方法全面，准确地渗透了多种形式的肿瘤克隆异质性和系统发育关系。 RETCHER在模拟数据上的现有方法优于现有方法，并在实际的多样本测序数据中从五种肿瘤类型的数据中进行了更清晰的亚克隆结构和进化关系。通过精确分析肿瘤中复杂的克隆异质性，Retcher提供了一种新的方法来进行肿瘤进化研究，并为制定精确和个性化的治疗策略提供了科学证据。预计这种方法将在肿瘤进化研究，临床诊断和治疗中发挥重要作用。 Retcher可在https://github.com/zlsys3/retcher免费提供。
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.