Single-cell methylation sequencing data reveal succinct metastatic migration histories and tumor progression models

生物 谱系(遗传) DNA甲基化 CpG站点 甲基化 亚硫酸氢盐测序 肿瘤进展 癌症 计算生物学 进化生物学 遗传学 癌症研究 基因 基因表达
作者
Yuelin Liu,Xuan Cindy Li,Farid Rashidi Mehrabadi,Alejandro A. Schäffer,Drew Pratt,David R. Crawford,Salem Malikić,Erin K. Molloy,Vishaka Gopalan,Stephen M. Mount,Eytan Ruppin,Kenneth Aldape,S. Cenk Sahinalp
出处
期刊:Genome Research [Cold Spring Harbor Laboratory]
卷期号:: gr.277608.122-gr.277608.122
标识
DOI:10.1101/gr.277608.122
摘要

Recent studies exploring the impact of methylation in tumor evolution suggest that while the methylation status of many of the CpG sites are preserved across distinct lineages, others are altered as the cancer progresses. Since changes in methylation status of a CpG site may be retained in mitosis, they could be used to infer the progression history of a tumor via single-cell lineage tree reconstruction. In this work, we introduce the first principled distance-based computational method, Sgootr, for inferring a tumor's single methylation lineage tree and jointly identifying lineage-informative CpG sites which harbor changes in methylation status that are retained along the lineage. We apply Sgootr on single-cell bisulfite-treated whole genome sequencing data of multi-regionally-sampled tumor cells from 9 metastatic colorectal cancer patients, as well as multi-regionally-sampled single-cell reduced-representation bisulfite sequencing data from a glioblastoma patient. We demonstrate that the tumor lineages constructed reveal a simple model underlying tumor progression and metastatic seeding. A comparison of Sgootr against alternative approaches shows that Sgootr can construct lineage trees with fewer migration events and more in concordance with the sequential-progression model of tumor evolution, with a running time a fraction of that used in prior studies. Lineage-informative CpG sites identified by Sgootr are in inter-CpG island (CGI) regions, as opposed to intra-CGIs, which have been the main regions of interest in genomic methylation-related analyses.
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