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

Yuelin Liu, Xuan Cindy Li, Farid Rashidi Mehrabadi, Alejandro A. Schäffer, Drew Pratt, David R. Crawford, Salem Malikic, Erin K. Molloy, Vishaka Gopalan, Stephen M. Mount, Eytan Ruppin, Kenneth D. Aldape, S. Cenk Sahinalp*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Recent studies exploring the impact of methylation in tumor evolution suggest that although the methylation status of many of the CpG sites are preserved across distinct lineages, others are altered as the cancer progresses. Because 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-cell methylation lineage tree and for jointly identifying lineage-informative CpG sites that harbor changes in methylation status that are retained along the lineage. We apply Sgootr on single-cell bisulfite-treated whole-genome sequencing data of multiregionally sampled tumor cells from nine metastatic colorectal cancer patients, as well as multiregionally sampled single-cell reduced-representation bisulfite sequencing data from a glioblastoma patient. We show 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 with 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 interCpG island (CGI) regions, as opposed to intra-CGIs, which have been the main regions of interest in genomic methylation-related analyses.

Original languageEnglish
Pages (from-to)1089-1100
Number of pages12
JournalGenome Research
Volume33
Issue number7
DOIs
StatePublished - Jul 2023
Externally publishedYes

Funding

FundersFunder number
NCI-UMD
National Institutes of Health
National Cancer Institute

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