Paper

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

Farid Rashidi Mehrabadi, Schäffer, Alejandro A, Sahinalp, S Cenk, Aldape, Kenneth, Ruppin, Eytan, Salem Malikić, Liu, Yuelin, Molloy, Erin, Gopalan, Vishaka, Mount, Steve M, Li, Xuan C, Pratt, Drew, Crawford, David R

2023

Overview

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-cell 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 the single-cell bisulfite-treated whole genome sequencing data of multiregionally-sampled tumor cells from 9 metastatic colorectal cancer patients made available by Bian et al., as well as multiregionally-sampled single-cell reduced-representation bisulfite sequencing data from a glioblastoma patient made available by Chaligne et al.. We demonstrate that the tumor lineages constructed reveal a simple model underlying colorectal 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, in time a fraction of that used in prior studies. Interestingly, lineage-informative CpG sites identified by Sgootr are in inter-CpG island (CGI) regions, as opposed to CGI's, which have been the main regions of interest in genomic methylation-related analyses. Sgootr is implemented as a Snakemake workflow, available at https://github.com/algo-cancer/Sgootr.Competing Interest StatementThe authors have declared no competing interest.Footnotes* Manuscript and supplementary materials updated and revised. Author list updated.

• Publisher: Cold Spring Harbor Laboratory Press,Cold Spring Harbor Laboratory
• Subject: Bioinformatics / Bisulfite / Colorectal cancer / Computer applications / Copy number / DNA methylation / Genomes / Heritability / Phylogeny / Sparsity / Tumor cells / Tumors / Whole genome sequencing