Scholarlink

<p>Tumor evolution is driven by various mutational processes, ranging from single-nucleotide variants (SNVs) to large structural variants (SVs) to dynamic shifts in DNA methylation. Current short-read sequencing methods struggle to accurately capture the full spectrum of these genomic and epigenomic alterations due to inherent technical limitations. To overcome that, here we introduce an approach to identify and analyze the genomic and epigenetic events in different stages of tumoral evolution from long-read sequencing of single-cell derived sublines. We then use it to profile 23 sublines of a mouse cutaneous melanoma cell line, characterized with distinct growth phenotypes and treatment responses. We develop a computational framework for harmonization and joint analysis of different variant types in the evolutionary context. Uniquely, our framework enables detection of recurrent amplifications of putative driver genes, generated by independent SVs across different lineages, suggesting parallel evolution. In addition, our approach revealed gradual and lineage-specific methylation changes associated with aggressive clonal phenotypes. We also show our set of phylogeny-constrained variant calls along with openly released sequencing data can be a valuable resource for the development and benchmarking of computational methods.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Long-read sequencing of single cell-derived melanoma sublines reveals divergent and parallel genomic and epigenomic evolutionary trajectories

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月16日

查看全文
  • Yuelin Liu,
  • Anton Goretsky,
  • Ayse G. Keskus,
  • Salem Malikic,
  • Tanveer Ahmad,
  • E. Michael Gertz,
  • Farid Rashidi Mehrabadi,
  • Michael Kelly,
  • Maria O. Hernandez,
  • Charlie Seibert,
  • Juan Manuel Caravaca,
  • Kayla Kline,
  • Yongmei Zhao,
  • Ying Wu,
  • Biraj Shrestha,
  • Bao Tran,
  • Arindam Ghosh,
  • Xiwen Cui,
  • Antonella Sassano,
  • Laksh Malik,
  • Breeana Baker,
  • Cornelis Blauwendraat,
  • Kimberley J. Billingsley,
  • Sandra Burkett,
  • Eva Perez-Guijarro,
  • Glenn Merlino,
  • Erin K. Molloy,
  • S. Cenk Sahinalp,
  • Chi-Ping Day,
  • Mikhail Kolmogorov

摘要

Tumor evolution is driven by various mutational processes, ranging from single-nucleotide variants (SNVs) to large structural variants (SVs) to dynamic shifts in DNA methylation. Current short-read sequencing methods struggle to accurately capture the full spectrum of these genomic and epigenomic alterations due to inherent technical limitations. To overcome that, here we introduce an approach to identify and analyze the genomic and epigenetic events in different stages of tumoral evolution from long-read sequencing of single-cell derived sublines. We then use it to profile 23 sublines of a mouse cutaneous melanoma cell line, characterized with distinct growth phenotypes and treatment responses. We develop a computational framework for harmonization and joint analysis of different variant types in the evolutionary context. Uniquely, our framework enables detection of recurrent amplifications of putative driver genes, generated by independent SVs across different lineages, suggesting parallel evolution. In addition, our approach revealed gradual and lineage-specific methylation changes associated with aggressive clonal phenotypes. We also show our set of phylogeny-constrained variant calls along with openly released sequencing data can be a valuable resource for the development and benchmarking of computational methods.