Scholarlink

<p>Computational modeling of somatic evolution, a process shaped by ecology and impacting both host cells and microbial communities in the human body, can capture important dynamics driving carcinogenesis. Here we considered models for esophageal adenocarcinoma (EAC), a cancer that has dramatically increased in incidence over the past few decades in Western populations, with high case fatality rates due to late-stage diagnoses. Despite advancements in genomic analyses of the precursor Barrett’s esophagus (BE), prevention of late-stage EAC remains a significant clinical challenge. Previous microbiome studies in BE/EAC have focused on quantifying static microbial abundance differences rather than determining population dynamics. Using whole genome sequencing data from a total of 505 esophageal samples, we first applied a robust bioinformatics pipeline to extract non-host DNA reads, mapped these putative reads to microbial taxa, and retained those taxa with high genomic coverage. When applying mathematical models of demographic stochasticity to sequential stages of progression to EAC, we observed evidence of neutral dynamics in community assembly within normal esophageal tissue and BE, but not EAC. In a large case–control study of BE patients who progressed to EAC versus BE patients with non-cancer outcomes (NCO) during follow-up (mean = 10.5&#xa0;years), we found that <i>Helicobacter pylori</i> deviated significantly from the neutral expectation in BE NCO only, suggesting that factors related to <i>H. pylori</i> or <i>H. pylori</i> infection itself may influence EAC risk. Additionally, stochastic simulations incorporating selection recapitulated non-neutral behaviors observed. Formally modeling dynamics during progression holds promise in clinical applications by offering a deeper understanding of microbial involvement in cancer development.</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

Community assembly modeling of the microbiome within Barrett’s esophagus and esophageal adenocarcinoma

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月10日

查看全文
  • Caitlin Guccione,
  • Igor Sfiligoi,
  • Antonio Gonzalez,
  • Justin P. Shaffer,
  • Mariya Kazachkova,
  • Yuhan Weng,
  • Daniel McDonald,
  • Shailja C. Shah,
  • Samuel S. Minot,
  • Thomas G. Paulson,
  • William M. Grady,
  • Ludmil B. Alexandrov,
  • Rob Knight,
  • Kit Curtius

摘要

Computational modeling of somatic evolution, a process shaped by ecology and impacting both host cells and microbial communities in the human body, can capture important dynamics driving carcinogenesis. Here we considered models for esophageal adenocarcinoma (EAC), a cancer that has dramatically increased in incidence over the past few decades in Western populations, with high case fatality rates due to late-stage diagnoses. Despite advancements in genomic analyses of the precursor Barrett’s esophagus (BE), prevention of late-stage EAC remains a significant clinical challenge. Previous microbiome studies in BE/EAC have focused on quantifying static microbial abundance differences rather than determining population dynamics. Using whole genome sequencing data from a total of 505 esophageal samples, we first applied a robust bioinformatics pipeline to extract non-host DNA reads, mapped these putative reads to microbial taxa, and retained those taxa with high genomic coverage. When applying mathematical models of demographic stochasticity to sequential stages of progression to EAC, we observed evidence of neutral dynamics in community assembly within normal esophageal tissue and BE, but not EAC. In a large case–control study of BE patients who progressed to EAC versus BE patients with non-cancer outcomes (NCO) during follow-up (mean = 10.5 years), we found that Helicobacter pylori deviated significantly from the neutral expectation in BE NCO only, suggesting that factors related to H. pylori or H. pylori infection itself may influence EAC risk. Additionally, stochastic simulations incorporating selection recapitulated non-neutral behaviors observed. Formally modeling dynamics during progression holds promise in clinical applications by offering a deeper understanding of microbial involvement in cancer development.