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

Abstract

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.

Supplementary Information: The online version contains supplementary material available at 10.1186/s12864-026-12545-w.

Keywords: Barrett’s esophagus; Cancer microbiome; Community assembly; Esophageal adenocarcinoma; Helicobacter pylori; Mathematical modeling.

Fig. 1

Neutral and non-neutral community assembly and model dynamics in the context of the esophagus microbiome. A Analysis workflow for included samples with available sequencing data: host DNA depletion, taxonomic classification, and modeling. B Model overview. C Neutral model with mixed microbes in the community and an equal birth and death rates among all microbial taxa. D Non-neutral model with potential ecological dominance of particular microbial taxa in the community enabled by different birth and death rates among microbial taxa

Fig. 2

Alpha- and beta-diversity metrics across disease sets. A Microbial alpha-diversity represented by taxon richness (i.e., unique observed features) across disease stages. B Microbial alpha-diversity represented by Shannon entropy across disease stages. C Differences in community structure represented by the RPCA-PCoA between disease stages (PERMANOVA p = 0.0001, pseudo-F = 487). Shape corresponds to the disease stage: square = Deshpande et al., 2018, circle = Paulson et al., 2022, diamond = Ross-Innes et al., 2015 & International Cancer Genome Consortium (ICGC) et al., 2020

Fig. 3

Community assembly dynamics in progression to EAC. Plots are ordered in sequence of disease state progression from normal to EAC (see Supplementary Table S1 for patient/sample inclusion). The red curves represent the neutral model fit to data points representing unique taxa at the species level. The gray regions delineated by red dotted lines represent 95% bootstrap confidence intervals (obtained by resampling the hosts 100 times with replacement and refitting) and the R² value represents goodness of fit to the neutral model. The color of the data point represents phyla. Helicobacter pylori is colored pink with a box outline. Vertical line indicates mean relative abundance = 10^–4. H. pylori is found with the following occurrence frequency in each group: 19.0% in Normal, 15.4% in GERD, 14.3% in BE NCO, 5.1% in BE CO, 8.8% in EAC cohort 1, 0.0% in EAC cohort 2

Fig. 4

Simulation results for BE patients with non-cancer outcome (NCO). A Same data as shown for BE NCO panel in Fig. 3, with neutral model fit for BE NCO patients. B Neutral simulation with data from the BE NCO patients using the adjusted source pool conditions for H. pylori. All (‘A’) microbes have an equal birth and death rate of 1. C Non-neutral simulation for BE NCO data with H. pylori birth and death rates equal to 1 and 0.5, respectively. For all other ('O') microbes, birth rates are drawn independently from a normal distribution (mean = 1, standard deviation = 0.1) and the death rate is set equal to 1. For A-C, pink designates data for H. pylori. For B-C, the source pool prevalence of the H. pylori taxa is set to 1e-5 (H. pylori _SP = 1e-5, see Results and Supplementary Figure S9 for details). SP = source pool; HP = H. pylori

Fig. 5

ICGC EAC cohort 1 data with fits assuming neutral model at steady-state, neutral simulation, and non-neutral simulation. A Same data as shown in Fig. 3, neutral model fit to data from EAC cohort 1 patients in ICGC dataset. B Neutral model simulation with data from the EAC cohort 1 patients in ICGC dataset. All (‘A’) microbes have an equal birth and death rate of 1. C Non-neutral model simulation with all microbes having variable birth rate drawn independently from a uniform distribution ranging from 1 to 6, and a death rate of 1. ICGC = International Cancer Genome Consortium