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Arturo Loaiza-Bonilla1,2, Yan Leyfman3, Viviana Cortiana4

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This summary is machine-generated.

A new multi-omic stool screening approach combining host DNA methylation and gut microbiome data with AI shows promise for improved early detection of advanced precancerous lesions in colorectal cancer screening. This innovation could significantly enhance cancer prevention by identifying more precursor lesions.

Keywords:
artificial intelligencecancer preventioncolorectal cancer screeningdiagnostic accuracy reportinggut microbiomehost DNA methylationmulti-omic biomarkersmultitarget stool DNA

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Area of Science:

  • Gastroenterology and Oncology
  • Bioinformatics and Artificial Intelligence
  • Molecular Diagnostics

Background:

  • Colorectal cancer (CRC) screening faces challenges in detecting advanced precancerous lesions (APLs) through current noninvasive methods.
  • While stool-based screening reduces CRC mortality, its preventive impact is limited by under-detection of APLs like advanced adenomas and sessile serrated lesions.
  • Next-generation multitarget stool DNA assays offer high CRC sensitivity but require innovation in APL detection.

Purpose of the Study:

  • To present a consensus framework for a multi-omic stool screening paradigm integrating host epigenetic markers (DNA methylation) and gut microbiome features using artificial intelligence (AI).
  • To outline strategies for mitigating batch effects in microbiome-based models for reliable clinical application.
  • To propose a translational roadmap for developing and validating next-generation stool tests for improved APL detection.

Main Methods:

  • Integration of host DNA methylation and gut microbiome data using AI to capture complementary layers of early tumor biology.
  • Review of cross-cohort microbiome studies to assess the additive information provided by microbial signatures.
  • Development of practical mitigation strategies for batch effects, including harmonized pre-analytics and computational harmonization.
  • Proposal of a translational roadmap aligned with reporting standards (e.g., TRIPOD + AI, STARD, PROBAST-AI).

Main Results:

  • Multi-omic approach captures host epigenetic signals (DNA methylation) and luminal microbial features, reflecting early tumor biology.
  • Microbial signatures offer additive information for detecting CRC and precursor lesions, but models are susceptible to batch effects.
  • Scenario modeling suggests improving APL sensitivity from ~43% to 55-65% at ~94% specificity could detect 13-23 additional APLs per 1000 individuals.

Conclusions:

  • A multi-omic stool screening paradigm integrating host methylation, microbiome data, and AI offers a promising strategy for enhancing APL detection.
  • Addressing batch effects in microbiome analysis is crucial for the practical implementation and validation of these tests.
  • This framework guides the development of next-generation stool tests to significantly improve precursor lesion detection and advance colorectal cancer prevention.