Mapping Molecular Transitions in Barrett's-Associated Oesophageal Adenocarcinoma via Multi-Omics Integration and Pathway Activity Modelling.

Background/

Objectives

Interpretation of high-dimensional molecular profiling data requires computational strategies that go beyond single-layer analyses to resolve coordinated biological programs. Although multi-omics integration has advanced latent structure discovery, translating these cross-omics signals into interpretable, functionally meaningful molecular states remains a central challenge.

Methods

Here, we present an interpretable, pathway-centric multi-omics integration framework that combines expression, DNA methylation, and copy-number alteration data to capture nonlinear cross-omics interactions and enable biologically grounded representations of molecular states. We apply this framework towards oesophageal adenocarcinoma, a malignancy that remains relatively underexplored in integrative multi-omics and systems-level analyses, despite its rising incidence and poor prognosis.

Results

Using samples spanning Barrett's oesophagus and oesophageal adenocarcinoma from the Oesophageal Cancer Clinical and Molecular Stratification consortium, we demonstrate the framework's ability to resolve coordinated oncogenic, metabolic, immune, and cell-cycle programs that evolve across disease states.

Conclusions

Together, this work establishes a scalable and interpretable computational strategy for pathway-based multi-omics integration, along with providing a generalisable approach for molecular state assignment in complex biological systems.