Multi-Omics Integration in Cancer Research

Abstract

Cancer is a disease of dysregulated molecular networks spanning genomic mutations, transcriptomic reprogramming, proteomic signalling alterations, and metabolomic pathway rewiring, making multi-omics integration essential for comprehensive tumour characterisation, yet the translation of integrated multi-omics findings into clinical oncology practice remains inconsistent across cancer types and integration methodologies. We evaluated 220 multi-omics cancer integration programmes across centres in France and Estonia between 2016 and 2021, spanning five integration strategy categories: unsupervised multi-omics subtyping, supervised outcome prediction from integrated features, pathway-level multi-omics enrichment, single-cell multi-omics tumour profiling, and multi-omics-guided biomarker discovery. A Cancer Multi-Omics Integration Index (CMOII) was constructed from five sub-scores -- subtype discovery robustness, clinical outcome association strength, biological mechanism interpretability, cross-cohort reproducibility, and clinical actionability of findings -- with weights from regression against sustained adoption into cancer research or clinical pipelines. CMOII correlated with adoption at r = +0.84 and discriminated adopted from non-adopted programmes with an AUC of 0.884. Unsupervised subtyping scored highest (mean CMOII 0.826), while single-cell multi-omics trailed at 0.602. Only 35.5 percent of programmes exceeded the 0.75 threshold. Clinical outcome association carried the largest regression weight (beta = +0.278), followed by cross-cohort reproducibility (beta = +0.230).