Multi-Omics Integration
Under scientific review
This introductory page is under scientific review. Use it for orientation, not clinical decision-making.
Educational note: multi-omics integration can generate strong hypotheses, but it does not automatically produce clinically valid biomarkers or treatment choices.
TL;DR
Multi-omics integration combines layers such as genomics, epigenomics, transcriptomics, proteomics, metabolomics, imaging, and clinical variables to understand tumor biology more completely than any single assay can.
The core risk is overfitting: high-dimensional data can create beautiful clusters and weakly validated predictions. Any clinical claim needs independent validation, reproducible preprocessing, and a clear endpoint.
What It Integrates
| Layer | Typical signal | Common use |
|---|---|---|
| Genomics | Mutations, copy-number changes, structural variants | Driver discovery, therapy matching, tumor evolution |
| Epigenomics | Methylation, chromatin accessibility | Cell state, lineage, silencing, enhancer programs |
| Transcriptomics | RNA abundance and splicing | Pathway activity, subtype, immune state |
| Proteomics | Protein abundance, phosphorylation | Signaling, target engagement, drug response |
| Metabolomics | Metabolites and flux proxies | Nutrient use, hypoxia, treatment resistance |
| Clinical/imaging | Stage, outcomes, pathology, radiology | Endpoint definition, risk models, validation |
Integration Strategies
- Early fusion: concatenate features before modeling. Simple, but vulnerable to scale and missingness.
- Late fusion: model each layer separately, then combine outputs. Easier to audit, sometimes less expressive.
- Intermediate fusion: learn shared latent representations across layers. Powerful, but can be hard to interpret.
- Graph-based integration: connect genes, proteins, pathways, variants, drugs, and phenotypes in a network.
- Multi-view clustering: discover subgroups using agreement across modalities.
Evidence Standards
Before treating a multi-omics result as actionable, check:
- Was the dataset split by patient, not by sample or tile?
- Was there an independent external cohort?
- Were batch effects, missingness, and ancestry/site bias addressed?
- Is the endpoint clinical, molecular, or only unsupervised clustering?
- Does the method explain which layer drove the result?
- Are code, features, and preprocessing steps reproducible?
What It Does Not Do
- It does not turn correlation into causality.
- It does not replace prospective clinical validation.
- It does not make a biomarker actionable without assay, threshold, indication, and treatment context.
- It does not remove privacy and governance requirements for patient data.
Useful Data Sources
- NCI Genomic Data Commons for harmonized cancer genomics.
- cBioPortal for cancer genomics exploration and clinical context.
- CPTAC for proteogenomic cancer datasets.
- ClinicalTrials.gov API for trial metadata when linking biomarkers to studies.
References
- Hoadley KA, Yau C, Hinoue T, et al. Cell-of-Origin Patterns Dominate the Molecular Classification of 10,000 Tumors from 33 Types of Cancer. Cell. 2018;173(2):291-304.e6. PMID: 29625048. https://doi.org/10.1016/j.cell.2018.03.022
- National Cancer Institute. Genomic Data Commons. https://gdc.cancer.gov/
- National Cancer Institute. Clinical Proteomic Tumor Analysis Consortium. https://proteomics.cancer.gov/programs/cptac
- cBioPortal. Cancer genomics portal. https://www.cbioportal.org/