At The Festival of Genomics and Biodata in Boston this summer, we heard from Aadel Chaudhuri (Vice Chair for Translational Research, Mayo Clinic) about the promise of multi-omics-based liquid biopsy assays for cancer detection and monitoring. Here, we summarise some key takeaways from his talk, and present some suggestions for the implementation of these approaches in the clinic.
Why use multi-omics-based liquid biopsy assays?
Our ability to perform multi-omics analyses is increasing, thanks in part to the rise of multi-analyte platforms, and computational frameworks for deconvolution and data integration. A growing interest in longitudinal monitoring has also emerged, alongside increasing availability of paired tissue, blood and outcome datasets for deep validation. All of these factors make multi-omics-based liquid biopsy more attainable, and more desirable, enabling a systems level view of cancer detection and treatment response.
How can the integration of multi-omic data improve the sensitivity and specificity of cancer detection and profiling?
Integrating multi-omic data improves the sensitivity and specificity of cancer detection and profiling in several key ways. In his presentation, Aadel discussed the following:
- Complementary Biomarkers: Different omic layers provide complementary information, filling gaps that one medium alone cannot. Combining these layers can provide more accurate distinctions between benign, pre-malignant and malignant states, allowing for more precise disease stratification.
- Enhanced Signal Detection: Despite the benefits of using a combined approach, some cancer-associated changes may only be detectable in one omic layer. However, by integrating multiple data types, subtle signals that might be missed by a single-omic approach can be captured, increasing sensitivity.
- Reduction of False Positives: Multi-omics integration helps filter out noise and non-specific findings, which can lead to false positives. For example, fragmentomics and methylomics together can better distinguish cancer-derived signals from background variation, improving specificity.
How can the multi-omic approach to liquid biopsy be further integrated into clinical workflows to improve cancer detection and profiling?
To further integrate the multi-omic approach to liquid biopsy into clinical workflows and improve cancer detection and profiling, the following steps could be considered:
- Adopt Multi-Omic Platforms: Moving beyond single-omic assays to platforms that combine genomics, epigenomics, transcriptomics, proteomics and metabolomics can help to provide a more comprehensive cancer profile. Read more about up-and-coming multi-omics platforms in our Multi-Omics Playbook, publishing this autumn!
- Utilise AI and Machine Learning: AI and machine learning techniques have already helped to advance the multi-omics field. By implementing these tools alongside advanced computational frameworks, complex multi-omics data can be integrated and interpreted so that diagnosis and treatment response prediction can be improved.
- Enable Longitudinal Monitoring: A benefit of using liquid biopsy approaches is that blood can be obtained easily and frequently with less distress for patients than a traditional tissue biopsy. By incorporating repeated, real-time sampling of blood into clinical workflows, you can carry out ongoing disease monitoring.
- Leverage Paired Datasets: Paired tissue, blood, and health outcome datasets can be used together to validate and refine these multi-omics approaches, ensuring clinical relevance and robustness.
- Predict Therapy Response with Epigenomic Signatures: Integrated cfDNA methylation and stemness-related signatures can be used to stratify patients by resistance risk and inform therapy selection. An example given in the talk was for prostate cancer.
- Standardise and Validate: As with all new techniques, it is important to develop standardised protocols and validate multi-omic assays in large, diverse patient cohorts to support regulatory approval and clinical adoption.
- Integrate with Clinical Decision Support: Embedding multi-omic results into electronic health records and clinical decision support tools can assist clinicians in making informed decisions.
Listen to Aadel’s recent interview here.
