Towards Personalised Medicine: One Type of Data Is Not Enough
To understand the biology of diseased organs, researchers can use different types of molecular data. One of the biggest computational challenges at the moment is integrating these multiple data types.
A new computational method, developed by researchers from the European Bioinformatics Institute, jointly analyses different types of molecular data and disentangles the sources of disease variability to guide personalised treatment.
The method, called Multi-Omics Factor Analysis, particularly useful for understanding cancer development, improving diagnosis, and suggesting new directions for personalised treatment, and is described in Molecular Systems Biology.
“The big challenge in cancer is that each patient’s disease is different from a molecular point of view, and has a unique set of molecular features that have led to its development,” explains Ricard Argelaguet, Predoctoral Fellow in the Stegle Group at the European Bioinformatics Institute (EMBL-EBI).
Multi-omics approaches integrate data from the genome, epigenome, transcriptome, metabolome, and other molecular data. These data types have different properties and dimensions and are difficult to integrate into a comprehensive analysis to build an individual’s molecular profile.
However, by combining multiple molecular data types (multi-omics), researchers can identify biomarkers — naturally occurring molecules, genes or molecular characteristics associated with a particular disease. Biomarkers are essential for clinical research and can be used to classify patients into different patient groups. By measuring biomarkers, we can understand a patient’s disease better and estimate what kind of treatment they will respond to best.
The method was tested on multi-omics data collected from 200 leukaemia patients, where it identified a series of factors that highlight the molecular variability between patients. This information could help researchers understand how cancer develops at an individual level. It could also help steer personalised treatment decisions.
“Our method allows researchers to do something that couldn’t be done before — to easily integrate complex molecular data from DNA, RNA, methylation and more to build a tumour’s molecular profile,” Argelaguet said.
“Using these profiles, the method can also stratify patients into groups that may benefit from different types of treatment,” he added.
In a second application, the researchers also used Multi-Omics Factor Analysis to analyse multi-omics data at a single-cell resolution. They are currently working on further improving the method so that it can cope with even larger data sets and additional experimental designs.