A Multi-omics Data Integration Approach for Precision Medicine and Improved Clinical Trial Success
Principal Investigator: Dr Richard McEachin
Approved Research ID: 57686
Approval date: February 17th 2020
This project will develop a novel analysis method and software package able to identify subtypes of disease based on the integration of multiple types of omics data. Many drug candidates fail and many patients receive inappropriate treatment because of our current inability to distinguish between subgroups of patients (respondent vs. non-respondents) and/or subtypes of disease (aggressive vs. non-aggressive). We are developing an approach that will be used both to optimize treatment by separating patients with aggressive disease from those with less aggressive disease, and to increase the success of clinical trials by sub-typing patient groups that are more likely to be respondents from those non-respondents. Our algorithm will first be used to sub-type patients based on variant data only, then clinical data and omics data will be incorporated to improve the sensitivity and specificity of the sub-typing. UK Biobank is a critical asset for this research because it offers high quality omics and clinical data from a very large population sample. After fine-tuning our algorithm and establishing its effectiveness based on UK Biobank data, we will replicate the research in an independent dataset derived from the US ClinicalTrials.gov database. We anticipate that this research will significantly increase the effectiveness of clinical trials, and make a contribution to the field consistent with peer-reviewed publication. The proposed work is scheduled for a total of two years.