Our approach

Since the advent of the Human Genome Project and widespread genotyping and sequencing of patients, analytical methods such as Genome Wide Association Studies (GWAS) have been used to try to identify new disease targets.

These methods have had some limited success, but work best when single variants are responsible for a large proportion of the disease risk, for example in some cancers and rarer genetic diseases.

In contrast, complex diseases are driven by multiple networks of interconnected causative factors and metabolic processes. In these diseases, patients’ disease risks, rates of progression and responses to therapy vary enormously due to combinations of their mutations, predisposing epidemiological features, co-morbidities and environmental influences.

PrecisionLife’s key innovation is a hypothesis-free combinatorial analytics platform that finds all significant combinations of these factors (disease signatures) that distinguish one patient sub-group accurately from another more.

Our combinatorial analytics approach allows us to deliver many key aspects of precision medicine:

• Generating more useful insights from population-scale patient datasets than GWAS, including disease risk and protective effects for complex diseases
• Distinguishing patient sub-groups more accurately with patient stratification biomarkers
• Identifying and validating novel disease targets with strong functional genomics and testable mechanistic hypotheses
• Predicting a novel target’s potential efficacy earlier to reduce late-stage failures
• Developing patient stratification criteria to design smaller, faster clinical trials and predictive complementary diagnostics products
• Creating Combinatorial Risk Scores to predict more accurately disease risk, trajectory and therapy response
• Building clinical decision support tools to help clinicians evaluate all available data and choose the right therapy for their patient