Disease Studies

Our approach

Biology is complex. Chronic diseases like dementia, schizophrenia, or metabolic disease don’t have simple, single gene explanations, so why should we expect our analyses to work one SNP at a time?

We developed the revolutionary PrecisionLife® platform to look deeper at the non-linear effects of combinations of genomic, clinical and epidemiological factors that influence disease.

We’re revealing what’s driving complex disease in different patient sub-groups and explaining why these patients show different disease risks and responses to therapy.


From Population to Personal®

We believe that the key to deliver better, more personalized precision medicine solutions is to find clinically relevant patient sub-groups and reveal the factors driving their different disease risks, progression rates and responses to drugs.

Our PrecisionLife platform enables us to stratify patient populations at the highest possible resolution, finding all of the factors associated with disease for sub-groups of patients.

These unique insights lead to new drug discovery and repurposing approaches to address unmet medical needs, provide patient stratification biomarkers that help select the right patients for clinical trials, and aid clinicians in selecting the most effective medicines for specific patients.


Driving precision medicine

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

Our methods

Our patented mathematical framework gives us an unparalleled ability to analyze huge multi-dimensional datasets and to accurately model the behaviour of complex systems. Developed by Dr Gert Møller over the last 30 years, this is uniquely capable of dealing with the complexity of patient datasets with millions of SNPs and other features, all of which may have an impact on a patient’s form of the disease and their response to therapy.

We use our PrecisionLife platform to identify high-order combinations of SNPs (including epistatic interactions) and other features in population-scale genomic, clinical, epidemiological and patient health datasets. These combinations capture the non-linearity of biological effects and the impact they have on disease phenotypes much better than existing methods based on single features (such as GWAS).

The disease insights generated enable novel, clinically relevant targets that were previously undetected to be identified and evaluated. They help explain the metabolic context and the functional role that key genes play in the disease. Such disease signatures provide strong, testable hypotheses for the mechanism of action of novel targets and also inform and accelerate downstream in-vitro and in-vivo target validation studies.


Figure 1. Schematic of a disease signature comprising a set of 6 SNPs (including several not found by GWAS analysis) that, as a combination, are found to be significantly co-associated with a highly reduced risk of developing breast cancer in a BRCA2 positive population. This signature can serve as a patient stratification biomarker, a component of a more personalized risk score, and provide a testable hypothesis relating the mechanism of action of a target to the patient's phenotype.

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Contact us

If you have any questions or would like to speak to us in terms of potential collaborations or partnership opportunities, please get in touch using the form below or email info@precisionlife.com and we will get back to you as soon as we can. Note that our Privacy Policy and Terms & Conditions apply.


UK

Unit 8b Bankside
Hanborough Business Park
Long Hanborough, OX29 8LJ

USA

1 Broadway Fl 14
Cambridge
MA 02142-1187

Denmark

Agern Allé 3
DK-2970
Hørsholm

Poland

CIC Ul
Chmielna 73m
00-801 Warszawa

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