DDW Editor Reece Armstrong speaks to Dr Steve Gardner, Co-Founder and CEO of PrecisionLife about work the company is doing to improve our understanding of endometriosis.
RA: You’ve recently announced a collaboration with the University of Oxford to access genotype data on women with endometriosis. How can analysing this data improve our understanding of the disease?
SG: Endometriosis is a common disease that causes chronic pain and reduced fertility, reducing the quality of life of over 200 million women around the world. It currently takes eight years on average to confirm a diagnosis and requires invasive laparoscopic surgery, which can make symptoms worse. This creates a high barrier to accurate diagnosis and slows down access to such treatments as are available, and these do not work for everyone. It’s therefore crucial to find and validate diagnostic biomarkers that can lead to less invasive, faster diagnosis and novel therapeutic options to improve treatment of the disease.
We previously conducted a hypothesis-free study using genotype data from the UK Biobank, which resulted in the first mechanism-based stratification of endometriosis patients.
By analysing additional clinical and phenotypic data from the University of Oxford’s Oxegene dataset we hope to both replicate those findings and provide greater insights into the underlying genetic factors relating to specific phenotypes and symptoms that are seen in the disease.
We would like to identify novel targets for the major patient subgroups that we’ve identified with accompanying biomarkers to define which patients would benefit from new personalised treatments. These biomarkers could potentially also be used to select patients most likely to benefit from a drug targeting a specific mechanism, so that smaller, faster, more targeted clinical trials can be designed.
RA: How will this data improve the chances of finding novel precision medicines for endometriosis?
SG: At PrecisionLife, our approach enables us to identify and understand the biological mechanisms that are driving disease within specific subgroups of patients at a higher resolution than has previously been possible. With these insights we find druggable targets to create new ways of treating a disease and generate biomarkers to improve diagnosis of the disease and inform the selection of the most effective therapies for individual patients.
Through our data agreement with the Nuffield Department of Women’s & Reproductive Health at the University of Oxford, we’re able to analyse the genotypes of nearly 1,000 endometriosis patients who were not included in our original studies. This enables us to confirm our previous findings and validate the most important mechanisms. Crucially these patients are also well characterised clinically. This will be crucial in advancing the mechanistic understanding of specific aspects and subtypes of the disease, evaluating the effect of different therapies, and creating better, more personalised disease detection, prevention, and treatment strategies.
RA: Why is there currently so little understanding of endometriosis and no diagnostic biomarkers for this chronic disease?
SG: Chronic diseases, like endometriosis, are polygenic, caused by a complex interplay of multiple genes and other factors. They are also heterogenous, and this complex set of underlying disease drivers means that patients with the same diagnosis may well have different mechanistic etiologies and will benefit from different treatments.
The unpredictable variations caused by these interactions can’t be detected by the current gold-standard genomic analyses such as genome-wide associationsStudies (GWAS), and such methods assume there is a broadly similar cause of the disease being studied, which is not true for very complex diseases, especially those that are routinely poorly diagnosed. Genomic medicine has therefore had limited impact on improving diagnosis or treatment in endometriosis and other chronic diseases.
To overcome this challenge, PrecisionLife has completely reimagined how to analyse patient datasets and capture non-linear interactions between multiple genes and external factors in complex, heterogenous diseases.
Our combinatorial analytics platform identifies interactions between multiple variants and other clinical, environmental, and epidemiological factors that together impact the patient’s phenotype. The combinations that we identify enable us to build a much more detailed view of the complex, interconnected biology of chronic diseases and perform mechanistic patient stratification, which is essential to improving and personalising diagnosis and treatment. This has huge implications for drug development and patient care.
Only with such a high-resolution understanding of the mechanistic drivers of disease can diagnostic biomarkers and precision-targeted therapies be developed for conditions like endometriosis.
RA: How do partnerships such as yours with University of Oxford help with your ability to de-risk drug development projects?
SG: Making patients’ data accessible for responsible commercial research is key to driving innovation from initial concept to clinic implementation. Combinatorial analytics consistently demonstrates its ability to get much more signal from much less data, which can be of great benefit to patients.
An example of the power of the combinatorial approach is our recent analysis of long Covid, which found 73 genes associated with the disease where only a single genetic association had previously been identified.
Once novel targets have been discovered, understanding which patients will likely benefit from them, and using that to guide clinical trial design and recruitment is key to accelerating and de-risking their clinical development.
However, to achieve high-resolution patient stratification and identify novel drug targets, mechanistic biomarkers, and other valuable insights, we require access to patient data. We achieve this through collaborations with research consortia and non‑profit organisations, subject to independent ethics approvals and strict data usage provisions.
As well as our collaboration with the University of Oxford to access the Oxegene endometriosis dataset, we now have over 30 data access agreements specific to the diseases in our pipeline, without which our work would not be possible.
RA: Through the FEMaLe EU Horizon 2020 project you’ve identified the first biological subtypes of endometriosis. How important is this milestone in helping discover new treatments?
SG: FEMaLe is a Horizon 2020 project focused on improving diagnosis, prevention, and care in endometriosis. As a project partner, PrecisionLife analysed Danish and UK Biobank data to mechanistically stratify endometriosis patients, identify subgroup-specific risk factors, and gain insights into possible new drug targets and drug repurposing opportunities.
In a disease as complex as endometriosis it is very unlikely that one drug will work equally for everyone or address the myriad of symptoms that it can cause. These groundbreaking findings therefore offer significant potential for precision medicine approaches to inform the development of personalised treatment options, diagnostic tools, and delivery of quicker and better care for patients.
RA: Precision medicine approaches have typically been used within oncology but how much potential do they have with things like chronic diseases?
SG: Precision medicine holds immense potential not only within oncology but also across chronic diseases, which affect billions of people and account for more than 80% of the $10 trillion healthcare spending worldwide. Chronic diseases represent populations with huge unmet medical need, whether that is as basic as having their disease diagnosed quickly and accurately (as with endometriosis, long Covid or ME/CFS), or finding new therapeutic options and tools to help select the right therapy for a specific patient.
While the initial focus of precision medicine has indeed been on oncology and rare diseases, its principles can be applied to more complex chronic diseases with equally transformative effects. But this requires a different way of thinking about disease than we usually apply in oncology.
Chronic diseases such as endometriosis, cardiovascular disorders, diabetes, neurodegenerative conditions, and autoimmune diseases are very often multifactorial in nature, resulting from combinations of genetic predisposition, environmental influences, and lifestyle factors. Like cancer where different mutations respond to different medicines, this means that within a disease population, a treatment which may be effective for one subgroup of patients may not be effective for others with the same diagnosis. However, because combinations of multiple genes and other factors are involved, identifying the real drivers of complex diseases has been more challenging.
The opportunity for biopharma is in understanding which targets and treatments will work for each distinct subgroup of patients within a single diagnostic label. By revealing these drivers of disease with mechanism-based patient stratification, PrecisionLife’s combinatorial approach is now enabling precision medicine beyond rare disease and oncology, in multiple complex chronic disease.
As our understanding of complex disease biology continues to advance, we will see precision medicine becoming a central pillar of how we approach and manage chronic diseases, with personalised diagnosis, treatment, and prevention strategies that are tailored to the specific needs of patient subgroups, ultimately improving patient outcomes and quality of life.
RA: You’ve been using combinatorial analytics to analyse patient data and discover new biomarkers for multiple common diseases. What opportunities does this present? Are drug repurposing strategies possible and can clinical trial outcomes be improved through more targeted recruitment methods?
SG: At the heart of our approach are our mechanistic patient stratification biomarkers. These describe the clinically relevant patient subgroups which have a specific and distinct mechanistic cause for their disease.
We’ve now identified every clinically relevant patient subgroup in over 50 chronic diseases and discovered over 300 novel drug targets for them all, many with multi-indication potential. All our novel targets are supported by our patient stratification biomarkers which can be used to inform and de-risk every stage of drug development, accelerate regulatory approval and enhance market adoption.
For example, when a disease has more than one mechanistic etiology, the maximum efficacy that any treatment will be able to demonstrate is equivalent to the prevalence of that disease mechanism in the patients recruited to a clinical trial. When used to inform inclusion criteria (patient recruitment) for clinical programmes, our patient stratification biomarkers allow for the design of mechanistically targeted trials where more of the participants are likely to benefit from the drug. This means that trials can be considerably smaller, faster to read out and more likely to demonstrate clinical efficacy.
We’ve also performed combinatorial analysis retrospectively on Phase III trials data for top-five pharmaceutical companies to identify biomarkers that differentiate the patients who showed a strong drug response from those who did not benefit at all or as much. For Phase III clinical trials with marginal or insufficient efficacy, developing these biomarkers of drug response help to refine clinical trial designs and revive stalled programs to recover sunk R&D costs and the potential revenue stream associated with an approved drug.
Our disease insights also offer an accurate and scalable way to map safe and effective drugs to patient subgroups across multiple new disease indications. This opens up the route for much more effective indication selection and/or drug repositioning – using an existing drug or candidate in multiple diseases.
With our insights into hundreds of mechanisms and targets and how they affect major patient subgroups across over 50 diseases, we’re able to systematically search for such indication extension or drug repurposing opportunities, which provide a faster, cheaper, and de-risked route to the approval of treatments for patients with unmet medical needs.
DDW Volume 24 – Issue 4, Fall 2023
Dr Steve Gardner has over 30 years’ experience developing and commercialising data science and informatics in the healthcare and life sciences sectors. He is Chair of the UK Bioindustry Association’s Genomic Advisory Committee, a former Global Director of Research Informatics for Astra and has consulted with drug discovery and safety teams in over 20 biopharma companies.