Endometriosis partners to discover personalised treatments

Endometriosis

PrecisionLife and the University of Oxford have signed a data access agreement to license the Oxford Endometriosis Gene (OXEGENE) dataset to develop new personalised treatments for endometriosis patients.

Endometriosis affects 10% of women globally, but on average it takes over seven years for patients to receive a diagnosis. There are currently no approved diagnostic biomarkers or cures for the disease.

The OXEGENE dataset contains anonymised genotype data including disease stage and infertility status, from 1,000 surgically confirmed patients.

In analysing the OXEGENE data, PrecisionLife hopes to identify the genetic differences in people with endometriosis and the mechanisms driving their disease, to reduce the time to a more personalised diagnosis for patients.

PrecisionLife also aims to find novel drug targets for these disease mechanisms with biomarkers linking them to the patients who will benefit from the development of new treatments.

Professor Krina Zondervan, Co-Director of the Endometriosis CaRe Centre and Head of the Nuffield Department of Women’s & Reproductive Health, University of Oxford, said: “Endometriosis is a major health issue affecting women’s lives. PrecisionLife is a leader in identifying innovative ways to consider the joint effects of combinations of genetic risk variants that may identify biological drivers of complex diseases like endometriosis. We hope that the analysis of our data will lead to the development of precision medicines to improve the lives of patients.”

PrecisionLife and the University of Oxford are partners in the FEMaLe (Finding Endometriosis using Machine Learning) EU Horizon 2020 project, which aims to enable the delivery of precision medicine in endometriosis and improve quality of life for patients.

As part of this collaboration, PrecisionLife has identified the first biological subtypes of endometriosis, shedding light on distinct patient subgroups defined by combinations of genetic risk factors.

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