Work with us

Are you a ovarian cancer researcher?

Work with us on a project combining multimodal data and AI to better understand OvHGSC patient subtyping and treatment response.

Are you interested in exploring these questions?

We are looking for ovarian cancer researchers with patient data cohorts to work with us on a project to better understand OvHGSC patient subtyping and treatment response.

The population of interest is adult patients with OvHGSC patients, stage II or above with the current 1L treatment standard of OvC (chemotherapy +/- bevacizumab +/- maintenance with bevacizumab a/o PARPi).

Owkin can help you:

RNA ready

Access cutting edge tech, such as generating RNA sequencing data from tumor samples

AI ready

Finance and support the collection, curation and enrichment of datasets to make them AI ready

AI ready datasets

Use these newly generated AI ready datasets to perform your own research after the project

Data generation

Simplify your data generation processes, make datasets interoperable and promote collaborations with other PIs

Unlock opportunities

Unlock new project-independent funding opportunities

About the project

Molecular characterisation and subtyping of ovarian high grade serous carcinoma (OvHGSC) has been extensively investigated during the last decade; however, patient outcomes remain extremely poor. In particular, resistance to platinum therapy and early relapse during maintenance treatment, especially for HR-proficient patients, continues to be a significant issue, highlighting the need to identify new potential targets for such patient populations.

Owkin scientists are using the latest AI techniques to explore critical questions in this field. These include better understanding and characterizing patients with poor outcomes and high unmet medical need with the current 1L treatment standard of OvC and identifying biomarkers of such poor outcomes and potential new therapeutic targets and/or alternative drug combinations. Overall, for ovarian cancer AI methodologies have been developed to answer medical needs but very few models so far have used a multimodal approach using H&E slides and genomics.

Considering that histology is the standard examination to assess the staging and grading of OvHGSC, and genomics allows a better definition of personalized treatment in oncology, we believe a multimodal approach could facilitate a better understanding of the disease’s treatment and mechanism.

This project will use a broad range of multimodal data to better understand the underlying cellular mechanisms driving the subtype of OvHGSC patients, including:

  • Clinical (demographics, history of treatment) and routine biological data at diagnosis and HR status
  • Treatment and follow up data
  • Imaging: whole slides H&E from surgical specimen
  • Transcriptomics data: RNAseq and/or scRNAseq from tumors samples if available (can be generated from FFPE of tumor - from primary cancer)
  • Genomics data: WES if HR status not available