HERCULES

Comprehensive characterization and effective combinatorial targeting of high-grade serous ovarian cancer via single-cell analysis

HERCULES was a collaboration project focused on finding solutions to drug resistance in high-grade serous ovarian cancer. The project had funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 667403 from 1.1.2016 until 30.6.2021. The project has officially ended in 2021. Publications that result from the project will still be updated on this website. Please see the DECIDER project for currently ongoing research on ovarian cancer.

See the latest blog posts by the project researchers, staff and collaborators here.

Ovarian cancer kills more than 40 000 women in Europe and more than 150 000 women globally each year. High-grade serous ovarian cancer is the most common and most difficult to treat subtype of the disease. The high-grade serous tumours consist of several heterogeneous cell populations with a large number of mutations. This genetic variability of the tumours makes it difficult to find drugs that would be able to kill all the cancer cells and to which some of the cells would not become resistant during treatment. Therefore, though most of the patients respond well to surgery and chemotherapy initially, more than half experience relapse.

In the HERCULES project, we are studying samples from patients with high-grade serous ovarian cancer. The samples are analysed using mass cytometry; DNA, RNA and ChIP (chromatin immunoprecipitation) sequencing; and computational tools to find optimal biomarkers that would allow the identification of different cell populations from tissue samples. The use of single-cell sequencing for DNA and RNA allows for an unprecedented level of information to be gained from the tumour cell populations. Fresh patient samples and cell lines established from them will be used for examining the cancer cells’ response to anti-cancer drugs.

The data from these experiments will be used to establish computational models and develop computational tools to predict the most effective drug combinations to kill the cell populations. The key results will be validated using existing high-grade serous ovarian cancer data together with fresh samples, old biobank samples and in vivo models. Based on the results, a prototype of a commercial test for predicting the best drug combinations to individual patients will be developed.

For more information on ovarian cancer in Finnish, Swedish, French, Italian and English, please see the following websites:

• Cancer Society of Finland
• Swedish Cancer Foundation
• French National Cancer Institute
• Italian Association for Cancer Research
• Cancer Research UK