1. Comprehensive characterisation and modelling of the spatial and temporal landscape of tumour cell subpopulations during disease progression and therapy using prospective collection of multiple subsections of freshly acquired tumours
2. Identification of genetic and transcriptomic biomarkers for tumour cell subpopulations, in particular drug resistant cell subpopulations, using mass cytometry and single-cell deep sequencing
3. Development and application of integrative computational tools that will predict efficient combination therapy modalities among molecularly-targeted anticancer compounds that are able to kill the identified subpopulations in cell cultures and in vivo
4. Development and validation of a marketable prototype biomarker kit for predicting high-grade serous ovarian cancer patients’ response to combinatorial therapeutic modalities based on a formalin-fixed, paraffin-embedded (FFPE) sample from a tumour
The project started on January 1, 2016, and its main results will be posted here and published in international peer reviewed journals.
The latest update of the HERCULES Data Management Plan can be downloaded from here
More than 150,000 women die of ovarian cancer every year. Typically, patients with high-grade serous ovarian cancer (HGS-OvCa), which is the most common and lethal subtype of ovarian cancer, respond well to the platinum-based first-line chemotherapy, but the disease becomes increasingly resistant to the treatments, leading to progressive disease and death. In the HERCULES project our goal was to develop methods to firstly gain understanding of mechanisms of drug resistance in HGS-OvCa and secondly identify effective interventions to overcome resistance and kill chemotherapy resistant cancer cells.
HGS-OvCa tumours contain millions of cancer cells that have acquired a numerous and diverse set of changes in their genome, such as mutations and copy number variations. Some genetic aberrations may give a cancer cell the ability to resist chemotherapy. Thus, while a drug kills most cancer cells that do not have resistance to the drug, the remaining small fraction of resistant cells has a growth advantage. Thus, the tumour mass contains more and more resistant cells causing reduced effect of the following chemotherapy cycles. In HERCULES, we used state-of-the-art measurement technologies to obtain information from tumour samples collected at surgery before and after chemotherapy to reveal genetic aberrations that drive chemotherapy resistance. The core of the project was to develop methods to analyse data from tumour samples to find biomarkers that characterise chemotherapy resistant cells.
HERCULES successfully produced new knowledge on chemoresistance mechanisms in HGS-OvCa. Some clinical benefits from the research materialised already during the project period. The core of the project was to develop novel computational methods that enable translating large amounts of molecular level data obtained from tumour samples into knowledge and medical benefits. All the methods developed in HERCULES are open-source and freely available with thorough documentation. Also, the data obtained in HERCULES are available for research purposes. However, as patient data are considered sensitive under EU legislation, the sequencing data are accessible only via the European Phenome Genome (EGA) archive, where a Data Access Committee reviews that the research plan and data security measures comply with the patient consent and EU legislation. As the project was based on a longitudinal patient cohort, the research results from larger numbers of patients as well as clinical benefits will take place later, when more patients have a longer follow-up time.
The main results of the project are 1) novel methods and mathematical models to transform data from tumour samples into knowledge, 2) knowledge of chemoresistance mechanisms that can be targeted pharmaceutically in HGS-OvCa, 3) a novel protocol to establish organoid cell cultures from tumour samples, 4) biomarkers that can be used to guide treatment for a subset of HGS-OvCa patients, and 5) improvements to HGS-OvCa patient care using results from genomics data. These results are published in scientific journals and can be found at https://www.project-hercules.eu/Publications.html.
Altogether, 180 HGS-OvCa patients from the Hospital District of Southwest Finland participated in the project. The number of tissue samples collected from these patients during surgery varied from 2 to 21 per patient, and in total over 1,400 samples from 12 different anatomical sites have been collected. To connect the information from the analyses to clinical information on how well the patient responded to the treatments, we compiled a database for storing the relevant clinical information. The clinicians working for the project were then able to export clinical information on the samples from the database in a pseudonymised format that allowed researchers in the project to use it in analyses.
The samples were first processed at University of Turku to deliver as good quality samples for further experiments and analysis as possible. To obtain in-depth information on the tumour cells, we used the latest sequencing and mass cytometry technologies. To identify and study the different cells that make up the tumours, we analysed DNA (genome), RNA (expression of the genes) and circulating tumour DNA (ctDNA; tumour DNA fragment in blood) from the samples at University of Helsinki (Finland) and set up new experimental methods to study the regulation of genes on cell cultures at University of Cambridge (UK). Measuring protein levels of important markers both on the surface and inside the cells to identify different cell populations was done at the Istituto Superiore di Sanita (Italy). At the Institute of Molecular Medicine Finland (FIMM), University of Helsinki, researchers tested the sensitivity or resistance of cell populations from patient tumour cell cultures to different drugs. New computational methods and models for the analysis and interpretation of the large amounts of data produced in the project have been developed at University of Helsinki and Institute Pasteur (France).
Biomarkers that were hypothesised to predict patient response were tested in biobank samples from Finland at University of Turku and from Italy at University of Trieste (Italy). The SME partner AB ANALITICA (Italy) optimised methods for DNA extraction from diagnostic formalin fixed samples samples and developed a prototype test for predicting patient response for chemotherapy.
Our results have been disseminated in scientific journals and symposiums for the research community and in collaboration with patient organisations to the HGS-OvCa patient communities. In addition, partners of HERCULES have been active in contributing to magazine articles, blogs, TV programs and newspapers to communicate the latest advances in HGS-OvCa research to the general public. All the of dissemination activities are listed in the Report on raising public participation and awareness (https://cordis.europa.eu/project/id/667403/results).
The use of single-cell measurement technologies and our experimental design of longitudinal, multi-regional sample collection before and after chemotherapy allowed us to characterise chemotherapy resistance in HGS-OvCa beyond any study published so far. For example, our HGS-OvCa tumour evolution study has ten times more patients with high-resolution sequencing than any previous HGS-OvCa tumour evolution study so far, which allowed for the first time to discover subgroups characterised with targetable genomic alterations.
One of the important results of HERCULES was a successful proof-of-concept of a novel approach for personalised medicine. We hypothesized that genomics data from patients could reveal the dominant mechanism of resistance and targeting it together with standard-of-care chemotherapy should lead to effective therapy. In our ctDNA publication we presented the result of this approach in patient care. Currently there are very few effective therapy options for patients with a chemotherapy resistant disease and more efficient and targeted treatment of patients, such as the new approached we developed, would reduce the number of expensive but inefficient and toxic treatments, hence improving both patient survival, wellbeing and savings in health care costs. This could also benefit the pharmaceutical industry and academic driven drug development projects.