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. In the HERCULES project we aim at overcoming drug resistance in high-grade serous ovarian cancer, which is the most common and lethal subtype of ovarian cancer. Typically, the patients respond well to the platinum-based first- line chemotherapy, but often the disease becomes increasingly resistant to the treatments, leading to progressive disease and death. The mechanism of chemotherapy resistance is poorly understood, and we lack effective treatment options for patients with chemotherapy resistant disease. The major objective of the HERCULES project is to develop effective therapy options for ovarian cancer patients.
Ovarian cancers contain millions of cancer cells that have acquired a numerous and diverse set changes in their genome, such as mutations and copy number variations. Some genetic aberrations may give a cancer cell the ability to resist chemotherapy. This means that even though the cancer drug kills most cancer cells, a small fraction of resistant cells has a growth advantage and the following chemotherapy cycles are less effective. In HERCULES, we use state-of-the-art measurement technologies to obtain information from cancer samples collected at surgery before and after the chemotherapy to reveal genetic aberrations that cause chemotherapy resistance. Major part of the project is dedicated to the analysis of data from tumour samples to find biomarkers that characterize chemotherapy resistant cells. This is followed by testing combinations of drugs in patient-derived cancer cell cultures. We will use this information to identify drugs or drug combinations that would be most efficient against the chemoresistant cells.
The HERCULES project started on January 2016 and the main results are 1) establishment of infrastructure where patient samples from surgery are processed and delivered to project partners for experiments, 2) functional data analysis pipelines for all data levels obtained in the project, 3) preliminary results that show high quality of the data and provide solid basis for subsequent research, and 4) a database for clinical data.
Within the first eighteen months, 92 ovarian cancer patients have been recruited for the study in the Hospital District of Southwest Finland; 62 of them diagnosed with high-grade serous ovarian cancer. The number of tissue samples collected from these patients during surgery has varied from 2 to 21 per patient, and in total 349 samples from 11 different metastatic sites have been collected. All the samples fulfilling quality standards have been processed for further analyses performed by the project partners. In order to connect the information from the analysis of the samples to clinical information to know how well the patient responded to the treatments, we have compiled a database where we store the clinical information relevant for the research project. The clinicians working for the project can then export clinical information concerning the samples from the database in an anonymized format that allows other researchers in the project to use it in their analyses.
The samples are first processed at University of Turku, where the researchers have optimised the methods to deliver as good quality samples for further experiments and analysis as possible. In order to obtain in-depth information on the tumour cells, we are using the latest sequencing and mass cytometry technologies. To identify and study the different cells that make up the tumours, we have been analysing the DNA (genome) and RNA (expression of the genes) from individual cells as well as tissue at University of Helsinki (Finland), and setting up methods to study the regulation of genes on cell lines at Karolinska Institutet (Sweden). Measuring protein levels of important markers both on the surface and inside the cells, which has been done at the Istituto Superiore di Sanita (Italy), is essential for identifying different cell populations. Based on the preliminary data from all these analyses, we have started narrowing down the markers that could be used to easily identify different cell populations from tumour samples. In the next phase of the project, the reduced set of markers will be used to sort the cancer cell populations and study the sensitivity or resistance of different cell populations to different drugs. In order to do that, the drug testing methods have been optimised at the Institute of Molecular Medicine (FIMM), University of Helsinki, using cell lines generated from the patient tumour samples.
At later stages of the project, once we have found markers that could be used to predict a patient’s response to the treatments, we will use biobank samples from Finland and Italy to validate those markers on a larger scale, and analyse their ability to predict disease progression. To prepare for that, we have started to identify the suitable tumour samples and collected clinical information on the treatments and outcome at University of Trieste, Italy, and University of Turku, Finland. We are also developing a test for predicting patient response for chemotherapy, and it is important to make sure that the test can be used in diagnostic samples, which are stored correspondingly as the biobank samples. To ensure that, the SME partner AB Analitica (Italy), in charge of developing the test, has been setting up techniques and facilities to prepare for further development. They have also performed feasibility and patent studies in the field.
Measurement technologies, especially single-cell technologies, used in this project are state-of- the art, and when combined with our sample logistic and data analysis expertise allow for unprecedented opportunities to identify chemotherapy resistant cells and therapies to kill them. Currently, chemoresistant cell characteristics are to a large degree unknown, and the ways to reliably identify them from a patient sample would help diagnostics, prognostication, as well as therapy decisions. Moreover, currently there are very few if any effective therapy options for patients with chemotherapy resistant disease and more efficient and targeted treatment of patients would reduce the number of expensive but inefficient and toxic treatments, hence improving both patient survival, well-being and savings in health care costs. When the tumours can be classified into more specific subtypes, it will also make drug development more efficient and cost effective, as the drugs can be targeted to the right patient groups in clinical trials, thus benefiting also the pharmaceutical industry and academic-driven drug development projects.