Individualized Systems Medicine (ISM) in Cancer
Hundreds of drugs have either been approved or are being developed for cancer treatment, but understanding which patients respond to these drugs is a grand challenge for the research community and the physicians treating the patients, as well as for the pharmaceutical industry.
The Individualised Systems Medicine (ISM) Grand Challenge programme was established in 2010 to meet these needs as a collaborative research effort initially between FIMM and the haematology department at the Helsinki University Central Hospital Comprehensive Cancer Center. Later this effort has expanded to other departments at HUS as well as to other hospitals in Finland and elsewhere. The programme aims to improve treatment decisions and outcomes for cancer patients through sensitivity and resistance testing of patients’ tumour cells against 525 different drugs, combined with deep molecular profiling of the samples. Uniquely, the results for an individual patient are given to the treating physician who can adjust the treatment based on this individualised profiling information.
The ISM project in acute myeloid leukaemia (AML) was initiated at the same time as the foundations were being laid for the Finnish Hematology Register and Biobank (FHRB). The initial focus was on AML but the scope has expanded to include other haematological malignancies, such as multiple myeloma, and alsosolid tumors, such as urological, ovarian and lung cancer. Development of computational machine learning models to predict treatment responses is an essential part of the programme.
The ISM projects have resulted in collaborations with many pharmaceutical companies. By working together on patient-derived material, it will be possible to reposition existing drugs to new indications, as well as to better prioritise new pre-clinical leads for drug development. These types of collaborative studies with the clinic have made FIMM an attractive partner for research and development projects.The ISM projects attract international attention and promote FIMM and Finland to the global forefront of precision medicine research.
FIMM ISM researchers are also actively involved in the iCAN Digital Precision Cancer Medicine flagship project.
FIMM Research Groups involved in this Grand Challenge:
FIMM Senior Researchers involved in this Grand Challenge:
FIMM TC Units and Biobanking Infrastructure involved in this Grand Challenge:
ISM is our new concept that combines multiple levels of medical, technological, scientific, and strategic aspects to practice translational cancer medicine as follows:
- Focus on individual patients: We will seek to understand and interpret the unique genomic and molecular profile of the disease in each individual patient.
- Direct prediction of response to all drugs: Functional, large-scale drug response data are acquired from ex vivo primary culture of cancer cells from all patients.
- Real-time science: Biobanking, profiling, analysis, and interpretation of each case in 1-4 weeks, with feedback to the clinician. Scientists work in parallel with clinical developments.
- Consecutive sampling from different stages of cancer evolution: Deep understanding of mechanisms of drug resistance and cancer evolution for each patient.
- Integration of in vivo, ex vivo and in vitro data: Model systems will be designed to understand mechanisms and causalities, such as drug combinations, based on ex vivo data from patient samples. Thus patient samples and models are compared to one another.
- Implementation: Patient consent and ethical permission allow implementation of actionable results in the clinic by physician’s discretion when no other therapy options exist.
- Aiming at strategic drug combinations: Identify synergistic drug regimens blocking multiple cancer subclones and “escape routes” for cancer cells.
- Systems medicine: Continuous circle of (re)analysis and improving models and understanding: learning from each patient and each consecutive sample.
- Aiming to design clinical trials: based on validated results across patients and model systems, building on mechanistic understanding and biomarkers for patient selection.