Individualized Systems Medicine (ISM) in Cancer, AML and beyond

We have established a close collaboration with hematologists at the Helsinki University Central Hospital (HUCH) to develop a novel Individualized Systems Medicine (ISM) strategy to accelerate translational cancer research. We combine biobanking, state-of-the-art genomic technology, high-throughput drug testing, and rapid clinical translation in a programme that has grown into a major effort at FIMM and HUCH. We selected adult Acute Myeloid Leukaemia (AML) as the primary model system for our studies due to lower complexity as compared to solid tumours. Millions of cells can be readily obtained for both molecular and ex vivo drug response studies. Sampling at the time of diagnosis, remission and relapse and drug resistance is easily accomplished. Indeed, analysis of temporal evolution patterns in individual patients represents the unique strength of our ISM strategy. AML patients, particularly those with treatment-refractory disease with less than 10% long-term survival expectation, desperately need new therapeutic options to replace the 30-50 year-old chemotherapeutic regimens. Refractory AML therefore represents an ideal initial indication to introduce individualized treatments to improve cancer care.

FIMM Research Groups involved in this Grand Challenge:

Group Kallioniemi    Group Aittokallio    Group Heckman    Group Wennerberg   Group Tang

FIMM Senior Researchers involved in this Grand Challenge:

Taija af Hällström   Milla Kibble    Pirkko Mattila  Vilja Pietiäinen  Gretchen Repasky  Markus Vähä-Koskela   Maija Wolf    Päivi Östling       

FIMM TC Units and Biobanking Infrastructure involved in this Grand Challenge:

Sequencing    High Throughput Biomedicine     Bioinformatics    Biobanking infrastructure

Main collaborators:

FHRB - Hematological biobank    HUS Comprehensive Cancer Center   

ISM is our new concept that combines multiple levels of medical, technological, scientific, and strategic aspects to practice translational cancer medicine as follows:


  1. Focus on individual patients: We will seek to understand and interpret the unique genomic and molecular profile of the disease in each individual patient.
  2. 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.
  3. 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.
  4. Consecutive sampling from different stages of cancer evolution: Deep understanding of mechanisms of drug resistance and cancer evolution for each patient.
  5. 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.
  6. Implementation: Patient consent and ethical permission allow implementation of actionable results in the clinic by physician’s discretion when no other therapy options exist.
  7. Aiming at strategic drug combinations: Identify synergistic drug regimens blocking multiple cancer subclones and “escape routes” for cancer cells.
  8. Systems medicine: Continuous circle of (re)analysis and improving models and understanding: learning from each patient and each consecutive sample.
  9. Aiming to design clinical trials: based on validated results across patients and model systems, building on mechanistic understanding and biomarkers for patient selection.


Last updated: 09.06.2017 - 20:16