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FIMM – Building a Bridge from Discovery to Medicine iCAN Digital Precision
Cancer Medicine Flagship

iCAN is an Academy of Finland national research and
innovation flagship for 2019-2026. At the core of iCAN is
a large pan-cancer public-private research partnership
project, combining cancer genetics, translational and
clinical cancer research, biobanks, information technology
and artificial intelligence (AI) in a novel way. The project
links deep molecular profiling of at least 15,000 tumours
with patients’ longitudinal health data. Using machine
learning and AI to extract relevant predictive features from
each dataset, the study is expected to increase knowledge
of immuno-oncology, the tumour microenvironment and
development of disease resistance, thereby resulting in
discoveries of new targets and contributing to the devel-
opment of improved therapeutics. The study currently in-
cludes 15 subprojects, many of which involve FIMM senior
staff. In addition, FIMM data scientists, cancer researchers
and technology platforms contribute to most subprojects.

Drug Sensitivity and Resistance
Testing platform

This unique platform developed at FIMM provides func-
tional profiling of patient-derived cancer cells or drug
responses to hundreds of approved and emerging inves-
tigational oncology compounds covering a wide range of
molecular targets. The most potent drugs are identified
based on cell viability or microscopy and image analysis.
The platform is used in FIMM’s translational precision
medicine pipeline and is applied in drug repurposing
studies, defining optimal patient groups for clinical trials
in collaboration with pharmaceutical companies.

Computational Systems
Medicine Approaches

A key area of focus for many FIMM researchers is the
development of AI and machine learning tools to integrate
clinical, functional, and genomic profiles to predict thera-
peutic targets and identify robust marker panels predictive
of treatment responses for improved clinical translation
and patient stratification. An additional key aspect is the
ability to predict synergistic, effective and safe drug com-
binations for individual patients. The work has resulted
in multiple high-profile publications. Furthermore, FIMM
researchers, along with their collaborators at the Helsinki
Institute for Information Technology (HIIT), have excelled
in several international crowdsourcing competitions with
solutions to highly demanding scientific problems, includ-
ing response prediction in cancer cells and patients.

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