Main Projects and Clinical Trials

PRESORT and COSENSE

How do we match patients with the right treatment?

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DrugLogics and ONCOLOGICS

How do we use computer models to represent cancer cells?

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COLOGIC

How do we process patient samples in the lab?

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ColoPaint and Canserv

How does patient samples look under the microscope?

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COSENSE-1 Clinical Trial

How do we move to functional precision oncology in Norway?

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RETROSPONSE

How well do today’s treatments actually work in real life?

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PRESORT and COSENSE projects

In the PRESORT and COSENSE projects, we are trying to find better ways to match colorectal cancer patients with effective anti-cancer treatment. In the PRESORT project we use surgical biopsies from colorectal cancer patients to generate tumoroids, and build a library of tumoroid drug-responses. In the COSENSE project we will use needlebiopsies from patients with metastatic colorectal cancer to generate tumoroids, and will use the tumoroid drug responses to assign patients to first-line chemotherapy treatment.
People affiliated to this project:
Cristoffer Sakshaug
Ingrid Aune Bergstrøm
Henri Colyn Harry Bwanika

DRUGLOGICS and ONCOLOGICS projects

In the DrugLogics project, we develop Boolean models to simulate the internal behavior of cells, such as cancer cells. These models help us explore how cells respond to different drug treatments. As part of the ONCOLOGICS project, we tailor these models to match individual patients’ genetic profiles, allowing us to predict treatment outcomes more accurately. We then compare these predictions to real responses observed in lab-grown cancer models, cell lines or tumoroids, to validate and refine our approach.
People affiliated to this project:
Viviam Bermudez Paiva
Henri Colyn Harry Bwanika
Funding: The ERA PerMed funded the OncoLogics project. It's coordinated by NTNU, and includes partners from Institut Curie, Barcelona Supercomputing Centre, Uppsala University, Charité and the Greek SME ProtAtOnce. The aim is to study colon cancer patients treated at Institut Curie, and suggesting additional therapies in patients that failed to respond to molecularly matched therapies. 
Read more about this project in:
ONCOLOGICS ERA PerMed
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COLOGIC

In the COLOGIC project, we are developing robust protocols for high-throughput robotic drug screening using patient-derived tumoroids (PDTs). By integrating drug response data with molecular profiles (scRNA-seq, proteomics), we aim to support therapeutic decision-making within clinically relevant timeframes. The project contributes to designing clinical trials where treatment is tailored to each patient’s PDT response. Established 3D screening methods at NTNU and SINTEF will be adapted to incorporate functional testing into precision oncology pipelines.
People affiliated to this project:
Henri Colyn Harry Bwanika

ColoPaint and Canserv

In ColoPaint, we explore how morphological profiling can benefit precision medicine by Cell Painting a panel of colorectal cancer cell lines. We then set up a Cell Painting protocol for 3D solid spheroids. In Canserv, we adapt the protocols to actual patient-derived tumoroids (PDTs) within a clinical timeframe to scout for phenotypic biomarkers.
People affiliated to this project:
Christa Ringers

COSENSE-1 Clinical Trial

In the COSENSE-1 trial we stratify patients with metastatic colorectal cancer to the most promising treatment regimen, based on our tumoroid drug screening. Each patient’s treatment recommendation is highly specific due to the use of patient-derived material from “living biopsies”, enabled by our collaboration with the radiology department. COSENSE-1 is the first functional precision oncology trial in Norway. In the future we will expand the number  of possible perturbations in our assay, with the ultimate goal of treating cancer as an individual disease, “providing the right treatment to the right patient”.
People affiliated to this project:
Ingrid Aune Bergstrøm
Cristoffer Sakshaug
Henri Colyn Harry Bwanika

RETROSPONSE

In RETROSPONSE, we look back at how patients with metastatic colorectal cancer responded to their first treatment. By collecting and analyzing real-world data from hospital records, we aim to map out how well current therapies work in everyday clinical practice. These updated benchmarks will help us understand what “good response” looks like today — and serve as a baseline to measure the impact of new treatments coming into the clinic.
People affiliated to this project:
Andreas Eikså