Main Projects and Clinical Trials
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.
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.
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.
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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”.
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
COSENSE-2: The Mona Lisa Project
In COSENSE-2: The Mona Lisa Project, we aim to build a broad drug-response library comprising both colorectal cancer and off-label drugs, using patient-derived tumoroids. This will help us to identify molecular signatures that predict treatment response and resistance. By optimizing Cell Painting – a high-content imaging technique that reveals early cellular stress and death signals – we will obtain rich image data. These data will be integrated with viability assays and DNA- and RNA sequencing to uncover biomarkers that support more personalized therapy decisions.
People affiliated to this project:
