Despite the demonstrated effectiveness of immunoregulatory agents such as immune checkpoint blockers (ICB) on refractory cancers, these therapies work satisfactorily only in a reduced subset of patients. Further, ICB treatments are not exempt of risks and are associated to very high costs. Reliable response biomarkers are needed to identify responders and non-responders, and conventional imaging modalities and/or wet biomarkers have not proved adequate.

Recently, the immune contexture of the tumor microenvironment was introduced as a new concept that classifies tumors by quantifying immune cell densities and other immune markers, and defines the chances for responding to immunotherapy. For the case of PD-1/PD-L1 inhibitors, patients are currently stratified by determining tumor expression of the target molecules from a biopsy collected prior treatment. However, the procedure is invasive, introduces risks of tumor cells dissemination and is associated with low sensitivity and specificity due to intratumoral heterogeneity.

Positron emission tomography (PET) is a powerful, quantitative, non-invasive imaging technique that permits longitudinal analyses of biological processes in vivo by administration of a radiolabeled probe. In this project we aim at exploiting PET technology for doing spatial and temporal tracking of intratumoral T lymphocytes and other relevant immune-markers to stratify patients amenable for immunotherapy, and to monitor responses to therapeutic interventions.

Principal investigators:

• Inigo Zubiaurre Martinez, Professor, UiT
• Turid Hellevik, Physicist and Researcher, UNN

The plasticity of GB tumor cells and their ability to infiltrate adjoining brain tissue limits the effectiveness of current cancer therapies. Microglia plays an important role in GB progression. Inhibition of EGFR and CSF-1R decreases microglia-stimulated invasion of GB cells.

Specific radiopharmaceuticals targeting EGFR or CSF-1R will be developed (WP3/Bergen) and applied as (i) diagnostic tool (¹¹C, ¹⁸F) and (ii) radionuclide targeted therapy with radiometals (radiotherapeutics) combined with chemo- and external radiotherapy.

To evaluate its EGFR or CSF-1R affinity and selectivity, radiopharmaceuticals will be tested by in vivo PET/SPECT/MR in healthy animals and orthotopic animals models of GB (UNN).

Principal Investigators:

• Mathias Kranz, Researcher, UNN
• Rune Sundset, Associate Professor, UiT

In spite of an enormous global research effort, astonishing preclinical cancer cures, and the approval of multiple formulations, nanomedicine’s impact on cancer patient care remains limited. Recently, it is becoming evident that this unsatisfactory exploitation may be tackled by considering nanodrugs’ extensive interaction with the immune system. Moreover, our collaborators recently demonstrated these interactions can be tuned and potentially utilized in (immuno)therapeutic settings.

At the same time, immunotherapy is rapidly developing into a powerful therapeutic modality in oncology. Nonetheless, it is now well-established that only a portion of treated patients respond and combination therapies are anticipated to greatly improve immunotherapeutic outcomes. In such synergistic therapeutic regimens, one component may serve to ‘prime’ patients for immunotherapy, for example through immune cell activation, or immune system rebalancing.

The innate immune system is emerging as an important regulator of anti-cancer adaptive immune responses; numerous mechanisms through which phagocytes and other innate immune cells can regulate anti-tumor immune responses are being discovered. Technology allowing for detection and imaging of these dynamic processes in patients will greatly benefit (immune)therapy selection and monitoring. Moreover, interference in these phagocyte specific pathways may be a potent approach to improve anti-cancer immune responses and boost immunotherapy.

In preliminary work for this project we have established that certain nanoparticles are specifically and extensively taken up by phagocytes. Now, in the “coastal collaboration”, we aim to develop these nanoparticles into diagnostic PET imaging and therapeutic agents in breast cancer. This will be facilitated by the integrative application of advanced in vivo imaging methods, including PET, MRI, and intravital microscopy, in conjunction with uniquely complementary and state-of-the-art ex vivo immune response and cell profiling methodology. Being part of the coastal collaboration offers exciting opportunities for interdisciplinary collaboration, validation of results, and free exchange of mutually beneficial tracers and protocols.

Principal Investigators:

• Sjoerd Hak, Researcher, NTNU
• Natasa Skalko-Basnet, Professor, UiT

The work package on machine learning will develop data analysis methods in support of the other work packages. The aim is to use expertise in artificial intelligence to help solving the medical research questions in the 180° North project.

Our work focuses in particular on how to fuse the information from data acquisitions with PET, MR and CT scanners. The individual imaging modalities provide insight in different aspects of the patient’s anatomy and physiology, but together they offer a more complete picture and can help reveal the underlying medical truth. In this context, we are particularly interested in developing methods for accurate translation between the medical image modalities (PET, MR and CT). By converting data from one image domain to another, we may complement the patient information with image modalities that have not been recorded, which may help to detect changes, anomalies and relevant artifacts in the images with a sensitivity that a human interpreter – meaning a qualified medical expert – cannot provide.

We will also address well-known physical, biological and technical limitations in preclinical PET imaging that lower the quantification accuracy during kinetic analysis in imaging of small animals, by developing novel PET quantification methods using machine learning. We will further use established machine learning methods, as well as own developed techniques, to detect, visualize, and quantify changes in the tumor tissue automatically over time from PET image data. This will be necessary in order to quantify subtle changes in the treated tissue at early time points after therapy. Lastly, we will build machine learning models that, based on collected data, will be able to predict outcome and efficiency of a given treatment already at an early stage of the treatment.

All these objectives will be essential for both brain, lung and breast cancer work packages. The research is performed in close collaboration with Siemens Healthineers, the producer of the medical sensors at Tromsø PET Center, and other research institutions within the 180° North project and its network.

Principal Investigators:

• Samuel Kuttner, Adjunct Associate Professor, UiT
• Luigi Tommaso Luppino, Postdoctor, UiT