Clinical trials

Currently, only a few validated biomarkers and models exist guiding or predicting treatment response for specific patient groups. Therefore, a patient-tailored clinical model needs to be developed to address tumour heterogeneity and thereby guide treatment selection on an individualized level. Organoids are patient-derived aggregates that grow in 3D and maintain self- renewal pluripotency and lineage-specific differentiation. Therefore, in contrast with conventional cell lines, they are thought to sustain patient heterogeneity and characteristics and are consequently already in use for drug response screening. This now offers the opportunity to investigate if the primary patient breast cancer organoid platform reflects disease progression, treatment response and relapse in patients with different clinical breast cancer subtypes. Goal: To develop a living biobank from prospective patient-derived breast cancer tissue. The questions we will address are: 1. Do patient-derived breast cancer organoids retain the clinical behaviour and characteristics of the primary patient tumour? 2. Can BC organoids be used to derive prognostic and predictive biomarkers to inform treatment decisions? 3. Can the investigators utilize BC organoids to discover novel actionable targets and combination treatments for therapeutic intervention for breast cancer patients? 4. Can BC organoids be used to discover mechanisms of treatment resistance and relapse?

For men with an aggressive form of prostate cancer, finding the right and effective treatment right away is challenging. Many of these men face a high risk of cancer recurrence: about half experience a relapse after surgery, and more than a third after undergoing radiation therapy. Men with metastatic prostate cancer have particularly poor prognoses, with a five-year survival rate of only 30% to 50%. In short, it is difficult to predict which treatment, or combination of treatments, will lead to longer survival for this group of men with aggressive (metastatic) prostate cancer. In the laboratory, it is possible to grow small samples of tumors into 3D mini-tumors. These mini-tumors retain the characteristics of the patient's original tumor tissue. Various treatments can be tested on these 3D mini-tumors to determine which therapy is most effective for each individual case. There are currently two techniques available for creating these 3D mini-tumors in the lab. In this project, we aim to investigate which of these two techniques works best in order to test and personalize treatments.

This registry will collect data from all patients treated with the MAASTRO HDR applicator. The goal is to deepen knowledge about dose-response relationships in rectal cancer boosting, which is essential for improving future treatment approaches and patient selection.

There is a high medical need to improve treatment outcome for high-grade and low-grade glioma since no curative treatment is available. To achieve this goal, a broader understanding is needed of the causes of inter-and intratumoral heterogeneity; glioma dedifferentiation and invasion; the major determinants of malignancy and treatment failure in glioma patients. Patient-derived organoid (PDOs) of high-grade gliomas and low-grade gliomas will be used to identify the mechanisms that underlie this malignant behaviour and treatment resistance. This insight may be used to develop patient avatars to simultaneously test multiple new treatment modalities that are predictive for survival and quality of life of glioma patients.

Meningioma are slow growing and frequently occurring intracranial tumors, responsible for 33% of all asymptomatic intracranial tumors and 13-26% of all symptomatic primary brain tumors. The 10-year survival rate is 72%. A variety of treatment options is available for symptomatic meningioma including surgical removal with or without radiotherapy or radiotherapy alone. These therapies can have negative impact on cerebral functioning. After high dose radiotherapy for primary or metastatic brain tumors 50-90% of \> 6 months' survivors develop irreversible disabling cognitive decline leading to premature loss of independence, reduced Quality of Life (QOL) as well as significant economic burden both at the individual as societal level. Especially for patients with a good prognosis like benign meningioma, maintaining neurocognitive function is crucial. Understanding the mechanisms underlying radiation induced cognitive decline is complex and which brain areas to spare are an important subject of research. Evaluation methods to assess cognitive function and predict cognitive decline are urgently needed, this will allow the development of optimized treatment strategies with the aim to preserve or even improve cognitive function in meningioma patients. Improvements in the field of neuroimaging techniques (i.e. advanced MRI techniques) have the possibility to identify areas susceptible to cognitive impairment. This allows in the future a more personalized radiation treatment by identifying patients at risk, by optimizing the radiotherapy dose to specific brain regions, that could eventually reduce or prevent, cognitive decline. Improvements in the field of radiotherapy for example by higher precision treatment such proton therapy have potential in obtaining these more individualized strategies.

The primary and general objective of the clinical introduction of the Standard Follow-up Program (SFP) as the current standard of care is to improve the quality of radiotherapy for head and neck cancer patients by reducing radiation-induced side effects without hampering treatment efficacy in terms of locoregional tumour control and overall survival and to systematically evaluate the beneficial effect of newly introduced radiation technology for this particular group of patients. The clinical introduction of the SFP will allow for a systematic and broad scale quality improvement cycle for head and neck cancer patients treated with radiotherapy. In fact, this methodology can be considered a kind of quality circle for the clinical introduction of new radiation techniques, aiming at continuous efforts for further improvement.

The aim of the study is to provide prospective data regarding microscopic tumor spread in all directions from the macroscopic tumor in pathology specimens, as seen by eye, and on imaging to define the target volume for endoluminal radiation boosting in rectal cancer patients.

Extra tissue will be taken from patient during a procedure in standard of care. Also, through an existing line, 10ml of extra blood will be drawn. From this material the investigator will try to establish matched normal and primary human lung cancer organoids.

The purpose of this study is to determine, by means of DNA and protein analysis, the relationship between DNA and protein profiles and a number of endpoints, which are important for the patient such as overall survival and side effects.

To acquire data during High Dose Rate (HDR) brachytherapy treatments aiming to track the brachytherapy source inside the patient verifying the delivery of the treatment plan. Medical images (CT,MRI and US) combined with software developed by the investigator will be employed to predict the response of the Investigational product during the treatment and will be used as reference.The IP will be placed on the side of the treatment table and will acquire data during the whole treatment, which will be compared against information derived from the treatment plan. The IP based method can detect errors and information about motion and treatment uncertainties that are not currently available.

The first proton therapy treatments in the Netherlands have taken place in 2018. Due to the physical properties of protons, proton therapy has tremendous potential to reduce the radiation dose to the healthy, tumour-surrounding tissues. In turn, this leads to less radiation-induced complications, and a decrease in the formation of secondary tumours. The Netherlands has spearheaded the development of the model-based approach (MBA) for the selection of patients for proton therapy when applied to prevent radiation-induced complications. In MBA, a pre-treatment in-silico planning study is done, comparing proton and photon treatment plans in each individual patient, to determine (1) whether there is a significant difference in dose in the relevant organs at risk (ΔDose), and (2) whether this dose difference translates into an expected clinical benefit in terms of NormalTissue Complication Probabilities (ΔNTCP). To translate ΔDose into ΔNTCP, NTCP-models are used, which are prediction models describing the relation between dose parameters and the likelihood of radiation-induced complications. The Dutch Society for Radiotherapy and Oncology (NVRO) setup the selection criteria for proton therapy in 2015, taking into account toxicity and NTCP. However, NTCP-models can be affected by changes in the irradiation technique. Therefore, it is paramount to continuously update and validate these NTCP-models in subsequent patient cohorts treated with new techniques. In ProTRAIT, a Findable, Accessible, Interoperable and Reusable (FAIR)data infrastructure for both clinical and 3D image and 3D dose information has been developed and deployed for proton therapy in the Netherlands. It allows for a prospective, standardized, multi-centric data from all Dutch proton and a representative group of photon therapy patients.