Niels Komen assistant professor

Research Niels Komen

Research team

Expertise

A few years ago we have erected ReSURG as a research unit within the department of abdominal surgery, as part of ASTARC. This group aims to perform surgical research of sound quality and impact, on different research levels. An additional objective is to train young colleagues in the field of research, either in the context of a "master thesis" or a PhD. Most projects can be considered a clinical research which can be considered a an expertise. I have initiated and executed many clinical trials, from retrospective case series and surveys to prospective randomized trials and meta-analysis. In clinical research large databases are essential and it seems only logical that cooperation and uniting different hospitals for a project will lead to bigger datasets and better results. Regarding are current research network, building and expanding a research network can also be considered an expertise. Based upon previous experience operating (with and without microscope) on pigs,rats and mice, animal experimental research is also an expertise. Since 2 years ReSURG is also active in the field of "patient derived organoids", in close collaboration with CORE. My expertise herein lies in the translational aspect of these project, being the connection to clinical reality.

Development of an image-based multiparametric drug response signature to predict clinical therapy response in cancer patients from ex vivo tumoroid screenings. 01/10/2022 - 30/09/2026

Abstract

Precision oncology has been shown to greatly improve outcomes of cancer patients, with tailored treatment approaches that consist of patient-directed therapies on the molecular characteristics of a patient. Despite this, chemo- and radiotherapy are still the basis of most standard treatment regimens, especially for gastrointestinal (GI) cancer patients. Importantly, there are significant differences in how GI cancer patients respond to standard-of-care (SOC) chemotherapy (CT) and chemoradiation (CRT), resulting in a majority of patients experiencing either over- or undertreatment and a delay in starting the optimal treatment. Tailored treatment approaches for SOC CT/CRT to enable precision oncology for these standard therapies is of high interest in order to improve quality-of-life and survival of GI cancer patients. With no existing predictive biomarkers for CT/CRT, and genomic profiling falling short on this front, there is therefore a clear unmet medical need for a novel model that can distinguish CT/CRT responders and non-responders in GI cancer patients. Patient-derived tumor organoids (PDOs), a functional precision oncology strategy, are 3D vivo models generated from individual patient tumor tissue and have recently emerged as a promising tool for predicting CT/CRT responses in cancer patients. PDO-guided treatment has not yet been implemented in the clinic, because some limitations need to be overcome first. With this study, we aim to overcome the most important limitations by developing a multiparametric, live-cell imaging-based drug response signature for ex vivo PDO screenings that enables monitoring of the true PDO drug response. We hypothesize that this will drastically improve the predictive value of PDOs and feasibility of using PDO drug screenings in routine clinical practice. To test this and as proof-of-concept we will also perform a multicentric prospective observational cohort study with our novel PDO screening platform for prediction of neoadjuvant CT/CRT response in rectal and esophageal cancer patients in regional hospitals. If successful, we aim to set up a prospective clinical phase-1 trial in the future, and on the long term implement our PDO drug response signature as a tool to help guide clinical decision-making of CT/CRT treatment choices for GI cancer patients.

Researcher(s)

Research team(s)

Project type(s)

  • Research Project

Predicting chemoradiation response for gastrointestinal cancer patients using patient-derived tumor organoids. 01/04/2022 - 31/03/2023

Abstract

Advances in precision oncology have been shown to greatly benefit outcomes of cancer patients, demonstrating the importance of shifting to tailored treatment approaches for all cancer patients. A significant proportion of gastrointestinal (GI) cancer patients treated with standard-of-care (SOC) chemo- and/or radiotherapy experience either under- or overtreatment and a delay in starting the optimal treatment. With no existing predictive biomarkers for chemo- and radiotherapy responses there is therefore a clear unmet medical need for a novel model that can distinguish chemoradiation therapy (CRT) responders and non-responders in GI cancer patients. Patient-derived tumor organoids (PDOs), three-dimensional (3D) ex-vivo models generated from individual patient tumor tissue biopsies or resections, have recently emerged as a promising tool for predicting chemo- and radiotherapy responses in GI cancer patients. Some limitations of PDOs, however, currently hamper the implementation of PDOs into the clinic. With this feasible 1-year project we will focus on overcoming the most important limitations: design and comparison of various ex-vivo drug panels and treatment schedules representative of clinical practice, optimization of our innovative in-house developed 3D organoid assay (OrBITS) for determining ex-vivo drug response, and reducing turn-around time. Our already successfully established tumor organoid biobank and validated 3D organoid assay will be great assets in this. Moreover, as a first proof-of-principal we will study the potential of PDO drug screenings for identifying in-vivo therapy response of CRT for GI patients, which will be obtained from retrospective clinical follow-up. Altogether, the project will enable us to reach our ultimate goal in the near future: implementing PDO drug screens at University Hospital Antwerp (UZA) as a tool to help guide clinical decision-making for cancer patients and for advancing precision medicine in oncology.

Researcher(s)

Research team(s)

Project type(s)

  • Research Project