Jeroen de Ridder

UMC Utrecht

In short, Jeroen de Ridder's research entails the following:

MaLMeC: Machine-Learned DNA Methylation Classification to enable tumor subtyping from liquid biopsies

Central Nervous System tumors are a rare and often lethal disease where accurate diagnosis plays a major role in making the right treatment choice. The precise molecular subtype of CNS tumors can be determined based on DNA methylation profiling. This will indicate the origin and mechanism of the particular tumor, guiding the treatment strategy.

However, accessing the tumor is often impossible without surgery, as these tumors usually reside inside the skull or spinal cord. This poses a challenge for neurosurgeons, as they have to operate on a tumor without knowing its specific type necessitating intraoperative diagnostics in which surgical samples are rapidly processed, and results are conveyed back to the operating room while the surgery is still ongoing.

We have recently developed ‘Sturgeon’, and AI application to sequence and classify tumor samples within 90 minutes, allowing exact molecular classification during surgery. Promisingly, tumor DNA fragments can also be obtained from cerebrospinal fluid. Our aim is to develop a variation of Sturgeon that is able to leverage the information from such circulating tumor DNA fragments, and to enable tumor classification from liquid biopsies (ie. Lumbar punctions or ventricular drain fluid).

A major advantage of this approach is that pre-operative tumor classification will be possible,allowing the surgical plan to be planned accordingly. The major challenge to be overcome is that the CSF contains a mixture of tumor and normal DNA. By leveraging real and simulated data we will train AI models to account for this mixture. In the four-year duration of this project we will both retrospectively and prospectively sequence at least 200 liquid biopsies from CNS tumor patients and strive for clinical implementation of this method.