Ependymomics: Multiomic Approach to Radioresistance of Ependymomas in Children and Adolescents

The resistance to treatment prompted us to perform the national PEPPI study (Pediatric Ependymoma Photons Protons and Imaging). PEPPI collated clinical, imaging and dosimetry data from children with intracranial ependymoma treated in France between 2000 and 2013. Since then, patients are treated in the current prospective SIOP II Ependymoma program. Our clinical results confirmed the classical clinical prognostic factors including radiotherapy dose, and we showed that imaging biomarkers from T2/FLAIR, perfusion and diffusion MRI were novel prognostic factors. We reported that relapse after RT occurs mainly locally within the high-dose region. The prognostic value of dose and the high rate of relapse with standard dose prompted us to perform an in silico dosimetry study of dose escalation comparing photons and protons, confirming the feasibility of this approach. In PEPPI series, the prediction of the site of relapse with advanced MR imaging was not possible due to small sample of imaging data and the recent biomolecular classification was not available. Recently, DNA methylation profiling provided a novel classification of ependymoma in molecular subgroups harbouring a strong prognostic value.

EPENDYMOMICS project aims to determine the prognostic role of multimodal imaging, to identify radioresistant clusters predictive of relapse and to analyze the link between radiomics features and genomic markers, hence leading to a radiogenomics study. It will build on a database with a larger number of patients called NETSPARE (Network to Structure and Share Pediatric data to Accelerate Research on Ependymoma) to develop a radiomics approach, i.e. predict clinical endpoints by relying on quantitative features extracted from medical images using either handcrafted or automated features extraction algorithms. These features will be exploited and combined with other available variables (clinical, genetic, etc.) as improved decision support.

1. Data collection MRI: Diagnostic, FU until relapse (DICOM format)

- Sequences: T1W, T2W, FLAIR, T1W with & without contrast enhancement, diffusion-weighted imaging (DWI), perfusion-weighted imaging (PWI) Radiotherapy: CT scan, RTDOSE, RTSS, RTPLAN (DICOM RT format) Clinical: Age, surgery, chemotherapy, doses, late effects, relapse date Histology: Hematoxylin and Eosin (H&E)-stained histopathology slides Molecular Biology: Methylation groups RELA, YAP, PFA, and PFB 2. Data quality check and delineation of volume of interest to curate data before post-processing and analysis.

To ensure preliminary robust image segmentation the Radiotherapy Structure Set will be checked for each patient. The referring radiologist will confirm all imaging changes after RT. An expert radiation oncologist will segment volumes of post-treatment abnormalities in T1WI post contrast, in T2W/FLAIR imaging, as well as site of relapse and missing OARs. 3. Imaging data post-processing and analysis of changes after radiotherapy We will process the DWI and PWI with the Olea Sphere®3.0 software, a post-processing solution for MRI and CT scanners. We will extract and generate Apparent Diffusion Coefficient maps from DWI data calculated on a voxel-by-voxel basis and relative cerebral blood volume maps calculated from PWI data with an oscillation-index singular value decomposition routine and correction for T1-weighted leakage effects.