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Clinical Trial Details — Status: Recruiting

Administrative data

NCT number NCT05151718
Other study ID # 21 HLTETE 01
Secondary ID
Status Recruiting
Phase
First received
Last updated
Start date September 30, 2021
Est. completion date September 29, 2024

Study information

Verified date December 2022
Source Institut Claudius Regaud
Contact Anne LAPRIE, PU-PH
Phone + 33 (0)5 31 15 50 50
Email laprie.anne@iuct-oncopole.fr
Is FDA regulated No
Health authority
Study type Observational

Clinical Trial Summary

Treatment of childhood ependymoma, the second most frequent pediatric brain tumor, is based on surgery and radiation therapy. However, 50% relapse, mainly locally. Progress in imaging, molecular biology and radiotherapy ballistics has led us to propose the EPENDYMOMICS project, a multi-omics approach using artificial intelligence to detect the predictive characteristics of relapse, and to define innovative radiotherapy targets using multimodal imaging. We previously reported that the relapse sites are mainly located in the high-dose radiotherapy zone and that there appear to be prognostic factors for relapse based on anatomical and functional MRI abnormalities by diffusion and perfusion. In addition, recent studies in molecular biology have identified significant prognostic factors. The challenge now is to use and correlate all these findings in larger cohorts to tackle the radio-resistance of this disease. Our objective is to collate in a single database called NETSPARE (Network to Structure and Share Pediatric data to Accelerate Research on Ependymoma) the clinical, histological, biological, imaging and radiotherapy data from two consecutive studies that included 370 children and adolescents with ependymoma since 2000 in France. The EPENDYMOMICS project will comprise a clinical research team, three imaging research teams, two histopathology teams, and a biostatistics team working on NETSPARE. Our goal is to obtain a radiogenomic signature of our data, which will be validated with the English external cohort of 200 patients that is currently being analyzed. The perspective is to optimize the indications and volumes of irradiation that could in the future be used in a European translational research trial to tackle radioresistance.


Description:

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. All the MRI data will be strictly co-registered with the T1WI-PC and planning CT. 4. Radiomics harmonization We will perform the increase of the level of harmonization of images and/or extracted features on highly heterogeneous dataset due to its multiple sources. We will develop a GAN-based framework, translating heterogeneous images to match the properties of a standard dataset, such as a template reference image, or alternatively, one set of images chosen as a reference. We need to determine the relevant properties within images (local or global metrics, texture, edges, contrast, signal-to-noise ratio, etc.) and to ensure the ability of the framework to harmonize images without losing their clinically relevant informative and content. In the features space, numerous statistical approaches can be applied, such as normalization or batch effect compensation. ComBat has been shown to outperform other similar approaches and we will rely on Monte Carlo estimation for small samples. We will investigate the combination of the batch-correction methods with unsupervised clustering to deal with data presenting very high heterogeneity and a very small number of samples per batch. It might be beneficial and complementary to combine image-based and feature-based harmonization methodologies for improving the results of multicenter radiomics studies. We will evaluate the potential added benefit of both previously developed approaches in improving the results. The goal of this task will be to establish whether the first or the second approach (or the combination of both) is the most efficient, taking into account not only the absolute improvement observed in the results, but also the computing time and effort required to implement each approach in practice. 5. Biomolecular analysis Biological resources from PEPPI and SIOPII ependymoma cohorts of patients will be included. First, the diagnosis of intracranial ependymomas will be confirmed and then pathological features will be investigated. WHO 2020 classification of ependymomas: the cIMPACT-NOW (Consortium to Inform Molecular and Practical Approaches to CNS Tumor Taxonomy - Not Official WHO) decided to classify ependymomas according to location and the underlying genetic alteration. The current WHO classification is under revision. Biological studies required to classify intracranial ependymoma of the project: Immunohistochemistry: p65-RelA, H3K27Me3. FISH analysis (classification and Copy Number Variations). DNA-methylation profiling. RNAseq analysis. 6. Deep learning (DL) for molecular biomarkers of ependymoma For this purpose, the Zimmer team has designed and trained deep convolutional neural networks based on the Inception V3 architecture to distinguish ependymoma from glioma grades III and IV. Preliminary results based on an imaging data set indicate a high accuracy of discrimination and suggest the possibility to predict clinically relevant mutations from H&E images alone (manuscript in preparation). We will extend these DL approaches to predict resistance to therapy and metastasis. This will be done by using the histopathology images alone either as input or in combination with classifications based on molecular assays. At a technical level, we will use state-of-the-art deep neural network architectures based on convolutional layers and skip connections. To compensate for the moderate size of the training data set, we will make heavy use of transfer learning. Here, we will make specific use of the TGCA database, which contains images from very different cancers such as breast cancer, and adapt successful models by retraining on a subset of the ependymoma data acquired in this study. We also will adopt multi-scale image representations for a better exploitation of the image information at cellular and tissue levels, use H&E specific color normalization and methods for automated elimination of regions subject to imaging artefacts. We also will incorporate the developed methods in Imjoy. By decoupling the graphical user interface from the computational back end, it enables users to access high-end computing capabilities from any workstation or laptop. 7. Statistical analysis Several signatures will be developed and validated : a radiomic signature (RAD-Score), a radiomic and clinical signature (RADClin-Score), a molecular signature (MOL-score) and a global signature (Radiomic, Molecular and clinical signature). - RAD-Score associated with progression-free survival (PFS). To avoid biases, NETSPARE built from retrospective multicenter study including independent cohorts is a training cohort. The validation cohort to test the performance of the radiomic signature corresponds to the UK cohort of SIOP II Program (n=220). This division allows external validation. Training Cohort: the median PFS is estimated to be 5 years in literature. This corresponds to a 3.5-year PFS of 62%. With 370 patients and a median follow-up of 3.5 years, we expect at least 120 events. According to the rule of thumb recommending 10 events per variable of interest, a multiparametric model combining 10 variables may be trained. An alternative modeling strategy based on a penalized approach will be used to study the association between imaging parameters and PFS. As imaging biomarker data far exceeds sample size and are intercorrelated, penalized methods (previously used in different situations with spatial data) will be used for the regularization and selection of variables, by encouraging the grouping effect and select explanatory variables. Validation cohort: 66 events are expected. With this number of events, it is possible to detect with 80% power a hazard ratio of 0.5 between the low- and high-risk group (Logrank test two-sided 0.05). Demographic data: Continuous variables will be summarized by cohort using median, minimum, maximum and number of available observations. Qualitative variables will be summarized using counts, percentages and number of missing data. Comparison between cohort will be performed using the chi-square test (or Fisher exact test if applicable) for qualitative variables and the Mann-Whitney test for quantitative variables. - Radiomics signature development and validation: To study the association between imaging features and PFS, we will use an alternative modeling strategy based on the penalized Cox regression model. Elastic Net method encourages grouping effect and selects explanatory variables. We will perform a 10-fold cross-validation to select the best penalty parameter lambda. The mixing parameter α, other parameter of the Elastic net method, will be set to a default value of 0.5. The regression coefficients associated with each state of the different variables will be used to determine the RAD score. As the outcome is time-dependent, the risk score will be dichotomized using a time-dependent ROC curve to obtain a classifier (risk groups: poor vs intermediate vs good prognosis). The optimal threshold has been determined to obtain at 3 years a sensitivity of at least 80% and the best specificity. An interval validation will be performed using a leave k-out cross-validation. We will then apply the RAD-score signature to the validation cohort. Performances of the score will be evaluated using different criteria: monotonicity of the prognostic stratification assessed using Kaplan-Meier survival curves and Hazard Ratio (HR) estimation, discrimination evaluated by Harrell's C-Index and the D statistics, quality of the model and the proportion of explained variation estimated by the Akaike Information Criterion (AIC) and the RD² statistic, respectively. The ability of prognostic score to identify patients at high risk of relapse during the first 3 years will be evaluated by determining the sensitivity, specificity and predictive value using a time-dependent ROC curve. - RADClin-Score development and validation: To incorporate clinical and imaging informations, we will combine both low-dimensional clinical and high-dimensional imaging data in a global prediction model. First, we will completely ignore the imaging features. A Cox model will be fitted to identify clinical covariates associated with PFS. We will build a clinical linear predictor using a regression coefficient. Next, we will use Elastic Net with the clinical linear predictor in offset to identify imaging features. Finally, we will compute the RADClin-Score using a regression coefficient. We will plot a Venn diagram to compare feature selection with the RADClin-Score and the RAD-Score. We will compare the model using the different criteria previously presented.


Recruitment information / eligibility

Status Recruiting
Enrollment 370
Est. completion date September 29, 2024
Est. primary completion date October 1, 2023
Accepts healthy volunteers No
Gender All
Age group N/A to 25 Years
Eligibility Inclusion Criteria: - children with intracranial ependymoma - treated - included in PEPPI study, pediaRT or in SIOP II Ependymoma french program Exclusion Criteria: - NA

Study Design


Related Conditions & MeSH terms


Locations

Country Name City State
France Institut Claudius Regaud Toulouse

Sponsors (6)

Lead Sponsor Collaborator
Institut Claudius Regaud Assistance Publique Hopitaux De Marseille, Centre Hospitalier St Anne, Centre Leon Berard, Institut National de la Santé Et de la Recherche Médicale, France, Institut Pasteur

Country where clinical trial is conducted

France, 

References & Publications (32)

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Parker M, Mohankumar KM, Punchihewa C, Weinlich R, Dalton JD, Li Y, Lee R, Tatevossian RG, Phoenix TN, Thiruvenkatam R, White E, Tang B, Orisme W, Gupta K, Rusch M, Chen X, Li Y, Nagahawhatte P, Hedlund E, Finkelstein D, Wu G, Shurtleff S, Easton J, Boggs K, Yergeau D, Vadodaria B, Mulder HL, Becksfort J, Gupta P, Huether R, Ma J, Song G, Gajjar A, Merchant T, Boop F, Smith AA, Ding L, Lu C, Ochoa K, Zhao D, Fulton RS, Fulton LL, Mardis ER, Wilson RK, Downing JR, Green DR, Zhang J, Ellison DW, Gilbertson RJ. C11orf95-RELA fusions drive oncogenic NF-kappaB signalling in ependymoma. Nature. 2014 Feb 27;506(7489):451-5. doi: 10.1038/nature13109. Epub 2014 Feb 19. Erratum In: Nature. 2014 Apr 24;508(7497):554. Becksford, Jared [corrected to Becksfort, Jared]. — View Citation

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Siegfried A, Rousseau A, Maurage CA, Pericart S, Nicaise Y, Escudie F, Grand D, Delrieu A, Gomez-Brouchet A, Le Guellec S, Franchet C, Boetto S, Vinchon M, Sol JC, Roux FE, Rigau V, Bertozzi AI, Jones DTW, Figarella-Branger D, Uro-Coste E. EWSR1-PATZ1 gene fusion may define a new glioneuronal tumor entity. Brain Pathol. 2019 Jan;29(1):53-62. doi: 10.1111/bpa.12619. Epub 2018 Jul 13. — View Citation

Tensaouti F, Ducassou A, Chaltiel L, Bolle S, Habrand JL, Alapetite C, Coche-Dequeant B, Bernier V, Claude L, Carrie C, Padovani L, Muracciole X, Supiot S, Huchet A, Leseur J, Kerr C, Hangard G, Lisbona A, Goudjil F, Ferrand R, Laprie A. Feasibility of Dose Escalation in Patients With Intracranial Pediatric Ependymoma. Front Oncol. 2019 Jun 21;9:531. doi: 10.3389/fonc.2019.00531. eCollection 2019. — View Citation

Tensaouti F, Ducassou A, Chaltiel L, Bolle S, Muracciole X, Coche-Dequeant B, Alapetite C, Bernier V, Claude L, Supiot S, Huchet A, Kerr C, le Prise E, Laprie A; radiotherapy committee of the French Society for Childhood Cancer (SFCE). Patterns of failure after radiotherapy for pediatric patients with intracranial ependymoma. Radiother Oncol. 2017 Mar;122(3):362-367. doi: 10.1016/j.radonc.2016.12.025. Epub 2017 Jan 12. — View Citation

Tensaouti F, Ducassou A, Chaltiel L, Sevely A, Bolle S, Muracciole X, Coche-Dequant B, Alapetite C, Supiot S, Huchet A, Bernier V, Claude L, Bertozzi-Salamon AI, Liceaga S, Lotterie JA, Peran P, Payoux P, Laprie A; radiotherapy committee of the French Society for Childhood Cancer (SFCE). Prognostic and predictive values of diffusion and perfusion MRI in paediatric intracranial ependymomas in a large national study. Br J Radiol. 2016 Oct;89(1066):20160537. doi: 10.1259/bjr.20160537. Epub 2016 Aug 23. — View Citation

Tensaouti F, Ducassou A, Chaltiel L, Sevely A, Bolle S, Padovani L, Jouin A, Alapetite C, Supiot S, Huchet A, Bernier V, Claude L, Kerr C, Le Prise E, Bertozzi-Salamon AI, Liceaga S, Lotterie JA, Peran P, Laprie A; French Society for Childhood Cancer (SFCE). Imaging biomarkers of outcome after radiotherapy for pediatric ependymoma. Radiother Oncol. 2018 Apr;127(1):103-107. doi: 10.1016/j.radonc.2018.02.008. Epub 2018 Mar 2. — View Citation

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* Note: There are 32 references in allClick here to view all references

Outcome

Type Measure Description Time frame Safety issue
Primary Definition of radiogenomics signatures that are predictive of patient outcome The primary endpoint is progression-free survival (PFS), i.e. the time from diagnosis to progression or death from any cause. Patients alive at last follow-up are censored at this date. 30 months
Secondary Definition of new RT target volumes based on radiomic features and to choose their indications based on biomolecular prognostic factors Several indexes will be used to evaluate discriminant capacity: Harrell's C-Index and the D statistics, Akaike Information Criterion (AIC) and the RD² statistic 30 months
Secondary To predict resistance to therapy by using the histological data in digital format and the biomolecular data for analysis by methods based on artificial deep neural networks . Several indexes will be used to evaluate discriminant capacity: Harrell's C-Index and the D statistics, Akaike Information Criterion (AIC) and the RD² statistic 30 months
See also
  Status Clinical Trial Phase
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Recruiting NCT05620862 - Phase I Dose Escalation and Pharmacokinetics Clinical Trial of Mitoxantrone Hydrochloride Liposome in Children With Relapsed and Refractory Lymphoma and Solid Tumors Phase 1
Active, not recruiting NCT02520713 - The iCat2, GAIN (Genomic Assessment Informs Novel Therapy) Consortium Study
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Completed NCT04944875 - Effects of Music and Maternal Voice on Sedation Depth and Sedative Use During Pediatric Magnetic Resonance Imaging. N/A
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Recruiting NCT04897321 - B7-H3-Specific Chimeric Antigen Receptor Autologous T-Cell Therapy for Pediatric Patients With Solid Tumors (3CAR) Phase 1
Completed NCT05024331 - A Prediction Model of Hematological Recovery After High-dose Chemotherapy in Pediatric Solid Tumor
Active, not recruiting NCT04483778 - B7H3 CAR T Cell Immunotherapy for Recurrent/Refractory Solid Tumors in Children and Young Adults Phase 1
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Recruiting NCT03273829 - Study of Carfilzomib in Combination With Cyclophosphamide and Etoposide for Children With Relapsed or Refractory Solid Tumors and Leukemias Phase 1
Completed NCT03455140 - A Study Evaluating the Safety and Activity of Pegylated Recombinant Human Arginase (BCT-100) Phase 1/Phase 2
Completed NCT01853345 - iCAT for Recurrent/Refractory/HR Solid Tumors N/A
Active, not recruiting NCT03478462 - Dose Escalation Study of CLR 131 in Children, Adolescents, and Young Adults With Relapsed or Refractory Malignant Tumors Including But Not Limited to Neuroblastoma, Rhabdomyosarcoma, Ewings Sarcoma, and Osteosarcoma Phase 1
Not yet recruiting NCT05322187 - Sequential PD-1/PD-L1 Inhibitor and LENvatinib in TLCT and Refractory Hepatoblastoma After Chemotherapy Phase 2/Phase 3