Multi-parametric Analysis of the Evolution and Progression of Low-Grade Glioma

In this Transcan project, coordinated by Andreas Bikfalvi (INSERM, University of Bordeaux), we have as partner 4, analysed low-grade gliomas (LGG). Tissue samples from 230 LGGs (Italian cohort) was first analyzed in Italy where the inclusion criteria were histological analysis and IDH1 status. In this cohort MRI and PET imaging data were also available. From these, we were able to perform robust proteomics analysis from 141 patients whereas in France gene expression analyses were performed on 104. Based on the radiological data where differences in radiological parameters were assessed (high signal vs. diffuse signal), two different cohorts were identified. The data were also clinically evaluated focusing on tumor progression parameters. Using all these data, the major aim of the Transcan project was to predict, using advanced bioinformatic analyses, LGG disease progression based on RNA-seq, proteomics and radiological data. During the project period we were able to increase the amount of proteins identified in each patient to > 5000. In collaboration with bioinformatics teams in France, a gene signature involving 15 genes and a proteomic signature involving 29 proteins have been identified that predict tumor recurrence. Our group is now in the process of validating these signatures using our REK approved biobank and also to validate these signatures in a clinical context. Further research will focus on getting a biological understanding of these signatures in a biological context.

The impacts of the TRANSCAN project as described in the grant application were: - Scientific impact: Better understanding of the evolution of LGG - Technological impact: Computation models and algorithms - Translational impact: Development of a tool for prediction of the clinical evolution of LGG, potential biomarkers. Towards this impact we have had a central role in finding a predictive protein biomarker profile for LGG

-Hypothesis Tumor evolution is of primary importance in IDH1-mutated LGG regarding clinical outcome. Global genetic profiling of the primary tumors is not sufficient. A detailed multi-layer analysis needs to be undertaken. We postulate that an integrative analysis of imaging, transcriptional, proteomic data coupled with molecular patient data and immunohistology will provide a better understanding of the clinical evolution of IDH1-mutated LGG. This is a multilevel approach, where we aim at obtaining insights into the heterogeneity profiles of the tumor. The aim is to integrate the data by computational modelling for prediction of clinical evolution of the disease. -Aims The aim is to elaborate a predictive model for LGG progression by using a novel approach coupling mathematical modelling and statistical learning adapted to high dimensional data. The models will be further refined by combining standard molecular analysis as well as expression analysis, proteomics and infrared imaging. -Methods The project is divided into several steps: i) Construction of the predictive model of clinical progression based on standard clinical, molecular and imaging characteristics in a cohort of 150-200 patients followed over up to 5 years ii) Deep evaluation of the misclassified patients based on further imaging, transcriptomics and proteomics iii) Supervised analysis of each high-dimensional datasets based on appropriate and new mathematical and statistical models iv) Construction of the predictive model and improving the model with the transcriptomics and proteomics analyses and Validation of the model using a test sample. -Expected results and potential impact A predictive model for patient stratification and prediction at onset of diagnosis is the ultimate deliverable that should improve the clinical management of patients. Additional results should be provided by the multilevel approach leading to a better understanding of the disease.