A pipeline for predicting cancer grades for brain cancers

New article in Analytical Chemistry published

Predicting cancer grade with epitranscriptomic marks and Machine Learning

In a consortium allying a Mass Spectrometry platform, a cancer biology lab, several clinicians, and a bioinformatic team, we investigated how epigenetic marks on the RNA (a.k.a., the epitranscriptome) can help distinguishing the different grades of glioma tumors from tissue samples. We came up with a pipeline that proceeds surgical samples, measures the level of epitranscriptomic marks, and predicts the grade of this cancer. Our method delivers grade prediction with unmet sensitivity and specificity.

This research effort combines several disciplines: chemistry, cancer biology, and computer science. It was supported by the Occitanie region which funded the Mass Spectrometry platform for clinical purposes, known as the SMART project.

For more details, check out our publication in Analytical Chemistry entitled:

Multivariate Analysis of RNA Chemistry Marks Uncovers Epitranscriptomics-Based Biomarker Signature for Adult Diffuse Glioma Diagnostics

Authors: S. Relier, A. Amalric, A. Attina, I.B. Koumare, V. Rigau, F. Burel Vandenbos, D. Fontaine, M. Baroncini, J.P. Hugnot, H. Duffau, L. Bauchet, C. Hirtz*, E. Rivals*, and A. David* – *: co-corresponding authors.

Anal. Chem. 2022, doi: https://doi.org/10.1021/acs.analchem.2c01526

Eric Rivals
CNRS Research Director in Computer Science and Bioinformatics

My research interests include string algorithms, bioinformatics, genomics.