Keywords: machine learning, bioinformatics, methylation data, brain tumor diagnosis
Webpages:
https://www.molecularneuropathology.org/mnp More than 100 brain tumor entities are listed in the World Health Organization (WHO) classification. Most of these are defined by morphological and histochemical criteria that may be ambiguous for some tumor entities and if the tissue material is of poor quality. This can make a histological diagnosis challenging, even for skilled neuropathologists. Molecular high-throughput technologies that can complement standard histological diagnostics have the potential to greatly enhance diagnostic accuracy. Profiling of genome-wide DNA methylation patterns, likely representing a ‘fingerprint’ of the cellular origin, is one such promising technology for tumor classification.
We have collected brain tumor DNA methylation profiles of almost 3,000 cases using the Illumina HumanMethylation450 (450k) array, covering over 90 brain tumor entities. Using this extensive dataset, we trained a Random Forest classifier which predicts brain tumor entities of diagnostic cases with high accuracy (Capper et al. 2017). 450k methylation data can also be used to generate genome-wide copy-number profiles and predict target gene methylation. We have developed a
R package that includes a data analysis pipeline which takes data of the Illumina 450k array and the new EPIC array as input and automatically generates diagnostic reports containing quality control metrics, brain tumor class predictions with tumor class probability estimates, copy number profiles and target gene methylation status.
Besides sharing this
R package with cooperating institutes worldwide, we also offer a web interface that allows researchers from other institutes to apply the pipeline to their own data. Practical experience from different cooperating institutes show that application of our pipeline to Illumina methylation array data represents a cost efficient method to greatly improve diagnostic accuracy and clinical decision making.
References Capper, David, David Jones, Martin Sill, Volker Hovestadt, Daniel Schrimpf, …, Andreas von Deimling, and Stefan Pfister. 2017. “DNA Methylation-Based Classification of Human Central Nervous System Tumors.”
Submitted to Nature.
