DNA methylation-based classification of central nervous system tumours

By D. Capper, David T. W. Jones, M. Sill, V. Hovestadt, D. Schrimpf, D. Sturm, Christian Koelsche, F. Sahm, L. Chavez, D. Reuss and 133 more. Nature, 2018

2,473 citations recorded by the source corpus.

Accurate pathological diagnosis is crucial for optimal management of patients with cancer. For the approximately 100 known tumour types of the central nervous system, standardization of the diagnostic process has been shown to be particularly challenging—with substantial inter-observer variability in the histopathological diagnosis of many tumour types. Here we present a comprehensive approach for the DNA methylation-based classification of central nervous system tumours across all entities and age groups, and demonstrate its application in a routine diagnostic setting. We show that the availability of this method may have a substantial impact on diagnostic precision compared to standard methods, resulting in a change of diagnosis in up to 12% of prospective cases. For broader accessibility, we have designed a free online classifier tool, the use of which does not require any additional onsite data processing. Our results provide a blueprint for the generation of machine-learning-based tumour classifiers across other cancer entities, with the potential to fundamentally transform tumour pathology.

Subfields: Cancer Research, Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/c3864c7e10f6b0bc0e78c1de18872261effda660

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cancer Research

1. Olivier Elemento
2. Olivier Gevaert

In Genetics

1. James Y. Zou
2. Olivier Elemento
3. Neel S. Madhukar

In Molecular Biology

1. James Y. Zou
2. Olivier Elemento
3. Kyle Swanson
4. B. Bhinder
5. C. Gilvary
6. Neel S. Madhukar
7. Olivier Gevaert

Citations in the corpus

• Multimodal data fusion for cancer biomarker discovery with deep learning (2023, result): “Of late, some reports also suggest the use of DNA-methylation-based classification of central nervous system tumours 34,35 .”
• Artificial Intelligence in Cancer Research and Precision Medicine (2021, background): “Their subclass predictions for 139 cases did not match pathologists’ diagnosis, but follow-up of those select cases revealed that approximately 93% of those mismatched cases were in fact accurately predicted by the model (27).”