Epigenetic profiling for the molecular classification of metastatic brain tumors.
Javier I J OrozcoTheo A KnijnenburgAyla O Manughian-PeterMatthew P SalomonGarni BarkhoudarianJohn R JalasJames S WilmottParvinder HothiXiaowen WangYuki TakasumiMichael E BucklandJohn F ThompsonGeorgina V LongCharles S CobbsIlya ShmulevichDaniel F KellyRichard A ScolyerDave S B HoonDiego M MarzesePublished in: Nature communications (2018)
Optimal treatment of brain metastases is often hindered by limitations in diagnostic capabilities. To meet this challenge, here we profile DNA methylomes of the three most frequent types of brain metastases: melanoma, breast, and lung cancers (n = 96). Using supervised machine learning and integration of DNA methylomes from normal, primary, and metastatic tumor specimens (n = 1860), we unravel epigenetic signatures specific to each type of metastatic brain tumor and constructed a three-step DNA methylation-based classifier (BrainMETH) that categorizes brain metastases according to the tissue of origin and therapeutically relevant subtypes. BrainMETH predictions are supported by routine histopathologic evaluation. We further characterize and validate the most predictive genomic regions in a large cohort of brain tumors (n = 165) using quantitative-methylation-specific PCR. Our study highlights the importance of brain tumor-defining epigenetic alterations, which can be utilized to further develop DNA methylation profiling as a critical tool in the histomolecular stratification of patients with brain metastases.
Keyphrases
- brain metastases
- dna methylation
- small cell lung cancer
- machine learning
- genome wide
- gene expression
- squamous cell carcinoma
- copy number
- single molecule
- circulating tumor
- cell free
- artificial intelligence
- single cell
- deep learning
- wastewater treatment
- clinical practice
- mass spectrometry
- smoking cessation
- basal cell carcinoma