Whole slide images reflect DNA methylation patterns of human tumors.
Hong ZhengAlexandre MomeniPierre-Louis CedozHannes VogelOlivier GevaertPublished in: NPJ genomic medicine (2020)
DNA methylation is an important epigenetic mechanism regulating gene expression and its role in carcinogenesis has been extensively studied. High-throughput DNA methylation assays have been used broadly in cancer research. Histopathology images are commonly obtained in cancer treatment, given that tissue sampling remains the clinical gold-standard for diagnosis. In this work, we investigate the interaction between cancer histopathology images and DNA methylation profiles to provide a better understanding of tumor pathobiology at the epigenetic level. We demonstrate that classical machine learning algorithms can associate the DNA methylation profiles of cancer samples with morphometric features extracted from whole slide images. Furthermore, grouping the genes into methylation clusters greatly improves the performance of the models. The well-predicted genes are enriched in key pathways in carcinogenesis including hypoxia in glioma and angiogenesis in renal cell carcinoma. Our results provide new insights into the link between histopathological and molecular data.
Keyphrases
- dna methylation
- genome wide
- gene expression
- deep learning
- papillary thyroid
- machine learning
- endothelial cells
- high throughput
- convolutional neural network
- optical coherence tomography
- copy number
- squamous cell
- renal cell carcinoma
- artificial intelligence
- lymph node metastasis
- multidrug resistant
- electronic health record
- wound healing