Whole slide images reflect DNA methylation patterns of human tumors

Abstract

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.

Hypoxia Inducible Factors’ Signaling in Pediatric High-Grade Gliomas: Role, Modelization and Innovative Targeted Approaches

“…DNA methylation patterns are described in adult cancers as a mechanism explaining the changes in splicing patterns under hypoxia. Recent publications in aHGG described this post-transcriptional process as a new hallmark of aggressiveness and linked intra-aHGG hypoxia to hypermethylation [67]. This RNA splicing is not present and induced in pHGG in association with hypoxia.…”

Neuroblastoma GD2 Expression and Computational Analysis of Aptamer-Based Bioaffinity Targeting

“…Feature extraction is a method used to represent the state of an image as a simple vector where each vector component defines a specific measurable attribute of the image. The main aim of feature extraction for GD2-stained tissues biopsies or cell culture WSIs is to provide a means for quantifying stained cells with the potential to map their quantities to disease state including, but not limited to stage [94][95][96][97], gene expression profile [98], GD2 concentration, etc. Due to the high dimensionality of the image data (typically 100,000 × 100,000 pixels [99,100]), performing feature extraction is a necessary step for many biological image processing techniques as these features could correspond directly to disease state.…”

Precision Health for Chagas Disease: Integrating Parasite and Host Factors to Predict Outcome of Infection and Response to Therapy

“…If all goes well with the training, the computer can then type the cancer with high accuracy (Esteva et al, 2017;Gertych et al, 2019). Extending the histology example further, there is information in a complex image like tissue histology that reflects underlying genetic modifications, such as DNA methylation, and machine learning can identify those subtleties in a way that the human eye never could (Zheng et al, 2020). This approach can also be applied to molecular data de novo for gene discovery (Wood et al, 2018).…”