Oncohistone interactome profiling uncovers contrasting oncogenic mechanisms and identifies potential therapeutic targets in high grade glioma.

Histone H3 mutations at amino acids 27 (H3K27M) and 34 (H3G34R) are recurrent drivers of pediatric-type high-grade glioma (pHGG). H3K27M mutations lead to global disruption of H3K27me3 through dominant negative PRC2 inhibition, while H3G34R mutations lead to local losses of H3K36me3 through inhibition of SETD2. However, their broader oncogenic mechanisms remain unclear. We characterized the H3.1K27M, H3.3K27M and H3.3G34R interactomes, finding that H3K27M is associated with epigenetic and transcription factor changes; in contrast H3G34R removes a break on cryptic transcription, limits DNA methyltransferase access, and alters mitochondrial metabolism. All 3 mutants had altered interactions with DNA repair proteins and H3K9 methyltransferases. H3K9me3 was reduced in H3K27M-containing nucleosomes, and cis-H3K9 methylation was required for H3K27M to exert its effect on global H3K27me3. H3K9 methyltransferase inhibition was lethal to H3.1K27M, H3.3K27M and H3.3G34R pHGG cells, underscoring the importance of H3K9 methylation for oncohistone-mutant gliomas and suggesting it as an attractive therapeutic target.

Integrating morphologic and molecular histopathological features through whole slide image registration and deep learning.

BACKGROUND: Modern molecular pathology workflows in neuro-oncology heavily rely on the integration of morphologic and immunohistochemical patterns for analysis, classification, and prognostication. However, despite the recent emergence of digital pathology platforms and artificial intelligence-driven computational image analysis tools, automating the integration of histomorphologic information found across these multiple studies is challenged by large files sizes of whole slide images (WSIs) and shifts/rotations in tissue sections introduced during slide preparation. METHODS: To address this, we develop a workflow that couples different computer vision tools including scale-invariant feature transform (SIFT) and deep learning to efficiently align and integrate histopathological information found across multiple independent studies. We highlight the utility and automation potential of this workflow in the molecular subclassification and discovery of previously unappreciated spatial patterns in diffuse gliomas. RESULTS: First, we show how a SIFT-driven computer vision workflow was effective at automated WSI alignment in a cohort of 107 randomly selected surgical neuropathology cases (97/107 (91%) showing appropriate matches, AUC = 0.96). This alignment allows our AI-driven diagnostic workflow to not only differentiate different brain tumor types, but also integrate and carry out molecular subclassification of diffuse gliomas using relevant immunohistochemical biomarkers (IDH1-R132H, ATRX). To highlight the discovery potential of this workflow, we also examined spatial distributions of tumors showing heterogenous expression of the proliferation marker MIB1 and Olig2. This analysis helped uncover an interesting and unappreciated association of Olig2 positive and proliferative areas in some gliomas (r = 0.62). CONCLUSION: This efficient neuropathologist-inspired workflow provides a generalizable approach to help automate a variety of advanced immunohistochemically compatible diagnostic and discovery exercises in surgical neuropathology and neuro-oncology.