The ERC grant “Elucidating the mechanisms, heterogeneity and role of epigenetic topological alterations in cancer” has three aims, and we have achieved progress in all aims:
Aim 1: Decipher the regulation of DNA methylation at CTCF binding sites, and its impact on topology and gene expression.
For this aim we have analyzed changes in DNA methylation at CTCF binding sites upon perturbation of DNA methylation regulators in human embryonic stem cells. We identified which CTCF binding sites gain, lose, or maintained methylation levels and characterized the sequence features that differ between the three groups. Based on these results we are training machine learning algorithms to predict how different CTCF binding sites will behave under DNA methylation perturbations in the lab and in cancer.
Aim 2: Uncover mechanisms of oncogenic epigenetic topological alterations and their role in cancer.
In this aim we are focusing on the impact of topological alterations in three cancer types- melanoma, glioma and gastric cancer. We have established cell line models to study these alterations in lab for all three cancer types, as well as profiled tumors from relevant patients, in collaboration with the Hadassah Medical Center. We have identified changes in CTCF binding in all three cancers in numerous sites across the genome and are now studying their effect on chromosome topology, gene regulation and their role in disease development.
Aim 3: Disentangle the subclonal structure of topological and regulatory alterations and its interplay with genetic and transcriptional intratumor heterogeneity.
To promote this aim we have followed several paths. First, we have studied the heterogeneity of adenoid cystic carcinoma, a biphasic cancer that exhibit two cell types (myoepithelial and luminal epithelial), each with its own epigenetic state and different set of active enhancers. We identified how these different malignant cells communicate with each other and how this communication drives tumorigenesis. Second, we developed a method to estimate pathway activity from single cell RNA-seq data, and applied it to understand pathway heterogeneity and disruption in disease in the case of glioblastoma and COVID-19. Third, we are developing a method to estimate topological heterogeneity by identifying sets of cells where given gene pairs are coregulated.
