An integrative ENCODE resource for cancer genomics

Abstract

ENCODE comprises thousands of functional genomics datasets, and the encyclopedia covers hundreds of cell types, providing a universal annotation for genome interpretation. However, for particular applications, it may be advantageous to use a customized annotation. Here, we develop such a custom annotation by leveraging advanced assays, such as eCLIP, Hi-C, and whole-genome STARR-seq on a number of data-rich ENCODE cell types. A key aspect of this annotation is comprehensive and experimentally derived networks of both transcription factors and RNAbinding proteins (TFs and RBPs). Cancer, a disease of system-wide dysregulation, is an ideal application for such a network-based annotation. Specifically, for cancer-associated cell types, we put regulators into hierarchies and measure their network change (rewiring) during oncogenesis. We also extensively survey TF-RBP crosstalk, highlighting how SUB1, a previously uncharacterized RBP, drives aberrant tumor expression and amplifies the effect of MYC, a wellknown oncogenic TF. Furthermore, we show how our annotation allows us to place oncogenic transformations in the context of a broad cell space; here, many normal-to-tumor transitions move towards a stem-like state, while oncogene knockdowns show an opposing trend. Finally, we organize the resource into a coherent workflow to prioritize key elements and variants, in addition to regulators. We showcase the application of this prioritization to somatic burdening, cancer differential expression and GWAS. Targeted validations of the prioritized regulators, elements and variants using siRNA knockdowns, CRISPR-based editing, and luciferase assays demonstrate the value of the ENCODE resource.

Automated CUT&Tag profiling of chromatin heterogeneity in mixed-lineage leukemia

“…To define the chromatin features around KMT2A fusion protein binding sites, we used AutoCUT&Tag to profile the active histone modifications H3K4me1, H3K4me3, and H3K36me3, and the silencing histone modifications H3K27me3 and H3K9me3. Together, these five histone modifications distinguish active promoters, enhancers, transcribed regions, developmentally silenced, and constitutively silenced chromatin 35 , and provide a straightforward picture of the regulatory status of a genome ( Supplementary Fig. 3c).…”

DiNeR: a Differential graphical model for analysis of co-regulation Network Rewiring

“…Many methods have been developed to address this goal. For instance, researchers investigated TF target gene gain and loss events and found distinct patterns between oncogenes and tumor suppressors [11]. Others combined expression changes with TF regulatory networks and identified TFs responsible for aberrant gene expression patterns, and then validated their discoveries Fig.…”

“…For example, scientists have constructed TF regulatory networks in order to mimic the physical binding of TFs to either enhancer or promoter regions during initiation of the transcriptional process of its target gene ( Fig. 2a, b) [11]. Edges in this type of regulatory network only focus on the local interaction between the TF and target gene pair, and do not consider the effect of the rest of the genome.…”

“…Studies have reported that disruption of the co-regulation relationships of TFs can result in gene expression alterations, which can consequently introduce phenotypical variations and even lead to disease [5][6][7][8][9]. However, various computational methods have been proposed to infer dysregulations of an individual TF by exploring differential expressions of the TF itself and its gene targets [10], or investigating the direct TF-gene gain and loss events [11], ignoring the higher-order combinatory binding patterns among TFs. Therefore, large-scale mining of TF co-binding changes in disease and normal states could provide new insights into gene dysregulation and opportunities for targeted therapies.…”

DiNeR: aDifferential Graphical Model for analysis of co-regulationNetworkRewiring

“…Many methods have been developed to address this goal. For instance, researchers investigated TF target gene gain and loss events and found distinct patterns between oncogenes and tumor suppressors [11]. Others combined expression changes with TF regulatory networks and identified TFs responsible for aberrant gene expression patterns, and then validated their discoveries through patient survival analysis [10].…”