Widespread perturbation of ETS factor binding sites in cancer

Abstract

Although >90% of somatic mutations reside in non-coding regions, few have been reported as cancer drivers. To predict driver non-coding variants (NCVs), we present a transcription factor (TF)-aware burden test based on a model of coherent TF function in promoters. We apply this test to NCVs from the Pan-Cancer Analysis of Whole Genomes cohort and predict 2555 driver NCVs in the promoters of 813 genes across 20 cancer types. These genes are enriched in cancer-related gene ontologies, essential genes, and genes associated with cancer prognosis. We find that 765 candidate driver NCVs alter transcriptional activity, 510 lead to differential binding of TF-cofactor regulatory complexes, and that they primarily impact the binding of ETS factors. Finally, we show that different NCVs within a promoter often affect transcriptional activity through shared mechanisms. Our integrated computational and experimental approach shows that cancer NCVs are widespread and that ETS factors are commonly disrupted.

A large-scale cancer-specific protein-DNA interaction network

“…We observed interactions involving all major TF families including homeodomains, Cys2His2 zinc fingers (ZF-C2H2), nuclear hormone receptors (NHRs), basic helix–loop–helix (bHLH), and basic leucine zippers (bZIP). Compared to the proportion of TF families in the array, we found an over-representation of interactions involving the EBF1, grainyhead, NHR, and AP-2 families (Figure 2E), which are known to play important roles in tumor growth and progression via diverse mechanisms (4,31–33). Interestingly, we found that AP-2, in particular TFAP2B, is also enriched compared to previous screens against developmental enhancers and cytokine gene promoters (Figure 2E and Supplementary figures 1A-B), suggesting that this TF family may be more actively involved in cancer regulation.…”

“…Gene expression is often dysregulated in cancer due to changes in copy number, mutation or epigenetic changes in promoter and enhancer regions, or changes in the expression or activity of transcription factors (TFs) and chromatin modifying enzymes (1,2). Among the affected genes are those involved in cell differentiation, proliferation, apoptosis, DNA repair, immune regulation, and general biological processes such as translation and RNA processing, ultimately contributing to cancer development, progression, and metastasis (3,4).…”

Rapid profiling of transcription factor-cofactor interaction networks reveals principles of epigenetic regulation

“…Critically, for each of the 346 consensus binding sites we also include all single-nucleotide variant (SV) sites on the microarray, defining 346 consensus+SV probe sets on the array ( Figure 1B). By profiling the differential recruitment of each COF to the consensus+SV probe sets we can define COF “recruitment motifs” that can be matched against large motif databases (e.g., CIS-BP 36 and JASPAR 37 ) to infer the identity of interacting TFs at the level of TF family 31,32 ( Figure 1C-D) . The CoRec approach is inherently multiplexed as COF recruitment to all 346 TF sites is assayed in parallel.…”

“…The CoRec approach is built upon the PBM technology for the high-throughput measurement of protein-DNA binding [33][34][35] and extends work from our lab using PBMs to analyze the DNA binding of TF-COF complexes from cell extracts. [30][31][32] In CoRec, we apply nuclear extracts to a DNA microarray and profile the recruitment of a target COF to thousands of customized DNA sequences representing different TF binding sites. We infer the identity of the TFs involved in COF recruitment based on the pattern of DNA sites to which the COF is recruited.…”

“…CoRec builds upon our recent work applying the proteinbinding microarray (PBM) technology to study TF-COF complexes present in cell nuclear extracts. [30][31][32] We use CoRec to profile cell-specific TF-COF interactions for different classes of COF. In addition, we use CoRec to examine KAT-TF interaction networks in resting and stimulated T cells and determine how these networks relate to mechanisms of epigenetic regulation.…”