The Cancer Genome Atlas (TCGA) project aims to provide a comprehensive catalog of the key genomic changes in major cancer types, to foster effective diagnosis, treatment and prevention. The recently published TCGA HNSCC (head and neck squamous cell carcinoma) project, including 279 patient tissues, revealed that >84% of human papillomavirus-negative HNSCC harbor genetic alterations in tumor suppressor TP53, concurrent with amplification and expression of related family member ΔNp63 in ~47% cases, and altered TNF-α-NF-κB/REL, inflammation and death pathways in ~60% of cases.1 In addition, the TCGA network PanCancer 12 project performed integrative analyses on 3527 specimens of 12 cancer types, using five genome-wide high-throughput platforms.2 The project revealed a unique convergence that classified squamous cell carcinoma (SCC) of head and neck, lung and a subset of bladder cancers into a common subtype called ‘squamous-like’.2 This genomic classification suggests that those squamous cancer types shared more molecular similarities than other tumor types from the same tissue of origin. Consistent with findings in the HNSCC TCGA project, the hallmarks of these squamous cancers include high rates of TP53 mutation, amplification and overexpression of oncogenic isoform ΔNp63, and altered activation of TP53/TP63/TP73 and immune pathway genes linked to NF-κB/REL transcription factors. These newly identified SCC signatures raised several critical questions, including how and to what extent do TP63/TP73 and NF-κB/REL family transcription factors interact to regulate global gene programs in squamous cancers?
The TP53 family comprises TP53, TP63 and TP73 and their isoforms. TP53 is the most frequently mutated tumor suppressor gene in cancer, especially in squamous cancers, and is described as the ‘guardian of the genome’. Mutation or inactivation of TP53 promotes genomic instability by disrupting cell cycle arrest, DNA repair, senescence, apoptosis and autophagy of irreversibly damaged cells.3 Interestingly, however, the other TP53 family members, TP63 and TP73, are infrequently mutated, and can potentially compensate for disrupted TP53,4, 5 but their shared and distinct functions appear to be more complex than those of TP53 in tumorigenesis, as suggested by TCGA findings. TP63 and TP73 isoforms share a core structural architecture, sequence homology and potential but usually weaker tumor suppressor function than TP53, whereas they also exhibit distinct roles in development, adhesion, tumor promotion and inflammatory responses in normal and malignant epithelia.4, 6, 7, 8 The TA isoforms contain full-length N-terminal-transactivating domains that function as TP53 homologs, whereas ΔN isoforms having a truncated N terminus can serve as antagonists of TP53 and its TA counterparts, as well as promote cancer gene programs.9, 10 However, the genome-wide role and mechanisms whereby ΔNp63α/TAp73 promote tumorigenesis and linked to inflammation in cancer are undefined.
We recently revealed that inflammatory cytokine TNF-α stimulates binding of NF-κB subunit cREL with ΔNp63 to form a protein complex and displacement of TAp73 DNA binding, leading to the repression of growth arrest and apoptotic genes p21, NOXA and PUMA.6 Complexes between cREL, RELA and ΔNp63 were also implicated in reciprocal activation of several NF-κB-regulated genes.8 Interestingly, the NF-κB family proto-oncogene cREL is amplified and overexpressed in a subset of HNSCC and other cancers,11 but its functions are less well characterized.12 These experimental data suggest the hypothesis that NF-κB and TP53 family members could coordinate wider global and reciprocal cross-talk between cell death, survival and inflammatory gene programs. Specifically, are these interactions between cREL, ΔNp63α and TAp73 part of a novel mechanism that reciprocally regulates a genome-wide oncogenic program? Do the reciprocal interactions between cREL and ΔNp63α with TAp73 indicate their capability to bind TP53, NF-κB or other DNA response elements (REs)? Finally, what is the fate and functional significance of TAp73 displaced by the cREL/ΔNp63α complexes?
To answer these questions, we performed genome-wide chromatin immunoprecipitation assay, followed by high-throughput sequencing (ChIP-seq), and microarray profiling in HNSCC cell lines to explore the genome-wide regulatory role of cREL/ΔNp63α and TAp73 complexes. Furthermore, bioinformatic analysis of in vivo data from TCGA, immunohistochemistry and tissue array of HNSCC, and a ΔNp63α transgenic mouse model supports their contribution in the regulation of the cancer gene program. These findings present a new paradigm for how TNF-α and these TFs orchestrate gene programs implicated in cancer-related inflammation, survival and migration, and help explain the mechanisms underpinning the dysregulated network of TP53/TP63 and inflammation observed in the Pan-TCGA project.
TNF-α promotes enrichment and colocalized binding of cREL, TP63α and TAp73 in the regulatory regions and around transcription start sites genome wide
To investigate cREL, p63α and TAp73 binding activity genome wide, ChIP-seq was performed using UM-SCC46 cells, previously shown to exhibit higher expression of mtTP53, TP63 and TP73 proteins, and TNF-α modulation of their interactions in target gene regulation representative of HNSCC with mtTP53.6 We confirmed that TNF-α induced cREL and ΔNp63α, and partially decreased multiple TAp73 isoforms in nuclear extracts (Figure 1a), where TAp73 is predominantly detected in UM-SCC46 and other HNSCC lines under baseline conditions (Supplementary Figure S1A). TNF-α increased total genomic binding peak numbers and peak associated genes by cREL and p63α, while partially decreasing TAp73 binding activity (Figure 1b and Supplementary Table S1), and similar patterns were seen on individual chromosomes (Supplementary Figures S1B and S2). We observed that the percentage of genome-wide bindings were disproportionately enriched near or within genes (promoter, intragenic, transcriptional termination site region) compared with much larger intergenic regions (Figure 1c, upper panels). Cumulatively, over half of the binding peaks were within the promoter (~7–12%) and intragenic regions (35–41%). These perigenic region peak distributions were significantly enriched compared with the whole-genome background distribution, tested using the exact binomial test (Supplementary Figure S3). The binding activities within the intragenic regions were enriched in the first intron (Figure 1c, lower panels). After TNF-α treatment, cREL and p63α binding were substantially induced near the transcriptional start sites (TSSs), whereas the basal TAp73 TSS binding peak did not appreciably change in the presence of TNF-α (Figure 1d). The intersection sets of three TF binding peaks within 1 kb distance were identified, showing that intersecting binding peaks under basal conditions (215) were significantly increased after TNF-α treatment (1159) (Figure 1e, upper and lower left; Fisher’s exact test, P=2.2e−16). The 793 TAp73 basal binding peaks intersected with TNF-induced cREL and p63α binding (Figure 1e, lower right). After combination of all binding activities, 1217 candidate binding peaks were observed, which aligned within the regulatory loci of genes and generated 530 genes displayed cobinding by the three TFs (Supplementary Figure S4). Furthermore, the physical distance between the intersections of three TF binding peaks was examined, defined as within 1 kb distance on the same chromosome (Figure 1f). TNF-α increased intersection binding peaks between cREL and p63α, or between p63α and TAp73, around the TSS within <200 bp (Figure 1f, upper two panels), when compared with other intersections (Figure 1f, lower panels). More detailed results were included in the Supplementary Information. Taken together, these data show that TNF-α modulated the colocalization of cREL, p63α and TAp73 binding enriched around TSSs.
TNF-α promotes genome-wide cREL, p63α and TAp73 binding activities in the regulatory regions. (a) UM-SCC46 cells were treated with TNF-α (20 ng/ml) for 1 h to induce altered cREL, p63 and p73 protein expression in the nucleus. Oct-1 was used as the loading control. (b) ChIP-seq was performed using antibodies against cREL, p63α and TAp73 for the UM-SCC46 cells treated (TNF) and untreated (NT) with TNF-α. The pull-down DNAs were sequenced by the high-throughput sequencer GAIIx from Illumina. Total peak numbers (left) and peak-bound genes (right) are presented in the bar graph. (c) Characterization of the modulation of TF binding in the regulatory regions. The distribution of binding peaks in different regions of the genome is presented in the pie charts, and peak numbers are labeled at the top. The upper panel shows the percentages of the peaks distributed among regulatory (promoter, TTS, intragenic) and intergenic regions. The lower panel shows the percentages of the peaks distributed only among the different intragenic regions. (d) The binding peak numbers of each TF were plotted within 20 kb upstream (−) and downstream of the TSSs across the whole genome. The blue line is the basal transcription factor binding without TNF-α treatment, whereas the green line is after TNF-α treatment. (e) The transcription factor binding sites within 1 kb distance were identified, and the number of overlapping sites are presented in the Venn diagrams. (f) Distance relationships between two peak sets under different conditions in the ChIP-seq experiments were analyzed. Intersected peaks were defined to be within 1 kb distance on the same chromosome. Y axis shows the quantity of intersected peaks at different intersection distance in bp (x axis). Blue, untreated; green, TNF-α treated.Full figure and legend (330K)
De novo motif search defines genome-wide binding on TP53 and AP-1 consensus elements modulated by TNF-α
Next, the top motifs most frequently bound by the three TFs under different treatment conditions were identified using Gibb’s motif sampler and summarized (Figures 2a–d and Supplementary Figure S5). The basal binding of TAp73- (Figure 2a, left) and TNF-α-induced p63α binding (Figure 2b, left) were enriched for a TP53/p63 consensus sequence. Surprisingly, basal cREL- (Figure 2c, left) and TNF-α-induced TAp73 bindings (Figure 2d, left) were enriched for AP-1 consensus motifs. A narrow distribution of ~200 bp for the basal TAp73- (Figure 2a, right) and TNF-α-induced p63α binding to TP53 motifs (Figure 2b, right) were observed. However, broader distribution patterns (~400 bp) were observed for the basal and TNF-α-induced cREL (Figure 2c, right and Supplementary Figure S5), and TNF-α-induced TAp73 binding on AP-1 sites (Figure 2d, right). On the TP53/p63 motifs, there was abundant overlapping binding activity of basal TAp73- versus TNF-α-induced p63α, ranging within ~200–300 bp (Figure 2e, left). TNF-α also induced concurrent binding of p63α on TP53 sites and TAp73 on AP-1 motifs, located in a broader range of ~400 bp (Figure 2e, right).
De novo motif search identified TP53 and AP-1 consensus sequences. The motifs most frequently bound by p63, TAp73 and cREL transcription factors were identified by MEME-ChIP55 or Gibbs Motif Sampler.54 They are shown as the sequence logos with known consensus (a–d, left). (a, left) The predominant TP53/p63 motifs consistent with the TP53.02 consensus sequences were identified in basal binding of TAp73. (b, left) The predominant motif of TP63 was identified by TP63α binding activities after TNF-α treatment. (c, left) AP-1 motif was identified in basal cREL binding. (d, left) The AP-1 motif was observed in TNF-α-induced TAp73 binding. (a–d, right) Distance relationships between different motifs detected by ChIP-seq were examined. Motifs are considered to be intersecting if they are located within 1 kb distance. The corresponding motifs were mapped back to peak sequences. Motif density (y axis) was plotted against the distance from the center of the binding peak (x axis), showing the distribution pattern of specific motifs in peaks (scales are labeled as × 102). (e) The relative interaction on the TP53 motif (y axis) was plotted against the distance of basal TAp73 binding vs TP63α after TNF-α treatment (left). Under TNF-treated condition, the relative interaction (x axis) of TP63 binding on TP53 motifs and TAp73 binding on AP-1 motif was plotted against the distance between the two binding motifs (x axis, right). (f) Sedimentation coefficient distribution of purified cREL Rel homology domain (RHD, left) and p73 DBD (right) binding to fluorescein-labeled AP-1 (black), cREL (red) and TP53 (blue) REs. Fluorescein absorbance was detected at 488 nm. The peaks corresponding to the protein dimers and tetramers are indicated. No tetrameric binding of p73 DBD was observed to the AP-1 and cREL REs. For clarity the peak at 2S for the unbound DNA species is not displayed.Full figure and legend (258K)
Binding of purified cREL homology domain and TP73 DNA-binding domain to cREL, TP53 and AP-1 REs
Next, we purified recombinant cREL Rel homology domain and TP73 DNA-binding domain (DBD) proteins and tested their ability to bind oligonucleotides with minimal consensus sequences from cREL, TP53 and AP-1 REs (Figure 2f). Analytical ultracentrifugation with fluorescein-labeled DNA allowed us to measure sedimentation velocities of the protein–DNA complexes and classify their oligomerization state. Remarkably, cREL exhibited binding activities for all three REs, with a higher affinity for its own RE (Figure 2f, left). TP73 DBD exhibited dimeric DNA–protein complexes for all three REs tested; however, the tetramer complex was only observed for the TP53 RE (Figure 2f, right). Thus, our binding experiments with purified proteins confirmed that both cREL Rel homology domain and p73 DBD are able to bind not only to their own consensus sequences but also to the other DNA REs, supporting the binding to motifs identified in the ChIP-seq experiment.
Validation of colocalized binding on individual gene promoters by ChIP-qPCR and oligo-based binding assays
Representative peak binding activities of four of the genes detected by ChIP-seq are presented in Supplementary Figure S6. We further validated the specific binding activities of the three TFs on regulatory regions of eight representative genes detected by ChIP-seq and confirmed the binding by ChIP-PCR (Figures 3a–c and Supplementary Table S2). Treatment with TNF-α significantly increased cREL and/or p63α binding (Figures 3a and b), whereas TAp73 binding was partially decreased or not significantly changed (Figure 3c). Next, we performed a different binding experiment using short ~50–70 bp synthetic oligonucleotides containing 10-20bp sequences of individual or a combination of predicted consensus motifs (Figure 3d and Supplementary Table S3). We observed a relatively stronger basal TAp73 binding activity when compared with p63α binding, consistent with the ChIP-seq and ChIP-qPCR results. Interestingly, the oligonucleotide containing a predicted AP-1 motif (p21-c) without the other motifs also exhibited TAp73 binding. These binding activities detected by ChIP-seq were validated by two different experimental methods. Thus, our current study provided evidence for the colocalization of cREL, ΔNp63 and TAp73 to multiple target promoters and REs in response to TNF-α, consistent with our previous publications.6, 8
Validation of colocalized binding activities of cREL, p63α and TAp73. Based on overlapped binding peaks in the regulatory regions, we predicted core binding sequences containing potential p63/TP53 or NF-κB/cREL binding motifs using a bioinformatic approach. PCR primer sequences were designed to flank the regions containing the binding motifs (Supplementary Table S2). In the genes of interest, the overlapping binding peaks were located in promoter/enhancer regions (SERPINE1, BCL3, CEBPA, HBEGF) or in first (CDKN1A, FOSL1, TNFSF10) or other (GADD45A) introns. qPCR was performed in independent ChIP experiments, and isotype antibody served as the negative control. (a–c) ChIP binding activity of cREL (a), p63α (b) and TAp73 (c). Blue, untreated; red, TNF-α treated. The data are presented as the mean±s.d. from three replicates. (d) The DNA sequences were extracted from ChIP-seq peaks and the core p53, p63, AP-1, NF-κB and cREL consensus motifs were predicted and depicted. Nuclear extracts were isolated from UM-SCC46 cells and the binding assays were performed using a 96-well colorimetric binding assay with 50–70mer oligos containing the 10-20bp motifs synthesized and labeled with biotin (Supplementary Table S3). Open bars, anti-p63α antibody; dashed bars, anti-TAp73 antibody. Neg, negative control using p21 control oligo without lysate. p21-P, positive control using a p21 oligo containing the known TP53/TP63 site. The data are presented as the mean±s.d. calculated from three replicates.Full figure and legend (179K)
cREL, ΔNp63α and TAp73 modulate transcriptional regulation and gene expression
Next, we performed functional validation using luciferase reporters containing p53-, AP-1- and NF-κB/REL-specific consensus REs after coexpression of ΔNp63α, TAp73, cREL or empty control vectors (Figure 4a). Overexpressed TAp73 was a strong inducer of classical TP53 RE reporter activity, which was downmodulated by TNF-α (Figure 4a, upper left). Both ΔNp63α and TAp73 also induced significant AP-1 RE-inducing activity, and TNF-α further enhanced it (upper middle). cREL induced strong NF-κB reporter activity that was increased by TNF-α, whereas ΔNp63α and TAp73 exhibited less pronounced effects (upper right). Then, the individual promoters of p21-containing TP53, p63 REs, SERPINE1 with AP-1 RE and IL-6 with AP-1 and NF-κB REs were tested for their activities (Figure 4a, lower panels). TAp73 was the strongest inducer of p21 and SERPINE1. ΔNp63α was the strongest inducer of IL-6, whereas mutation of either the NF-κB or the AP-1 RE significantly suppressed the activity. Taken together, these data further confirm the overlapping capability of these TFs to modulate p53, AP-1 and NF-κB/REL transcriptional activities.
cREL, ΔNp63α and TAp73 modulate transcriptional regulation and differential gene expression. (a) UM-SCC46 cells were transfected with luciferase reporter plasmids containing specific TP53, AP-1 or NF-κB REs (upper panels), or the promoter sequences of p21 (TP53, TP63 REs), SERPINE1 (AP-1 REs) or IL-6 (AP-1 and NF-κB REs; lower panels). Overexpression of cREL, ΔNp63α or TAp73α was induced by TNF-α treatment (20 ng/ml) for 48 h. IL-6 binding site-specific point mutant promoter constructs included the deletion mutation of NF-κB or AP-1 binding sites without TNF-α (bottom right panel). The relative reporter activity was normalized to the corresponding β-gal activity and/or compared with the control vectors. Blue, untreated; red, TNF-α treated, except in IL-6 reporter assay (bottom right panel). In IL-6 reporter assay without TNF-α treatment, blue: reporter with full-length IL-6 promoter; red: IL-6 promoter with the NF-κB binding motif deleted; green: IL-6 promoter with the AP-1 binding motif deleted. (b) The newly identified target genes were validated by qRT–PCR 48 h after cREL, ΔNp63α or TAp73α overexpression under TNF-α treatment (20 ng/ml). The data are presented as the mean±s.d. of three replicates from one representative experiment. Blue, untreated; red, TNF-α treated. Statistical significance was calculated using a two-tailed Student's T-test, P<0.05. *Statistical significance when comparing the conditions with overexpressed plasmids versus control plasmid. #Statistical significance when comparing untreated versus TNF-α-treated condition.Full figure and legend (226K)
We examined how TNF-α and three TFs modulated the expression of six candidate genes identified through ChIP-seq and confirmed by microarray (Figure 5 below and Supplementary Figure S6). ΔNp63α and TAp73 strongly induced GADD45A, a known TP53 target,13 as well as AP-1 subunit FOSL1 and SERPINE1 (Figure 4b, upper panels). CEBPA was strongly repressed by ΔNp63α and TAp73 (Figure 4b, lower left panel). The modulation of MAP4K4 and CFLAR (FLIP) by the three TFs was distinct, in that cREL plus TNF-α, ΔNp63α alone and ΔNp63α plus TNF-α significantly induced gene expression. Collectively, TNF-α-modulated cREL, ΔNp63α and TAp73 can differentially regulate a functionally diverse gene repertoire.
TNF-α modulates the expression of a global gene repertoire bound by cREL, p63α and TAp73. RNA was isolated from cells treated with TNF-α for 1, 3, 6, 12 and 24 h and the differential gene expression was examined by an Illumina bead-based array. (a) Differential expression of up- and downregulated genes in UM-SCC46 cells under basal level conditions when compared with normal human oral keratinocyte cells, and (b) altered gene expression of UM-SCC46 cells upon TNF-α treatment. The colored sections represent the percentages of differentially expressed genes with binding activities for cREL, p63 or TAp73. (c) Gene numbers with individual and intersecting binding activities among the three TFs at the basal level, or (d) after TNF-α treatment. Top Venn diagrams represent upregulated genes, and bottom Venn diagrams represent downregulated genes. (e) Heat maps of hierarchical cluster analysis of 46 upregulated (left) and 27 downregulated (right) genes with overlapped TF binding activities induced by TNF-α. Red, increased expression; blue, decreased expression (compared with untreated controls). Color key, Z-score, reflects the relationship of the value of gene expression in a specific sample to the mean of the expression values of the same gene in all the samples. Euclidean distance with complete linkage was used to constitute the gene cluster.Full figure and legend (307K)
TNF-α globally modulates the expression of genes with colocalized binding of TFs
Next, we tested if TNF-α globally modulates the expression of genes exhibiting colocalized binding of cREL, p63α and TAp73 (Figure 5). A total of 1050 genes with 1.5-fold differential expression were identified during at least one time point after TNF-α treatment. Without TNF-α, a quarter of altered genes exhibited TF binding, while TNF-α significantly increased up- and downregulated genes bound by the TFs by ~10%, together accounting for a third of differentially expressed genes (Figures 5a and b, colored sections). TNF-α treatment significantly altered gene numbers with binding activities (χ2, upregulated genes, P=4.3E−07; downregulated genes, P=5.5E−05). The number of differentially expressed genes with individual or overlapping binding activities for the three TFs are presented for basal activities (Figure 5c), or for TNF-α-modulated activities (Figure 5d). From these, we identified those genes displaying cobinding of all three transcription factors that were up- or downregulated by TNF-α from Figures 5c and d, and removed the redundant genes. Altogether, TNF-α upregulated 46 genes and downregulated 27 genes with overlapping binding of cREL, p63α and TAp73, which were selected for hierarchical gene clustering and displayed in heatmaps (Figure 5e). Supporting the potential for bidirectional gene modulation, the expression of a selection of target genes up- and downmodulated by TNF-α treatment was validated by qRT–PCR (quantitative reverse transcription–PCR) (Supplementary Figure S7). Using a less stringent difference of 1.3-fold change in gene expression by TNF-α treatment, we detected 84 upregulated genes and 61 downregulated genes bound by all three transcription factors (Supplementary Tables S4a and 4b). Using either 1.5- or 1.3-fold criteria, the TNF-α-modulated genes bound by the TFs include multiple molecules implicated in inflammation, oncogenesis and NF-κB- and AP-1-mediated signaling.
cREL, ΔNp63 and TAp73 expression and altered gene and protein signatures in HNSCC tissues and ΔNp63α transgenic mouse skin
We examined the 46 TNFα-upregulated genes and 27 downregulated genes cobound by all three TFs from Figure 5e using the HNSCC mRNAseq expression data set recently published for 279 primary tumor and 16 mucosa specimens from TCGA.1 Using these data, we found that 23/73 genes (31.5%), including 13 of 46 activated genes and 10 of 27 repressed genes, showed significant concordance when comparing primary HNSCC tumor with mucosa specimens (Figure 6a; fold change 1.5, P<0.05, Student's t-test with false discovery rate correction <1% cutoff). An additional 23 genes (31.5%) were also expressed in the same direction as in cell lines, but below the significance threshold (Supplementary Table S6). Thus, overall 63% of genes showed consistent changes in direction of expression when comparing primary tumors with mucosa between cell line data and TCGA human tissue data. Consistent with this, an unsupervised cluster analysis and heatmap of the TCGA data for all 73 genes confirms that many of the TNFα-inducible and downregulated genes in cell lines cocluster among four major tumor subsets (Supplementary Figure S8, clusters a–d). Among the TNF-α-inducible genes, several of the most significantly upregulated genes cocluster predominantly in major clusters (a and/or b) (SERPINE1, MET, SPOCK1, CRISPLD2, TINAGL1, LEPREL1, TNS3, PARP14, CDH3), which have previously been reported in PubMed to be deregulated in cancer and/or metastasis. MET and SERPINE1 are implicated in cell migration, invasion and metastasis, and SERPINE1 is also a downstream target of MET signaling.14, 15, 16, 17
cREL, ΔNp63 and TAp73 nuclear and cytoplasmic localization and target gene expression in human HNSCC tissues and skin of ΔNp63α transgenic mice. (a) Forty-six upregulated and 27 downregulated genes from Figure 5d were queried using the mRNA expression data set from TCGA HNSCC project, which includes 279 tumor and 16 mucosa specimens. Significant upregulation of 13/46 activated genes (red) and downregulation of 10/27 repressed genes (blue) are detected in TCGA tumors compared with normal samples (fold change 1.5, Student's t-test with false discovery rate (FDR) <1% multivariate comparison correction cut off). (b) Three publicly available data sets of gene profiling microarrays were investigated, which included a total of 125 (67 metastatic and 58 non-metastatic) HNSCC tissues. Pearson's correlation coefficients of gene expression between p63 and potential target genes were calculated, and genes exhibiting statistically significant correlation are presented. The line indicates P<0.05. (c) RNA was isolated from the skins of ΔNp63α transgenic mice (red). qRT–PCR was performed to quantify gene expression levels with a statistically significant increase compared with their age-matched non-transgenic littermates (blue, P<0.05). The data were calculated from triplicates of one representative experiment and presented as the mean±s.d. (d) Human HNSCC and normal mucosa frozen sections were stained for cREL, ΔNp63 and TAp73, and intensities within nuclei or cytoplasm in three × 200 fields per slide were acquired and quantified using an Aperio Scanscope and Cell Quantification Software (Vista, CA, USA), and presented as mean histoscores±s.d. for 8–13 samples. *P<0.05, HNSCC vs normal mucosa by t-tests. (e) Immunohistochemistry comparing transcription factors JUNB and FOSL1 nuclear staining in human HNSCC tissue array. Images were acquired using an Aperio Scanscope at × 200 magnification, and staining intensity was quantified using Aperio Cell Quantification Software. Tumor protein expression of evaluable specimens for JUNB and FOSL1 (n=66) and mucosa (n=11) samples. Student's t-test, P<0.05. (f) Associations in expression levels between the transcription factors nuclear cREL with targets nuclear JUNB and IKKε (stage III tumors), nuclear cREL with nuclear FOSL1 (in all tumor stages), and nuclear TP63 with nuclear SERPINE1/PAI1 (metastatic tumors). A non-directional test for the significance of the Pearson's product-moment correlation coefficient with the computed histoscores for each protein was used.Full figure and legend (283K)
As additional TP63-bound and -modulated genes identified in our data set are implicated in cancer, inflammation and metastasis, we also examined 48 TP63 targets, including 31 curated from the literature, and 17 from our prior8 or present study of TNF-α-modulated genes (Supplementary Figure S7), as described in Supplementary Methods. An expression-based method was used to cluster a group of genes with common profiling patterns and TP63 transcription factor motifs, to computationally infer coexpressed networks, using three independent publicly available microarray data sets containing 125 HNSCC samples that include 58 non-metastatic and 67 metastatic HNSCC tissues.18, 19, 20 Among these genes, a statistically significant correlation was established between the expression of TP63 and 35 genes (71.4%, Pearson's correlation coefficient P<0.05; Figure 6b). This analysis provided evidence for differential expression of multiple TP63-linked genes that are implicated in cancer cell phenotype (e.g. JUNB/FOSL1, CDKN1A, JAG2/NOTCH2), invasion and metastasis (e.g., SERPINE1, MET, SNAI2, MMP10) and inflammation (e.g. IL-1A, IL-8, RELB). Several of these TP63 target genes also comodulated by TNF-α cocluster together in major subsets of TCGA HNSCC tumors (Supplementary Figure S8; e.g., FOSL1, CDKN1A, SERPINE1, MET, TNFAIP8).
We further compared ΔNp63-bound and -upregulated genes in a conditional K5-ΔNp63α transgenic mouse model, where increased TNF-α expression and a similar nuclear redistribution of cREL/ΔNp63 and TAp73 was observed.21, 22 Array profiling detected increased expression of a panel of upregulated genes, including AP-1 subunits Fosl1 and Junb, and NF-κB pathway-related genes, Traf1, Ikkε and Relb (Figure 6c).
We next validated if proteins of TNF-α-comodulated TFs and four target genes identified in multiple platforms above display corresponding alterations in human HNSCC tumor and tissue arrays. The nuclear-cytoplasmic distribution of cREL, ΔNp63 and TAp73 in human specimens was quantified by immunohistochemistry score. We observed increased cREL and ΔNp63, and decreased TAp73 nuclear distribution, with a reciprocal decrease in cytoplasmic cREL and increased TAp73 cytoplasmic staining in HNSCC (Figure 6d and Supplementary Figure S9), consistent with TNF-α-modulated nuclear redistribution and DNA binding of these TFs in HNSCC in vitro (see Figures 1a and b) and prior studies.6,8 Next, we examined the protein expression of target genes in human HNSCC tissue array, which contains 61 primary tumors of stages I–IV at different anatomic locations, 8 lymph node metastatic tumors and 11 normal mucosa tissues (Supplementary Methods). Notably, key AP-1 family transcription factor targets, JUNB and FOSL1, showed significantly elevated nuclear staining in HNSCC tumors relative to mucosa (Figure 6e), consistent with altered AP-1 promoter binding and expression of JUN1B and FOSL1 observed above (Figures 3, 4, 5). Further, the nuclear staining of JUNB and FOSL1 was significantly correlated when compared with cREL (Figure 6f). Increase in staining for two additional targets, IKKε and SERPINE1, were also significantly correlated with cREL and p63 staining, respectively. Taken together, these data from human and mouse tissues support our hypothesis for the altered cellular distribution and functional comodulation of cREL, ΔNp63α, TAp73, with AP-1 subunits JUNB/FOSL1, and a subset of other target genes detected in different in vitro and in vivo systems.
CircleMap and Pearson's correlation analyses support TP53/p63/p73-modulated gene signatures in squamous cancers
We next examined how our current findings may relate to large data sets and analyses from the recently published TCGA PanCancer 12 project, which identified inflammatory as well as a ‘TP53-like’ compensatory gene expression activity pattern associated with increased ΔNp63 and TP73 activities, and frequent genomic inactivation of TP53 in the squamous-like tumors.2 These squamous cancers included 293 HNSCCs, 156 lung SCCs and 24 bladder SCCs, which were analyzed for TP53/63/73 status and expression and function of eight genes identified among their known and new targets in the present study. Figure 7a shows CircleMaps, which enable a multidimensional comparison of the genomic alterations, expression and inferred pathway activities for TP53, ΔNp63, TP73 and these target genes, using the annotated PARADIGM bioinformatics analytic platform.23, 24 The TP53 CircleMap shows that the majority of SCC tissues contain TP53 mutations (second circle), and/or copy number loss (third circle) with corresponding decrease in TP53 expression (fourth circle). However, they retain weak inferred PARADIGM activity for TP53 (the outer circle), which cannot be explained by inactivated TP53, but could be compensated by overlapping function of other family members. Consistent with this, the TP63 CircleMap exhibited high copy number gain, expression and PARADIGM gene signatures, supporting a role for copy number gain and expression in TP63 PARADIGM gene activity. Similarly, TP73 overexpression and PARADIGM activity are also congruent. We further analyzed eight targets identified in this study, including genes involved in cell cycle (CDKN1A, GADD45), signal receptor and kinase (MET, MAP4K4), transcription factors (AP-1/FOSL1, CEBPA) and secreted factors (SERPINE1, IL-6). The expression and PARADIGM activity of ΔNp63, TP73 and these target genes are higher across SCC tumors compared with other cancer types. By Pearson's correlation analysis (Figure 7b), ΔNp63 exhibited significant positive correlation with four genes bound and inducible by TNF-α, including MAP4K4, FOSL1 and GADD45, whose activation ΔNp63 and TNF-α promotes, and a negative correlation with CEBPA, repressed by ΔNp63 and TNF-α (Figures 4b and 5e). TP73 exhibited a negative association with IL-6, which TAp73 suppresses, and a positive correlation with CDKN1A (p21), which it activates (Figure 4a). SERPINE1 showed only a weak negative association with TP73, which was observed with TNF-α treatment by reporter but not by RT–PCR (Figures 4a and b).
Squamous cancer signatures by CircleMaps and PARADIGM SuperPathway analyses. (a) CircleMap of PARADIGM-Shift differences associated with SCC tissue origins and TP53 mutation status were created for TP53, TP63, TP73 and target genes identified in this study. Samples were ordered first by tissue origin of SCC (innermost ring), then by TP53 mutation status (second ring), GISTIC score (indicating CNV), mRNA expression level and finally by PARADIGM activities (outer ring). The red-blue color intensity reflects the magnitude of CNV, expression and PARADIGM activities (red: high; blue: low). TP53 mutation is highlighted (black: truncation; gray: missense). Samples were restricted to the C2-squamous-like cluster-of-cluster-assignments (COCA), including 156 lung SSC, 293 HNSCC and 24 bladder cancer samples. Each plot illustrates multiple data types across many samples for a given gene. (b) Pearson correlation coefficients for ΔNp63 and Tp73 PARADIGM activity and the 8 target genes were calculated, and the significance (-log10) for positive (red) and negative (blue) correlations is shown. (c) PARADIGM SuperPathway subnetwork defining C2-squamous-like PanCancer 12 integrative subtype. Zoom-in view of network neighborhood surrounding the ΔNp63α tetramer, TAp63γ tetramer, RelA/p50 and Jun/Fos complexes. Color of the nodes reflects activation (red) or repression (blue) within the squamous subtype when compared with the mean of other tumor types. Edge color denotes interaction type: inhibitory (green) and activating (yellow). Node shape reflects feature type: protein (circle), complex (diamond), family or miRNA or RNA (square) and abstract concepts (arrowhead). The target genes with cRel, p63 and TAp73 binding identified in this study are highlighted with gold colored outer rings as showing in Supplementary Figure S10.Full figure and legend (390K)
PARADIGM SuperPathway analyses connect p63, NF-κB and AP-1 subnetworks in squamous cancers
Our current and prior experimental data indicate that ΔNp63 and REL NF-κB family members bind and modulate inflammation and survival genes (Figures 3, 4, 5).6, 8, 25 Analyses of TCGA PanCancer 12 data sets also revealed a strong TP63 gene signature associated with altered immune and inflammatory gene signatures in squamous cancers.2 However, the network(s) linking TP63 and NF-κB family members and target genes has not been well elucidated. We thus searched within the interconnected network of differentially activated PARADIGM pathways, linked through regulatory hubs with >15 downstream targets, derived from a larger interconnected network displaying differentially activated proteins inferred between the C2-squamous-like subtype and other tumors, as detailed in Supplementary Methods, Supplementary Table S5 and Supplementary Figure S10.26 Interestingly, we observed increased activation of the network neighborhood surrounding TP63, connected with NF-κB complex (RELA/p50) through AP-1 (JUN/FOS) complexes within the squamous cancers (Figure 7c). The activation of these networks and targets are consistent with our evidence that TNF-α orchestrates cREL/ΔNp63 binding and induction of AP-1 subunits, reprograming of cREL/p73 binding via AP-1 binding consensus sites and their related gene signatures in vitro and in vivo (Figures 2, 3, 4, 5, 6). Several genes shown on the network identified in this study have been independently validated previously, such as TP63-regulated CDKN1A, GADD45A, IL-1A, CHUK, YAP1, IGFBP3 and KRT14, as well as NF-κB- and AP-1-related genes IL-6, IL-8, and MAPK1/ERK/JUN/FOS pathways. Most of these related genes in squamous cancers are activated (red) when compared with other cancer types. Furthermore, a more extended network connecting ΔNp63α, TP53 and AP-1 transcription complexes (among other regulatory hubs) links a larger number of target genes identified in this study by existing experimental data (Supplementary Figure S10A). Although not all target genes identified from our current study are linked by this expanded network (Supplementary Figure S10A-B), they are significantly enriched among the 4213 differentially activated PARADIGM proteins inferred between squamous and other cancers (hypergeometric test P=6E−9 for basal targets, and P=1.22E−10 for TNF-α-responsive targets, Supplementary Table S5).
Here, we provide evidence for a novel and dynamic genome-wide TF binding paradigm, whereby the inflammatory cytokine TNF-α modulated binding activities of cREL, ΔNp63α and TAp73 genome wide, intersecting a diverse repertoire of genes implicated in the malignant phenotype. This modulation was linked to attenuated expression of specific TP53 tumor suppressor targets and promotion of REL/NF-κB and AP-1 oncogenic and inflammatory gene programs (Figure 8). Our study provides a link between global TP63/TP73-, AP-1- and NF-κB-mediated survival and inflammatory gene programs, which helps explain the finding of unique TP63/TP73 gene signatures compensating for mutant TP53 identified recently in squamous cancers from TCGA HNSCC and PanCancer projects.1, 2 Furthermore, our current data are consistent with previous mechanistic studies demonstrating how TNF-α-induced changes alter the DNA binding of cREL, ΔNp63 and TAp73 family members.6, 8 Our studies showed that these transcription factors regulate genes involved in several aspects of cell fate including cell proliferation, stemness, survival/apoptosis and migration, as well as epithelial cell growth, inflammation and immune responses in murine keratinocyte and ΔNp63 transgenic mouse models.27, 28
Mechanistic model illustrating effects of TNF-α modulation of cREL, ΔNp63α and TAp73 chromatin occupancy and reprogramming of TAp73 from TP53 to AP-1 sites to promote inflammatory and cancer gene programs. Most head and neck and solid cancers have high frequencies of TP53 mutation, where TAp73 can serve as a potential tumor suppressor. In this model, without TNF-α, TAp73 predominantly occupies TP53 or p63 binding sites, whereas NF-κB family member cREL either resides in the cytoplasm or in the nucleus with binding on AP-1 sites, and ΔNp63α is found in the nucleus unbound to DNA (upper left). TNF-α, a major inflammatory cytokine produced in the tumor microenvironment, can promote cREL nuclear translocation, and complexes with ΔNp63α, to occupy TP53/p63 binding sites. TNF-α also induces nuclear displacement or reprogramming of TAp73 to bind neighboring AP-1 sites (right panels). These dynamic alterations diminish TAp73 tumor suppressor activity, while enhancing cREL/ΔNp63α- and TAp73-mediated inflammatory and cancer gene programs. This model helps explain how TNF-α modulates NF-κB and AP-1 signaling while altering tumor suppressor activity, to promote gene programs implicated in inflammation, survival and metastasis (lower left panels).Full figure and legend (81K)
After cross-comparison of 12 different cancer types, TCGA PanCancer 12 project uncovered several unique signatures that differentiate squamous cancers from other cancer types, such as high rates of TP53 mutation and TP63 amplification.2 In-depth genetic analyses of the gene signatures of squamous cancers revealed attenuated TP53 and increased expression of compensatory TP63/TP73 gene signatures. These were not strictly linked with the relatively infrequent loss of heterozygosity or TP53-truncating loss-of-function mutations, but with TP63 amplification and TP63/73 gene expression by PARADIGM-Shift analysis.23
Interestingly, our previous experimental results supported a relationship between attenuated TP53 target gene expression and partial compensatory function of TP53 family members TP63 and/or TP73 in SCC.6, 8 HNSCC with mtTP53 displayed higher ΔNp63 and TAp73, and intermediate ‘compensatory’ basal expression of several TP53/TP63/TP73-regulated apoptotic genes. Expression of these genes was further attenuated by TNF-α, DNA binding interaction of cREL with ΔNp63 and displacement of TAp73.6 In the present study, ChIP-seq, ChIP PCR and binding analyses provide evidence for genome-wide reprogramming of TAp73 binding to AP-1 RE-containing promoters, and varying regulation of target genes of TP53 as well as AP-1 and REL/NF-κB. Such TP63/TP73 compensatory TP53 function has been associated with higher sensitivity to cisplatin chemotherapy in both HNSCC29, 30 and in BRCA1-related triple-negative breast cancer,31 but together with promotion of opposing inflammatory and oncogenic gene programs, is apparently insufficient to prevent development of these cancers.
TP53 is highly mutated in HNSCC, ~80–90% in human papillomavirus-negative tumors, as evident by recent TCGA data.1 As we previously reported in the manuscript by Lu et al.,6 the increased expression of TAp73 is predominantly observed in mtTP53 cell lines, and reduced TAp73 occupancy is associated with replacement by cREL, but not mtTP53, in response to TNF-α. The mtTP53 does not co-immunoprecipitate with the cREL/ΔNp63 complex, and is not recruited to p21, NOXA or PUMA promoters by ChIP assay following TNF-α treatment. In addition, dependence on cREL and ΔNp63 for TAp73 binding to p21 promoter was shown, whereby cREL inversely modulated TAp73 binding. Similar results for TNF-α treatment or cREL modulation of TAp73 were obtained in three UM-SCC lines.6 Furthermore, our prior data are also consistent with other’s evidence for a strong protein interaction is between p63 and p73, but not with TP53, because TP53 is lacking the SAM domain at the C terminal, which mediates heterotetramerization between these family members.32, 33
Our prior and current findings help further clarify the apparent function of TP63/TP73 in compensating for TP53 and promoting oncogene expression. First, such compensatory activity is associated with enhanced expression of typical TP53 target genes, such as CDNK1A/p21 and GADDA45, which control cell cycle,9, 30 observed in our cell line model (Figures 3, 4, 5), broader microarray studies of HNSCC tissues (Figure 6b) and PanCancer 12 TCGA project (Figure 7). However, TNF-α, cREL and ΔNp63 modulate TAp73, attenuating its TP53 compensatory function, while also enhancing a much broader oncogene program implicated in cancer promotion.6, 8 This includes growth factor-mediated signaling, inflammatory cytokines, prosurvival transcription factors such as AP-1 and RELs and antiapoptotic genes (e.g. CFLAR) (Figures 4, 5, 6), which can potentially counter and limit compensatory TP53 tumor suppressor function. Second, as shown by our and other laboratories, as well as in PanCancer 12 project, ΔNp63α is the dominant isoform expressed in squamous cancers.2 We and others have shown the strong oncogenic activity of ΔNp63α in squamous cancers, including promoting tumor cell proliferation, migration, colony formation and inflammatory responses.8 Third, as our and other laboratories previously showed and supported by HNSCC and PanCancer 12 TCGA project, a subset of squamous cancers exhibited inflammatory signatures concurrent with TP63/TP73 compensatory activity.2 Such inflammatory signaling mediated by TNF-α could reprogram TP73 tumor suppressor to oncogenic activity by binding to AP-1 sites, as supported by ChIP-seq, ChIP RT–PCR and binding assays (Figures 1, 2, 3, 4) in this study. This novel finding is also supported by the PanCancer 12 data, indicating that inflammatory gene signatures linked by cytokine TNF-α and NF-κB transcription factors are connected to TP63 gene signatures through JUN/FOS-mediated pathway and network (Figure 7c and Supplementary Figure S10).
The novel finding from this study of physical interaction among these TFs with genomic DNA is supported, among other evidence, by a narrow binding peak within ~200 bp surrounding TP53/p63 consensus sites, and a broader peak of ~400–500 bp within which nearby AP-1 motifs are distributed. Interestingly, a ~500 bp distance between p73 binding and AP-1 sites was independently identified in ChIP-seq experiments in osteosarcoma cells, where anti-TAp73α and c-Jun antibodies detected cobinding by ChIP-re-ChIP assay.34 In addition, Pietenpol and co-workers35 identified overlapping AP-1 and p73 binding sites in ChIP-seq experiments, and confirmed c-Jun binding within 500 bp of p73 binding sites in rhabdomyosarcoma cells. Furthermore, we identified palindromic binding sequences for TP53/p63 and AP-1 (Figure 2), which are similar to the sequences of TP53/TP63/TP73 and nearby AP-1 binding motifs observed in previous studies.34, 35, 36, 37, 38, 39 Our study reveals that in the absence of TNF-α, TAp73 was mainly bound to TP53/TP63 consensus motifs, consistent with that found for growth arrest and proapoptotic genes in multiple HNSCC lines including UM-SCC46,6 where TAp73 exhibited a partial compensatory function for mutated TP53.9 However, previous studies have not observed how TNF-α induced dynamic alterations of these TF binding activities. After TNF-α treatment, ΔNp63α bound at TP53/TP63 sites previously occupied by TAp73, while TAp73 bound to AP-1 sites (Figures 2c and d). These dynamic alterations of binding activities reversed TAp73 tumor suppressor activity, while increased cREL/ΔNp63α and TAp73 modulated the expression of inflammatory- and cancer-related gene programs.
Our previous study demonstrated that TNF-α modulates reciprocal and mutually exclusive protein–protein interactions between endogenous nuclear ΔNp63α/TAp73 and cREL/ΔNp63α complexes.6, 8 Our prior ChIP studies did not resolve if cREL or TAp73 can individually bind the same minimal TP53 RE, or if TP73 can bind to AP-1 REs independent of ΔNp63α or other cofactors. Our sedimentation velocity centrifugation experiments revealed that both cREL and TP73 bind these DNA REs and can form dimers and/or tetramers upon DNA binding. These results provide biophysical support to the model of colocalized binding of cREL and TP73 suggested by the ChIP-seq and bioinformatic analyses. Collectively, the different experimental approaches provide mechanistic details of how TFs can occupy binding sites at the same as well as very close promoter sequences.
Our reporter assays provide evidence for functional overlap in transcriptional activities in addition to binding, which demonstrate differing effects of the TFs and TNF-α on modulation of transcription (Figure 4a). The reporters contained TP53, AP-1 or NF-κB REs. ΔNp63α exhibited minimal effects on TP53, decreased p21 and significantly induced IL-6 expression. This induction was significantly reduced by mutation of the NF-κB and AP-1 REs, which is consistent with our and others’ publications.8, 40 Significantly, TAp73α strongly induced p21 and SERPINE1 reporter activity, consistent with its compensatory function for mutant TP53,9, 31, 35, 41, 42 as well as induced specific AP-1 promoter activity.43, 44 In addition, reporters with specific TP53 or AP-1 REs demonstrated the ability of TNF-α to repress TAp73-induced TP53 RE, or enhance TAp73-mediated AP-1 RE transactivation, supporting a reciprocal role for TNF-α in modulating these REs.
TNF-α, an inflammatory cytokine produced in the tumor microenvironment of many cancers, was previously shown to activate both NF-κB and AP-1,45, 46 the latter through MEK/ERK signaling. These findings are also consistent with the Pan-TCGA data, where the squamous-like cancer type exhibited elevated activity of MAPK signaling pathways.2 Here, we propose an expanded model, wherein TNF-α-modulated TAp73 also appears capable of reprogrammed binding of AP-1-containing REs, while inducing coexpression of AP-1 family member FOSL1/Fra1 and reporters containing AP-1 REs. In addition, FOSL1 and JunB expression were significantly associated with p63 and p73 expression in human HNSCC cells and ΔNp63α transgenic mice. This is consistent with our finding that FOSL/Fra1, c-Jun and JunB are the major AP-1 protein family members expressed and bound to AP-1 sites, and which mediate cell proliferation and migration in HNSCC cell lines.47, 48 Our data are supported by others’ observations that p73 and AP-1 family members can enhance each other’s binding and transcription activities.35, 43, 44 Our present study adds new evidence that TNF-α is a key factor that induces binding of TAp73 to AP-1 sites and increases AP-1 family member transcription, and suggests a new model whereby TNF-α can comodulate AP-1 and reprogram TAp73 to cooperatively activate AP-1 target genes.
Thus, our and others’ studies help explain why when TP53 is mutated or defective in function, the TAp73 compensatory function for TP53 is repressed. First, overexpressed ΔNp63α is able to form a complex with TAp73 to block its tumor suppressor function.6, 8, 31 Second, TNF-α promotes displacement of TAp73 from proapoptotic genes and reprograms its binding and activation of AP-1 sites (Figures 2a, 2d, 2e and 3c). Third, nuclear TAp73 binding is displaced from TP53/p63 sites by the cREL/ΔNp63α complex as supported by experimental validations (Figures 2 and 3).6 Instead, TAp73 function is converted to potentiate AP-1 activity and promote survival and inflammatory gene expression (Figure 5), as supported by other studies.34, 43 Furthermore, in an independent study of a group of genes predicted to be controlled by c-Jun/NF-κB,49 20% of genes overlapped with the genes differentially regulated by TAp73.34
Taken together, our current and recent studies6, 8, 48 reveal a complex, genome-wide transcriptional regulatory mechanism whereby TP53, NF-κB and AP-1 family members interact to promote a broad signaling network favoring tumor survival and inflammation. Our current study helps explain the global mechanisms underlying the newly discovered deregulated TP53/p63 network and inflammation, presented recently by TCGA PanCancer project,2 and a study in neuroblastoma.50 The transcriptional regulatory mechanisms mediated by TP53, NF-κB and AP-1 family members genome wide are far more complex and nuanced than originally anticipated, and understanding the mechanisms will shed light on more effective means for targeting of cancer therapeutics.
Materials and methods
ChIP and next-generation sequencing (ChIP-seq)
DNA fragments bound by antibodies were prepared following the ChIP protocol using sheared DNA (~100–400 bp). The DNA samples were sequenced using a next-generation GAIIx sequencer from Illumina (San Diego, CA, USA) following the manufacturer’s protocol. Sequence reads were mapped to the UCSC human genome Hg18 assembly using the Eland algorithm (Illumina), permitting up to two mismatches and no gaps. Only unique mapped reads were used in the binding peak calling analysis. To identify binding peaks, we used MACS,51 in which a dynamic Poisson distribution model was used to detect the statistically significant binding peaks in ChIP samples compared with DNA input controls. MACS is a widely used algorithm for detection of protein–DNA binding sites when analyzing ChIP-seq data. This method is specifically designed to detect the promoter transcription factor binding sites, which are typically located within a few hundred DNA base pairs. In our study, the default setting was selected, which is commonly used and suitable to our data sets. Detected peak regions were visualized mainly by using the CisGenome browser52 together with gene structure, DNA sequences and conservation scores. We compared the fraction of peaks residing in various genomic features with the corresponding genome background using the CEAS program.53 A binomial test was used in this method to obtain the P-value of each comparison. Gibbs Sampler54 was used for the de novo motif search for binding site sequences and MEME-ChIP suit55 was used to process the found motifs and compare the results to known motifs in database. Peak location, distance to TSSs, peak intersection, motif location and intersection analysis were performed using customized python/R scripts. ChIP-seq experiments were repeated in UM-SCC46 cell lines, and a representative experiment was analyzed and presented in Supplementary Table S1. Minimal background peaks were observed in the samples of input DNA or DNA from isotype control antibodies. This study used the high-performance computational capabilities of the Biowulf Linux cluster at the National Institutes (Health, Bethesda, MD, USA; http://biowulf.nih.gov).
Conflict of interest
The authors declare no conflict of interest.
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We thank Dr Fan Yang (NCI/NIH), Dr Kairong Cui (NHLBI/NIH), Dr Bingmei Zhu (NIDDK/NIH), Jeffery Burnett (NIDCD/NIH), Jamie Coupar (NIDCD/NIH), Guanmei Liang (Thomas Jefferson High School for Science and Technology, Alexandria, VA, USA) and Eric Nicolson (Ithaca High School, Ithaca, New York, NY, USA) for their technical assistance and suggestions. The authors express appreciation to Drs James W Rocco and Leif W Ellisen (Harvard University) for providing ΔNp63 and TAp63 expression vectors, Dr Thomas Gilmore (Boston University) for cRel expression plasmids, Professor Gerry Melino (University of Leicester) for TAp73α expression plasmids, Dr J Silvio Gutkind (NIDCR/NIH) for IL-6 promoter reporter plasmids, Dr Gourisankar Ghosh (UCSD) for the cRel expression plasmid, and Drs Michal Karin (University of California, San Diego), Cheng-Ming Chiang (University of Texas, Southwestern) and Xuan Liu (University of California, Riverside) for critique of and helpful suggestions for the manuscript. HL, HS, XY, AM, MJ, YB, CVW and ZC are supported by intramural projects ZIA-DC-000073, ZIA-DC-000074 and RAR and SS are supported by a grant from NIH (R01AR049238). JMS acknowledges support from NCI (R01-CA180778 and U24-CA143858), Stand Up to Cancer, Prostate Cancer Foundation and the Movember Foundation.
Supplementary Information accompanies this paper on the Oncogene website