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Epigenetic Alterations in Keratinocyte Carcinoma

Open ArchivePublished:November 15, 2020DOI:https://doi.org/10.1016/j.jid.2020.10.018
      Basal cell carcinoma (BCC) and squamous cell carcinoma (SCC) are both derived from epidermal keratinocytes but are phenotypically diverse. To improve the understanding of keratinocyte carcinogenesis, it is critical to understand epigenetic alterations, especially those that govern gene expression. We examined changes to the enhancer-associated histone acetylation mark H3K27ac by mapping matched tumor-normal pairs from 11 patients (five with BCC and six with SCC) undergoing Mohs surgery. Our analysis uncovered cancer-specific enhancers on the basis of differential H3K27ac peaks between matched tumor-normal pairs. We also uncovered biological pathways potentially altered in keratinocyte carcinoma, including enriched epidermal development and Wnt signaling pathways enriched in BCCs and enriched immune response and cell activation pathways in SCCs. We also observed enrichment of transcription factors that implicated SMAD and JDP2 in BCC pathogenesis and FOXP1 in SCC pathogenesis. On the basis of these findings, we prioritized three loci with putative regulation events (FGFR2 enhancer in BCC, intragenic regulation of FOXP1 in SCC, and WNT5A promoter in both subtypes) and validated our findings with published gene expression data. Our findings highlight unique and shared epigenetic alterations in histone modifications and potential regulators for BCCs and SCCs that likely impact the divergent oncogenic pathways, paving the way for targeted drug discoveries.

      Abbreviations:

      BCC (basal cell carcinoma), GO (gene ontology), GREAT (Genomic Regions Enrichment of Annotations Tool), KC (keratinocyte), MsigDB (Molecular Signatures Database), PCA (principal component analysis), SCC (squamous cell carcinoma), TF (transcription factor), Treg (regulatory T cell)

      Introduction

      Keratinocyte (KC) carcinoma, comprised of basal cell carcinoma (BCC) and squamous cell carcinoma (SCC), is the most common cancer in the United States, and its incidence is rising (
      • Rogers H.W.
      • Weinstock M.A.
      • Feldman S.R.
      • Coldiron B.M.
      Incidence estimate of nonmelanoma skin cancer (keratinocyte carcinomas) in the US population, 2012.
      ). KC carcinomas are derived from epidermal KCs, which constitute 90% of the human epidermis (
      • Rook A.
      • Burns T.
      Rook’s Textbook of Dermatology.
      ). KC carcinomas can serve as a unique model for examining the molecular changes that arise because a progenitor cell, the KC, gives rise to two phenotypically distinct tumors, BCCs and SCCs. Although previous GWASs have identified genes associated with KC carcinoma risk (
      • Asgari M.M.
      • Wang W.
      • Ioannidis N.M.
      • Itnyre J.
      • Hoffmann T.
      • Jorgenson E.
      • et al.
      Identification of susceptibility loci for cutaneous squamous cell carcinoma.
      ;
      • Chahal H.S.
      • Wu W.
      • Ransohoff K.J.
      • Yang L.
      • Hedlin H.
      • Desai M.
      • et al.
      Genome-wide association study identifies 14 novel risk alleles associated with basal cell carcinoma.
      ;
      • Liyanage U.E.
      • Law M.H.
      • Han X.
      • An J.
      • Ong J.S.
      • Gharahkhani P.
      • et al.
      Combined analysis of keratinocyte cancers identifies novel genome-wide loci.
      ;
      • Roberts M.R.
      • Asgari M.M.
      • Toland A.E.
      Genome-wide association studies and polygenic risk scores for skin cancer: clinically useful yet?.
      ;
      • Sarin K.Y.
      • Lin Y.
      • Daneshjou R.
      • Ziyatdinov A.
      • Thorleifsson G.
      • Rubin A.
      • et al.
      Genome-wide meta-analysis identifies eight new susceptibility loci for cutaneous squamous cell carcinoma.
      ), no studies, to our knowledge, have examined genome-wide epigenetic alterations in KC carcinoma.
      The epigenome plays a pivotal role in oncogenesis and is important to characterize because, unlike the genome, it is amenable to therapeutic intervention. Yet, for KC carcinogenesis, it remains poorly understood. Epigenetic modifications may signal reversible changes to gene function and expression that play a critical role in cancer initiation, development, and progression (
      • Zhao Z.
      • Shilatifard A.
      Epigenetic modifications of histones in cancer.
      ). Histone modifications serve as important markers to identify regulatory elements in the genome. Histones can undergo multiple post-translational modifications that associate with open and closed chromatin states, activating or repressing gene expression. Unlike the genes inactivated by nucleotide sequence variation, the genes silenced by epigenetic mechanisms are intact and retain the potential to be reactivated by the intervention. Furthermore, epigenetic changes can serve as biomarkers for detection, prognosis, risk assessment, and disease monitoring.
      We sought to identify epigenetic changes in histone modifications in BCC and SCC compared with those in matched normal KCs derived from patient samples undergoing surgical resection to identify shared and unique epigenetic alterations. The overarching goal is to pave the way for identifying possible new therapeutic pathways for these very common tumors.

      Results

      Identification of KC carcinoma‒specific enhancers

      The paired samples (five BCC-normal and six SCC-normal pairs) were collected from patients (aged 48–84 years) and sequenced at a depth of 30–45 million reads (Table 1). We performed chromatin immunoprecipitation sequencing for H3K27ac, an epigenetic modification to the DNA-packaging protein histone H3, which is associated with active enhancers (
      • Davis C.A.
      • Hitz B.C.
      • Sloan C.A.
      • Chan E.T.
      • Davidson J.M.
      • Gabdank I.
      • et al.
      The Encyclopedia of DNA elements (ENCODE): data portal update.
      ;
      ENCODE Project Consortium
      An integrated encyclopedia of DNA elements in the human genome.
      ). Running a peak calling pipeline based on guidelines from the Encyclopedia of DNA Elements consortium (
      • Davis C.A.
      • Hitz B.C.
      • Sloan C.A.
      • Chan E.T.
      • Davidson J.M.
      • Gabdank I.
      • et al.
      The Encyclopedia of DNA elements (ENCODE): data portal update.
      ;
      ENCODE Project Consortium
      An integrated encyclopedia of DNA elements in the human genome.
      ), we recovered ∼60,000–70,000 peaks per sample, which were combined into a 250,344 union peak set and analyzed using Deeptools (
      • Ramírez F.
      • Ryan D.P.
      • Grüning B.
      • Bhardwaj V.
      • Kilpert F.
      • Richter A.S.
      • et al.
      deepTools2: a next generation web server for deep-sequencing data analysis.
      ) to quantify signal intensity. Comparing these peaks with peaks from previously profiled skin samples, we observed high concordance (95% peak overlap), whereas other noncutaneous tissue types showed modest concordance (75–82% peak overlap), suggesting strong validity in our data pipeline (Supplementary Figure S1). Figure 1a shows a genomic region at chr2:132426825‒132427790 that visually highlights the differential peaks between normal and tumor samples.
      Table 1Baseline Characteristics and Read Depth of BCC and SCC Tissue Samples
      IDAge (y)SexPhenotypeAligned Read Depth
      Millions of unique fragments.
      KC CarcinomaMatched

      Normal
      H3K27acWCEH3K27acWCE
      BCC
       Donor 96177MaleSuperficial, nodular, and infiltrative34.55740.953
       Donor 96548FemaleSuperficial, nodular, and metatypical37.26232.132
       Donor 96667MaleSuperficial and nodular34.35923.519
       Donor 113965MaleSuperficial, nodular, and infiltrative41.18027.653
       Donor 114058MaleSuperficial and nodular43.65623.048
      SCC
       Donor 96284MaleIn situ30.15329.053
       Donor 96378MaleInvasive, well-differentiated35.95539.557
       Donor 114264FemaleInvasive, well-differentiated42.95837.961
       Donor 114771FemaleInvasive, well-differentiated41.95641.169
       Donor 114875MaleInvasive, well-differentiated38.76340.659
       Donor 114980FemaleInvasive, well-differentiated41.07541.058
      Abbreviations: BCC, basal cell carcinoma; ID, identification document; KC, keratinocyte; SCC, squamous cell carcinoma; WCE, whole-cell extract.
      a Millions of unique fragments.
      Figure thumbnail gr1
      Figure 1H3K27ac-specific regions for SCC and BCC. (a) H3K27ac signal tracks for tumor-normal samples at a single locus (chr2:132426825-132427790) show cancer-specific alteration. (b) Volcano plots showing the log2 fold-change and the P-value for BCC and SCC samples. Red dots are the regions with significant P-value (<5%) and absolute log2 fold-change larger than 1. (c) Venn diagram for differential unique and shared peaks in BCC and SCC samples. (d) Significant H3K27ac peaks identified by DESeq2 separates tumor-normal samples using the first two principal components. BCC, basal cell carcinoma; PCA, principal component analysis; SCC, squamous cell carcinoma.
      Using DESeq2 (
      • Love M.I.
      • Huber W.
      • Anders S.
      Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2.
      ), we searched for genome-wide differential peaks (P < 5% and log2[fold-change] > 1) to identify key regulatory elements for KC carcinogenesis. We identified numerous BCC- and SCC-specific differential peaks in H3K27ac, including peaks that could correspond to novel cancer-specific enhancers (562 for BCCs and 3,863 for SCCs) or lost wild-type enhancers (139 for BCC and 995 for SCC) (Figure 1b). When we compared SCCs with BCCs, we identified 66 peaks specifically enriched in SCC samples and 26 peaks enriched in BCC samples (Supplementary Table S1).
      Comparing the gained H3K27ac peaks between BCCs and SCC (Figure 1c), we found 180 unique BCC peaks, 3,481 unique SCC peaks, and 382 shared peaks. Comparing the lost peaks, we observed 865 unique SCC peaks, 9 unique BCC peaks, and 130 shared peaks (Supplementary Table S1).
      We performed principal component analysis (PCA) before and after peak selection to assess how the selection strategies affected sample heterogeneity and grouping (Figure 1d). Using the top two principal components, we observed that the nine normal tissue samples formed a discrete cluster, whereas BCC and SCC samples formed their own admixed cluster. PCA plots demonstrated that samples were clustered by cancer subtype and not by the donor, suggesting shared regulatory mechanisms across donors (Supplementary Figure S2a). Analysis after differential peak selection revealed a clear separation of BCCs, SCCs, and normal samples (Supplementary Figure S2b). The boundary of BCC and SCC was observable; however, SCC samples were more scattered. The PCA differential peak and PCA analyses suggest that the two cancer types share features and that the separation from the normal samples is clear and reproducible.

      Unique epigenetic landscapes for BCC and SCC correspond to different gene expression profiles and biological processes

      To investigate the effect of the cancer-specific gain or loss of function of H3K27ac on BCCs and SCCs, we used Genomic Regions Enrichment of Annotations Tool (GREAT, version 3.0.0) (
      • McLean C.Y.
      • Bristor D.
      • Hiller M.
      • Clarke S.L.
      • Schaar B.T.
      • Lowe C.B.
      • et al.
      Great improves functional interpretation of cis-regulatory regions.
      ), a computational tool that helps to determine the biological processes related to a set of noncoding genomic regions by analyzing the annotations of nearby genes. Using GREAT on the genomic intervals that distinguish tumor-normal H3K27ac levels, we inferred functionally important differences between tumor and normal samples, as illustrated in Figure 2, which also included Molecular Signatures Database (MsigDB) perturbation signatures for BCC and SCC. On the basis of gene ontology (GO) annotations, we observed several enriched biological processes. For BCCs, the top five enriched pathways included skin development (P = 1.0e-14), skeletal system development (P = 1.0e-13), epidermis development (P = 1.0e-12), Wnt receptor signaling pathway (P = 1.0e-12), and hair follicle development (P = 1.0e-12). Kinase activities were also enriched (Figure 2a).
      Figure thumbnail gr2
      Figure 2H3K27ac cancer‒specific regions and GREAT analysis. (a) BCC upregulated biological process, (b) SCC upregulated biological process, (c) BCC upregulated MsigDB perturbation signatures, and (d) SCC upregulated MsigDB perturbation signatures. ADH, atypical ductal hyperplasia; ADHC, atypical ductal hyperplasia with concurrent cancer; AGC, advances gastric cancer; BCC, basal cell carcinoma; CHIP, chromatin immunoprecipitation; DCIS, ductal carcinoma in situ; EGC, early gastric cancer; FSHD, faciocapulohumeral muscular dystrophy; GREAT, Genomic Regions Enrichment of Annotations Tool; GVHD, graft-versus-host disease; HCC, hepatocellular carcinoma; HSC, hematopoietic stem cell; ID, identification document; IDC, invasive ductal carcinoma; LPS, lipopolysaccharide; MDDC, monocyte-derived dendritic cell; MEMN, macrophage-enriched metabolic network; MsigDB, Molecular Signatures Database; RNAi, RNA interference; SCC, squamous cell carcinoma; T-PLL, T-cell prolymphocytic leukemia; TSC, thymic stromal culture; UCC, urothelial cell carcinoma.
      In contrast, the top five enriched biological processes in SCCs (Figure 2b) were the regulation of the immune system, cell activation, immune response, leukocyte activation, and positive regulation of immune system process (P < 10e-20). This suggests that SCC pathogenesis involves dysfunction in immune regulation, a finding backed up by its increased incidence among immunosuppressed individuals (
      • Bottomley M.J.
      • Thomson J.
      • Harwood C.
      • Leigh I.
      The role of the immune system in cutaneous squamous cell carcinoma.
      ;
      • Clark R.A.
      • Huang S.J.
      • Murphy G.F.
      • Mollet I.G.
      • Hijnen D.
      • Muthukuru M.
      • et al.
      Human squamous cell carcinomas evade the immune response by down-regulation of vascular E-selectin and recruitment of regulatory T cells.
      ), whereas BCC is related to the dysregulation in epidermal development pathways. Wnt signaling pathway was associated with both cancer types, as previously reported (
      • Lang C.M.R.
      • Chan C.K.
      • Veltri A.
      • Lien W.H.
      Wnt signaling pathways in keratinocyte carcinomas.
      ); stronger enrichment was observed in BCC than in SCCs (
      • Noubissi F.K.
      • Yedjou C.G.
      • Spiegelman V.S.
      • Tchounwou P.B.
      Cross-talk between Wnt and hh signaling pathways in the pathology of basal cell carcinoma.
      ). In addition, SCCs lack a mechanism of apoptosis compared with normal samples (Supplementary Figure S3).
      MsigDB-enriched categories showed that BCCs and SCCs have similar areas of enrichment as other common cancer, including breast cancer, which implies potentially shared functional genes involved in carcinogenesis (Figure 2c and 2d). MsigDB enrichment results also highlighted different transcription factors (TFs) in BCCs and SCCs, with BCCs gaining enhancers closer to the genes with promoters bound by SMAD2 and SMAD3 (Figure 2c). In addition, MSigDB predicted promoter motifs in the oncogenes JUN, MSX, and MYC. In SCCs, the gained enhancers are closer to the genes with promoters bound by FOXP3 (Figure 2d). MsigDB also uncovered several enriched motifs in promoter regions, including ETS1 and ETV4.

      Different TFs and pervasive transcription regulation may mediate distinct BCC and SCC expression profiles

      To understand the potential regulatory role of the altered H3K27ac peaks, we systematically explored the correlation between H3K27ac peaks and the RNA expressions of putative target genes. We used the gene expression data (GSE125285) from a recent publication (
      • Wan J.
      • Dai H.
      • Zhang X.
      • Liu S.
      • Lin Y.
      • Somani A.K.
      • et al.
      Distinct transcriptomic landscapes of cutaneous basal cell carcinomas and squamous cell carcinomas [e-pub ahead of print].
      ), where BCC and SCC samples were profiled by RNA sequencing. Using GREAT to identify putative target genes of the H3K27ac peaks, we observed elevated expression level of the genes near SCC- and BCC-upregulated peaks and decreased expression of genes near SCC- and BCC-downregulated peaks in cancer samples as compared with the expression of genes near nonsignificant peaks (P > 5% or log2[fold-change] < 1) (Supplementary Figure S4). The overlap and the full list of the genes passing these filters are displayed in Supplementary Figure S5 and reported in Supplementary Table S2.
      We further observed this trend quantitatively by calculating the correlation between the log2 fold-change of H3K27ac peaks and the expression of their potential target genes (Supplementary Figure S6). The correlation improved by narrowing down to the regulatory peaks and the genes associated with immune response and skin development GO terms, demonstrating the importance of the uncovered H3K27ac cancer‒specific gain or loss in these pathways.
      To investigate the potential TFs that mediate the cancer-specific gain or loss of H3K27ac in KC carcinoma, we performed an unbiased motif analyses using Homer and Haystack. Haystack calculates the enrichment of known and annotated regions on the basis of available TFs databases, whereas Homer is a powerful tool for de novo motif discovery.
      Using Haystack, we inferred TFs that are enriched in gained putative enhancers. For BCCs, we inferred factors from the JUN family and its heterodimers (JUNB, JUND, BATF-JUN, FOS-JUN) as well as JDP2, FOS, and HOX (Figure 3a). Using recently published gene expression data (
      • Wan J.
      • Dai H.
      • Zhang X.
      • Liu S.
      • Lin Y.
      • Somani A.K.
      • et al.
      Distinct transcriptomic landscapes of cutaneous basal cell carcinomas and squamous cell carcinomas [e-pub ahead of print].
      ), we confirmed that JDP2 from the JUN family (also the most enriched BCC-specific factor in Haystack analysis) was significantly upregulated in BCCs and downregulated in SCC (Supplementary Figure S7a). All members of the SMAD family were upregulated in BCCs, although the difference did not reach statistical significance (Supplementary Figure S7b). For SCCs, we observed that the enrichment of FLI1, ETS1, ERG, ERF, ETV2, and RUNX. FOS factors were enriched for regions gained in both BCC and SCC (Figure 3b). However, no differential expression patterns were observed for these genes in BCCs and SCCs (
      • Wan J.
      • Dai H.
      • Zhang X.
      • Liu S.
      • Lin Y.
      • Somani A.K.
      • et al.
      Distinct transcriptomic landscapes of cutaneous basal cell carcinomas and squamous cell carcinomas [e-pub ahead of print].
      ).
      Figure thumbnail gr3ab
      Figure 3Putative TFs located at regulatory elements defined by H3K27ac-specific peaks in BCC and SCC samples from Haystack and Homer analyses. (a) Top 10 TFs for BCC from Haystack analyses, (b) Top 10 TFs for SCC from Haystack analyses, (c) Top 10 TFs in BCC from Homer analyses, and (d) Top 10 TFs in SCC from Homer analyses. BCC, basal cell carcinoma; Bg, background; ID, identification document; SCC, squamous cell carcinoma; TF, transcription factor; STD, standard deviation of motif occurrence.
      Figure thumbnail gr3cd
      Figure 3Putative TFs located at regulatory elements defined by H3K27ac-specific peaks in BCC and SCC samples from Haystack and Homer analyses. (a) Top 10 TFs for BCC from Haystack analyses, (b) Top 10 TFs for SCC from Haystack analyses, (c) Top 10 TFs in BCC from Homer analyses, and (d) Top 10 TFs in SCC from Homer analyses. BCC, basal cell carcinoma; Bg, background; ID, identification document; SCC, squamous cell carcinoma; TF, transcription factor; STD, standard deviation of motif occurrence.
      Homer analysis revealed FOSL2 as a top regulated factor for BCC-specific regions (Figure 3c); whereas for the SCC regions, we found ETS and RUNX, two factors also observed by the Haystack analysis (Figure 3d). When contrasting SCC and BCC regions, we discovered REL and NF-κB as SCC-specific enhancers, again implicating immune response pathways in SCC pathogenesis (Supplementary Figure S8). NF-kB expression was not significantly changed in BCC or SCC (Supplementary Figure S7c).
      Homer analysis of H3K27ac lost peaks showed 15 enriched TFs in SCCs (Supplementary Figures S9a) and 3 enriched motifs in BCCs (Supplementary Figures S9b). Interestingly, in SCCs, enrichment was noted in the RAR/RXR motif, which plays a key role in the recruitment of chromatin regulators (
      • Wang S.P.
      • Tang Z.
      • Chen C.W.
      • Shimada M.
      • Koche R.P.
      • Wang L.H.
      • et al.
      A UTX-MLL4-p300 transcriptional regulatory network coordinately shapes active enhancer landscapes for eliciting transcription.
      ) and has been associated with SCC pathogenesis (
      • Crowe D.L.
      • Shuler C.F.
      Increased cdc2 and cdk2 kinase activity by retinoid X receptor gamma-mediated transcriptional down-regulation of the cyclin-dependent kinase inhibitor p21Cip1/WAF1 correlates with terminal differentiation of squamous cell carcinoma lines.
      ).

      Prioritizing regulatory loci by integrating information from differential enhancer signals and TFs

      To illustrate the utility of our unbiased and genome-wide characterization of H3k27ac gain and loss in BCC and SCC, we prioritized three loci on the basis of enriched functional annotations from the GREAT analysis. In BCC, FGFR2 is an important gene in both hair follicle development (GO: 0001942) and skin development (GO: 0043588) (
      • Czyz M.
      Fibroblast growth factor receptor signaling in skin cancers.
      ). A proximal H3K27ac peak (1 kb downstream of FGFR2) was enriched in BCCs (Figure 4a), suggesting that these differentially active enhancers are related to dysfunctional epidermal development. The expression of FGFR2 has also been shown to be significantly upregulated in BCCs (Supplementary Figure S7d) (
      • Wan J.
      • Dai H.
      • Zhang X.
      • Liu S.
      • Lin Y.
      • Somani A.K.
      • et al.
      Distinct transcriptomic landscapes of cutaneous basal cell carcinomas and squamous cell carcinomas [e-pub ahead of print].
      ).
      Figure thumbnail gr4a
      Figure 4Three prioritized regions of differential regulation in KC carcinoma samples. (a) BCC unique peak in FGFR2 locus (chr10:123092432-123092933), (b) SCC unique peaks in FOXP1 introns (chr3:71499999-71507918, chr3:71534090-71548628, chr3:71550666-71556241) and overlap with putative FOXP1/3-binding sites, and (c) both BCC- and SCC-specific peaks in WNT5A locus (chr3:55515028-55516034 and chr3:55519129-55522574) and overlap with putative SMAD-binding sites. BCC, basal cell carcinoma; CHIP-seq, chromatin immunoprecipitation sequencing; KC, keratinocyte; SCC, squamous cell carcinoma.
      Figure thumbnail gr4b
      Figure 4Three prioritized regions of differential regulation in KC carcinoma samples. (a) BCC unique peak in FGFR2 locus (chr10:123092432-123092933), (b) SCC unique peaks in FOXP1 introns (chr3:71499999-71507918, chr3:71534090-71548628, chr3:71550666-71556241) and overlap with putative FOXP1/3-binding sites, and (c) both BCC- and SCC-specific peaks in WNT5A locus (chr3:55515028-55516034 and chr3:55519129-55522574) and overlap with putative SMAD-binding sites. BCC, basal cell carcinoma; CHIP-seq, chromatin immunoprecipitation sequencing; KC, keratinocyte; SCC, squamous cell carcinoma.
      Figure thumbnail gr4c
      Figure 4Three prioritized regions of differential regulation in KC carcinoma samples. (a) BCC unique peak in FGFR2 locus (chr10:123092432-123092933), (b) SCC unique peaks in FOXP1 introns (chr3:71499999-71507918, chr3:71534090-71548628, chr3:71550666-71556241) and overlap with putative FOXP1/3-binding sites, and (c) both BCC- and SCC-specific peaks in WNT5A locus (chr3:55515028-55516034 and chr3:55519129-55522574) and overlap with putative SMAD-binding sites. BCC, basal cell carcinoma; CHIP-seq, chromatin immunoprecipitation sequencing; KC, keratinocyte; SCC, squamous cell carcinoma.
      In SCCs, FOXP1, a member of the FOXP family, is involved in the regulation of immune system processes (GO: 0002682). Three SCC-specific peaks were enriched in one FOXP1 intron (Figure 4b), indicating that a possible intronic enhancer can modulate the FOXP1 function. FOXP3 is a TF identified in our GREAT analysis, which indicates that the FOXP family could play important roles in SCC development. Expression data suggest that FOXP1 (and not FOXP3) has a self-regulatory mechanism that is potentially associated with SCCs (Supplementary Figure S10).
      WNT5A, a gene involved in both the regulation of immune system processes and Wnt signaling pathways, is a shared feature of both tumor subtypes and has been reported to be upregulated in KC carcinoma (
      • Lang C.M.R.
      • Chan C.K.
      • Veltri A.
      • Lien W.H.
      Wnt signaling pathways in keratinocyte carcinomas.
      ). Other Wnt members, including WNT2 and WNT2B, also have differential enhancer signals. We detected two H3K27ac cancer‒specific peaks in both BCCs and SCCs in the WNT5A promoter and the first intron (Figure 4c). In addition, there are two putative SMAD-binding sites within the 20-kb region upstream WNT5A, which is consistent with our motif analysis. Expression data show that WNT5A is significantly upregulated in both BCCs and SCCs (Supplementary Figure S7e) (
      • Wan J.
      • Dai H.
      • Zhang X.
      • Liu S.
      • Lin Y.
      • Somani A.K.
      • et al.
      Distinct transcriptomic landscapes of cutaneous basal cell carcinomas and squamous cell carcinomas [e-pub ahead of print].
      ). Our findings highlighting the gain of cancer-specific enhancers suggests that the Wnt signaling may be a shared pathway in KC carcinogenesis. Importantly, our H3k27ac differential map will allow other researchers to perform further detailed analyses with other genes.

      Discussion

      Using chromatin profiling for H3K27ac in KC carcinoma matched tumor-normal samples, we uncovered putative novel enhancers that are specific to BCCs and SCCs and shared across different individuals. Biological processes, including skin, skeletal system, epidermis, and hair follicle development, as well as the Wnt signaling pathway are enriched in BCC. In contrast, the regulation of immune system processes, immune response, and cell activation are enriched in SCCs. Our findings uncover unique and shared epigenetic alterations that may contribute to the pathogenesis of BCC and SCC. Epigenetic alterations hold promise for the development of robust biomarkers for the detection, monitoring, and prognosis of patients with KC carcinoma.
      Given that epigenetic as well as genetic abnormalities are important in malignant transformation, it is critical to understand the shared and unique features of the epigenetic landscape of KC carcinomas. Epigenetic alternations in tumor suppressor genes such as p53, p16(INK4a), and p14(ARF) and TF such as FOXE1 have been associated with SCC (
      • Brown V.L.
      • Harwood C.A.
      • Crook T.
      • Cronin J.G.
      • Kelsell D.P.
      • Proby C.M.
      p16INK4a and p14ARF tumor suppressor genes are commonly inaivated in cutaneous squamous cell carcinoma.
      ;
      • Murao K.
      • Kubo Y.
      • Ohtani N.
      • Hara E.
      • Arase S.
      Epigenetic abnormalities in cutaneous squamous cell carcinomas: frequent inactivation of the RB1/p16 and p53 pathways.
      ;
      • Venza I.
      • Visalli M.
      • Tripodo B.
      • De Grazia G.
      • Loddo S.
      • Teti D.
      • et al.
      FOXE1 is a target for aberrant methylation in cutaneous squamous cell carcinoma.
      ). Epigenetic changes in Sonic Hedgehog and Wnt pathway have also been reported in BCC (
      • Brinkhuizen T.
      • van den Hurk K.
      • Winnepenninckx V.J.L.
      • de Hoon J.P.
      • van Marion A.M.
      • Veeck J.
      • et al.
      Epigenetic changes in basal cell carcinoma affect SHH and WNT signaling components.
      ;
      • Goldberg M.
      • Rummelt C.
      • Laerm A.
      • Helmbold P.
      • Holbach L.M.
      • Ballhausen W.G.
      Epigenetic silencing contributes to frequent loss of the fragile histidine triad tumour suppressor in basal cell carcinomas.
      ). Our findings expand on the previous reports of the association between epigenetic changes and carcinogenesis of KC carcinoma by examining genome-wide histone modifications in BCC and SCC.
      Using the enriched regions as a guide, we uncovered transcriptional regulators, signaling pathways, and biological processes that may mediate the malignant transformation of BCC and SCC. We identified FOXP as a potential key TF family for SCC. FOXP1 is a gene that encodes a TF involved in maintaining quiescence in hair follicle stem cells (
      • Leishman E.
      • Howard J.M.
      • Garcia G.E.
      • Miao Q.
      • Ku A.T.
      • Dekker J.D.
      • et al.
      Foxp1 maintains hair follicle stem cell quiescence through regulation of Fgf18.
      ). Loss of FOXP1 in KCs results in stem cell activation, whereas the overexpression prevents cell proliferation by promoting cell-cycle arrest (
      • Leishman E.
      • Howard J.M.
      • Garcia G.E.
      • Miao Q.
      • Ku A.T.
      • Dekker J.D.
      • et al.
      Foxp1 maintains hair follicle stem cell quiescence through regulation of Fgf18.
      ). FOXP3, also known as scurfin, is a TF involved in regulatory T cell (Treg) function. FOXP3 mutations that alter expression can lead to a lack of Treg (
      • Roncarolo M.G.
      • Gregori S.
      Is FOXP3 a bona fide marker for human regulatory T cells?.
      ). Germline mutations in FOXP3 cause immune dysregulation, polyendocrinopathy, enteropathy, and X-linked syndrome, which is a fatal autoimmune disease (
      • Roncarolo M.G.
      • Gregori S.
      Is FOXP3 a bona fide marker for human regulatory T cells?.
      ). Intense FOXP3 + Treg cell infiltration has been associated with high-grade, aggressive cutaneous SCC (
      • Azzimonti B.
      • Zavattaro E.
      • Provasi M.
      • Vidali M.
      • Conca A.
      • Catalano E.
      • et al.
      Intense Foxp3+ CD25+ regulatory T-cell infiltration is associated with high-grade cutaneous squamous cell carcinoma and counterbalanced by CD8+/Foxp3+ CD25+ ratio.
      ). Thus, a proposed mechanism of action for the alterations in FOXP1 or FOXP3 expression in SCC pathogenesis is through its impact on Treg function, which can lead to immune dysregulation and allow SCC to develop in the setting of immune tolerance. The clinical impact of this pathway for SCC carcinogenesis lies in the ability to modulate FOXP3 expression and Treg function by targeting newly discovered regulatory nodes that can potentially lead to the development of novel immunotherapies (
      • Lu L.
      • Barbi J.
      • Pan F.
      The regulation of immune tolerance by FOXP3.
      ).
      The Wnt genes express signaling molecules that are components of a group of signal transduction pathways (
      • Kretzschmar K.
      • Clevers H.
      Wnt/β-catenin signaling in adult mammalian epithelial stem cells.
      ), which regulate various cell functions, including cell growth, proliferation, differentiation, apoptosis, migration, and angiogenesis (
      • DO Carmo N.G.
      • Sakamoto L.H.
      • Pogue R.
      • DO Couto Mascarenhas C.
      • Passos S.K.
      • Felipe M.S.
      • et al.
      Altered expression of PRKX, WNT3 and WNT16 in human nodular basal cell carcinoma.
      ;
      • Noubissi F.K.
      • Yedjou C.G.
      • Spiegelman V.S.
      • Tchounwou P.B.
      Cross-talk between Wnt and hh signaling pathways in the pathology of basal cell carcinoma.
      ). Aberrant activation of the Wnt signaling pathway is involved in tumor initiation, progression, and invasion and maintaining cancer stem cells (
      • Lang C.M.R.
      • Chan C.K.
      • Veltri A.
      • Lien W.H.
      Wnt signaling pathways in keratinocyte carcinomas.
      ). Wnt signaling plays a role in the development of epidermal stem cells (
      • Kretzschmar K.
      • Clevers H.
      Wnt/β-catenin signaling in adult mammalian epithelial stem cells.
      ), in the proliferation of KC proliferation (
      • Teh M.T.
      • Blaydon D.
      • Ghali L.R.
      • Briggs V.
      • Edmunds S.
      • Pantazi E.
      • et al.
      Role for WNT16B in human epidermal keratinocyte proliferation and differentiation.
      ), and in homeostasis and regeneration of the skin (
      • Lang C.M.R.
      • Chan C.K.
      • Veltri A.
      • Lien W.H.
      Wnt signaling pathways in keratinocyte carcinomas.
      ). Activating mutations of the Wnt signaling has been implicated in the pathogenesis of BCC and SCC, as shown by overexpression of Wnt proteins (
      • Lang C.M.R.
      • Chan C.K.
      • Veltri A.
      • Lien W.H.
      Wnt signaling pathways in keratinocyte carcinomas.
      ;
      • Noubissi F.K.
      • Yedjou C.G.
      • Spiegelman V.S.
      • Tchounwou P.B.
      Cross-talk between Wnt and hh signaling pathways in the pathology of basal cell carcinoma.
      ;
      • Salto-Tellez M.
      • Peh B.K.
      • Ito K.
      • Tan S.H.
      • Chong P.Y.
      • Han H.C.
      • et al.
      RUNX3 protein is overexpressed in human basal cell carcinomas.
      ;
      • Youssef K.K.
      • Lapouge G.
      • Bouvrée K.
      • Rorive S.
      • Brohée S.
      • Appelstein O.
      • et al.
      Adult interfollicular tumour-initiating cells are reprogrammed into an embryonic hair follicle progenitor-like fate during basal cell carcinoma initiation.
      ). In particular, the expression of mediators in the Wnt signaling pathway such as WNT1, WNT2, WNT5A, WNT11, WNT13, and WNT16 promotes the progression of BCC (
      • Lang C.M.R.
      • Chan C.K.
      • Veltri A.
      • Lien W.H.
      Wnt signaling pathways in keratinocyte carcinomas.
      ). Our findings suggested that the Wnt signaling pathway is a shared pathway in KC carcinogenesis.
      Several limitations should be considered when interpreting the results. Despite our small sample size, our chromatin profiling identified clear cancer-specific alterations in the epigenome, which are shared across KC carcinomas as well as distinct BCC and SCC epigenetic alterations. Although we did not have gene expression profiling data from our patient samples, we incorporated recently published BCC and SCC gene expression data in our analyses to distinguish important regulators that may share binding sequences. Finally, although all study samples were procured by a Mohs surgeon, with particular attention paid to removing tumor-normal samples at the dermal‒epidermal junction to ensure that predominantly KC-derived cells were harvested, the samples may have contained a mixture of cells, including immune infiltrates, which may have contributed to the chromatin profiles. However, the examination of the protein expression level of these genes using the Human Protein Atlas (http://www.proteinatlas.org) (
      • Uhlen M.
      • Zhang C.
      • Lee S.
      • Sjöstedt E.
      • Fagerberg L.
      • Bidkhori G.
      • et al.
      A pathology atlas of the human cancer transcriptome.
      ) suggests that our observed findings are more likely to reflect KC rather than inflammatory cell profiling. Future studies using single-cell profiling to assess tumor composition can address this limitation. The selection of tumor and adjacent normal paired samples allows for the elimination of interpatient variability as a potential confounding variable.
      Our findings suggest that FGFR2 enhancers are involved in BCC carcinogenesis, whereas epigenetic regulators of FOXP1/3 are involved in SCC carcinogenesis, and WNT5A promoters are involved in both subtypes. This highlights unique and shared epigenetic alterations in histone modifications and potential regulators for BCCs and SCCs that likely impact divergent pathways in KC oncogenesis. Future studies should not only aim to replicate these findings but also to perform chromatin immunoprecipitation-sequencing profiling for the TFs we have uncovered in this study to validate their binding activity. Furthermore, gene expression profiling can help decipher which expression programs are responsible for the malignant transformation and to uncover their functional mechanisms. Ultimately, multiomic profiling of epigenomic alterations could contribute to the development of novel therapies.

      Materials and Methods

      Study population

      We enrolled 11 patients with KC carcinomas (five with BCCs and six with SCCs) at the Massachusetts General Hospital (Boston, MA) who were undergoing Mohs surgery for the resection of their tumors. Written informed consent was obtained preoperatively by study staff from each patient, which included the donation of discarded tumor tissue and normal skin removed in the reconstruction of the surgical defect (tumor and normal pairs). The epidermis from each sample was carefully surgically dissected at the level of the dermal‒epidermal junction by the study principal investigator and Mohs surgeon (MMA) and subsequently snap frozen. The institutional review board of Partners Healthcare approved the study.

      Chromatin immunoprecipitation sequencing of KC carcinoma

      Tumor and/or normal pairs were snap frozen in liquid nitrogen, then pulverized using a Covaris Cryoprep (Covaris, Woborn, Massachusetts). Tissues remained frozen until they were cross-linked (20 °C at room temperature, 1% formaldehyde), quenched with 2.5 M glycine, and washed two times in PBS. Fixed cells were solubilized using a two-step lysis protocol (cytoplasm, then nucleus), sonicated (Branson Sonifier), and immunoprecipitated with antibodies to H3K27ac (active enhancers). Immunoprecipitated DNA was recovered and prepared for DNA sequencing, along with antibody-free control libraries. Libraries were made using 8-ng chromatin immunoprecipitation DNA per library and were prepared using the KAPA Hyper prep kits (Roche Sequencing and Life Science, Wilmington, MA). Library quality was then assessed by the use of an Agilent 2100 (Agilent Technologies, Santa Clara, CA) to infer fragment size distribution and by the use of a Qubit to infer DNA concentration. Libraries were sequenced on an Illumina HiSeq2500 (Illumina, Inc, San Diego, CA), using 76-cycle, single-end sequencing. DNA sequences were aligned to the human genome scaffold. Maps reflecting different chromatin modifications in normal KCs and KC carcinomas derived from the same patient were integrated to derive a comprehensive set of sequence elements in normal human KCs, annotated by their predicted functions and cell type-specificities, as well as in two different kinds of KC-derived carcinomas.

      Differentially enhancer definition

      Homer (histone mode) was used to segment H3K27ac chromatin immunoprecipitation-sequencing BAM files to identify candidate active enhancers. Peak calling was achieved using Homer findpeaks-style histone. Deeptools (
      • Ramírez F.
      • Ryan D.P.
      • Grüning B.
      • Bhardwaj V.
      • Kilpert F.
      • Richter A.S.
      • et al.
      deepTools2: a next generation web server for deep-sequencing data analysis.
      ) was used to quantify the signal strength under each peak with the setting “normalizeUsing RPKM” to normalize each sample toward Reads Per Kilobase per Million mapped reads (RPKM). Using DESeq2 (
      • Love M.I.
      • Huber W.
      • Anders S.
      Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2.
      ), we compared cancer samples with normal samples and searched for differential peaks genome wide (corrected P < 5% and log2[fold-change] > 1).

      Dimensionality reduction and visualization

      For each sample, we obtained a vector of signal intensity at all identified H3K27ac-enriched peaks. We applied sklearn python package to perform the PCA. We plotted samples using the largest three components from PCA to verify the separation of sample space.

      Gene set enrichment analysis

      For enriched BCC- and SCC-specific H3K27ac peaks, we applied GREAT (
      • McLean C.Y.
      • Bristor D.
      • Hiller M.
      • Clarke S.L.
      • Schaar B.T.
      • Lowe C.B.
      • et al.
      Great improves functional interpretation of cis-regulatory regions.
      ) and used 5-kb upstream and 1-kb downstream of each gene as the regulatory domain to identify the enriched GO terms (i.e., biological processes) and MsigDB functions.

      TF motif analysis

      Haystack (
      • Pinello L.
      • Farouni R.
      • Yuan G.C.
      Haystack: systematic analysis of the variation of epigenetic states and cell-type specific regulatory elements.
      ) and Homer (
      • Heinz S.
      • Benner C.
      • Spann N.
      • Bertolino E.
      • Lin Y.C.
      • Laslo P.
      • et al.
      Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities.
      ) are complementary tools used for TF motif analysis. Haystack is a bioinformatics pipeline that identifies hotspots of epigenetic variability in TFs. We used H3K27ac peaks with a corrected P <5% and log2(fold-change) >1 to capture cancer-specific enhancers regions and used peaks regions with log2(fold-change) <0.1 as background to avoid recovering general factors shared with ubiquitous peaks. Homer is a motif discovery tool that works by inspecting enriched motifs, and we used findMotifs.pl in the same set of H3K27ac regions to obtain motif lists.

      Data availability statement

      The accession identifiers of datasets generated and analyzed for this study can be found at the uniform resource locators supplied in Supplementary Table S3, hosted by the National Human Genome Research Institute ENCODE Data Coordination Center (https://www.encodeproject.org/) at Stanford University, California. The characterized basal cell carcinoma‒ and squamous cell carcinoma‒specific H3K27ac peaks described in this paper are summarized in Supplementary Table S1 (Excel file).

      Conflict of Interest

      MMA has research funding from Pfizer Inc to her institution. BEB declares outside interests in Fulcrum Therapeutics, 1CellBio, HiFiBio, Arsenal Biosciences, Cell Signaling Technologies, BioMillenia, and Nohla Therapeutics. The remaining authors state no conflict of interest.

      Acknowledgments

      This work was supported by the National Institute of Arthritis and Musculoskeletal and Skin Diseases (K24 AR069760 to MMA), the National Cancer Institute (R01CA231264 to MMA), the National Human Genome Research Institute (R00HG008399 and R35HG010717 to LP), and the National Human Genome Research Institute (5UM1HG009390 to BEB).

      Author Contributions

      Conceptualization: LP, MMA; Data Curation: QY, CBE, SB, RI, MMA; Formal Analysis: QY, CBE; Funding Acquisition: SB, LP, MMA; Investigation: QY, CBE, LP, MMA; Methodology: LP, MMA; Project Administration: MMA; Resources: LP, CBE, MMA; Software: QY, SB, RI; Supervision: LP, MMA; Validation: QY; Visualization: QY, YK; Writing - Original Draft Preparation: QY, LP, YK, MMA; Writing - Review and Editing: QY, LP, CBE, YK, MMA

      Supplementary Material

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      Linked Article

      • From Enhancers to Keratinocyte Cancers
        Journal of Investigative DermatologyVol. 141Issue 5
        • Preview
          Epigenetic dysregulation and disruption of gene enhancer networks are both pervasive in human cancers, and yet, their roles in keratinocyte cancers are poorly understood. Utilizing patient samples, Yao et al. (2020) provide an initial framework for understanding the underlying mechanisms by integrating enhancer and transcriptional alterations that occur during the progression of basal cell and squamous cell carcinomas.
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