Landscape of enhancer disruption and functional screen in melanoma cells

Wang, Zhao; Luo, Menghan; Liang, Qian; Zhao, Ke; Hu, Yuelin; Wang, Wei; Feng, Xiangling; Hu, Bolang; Teng, Jianjin; You, Tianyi; Li, Ran; Bao, Zhengkai; Pan, Wenhao; Yang, Tielong; Zhang, Chao; Li, Ting; Dong, Xiaobao; Yi, Xianfu; Liu, Ben; Zhao, Li; Li, Miaoxin; Chen, Kexin; Song, Weihong; Yang, Jilong; Li, Mulin Jun

doi:10.1186/s13059-023-03087-5

Research
Open access
Published: 30 October 2023

Landscape of enhancer disruption and functional screen in melanoma cells

Zhao Wang^1,2^na1,
Menghan Luo^2,3^na1,
Qian Liang^1,2,4^na1,
Ke Zhao³^na1,
Yuelin Hu¹,
Wei Wang²,
Xiangling Feng³,
Bolang Hu¹,
Jianjin Teng³,
Tianyi You³,
Ran Li¹,
Zhengkai Bao¹,
Wenhao Pan¹,
Tielong Yang⁵,
Chao Zhang⁵,
Ting Li⁵,
Xiaobao Dong³,
Xianfu Yi³,
Ben Liu²,
Li Zhao⁶,
Miaoxin Li⁷,
Kexin Chen²,
Weihong Song¹,
Jilong Yang⁵ &
…
Mulin Jun Li ORCID: orcid.org/0000-0003-3598-3679^2,3

Genome Biology volume 24, Article number: 248 (2023) Cite this article

2725 Accesses
2 Altmetric
Metrics details

Abstract

Background

The high mutation rate throughout the entire melanoma genome presents a major challenge in stratifying true driver events from the background mutations. Numerous recurrent non-coding alterations, such as those in enhancers, can shape tumor evolution, thereby emphasizing the importance in systematically deciphering enhancer disruptions in melanoma.

Results

Here, we leveraged 297 melanoma whole-genome sequencing samples to prioritize highly recurrent regions. By performing a genome-scale CRISPR interference (CRISPRi) screen on highly recurrent region-associated enhancers in melanoma cells, we identified 66 significant hits which could have tumor-suppressive roles. These functional enhancers show unique mutational patterns independent of classical significantly mutated genes in melanoma. Target gene analysis for the essential enhancers reveal many known and hidden mechanisms underlying melanoma growth. Utilizing extensive functional validation experiments, we demonstrate that a super enhancer element could modulate melanoma cell proliferation by targeting MEF2A, and another distal enhancer is able to sustain PTEN tumor-suppressive potential via long-range interactions.

Conclusions

Our study establishes a catalogue of crucial enhancers and their target genes in melanoma growth and progression, and illuminates the identification of novel mechanisms of dysregulation for melanoma driver genes and new therapeutic targeting strategies.

Background

Melanoma, one of the most aggressive and malignant tumors, has a high fatality rate with over fifty thousand deaths reported each year worldwide [1]. Previous large-scale genome sequencing studies have identified many putative cancer genes and hotspots in different types of melanomas [2,3,4,5,6,7,8]. However, about 15% cutaneous melanoma and over 50% mucosal/acral melanoma patients are unclassified based on the pattern of the most prevalent significantly mutated genes, including BRAF, RAS, and NF1 [2, 3]. Few studies have systematically characterized genomic events outside of protein-coding genes and their functions in melanoma development. The major challenge comes from the predominantly high mutation burden of the melanoma genome with a C > T nucleotide transition signature attributable to ultraviolet radiation [9]. This hampers the identification of true melanoma drivers from background mutations only relying on genome sequencing and computational modelling, especially in the non-coding genomic region [10].

Given the poorly understood localized hypermutation processes, establishing and elucidating the melanoma regulatory landscape, such as enhancer disruption, could provide an effective avenue for identification of non-coding drivers [11,12,13]. It has been demonstrated that enhancer signatures are strongly enriched for SOX10/MITF and AP-1/TEAD regulome in melanoma [14,15,16,17]. Furthermore, KMT2D or STAG2 mutant melanomas could rewire metabolic pathways or interferon signaling through enhancer reprogramming [18, 19]. Importantly, CRISPR-based screening of cis-regulatory elements (CREs) provides a powerful tool to comprehensively investigate functional mutation events in the non-coding genome of cancers [20,21,22]. A high-resolution CRISPR screen of non-coding functional elements surrounding three genes, including NF1, NF2, and CUL3, uncovered several critical CREs that modulate drug resistance in melanoma [23]. These studies highlighted the importance of CRE dysregulations during melanoma tumorigenesis and emphasized the necessity of systematic deciphering of enhancer-altered events in melanoma genomes.

By systematically integrating 297 high-coverage melanoma whole-genome sequencing (WGS) datasets, we prioritized highly recurrent regions (HRRs) across the whole melanoma genome. We designed a genome-scale CRISPR interference (CRISPRi) screening and identified 66 significant HRR-associated enhancers that could play growth-suppressive roles in melanoma cells. Target gene analysis for these essential enhancers revealed many hidden mechanisms underlying melanoma growth, with others already confirmed. We further demonstrated that two high-level screened HRR-associated enhancers are crucial in sustaining MEF2A and PTEN tumor-suppressive potential in melanoma cells (Fig. 1a).

Results

Genome-wide prioritization of highly recurrent regions (HRRs) in 297 melanoma WGS datasets

To gain sufficient power in detection of recurrent alterations in the melanoma non-coding genome, we comprehensively integrated 297 melanoma genomes with paired (tumor and normal tissues) and high-coverage (> 30 ×) WGS data from three different projects, deposited in the International Cancer Genome Consortium (ICGC) [29]. This included SKCM-US (38 patients), MELA-AU (183 patients), and SKCA-BR (76 patients) (Additional file 2: Table S1). Point somatic mutations including single-nucleotide variants (SNVs) and insertions/deletions (INDELs) were uniformly identified via GATK Mutect2 [30] (Additional file 1: Fig. S1a). To increase detection sensitivity for structural variant (SV) calling, we used two robust somatic SV detection tools, Manta [31] and GRIDSS [32], to identify four types of simple somatic SV, including deletions (DELs), duplications (DUPs), inversions (INVs), and translocations (TRAs) (Additional file 1: Fig. S1b). Based on these mutation spectra from 297 melanoma samples, we applied a Gamma-Poisson model [33] to test genomic intervals showing an excess of somatic SNVs/INDELs (Additional file 1: Fig. S1c) and leveraged a sliding window strategy to prioritize the recurrence of somatic SVs among melanoma patients (Additional file 1: Fig. S1d) (see “Methods” for details).

Consequently, we identified 5508 SNV/INDEL-associated HRRs (SNV/INDEL-HRRs) (FDR < 5%) and 13,736 SV-associated HRRs (SV-HRRs) (≥ 10 donors) in the melanoma genomes, including 8265 DEL-HRRs, 3961 DUP-HRRs, 1224 INV-HRRs, and 2717 TRA-HRRs (Fig. 1b, Additional file 3: Table S2, Additional file 4: Table S3). We prioritized these HRRs according to their mutation recurrence frequency and found that over half of the high priority HRRs incorporate known melanoma driver genes revealed by genomic analysis, CRISPR fitness screening, and other literature evidence (Fig. 1b). Among SNV/INDEL-HRRs, our results showed that DPH3, TERT, UBXN8, RPL18A, PSMC6 promoter, and RPS27, SASS6, MRPS31 5’UTR were significantly mutated, which is consistent with previous WGS studies [3] (Fig. 1c, Additional file 3: Table S2). Notably, about 66% of the high priority SNV/INDEL-HRRs were located in the non-coding region, suggesting that alteration of the regulatory code could play an essential role in melanoma growth. Besides, 72 and 46% melanoma driver genes were affected by DEL and DUP, respectively, but few were influenced by other types of SV. These include some canonical melanoma tumor-suppressive genes such as BRD9 [34, 35] and CDKN2A/CDKN2B [36, 37], and several critical melanoma oncogenes like BIRC5 [38] and YAP1 [39]. Nevertheless, the majority of SV-HRRs occurred at the non-coding region with unknown function (Fig. 1d, Additional file 4: Table S3).

CRISPR tiling screen of HRR-associated enhancers for melanoma cell growth

Given the highest mutation frequency and distinct mutational processes of melanoma compared with other cancer types, identification of true non-coding drivers from these candidate melanoma HRRs remains a challenge. Many research studies have supported that enhancer elements are highly mutated in cancers and constantly shaped via tumor evolutionary selection [11, 40,41,42]. Thus, we applied publicly available melanoma H3K27ac ChIP-seq to define active enhancers and intersected them with our identified melanoma HRRs (see “Methods” for details). We uncovered 4645 HRR-associated enhancers across the melanoma genome. To explore whether HRR-associated enhancers play a potential role in melanoma growth and progression, we aligned all active melanoma enhancers with their potential target genes (1 Mb upstream and downstream). Their tumor proliferation potential was then evaluated based on previous CRISPR knockout (KO) screens in melanoma [43,44,45]. Our analysis revealed that genes associated with DEL- and DUP-associated enhancers elicited more significant signals in the positive/negative selection of the CRISPR KO screens than those linked to HRR-unrelated enhancers (p-value < 0.05, Mann–Whitney U test) (Fig. 2a, Additional file 1: Fig. S2a). This implies that enhancer disruption through recurrent mutation trends to modulate genes suppressive of melanoma proliferation/survival.

To systematically inspect the functional consequence of enhancer disruption within melanoma HRRs, we performed a CRISPR interference (CRISPRi) screen on the A375 human melanoma cell line by employing the dCas9-KRAB system and dispersedly tiled sgRNAs for each of selected HRR-associated enhancers. Specifically, to reduce the library size and improve screening power due to the abundant enhancers, we initially collected 190 essential candidate genes associated with melanoma cell growth and drug resistance from published CRISPR/shRNA screening studies (Additional file 5: Table S4). We selected 665 HRR-associated enhancers harboring both H3K27ac and H3K4me1 signals in A375 cells within 1 Mb region of candidate essential genes. Since many H3K27ac and H3K4me1 peaks span a long distance, we also tailored enhancer regions with chromatin accessibility signal based on ATAC-seq peaks, yielding 805 enhancer core regions. Finally, we designed a pooled sgRNA library (total 9981 sgRNAs, including 1000 non-targeting control sgRNAs) via an intersected tiling strategy, in which each selected enhancer core region received a mean of 11 sgRNAs (range 2–34) (Fig. 2b, Additional file 6: Table S5). During screening, we generated melanoma cell lines stably expressing CRISPRi transgenes and performed lentiviral transduction of pooled sgRNAs at low multiplicity of infection (MOI) = 0.2. The transduced cells were selected using puromycin for a week and subsequently cultured for 7 or 21 days before harvesting the genomic DNA. The sgRNA sequences were PCR-amplified and sequenced to determine enrichment at day 7 or 21 relative to day 0 cells (Fig. 2b).

We first performed two independent CRISPRi screens on A375 (BRAF^V600E melanoma cell line) cell proliferation with the pooled sgRNA library (Additional file 7: Table S6). The MAGeCK-RRA algorithm [46] was used to analyze the positively selected enhancers (see “Methods” for details). The results from the replicate screens were highly consistent (r > 0.9, Spearman’s correlation test) and the non-targeting control showed limited effect on cell viability (Additional file 1: Fig. S2b). By positive selection analysis of CRISPRi screening at day 21, we revealed 66 significant enhancers (false discovery rate (FDR) < 0.05) which displayed a strong pro-proliferative effect (Fig. 2c, Additional file 1: Fig. S2c, Additional file 8: Table S7), suggesting these enhancers could function as tumor-suppressive regulatory elements in melanoma growth. Analysis of tiled sgRNA enrichment for the top four functional enhancers, including E_349, E_156, E_409, and E_653, further demonstrated that the enhancer effect on tumor growth can be robustly estimated (Fig. 2d). To delineate the potential target genes for the 66 significant HRR-associated enhancers, we performed in situ Hi-C followed by chromatin immunoprecipitation (HiChIP) of enhancer mark H3K27ac on A375 cells (Fig. 2e). Several target genes of top hits are regarded as classical melanoma tumor suppressors, such as PTEN and NF1. These enhancers are associated with highly recurrent loss-of-function events in melanoma patients, which implies that tumors may acquire survival advantages by disrupting the genomic sequence of these enhancers, thus decreasing the expression level of their tumor suppressor targets.

To explore the stability of the CRISPRi screening at different time points, we compared positive selection results between day 21 and day 7 screens (Additional file 1: Fig. S3a, c; Additional file 7: Table S6). We found that 14 of 66 hits from the A375 day 21 screen were also enriched on day7, including top-performing enhancer E_349. Such moderate overlap may suggest a premature screen at day7 or potential background amplification at day21. To investigate cell-type specificity of functionally enriched enhancers, we performed CRISPRi screening on a BRAF wild-type melanoma cell line, SK-MEL-2. In the positive selection from SK-MEL-2 day7 screen, we identified 25 significant enhancers, with 19 shared by either A375 day7 or day21 screens. Notably, another high-performing enhancer, E_156, showed enrichment in SK-MEL-2 cells, meriting further investigation (Additional file 8: Table S7). Additionally, we analyzed negative selection from CRISPRi screens on A375 and SK-MEL-2 (Additional file 1: Fig. S3b, d). Day7 screening revealed more functional drop-out enhancers, but fewer were detected in the day21 screen, with nearly half being of DUP type (Additional file 8: Table S7). Interestingly, enhancer E_763, potentially targeting the classic melanoma driver gene MITF, was supported by our HiChIP data.

Genomic and epigenomic features of functional enhancers in melanoma

To investigate the potential tumor-suppressive mechanism and clinical significance of HRR-associated enhancers characterized from the A375 CRISPRi positive selection screen, we initially analyzed the recurrent mutation pattern around the 66 significant HRR-associated enhancers. As expected, inhibition of enhancers associated with DEL-HRR showed a stronger growth-promoting effect of tumor than perturbation of enhancers linked to other types of HRR (Fig. 3a). This indicates that these DEL events may drive cancer progression through disruption of tumor-suppressive enhancers. To inspect the mechanism involved in these functional enhancers, we extracted single-base-pair substitutions (SBS) in enhancer regions and calculated de novo mutational signatures using Palimpsest [47]. By examining the COSMIC mutational signature database [48], we identified two predominant signatures corresponding to ultraviolet light exposure (SBS7a) and platinum treatment (SBS31) (Fig. 3b). Analysis on all 4645 HRR-associated enhancers and separate significant enhancers also support this finding (Additional file 1: Fig. S4a, b). Thus, consistent with highly mutated protein-coding genes, exposure to ultraviolet light is still a major cause of enhancer hypermutation in melanoma. To exploit the epigenomic features associated with the 66 significant enhancers, we collected A375 ChIP-seq for some relevant transcription factors (TFs) and histone modifications (including Pol II, CTCF, MITF, H3K27ac, H3K4me3), and PRO-seq for measuring nascent RNAs. By inspecting the preference of the aforementioned loci at these functional enhancers (Additional file 9: Table S8), we found that most of the enhancers harbor the Pol II binding signal and nearly half of them show promoter-like function. In addition, 10% of enhancers display a super enhancer signature, including the most significant ones E_349 and E_740 (Fig. 3c). Among the signals from the 13 melanoma essential TFs, the ChIP-seq results suggested that the classical melanoma driver TF, MITF, might not be associated with these enhancers (Additional file 1: Fig. S4c). However, we observed HEXIM1 binding signals present in 27 functional enhancer regions, including top hits E_349 and E_156. Additionally, we discovered that binding signals from AP2A, AR, and EGFR were present on many enhancers (Additional file 1: Fig. S4c). In addition, we analyzed the enrichment of transcription regulators with the 66 significant enhancers and found that several critical melanoma-associated TFs (e.g., SP1, SNAI2, HEXIM1, ASCL1) preferentially bind at these enhancer loci (Fig. 3d). Consistent with our result, SP1 and HEXIM1 have been documented to play an important regulatory role in inhibiting melanoma-specific gene transcription. The melanoma tumor suppressor gene HEXIM1 is upregulated by obligatory binding of SP1 under nucleotide stress conditions [49]. Furthermore, several chromatin remodelers (e.g., SMARCA4, H2AZ, PRMT1, RCOR1) and architecture proteins (e.g., CTCF, MAX) displayed high enrichment scores at these enhancers (Fig. 3d), suggesting that disruption of epigenomic regulation on non-coding regulatory elements may be essential in melanoma tumorigenesis.

We applied a latent block model [50] to cluster the 66 significant enhancers according to their associated HRR type and degree of mutation, and we found that these enhancers could generally be partitioned into four major subsets (Fig. 3e). Class I enhancers are frequently lost in melanoma patients including some that are functional towards classical tumor suppressor genes (e.g., PTEN, NF1). Class II enhancers exhibit a heterogeneous mutation pattern wherein melanoma genomes can acquire different types of mutation in some functional loci (such as the non-coding region of KLF6). This highlights the context-specific regulatory re-wiring that occurs during tumorigenesis. Compared to Class I enhancers, Class III enhancers are sparsely mutated by different forms among melanoma patients, while Class IV enhancers are highly duplicated across many oncogene loci (such as YAP1). Previous genomic classification of melanoma patient based on the pattern of the most prevalent significantly mutated genes leads to four subtypes, including mutant BRAF, mutant RAS, mutant NF1, and Triple-WT (wild-type) [2]. However, we found that the mutation spectrum of these functional enhancers is generally independent of the mutational pattern, relying on presence of significantly mutated genes in melanoma.

To elucidate the epigenomic heterogeneity of enhancers across different melanoma contexts, we utilized publicly available H3K27ac and open chromatin signal data from tissue samples of melanoma patient [51,52,53]. We examined the occurrence of the 66 significant HRR-associated enhancers identified from our CRISPRi screening. Our findings suggested that over half of these enhancers are actively prevalent across various melanoma conditions (Additional file 10: Table S9, Additional file 11: S10). To investigate genetic heterogeneity, we examined the mutation status around the 66 significant enhancers, comparing these with melanocyte/melanoma cell lines in various carcinogenic stages from the Cancer Cell Line Encyclopedia (CCLE) [54]. In all 71 melanocyte/melanoma cell lines from the CCLE, including A375, we evaluated the presence of somatic copy number variations and structural variations (DEL, DUP, INV, TRA) within the enhancer genomic region. Our findings revealed that 21 of 66 enhancers, including E_156 and E_349, exhibited copy number gain events, and 17 of 66 showed heterozygous loss events (Additional file 12: Table S11). Together, these enhancer dysregulations may be influenced by the specific tumor evolution patterns at both genetic and epigenetic levels.

Target gene analysis between melanoma-suppressive enhancers and known or novel cancer genes

In order to systematically probe the relationship between functional enhancers and cancer-related genes, we conducted an in-depth analysis of H3K27ac HiChIP data. This analysis led to the identification of 266 loops between 66 significantly screened enhancers via CRISPRi and 200 target genes, of which some have been previously recognized to play a critical role in constraining melanoma development (e.g., PTEN, NF1) (Additional file 13: Table S12). By systematically curating melanoma-associated cancer genes and other cancer genes from different resources, we annotated the 200 target genes linked to these essential enhancers (Fig. 4a, Additional file 14: Table S13). We noted that half of these target genes were previously confirmed to be cancer-associated genes, of which 18.5% are melanoma-associated genes with different types of evidence. This indicates that HRR-associated enhancers could modulate novel melanoma driver genes in carcinogenesis. Next, we performed the Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis on these target genes, and we demonstrated that the functional HRR-associated enhancers revealed by our CRISPRi screen can drive tumor growth through several canonical cancer pathways, such as the EGFR signaling pathway, central carbon metabolism, and AMPK signaling pathway (Fig. 4b).

We illustrated the connectome for the top 20 essential enhancers and found that most enhancers contact multiple genes beyond the designed targets in the linear genome (Fig. 4c). The most significant CRISPRi screened enhancer E_349 interacts with MEF2A and LRRC28 instead of its designed target IGF1R, which implies that HRR-associated enhancers could modulate multiple uncharacterized melanoma cancer genes for tumor survival. Furthermore, another significant enhancer E_223 targets the Hippo pathway melanoma oncogene YAP1 and a multi-functional tumor suppressor BIRC2. This suggests that inhibition of certain HRR-associated enhancers could promote oncogenic expression by several unknown regulatory mechanisms in A375 cells. In addition, we found that some target genes, which interacted with the most active enhancers, were positively selected hits from the previous CRIPSR KO screens (Fig. 4d) or were differentially expressed between tumor and normal samples in the TCGA-SKCM cohort (Fig. 4e). Notably, several target genes were supported by complementary evidence to be tumor-suppressive in melanoma. For instance, MEF2A, EMP2, ZNF440, PIGL, FRMD4B, and HIF3A are not only downregulated in TCGA-SKCM melanoma samples but also essential for restraining tumor growth in CRIPSR KO screens (Fig. 4f). Similarly, PTEN, NF1, KLF6, and THBS1 genes were frequently reported in different cancer gene resources as melanoma tumor suppressors (Fig. 4g). In summary, the integration of the enhancer connectome and CRISPRi screen identified several known or novel cancer genes in melanoma carcinogenesis.

A super enhancer element modulates melanoma cell proliferation and apoptosis by targeting MEF2A

We identified the enhancer E_349 (GRCh37/hg19 chr15: 99,982,353–99,983,277), located at a super enhancer locus and approximately 475 kb downstream of the designed gene IGF1R, as the most significant hit in our CRISPRi screens. IGF1R is overexpressed in melanoma and plays an oncogenic role by mediating tumor proliferation, motility, and protection from apoptosis [55], yet the relationship between E_349 and IGF1R gene was not supported by our A375 H3K27ac HiChIP. Instead, we found that E_349 can loop to the promoter region (125 kb downstream) of a developmental TF MEF2A gene (Fig. 5a), which has been documented to be important in cell differentiation, proliferation, and death [56, 57]. Given E_349 overlaps a DEL-HRR (30% recurrence rate) and its core region is highly mutated in melanoma patients (Fig. 5b), we sought to investigate whether this tumor-suppressive super enhancer element is essential in controlling melanoma growth by regulating MEF2A.

First, we performed the circularized chromosome conformation capture (4C) assay on the A375 cell line to evaluate the spatial proximity between E_349 and MEF2A. By selecting the promoter region (GRCh37/hg19 chr15: 100,125,127–100,127,180) of MEF2A as a viewpoint (VP), the 4C result showed that the VP region has a higher interaction frequency with the E_349 chromosomal region (Fig. 5c). This chromosome loop was further confirmed via the chromosome conformation capture (3C) assays on A375, SK-MEL-28 (another BRAF^V600E melanoma cell line), and SK-MEL-2 (no BRAF mutation melanoma cell line) cells at different conditions (Fig. 5c and Additional file 1: Fig. S5a-f). Then, we found that MEF2A is significantly downregulated in TCGA-SKCM tumor samples (tumor cell: 461 and normal cell: 558, adjusted p-value = 5.59e − 68, ANOVA test) (Fig. 5d). Furthermore, a lower expression of MEF2A was associated with a significantly decreased overall survival among TCGA patients (p-value = 0.0013, Kaplan–Meier estimate) (Fig. 5e). These data suggest a critical tumor-suppressive role for E_349 and its target gene MEF2A in melanoma growth and prognosis. In contrast, LRRC28, an alternative target of E_349 did not show clinical importance in the TCGA cohort (Additional file 1: Fig. S6a). We then evaluated the regulatory activity of E_349 in vitro by performing luciferase reporter assays, cloning the sequence of the E_349 core region into reporter vectors with promoter in 293T, A375, SK-MEL-28, and SK-MEL-2 cells, respectively. We observed that the E_349 core region significantly promotes luciferase activity in these four cell lines (all p-value < 0.0001) (Fig. 5f, g; Additional file 1: Fig. S7a, b). To further validate the regulatory function of E_349 in MEF2A, we packaged the lentivirus carrying the most significant enriched sgRNA for targeting of E_349, then transduced this into the A375-KRAB, A375-VP64, SK-MEL-28-KRAB, SK-MEL-28-VP64, SK-MEL-2-KRAB, and SK-MEL-2-VP64 cells, respectively. The results indicated that the expression of MEF2A is downregulated when E_349 is inhibited in A375 (Fig. 5h), SK-MEL-28 (Additional file 1: Fig. S7c), and SK-MEL-2 (Additional file 1: Fig. S7e) cells, and the expression of MEF2A is upregulated when E_349 is over-activated in A375 (Fig. 5i), SK-MEL-28 (Additional file 1: Fig. S7d), and SK-MEL-2 (Additional file 1: Fig. S7f) cells (all p-value < 0.001). Besides, we found that inhibition of E_349 cannot affect the expression of our designed gene IGF1R in A375, SK-MEL-28, and SK-MEL-2 cells (Additional file 1: Fig. S10a, d). Collectively, these data indicate that E_349 is a bona fide enhancer that directly regulates MEF2A expression.

Since the expression level of MEF2A is associated with patient prognosis, we speculated whether inhibition of E_349 could affect tumor survival phenotypes and drug sensitivity (e.g., vemurafenib). Notably, our 3C data indicated that vemurafenib cannot affect the interaction between E_349 and the MEF2A promoter (Additional file 1: Fig. S5d, e). We performed the cell proliferation, apoptosis, and flat colony formation assays on A375-KRAB, SK-MEL-28-KRAB, or SK-MEL-2-KRAB cells under treatment, with or without vemurafenib. We found that the proliferative abilities of the cells in the E_349-inhibited group were markedly higher than those in the control group (all p-value < 0.001) (Additional file 1: Fig. S7h, i, k). Furthermore, the cell apoptosis abilities of the cells in the E_349-inhibited group were significantly lower than those in the control group (all p-value < 0.05) (Fig. 5j and Additional file 1: Fig. S7l). Finally, the colony formation abilities of the cells in the E_349-inhibited group were significantly higher than those in the control group (all p-value < 0.01) (Fig. 5k, Additional file 1: Fig. S7m, n). To further access the E_349’s function in melanoma growth, we used CRISPR technology to knock out the E_349 core region in A375 cells (Additional file 1: Fig. S11a, b). Expectedly, we acquired the similar phenotypes with the E_349 inhibition (all p-value < 0.0001) (Additional file 1: Fig. S7g, j); when the E_349 core region was deleted, the interaction between MEF2A promoter and the region harboring E_349 was lost (Additional file 1: Fig. S5g), further validating the E_349 directly regulates MEF2A expression in A375 cells. Next, we used the cell-derived xenograft (CDX) model to further investigate the function of E_349 in melanoma growth in the nude mice. As a result, the subcutaneous A375-derived cells in the E_349-inhibited group were significantly larger (all p-value < 0.05) (Fig. 5l) and heavier (p-value < 0.01) (Fig. 5m) than those in the control group, highlighting the important function of E_349 in A375 tumor growth. Finally, to access the E_349’s function in melanoma patients, we collected patient melanoma tissues, extracted the genomes, and amplified the DNA sequence harboring E349 core region for Sanger sequencing. Fortunately, of 122 melanomas, we identified two samples with deletions or extensive mutations in E349 (Additional file 1: Fig. S12a, b). The immunohistochemistry (IHC) results indicated that E_349, when deleted or extensively mutated, showed a relatively low expression of MEF2A and a markedly stronger ability for tumor proliferation (Additional file 1: Fig. S12c). This highlights the importance of E349 in melanoma growth. Taken together, these results demonstrate loss of E_349 in melanoma can unlock tumor growth potential and promote vemurafenib resistance.

A distal enhancer sustains PTEN tumor-suppressive potential in melanoma cells

Recurrent deletion of PTEN gene is a critical driver for melanoma tumorigenesis and metastasis [58, 59]. However, not all of the melanoma cases with loss of PTEN expression can be explained by the disruptions in its coding or splicing regions, emphasizing the importance for the identification of non-coding regulatory mechanisms mediating PTEN loss. In our endogenous screen, we revealed a significant enhancer (E_156) located near PTEN whose inactivation could facilitate melanoma cell growth. E_156 (GRCh37/hg19 chr10: 89,986,509–89,987,396) was the second-ranked hit in the screen, which was located 375 kb downstream of the PTEN gene and shows frequent interaction with the PTEN promoter (Fig. 6a). In melanoma patients, in addition to highly recurrent mutations in the protein-coding region, PTEN acquires many independent upstream/downstream non-coding mutations, and many of them overlap with HRR-associated enhancers (Fig. 6b). To verify whether E_156 directly regulates the expression of PTEN, we performed 4C and 3C assays on melanoma cell lines by selecting the promoter region (GRCh37/hg19 chr10: 89,620,814–89,627,996) of PTEN as a VP. Both 4C and 3C results showed that the PTEN promoter had a higher interaction frequency with the enhancer core region of E_156 at different conditions (Fig. 6c, Additional file 1: Fig. S8a-f), supporting the regulatory relationship of the distal enhancer E_156 on PTEN in melanoma.

Given the clinical significance of PTEN, but not the alternative target gene ATAD1 and KLLN (Fig. 6d, e; Additional file 1: Fig. S6b), as well as the evidence of direct interaction between E_156 and the PTEN promoter in melanoma, we evaluated the tumor-suppressive function of E_156 in vitro. For comparison, we also measured the effect of two closer enhancers, E_155 and E_154, which show moderate signals in our screen. We first performed luciferase reporter assays by cloning the sequence of their core regions into reporter vectors with promoters in 293T, A375, SK-MEL-28, and SK-MEL-2 cells, respectively. We observed that all these enhancer core regions significantly promote luciferase activity in all four cell lines (all p-value < 0.0001) (Additional file 1: Fig. S9a-c, n), in which E_156 displays relatively higher activity. By constructing enhancer-perturbed A375-KRAB, A375-VP64, SK-MEL-28-KRAB, SK-MEL-28-VP64, SK-MEL-2-KRAB, and SK-MEL-2-VP64 cell lines, we found that the expression of PTEN was downregulated or upregulated when the enhancers were inhibited (all p-value < 0.05) (Fig. 6f, Additional file 1: Fig. S9d, g, o) or activated (all p-value < 0.05) (Additional file 1: Fig. S9e,f,h, p), further validating our CRISPR screen result. Besides, we found that inhibition of E_156 cannot affect the expression of the alternative target gene ATAD1 and KLLN in A375, SK-MEL-28, and SK-MEL-2 cells (Additional file 1: Fig. S10b, c, e, f). Since the PTEN/PI3K/AKT signaling pathway plays a key role in cell proliferation, growth, and apoptosis [60], we speculated whether these enhancers could affect tumor viability or drug response via the PI3K/AKT signaling pathway in melanoma cells. As expected, the phosphorylated AKT was upregulated in the enhancer-inhibited A375-KRAB and SK-MEL-28-KRAB cells (all p-value < 0.05) (Fig. 6g, Additional file 1: Fig. S9i), in which E_156 exhibited a stronger effect on AKT activation. Furthermore, cell proliferation, apoptosis, and flat colony formation assays were applied to A375-KRAB, SK-MEL-28-KRAB, and SK-MEL-2-KRAB cells treated with or without vemurafenib. Consequently, the proliferative abilities of the cells in the enhancer-inhibited group were all markedly higher than those in the A375-KRAB, SK-MEL-28-KRAB, and SK-MEL-2-KRAB control group (all p-value < 0.01) (Additional file 1: Fig. S9j, k, t). For cell apoptosis, abilities of the cells in the enhancer-inhibited group were all significantly lower than those in the A375-KRAB, SK-MEL-28-KRAB, and SK-MEL-2-KRAB control group (all p-value < 0.01) (Fig. 6h, Additional file 1: Fig. S9l). Finally, for the colony formation abilities of the cells in the enhancer-inhibited group, these were all markedly higher than those in the A375-KRAB, SK-MEL-28-KRAB, and SK-MEL-2-KRAB control group (all p-value < 0.05) (Fig. 6i, Additional file 1: Fig. S9m, r). To further access the E_156’s function in melanoma growth, we used CRISPR technology to knock out the E_156 core region in A375 cells (Additional file 1: Fig. S11c, d). As expected, we acquired the similar phenotypes with the E_156 inhibition (all p-value < 0.01) (Additional file 1: Fig. S9q, s); when E_156 core region was deleted, the interaction between PTEN promoter and the region harboring E_156 was lost (Additional file 1: Fig. S8g), further validating the E_156 directly regulates PTEN expression in melanoma growth. Next, we used the CDX model to further investigate the E_156’s function in melanoma growth in the nude mice. As a result, the subcutaneous A375 cells in the E_156-inhibited group was markedly larger (all p-value < 0.05) (Fig. 6j) and heavier (all p-value < 0.05) (Fig. 6k) than those in the control group, highlighting the important function of E_156 in A375 tumor growth. However, due to scarcity of collected samples, we did not find the E156 deletions or extensive mutations among the 122 melanoma patient’s samples. Taken together, among the three enhancers, inhibition of E_156 consistently resulted in a greater effect on tumor survival phenotypes, which highlights the importance of the distal enhancer in sustaining PTEN tumor-suppressive potential in melanoma cells.

Discussion

The high mutation burden in the melanoma genome provides a major challenge in identifying true driver events from the background mutations, especially in the non-coding genomic regions. The dilemma could be attributed to multiple factors such as limited sequenced melanoma samples, overestimated mutation hotspots, paucity of non-coding drivers, and relatively low-throughput functional validations. In the present study, we systematically integrated 297 melanoma WGS datasets and developed a strategy to estimate HRRs for both SV and SNV/INDEL. Consequently, we present one of the largest genome-wide studies on melanoma genomics. We designed a genome-scale CRISPRi screening method to investigate the functional consequence of HRR-associated enhancers and uncovered 66 significant enhancers, which could play growth-suppressive roles in melanoma cells. These functional enhancers colocalize with several critical melanoma-associated TFs and are generally independent of the mutational pattern of classically significantly mutated genes in melanoma. By applying H3K27ac HiChIP chromatin loop detection, we identified 200 target genes linked to the functional HRR-associated enhancers, and some of these may be implicated as being potentially novel mechanisms in melanoma. In-depth functional assays demonstrated that the two highest ranked enhancers are critical in sustaining MEF2A and PTEN tumor-suppressive potential in melanoma cells.

An increasing number of studies have confirmed that disruption of CREs, such as enhancer, represents an essential tumor-driven mechanism in multiple cancers [40, 42, 61,62,63]. This demonstrates that CREs could be an attractive area for therapeutic targets in cancers. Despite the constant endeavor to identify driver events in highly mutated melanoma genomes [2, 3, 5,6,7], study for systematic assessing non-coding HRRs and their associated functional elements on substantial melanoma samples is still lacking. In order to attain the requisite sample size, this study combined high-coverage melanoma WGS data from different cohorts (including European, Australian, Brazilian, and other populations) and various cancer subtypes (including cutaneous melanoma and few mucosal/acral melanoma), which may confound the identification of true HRR and compromise the detection specificity. On the other hand, in this study, despite the significant insights into functional HRR-associated enhancers in melanoma that our study provides, we recognize its limitations. In our analysis, the selection of screened enhancers and chromatin interaction measurement based on A375 cell line did not fully account for the complexities inherent in epigenomic heterogeneity and 3D genome looping structures across different melanoma contexts. Epigenomic heterogeneity, a fundamental aspect of cancer cells, and 3D genome organization vastly influence gene expression and cellular functions [64], with evidence of substantial variances across different cell types. Thus, to advance our understanding of the intricate gene regulation in melanoma, future investigations should systematically consider these factors.

Previous CRISPR screen of CREs on melanoma cells has been performed on several critical genes, including NF1, NF2, and CUL3, whose loss-of-function mutations resulted in vemurafenib resistance [23]. By designing sgRNA libraries tiling across 100-kb regions surrounding the three genes, the CRISPR KO screen yielded many functional CREs in the non-coding region. Intriguingly, among 66 significant HRR-associated enhancers in our CRISPRi screen, two are located proximal to NF1 and another two are in the vicinity of NF2. This indicates that loss of NF1 or NF2 expression will benefit tumor growth and thus primarily confer vemurafenib resistance. In addition, compared with existing genome-wide CRISPR screening of CREs in other cancer types [21, 22, 65, 66], our CRISPRi screen focused on potential enhancers for all candidate essential genes associated with melanoma cell growth and drug resistance. Nevertheless, we accept that this screening strategy will inevitably miss other types of CREs (e.g., insulators, silencers, or unmarked regulatory elements [67]) and most of the undiscovered melanoma driver genes, which are out of the regulatory scope of the desired enhancers. In addition, background amplification could impact positive selection screens at day21. In this study, we attempted to control for background amplification by eliminating low-count guides, using non-targeting control guides for normalization, and estimating significant enhancer-level enrichment with tiling sgRNAs. However, our screen strategy without selecting for a specific phenotype, based on continuous melanoma cell proliferation, might introduce unwanted background amplification. Furthermore, comparisons between day7 and day21 screens revealed limited consistent hits, likely due to the diminishing power of drop-out screening over time. This highlights the need to carefully select screening timeframes and enrichment strategies in future studies, taking into account the specific readouts.

Given that we found 66 significant HRR-associated enhancers in our CRISPRi screen, many connect to distal targets or regulate uncharacterized melanoma driver genes according to the H3K27ac HiChIP. The highest significant screened enhancer E_349 loops to the promoter of MEF2A instead of its designed target IGF1R. The context-dependent function of MEF2A confers its tumor-suppressive or oncogenic activity in different cancers [68]. However, MEF2A is poorly characterized and has been rarely studied in melanoma. By combining genomic, functional, and clinical evidence, we discovered that the critical enhancer, E_349, within a super enhancer region, plays a tumor-suppressive role in melanoma through modulation of MEF2A expression. In contrast, a PTEN enhancer was previously identified to be essential in maintaining tumor-suppressive function in T-cell leukemia [24]. Consistent with this finding, we found several distal downstream enhancers could sustain PTEN tumor-suppressive potential in melanoma. In summary, our study provides a comprehensive catalogue of crucial enhancers and their target genes in melanoma growth and progression. These results will not only facilitate the discovery of novel melanoma drivers, but also promote the fine-grained molecular classification and therapeutic optimization of melanoma. Interestingly, we also discovered that some significant HRR-associated enhancers are spatially proximal to several well-known melanoma oncogenes, such as YAP1. This proximity is likely due to the specific effects of CRISPRi at certain loci or a non-traditional gene regulatory mechanism. These findings merit further in-depth research [25].

Conclusions

Our study establishes a catalogue of crucial enhancers and their target genes in melanoma growth and progression, and illuminates the identification of novel mechanisms of dysregulation for melanoma driver genes and new therapeutic targeting strategies.

Methods

Cell lines

A375 (American Type Culture Collection [ATCC], CRL-1619) and A375-derived human melanoma cell lines, 293T cell line (ATCC, CRL-3216), and 293FT cell line (ThermoFisher, R70007) were all cultured in Dulbecco’s modified Eagle’s medium (DMEM; ThermoFisher, 11965092) supplemented with 10% fetal bovine serum (FBS; ThermoFisher, R7007). SK-MEL-2 (ATCC, HTB-68), SK-MEL-2-derived, SK-MEL-28 (ATCC, HTB-72), and SK-MEL-28-derived human melanoma cell lines were maintained in Eagle’s Minimum Essential Medium (EMEM; ATCC, 30–2003) supplemented with 10% FBS. All cell lines were cultured at 37 °C in a humidified atmosphere containing 5% CO₂ and were verified mycoplasma-free using the MycoAlert Mycoplasma Detection Kit (Lonza, LT07-218).

WGS data and somatic mutation calling

WGS raw data of 297 paired melanoma tumor and normal samples were collected (MELA-AU: 183 samples, SKCM-US: 38 samples, and SKCA-BR: 76 samples) from the International Cancer Genome Consortium (ICGC) and European Genome-phenome Archive (EGAD00001003388) databases. For SNVs/INDELs, we directly extracted the GATK Mutect2 call sets [30] of MELA-AU and SKCM-US from the ICGC. To ensure consistent mutation calling, GATK (v4.1.4.0) and Mutect2 [30] were used to pre-process and call somatic SNV/INDELs on each tumor-normal paired sample of SKCA-BR using the parameters recommended in the GATK Best Practices. The generated mutation calls were further filtered using GATK FilterMutectCalls module and the final somatic output in VCF format was annotated and converted by VEP (v1.6.19) [69]. Considering the diversity and complexity of SV detection tools, we applied both Manta (v1.6.0) [31] and GRIDSS (v2.9.2) [32] to detect somatic SVs with default parameters for tumor-normal paired samples. The two SV calling results were used for subsequent analysis after removing all insertions and other SVs with fragment lengths over 1 Mb. The human assembly GRCh37/hg19 was used across the whole analysis.

HRR detection and prioritization

We used fishHook [33] to detect genome-wide HRRs on somatic SNV/INDELs. Briefly, we first clustered mutations within 200 bp and collected three cell type-specific epigenomic features of epidermal keratinocyte primary cell (NHEK) as covariates, including chromatin accessibility, ChromHMM chromatin state, and DNA replication timing. Then, fishHook Gamma-Poisson model was used to correct the three epigenomic features, nucleotide context, and mutation cluster length for SNV/INDEL-HRR estimation. For detecting and prioritizing SV-HRRs, we leveraged a sliding window strategy to calculate the recurrence of somatic SVs among melanoma patients within each window (10 kb window size and 2 kb step size for DELs and DUPs, 1 kb window size, and 200 bp step size for INVs and TRAs). Subsequently, we located the window with the largest sample counts (at least four patients) and merged the adjacent windows on both sides. The merged interval whose total recurrent count ≥ 10 was defined as candidate SV-HRR. Since we produced two sets of SV-HRR by different SV callers (Manta and GRIDSS), we used BEDTools [70] merge function to obtain unique HRRs, wherein the SV-HRRs were labeled as “Dual” if they are detected by both tools. To facilitate the prioritization of SV-HRRs at patient level, we also merged all types of overlapped SV-HRRs and recalculated the recurrence count.

HRR and HRR-associated enhancer annotation

We annotated the genomic attributes of each HRR based on RefSeq database [71], and we defined melanoma driver/essential genes from different angles, including genetic evidence, functional evidence, and literature evidence. Candidate melanoma driver genes with genetic evidence carrying significant mutations were integrated from several large-scale sequencing studies and reviews [2, 3, 10]. Melanoma essential genes with functional evidence were collected from The Cancer Dependency Map (DepMap) [72, 73] skin-related results (p-value < 0.05). Putative melanoma driver/essential genes or other non-melanoma cancer genes were compiled from public cancer gene databases including CancerMine [26], IntOGen [27], and NCG [28]. In addition, we uniformly processed H3K27ac ChIP-seq from 10 melanoma cell lines, including LOX_IMVI, M14, MALME-3 M, MDA-MB-435, SK-MEL-2, SK-MEL-28, SK-MEL-5, UACC-257, and UACC-62 were from GSE143653 [74], and A375 cell line was from GSE99835 [75] and GSE82332 [76]. All ChIP-seq raw data were mapped to the human genome (GRCh37/hg19) using Bowtie2 [77] and narrow peaks were called by MACS2 [78] with default parameters. We intersected all significant SNV/INDEL-HRRs (FDR < 5%) and SV-HRRs (≥ 10 donors) with these H3K27ac peaks to define HRR-associated enhancers.

Functional evaluation of HRR-associated enhancer

We re-analyzed three public A375 CRISPR KO screening datasets, including two for positive screen [44, 79] and one for negative screen [45], to retrieve melanoma essential genes using MAGeCK-RRA algorithm [46], yielding two significant gene sets from positive and negative selection, respectively. By assigning presumed target gene (1 Mb near enhancer) to each HRR-associated enhancer or HRR-unrelated enhancer, we tested the difference of CRISPR enrichment scores between gene groups via Mann–Whitney U test across five HRR types.

CRISPRi sgRNA library construction

We first comprehensively curated essential genes associated with melanoma cell growth and drug resistance from published CRISPR/shRNA screening studies. Then, we selected HRR-associated enhancers receiving both H3K27ac and H3K4me1 signals in A375 cells within 1 Mb region of each melanoma essential genes. These enhancer regions were further narrowed with chromatin accessibility signal based on ATAC-seq peaks. For the remaining enhancer core regions, we divided each region into several bins consisting of 100 bp and scored all eligible sgRNAs in each bin using FlashFry [80]. By selecting the best sgRNA in each bin, the intersected tiling strategy can yield a sgRNA library for all selected enhancers, and a total of 1000 unmatched sgRNAs were added into the library as negative control. The sgRNA sequences were synthesized by Synbio Technologies and cloned into lentiGuide-Puro vector (Addgene, 52963) in the form of homologous recombination to construct the plasmid library, and then packaged into the lentivirus library using the pMD2.G (Addgene, 12259) and psPAX2 (Addgene, 12260) packaging plasmids as mentioned below for subsequent screening.

CRISPRi screen

We generated A375 or SK-MEL-2 cell lines stably expressing Lenti-dCas9-KRAB (Addgene, 89567) transgenes and performed lentiviral transduction of pooled sgRNAs at low multiplicity of infection (MOI) = 0.2. After 24 h, puromycin was added for selecting the effective infected A375-KRAB or SK-MEL-2 cells. After 7 days, the cells were divided into two groups. One group (day 0) of cells was collected and frozen as a control group, and the other groups (day 7 or 21) of cells were selected at 7 or 21 days. Genomic DNAs of the two group cells were extracted by FastPure Cell/Tissue DNA Isolation Mini Kit (Vazyme, DC102) for PCR amplification (Vazyme, N616), and the amplified products were subject to library construction using the VAHTS Universal DNA Library Prep Kit for Illumina V3 (Vazyme, ND607) according to the kit’s instructions and then performed the next-generation sequence on a HiSeq X Ten platform. The CRISPRi screen was independently performed for two times.

CRISPRi positive selection enrichment

Two replicates of day 0 group and day 21 group were sequenced and undergone quality control using MAGeCK-VISPR [81]. We first extracted the sgRNA sequence from the 150 bp original reads, and the abundance of guides was first determined by the MAGeCK “count” module from the extracted sgRNAs. Second, we removed 1% guides of each sample with lowest count. We used the non-targeting control guides to estimate the size factor for normalization, and estimated significant enrichment according to MAGeCK-RRA algorithm [46] at enhancer level. Finally, significant enhancers were identified with FDR < 0.05.

Mutational signature analysis

To investigate the mechanism involved in functional HRR-associated enhancer, we extracted single-base-pair substitutions (SBS) in enhancer regions and used Palimpsest [47] to calculate de novo mutational signatures. We then calculated cosine similarity values between de novo signature and known COSMIC mutational signatures. COSMIC signature with highest correlation coefficient was regarded as candidate predominant signatures among enhancers. We also performed component analysis of known melanoma signatures for each functional enhancer.

Genomic and epigenomic features

We downloaded epigenetic signatures of A375 cell line from public repositories, including CTCF (GSM3671692) [82], H3K4me3 (GSM2653904) [83], and Pol II ChIP-seq (GSM1661790) [49], together with PRO-seq (GSE128081) [84] and super enhancer [76]. The TFs narrowPeak were downloaded from Gene Expression Omnibus (GEO), including MITF (GSE149929) [85], HEXIM1 (GSM1661788) [49], TFAP2C (GSM1011562) [86], ETV1 (GSM2127445) [87], BRD4 (GSM2359435) [88], AR (GSM3212792) [89], EGR1 (GSM3212795) [89], DDX21 (GSE149929) [85], CDK9 (GSM3664674) [84], USP7 (GSM3928164) [90], EZH2 (GSM3928166) [90], and AP2A (GSM4950452) [91]. All ChIP-seq datasets were uniformly processed by MACS2 [78]. PRO-seq was analyzed using PEPPRO [92] and dREG [93] for predicting nascent enhancer RNA with bidirectional transcription. Transcription factor colocalization analysis was performed by epiCOLOC [94]. To investigate the mutation pattern of significant enhancers, we first built a categorical matrix based on the HRR type on enhancer region for MELA-AU cutaneous melanoma patients (N = 137). Then we applied a latent block model [50] to cluster the categorical matrix and compared with previous molecular classification (mutant BRAF, mutant RAS, mutant NF1, and Triple-WT) defined by TCGA.

In situ Hi-C followed by chromatin immunoprecipitation (HiChIP)

H3K27ac HiChIP was conducted following the previously published procedures [95]. Briefly, A375 cells were counted, and ~ 1.5 × 10⁷ cells were crosslinked with 1% formaldehyde (Sigma-Aldrich, F8775) in phosphate-buffered saline (PBS; ThermoFisher, 10010023) at room temperature for 10 min, and then quenched with 0.125 M Glycine (Sigma-Aldrich, 50046) on ice for 5 min. Next, crosslinked cells were lysed in Hi-C lysis buffer (10 mM Tris–HCl pH 8.0 [ThermoFisher, 15568025], 10 mM NaCl [Sigma-Aldrich, S7653], and 0.2% Igepal CA630 [Sigma-Aldrich, I3021]) with 50 μl of protease inhibitors (Sigma, P8340), and nuclei were extracted and digested with MboI restriction enzyme (NEB, R0147) for 2 h. After digestion, nuclei were resuspended in NEB buffer supplemented with DNA polymerase I, Large (Klenow) fragment (NEB, M0210) to fill in the restriction fragment overhangs with Biotin-14-dATP (Life Technologies, 19524–016) for 1 h. Proximal ligation was performed with T4 DNA ligase (NEB, M0202) for 4 h at 16 °C and nuclei were harvested. The nuclei were transferred to shear chromatin using a Biorupter system (Diagenode, B01060001). For each sample, the sheared chromatin was incubated overnight with 8 μl of H3K27ac antibody (Abcam, ab4729), and then Protein A-agarose beads (Millipore, 16–157) were used to capture H3K27Ac-associated chromatin. The whole H3K27ac-ChIP process was performed with the Pierce™ Magnetic ChIP kit (ThermoFisher, 26157), the final DNA was eluted with 15 μl Nuclease-Free Water (ThermoFisher, AM9930). The eluted DNA was sent for DNA biotin pull-down with Dynabeads MyOne Streptavidin T1 beads (Life technologies, 65602). Sixty micrograms of DNA was used for PCR amplification, and then AMPure XP beads (Beckman Coulter, A63880) was used to select a size range of 300 ~ 600 bp products. Finally, the HiChIP libraries were constructed using the VAHTS Universal DNA Library Prep Kit for Illumina V3 according to the kit’s instructions and then performed the next-generation sequence on a HiSeq X Ten platform.

HiChIP loop detection and target gene annotation

HiChIP paired-end reads were aligned and analyzed using the FitHiChIP pipeline [96] with default settings to remove duplicate reads, assign reads to MboI restriction fragments, filter for valid interactions, and generate binned interaction matrices. Chromatin interactions were filtered from a minimum distance of 20 kb to a maximum of 2 Mb. To identify target gene of significant functional enhancers, we defined gene promoters as 20 kb upstream and 5 kb downstream to the transcription start sites. We intersected these promoters with significant contacts and left only those contacts for which one of the anchors fell inside the promoter region, and another anchor, therefore, was assigned to functional enhancer region we defined above. We performed KEGG enrichment analysis on target genes with cancer-related evidence using ClusterProfiler [97]. Supporting evidence of target genes on melanoma growth were further mined by differential expression analysis of TCGA-SKCM samples using GEPIA2 [98], as well as public A375 CRISPR KO positive screen [44, 79]. TSGene [99] and CancerMine [26] were also used to annotate known tumor suppressive genes. Our A375 H3K27ac HiChIP identified a total of 121,775 loops, of which 68,998 loops contain valid Enhancer-Promoter pairs. Enhancers are co-defined by A375 H3K27ac, H3K4me1, and ATAC-seq signals, and promoters of the genes are defined as 3 kb near the transcriptional start site (TSS).

Circularized chromosome conformation capture (4C) assay

4C assays were performed as previously described [100] with slight modifications. For MEF2A, the NlaIII-cutting chromosome region harboring the MEF2A promoter (GRCh37/hg19 chr15: 100,125,127–100,127,180) was designed as a viewpoint (VP), and DpnII was the second enzyme. For PTEN, the HindIII-cutting chromosome region harboring the PTEN promoter (GRCh37/hg19 chr10: 89,620,814–89,627,996) was designed as a VP, and NlaIII was the second enzyme. Briefly, ~ 1 × 10⁷ A375 cells were crosslinked by 2% formaldehyde, quenched with 0.125 glycine (Sigma-Aldrich, 50046), lysed with cold lysis buffer (50 mM Tris pH 7.5 [ThermoFisher, 15567027], 0.5% NP-40 [ThermoFisher, FNN0021], 150 mM NaCl, 1% Triton X-100 [ThermoFisher, T8787], 5 mM EDTA [ThermoFisher, 15575020], and 1 × protease inhibitors [ThermoFisher, 78425]), digested with NlaIII (NEB, R0125L) or HindIII (NEB, R0104) for 4 h at 37 °C while shaking at 800 revolutions per minute (RPM) for more than three times until the digestion efficiency reached over 90%, and then ligated with T4 DNA ligase for overnight in a 7-ml reaction system after enzyme inactivation. The ligation sample was purified using the Phenol/Chloroform/Isoamyl Alcohol (25:24:1) (ThermoFisher, 15593–049) before reversing the cross-links by adding Protein K (ThermoFisher, AM2548) for 6 h at 65 °C and RNase A (ThermoFisher, 15596–018) for 1 h at 37 °C. Next, the DNA samples were digested with DpnII (NEB, R0543L) or NlaIII and ligated with T4 DNA ligase for more than 6 h in a 14-ml reaction system. The ligation product was purified using the Phenol/Chloroform/Isoamyl Alcohol (25:24:1) and further purified using the QIAquick PCR Purification Kit (QIAGEN, 28106). The purified product was amplified by PCR using a high-fidelity DNA polymerase (Vazyme, N616-02) with 8 × 200 ng DNA. 4C-seq library was constructed using the VAHTS Universal DNA Library Prep Kit for Illumina V3 according to the manufacturer’s description and sent for next-generation sequencing. pipe4C [101] were used to filter, analyze, and visualize the 4C-seq data. Primers used are listed in Additional file 15: Table S14.

Chromosome conformation capture (3C) assay

3C assays were conducted as previously described [102, 103]. For MEF2A, we chose the promoter-containing region (GRCh37/hg19 chr15: 100,123,291–100,129,474) as a VP. For PTEN, the bait in 3C assays is the same as the VP used in the promoter 4C assay. Briefly, a total of 1 × 10⁷ A375-KRAB, SK-MEL-28-KRAB, or SK-MEL-2-KRAB cells treated with 1 μM vemurafenib (Selleck, RG7204) for 2 days or no treatment were used for 3C assays. The cells were crosslinked utilizing 2% formaldehyde, quenched with 0.125 M glycine, lysed with cold lysis buffer (50 mM Tris pH 7.5, 0.5% NP-40, 150 mM NaCl, 1% Triton X-100, 5 mM EDTA, and 1 × protease inhibitors), digested with HindIII for 4 h at 37 °C while shaking at 800 RPM for more than three times until the digestion frequency reached over 90%, and then ligated with T4 DNA ligase for overnight in a 7-ml reaction system after enzyme inactivation. The ligation samples were purified using the Phenol/Chloroform/Isoamyl Alcohol (25:24:1) before reversing the cross-links by adding Protein K for 6 h at 65 °C and RNase A for 1 h at 37 °C. The potential crosslinked-fragments were amplified using Premix Taq enzyme (TaKaRa, RR901A). The sequences harboring the detected HindIII loci were separately amplified by the Phanta Max Super-Fidelity DNA Polymerase (Vazyme, P505-d3). Concentrations of the sequences were determined by the Qubit dsDNA HS Assay Kit (ThermoFisher, Q32851). The same copy number of each sequence was blended as a reference control and subjected to proportional treatment as the experimental group. PCR products were electrophoresed on a 1.5% agarose gel and analyzed by ImageJ2 (https://imagej.net/ImageJ). Interaction frequency of each chromatin fragment was normalized by its reference control. Primers used are listed in Additional file 15: Table S14.

Luciferase reporter assay

Genomic sequences harboring enhancer E_349 (GRCh37/hg19 chr15: 99,982,353–99,983,277), E_155 (GRCh37/hg19 chr10: 89,912,175–89,913,031), E_154 (GRCh37/hg19 chr10: 89,875,711–89,876,693), or E_156 (GRCh37/hg19 chr10: 89,986,509–89,987,396) core region were amplified from the genomic DNA of A375 cells, SK-MEL-28, or SK-MEL-2 cells and separately cloned into the downstream of luciferase gene in the pGL3-Promoter vector (Promega, E1761). After sequencing at the Beijing Genomics Institute (BGI), concentrations of the recombinant plasmids were exactly determined by the Qubit dsDNA HS Assay Kit (ThermoFisher, Q32851). The same copy numbers (equal to 1 µg of the pGL3-Promoter vector) of each recombinant plasmid together with 40 ng of pRL-TK renilla luciferase control vector (Promega, E2241) was transfected into 293T, A375, SK-MEL-28, or SK-MEL-2 cells in 24-well plates using the Lipofectamine 2000 transfection reagent (ThermoFisher, 1168019) following the manufacturer’s description. After 12 h, the transfected cells were lysed and assayed for fluorescence levels before assaying luciferase activity using the Dual-Luciferase Reporter Assay System (Promega, E1960) according to the manufacturer’s protocol. Relative luminescent signals were determined by normalizing firefly luciferase signals with renilla luciferase signals. Primers used are listed in Additional file 15: Table S14.

Enhancer perturbation and knockout assays

For enhancer perturbation assay, A375-KRAB, SK-MEL-28-KRAB, SK-MEL-2-KRAB, A375-VP64, SK-MEL-28-VP64, and SK-MEL-2-VP64 cells were generated using the Lenti-dCas9-KRAB (Addgene, 89,567) and Lenti-dCas9-VP64 (Addgene, 61,425) plasmids. Briefly, lentiviral particles were packaged in 293FT cells utilizing the psPAX2 (Addgene, 12,260) and pMD2.G (Addgene, 12,259) packing plasmids. Virus titers were determined by Lenti-Pac HIV qRT-PCR Titration Kit (Genecopoeia, LT005). A375, SK-MEL-28, and SK-MEL-2 cells were separately transduced the lentiviruses at a multiplicity of infection (MOI) of 0.1 for 24 h and selected with 6 or 9 µg/ml blasticidin (ThermoFisher, 461,120) for 5 days, and then expanded cultivation. To perturb specific enhancers, we cloned the most significant enriched sgRNA DNA sequences into the lentiGuide-Puro (Addgene, 52,963) plasmid, respectively, each recombinant plasmid and empty vector were packaged into lentivirus as mentioned above. Then, we separately transduced the recombinant lentiviruses and empty lentivirus at an MOI of 10 into A375-KRAB, SK-MEL-28-KRAB, SK-MEL-2-KRAB, A375-VP64, SK-MEL-28-VP64, and SK-MEL-2-VP64 cells, and then expanded cultivation after selecting with 1 or 2 µg/ml puromycin (Sigma-Aldrich, P7255) for 4 days. For enhancer knockout assay, sgRNAs for E349 or E156 were designed by the CRISPOR (v4.99) Web Portal [104], and then separately cloned into the pGL3-U6-sgRNA-EGFP plasmid (Addgene, 107721). Single cell-derived stable A375-Cas9 cell line was generated using the LentiCas9-Blast (Addgene, 52962) vector as mentioned above. The recombinant plasmids (couple plasmids for each enhancer) were then separately transfected into the single cell-derived A375-Cas9 cells using the Lipofectamine 2000 transfection reagent. After 48 h, the transfected cells were digested and diluted into 50–100 cells/100 µl, and inoculated into each well of 96-well plates. After expanding culture of the cells from 96-well plates to 24-well plates, genomes of single-derived GFP-positive cells were extracted using a commercial DNA isolation kit (QIAGEN, 51304). The sequences harboring the edited loci were amplified with the Premix Taq enzyme, and the products were subsequently sent to BGI for Sanger sequencing. The sequencing results were analyzed and visualized on the CRISP-ID Web Portal [105] or SnapGene Viewer (v4.3.10) software. Primers and oligonucleotide sequences used are listed in Additional file 15: Table S14.

Western blotting and quantitative real-time RT-PCR (RT-qPCR)

Anti-Human MEF2A Antibody (1:1000; Abcam, ab76063), Anti-Human PTEN Antibody (1:1000; Cell Signaling Technology, 9188 T), Anti-Human phosphor-AKT Antibody (1:2000; Cell Signaling Technology, 4060), Anti-Human total AKT Antibody (1:1000; Cell Signaling Technology, 4691), Anti-Human KLLN Antibody (1:1000; Abcam, ab197892), Anti-human ATAD1 Antibody (1:1000; CUSABIO, CSB-PA850855LA01HU), Anti-human IGF1R Antibody (1:1000; Abcam, ab182408), Anti-Human β-actin Antibody (1:200,000; ABclonal, AC026), and Anti-human GAPDH Antibody (1:100,000; ABclonal, AC001) were used to perform western blotting. Western blotting was performed as previously described [106]. For western blotting analysis, the gray value of protein bands was measured by ImageJ2 (https://imagej.net/ImageJ). The ratio of gray value of the target protein to that of the housekeeping protein was calculated and then normalized to control. For RT-qPCR, total RNA in cells was extracted utilizing TRIzol (ThermoFisher, 15596026) and reverse-transcribed using SuperScript III Reverse Transcriptase (ThermoFisher, 12574026). Each RT-qPCR was performed with approximately 200 ng of DNase-treated RNA using the SYBR Premix Ex Taq II (TaKaRa, RR820A). The relative expression of gene was calculated using the 2^−ΔΔCt method, and fold changes were calculated as described in the figure legends. Error bars represent the standard deviations (SDs) of the average fold changes based on three experimental duplicates as indicated the figure legends. Primers used are listed in Additional file 15: Table S14.

Cell proliferation assay

A375-KRAB infected with the empty lentivirus, enhancer-inhibited A375-KRAB, SK-MEL-28-KRAB infected with the empty lentivirus, enhancer-inhibited SK-MEL-28-KRAB, SK-MEL-2-KRAB infected with the empty lentivirus, and enhancer-inhibited SK-MEL-2-KRAB cells were separately seeded 100 µl into 96-well microplates with 3000 cells per chamber, and four replicates were performed for each group. After 24 h, culture medium with 1 or 2 µM vemurafenib or no treatment was added into each chamber. Cells were further incubated for 24, 48, 60, and 72 h, and cell viability was detected using the CCK-8 Cell Counting Kit (Vazyme, A311-01) according to the manufacturer’s description. Briefly, the original culture medium in the 96-well microplates was discarded, 100 µl of 10% CCK-8-DMEM high glucose solution was added into each chamber, and then the microplates were incubated at 37 °C for 1 h. The absorbance was measured at 450 nm wave length by a microplate reader (Molecular devices, SpectraMax M). Viability was calculated as a percentage of control (A375-KRAB, SK-MEL-28-KRAB, or SK-MEL-2-KRAB cells without treatment) after background subtraction.

Apoptosis assay

A375-KRAB infected with the empty lentivirus, enhancer-inhibited A375-KRAB, SK-MEL-28-KRAB infected with the empty lentivirus, enhancer-inhibited SK-MEL-28-KRAB, SK-MEL-2-KRAB infected with the empty lentivirus, and enhancer-inhibited SK-MEL-2-KRAB cells were separately seeded 2 ml into 6-well plates at a density of 2 × 10⁵ cells/ml, and three replicates were performed for each group. After 24 h, cells were added 1 µM vemurafenib or no treatment and then incubated in an incubator with the atmosphere of 5% CO₂ at 37 °C for 2 days. Adherent cells were digested into single cells using No-EDTA Trypsin (ThermoFisher, 15050065) and stained with Annexin V-FITC/propidium iodide (PI) Apoptosis Detection Kit (Vazyme, A211-02) according to the manufacturer’s description. Briefly, the harvested cells were washed two times with pre-cooled PBS and incubated with 5 µl FITC-labeled Annexin V and 5 µl PI Staining Solution at room temperature for 10 min in the dark room. Early apoptotic (Annexin V-FITC stained only) and late apoptotic (Annexin V-FITC and PI double-stained) cells were detected by flow cytometry (BD Biosciences, USA), and the results were analyzed and visualized with FlowJo (FlowJo LLC.).

Flat colony formation assay

A375-KRAB infected with the empty lentivirus, enhancer-inhibited A375-KRAB, SK-MEL-28-KRAB infected with the empty lentivirus, enhancer-inhibited SK-MEL-28-KRAB, SK-MEL-2-KRAB infected with the empty lentivirus, and enhancer-inhibited SK-MEL-2-KRAB cells were separately seeded 2 ml in 6-well plates with 500 cells for A375-derived or SK-MEL-2-derived cells and 1000 cells for SK-MEL-28-derived cells per chamber, and three replicates were performed for each group. Each chamber was added 1 µM vemurafenib or no treatment in a 2-ml culture system, and incubated in an incubator with the atmosphere of 5% CO₂ at 37 °C. Vemurafenib was replaced every 3 days. After 12 days, the culture supernatant was discarded and washed two times with PBS, and then 4% paraformaldehyde (ThermoFisher, R37814) was added and fixed for 15 min. The stationary solution was discarded, and the plates were washed two times with PBS. The number of colonies was counted and photographed with camera after staining with 0.5% crystal violet (ThermoFisher, R40052) for 30 min and washed extensively with sterilized distilled water.

Cell-derived xenograft (CDX)

A375-KRAB infected with the empty lentivirus and enhancer-inhibited A375-KRAB melanoma cells were used for the CDX. Briefly, 1 × 10⁶ viable cells were resuspended in 100 µl DMEM and subcutaneously injected into the armpit of 5-week-old female nude mice (C57BL/6 background). At 14 or 21 days after injection, the tumor volumes were detected and analyzed by a high-resolution ultrasound imaging system for small animals (FUJIFILM VisualSonics, model Vevo 3100) according to the manufacturer’s instructions. Above 3 weeks after tumor cell inoculation, when the tumors reached a volume of 150–200 mm³, the nude mice were killed by cutting their necks, and the tumor tissues were harvested and macro dissected to minimize the content of necrotic tissue. The acquired tumors were weighed by an electronic balance (METTLER TOLEDO, model ME204E).

Immunohistochemistry (IHC)

Paraffin-embedded melanoma samples of two patients with E_349 mutation were retrieved from the tissue sample library of Tianjin Medical University Cancer Institute and Hospital. The IHC assay was used to detect the expression of MEF2A and Cell proliferation-related proteins in paraffin-embedded E_349 deletion or extensive mutations melanoma tissues and matched control melanoma tissues. The slices were heated, dewaxed, rehydrated, and put into sodium citrate buffer (pH buffer = 6.0; ThermoFisher, 005000) for antigen repair. The slide was then soaked in 3% hydrogen peroxide (H₂O₂; ThermoFisher, 241020010) to inhibit endogenous peroxidase activity. After rinsing three times with PBS, the slices were incubated overnight with the first antibody, including rabbit or mouse antibodies against MEF2A (1:800; Affinity, AF6381) or Ki-67 (1:800; Abcam, ab16667) at 4 °C, and antibody diluention buffer was used the Superkine Enhanced Antibody Dilution Buffer (Abbkine, BMU103). Slices were washed three times using PBS and then treated with a second antibody including anti-rabbit IgG (1:2000, Cell Signal Technology, 7074) or anti-mouse IgG (1:2000, Cell Signal Technology, 7076) for 40 min at 37 °C. After being stained with Diaminobenzidine (DAB; Vector Laboratories, Cat# SK-4100), it was stained with hematoxylin (Sigma, H3136), dehydrated, sealed, and visualized under a microscopy.

Statistical analyses

Statistical analyses were carried out with GraphPad Prism 8.0 (GraphPad Software). All experiments were performed at least 3 replicates, unless otherwise noted. Differences in means were compared using an unpaired two-tailed Student’s t test, and graphed as the means ± standard deviations (SD) or means ± standard error of mean (SEM). Statistical significance denoted as follows: *, p < 0.05; **, p < 0.01; ***, p < 0.001; ****, p < 0.0001.

Availability of data and materials

Raw CRISPRi screening, H3K27ac HiChIP and 4C-seq data in this study were deposited in the GEO database under accession number GSE210424 [107]. CRISPRi screening sgRNA counts and all primer sequences used in this study are listed in Additional files. Raw WGS data for 297 paired melanoma tumor and normal samples (MELA-AU: 183 samples, SKCM-US: 38 samples, and SKCA-BR: 76 samples) were downloaded from the International Cancer Genome Consortium (ICGC) data portal (https://dcc.icgc.org/repositories) and European Genome-phenome Archive (EGAD00001003388). H3K27ac ChIP-seq data from the 10 melanoma cell lines were downloaded from the GEO database under accession number GSE143653, GSE99835 and GSE82332. A375 epigenomic data were downloaded from the GEO database, including CTCF (GSM3671692), H3K4me3 (GSM2653904), Pol II ChIP-seq (GSM1661790), and PRO-seq (GSE128081). A375 Hi-C data was acquired from the GEO database under accession number GSE143676. HiChIP loop data for WM (GSM5680738) and COLO (GSM5680737) cell lines were downloaded from the GEO database.

References

Saginala K, Barsouk A, Aluru JS, Rawla P, Barsouk A. Epidemiology of melanoma. Med Sci (Basel). 2021;9:63.
CAS PubMed Google Scholar
Cancer Genome Atlas N. Genomic classification of cutaneous melanoma. Cell. 2015;161:1681–96.
Article Google Scholar
Hayward NK, Wilmott JS, Waddell N, Johansson PA, Field MA, Nones K, Patch AM, Kakavand H, Alexandrov LB, Burke H, et al. Whole-genome landscapes of major melanoma subtypes. Nature. 2017;545:175–80.
Article CAS PubMed Google Scholar
Newell F, Kong Y, Wilmott JS, Johansson PA, Ferguson PM, Cui C, Li Z, Kazakoff SH, Burke H, Dodds TJ, et al. Whole-genome landscape of mucosal melanoma reveals diverse drivers and therapeutic targets. Nat Commun. 2019;10:3163.
Article PubMed PubMed Central Google Scholar
Newell F, Wilmott JS, Johansson PA, Nones K, Addala V, Mukhopadhyay P, Broit N, Amato CM, Van Gulick R, Kazakoff SH, et al. Whole-genome sequencing of acral melanoma reveals genomic complexity and diversity. Nat Commun. 2020;11:5259.
Article CAS PubMed PubMed Central Google Scholar
Johansson PA, Brooks K, Newell F, Palmer JM, Wilmott JS, Pritchard AL, Broit N, Wood S, Carlino MS, Leonard C, et al. Whole genome landscapes of uveal melanoma show an ultraviolet radiation signature in iris tumours. Nat Commun. 2020;11:2408.
Article CAS PubMed PubMed Central Google Scholar
Wilmott JS, Johansson PA, Newell F, Waddell N, Ferguson P, Quek C, Patch AM, Nones K, Shang P, Pritchard AL, et al. Whole genome sequencing of melanomas in adolescent and young adults reveals distinct mutation landscapes and the potential role of germline variants in disease susceptibility. Int J Cancer. 2019;144:1049–60.
Article CAS PubMed Google Scholar
Newell F, Johansson PA, Wilmott JS, Nones K, Lakis V, Pritchard AL, Lo SN, Rawson RV, Kazakoff SH, Colebatch AJ, et al. Comparative genomics provides etiologic and biological insight into melanoma subtypes. Cancer Discov. 2022;12:2856–79.
Article CAS PubMed PubMed Central Google Scholar
Alexandrov LB, Nik-Zainal S, Wedge DC, Aparicio SA, Behjati S, Biankin AV, Bignell GR, Bolli N, Borg A, Borresen-Dale AL, et al. Signatures of mutational processes in human cancer. Nature. 2013;500:415–21.
Article CAS PubMed PubMed Central Google Scholar
Zhang T, Dutton-Regester K, Brown KM, Hayward NK. The genomic landscape of cutaneous melanoma. Pigment Cell Melanoma Res. 2016;29:266–83.
Article CAS PubMed Google Scholar
Rheinbay E, Nielsen MM, Abascal F, Wala JA, Shapira O, Tiao G, Hornshoj H, Hess JM, Juul RI, Lin Z, et al. Analyses of non-coding somatic drivers in 2,658 cancer whole genomes. Nature. 2020;578:102–11.
Article CAS PubMed PubMed Central Google Scholar
Mauduit D, Taskiran II, Minnoye L, de Waegeneer M, Christiaens V, Hulselmans G, Demeulemeester J, Wouters J, Aerts S. Analysis of long and short enhancers in melanoma cell states. Elife. 2021;10:e71735.
Article CAS PubMed PubMed Central Google Scholar
Minnoye L, Taskiran II, Mauduit D, Fazio M, Van Aerschot L, Hulselmans G, Christiaens V, Makhzami S, Seltenhammer M, Karras P, et al. Cross-species analysis of enhancer logic using deep learning. Genome Res. 2020;30:1815–34.
Article CAS PubMed PubMed Central Google Scholar
Verfaillie A, Imrichova H, Atak ZK, Dewaele M, Rambow F, Hulselmans G, Christiaens V, Svetlichnyy D, Luciani F, Van den Mooter L, et al. Decoding the regulatory landscape of melanoma reveals TEADS as regulators of the invasive cell state. Nat Commun. 2015;6:6683.
Article CAS PubMed Google Scholar
Cunningham RL, Kramer ET, DeGeorgia SK, Godoy PM, Zarov AP, Seneviratne S, Grigura V, Kaufman CK. Functional in vivo characterization of sox10 enhancers in neural crest and melanoma development. Commun Biol. 2021;4:695.
Article CAS PubMed PubMed Central Google Scholar
Fufa TD, Baxter LL, Wedel JC, Gildea DE, Program NCS, Loftus SK, Pavan WJ. MEK inhibition remodels the active chromatin landscape and induces SOX10 genomic recruitment in BRAF(V600E) mutant melanoma cells. Epigenetics Chromatin. 2019;12:50.
Article PubMed PubMed Central Google Scholar
Eliades P, Abraham BJ, Ji Z, Miller DM, Christensen CL, Kwiatkowski N, Kumar R, Njauw CN, Taylor M, Miao B, et al. High MITF expression is associated with super-enhancers and suppressed by CDK7 inhibition in melanoma. J Invest Dermatol. 2018;138:1582–90.
Article CAS PubMed PubMed Central Google Scholar
Maitituoheti M, Keung EZ, Tang M, Yan L, Alam H, Han G, Singh AK, Raman AT, Terranova C, Sarkar S, et al. Enhancer reprogramming confers dependence on glycolysis and IGF signaling in KMT2D mutant melanoma. Cell Rep. 2020;33:108293.
Article CAS PubMed PubMed Central Google Scholar
Chu Z, Gu L, Hu Y, Zhang X, Li M, Chen J, Teng D, Huang M, Shen CH, Cai L, et al. STAG2 regulates interferon signaling in melanoma via enhancer loop reprogramming. Nat Commun. 2022;13:1859.
Google Scholar
Li K, Zhang Y, Liu X, Liu Y, Gu Z, Cao H, Dickerson KE, Chen M, Chen W, Shao Z, et al. Noncoding variants connect enhancer dysregulation with nuclear receptor signaling in hematopoietic malignancies. Cancer Discov. 2020;10:724–45.
Article CAS PubMed PubMed Central Google Scholar
Fei T, Li W, Peng J, Xiao T, Chen CH, Wu A, Huang J, Zang C, Liu XS, Brown M. Deciphering essential cistromes using genome-wide CRISPR screens. Proc Natl Acad Sci U S A. 2019;116:25186–95.
Article CAS PubMed PubMed Central Google Scholar
Ahmed M, Soares F, Xia JH, Yang Y, Li J, Guo H, Su P, Tian Y, Lee HJ, Wang M, et al. CRISPRi screens reveal a DNA methylation-mediated 3D genome dependent causal mechanism in prostate cancer. Nat Commun. 2021;12:1781.
Article CAS PubMed PubMed Central Google Scholar
Sanjana NE, Wright J, Zheng K, Shalem O, Fontanillas P, Joung J, Cheng C, Regev A, Zhang F. High-resolution interrogation of functional elements in the noncoding genome. Science. 2016;353:1545–9.
Article CAS PubMed PubMed Central Google Scholar
Tottone L, Lancho O, Loh JW, Singh A, Kimura S, Roels J, Kuchmiy A, Strubbe S, Lawlor MA, da Silva-Diz V, et al. A tumor suppressor enhancer of PTEN in T-cell development and leukemia. Blood Cancer Discov. 2021;2:92–109.
Article PubMed Google Scholar
Zhao K, Zhou Y, Wu C, Wang J, Yao H, Cheng X, Zhao L, Wang W, Chu X, Yi X, et al. Endogenous fine-mapping of functional regulatory elements in complex genetic loci. bioRxiv. 2023:05.06.539696.
Lever J, Zhao EY, Grewal J, Jones MR, Jones SJM. CancerMine: a literature-mined resource for drivers, oncogenes and tumor suppressors in cancer. Nat Methods. 2019;16:505–7.
Article CAS PubMed Google Scholar
Gonzalez-Perez A, Perez-Llamas C, Deu-Pons J, Tamborero D, Schroeder MP, Jene-Sanz A, Santos A, Lopez-Bigas N. IntOGen-mutations identifies cancer drivers across tumor types. Nat Methods. 2013;10:1081–2.
Article CAS PubMed PubMed Central Google Scholar
Repana D, Nulsen J, Dressler L, Bortolomeazzi M, Venkata SK, Tourna A, Yakovleva A, Palmieri T, Ciccarelli FD. The Network of Cancer Genes (NCG): a comprehensive catalogue of known and candidate cancer genes from cancer sequencing screens. Genome Biol. 2019;20:1.
Article PubMed PubMed Central Google Scholar
Zhang J, Bajari R, Andric D, Gerthoffert F, Lepsa A, Nahal-Bose H, Stein LD, Ferretti V. The international cancer genome consortium data portal. Nat Biotechnol. 2019;37:367–9.
Article CAS PubMed Google Scholar
Benjamin D, Sato T, Cibulskis K, Getz G, Stewart C, Lichtenstein L: Calling Somatic SNVs and Indels with Mutect2. bioRxiv. 2019:861054.
Chen X, Schulz-Trieglaff O, Shaw R, Barnes B, Schlesinger F, Källberg M, Cox AJ, Kruglyak S, Saunders CT. Manta: rapid detection of structural variants and indels for germline and cancer sequencing applications. Bioinformatics. 2016;32:1220–2.
Article CAS PubMed Google Scholar
Cameron DL, Schröder J, Penington JS, Do H, Molania R, Dobrovic A, Speed TP, Papenfuss AT. GRIDSS: sensitive and specific genomic rearrangement detection using positional de Bruijn graph assembly. Genome Res. 2017;27:2050–60.
Article CAS PubMed PubMed Central Google Scholar
Imielinski M, Guo G, Meyerson M. Insertions and deletions target lineage-defining genes in human cancers. Cell. 2017;168:460–472 e414.
Article CAS PubMed PubMed Central Google Scholar
Inoue D, Chew GL, Liu B, Michel BC, Pangallo J, D’Avino AR, Hitchman T, North K, Lee SC, Bitner L, et al. Spliceosomal disruption of the non-canonical BAF complex in cancer. Nature. 2019;574:432–6.
Article CAS PubMed PubMed Central Google Scholar
Huang FW, Hodis E, Xu MJ, Kryukov GV, Chin L, Garraway LA. Highly recurrent TERT promoter mutations in human melanoma. Science. 2013;339:957–9.
Article CAS PubMed PubMed Central Google Scholar
McNeal AS, Liu K, Nakhate V, Natale CA, Duperret EK, Capell BC, Dentchev T, Berger SL, Herlyn M, Seykora JT, Ridky TW. CDKN2B loss promotes progression from benign melanocytic nevus to melanoma. Cancer Discov. 2015;5:1072–85.
Article CAS PubMed PubMed Central Google Scholar
Cancer Genome Atlas N. Genomic classification of cutaneous melanoma. Cell. 2015;161:1681–96.
McKenzie JA, Liu T, Jung JY, Jones BB, Ekiz HA, Welm AL, Grossman D. Survivin promotion of melanoma metastasis requires upregulation of α5 integrin. Carcinogenesis. 2013;34:2137–44.
Article CAS PubMed PubMed Central Google Scholar
Zhang X, Yang L, Szeto P, Abali GK, Zhang Y, Kulkarni A, Amarasinghe K, Li J, Vergara IA, Molania R, et al. The Hippo pathway oncoprotein YAP promotes melanoma cell invasion and spontaneous metastasis. Oncogene. 2020;39:5267–81.
Article CAS PubMed Google Scholar
Khurana E, Fu Y, Chakravarty D, Demichelis F, Rubin MA, Gerstein M. Role of non-coding sequence variants in cancer. Nat Rev Genet. 2016;17:93–108.
Article CAS PubMed Google Scholar
Kron KJ, Bailey SD, Lupien M. Enhancer alterations in cancer: a source for a cell identity crisis. Genome Med. 2014;6:77.
Article PubMed PubMed Central Google Scholar
Bal E, Kumar R, Hadigol M, Holmes AB, Hilton LK, Loh JW, Dreval K, Wong JCH, Vlasevska S, Corinaldesi C, et al. Super-enhancer hypermutation alters oncogene expression in B cell lymphoma. Nature. 2022;607:808–15.
Article CAS PubMed PubMed Central Google Scholar
Shalem O, Sanjana NE, Hartenian E, Shi X, Scott DA, Mikkelson T, Heckl D, Ebert BL, Root DE, Doench JG, Zhang F. Genome-scale CRISPR-Cas9 knockout screening in human cells. Science. 2014;343:84–7.
Article CAS PubMed Google Scholar
Aguirre AJ, Meyers RM, Weir BA, Vazquez F, Zhang CZ, Ben-David U, Cook A, Ha G, Harrington WF, Doshi MB, et al. Genomic copy number dictates a gene-independent cell response to CRISPR/Cas9 targeting. Cancer Discov. 2016;6:914–29.
Article CAS PubMed PubMed Central Google Scholar
Hart T, Chandrashekhar M, Aregger M, Steinhart Z, Brown KR, MacLeod G, Mis M, Zimmermann M, Fradet-Turcotte A, Sun S, et al. High-resolution CRISPR screens reveal fitness genes and genotype-specific cancer liabilities. Cell. 2015;163:1515–26.
Article CAS PubMed Google Scholar
Li W, Xu H, Xiao T, Cong L, Love MI, Zhang F, Irizarry RA, Liu JS, Brown M, Liu XS. MAGeCK enables robust identification of essential genes from genome-scale CRISPR/Cas9 knockout screens. Genome Biol. 2014;15:554.
Article PubMed PubMed Central Google Scholar
Shinde J, Bayard Q, Imbeaud S, Hirsch TZ, Liu F, Renault V, Zucman-Rossi J, Letouzé E. Palimpsest: an R package for studying mutational and structural variant signatures along clonal evolution in cancer. Bioinformatics. 2018;34:3380–1.
Article CAS PubMed Google Scholar
Alexandrov LB, Kim J, Haradhvala NJ, Huang MN, Tian Ng AW, Wu Y, Boot A, Covington KR, Gordenin DA, Bergstrom EN, et al. The repertoire of mutational signatures in human cancer. Nature. 2020;578:94–101.
Article CAS PubMed PubMed Central Google Scholar
Tan JL, Fogley RD, Flynn RA, Ablain J, Yang S, Saint-André V, Fan ZP, Do BT, Laga AC, Fujinaga K, et al. Stress from nucleotide depletion activates the transcriptional regulator HEXIM1 to suppress melanoma. Mol Cell. 2016;62:34–46.
Article CAS PubMed PubMed Central Google Scholar
Leger JB. Blockmodels: a R-package for estimating in Latent Block Model and Stochastic Block Model, with various probability functions, with or without covariates. arXiv. 2016:1602.07587.
Terranova CJ, Tang M, Maitituoheti M, Raman AT, Ghosh AK, Schulz J, Amin SB, Orouji E, Tomczak K, Sarkar S, et al. Reprogramming of bivalent chromatin states in NRAS mutant melanoma suggests PRC2 inhibition as a therapeutic strategy. Cell Rep. 2021;36:109410.
Article CAS PubMed PubMed Central Google Scholar
Terranova C, Tang M, Maitituoheti M, Raman AT, Schulz J, Amin SB, Orouji E, Tomczak K, Sarkar S, Oba J, et al. Bivalent and broad chromatin domains regulate pro-metastatic drivers in melanoma. bioRxiv. 2019:721480.
Corces MR, Granja JM, Shams S, Louie BH, Seoane JA, Zhou W, Silva TC, Groeneveld C, Wong CK, Cho SW, et al. The chromatin accessibility landscape of primary human cancers. Science. 2018;362:eaav1898.
Google Scholar
Ghandi M, Huang FW, Jane-Valbuena J, Kryukov GV, Lo CC, McDonald ER 3rd, Barretina J, Gelfand ET, Bielski CM, Li H, et al. Next-generation characterization of the cancer cell line encyclopedia. Nature. 2019;569:503–8.
Article CAS PubMed PubMed Central Google Scholar
Yeh AH, Bohula EA, Macaulay VM. Human melanoma cells expressing V600E B-RAF are susceptible to IGF1R targeting by small interfering RNAs. Oncogene. 2006;25:6574–81.
Article CAS PubMed Google Scholar
McKinsey TA, Zhang CL, Olson EN. MEF2: a calcium-dependent regulator of cell division, differentiation and death. Trends Biochem Sci. 2002;27:40–7.
Article CAS PubMed Google Scholar
Naya FJ, Black BL, Wu H, Bassel-Duby R, Richardson JA, Hill JA, Olson EN. Mitochondrial deficiency and cardiac sudden death in mice lacking the MEF2A transcription factor. Nat Med. 2002;8:1303–9.
Article CAS PubMed Google Scholar
Stahl JM, Cheung M, Sharma A, Trivedi NR, Shanmugam S, Robertson GP. Loss of PTEN promotes tumor development in malignant melanoma. Cancer Res. 2003;63:2881–90.
CAS PubMed Google Scholar
Dankort D, Curley DP, Cartlidge RA, Nelson B, Karnezis AN, Damsky WE Jr, You MJ, DePinho RA, McMahon M, Bosenberg M. Braf(V600E) cooperates with Pten loss to induce metastatic melanoma. Nat Genet. 2009;41:544–52.
Article CAS PubMed PubMed Central Google Scholar
Carracedo A, Pandolfi PP. The PTEN-PI3K pathway: of feedbacks and cross-talks. Oncogene. 2008;27:5527–41.
Article CAS PubMed Google Scholar
Chen H, Li C, Peng X, Zhou Z, Weinstein JN, Cancer Genome Atlas Research N, Liang H. A pan-cancer analysis of enhancer expression in nearly 9000 patient samples. Cell. 2018;173:386–399 e312.
Article CAS PubMed PubMed Central Google Scholar
Bradner JE, Hnisz D, Young RA. Transcriptional addiction in cancer. Cell. 2017;168:629–43.
Article CAS PubMed PubMed Central Google Scholar
Elliott K, Larsson E. Non-coding driver mutations in human cancer. Nat Rev Cancer. 2021;21:500–9.
Article CAS PubMed Google Scholar
Donohue LKH, Guo MG, Zhao Y, Jung N, Bussat RT, Kim DS, Neela PH, Kellman LN, Garcia OS, Meyers RM, et al. A cis-regulatory lexicon of DNA motif combinations mediating cell-type-specific gene regulation. Cell Genom. 2022;2:100191.
Yao H, Liang Q, Qian X, Wang J, Sham PC, Li MJ. Methods and resources to access mutation-dependent effects on cancer drug treatment. Brief Bioinform. 2020;21:1886–903.
Article CAS PubMed Google Scholar
Korkmaz G, Lopes R, Ugalde AP, Nevedomskaya E, Han R, Myacheva K, Zwart W, Elkon R, Agami R. Functional genetic screens for enhancer elements in the human genome using CRISPR-Cas9. Nat Biotechnol. 2016;34:192–8.
Article CAS PubMed Google Scholar
Ding B, Liu Y, Liu Z, Zheng L, Xu P, Chen Z, Wu P, Zhao Y, Pan Q, Guo Y, et al. Noncoding loci without epigenomic signals can be essential for maintaining global chromatin organization and cell viability. Sci Adv. 2021;7:eabi6020.
Article CAS PubMed PubMed Central Google Scholar
Pon JR, Marra MA. MEF2 transcription factors: developmental regulators and emerging cancer genes. Oncotarget. 2016;7:2297–312.
Article PubMed Google Scholar
McLaren W, Gil L, Hunt SE, Riat HS, Ritchie GR, Thormann A, Flicek P, Cunningham F. The ensembl variant effect predictor. Genome Biol. 2016;17:122.
Article PubMed PubMed Central Google Scholar
Quinlan AR, Hall IM. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics. 2010;26:841–2.
Article CAS PubMed PubMed Central Google Scholar
O’Leary NA, Wright MW, Brister JR, Ciufo S, Haddad D, McVeigh R, Rajput B, Robbertse B, Smith-White B, Ako-Adjei D, et al. Reference sequence (RefSeq) database at NCBI: current status, taxonomic expansion, and functional annotation. Nucleic Acids Res. 2016;44:D733–745.
Article CAS PubMed Google Scholar
Pacini C, Dempster JM, Boyle I, Gonçalves E, Najgebauer H, Karakoc E, van der Meer D, Barthorpe A, Lightfoot H, Jaaks P, et al. Integrated cross-study datasets of genetic dependencies in cancer. Nat Commun. 2021;12:1661.
Article CAS PubMed PubMed Central Google Scholar
Dempster JM, Boyle I, Vazquez F, Root DE, Boehm JS, Hahn WC, Tsherniak A, McFarland JM. Chronos: a cell population dynamics model of CRISPR experiments that improves inference of gene fitness effects. Genome Biol. 2021;22:343.
Article PubMed PubMed Central Google Scholar
Gopi LK, Kidder BL. Integrative pan cancer analysis reveals epigenomic variation in cancer type and cell specific chromatin domains. Nat Commun. 2021;12:1419.
Article CAS PubMed PubMed Central Google Scholar
Joung J, Engreitz JM, Konermann S, Abudayyeh OO, Verdine VK, Aguet F, Gootenberg JS, Sanjana NE, Wright JB, Fulco CP. Genome-scale activation screen identifies a lncRNA locus regulating a gene neighbourhood. Nature. 2017;548:343–6.
Article CAS PubMed PubMed Central Google Scholar
Zhou B, Wang L, Zhang S, Bennett BD, He F, Zhang Y, Xiong C, Han L, Diao L, Li P, et al. INO80 governs superenhancer-mediated oncogenic transcription and tumor growth in melanoma. Genes Dev. 2016;30:1440–53.
Article CAS PubMed PubMed Central Google Scholar
Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nat Methods. 2012;9:357–9.
Article CAS PubMed PubMed Central Google Scholar
Feng J, Liu T, Qin B, Zhang Y, Liu XS. Identifying ChIP-seq enrichment using MACS. Nat Protoc. 2012;7:1728–40.
Article CAS PubMed Google Scholar
Shalem O, Sanjana NE, Hartenian E, Shi X, Scott DA, Mikkelsen TS, Heckl D, Ebert BL, Root DE, Doench JG. Genome-scale CRISPR-Cas9 knockout screening in human cells. Science. 2014;343:84–7.
Article CAS PubMed Google Scholar
McKenna A, Shendure J. FlashFry: a fast and flexible tool for large-scale CRISPR target design. BMC Biol. 2018;16:74.
Article PubMed PubMed Central Google Scholar
Li W, Koster J, Xu H, Chen CH, Xiao T, Liu JS, Brown M, Liu XS. Quality control, modeling, and visualization of CRISPR screens with MAGeCK-VISPR. Genome Biol. 2015;16:281.
Article CAS PubMed PubMed Central Google Scholar
Liu EM, Martinez-Fundichely A, Diaz BJ, Aronson B, Cuykendall T, MacKay M, Dhingra P, Wong EWP, Chi P, Apostolou E, et al. Identification of cancer drivers at CTCF insulators in 1,962 whole genomes. Cell Syst. 2019;8:446–455.e448.
Article CAS PubMed PubMed Central Google Scholar
Konermann S, Brigham MD, Trevino AE, Joung J, Abudayyeh OO, Barcena C, Hsu PD, Habib N, Gootenberg JS, Nishimasu H. Genome-scale transcriptional activation by an engineered CRISPR-Cas9 complex. Nature. 2015;517:583–8.
Article CAS PubMed Google Scholar
Santoriello C, Sporrij A, Yang S, Flynn RA, Henriques T, Dorjsuren B, Custo Greig E, McCall W, Stanhope ME, Fazio M, et al. RNA helicase DDX21 mediates nucleotide stress responses in neural crest and melanoma cells. Nat Cell Biol. 2020;22:372–9.
Article CAS PubMed PubMed Central Google Scholar
Johansson JA, Marie KL, Lu Y, Brombin A, Santoriello C, Zeng Z, Zich J, Gautier P, von Kriegsheim A, Brunsdon H, et al. PRL3-DDX21 transcriptional control of endolysosomal genes restricts melanocyte stem cell differentiation. Dev Cell. 2020;54:317–332.e319.
Article CAS PubMed PubMed Central Google Scholar
Lal G, Contreras PG, Kulak M, Woodfield G, Bair T, Domann FE, Weigel RJ. Human Melanoma cells over-express extracellular matrix 1 (ECM1) which is regulated by TFAP2C. PLoS One. 2013;8:e73953.
Article CAS PubMed PubMed Central Google Scholar
Xie Y, Cao Z, Wong EW, Guan Y, Ma W, Zhang JQ, Walczak EG, Murphy D, Ran L, Sirota I, et al. COP1/DET1/ETS axis regulates ERK transcriptome and sensitivity to MAPK inhibitors. J Clin Invest. 2018;128:1442–57.
Article PubMed PubMed Central Google Scholar
Rusan M, Li K, Li Y, Christensen CL, Abraham BJ, Kwiatkowski N, Buczkowski KA, Bockorny B, Chen T, Li S, et al. Suppression of adaptive responses to targeted cancer therapy by transcriptional repression. Cancer Discov. 2018;8:59–73.
Article CAS PubMed Google Scholar
Schmidt K, Carroll JS, Yee E, Thomas DD, Wert-Lamas L, Neier SC, Sheynkman G, Ritz J, Novina CD. The lncRNA SLNCR recruits the androgen receptor to EGR1-bound genes in melanoma and inhibits expression of tumor suppressor p21. Cell Rep. 2019;27:2493–2507.e2494.
Article CAS PubMed PubMed Central Google Scholar
Su D, Wang W, Hou Y, Wang L, Yi X, Cao C, Wang Y, Gao H, Wang Y, Yang C, et al. Bimodal regulation of the PRC2 complex by USP7 underlies tumorigenesis. Nucleic Acids Res. 2021;49:4421–40.
Article CAS PubMed PubMed Central Google Scholar
White JR, Thompson DT, Koch KE, Kiriazov BS, Beck AC, van der Heide DM, Grimm BG, Kulak MV, Weigel RJ. AP-2α-mediated activation of E2F and EZH2 drives melanoma metastasis. Cancer Res. 2021;81:4455–70.
Article CAS PubMed PubMed Central Google Scholar
Smith JP, Dutta AB, Sathyan KM, Guertin MJ, Sheffield NC. PEPPRO: quality control and processing of nascent RNA profiling data. Genome Biol. 2021;22:155.
Article CAS PubMed PubMed Central Google Scholar
Wang Z, Chu T, Choate LA, Danko CG. Identification of regulatory elements from nascent transcription using dREG. Genome Res. 2019;29:293–303.
Article CAS PubMed PubMed Central Google Scholar
Zhou Y, Sun Y, Huang D, Li MJ. epiCOLOC: integrating large-scale and context-dependent epigenomics features for comprehensive colocalization analysis. Front Genet. 2020;11:53.
Article CAS PubMed PubMed Central Google Scholar
Mumbach MR, Rubin AJ, Flynn RA, Dai C, Khavari PA, Greenleaf WJ, Chang HY. HiChIP: efficient and sensitive analysis of protein-directed genome architecture. Nat Methods. 2016;13:919–22.
Article CAS PubMed PubMed Central Google Scholar
Bhattacharyya S, Chandra V, Vijayanand P, Ay F. Identification of significant chromatin contacts from HiChIP data by FitHiChIP. Nat Commun. 2019;10:4221.
Article PubMed PubMed Central Google Scholar
Wu T, Hu E, Xu S, Chen M, Guo P, Dai Z, Feng T, Zhou L, Tang W, Zhan L. clusterProfiler 4.0: a universal enrichment tool for interpreting omics data. Innovation. 2021;2:100141.
CAS PubMed PubMed Central Google Scholar
Tang Z, Kang B, Li C, Chen T, Zhang Z. GEPIA2: an enhanced web server for large-scale expression profiling and interactive analysis. Nucleic Acids Res. 2019;47:W556–60.
Article CAS PubMed PubMed Central Google Scholar
Zhao M, Kim P, Mitra R, Zhao J, Zhao Z. TSGene 2.0: an updated literature-based knowledgebase for tumor suppressor genes. Nucleic Acids Res. 2016;44:D1023–31.
Article CAS PubMed Google Scholar
Splinter E, de Wit E, van de Werken HJ, Klous P, de Laat W. Determining long-range chromatin interactions for selected genomic sites using 4C-seq technology: from fixation to computation. Methods. 2012;58:221–30.
Article CAS PubMed Google Scholar
Krijger PHL, Geeven G, Bianchi V, Hilvering CRE, de Laat W. 4C-seq from beginning to end: a detailed protocol for sample preparation and data analysis. Methods. 2020;170:17–32.
Article CAS PubMed Google Scholar
Splinter E, Grosveld F, de Laat W. 3C technology: analyzing the spatial organization of genomic loci in vivo. Methods Enzymol. 2004;375:493–507.
Article CAS PubMed Google Scholar
Tolhuis B, Palstra RJ, Splinter E, Grosveld F, de Laat W. Looping and interaction between hypersensitive sites in the active beta-globin locus. Mol Cell. 2002;10:1453–65.
Article CAS PubMed Google Scholar
Haeussler M, Schonig K, Eckert H, Eschstruth A, Mianne J, Renaud JB, Schneider-Maunoury S, Shkumatava A, Teboul L, Kent J, et al. Evaluation of off-target and on-target scoring algorithms and integration into the guide RNA selection tool CRISPOR. Genome Biol. 2016;17:148.
Article PubMed PubMed Central Google Scholar
Dehairs J, Talebi A, Cherifi Y, Swinnen JV. CRISP-ID: decoding CRISPR mediated indels by Sanger sequencing. Sci Rep. 2016;6:28973.
Article CAS PubMed PubMed Central Google Scholar
Wang Z, Liang Q, Qian X, Hu B, Zheng Z, Wang J, Hu Y, Bao Z, Zhao K, Zhou Y, et al. An autoimmune pleiotropic SNP modulates IRF5 alternative promoter usage through ZBTB3-mediated chromatin looping. Nat Commun. 2023;14:1208.
Article CAS PubMed PubMed Central Google Scholar
Li MJ. Landscape of enhancer disruption and functional screen in melanoma, GSE210424. Gene Expression Omnibus. 2023, https://0-www-ncbi-nlm-nih-gov.brum.beds.ac.uk/geo/query/acc.cgi?acc=GSE210424.

Download references

Acknowledgements

We thank the described patients for participating in our studies.

Review history

The review history is available as Additional file 17.

Peer review information

Kevin Pang was the primary editor of this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

Funding

The work was supported by the following grants: Chinese National Key Research and Development Project (2021YFC2500403), National Natural Science Foundation of China (32270717, 32070675 to M.J.L. and 32000640 to Z.W.), and the Tianjin Committee of Science and Technology (19JCJQJC63600 to M.J.L.).

Author information

Zhao Wang, Menghan Luo, Qian Liang and Ke Zhao contributed equally to this work.

Authors and Affiliations

Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), Institute of Aging, Key Laboratory of Alzheimer’s Disease of Zhejiang Province, The Second Affiliated Hospital, Wenzhou Medical University, Wenzhou, China
Zhao Wang, Qian Liang, Yuelin Hu, Bolang Hu, Ran Li, Zhengkai Bao, Wenhao Pan & Weihong Song
Department of Epidemiology and Biostatistics, Tianjin Key Laboratory of Molecular Cancer Epidemiology, The Province and Ministry Co-Sponsored Collaborative Innovation Center for Medical Epigenetics, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin, China
Zhao Wang, Menghan Luo, Qian Liang, Wei Wang, Ben Liu, Kexin Chen & Mulin Jun Li
Department of Bioinformatics, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China
Menghan Luo, Ke Zhao, Xiangling Feng, Jianjin Teng, Tianyi You, Xiaobao Dong, Xianfu Yi & Mulin Jun Li
Scientific Research Center, Wenzhou Medical University, Wenzhou, China
Qian Liang
Department of Bone and Soft Tissue Tumor, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin, China
Tielong Yang, Chao Zhang, Ting Li & Jilong Yang
Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China
Li Zhao
Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, China
Miaoxin Li

Authors

Zhao Wang
View author publications
You can also search for this author in PubMed Google Scholar
Menghan Luo
View author publications
You can also search for this author in PubMed Google Scholar
Qian Liang
View author publications
You can also search for this author in PubMed Google Scholar
Ke Zhao
View author publications
You can also search for this author in PubMed Google Scholar
Yuelin Hu
View author publications
You can also search for this author in PubMed Google Scholar
Wei Wang
View author publications
You can also search for this author in PubMed Google Scholar
Xiangling Feng
View author publications
You can also search for this author in PubMed Google Scholar
Bolang Hu
View author publications
You can also search for this author in PubMed Google Scholar
Jianjin Teng
View author publications
You can also search for this author in PubMed Google Scholar
Tianyi You
View author publications
You can also search for this author in PubMed Google Scholar
Ran Li
View author publications
You can also search for this author in PubMed Google Scholar
Zhengkai Bao
View author publications
You can also search for this author in PubMed Google Scholar
Wenhao Pan
View author publications
You can also search for this author in PubMed Google Scholar
Tielong Yang
View author publications
You can also search for this author in PubMed Google Scholar
Chao Zhang
View author publications
You can also search for this author in PubMed Google Scholar
Ting Li
View author publications
You can also search for this author in PubMed Google Scholar
Xiaobao Dong
View author publications
You can also search for this author in PubMed Google Scholar
Xianfu Yi
View author publications
You can also search for this author in PubMed Google Scholar
Ben Liu
View author publications
You can also search for this author in PubMed Google Scholar
Li Zhao
View author publications
You can also search for this author in PubMed Google Scholar
Miaoxin Li
View author publications
You can also search for this author in PubMed Google Scholar
Kexin Chen
View author publications
You can also search for this author in PubMed Google Scholar
Weihong Song
View author publications
You can also search for this author in PubMed Google Scholar
Jilong Yang
View author publications
You can also search for this author in PubMed Google Scholar
Mulin Jun Li
View author publications
You can also search for this author in PubMed Google Scholar

Contributions

M.J.L., M.H.L., and Z.W. designed the studies and wrote the manuscript. Z.W., Q.L., W.W., Y.L.H., B.L.H., J.J.T., Z.K.B., R.L., W.H.P., T.L.Y., and T.L. performed the experiments and analyzed data. M.H.L., K.Z., X.L.F., T.Y.Y., C.Z., X.B.D., and X.F.Y. conducted the bioinformatics and data analysis. B.L., L.Z., M.X.L., and K.X.C. contributed scientific expertise and reviewed the manuscript. M.J.L., J.L.Y., and W.H.S. supervised the study. All authors approved the final version of the manuscript.

Corresponding authors

Correspondence to Zhao Wang, Weihong Song, Jilong Yang or Mulin Jun Li.

Ethics declarations

Ethics approval and consent to participate

All animal procedures were performed in accordance with guidelines established by the Tianjin Science and Technology Commission guidelines and approved by the Animal Ethic Committee of Tianjin Medical University (WSJK-AE-2022004). The use of human melanoma samples was ethically approved by the Tianjin Medical University Cancer Institute and Hospital (bc2022110).

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1: Fig S1.

Workflow of genome-wide HRR identification based on melanoma WGS data. Fig S2. Reproducibility and annotation of functional HRR-associated enhancer in CRISPRi screen. Fig S3. CRISPRi screen day7 results on A375 and SK-MEL-2 cells. Fig S4. Mutational signature analysis for HRR-associated enhancers. Fig S5. 3C assay results between the MEF2A promoter and E_349. Fig S6. Survival evidence for alternative target genes of the top functional enhancers in TCGA-SKCM patients. Fig S7. E_349 modulates melanoma cell proliferation and apoptosis by targeting MEF2A. Fig S8. 3C assay results between the PTEN promoter and E_156. Fig S9. Distal enhancer-sustaining PTEN tumor-suppressive potential in melanoma cells. Fig S10. Detection the function of E_349 or E_156 on their predicted genes. Fig S11. Identification the knockout of E_349 or E_156 in A375 cells. Fig S12. Immunohistochemistry (IHC) results of melanoma patient samples with E_349 deletion or extensive mutations.

Additional file 2: Table S1.

Integrated clinical information of 297 melanoma patients from ICGC.

Additional file 3: Table S2.

Summary information and supporting evidence of significant SNV/INDEL-HRRs in melanoma.

Additional file 4: Table S3.

Summary information and supporting evidence of prioritized SV-HRRs in melanoma.

Additional file 5: Table S4.

Curated melanoma essential candidate genes from high-throughput screening.

Additional file 6: Table S5.

Information of sgRNAs and associated enhancers.

Additional file 7: Table S6.

sgRNA count table of CRISPRi screen for A375 and SK-MEL-2 cells.

Additional file 8: Table S7.

Significant enhancers of CRISPRi screen identified by MAGeCK.

Additional file 9: Table S8.

Epigenetic annotations for significant functional enhancers.

Additional file 10: Table S9.

The presence of functional enhancers in different cell lines.

Additional file 11: Table S10.

H3k27ac signal of patients captured by enhancers.

Additional file 12: Table S11.

Melanoma cell lines genetic profile in enhancer regions.

Additional file 13: Table S12.

A375 H3K27ac HiChIP interactions identified by FitHiChIP.

Additional file 14: Table S13.

Target gene annotation and supporting evidence functional enhancers in melanoma.

Additional file 15: Table S14.

Primers and oligonucleotide sequences used in the experiments.

Additional file 16.

Source data including uncropped WB and gel images.

Additional file 17.

Review history.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Cite this article

Wang, Z., Luo, M., Liang, Q. et al. Landscape of enhancer disruption and functional screen in melanoma cells. Genome Biol 24, 248 (2023). https://0-doi-org.brum.beds.ac.uk/10.1186/s13059-023-03087-5

Download citation

Received: 18 August 2022
Accepted: 12 October 2023
Published: 30 October 2023
DOI: https://0-doi-org.brum.beds.ac.uk/10.1186/s13059-023-03087-5

Landscape of enhancer disruption and functional screen in melanoma cells

Abstract

Background

Results

Conclusions

Background

Results

Genome-wide prioritization of highly recurrent regions (HRRs) in 297 melanoma WGS datasets

CRISPR tiling screen of HRR-associated enhancers for melanoma cell growth

Genomic and epigenomic features of functional enhancers in melanoma

Target gene analysis between melanoma-suppressive enhancers and known or novel cancer genes

A super enhancer element modulates melanoma cell proliferation and apoptosis by targeting MEF2A

A distal enhancer sustains PTEN tumor-suppressive potential in melanoma cells

Discussion

Conclusions

Methods

Cell lines

WGS data and somatic mutation calling

HRR detection and prioritization

HRR and HRR-associated enhancer annotation

Functional evaluation of HRR-associated enhancer

CRISPRi sgRNA library construction

CRISPRi screen

CRISPRi positive selection enrichment

Mutational signature analysis

Genomic and epigenomic features

In situ Hi-C followed by chromatin immunoprecipitation (HiChIP)

HiChIP loop detection and target gene annotation

Circularized chromosome conformation capture (4C) assay

Chromosome conformation capture (3C) assay

Luciferase reporter assay

Enhancer perturbation and knockout assays

Western blotting and quantitative real-time RT-PCR (RT-qPCR)

Cell proliferation assay

Apoptosis assay

Flat colony formation assay

Cell-derived xenograft (CDX)

Immunohistochemistry (IHC)

Statistical analyses

Availability of data and materials

References

Acknowledgements

Review history

Peer review information

Funding

Author information

Authors and Affiliations

Contributions

Corresponding authors

Ethics declarations

Ethics approval and consent to participate

Competing interests

Additional information

Publisher’s Note

Supplementary Information

Rights and permissions

About this article

Cite this article

Share this article

Keywords

Genome Biology

Contact us