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Review

Epigenetic Mechanisms Underlying Melanoma Resistance to Immune and Targeted Therapies

1
Department of Pathology, NYU Grossman School of Medicine, New York, NY 10016, USA
2
Interdisciplinary Melanoma Cooperative Group, Perlmutter Cancer Center, NYU Langone Health, New York, NY 10016, USA
*
Author to whom correspondence should be addressed.
Cancers 2022, 14(23), 5858; https://doi.org/10.3390/cancers14235858
Submission received: 11 November 2022 / Accepted: 22 November 2022 / Published: 28 November 2022
(This article belongs to the Special Issue Epigenetic Dysregulation in Melanoma)

Abstract

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Simple Summary

Despite major recent therapeutic advances, melanoma remains the deadliest form of skin cancer due to the capacity of melanoma cells to adapt to drug treatment and become resistant. Improved understanding of melanoma suggests that it resists treatment not just due to DNA changing mutations but also due to changes in DNA accessibility with respect to the reading and creation of different proteins. Here, we summarize the various ways in which different DNA regions become more or less open, impacting patient response to anti-cancer therapies. For instance, targeting specific proteins governing the expression of viral-like genes can alert the immune system and enhance the effects of anti-cancer therapies. Alternatively, changes in DNA accessibility allow the activation of alternative survival signals that allow melanoma cells to escape death upon treatment. Targeting factors modulating DNA accessibility in cancer cells has recently become possible through technological developments. These novel therapies, administered alone or in combination with existing ones, represent the next frontier in the treatment of advanced melanoma.

Abstract

Melanoma is an aggressive skin cancer reliant on early detection for high likelihood of successful treatment. Solar UV exposure transforms melanocytes into highly mutated tumor cells that metastasize to the liver, lungs, and brain. Even upon resection of the primary tumor, almost thirty percent of patients succumb to melanoma within twenty years. Identification of key melanoma genetic drivers led to the development of pharmacological BRAFV600E and MEK inhibitors, significantly improving metastatic patient outcomes over traditional cytotoxic chemotherapy or pioneering IFN-α and IL-2 immune therapies. Checkpoint blockade inhibitors releasing the immunosuppressive effects of CTLA-4 or PD-1 proved to be even more effective and are the standard first-line treatment. Despite these major improvements, durable responses to immunotherapy and targeted therapy have been hindered by intrinsic or acquired resistance. In addition to gained or selected genetic alterations, cellular plasticity conferred by epigenetic reprogramming is emerging as a driver of therapy resistance. Epigenetic regulation of chromatin accessibility drives gene expression and establishes distinct transcriptional cell states. Here we review how aberrant chromatin, transcriptional, and epigenetic regulation contribute to therapy resistance and discuss how targeting these programs sensitizes melanoma cells to immune and targeted therapies.

1. Introduction

Representing only 1% of skin cancer cases but accounting for over 80% of deaths, melanoma is the deadliest form of skin cancer [1]. Ultraviolet exposure [2] of neural-crest-derived epidermal melanocytes [3] (or melanocyte stem cells [4,5]) generates highly mutated primary tumors [6,7,8] capable of rapidly metastasizing to the liver, lungs, and brain, even after complete surgical resection [9]. Half of melanoma patients carry conformation-changing BRAFV600E mutations [10,11] which constitutively activate the mitogen-activated protein kinase (MAPK) pathway. BRAFV600E inhibitor vemurafenib (FDA-approved in 2011) increased objective response rates (ORRs) from 5% to 50% compared to dacarbazine chemotherapy, and improved 6-month overall survival (OS) from 64% to 84% [12]. Following FDA approval of the MEK inhibitors cobimetinib and trametinib, combinatorial BRAF inhibition (BRAFi) and MEK inhibition (MEKi) increased 5-year OS to 34% [13] from 7% with dacarbazine plus interferon alpha (IFN-α) [14]. Targeted MAPK therapy recast metastatic melanoma from a rapid death sentence to a treatable condition with favorable outcomes for many patients.
Unfortunately, of the 40–60% eligible (BRAFV600E) patients, half of targeted therapy recipients display intrinsic or develop acquired resistance [15,16]. The intratumoral heterogeneity characteristic of melanoma gives rise to coexisting subclones with varying sensitivities to targeted therapy [17,18]. Intrinsic and acquired resistance are predominantly explained by MAPK re-activation [17]. Upstream of BRAF, gain-of-function mutations in NRAS and receptor tyrosine kinases (AXL, EGFR, IFG-1R, KIT, MET, and PDGFR) [19,20,21] or loss-of-function mutations in the tumor suppressor NF1 [22,23] hinder the efficacy of BRAFi and MEKi. Downstream of BRAF, MEK1/2 gain-of-function mutations [24,25] and increased levels of COT1, a MAPK kinase activating MEK1, also enable therapy resistance [26]. Additionally, resistance frequently involves activation of the PI3K/AKT/mTOR survival pathway via loss of the tumor suppressors PTEN and RB1 [27]. Similarly, acquired resistance typically occurs via delayed re-activation of MAPK (or analogous survival pathways), RAF isoform switching [28], BRAF copy number amplification [29], and alternative splicing isoforms [30].
First-generation immune therapy based on broad immune activation via IFN-α or IL-2 proved ineffective or exceedingly toxic [31,32]. Immune checkpoint blockade emerged chronologically parallel with targeted therapy, pioneered by the Nobel Prize-granted discovery of cytotoxic CD8+ T cell inhibition by CTLA-4 [33]. T cell surface marker CTLA-4 outcompetes the lower-affinity co-stimulatory receptor CD28 for binding to CD80 and CD86 on antigen-presenting cells (APCs), deactivating T cells via IL-2 inhibition and cell-cycle arrest [34]. FDA approval of ipilimumab, a monoclonal human CTLA-4 antibody, unlocked a novel approach to cancer treatment and improved patient outcomes [35], further potentiated by addition of anti-PD-1 antibodies [36,37]. Binding of T cell surface marker PD-1 to ligand PD-L1 inhibits T cell function via de-phosphorylation of the T cell receptor (TCR) [34]. While CTLA-4 inhibits early priming events between APCs and TCRs in the lymph nodes, PD-1 restrains activated T cells in peripheral tissues [34]. However, despite the monumental success of checkpoint blockade, half of treated patients fail to achieve long-term benefits [38]. Targeting of the alternative immune checkpoints LAG-3, TIM-3, and TIGIT has been explored [39,40]. However, despite recent FDA approval of combinatorial LAG-3 and PD-1 treatment [41], clinical progress has stagnated. A plateau in responses along with a lack of robust biomarkers for patient selection warrants a search for molecular mechanisms facilitating intrinsic and acquired resistance.
Intrinsic resistance affects half of anti-PD-1 and over 70% of anti-CTLA-4 immunotherapy recipients. Tumor-inherent factors facilitating intrinsic resistance include alterations in interferon signaling within the tumor and its microenvironment, reduced antigenicity and immunogenicity, and disrupted T cell infiltration and activity [42]. Tumor neoantigens matching patient TCRs represent a key component of tumor immune clearance in checkpoint blockade responders [43,44]. High tumor mutational burden precedes increased neoantigen generation [45] and is significantly associated with but not predictive of clinical treatment benefit [46,47,48]. Unsurprisingly, as a regulator of antigen presentation, interferon gamma (IFN-γ) signaling has been implicated in immunotherapy resistance. Binding of IFN-γ to its tumoral receptors induces inhibitory PD-L1 expression via JAK-STAT signaling [49]. Genetic defects in IFN-γ curb PD-L1 expression and suppress the efficacy of PD-1 immunotherapy [50]. However, clinical trials combining JAK inhibitors with PD-1 blockade have been generally unsuccessful [42,51]. Furthermore, tumors insensitive to IFN-γ escape immune surveillance via downregulation of MHC class I antigen presentation [52,53]. Unfortunately, the epigenetic alterations producing active IFN signaling cell states are still poorly understood.
Acquired resistance to immune checkpoint blockade prevents durable responses through mechanisms analogous to intrinsic resistance. Two fundamental models are proposed [54]. The first describes Darwinian selection of resistant clones present from treatment onset (as a consequence of intratumoral heterogeneity) following depletion of sensitive clones with treatment. The second, homeostatic resistance, suggests the emergence of resistant clones due to the selective pressure imposed by treatment. Although the primary mode remains unknown, the underlying mechanisms are strikingly similar to intrinsic resistance and include lack of neoantigens, impaired antigen presentation, diminished IFN signaling, cellular plasticity defined by de-differentiation, epithelial-to-mesenchymal (EMT) transition [55,56], and WNT activation [57,58]. Recent studies have focused on the contribution of epigenetic regulators to resistance due to their ability to alter global transcriptional networks and produce resistant cell states.

2. Role of the Epigenome in Therapeutic Resistance

Epigenetic alterations enable more rapid and reversible modulation of cellular responses than changes to the coding genome. Melanoma tumorigenesis and progression has been linked to dysregulation of epigenetic mechanisms, such as chromatin remodeling complexes (INO80 [59,60], ISWI [61,62], and SWI/SNF [63,64,65]); histone post-translational modifications (PTMs) by histone acetyltransferases (HATs) [66], deacetylases (HDACs) [67], methyltransferases (HMTs) [68], and demethylases (HDMs) [69]; histone variants [70,71,72]; DNA [73,74,75] and RNA [76,77,78] methylation; plus non-coding [79,80,81,82], micro- [83,84,85], and circular [86,87,88] RNA. Unsurprisingly, epigenetic changes are also linked to immune [89,90,91,92,93,94,95,96,97] and targeted [98,99,100,101,102,103] therapy resistance. Therapy resistance develops rapidly and is unlikely to be driven by de novo mutations [17,104]. Rather than undergoing an irreversible and time-consuming mutagenic process, melanoma cells respond to microenvironmental signals and/or stochastic intracellular fluctuations to remodel epigenetic landscapes and adopt reversible gene expression programs.
For instance, decreased PD-L1 expression and melanoma treatment-resistance is associated with global hypermethylation [105] and is reversible with DNA methyltransferase (DNMT) inhibitors [106]. Interestingly, DNMT inhibitors sensitized a pre-clinical melanoma model to CTLA-4 immunotherapy [107] by de-repressing endogenous retroviruses (ERVs) and activating an IFN response. Additionally, cyclin-dependent kinase 9 (CDK9) inhibition with toyocamycin activated the SWI/SNF catalytic subunit BRG1 and synergized with DNMT inhibition to de-repress endogenous retroviral elements and interferon signaling, sensitizing an in vivo ovarian cancer model to PD-1 immunotherapy [108,109]. Here we examine recent studies advancing our understanding of epigenetic regulation of ERVs and the emerging role of viral mimicry in overcoming melanoma immunotherapy resistance. Furthermore, targeted therapy resistance is not entirely explained by the acquisition of further mutations or genetically resistant clones but rather phenotypic alterations of cell states [110,111,112,113]. Interconnected oncogenic networks enable melanoma cells to circumvent MAPK inhibition via upregulation of survival pathways, molecular alteration of targets, or downregulating target expression. Here, we explore the role of epigenetic modulators in activating survival pathways and the contribution of phenotype switching to targeted therapy resistance.

2.1. Interferon-Mediated Viral Mimicry Facilitated by ERV De-Repression Enhances Response to Immunotherapy

Endogenous retroviral elements are genomically integrated fossil records of former retroviral infections in humans. Characterized as autonomous retrotransposons, ERV sequences occupy up to 8% of the human genome [114] but are often epigenetically repressed [115,116]. Human ERVs (HERVs) have been classified into over twenty families primarily on the basis of their tRNA-binding specificity [117]. While most HERVs exist defectively as long terminal repeats (LTRs), some HERV-K (HML2) members possess open reading frames (ORFs) for gag, pol, env, Rec [118], and Np9 [119] and produce viral particles [120,121]. A regulatory loop between Rec and MITF inhibits epigenetic phenotype switching between proliferative and invasive melanoma states [122]. Additional ERVs have been co-opted by mammalian genomes in regulating cellular immunity [123,124]. HERV-K mRNA is expressed in primary and metastatic melanoma [125,126] and generates viral proteins containing antigenic and therapeutically targetable epitopes [127,128]. ERV retention of viral features endows immunogenicity, triggering immune responses [124,127] and activating viral mimicry [129]—a phenomenon of beneficial cellular hypochondria characterized by a viral-like immune response in lieu of infection. Driven by interferon signaling following aberrant dsRNA accumulation, viral mimicry is gathering momentum as a major mechanism of immunotherapy response [130,131].
Recent pre-clinical studies have demonstrated significantly improved melanoma immunotherapy responses due to viral mimicry. The PRMT family of methyltransferases, commonly upregulated in cancer [132], facilitate methyl deposition on arginine residues. PRMTs regulate antiviral responses [133,134] through mitochondrial antiviral-signaling-protein (MAVS)-dependent interferon and proinflammatory responses [135]. PRMT7 inhibition induced viral mimicry and sensitized therapy-resistant B16F10 melanoma tumors to CTLA-4 and PD-1 treatment in vivo [136]. Pharmacological inhibition or CRISPR/Cas9-mediated knockout of PRMT7 hypomethylates ERVs through DNMT silencing and leads to dsRNA accumulation—a common characteristic of viral infections. In addition, PRMT7 repressively bi-methylates H4R3 residues in the RIG-I and MDA5 promoters. Their increased transcription following PRMT7 knockout enhances dsRNA detection, activating IFN and interferon-stimulated gene (ISG) transcription through MAVS. This enhanced immunogenicity, antigenicity, and CD8+ T cell infiltration, improving responses to immunotherapy. Unfortunately, efforts to inhibit the PRMT family, including Phase I clinical trials, have focused on Type I and II PRMTs, leaving PRMT7 (Type III) behind [137]. SGC3027, used in this study, was recently reported as the first PRMT7-specific pharmacological inhibitor [138], but clinical testing has not been conducted yet.
Genetic or pharmacological inhibition of histone demethylase LSD1 (also known as KDM1A) also stimulates ERV expression in melanoma [139]. Intriguingly, LSD1 ablation resulted in decreased protein levels of DICER, AGO2, and TRBO2—key components of the dsRNA-recycling RISC complex. In addition to physically interacting with RISC, AGO2 protein stability is diminished upon LSD1 knockout via demethylation of the AGO2 K726me1 residue. Detected by upregulated TLR3 and MDA5, supraphysiological dsRNA levels plus reduced RISC activity induced IFN-β signaling. Consequentially, upregulation of MHC-I complexes and PD-L1 expression sensitized treatment-resistant B16F10 melanoma cells to PD-1 checkpoint blockade due to increased CD4+ and CD8+ T cell infiltration. At least half a dozen LSD1 inhibitors have gone through or are currently undergoing clinical assessment for oncological and neurological treatments; however, none have reached Phase III yet [140].
A string of recent publications have validated histone methyltransferase SETDB1 as an important regulator of melanoma immune responses. Initially, SETDB1 loss was demonstrated to de-repress transposable elements (TEs) resembling ERVs, immune gene clusters, and MHC-I loci [141]. Surprisingly, viral mimicry was not observed due to lack of bi-directional transcription and interferon signaling, despite the emergence of accessible interferon gene clusters following SETDB1 knockout. However, due to the presence of long terminal repeats and intact open reading frames containing viral gag, pol, and env genes, de-repressed TEs encoded immunogenic peptides predicted to bind MHC-I complexes. Augmented antigenicity enhanced infiltration of T cells carrying corresponding TCRs, sensitizing treatment-resistant B16F10 cells to in vivo PD-1 and CTLA-4 checkpoint blockade (however, these effects have only been tested in GVAX melanoma cells expressing GM-CSF [142]).
Shortly after, it was demonstrated that histone demethylase KDM5B recruits SETDB1 to silence ERVs in a demethylase-independent manner [143]. KDM5B-knockout melanoma cells treated with proteasome inhibitor MG132 significantly increased SETDB1 levels, suggesting a protective role for KDM5B in proteasome-dependent degradation of SETDB1. Surprisingly, KDM5B loss (with concomitant SETDB1 reduction) increased bi-directional transcription of ERVs (such as MMVL30), accumulating dsRNA stress, activating type I interferon (IFN-I) signaling, upregulating MHC-I signaling, and inducing ISG expression. Accordingly, KDM5B knockout sensitized treatment-resistant YUMM1.7 melanoma cells to in vivo anti-PD-1 treatment through boosted CD8+ T cell activity. Corroborating the direct involvement of the IFN-I response, depletion of cytosolic DNA sensor cGAS or RNA sensor MDA5 ablated ISG expression and rescued YUMM1.7 in vivo tumor growth.
Finally, knockout of chromatin regulator ATF7IP or its interacting partner SETDB1 augments immunogenicity due to ERV expression and mRNA intron retention [144]. ATF7IP knockout induces interferon signaling through IRF7 and IRF9 upregulation, enhancing tumor immunogenicity and anti-tumor immunity via increased T cell activity. In MC38 colon carcinoma and KP lung carcinoma cell lines, ATF7IP knockout inhibited spliceosome activity, increasing intron expression and producing antigenic neoepitopes. Although no SETDB1-specific inhibitors have yet been developed, several histone lysine methyltransferase inhibitors have been tested in multiple pathological settings [145]. Recently and relevantly, Mithramycin A and Mithralog EC-8042 were shown to inhibit melanoma SETDB1 levels and improve the efficacy of MAPKi treatment [146]. KDM5B, upregulated in aggressive phenotypes of multiple cancer types, has been the target of numerous pharmacological inhibitors, but none have advanced to the clinic due to off-target effects and toxicity [147,148].
Viral mimicry defines a cell state characterized by a viral immune response in absence of infection. Induction of an anti-viral gene signature, driven by interferon signaling, activates MHC-I and PD-L1 expression (Figure 1). The resultant antigenicity alerts host immunity and reduces tumoral immune evasion. Despite some discrepancies in mechanisms and effector genes, LSD1, PRMT7, SETDB1, KDM5B, and ATF7IP functionally converge to evade melanoma immune clearance via repression of ancient genomic ERV elements that induce interferon signaling and translate immunogenic peptides for MHC-I presentation to CD8+ T cells (Table 1). Thus, inhibition of these immune-suppressive mechanisms could synergize with clinical immune checkpoint blockade.

2.2. Targeted Therapy Resistance via Epigenetic Upregulation of Survival Pathways Parallel to MAPK Signaling

Epigenetic mechanisms induce melanoma resistance to BRAF and MEK inhibitors, often by upregulating survival pathways functionally analogous to MAPK signaling, as illustrated by the following examples (Table 2).
BMI1 is a transcriptional repressor within the polycomb repressive complex 1 (PRC1) that mono-methylates H3K27, enabling PRC1 binding, and inhibiting transcription factor access via chromatin compaction [149]. BMI1 overexpression induced resistance in melanoma cells sensitive to pharmacological BRAFV600E inhibition, while BMI1 silencing increased BRAFi sensitivity proportional to the degree of knockdown. Upregulation of both WNT5a and its receptor ROR2 in BMI1-overexpressing persister cells revealed the contribution of WNT signaling to BRAFi sensitivity. In fact, treatment sensitivity was also proportional to the degree of WNT5a knockdown. In medulloblastomas, BMI1 modulation of receptor tyrosine kinases in the MAPK pathway altered patient responsiveness to MEK inhibition [150]. BMI1 upregulation in multiple cancer types and a described role in cancer stem cells (CSCs) has led to the development of pharmacological inhibitors. BMI1 hyperphosphorylation by the small molecule PTC596 impairs protein function [151]. Recent Phase I results indicate that orally bioavailable PTC596 has a tolerable human safety profile and is pre-clinically efficacious in mice as a monotherapy against leiomyosarcomas and glioblastoma, supporting its further development [152]. This is exciting, considering that clinical trials targeting the polycomb complex have to date focused solely on EZH2, the catalytic domain of polycomb repressive complex 2.
Activation of the PI3K/AKT/mTOR pathway facilitates melanoma resistance to BRAF and MEK inhibition. A CRISPR-Cas9 sgRNA knockout screen targeting chromatin factors in BRAFV600E melanoma cells in the presence of MAPKi rendered histone acetyltransferase (HAT) and deacetylase (HDAC) enzymes as hits [153,154]. Intriguingly, histone deacetylase SIRT6 haploinsufficiency promoted melanoma MAPKi resistance, while complete loss conferred sensitivity due to induction of the DNA damage response. SIRT6 haploinsufficiency resulted in increased H3K56 acetylation at the IGFBP2 locus, increasing chromatin accessibility and IGFBP2 expression. IGFBP2 activates IGF-1 receptor (IGF-1R), which triggers PI3K/AKT/mTOR survival signaling, enabling cell persistence in the presence of BRAF and MEK inhibition (MAPKi). Treatment with IGF-1R inhibitor linsitinib, which prevents IGF-1R autophosphorylation, overcomes SIRT6 haploinsufficiency resistance in vitro and in vivo. Supporting these findings, the authors showed that IGFBP2 transcript and protein levels were associated with poor prognoses for primary melanoma patients. Single-agent cixutumumab, a monoclonal antibody targeting IGF-1R, was tested on eighteen patients with metastatic uveal melanoma and exhibited low toxicity but only incomplete or partial responses, requiring further combinatorial studies with MAPK inhibitors to assess its utility [155]. IGF-1R inhibition has received abundant attention over the last decade for multiple cancer types, with the first FDA IGF-1R inhibitor, teprotumumab, a monoclonal antibody indicated for autoimmune Graves’ orbitopathy [156], approved in 2022. Further pre-clinical characterization may uncover a benefit in resistance delay or prevention by combining IGF-1R and MAPK inhibition in BRAFV600E patients with high IGFBP2 expression. Complete SIRT6 loss results in hyperacetylation and global chromatin disarray, also enhancing MAPKi sensitivity. SIRT6 inhibitors have not reached the clinic, but numerous small-molecule modulators are available [157]. Four HDAC inhibitors are currently FDA-approved as cancer therapeutics and represent the most clinically successful epigenetic regulatory mechanisms, with almost two dozen completed, ongoing, or recruiting trials in melanoma alone [158]. Unfortunately, benefits are observed only in a minority of patients, in combination with other treatments, and are typically quickly followed by resistance and disease progression.
The WNT, PI3K/AKT/mTOR, and IGF signaling pathways have been shown to play key roles in melanocytes and the surrounding stroma throughout various developmental stages [159,160,161]. Accordingly, their dysregulation is associated with multiple stages of malignancy, from initiation to metastasis. Accumulating both a favorable and sufficient quantity of oncogenic mutations in these survival pathways by random chance is a time-intensive process. Epigenetic modulation facilitates complex changes in cell states without alterations in coding sequences (Figure 2). Shifting chromatin accessibility in multiple genomic loci enables the expression of multiple signaling components simultaneously—a necessary feature for sufficient pathway activation.

2.3. Phenotype Switching and Targeted Therapy Resistance

Cellular plasticity enables tumor cells to respond to stress and has been implicated as a mechanism of therapy resistance. Melanoma cells, regardless of their genetic subtype, are not truly epithelial or mesenchymal and rapidly switch between different phenotypes through EMT/MET-like mechanisms [162]. Accordingly, bulk and single-cell RNA sequencing of melanoma biopsies differentiated cells into two phenotypic states corresponding to two mutually exclusive gene expression signatures driven by the transcription factors MITF and AP-1/TEAD [18,163,164]. Further sequencing in additional melanoma cell lines and patient-derived xenograft (PDX) models unveiled a greater complexity beyond binary melanocytic–mesenchymal phenotypes [111,165,166,167]. Currently, at least seven in vivo melanoma phenotypes have been characterized by low to high MITF activity: undifferentiated (mesenchymal-like), neural crest stem cell-like (NCSC), interferon-active (IFN-active), starved melanoma cells (SMCs), intermediate, melanocytic, and hyper-differentiated. Fluctuating MITF activity during tumorigenesis and tumor progression confounds the ability to define its oncogenic role [164]. Additionally, MITF activity does not simply correlate with expression levels but also correlates with cell background, which dictates MITF protein stability [168], subcellular localization [169], protein interactors [170,171], microRNA regulation [172,173,174,175], DNA binding affinity [176], and DNA accessibility [64]. Furthermore, MITF activity depends on tumor microenvironment (TME) conditions, such as nutrient depletion [177,178], immune surveillance and inflammation [179,180,181], heterotypic interactions with normal cells [182], hypoxia [183,184], extracellular matrix (ECM) composition [185], and drug treatment [111,180,186,187]. Hence, an integrative adaptive response tuning MITF activity alters a cell’s probability of adopting a particular phenotypic state [167,188]. Genetic mutations drive tumorigenesis, and selected cancerous clones can overcome evolved host barriers against neoplastic growth. While genetic changes are irreversible, stochastic intra- and extra-cellular adaptive responses facilitate epigenetic plasticity, enabling cells to adopt reversible phenotypic states with temporospatial advantages (Figure 3).
Due to the expression of a mélange of melanocytic, mesenchymal, and neural-crest-like genes, starved and intermediate cells are considered poised state founders [111,167]. Starved cells present as metabolically hungry and arise following stressful conditions, such as hypoxia, nutrient depletion, and drug treatment. Undifferentiated, NCSC, and melanocytic states represent dormant cell reservoirs tolerant of cellular stress until the emergence of favorable microenvironmental conditions. Alternatively, cells can switch to highly proliferative intermediate or hyper-differentiated states which fuel tumor growth and replenish lesions with new clones.
KDM5B, the histone demethylase which modulates melanoma response to immunotherapy via SETDB1 recruitment and ERV repression, has also been shown to regulate BRAF and MEK inhibitor resistance by controlling a shift between CD34+ and CD34 subpopulations that vary widely in their treatment sensitivities [189]. BRAFi induces the enrichment of drug-resistant CD34 melanoma cells, upregulation of KDM5B, and global reduction in H3K4me3. KDM5B’s enzymatic JmjC domain and enzyme-independent domains involved in chromatin binding both regulate the shift between drug-resistant and sensitive populations. Genetic and pharmacological inhibition of KDM5B diminishes the degree of phenotype switching from CD34+ to CD34 during BRAFi treatment. KDM5B regulates transitions between melanoma-propagating cells with varying drug sensitivities that could potentially be exploited therapeutically. Additionally, the utility of CD34 expression as a prognostic biomarker for BRAFi sensitivity should be explored.
In addition to regulating immune checkpoint blockade resistance, SETDB1 may be a therapeutic target for patients resistant to combinatorial BRAF and MEK inhibition [190]. SETDB1 promotes H3K4 mono-methylation upstream of the thrombospondin 1 (THBS1) promoter, altering chromatin to an open and active state. THBS1 promotes melanoma aggressiveness and may facilitate therapeutic resistance through regulation of EMT phenotype switching [191,192,193]. Targeting of SETDB1 with a small-molecule inhibitor synergized with MAPK inhibition in sensitive cell lines and induced cell death in MAPKi-resistant melanoma cell lines.
Table 2. Epigenetic regulators of targeted therapy.
Table 2. Epigenetic regulators of targeted therapy.
GeneFunctionMechanism
SETDB1 [190]Histone methyltransferaseSETDB1 inhibition is cytotoxic to BRAFi-resistant melanoma cells, and inhibition synergizes with BRAFi and MEKi.
SIRT6 [153]Histone deacetylaseSIRT6 haploinsufficiency results in H3K56 acetylation at the IGFBP2 locus, activating PI3K/AKT/mTOR, and facilitating melanoma resistance to MAPKi. IGF-1R inhibition restores sensitivity to MAPKi. Complete SIRT6 loss activates DNA damage response due to global chromosomal instability and increases MAPKi sensitivity.
BMI1 [149]Polycomb ring finger oncogeneBMI1 expression shifts melanoma to a metastatic state by inducing an invasive gene signature through WNT5A, ROR2, EGFR, and PDGFR, without a decrease in proliferation. WNT signaling maintains MITF expression, preventing proliferation defects typical of the invasive state, while concurrent EGFR and PDGFR upregulation maintains BRAFi resistance. BMI1 inhibition restored BRAFi sensitivity through WNT5A.
KDM5B [189]Histone demethylaseKDM5B upregulation following BRAFi treatment decreases H3K4me3 levels and triggers the conversion of melanoma cells from drug-sensitive CD34+ to drug-resistant CD34- cell states.

2.4. Non-Coding RNAs Involved in MAPKi Therapy Resistance

Non-coding RNAs (ncRNAs) are involved in several molecular pathways underpinning physiological and pathological conditions, including cancer [194]. ncRNAs can arise from active enhancers, promoters, intragenic and intergenic regions, or alternative splicing of canonical transcripts [195]. They are classified into two major groups according to their size and structure: (i) small non-coding RNAs, which are smaller than 200 bp and include microRNAs (miRNAs); and (ii) long non-coding RNAs (lncRNAs) which account for 15,000–90,000 annotated transcripts [196,197], are more than 200 nucleotides in length, and include the subclass of circular RNAs (circRNAs). The expression of non-coding RNAs (ncRNAs) is often altered in cancer by single-nucleotide polymorphisms (SNPs) and copy-number variations (CNVs). ncRNAs can interact with chromatin, proteins, and other RNAs involved in tumorigenesis [198].
Ninety percent of melanoma patients express lncRNA SAMMSON, which is undetectable in normal melanocytes [80]. SAMMSON induction is due to nearby MITF locus amplification and upregulation of the transcription factors SOX10 and SOX9, which directly bind the SAMMSON promoter. SAMMSON fosters melanoma proliferation and survival by bursting mitochondrial activity and biogenesis through the p32 pathway [81]. Similarly, melanoma cells can overexpress the lncRNA LENOX (LINC00518) following LENOX genomic amplification or increased activity of SOX10 and TFAP2A [82]. LENOX works in concert with SAMMSON to promote mitochondrial oxidative phosphorylation adaptation during tumor progression and upon MAPKi treatment and resistance.
On the other hand, lncRNA TINCR is a negative translation regulator of the integrated stress response (ISR) transcription factor ATF4 through the direct binding of mRNAs driving the mesenchymal-like melanoma phenotype [199]. Even though TINCR regulation is not well-characterized, it is one of the few examples of ncRNAs indirectly regulating melanoma transcriptional states. In addition to lncRNAs, multiple miRNAs (e.g., miR-7 [200], miR-125a [103], miR-204-5p [201], and miR-211-5p [202]) have been found to be involved in resistance to MAPKi.

3. Concluding Remarks

Remarkable advances within the last decade have shattered the enduring untreatable paradigm of metastatic melanoma. Pharmacological inhibition of the MAPK pathway members BRAF and MEK or monoclonal antibodies against immune checkpoint markers CTLA-4 and PD-1 have provided metastatic melanoma patients with previously unattainable opportunities for meaningful and durable responses. Unfortunately, intrinsic or acquired resistance takes this benefit away for half of patients. An evolving understanding of cellular biology has driven scientific exploration beyond the limits of the genome and has reinforced the role of epigenetic mechanisms in regulating homeostatic and oncogenic cell states. Efforts to enhance patient responses have also shifted toward a deeper understanding of the epigenomic landscapes that facilitate treatment-sensitive cell states, the mechanisms to unlock them, and their downstream effectors.
We chose to focus a major portion of this review on the emerging role of viral mimicry in regulating melanoma response to immunotherapy, the chromatin writers and erasers co-opted by melanoma for its repression, and the interactions with the immune system elicited by its induction. The convergence of multiple mechanisms suggests the possibility that this phenomenon may be a major avenue of melanoma progression through immune evasion and, conversely, an exploitable susceptibility for treatment. However, our understanding of viral mimicry, which we define as an instance of beneficial cellular hypochondria, requires a deeper mechanistic dive into its components, such as dsRNA accumulation, interferon signaling, or the generation and presentation of neoantigens containing certain viral features. Most of the mechanisms enabling viral mimicry examined in this review focus on post-translational modifications of histone topography. Post-translational modifiers are well-described in the literature, but their nuclear localization has hindered their effective targeting. In addition, their ubiquitous expression in healthy and cancerous cells limits their usefulness as oncolytic targets. Since most of the chromatin regulators described here (LSD1, PRMT7, SETDB1, KDM5B, and ATF7IP) converge functionally, it will be crucial to identify additional cancer-specific factors the targeting of which may be less toxic. Proper identification of such targets mandates the analysis of clinical samples—a major limitation of the studies reviewed here, which focused on in vitro cell cultures or in vivo mouse models. The limited quantities and sizes of melanoma patient tissues available for experimentation demand collaboration amongst researchers to interrogate panels of markers, rather than assessing genes of interest on an individual basis.
The reviewed publications reiterate the notion that, without simultaneous inhibition of multiple pathways, overcoming targeted therapy resistance becomes a perpetual whack-a-mole game against emerging survival pathways. Additionally, these studies uphold that targeting the epigenome enables simultaneous activation or repression of multiple pathways. Of particular interest are the lessons learned from SIRT6, demonstrating that complete loss can be beneficial for therapy sensitization due to DNA damage responses following global chromosomal disarray, provided that targeting is sufficiently discriminatory between healthy and malignant cells to leave a therapeutic window. Additionally, BMI1 epigenetic regulation demonstrates that melanoma cell states are not as binary (invasive or proliferative) as typically described; paradoxical epigenomic states exist which enable MITF alterations without proliferation changes or shifts in therapeutic BRAFi sensitivity. Melanocytic, intermediate, mesenchymal-like, and mitotic states are detectable in drug-naïve tumors [203], while NCSC, SMC, pigmented, and IFN-active states arise upon drug treatment [111,167,187]. The emergence of an IFN-active state in PDX models elicits particular interest due to the absence of functional immune cells. Moreover, this state arises upon MAPKi treatment, reinforcing the molecular link between inflammation and therapy resistance. Accordingly, IFN-active melanoma cells overexpress the multi-drug resistance pump ABCG2 and interferon-related genes (e.g., PD-L1 and HLA-A, -B, and -C), irrespective of the presence of cytokines in vitro [187], conferring a potential cross-resistance to in vivo MAPKi and immunotherapy. Further exploration of a possible connection between epigenetic ERV de-repression and phenotype switching to the IFN-active state should be investigated. Finally, although mechanistically unclear, SETDB1 and KDM5B demonstrate pleiotropic effects in melanoma targeted therapy and immunotherapy which demand deeper mechanistic exploration.

4. Future Perspectives

Tumorigenesis and tumor progression rely in part on dysfunctional epigenetic regulation following genetic mechanisms, providing attractive therapeutic opportunities. Historically, natural compounds have been bountiful medicinal sources. As such, triptolide, a transcription factor II H inhibitor extracted from the Tripteryugium wilfordii plant [204], and lurbinectedin, an RNA Pol II degrader obtained from the sea squirt Ecteinascidia turbinate [205], are efficacious transcriptional inhibitors that impair cancer survival. Minnelide, a water-soluble triptolide prodrug, inhibits MYC and is undergoing Phase II clinical testing in advanced refractory adenosquamous pancreatic carcinoma [206]. However, these approaches frequently lack specificity and exert significant toxicity against healthy cells [207]. To enhance cancer specificity, de novo drug synthesis and medicinal chemistry have focused on disrupting cancer-specific genes or the unique protein–protein interfaces of epigenetic regulators.
Extensive compound profiling combined with titration of molecular 3D structures using nuclear magnetic resonance (NMR) or cryogenic electron microscopy (cryo-EM) are guiding the development of successful synthetic drugs. PROteolysis TArgeting Chimeras (PROTACs) are bi-modular molecules containing a ubiquitin ligase E3 and a target-protein-specific domain for proteosome-dependent ubiquitination and degradation [208]. AU-15330 is the first effective PROTAC against BRG1, BRM, and PBRM1 of the SWI/SNF complex, which account for ~20% of mutations in human cancers, including cutaneous melanoma [209]. Due to cancer cells’ addiction to dysregulated SWI/SNF, AU-15330 has enhanced cytotoxicity against malignant cells. Additionally, direct inhibition of MYC, one of the most amplified and de-regulated oncogenic transcription factors, has been a great ambition and an even greater challenge [210]. MYCi361, a PROTAC that disrupts the MYC-MAX heterodimer and promotes MYC degradation, has proven to be synergistic with anti-PD1 immunotherapy in a prostate cancer mouse model, highlighting the exciting possibilities of PROTAC technology [211].
Alternatively, epigenetic regulators can be disrupted at the RNA level with antisense oligonucleotides (ASOs). DNA or RNA sequences of 15 to 25 base pairs, ASOs hybridize with complementary RNA to block activity or trigger RNAse-H-dependent degradation. Bearing locked nucleic acids (LNAs) at both ends, ASOs’ high stability facilitate their administration in the absence of nanoparticles or liposomal vehicles and enable targeting of coding and non-coding RNAs in any subcellular compartment. ASOs are being tested in clinical trials of orphan genetic diseases and have proven to be safe and sufficient to reduce the symptoms of Duchenne muscular dystrophy (DMD) and neuronal ceroid lipofuscinosis (CLN7) patients [212]. Systemic administration of ASOs results in liver and kidney accumulation, reducing uptake by other tissues [213,214,215], but is preventable by conjugation with other molecules or local injection to improve tissue-specific delivery [216,217]. ASOs are finding their way in cancer therapy, as witnessed by their rapid development and clinical testing over the last few years.
The targeting of epigenetic regulators has the potential to be used in a wide spectrum of cancers, irrespective of lineage identity and governing oncogenic networks. Alone or in combination with conventional treatments, PROTACs and ASOs may represent the future for personalized medicine in patients bearing oncogenic drivers traditionally perceived as undruggable.

Author Contributions

Conceptualization, A.R., P.B. and E.H.; investigation, A.R. and P.B.; writing—original draft preparation, A.R. and P.B.; writing—review and editing, E.H. All authors have read and agreed to the published version of the manuscript.

Funding

A.R. is supported by the NIGMS/NIH Training Program in Cell Biology (T32GM136542). P.B. is supported by a National Cancer Center Fellowship. E.H.’s laboratory is funded by NCI/NIH R01CA274100, R01CA243446, and NYULH SPORE P50CA225450 and grants from the Department of Defense, the American Cancer Society, and the Melanoma Research Alliance.

Acknowledgments

Figures were created with Biorender.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. National Cancer Institute. Melanoma of the Skin-Cancer Stat Facts. Available online: https://seer.cancer.gov/statfacts/html/melan.html (accessed on 7 July 2022).
  2. Arnold, M.; De Vries, E.; Whiteman, D.C.; Jemal, A.; Bray, F.; Parkin, D.M.; Soerjomataram, I. Global burden of cutaneous melanoma attributable to ultraviolet radiation in 2012. Int. J. Cancer 2018, 143, 1305–1314. [Google Scholar] [CrossRef] [PubMed]
  3. Köhler, C.; Nittner, D.; Rambow, F.; Radaelli, E.; Stanchi, F.; Vandamme, N.; Baggiolini, A.; Sommer, L.; Berx, G.; Oord, J.J.V.D.; et al. Mouse Cutaneous Melanoma Induced by Mutant BRaf Arises from Expansion and Dedifferentiation of Mature Pigmented Melanocytes. Cell Stem Cell 2017, 21, 679–693.e6. [Google Scholar] [CrossRef] [PubMed]
  4. Moon, H.; Donahue, L.R.; Choi, E.; Scumpia, P.O.; Lowry, W.E.; Grenier, J.K.; Zhu, J.; White, A.C. Melanocyte Stem Cell Activation and Translocation Initiate Cutaneous Melanoma in Response to UV Exposure. Cell Stem Cell 2017, 21, 665–678.e6. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  5. Sun, Q.; Lee, W.; Mohri, Y.; Takeo, M.; Lim, C.H.; Xu, X.; Myung, P.; Atit, R.P.; Taketo, M.M.; Moubarak, R.S.; et al. A novel mouse model demonstrates that oncogenic melanocyte stem cells engender melanoma resembling human disease. Nat. Commun. 2019, 10, 5023. [Google Scholar] [CrossRef] [Green Version]
  6. Alexandrov, L.B.; Nik-Zainal, S.; Wedge, D.C.; Aparicio, S.A.J.R.; Behjati, S.; Biankin, A.V.; Bignell, G.R.; Bolli, N.; Borg, A.; Børresen-Dale, A.-L.; et al. Signatures of mutational processes in human cancer. Nature 2013, 500, 415–421. [Google Scholar] [CrossRef] [Green Version]
  7. Chalmers, Z.R.; Connelly, C.F.; Fabrizio, D.; Gay, L.; Ali, S.M.; Ennis, R.; Schrock, A.; Campbell, B.; Shlien, A.; Chmielecki, J.; et al. Analysis of 100,000 human cancer genomes reveals the landscape of tumor mutational burden. Genome Med. 2017, 9, 34. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  8. Zehir, A.; Benayed, R.; Shah, R.H.; Syed, A.; Middha, S.; Kim, H.R.; Srinivasan, P.; Gao, J.; Chakravarty, D.; Devlin, S.M.; et al. Mutational landscape of metastatic cancer revealed from prospective clinical sequencing of 10,000 patients. Nat. Med. 2017, 23, 703–713. [Google Scholar] [CrossRef] [PubMed]
  9. Lo, S.N.; Scolyer, R.A.; Thompson, J.F. Long-Term Survival of Patients with Thin (T1) Cutaneous Melanomas: A Breslow Thickness Cut Point of 0.8 mm Separates Higher-Risk and Lower-Risk Tumors. Ann. Surg. Oncol. 2018, 25, 894–902. [Google Scholar] [CrossRef] [PubMed]
  10. Ascierto, P.A.; Kirkwood, J.M.; Grob, J.-J.; Simeone, E.; Grimaldi, A.M.; Maio, M.; Palmieri, G.; Testori, A.; Marincola, F.M.; Mozzillo, N. The role of BRAF V600 mutation in melanoma. J. Transl. Med. 2012, 10, 85. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  11. Alqathama, A. BRAF in malignant melanoma progression and metastasis: Potentials and challenges. Am. J. Cancer Res. 2020, 10, 1103–1114. [Google Scholar] [PubMed]
  12. Chapman, P.B.; Hauschild, A.; Robert, C.; Haanen, J.B.; Ascierto, P.; Larkin, J.; Dummer, R.; Garbe, C.; Testori, A.; Maio, M.; et al. Improved survival with vemurafenib in melanoma with BRAF V600E mutation. N. Engl. J. Med. 2011, 364, 2507–2516. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  13. Robert, C.; Grob, J.J.; Stroyakovskiy, D.; Karaszewska, B.; Hauschild, A.; Levchenko, E.; Chiarion Sileni, V.; Schachter, J.; Garbe, C.; Bondarenko, I.; et al. Five-Year Outcomes with Dabrafenib plus Trametinib in Metastatic Melanoma. N. Engl. J. Med. 2019, 381, 626–636. [Google Scholar] [CrossRef] [PubMed]
  14. Vuoristo, M.-S.; Hahka-Kemppinen, M.; Parvinen, L.-M.; Pyrhönen, S.; Seppä, H.; Korpela, M.; Kellokumpu-Lehtinen, P. Randomized trial of dacarbazine versus bleomycin, vincristine, lomustine and dacarbazine (BOLD) chemotherapy combined with natural or recombinant interferon-alpha in patients with advanced melanoma. Melanoma Res. 2005, 15, 291–296. [Google Scholar] [CrossRef] [PubMed]
  15. Griffin, M.; Scotto, D.; Josephs, D.H.; Mele, S.; Crescioli, S.; Bax, H.J.; Pellizzari, G.; Wynne, M.D.; Nakamura, M.; Hoffmann, R.M.; et al. BRAF inhibitors: Resistance and the promise of combination treatments for melanoma. Oncotarget 2017, 8, 78174–78192. [Google Scholar] [CrossRef] [Green Version]
  16. Patel, H.; Yacoub, N.; Mishra, R.; White, A.; Yuan, L.; Alanazi, S.; Garrett, J.T. Current Advances in the Treatment of BRAF-Mutant Melanoma. Cancers 2020, 12, 482. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  17. Shi, H.; Hugo, W.; Kong, X.; Hong, A.; Koya, R.C.; Moriceau, G.; Chodon, T.; Guo, R.; Johnson, D.B.; Dahlman, K.B.; et al. Acquired resistance and clonal evolution in melanoma during BRAF inhibitor therapy. Cancer Discov. 2014, 4, 80–93. [Google Scholar] [CrossRef] [Green Version]
  18. Tirosh, I.; Izar, B.; Prakadan, S.M.; Wadsworth, M.H.; Treacy, D.; Trombetta, J.J.; Rotem, A.; Rodman, C.; Lian, C.; Murphy, G.; et al. Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq. Science 2016, 352, 189–196. [Google Scholar] [CrossRef] [Green Version]
  19. Nazarian, R.; Shi, H.; Wang, Q.; Kong, X.; Koya, R.C.; Lee, H.; Chen, Z.; Lee, M.-K.; Attar, N.; Sazegar, H.; et al. Melanomas acquire resistance to B-RAF(V600E) inhibition by RTK or N-RAS upregulation. Nature 2010, 468, 973–977. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  20. Patel, H.; Mishra, R.; Yacoub, N.; Alanazi, S.; Kilroy, M.K.; Garrett, J.T. IGF1R/IR Mediates Resistance to BRAF and MEK Inhibitors in BRAF-Mutant Melanoma. Cancers 2021, 13, 5863. [Google Scholar] [CrossRef]
  21. Taniguchi, H.; Yamada, T.; Wang, R.; Tanimura, K.; Adachi, Y.; Nishiyama, A.; Tanimoto, A.; Takeuchi, S.; Araujo, L.H.; Boroni, M.; et al. AXL confers intrinsic resistance to osimertinib and advances the emergence of tolerant cells. Nat. Commun. 2019, 10, 259. [Google Scholar] [CrossRef] [PubMed]
  22. Maertens, O.; Johnson, B.; Hollstein, P.; Frederick, D.T.; Cooper, Z.A.; Messiaen, L.; Bronson, R.T.; McMahon, M.; Granter, S.; Flaherty, K.; et al. Elucidating distinct roles for NF1 in melanomagenesis. Cancer Discov. 2013, 3, 338–349. [Google Scholar] [CrossRef] [Green Version]
  23. Whittaker, S.R.; Theurillat, J.-P.; Van Allen, E.; Wagle, N.; Hsiao, J.; Cowley, G.S.; Schadendorf, D.; Root, D.E.; Garraway, L.A. A genome-scale RNA interference screen implicates NF1 loss in resistance to RAF inhibition. Cancer Discov. 2013, 3, 350–362. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  24. Wagle, N.; Emery, C.; Berger, M.F.; Davis, M.J.; Sawyer, A.; Pochanard, P.; Kehoe, S.M.; Johannessen, C.M.; MacConaill, L.E.; Hahn, W.C.; et al. Dissecting therapeutic resistance to RAF inhibition in melanoma by tumor genomic profiling. J. Clin. Oncol. 2011, 29, 3085–3096. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  25. Long, G.V.; Fung, C.; Menzies, A.M.; Pupo, G.M.; Carlino, M.S.; Hyman, J.; Shahheydari, H.; Tembe, V.; Thompson, J.F.; Saw, R.P.; et al. Increased MAPK reactivation in early resistance to dabrafenib/trametinib combination therapy of BRAF-mutant metastatic melanoma. Nat. Commun. 2014, 5, 5694. [Google Scholar] [CrossRef] [Green Version]
  26. Johannessen, C.M.; Boehm, J.S.; Kim, S.Y.; Thomas, S.R.; Wardwell, L.; Johnson, L.A.; Emery, C.M.; Stransky, N.; Cogdill, A.P.; Barretina, J.; et al. COT drives resistance to RAF inhibition through MAP kinase pathway reactivation. Nature 2010, 468, 968–972. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  27. Xing, F.; Persaud, Y.; A Pratilas, C.; Taylor, B.S.; Janakiraman, M.; She, Q.-B.; Gallardo, H.; Liu, C.; Merghoub, T.; Hefter, B.; et al. Concurrent loss of the PTEN and RB1 tumor suppressors attenuates RAF dependence in melanomas harboring (V600E)BRAF. Oncogene 2012, 31, 446–457. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  28. Villanueva, J.; Vultur, A.; Lee, J.T.; Somasundaram, R.; Fukunaga-Kalabis, M.; Cipolla, A.K.; Wubbenhorst, B.; Xu, X.; Gimotty, P.A.; Kee, D.; et al. Acquired resistance to BRAF inhibitors mediated by a RAF kinase switch in melanoma can be overcome by cotargeting MEK and IGF-1R/PI3K. Cancer Cell 2010, 18, 683–695. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  29. Corcoran, R.B.; Dias-Santagata, D.; Bergethon, K.; Iafrate, A.J.; Settleman, J.; Engelman, J.A. BRAF gene amplification can promote acquired resistance to MEK inhibitors in cancer cells harboring the BRAF V600E mutation. Sci. Signal. 2010, 3, ra84. [Google Scholar] [CrossRef] [Green Version]
  30. Poulikakos, P.I.; Persaud, Y.; Janakiraman, M.; Kong, X.; Ng, C.; Moriceau, G.; Shi, H.; Atefi, M.; Titz, B.; Gabay, M.T.; et al. RAF inhibitor resistance is mediated by dimerization of aberrantly spliced BRAF(V600E). Nature 2011, 480, 387–390. [Google Scholar] [CrossRef] [Green Version]
  31. Pachella, L.A.; Madsen, L.T.; Dains, J.E. The Toxicity and Benefit of Various Dosing Strategies for Interleukin-2 in Metastatic Melanoma and Renal Cell Carcinoma. J. Adv. Pract. Oncol. 2015, 6, 212–221. [Google Scholar]
  32. Schuchter, L.M. Adjuvant interferon therapy for melanoma: High-dose, low-dose, no dose, which dose? J. Clin. Oncol. 2004, 22, 7–10. [Google Scholar] [CrossRef] [PubMed]
  33. Leach, D.R.; Krummel, M.F.; Allison, J.P. Enhancement of antitumor immunity by CTLA-4 blockade. Science 1996, 271, 1734–1736. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  34. Buchbinder, E.I.; Desai, A. CTLA-4 and PD-1 Pathways: Similarities, Differences, and Implications of Their Inhibition. Am. J. Clin. Oncol. 2016, 39, 98–106. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  35. Hodi, F.S.; O’Day, S.J.; McDermott, D.F.; Weber, R.W.; Sosman, J.A.; Haanen, J.B.; Gonzalez, R.; Robert, C.; Schadendorf, D.; Hassel, J.C.; et al. Improved survival with ipilimumab in patients with metastatic melanoma. N. Engl. J. Med. 2010, 363, 711–723. [Google Scholar] [CrossRef] [PubMed]
  36. Topalian, S.L.; Sznol, M.; McDermott, D.F.; Kluger, H.M.; Carvajal, R.D.; Sharfman, W.H.; Brahmer, J.R.; Lawrence, D.P.; Atkins, M.B.; Powderly, J.D.; et al. Survival, durable tumor remission, and long-term safety in patients with advanced melanoma receiving nivolumab. J. Clin. Oncol. 2014, 32, 1020–1030. [Google Scholar] [CrossRef] [PubMed]
  37. Wolchok, J.D.; Kluger, H.; Callahan, M.K.; Postow, M.A.; Rizvi, N.A.; Lesokhin, A.M.; Segal, N.H.; Ariyan, C.E.; Gordon, R.-A.; Reed, K.; et al. Nivolumab plus ipilimumab in advanced melanoma. N. Engl. J. Med. 2013, 369, 122–133. [Google Scholar] [CrossRef] [Green Version]
  38. Wolchok, J.D.; Chiarion-Sileni, V.; Gonzalez, R.; Grob, J.-J.; Rutkowski, P.; Lao, C.D.; Cowey, C.L.; Schadendorf, D.; Wagstaff, J.; Dummer, R.; et al. Long-Term Outcomes With Nivolumab Plus Ipilimumab or Nivolumab Alone Versus Ipilimumab in Patients With Advanced Melanoma. J. Clin. Oncol. 2022, 40, 127–137. [Google Scholar] [CrossRef]
  39. Anderson, A.C.; Joller, N.; Kuchroo, V.K. Lag-3, Tim-3, and TIGIT: Co-inhibitory Receptors with Specialized Functions in Immune Regulation. Immunity 2016, 44, 989–1004. [Google Scholar] [CrossRef] [Green Version]
  40. Keilholz, U.; Mehnert, J.M.; Bauer, S.; Bourgeois, H.; Patel, M.R.; Gravenor, D.; Nemunaitis, J.J.; Taylor, M.H.; Wyrwicz, L.; Lee, K.-W.; et al. Avelumab in patients with previously treated metastatic melanoma: Phase 1b results from the JAVELIN Solid Tumor trial. J. Immunother. Cancer 2019, 7, 12. [Google Scholar] [CrossRef] [Green Version]
  41. Tawbi, H.A.; Schadendorf, D.; Lipson, E.J.; Ascierto, P.A.; Matamala, L.; Gutiérrez, E.C.; Rutkowski, P.; Gogas, H.J.; Lao, C.D.; De Menezes, J.J.; et al. Relatlimab and Nivolumab versus Nivolumab in Untreated Advanced Melanoma. N. Engl. J. Med. 2022, 386, 24–34. [Google Scholar] [CrossRef]
  42. Kalbasi, A.; Ribas, A. Tumour-intrinsic resistance to immune checkpoint blockade. Nat. Rev. Immunol. 2020, 20, 25–39. [Google Scholar] [CrossRef] [PubMed]
  43. van Rooij, N.; Van Buuren, M.M.; Philips, D.; Velds, A.; Toebes, M.; Heemskerk, B.; Van Dijk, L.J.; Behjati, S.; Hilkmann, H.; El Atmioui, D.; et al. Tumor exome analysis reveals neoantigen-specific T-cell reactivity in an ipilimumab-responsive melanoma. J. Clin. Oncol. 2013, 31, e439–e442. [Google Scholar] [CrossRef]
  44. Tran, E.; Turcotte, S.; Gros, A.; Robbins, P.F.; Lu, Y.-C.; Dudley, M.E.; Wunderlich, J.R.; Somerville, R.P.; Hogan, K.; Hinrichs, C.S.; et al. Cancer immunotherapy based on mutation-specific CD4+ T cells in a patient with epithelial cancer. Science 2014, 344, 641–645. [Google Scholar] [CrossRef] [PubMed]
  45. Sha, D.; Jin, Z.; Budczies, J.; Kluck, K.; Stenzinger, A.; Sinicrope, F.A. Tumor Mutational Burden as a Predictive Biomarker in Solid Tumors. Cancer Discov. 2020, 10, 1808–1825. [Google Scholar] [CrossRef] [PubMed]
  46. Snyder, A.; Makarov, V.; Merghoub, T.; Yuan, J.; Zaretsky, J.M.; Desrichard, A.; Walsh, L.A.; Postow, M.A.; Wong, P.; Ho, T.S.; et al. Genetic basis for clinical response to CTLA-4 blockade in melanoma. N. Engl. J. Med. 2014, 371, 2189–2199. [Google Scholar] [CrossRef] [Green Version]
  47. Yarchoan, M.; Hopkins, A.; Jaffee, E.M. Tumor Mutational Burden and Response Rate to PD-1 Inhibition. N. Engl. J. Med. 2017, 377, 2500–2501. [Google Scholar] [CrossRef]
  48. Wang, P.; Chen, Y.; Wang, C. Beyond Tumor Mutation Burden: Tumor Neoantigen Burden as a Biomarker for Immunotherapy and Other Types of Therapy. Front. Oncol. 2021, 11, 672677. [Google Scholar] [CrossRef]
  49. Garcia-Diaz, A.; Shin, D.S.; Moreno, B.H.; Saco, J.; Escuin-Ordinas, H.; Rodriguez, G.A.; Zaretsky, J.M.; Sun, L.; Hugo, W.; Wang, X.; et al. Interferon Receptor Signaling Pathways Regulating PD-L1 and PD-L2 Expression. Cell Rep. 2017, 19, 1189–1201. [Google Scholar] [CrossRef] [Green Version]
  50. Cui, C.; Xu, C.; Yang, W.; Chi, Z.; Sheng, X.; Si, L.; Xie, Y.; Yu, J.; Wang, S.; Yu, R.; et al. Ratio of the interferon-γ signature to the immunosuppression signature predicts anti-PD-1 therapy response in melanoma. NPJ Genom. Med. 2021, 6, 7. [Google Scholar] [CrossRef]
  51. Kirkwood, J.M.; Iannotti, N.; Cho, D.; O’Day, S.; Gibney, G.; Hodi, F.S.; Munster, P.; Hoyle, P.; Owens, S.; Smith, M.; et al. Abstract CT176: Effect of JAK/STAT or PI3Kδ plus PD-1 inhibition on the tumor microenvironment: Biomarker results from a phase Ib study in patients with advanced solid tumors. Cancer Res. 2018, 78, CT176. [Google Scholar] [CrossRef]
  52. Propper, D.J.; Chao, D.; Braybrooke, J.P.; Bahl, P.; Thavasu, P.; Balkwill, F.; Turley, H.; Dobbs, N.; Gatter, K.; Talbot, D.C.; et al. Low-dose IFN-gamma induces tumor MHC expression in metastatic malignant melanoma. Clin. Cancer Res. 2003, 9, 84–92. [Google Scholar] [PubMed]
  53. Liu, D.; Schilling, B.; Liu, D.; Sucker, A.; Livingstone, E.; Jerby-Arnon, L.; Zimmer, L.; Gutzmer, R.; Satzger, I.; Loquai, C.; et al. Author Correction: Integrative molecular and clinical modeling of clinical outcomes to PD1 blockade in patients with metastatic melanoma. Nat. Med. 2020, 26, 1147. [Google Scholar] [CrossRef] [PubMed]
  54. Zhou, B.; Gao, Y.; Zhang, P.; Chu, Q. Acquired Resistance to Immune Checkpoint Blockades: The Underlying Mechanisms and Potential Strategies. Front. Immunol. 2021, 12, 693609. [Google Scholar] [CrossRef]
  55. Kim, Y.J.; Sheu, K.M.; Tsoi, J.; Abril-Rodriguez, G.; Medina, E.; Grasso, C.S.; Torrejon, D.Y.; Champhekar, A.S.; Litchfield, K.; Swanton, C.; et al. Melanoma dedifferentiation induced by IFN-gamma epigenetic remodeling in response to anti-PD-1 therapy. J. Clin. Invest. 2021, 131, e145859. [Google Scholar] [CrossRef] [PubMed]
  56. Shields, B.D.; Koss, B.; Taylor, E.M.; Storey, A.J.; West, K.L.; Byrum, S.D.; Mackintosh, S.G.; Edmondson, R.; Mahmoud, F.; Shalin, S.C.; et al. Loss of E-Cadherin Inhibits CD103 Antitumor Activity and Reduces Checkpoint Blockade Responsiveness in Melanoma. Cancer Res. 2019, 79, 1113–1123. [Google Scholar] [CrossRef] [Green Version]
  57. Trujillo, J.A.; Luke, J.J.; Zha, Y.; Segal, J.P.; Ritterhouse, L.L.; Spranger, S.; Matijevich, K.; Gajewski, T.F. Secondary resistance to immunotherapy associated with beta-catenin pathway activation or PTEN loss in metastatic melanoma. J. Immunother. Cancer 2019, 7, 295. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  58. Grasso, C.S.; Tsoi, J.; Onyshchenko, M.; Abril-Rodriguez, G.; Ross-Macdonald, P.; Wind-Rotolo, M.; Champhekar, A.; Medina, E.; Torrejon, D.Y.; Shin, D.S.; et al. Conserved Interferon-gamma Signaling Drives Clinical Response to Immune Checkpoint Blockade Therapy in Melanoma. Cancer Cell 2020, 38, 500–515.e3. [Google Scholar] [CrossRef]
  59. Prendergast, L.; McClurg, U.L.; Hristova, R.; Berlinguer-Palmini, R.; Greener, S.; Veitch, K.; Hernandez, I.; Pasero, P.; Rico, D.; Higgins, J.M.G.; et al. Resolution of R-loops by INO80 promotes DNA replication and maintains cancer cell proliferation and viability. Nat. Commun. 2020, 11, 4534. [Google Scholar] [CrossRef]
  60. Zhou, B.; Wang, L.; Zhang, S.; Bennett, B.D.; 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, 1440–1453. [Google Scholar] [CrossRef] [Green Version]
  61. Dar, A.A.; Majid, S.; Bezrookove, V.; Phan, B.; Ursu, S.; Nosrati, M.; De Semir, D.; Sagebiel, R.W.; Miller, J.R.; Debs, R.; et al. BPTF transduces MITF-driven prosurvival signals in melanoma cells. Proc. Natl. Acad. Sci. USA 2016, 113, 6254–6258. [Google Scholar] [CrossRef] [Green Version]
  62. Eckey, M.; Kuphal, S.; Straub, T.; Rümmele, P.; Kremmer, E.; Bosserhoff, A.; Becker, P.B. Nucleosome remodeler SNF2L suppresses cell proliferation and migration and attenuates Wnt signaling. Mol. Cell. Biol. 2012, 32, 2359–2371. [Google Scholar] [CrossRef] [PubMed]
  63. Dreier, M.R.; de la Serna, I.L. SWI/SNF Chromatin Remodeling Enzymes in Melanoma. Epigenomes 2022, 6, 10. [Google Scholar] [CrossRef]
  64. Laurette, P.; Strub, T.; Koludrovic, D.; Keime, C.; Le Gras, S.; Seberg, H.; Van Otterloo, E.; Imrichova, H.; Siddaway, R.; Aerts, S.; et al. Transcription factor MITF and remodeller BRG1 define chromatin organisation at regulatory elements in melanoma mcells. Elife 2015, 4, e06857. [Google Scholar] [CrossRef] [PubMed]
  65. Carcamo, S.; Nguyen, C.B.; Grossi, E.; Filipescu, D.; Alpsoy, A.; Dhiman, A.; Sun, D.; Narang, S.; Imig, J.; Martin, T.C.; et al. Altered BAF occupancy and transcription factor dynamics in PBAF-deficient melanoma. Cell Rep. 2022, 39, 110637. [Google Scholar] [CrossRef] [PubMed]
  66. Kim, E.; Zucconi, B.E.; Wu, M.; Nocco, S.E.; Meyers, D.J.; McGee, J.S.; Venkatesh, S.; Cohen, D.L.; Gonzalez, E.C.; Ryu, B.; et al. MITF Expression Predicts Therapeutic Vulnerability to p300 Inhibition in Human Melanoma. Cancer Res. 2019, 79, 2649–2661. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  67. Woan, K.V.; Lienlaf, M.; Perez-Villaroel, P.; Lee, C.; Cheng, F.; Knox, T.; Woods, D.M.; Barrios, K.; Powers, J.; Sahakian, E.; et al. Targeting histone deacetylase 6 mediates a dual anti-melanoma effect: Enhanced antitumor immunity and impaired cell proliferation. Mol. Oncol. 2015, 9, 1447–1457. [Google Scholar] [CrossRef] [Green Version]
  68. Ceol, C.J.; Houvras, Y.; Jane-Valbuena, J.; Bilodeau, S.; Orlando, D.A.; Battisti, V.; Fritsch, L.; Lin, W.M.; Hollmann, T.J.; Ferré, F.; et al. The histone methyltransferase SETDB1 is recurrently amplified in melanoma and accelerates its onset. Nature 2011, 471, 513–517. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  69. Moubarak, R.S.; de Pablos-Aragoneses, A.; Ortiz-Barahona, V.; Gong, Y.; Gowen, M.; Dolgalev, I.; Shadaloey, S.A.A.; Argibay, D.; Karz, A.; Von Itter, R.; et al. The histone demethylase PHF8 regulates TGFβ signaling and promotes melanoma metastasis. Sci. Adv. 2022, 8, eabi7127. [Google Scholar] [CrossRef] [PubMed]
  70. Kapoor, A.; Goldberg, M.S.; Cumberland, L.K.; Ratnakumar, K.; Segura, M.F.; Emanuel, P.O.; Menendez, S.; Vardabasso, C.; LeRoy, G.; Vidal, C.I.; et al. The histone variant macroH2A suppresses melanoma progression through regulation of CDK8. Nature 2010, 468, 1105–1109. [Google Scholar] [CrossRef] [Green Version]
  71. Vardabasso, C.; Gaspar-Maia, A.; Hasson, D.; Pünzeler, S.; Valle-Garcia, D.; Straub, T.; Keilhauer, E.C.; Strub, T.; Dong, J.; Panda, T.; et al. Histone Variant H2A.Z.2 Mediates Proliferation and Drug Sensitivity of Malignant Melanoma. Mol. Cell 2015, 59, 75–88. [Google Scholar] [CrossRef] [Green Version]
  72. Duarte, L.F.; Young, A.; Wang, Z.; Wu, H.-A.; Panda, T.; Kou, Y.; Kapoor, A.; Hasson, D.; Mills, N.R.; Ma’Ayan, A.; et al. Histone H3.3 and its proteolytically processed form drive a cellular senescence programme. Nat. Commun. 2014, 5, 5210. [Google Scholar] [CrossRef] [PubMed]
  73. Micevic, G.; Theodosakis, N.; Bosenberg, M. Aberrant DNA methylation in melanoma: Biomarker and therapeutic opportunities. Clin. Epigenet. 2017, 9, 34. [Google Scholar] [CrossRef] [Green Version]
  74. Koroknai, V.; Szász, I.; Hernandez-Vargas, H.; Fernandez-Jimenez, N.; Cuenin, C.; Herceg, Z.; Vízkeleti, L.; Ádány, R.; Ecsedi, S.; Balázs, M. DNA hypermethylation is associated with invasive phenotype of malignant melanoma. Exp. Dermatol. 2020, 29, 39–50. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  75. Bonvin, E.; Radaelli, E.; Bizet, M.; Luciani, F.; Calonne, E.; Putmans, P.; Nittner, D.; Singh, N.K.; Santagostino, S.F.; Petit, V.; et al. TET2-Dependent Hydroxymethylome Plasticity Reduces Melanoma Initiation and Progression. Cancer Res. 2019, 79, 482–494. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  76. Liu, J.; Zhou, Z.; Ma, L.; Li, C.; Lin, Y.; Yu, T.; Wei, J.-F.; Zhu, L.; Yao, G. Effects of RNA methylation N6-methyladenosine regulators on malignant progression and prognosis of melanoma. Cancer Cell Int. 2021, 21, 453. [Google Scholar] [CrossRef] [PubMed]
  77. Feng, Z.-Y.; Wang, T.; Su, X.; Guo, S. Identification of the m6A RNA Methylation Regulators WTAP as a Novel Prognostic Biomarker and Genomic Alterations in Cutaneous Melanoma. Front. Mol. Biosci. 2021, 8, 665222. [Google Scholar] [CrossRef]
  78. Meng, J.; Huang, X.; Qiu, Y.; Yu, M.; Lu, J.; Yao, J. Characterization of m6A-Related Genes Landscape in Skin Cutaneous Melanoma to Aid Immunotherapy and Assess Prognosis. Int. J. Gen. Med. 2021, 14, 5345–5361. [Google Scholar] [CrossRef]
  79. Yu, X.; Zheng, H.; Tse, G.; Chan, M.T.; Wu, W.K. Long non-coding RNAs in melanoma. Cell Prolif. 2018, 51, e12457. [Google Scholar] [CrossRef] [Green Version]
  80. Leucci, E.; Vendramin, R.; Spinazzi, M.; Laurette, P.; Fiers, M.; Wouters, J.; Radaelli, E.; Eyckerman, S.; Leonelli, C.; Vanderheyden, K.; et al. Melanoma addiction to the long non-coding RNA SAMMSON. Nature 2016, 531, 518–522. [Google Scholar] [CrossRef]
  81. Vendramin, R.; Verheyden, Y.; Ishikawa, H.; Goedert, L.; Nicolas, E.; Saraf, K.; Armaos, A.; Ponti, R.D.; Izumikawa, K.; Mestdagh, P.; et al. SAMMSON fosters cancer cell fitness by concertedly enhancing mitochondrial and cytosolic translation. Nat. Struct. Mol. Biol. 2018, 25, 1035–1046. [Google Scholar] [CrossRef]
  82. Gambi, G.; Mengus, G.; Davidson, G.; Demesmaeker, E.; Cuomo, A.; Bonaldi, T.; Katopodi, V.; Malouf, G.G.; Leucci, E.; Davidson, I. The lncRNA LENOX interacts with RAP2C to regulate metabolism and promote resistance to MAPK inhibition in melanoma. Cancer Res. 2022. [Google Scholar] [CrossRef] [PubMed]
  83. Varrone, F.; Caputo, E. The miRNAs Role in Melanoma and in Its Resistance to Therapy. Int. J. Mol. Sci. 2020, 21, 878. [Google Scholar] [CrossRef] [Green Version]
  84. Couts, K.L.; Anderson, E.M.; Gross, M.M.; Sullivan, K.; Ahn, N.G. Oncogenic B-Raf signaling in melanoma cells controls a network of microRNAs with combinatorial functions. Oncogene 2013, 32, 1959–1970. [Google Scholar] [CrossRef] [PubMed]
  85. Choe, M.H.; Yoon, Y.; Kim, J.; Hwang, S.-G.; Han, Y.-H.; Kim, J.-S. miR-550a-3-5p acts as a tumor suppressor and reverses BRAF inhibitor resistance through the direct targeting of YAP. Cell Death Dis. 2018, 9, 640. [Google Scholar] [CrossRef]
  86. Hallajzadeh, J.; Amirani, E.; Mirzaei, H.; Shafabakhsh, R.; Mirhashemi, S.M.; Sharifi, M.; Yousefi, B.; A Mansournia, M.; Asemi, Z. Circular RNAs: New genetic tools in melanoma. Biomark. Med. 2020, 14, 563–571. [Google Scholar] [CrossRef] [PubMed]
  87. Qian, P.; Linbo, L.; Xiaomei, Z.; Hui, P. Circ_0002770, acting as a competitive endogenous RNA, promotes proliferation and invasion by targeting miR-331-3p in melanoma. Cell Death Dis. 2020, 11, 264. [Google Scholar] [CrossRef] [Green Version]
  88. Liu, Q.; Cui, W.; Yang, C.; Du, L.P. Circular RNA ZNF609 drives tumor progression by regulating the miR-138-5p/SIRT7 axis in melanoma. Aging 2021, 13, 19822–19834. [Google Scholar] [CrossRef]
  89. Fukumoto, T.; Lin, J.; Fatkhutdinov, N.; Liu, P.; Somasundaram, R.; Herlyn, M.; Zhang, R.; Nishigori, C. ARID2 Deficiency Correlates with the Response to Immune Checkpoint Blockade in Melanoma. J. Investig. Dermatol. 2021, 141, 1564–1572.e4. [Google Scholar] [CrossRef]
  90. Filipski, K.; Scherer, M.; Zeiner, K.N.; Bucher, A.; Kleemann, J.; Jurmeister, P.; Hartung, T.I.; Meissner, M.; Plate, K.H.; Fenton, T.R.; et al. DNA methylation-based prediction of response to immune checkpoint inhibition in metastatic melanoma. J. Immunother. Cancer 2021, 9, e002226. [Google Scholar] [CrossRef]
  91. Yang, S.; Wei, J.; Cui, Y.-H.; Park, G.; Shah, P.; Deng, Y.; Aplin, A.E.; Lu, Z.; Hwang, S.; He, C.; et al. m6A mRNA demethylase FTO regulates melanoma tumorigenicity and response to anti-PD-1 blockade. Nat. Commun. 2019, 10, 2782. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  92. Du, F.; Li, H.; Li, Y.; Liu, Y.; Li, X.; Dang, N.; Chu, Q.; Yan, J.; Fang, Z.; Wu, H.; et al. Identification of m6A Regulator-Associated Methylation Modification Clusters and Immune Profiles in Melanoma. Front. Cell Dev. Biol. 2021, 9, 761134. [Google Scholar] [CrossRef] [PubMed]
  93. Li, G.; Kryczek, I.; Nam, J.; Li, X.; Li, S.; Li, J.; Wei, S.; Grove, S.; Vatan, L.; Zhou, J.; et al. LIMIT is an immunogenic lncRNA in cancer immunity and immunotherapy. Nat. Cell Biol. 2021, 23, 526–537. [Google Scholar] [CrossRef]
  94. Wei, C.-Y.; Zhu, M.-X.; Lu, N.-H.; Liu, J.-Q.; Yang, Y.-W.; Zhang, Y.; Shi, Y.-D.; Feng, Z.-H.; Li, J.-X.; Qi, F.-Z.; et al. Circular RNA circ_0020710 drives tumor progression and immune evasion by regulating the miR-370-3p/CXCL12 axis in melanoma. Mol. Cancer 2020, 19, 84. [Google Scholar] [CrossRef]
  95. Mastroianni, J.; Stickel, N.; Andrlova, H.; Hanke, K.; Melchinger, W.; Duquesne, S.; Schmidt, D.; Falk, M.; Andrieux, G.; Pfeifer, D.; et al. miR-146a Controls Immune Response in the Melanoma Microenvironment. Cancer Res. 2019, 79, 183–195. [Google Scholar] [CrossRef] [Green Version]
  96. Huber, V.; Vallacchi, V.; Fleming, V.; Hu, X.; Cova, A.; Dugo, M.; Shahaj, E.; Sulsenti, R.; Vergani, E.; Filipazzi, P.; et al. Tumor-derived microRNAs induce myeloid suppressor cells and predict immunotherapy resistance in melanoma. J. Clin. Investig. 2018, 128, 5505–5516. [Google Scholar] [CrossRef] [Green Version]
  97. Echevarría-Vargas, I.M.; Reyes-Uribe, P.I.; Guterres, A.; Yin, X.; Kossenkov, A.V.; Liu, Q.; Zhang, G.; Krepler, C.; Cheng, C.; Wei, Z.; et al. Co-targeting BET and MEK as salvage therapy for MAPK and checkpoint inhibitor-resistant melanoma. EMBO Mol. Med. 2018, 10, e8446. [Google Scholar] [CrossRef]
  98. Grigore, F.; Yang, H.; Hanson, N.D.; VanBrocklin, M.W.; Sarver, A.L.; Robinson, J.P. BRAF inhibition in melanoma is associated with the dysregulation of histone methylation and histone methyltransferases. Neoplasia 2020, 22, 376–389. [Google Scholar] [CrossRef]
  99. Zakharia, Y.; Monga, V.; Swami, U.; Bossler, A.D.; Freesmeier, M.; Frees, M.; Khan, M.; Frydenlund, N.; Srikantha, R.; Vanneste, M.; et al. Targeting epigenetics for treatment of BRAF mutated metastatic melanoma with decitabine in combination with vemurafenib: A phase lb study. Oncotarget 2017, 8, 89182–89193. [Google Scholar] [CrossRef] [Green Version]
  100. Dar, A.A.; Nosrati, M.; Bezrookove, V.; de Semir, D.; Majid, S.; Thummala, S.; Sun, V.; Tong, S.; Leong, S.P.L.; Minor, D.; et al. The role of BPTF in melanoma progression and in response to BRAF-targeted therapy. J. Natl. Cancer Inst. 2015, 107, djv034. [Google Scholar] [CrossRef] [Green Version]
  101. Vergani, E.; Di Guardo, L.; Dugo, M.; Rigoletto, S.; Tragni, G.; Ruggeri, R.; Perrone, F.; Tamborini, E.; Gloghini, A.; Arienti, F.; et al. Overcoming melanoma resistance to vemurafenib by targeting CCL2-induced miR-34a, miR-100 and miR-125b. Oncotarget 2016, 7, 4428–4441. [Google Scholar] [CrossRef] [Green Version]
  102. Kolenda, T.; Rutkowski, P.; Michalak, M.; Kozak, K.; Guglas, K.; Ryś, M.; Galus, Ł.; Woźniak, S.; Ługowska, I.; Gos, A.; et al. Plasma lncRNA expression profile as a prognostic tool in BRAF-mutant metastatic melanoma patients treated with BRAF inhibitor. Oncotarget 2019, 10, 3879–3893. [Google Scholar] [CrossRef] [PubMed]
  103. Koetz-Ploch, L.; Hanniford, D.; Dolgalev, I.; Sokolova, E.; Zhong, J.; Díaz-Martínez, M.; Bernstein, E.; Darvishian, F.; Flaherty, K.T.; Chapman, P.B.; et al. MicroRNA-125a promotes resistance to BRAF inhibitors through suppression of the intrinsic apoptotic pathway. Pigment Cell Melanoma Res. 2017, 30, 328–338. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  104. Rizos, H.; Menzies, A.M.; Pupo, G.M.; Carlino, M.S.; Fung, C.; Hyman, J.; Haydu, L.E.; Mijatov, B.; Becker, T.M.; Boyd, S.C.; et al. BRAF inhibitor resistance mechanisms in metastatic melanoma: Spectrum and clinical impact. Clin. Cancer Res. 2014, 20, 1965–1977. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  105. Madore, J.; Strbenac, D.; Vilain, R.; Menzies, A.M.; Yang, J.Y.H.; Thompson, J.F.; Long, G.V.; Mann, G.J.; Scolyer, R.A.; Wilmott, J.S. PD-L1 Negative Status is Associated with Lower Mutation Burden, Differential Expression of Immune-Related Genes, and Worse Survival in Stage III Melanoma. Clin. Cancer Res. 2016, 22, 3915–3923. [Google Scholar] [CrossRef] [Green Version]
  106. Al Emran, A.; Chatterjee, A.; Rodger, E.J.; Tiffen, J.C.; Gallagher, S.J.; Eccles, M.R.; Hersey, P. Targeting DNA Methylation and EZH2 Activity to Overcome Melanoma Resistance to Immunotherapy. Trends Immunol 2019, 40, 328–344. [Google Scholar] [CrossRef] [Green Version]
  107. Chiappinelli, K.B.; Strissel, P.L.; Desrichard, A.; Li, H.; Henke, C.; Akman, B.; Hein, A.; Rote, N.S.; Cope, L.M.; Snyder, A.; et al. Inhibiting DNA Methylation Causes an Interferon Response in Cancer via dsRNA Including Endogenous Retroviruses. Cell 2015, 162, 974–986. [Google Scholar] [CrossRef] [Green Version]
  108. Pandey, S.; Djibo, R.; Darracq, A.; Calendo, G.; Zhang, H.; Henry, R.A.; Andrews, A.J.; Baylin, S.B.; Madzo, J.; Najmanovich, R.; et al. Selective CDK9 Inhibition by Natural Compound Toyocamycin in Cancer Cells. Cancers 2022, 14, 3340. [Google Scholar] [CrossRef]
  109. Zhang, H.; Pandey, S.; Travers, M.; Sun, H.; Morton, G.; Madzo, J.; Chung, W.; Khowsathit, J.; Perez-Leal, O.; Barrero, C.A.; et al. Targeting CDK9 Reactivates Epigenetically Silenced Genes in Cancer. Cell 2018, 175, 1244–1258.e26. [Google Scholar] [CrossRef] [Green Version]
  110. Menon, D.R.; Das, S.; Krepler, C.; Vultur, A.; Rinner, B.; Schauer, S.; Kashofer, K.; Wagner, K.; Zhang, G.; Rad, E.B.; et al. A stress-induced early innate response causes multidrug tolerance in melanoma. Oncogene 2015, 34, 4545. [Google Scholar] [CrossRef] [Green Version]
  111. Rambow, F.; Rogiers, A.; Marin-Bejar, O.; Aibar, S.; Femel, J.; Dewaele, M.; Karras, P.; Brown, D.; Chang, Y.H.; Debiec-Rychter, M.; et al. Toward Minimal Residual Disease-Directed Therapy in Melanoma. Cell 2018, 174, 843–855.e19. [Google Scholar] [CrossRef] [Green Version]
  112. Sharma, S.V.; Lee, D.Y.; Li, B.; Quinlan, M.P.; Takahashi, F.; Maheswaran, S.; McDermott, U.; Azizian, N.; Zou, L.; Fischbach, M.A.; et al. A chromatin-mediated reversible drug-tolerant state in cancer cell subpopulations. Cell 2010, 141, 69–80. [Google Scholar] [CrossRef]
  113. Su, Y.; Wei, W.; Robert, L.; Xue, M.; Tsoi, J.; Garcia-Diaz, A.; Moreno, B.H.; Kim, J.; Ng, R.H.; Lee, J.W.; et al. Single-cell analysis resolves the cell state transition and signaling dynamics associated with melanoma drug-induced resistance. Proc. Natl. Acad. Sci. USA 2017, 114, 13679–13684. [Google Scholar] [CrossRef] [Green Version]
  114. Lander, E.S.; Linton, L.M.; Birren, B.; Nusbaum, C.; Zody, M.C.; Baldwin, J.; Devon, K.; Dewar, K.; Doyle, M.; FitzHugh, W.; et al. Initial sequencing and analysis of the human genome. Nature 2001, 409, 860–921. [Google Scholar] [CrossRef] [Green Version]
  115. Hurst, T.P.; Magiorkinis, G. Epigenetic Control of Human Endogenous Retrovirus Expression: Focus on Regulation of Long-Terminal Repeats (LTRs). Viruses 2017, 9, 130. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  116. Katoh, I.; Kurata, S.-I. Association of endogenous retroviruses and long terminal repeats with human disorders. Front. Oncol. 2013, 3, 234. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  117. Tristem, M. Identification and characterization of novel human endogenous retrovirus families by phylogenetic screening of the human genome mapping project database. J. Virol. 2000, 74, 3715–3730. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  118. Löwer, R.; Tönjes, R.R.; Korbmacher, C.; Kurth, R.; Löwer, J. Identification of a Rev-related protein by analysis of spliced transcripts of the human endogenous retroviruses HTDV/HERV-K. J. Virol. 1995, 69, 141–149. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  119. Armbruester, V.; Sauter, M.; Krautkraemer, E.; Meese, E.; Kleiman, A.; Best, B.; Roemer, K.; Mueller-Lantzsch, N. A novel gene from the human endogenous retrovirus K expressed in transforMed. cells. Clin. Cancer Res. 2002, 8, 1800–1807. [Google Scholar]
  120. Contreras-Galindo, R.; Kaplan, M.H.; Dube, D.; Gonzalez-Hernandez, M.J.; Chan, S.; Meng, F.; Dai, M.; Omenn, G.S.; Gitlin, S.D.; Markovitz, D.M. Human Endogenous Retrovirus Type K (HERV-K) Particles Package and Transmit HERV-K-Related Sequences. J. Virol. 2015, 89, 7187–7201. [Google Scholar] [CrossRef] [Green Version]
  121. Singh, S.; Kaye, S.; Gore, M.E.; McClure, M.O.; Bunker, C.B. The role of human endogenous retroviruses in melanoma. Br. J. Dermatol. 2009, 161, 1225–1231. [Google Scholar] [CrossRef]
  122. Singh, M.; Cai, H.; Bunse, M.; Feschotte, C.; Izsvak, Z. Human Endogenous Retrovirus K Rec forms a Regulatory Loop with MITF that Opposes the Progression of Melanoma to an Invasive Stage. Viruses 2020, 12, 1303. [Google Scholar] [CrossRef] [PubMed]
  123. Greenig, M. HERVs, immunity, and autoimmunity: Understanding the connection. PeerJ 2019, 7, e6711. [Google Scholar] [CrossRef] [PubMed]
  124. Chuong, E.B.; Elde, N.C.; Feschotte, C. Regulatory evolution of innate immunity through co-option of endogenous retroviruses. Science 2016, 351, 1083–1087. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  125. Buscher, K.; Trefzer, U.; Hofmann, M.; Sterry, W.; Kurth, R.; Denner, J. Expression of human endogenous retrovirus K in melanomas and melanoma cell lines. Cancer Res. 2005, 65, 4172–4180. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  126. Singh, S.; Kaye, S.; Francis, N.; Peston, D.; Gore, M.; McClure, M.; Bunker, C. Human endogenous retrovirus K (HERV-K) rec mRNA is expressed in primary melanoma but not in benign naevi or normal skin. Pigment Cell Melanoma Res. 2013, 26, 426–428. [Google Scholar] [CrossRef] [PubMed]
  127. Schiavetti, F.; Thonnard, J.; Colau, D.; Boon, T.; Coulie, P.G. A human endogenous retroviral sequence encoding an antigen recognized on melanoma by cytolytic T lymphocytes. Cancer Res. 2002, 62, 5510–5516. [Google Scholar]
  128. Krishnamurthy, J.; Rabinovich, B.A.; Mi, T.; Switzer, K.C.; Olivares, S.; Maiti, S.N.; Plummer, J.B.; Singh, H.; Kumaresan, P.R.; Huls, H.M.; et al. Genetic Engineering of T Cells to Target HERV-K, an Ancient Retrovirus on Melanoma. Clin. Cancer Res. 2015, 21, 3241–3251. [Google Scholar] [CrossRef] [Green Version]
  129. Bannert, N.; Hofmann, H.; Block, A.; Hohn, O. HERVs New Role in Cancer: From Accused Perpetrators to Cheerful Protectors. Front. Microbiol. 2018, 9, 178. [Google Scholar] [CrossRef]
  130. Chen, R.; Ishak, C.A.; De Carvalho, D.D. Endogenous Retroelements and the Viral Mimicry Response in Cancer Therapy and Cellular Homeostasis. Cancer Discov. 2021, 11, 2707–2725. [Google Scholar] [CrossRef]
  131. Chiaro, J.; Kasanen, H.H.; Whalley, T.; Capasso, C.; Grönholm, M.; Feola, S.; Peltonen, K.; Hamdan, F.; Hernberg, M.; Mäkelä, S.; et al. Viral Molecular Mimicry Influences the Antitumor Immune Response in Murine and Human Melanoma. Cancer Immunol. Res. 2021, 9, 981–993. [Google Scholar] [CrossRef]
  132. Yang, Y.; Bedford, M.T. Protein arginine methyltransferases and cancer. Nat. Rev. Cancer 2013, 13, 37–50. [Google Scholar] [CrossRef] [PubMed]
  133. Xu, J.; Richard, S. Cellular pathways influenced by protein arginine methylation: Implications for cancer. Mol. Cell 2021, 81, 4357–4368. [Google Scholar] [CrossRef] [PubMed]
  134. Zhu, J.; Liu, X.; Cai, X.; Ouyang, G.; Fan, S.; Wang, J.; Xiao, W. Zebrafish prmt7 negatively regulates antiviral responses by suppressing the retinoic acid-inducible gene-I-like receptor signaling. FASEB J. 2020, 34, 988–1000. [Google Scholar] [CrossRef] [PubMed]
  135. Zhu, J.; Li, X.; Cai, X.; Zha, H.; Zhou, Z.; Sun, X.; Rong, F.; Tang, J.; Zhu, C.; Liu, X.; et al. Arginine monomethylation by PRMT7 controls MAVS-mediated antiviral innate immunity. Mol. Cell 2021, 81, 3171–3186.e8. [Google Scholar] [CrossRef]
  136. Srour, N.; Villarreal, O.D.; Hardikar, S.; Yu, Z.; Preston, S.; Miller, W.H.; Szewczyk, M.M.; Barsyte-Lovejoy, D.; Xu, H.; Chen, T.; et al. PRMT7 ablation stimulates anti-tumor immunity and sensitizes melanoma to immune checkpoint blockade. Cell Rep. 2022, 38, 110582. [Google Scholar] [CrossRef]
  137. Hwang, J.W.; Cho, Y.; Bae, G.U.; Kim, S.N.; Kim, Y.K. Protein arginine methyltransferases: Promising targets for cancer therapy. Exp. Mol. Med. 2021, 53, 788–808. [Google Scholar] [CrossRef]
  138. Szewczyk, M.M.; Ishikawa, Y.; Organ, S.; Sakai, N.; Li, F.; Halabelian, L.; Ackloo, S.; Couzens, A.L.; Eram, M.; Dilworth, D.; et al. Pharmacological inhibition of PRMT7 links arginine monomethylation to the cellular stress response. Nat. Commun. 2020, 11, 2396. [Google Scholar] [CrossRef]
  139. Sheng, W.; LaFleur, M.W.; Nguyen, T.H.; Chen, S.; Chakravarthy, A.; Conway, J.R.; Li, Y.; Chen, H.; Yang, H.; Hsu, P.-H.; et al. LSD1 Ablation Stimulates Anti-tumor Immunity and Enables Checkpoint Blockade. Cell 2018, 174, 549–563.e19. [Google Scholar] [CrossRef] [Green Version]
  140. Fang, Y.; Liao, G.; Yu, B. LSD1/KDM1A inhibitors in clinical trials: Advances and prospects. J. Hematol. Oncol. 2019, 12, 129. [Google Scholar] [CrossRef] [Green Version]
  141. Griffin, G.K.; Wu, J.; Iracheta-Vellve, A.; Patti, J.C.; Hsu, J.; Davis, T.; Dele-Oni, D.; Du, P.P.; Halawi, A.G.; Ishizuka, J.J.; et al. Epigenetic silencing by SETDB1 suppresses tumour intrinsic immunogenicity. Nature 2021, 595, 309–314. [Google Scholar] [CrossRef]
  142. Dranoff, G.; Jaffee, E.; Lazenby, A.; Golumbek, P.; Levitsky, H.; Brose, K.; Jackson, V.; Hamada, H.; Pardoll, D.; Mulligan, R.C.; et al. Vaccination with irradiated tumor cells engineered to secrete murine granulocyte-macrophage colony-stimulating factor stimulates potent, specific, and long-lasting anti-tumor immunity. Proc. Natl. Acad. Sci. USA 1993, 90, 3539–3543. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  143. Zhang, S.-M.; Cai, W.L.; Liu, X.; Thakral, D.; Luo, J.; Chan, L.H.; McGeary, M.K.; Song, E.; Blenman, K.R.M.; Micevic, G.; et al. KDM5B promotes immune evasion by recruiting SETDB1 to silence retroelements. Nature 2021, 598, 682–687. [Google Scholar] [CrossRef] [PubMed]
  144. Hu, H.; Khodadadi-Jamayran, A.; Dolgalev, I.; Cho, H.; Badri, S.; Chiriboga, L.A.; Zeck, B.; Gregorio, M.L.D.R.; Dowling, C.M.; Labbe, K.; et al. Targeting the Atf7ip-Setdb1 Complex Augments Antitumor Immunity by Boosting Tumor Immunogenicity. Cancer Immunol. Res. 2021, 9, 1298–1315. [Google Scholar] [CrossRef] [PubMed]
  145. Lazaro-Camp, V.J.; Salari, K.; Meng, X.; Yang, S. SETDB1 in cancer: Overexpression and its therapeutic implications. Am. J. Cancer Res. 2021, 11, 1803–1827. [Google Scholar]
  146. Federico, A.; Steinfass, T.; Larribère, L.; Novak, D.; Morís, F.; Núñez, L.-E.; Umansky, V.; Utikal, J. Mithramycin A and Mithralog EC-8042 Inhibit SETDB1 Expression and Its Oncogenic Activity in Malignant Melanoma. Mol. Ther. Oncolytics 2020, 18, 83–99. [Google Scholar] [CrossRef]
  147. Jose, A.; Shenoy, G.G.; Rodrigues, G.S.; Kumar, N.A.N.; Munisamy, M.; Thomas, L.; Kolesar, J.; Rai, G.; Rao, P.P.N.; Rao, M. Histone Demethylase KDM5B as a Therapeutic Target for Cancer Therapy. Cancers 2020, 12, 2121. [Google Scholar] [CrossRef]
  148. Zheng, Y.-C.; Chang, J.; Wang, L.-C.; Ren, H.-M.; Pang, J.-R.; Liu, H.-M. Lysine demethylase 5B (KDM5B): A potential anti-cancer drug target. Eur. J. Med. Chem. 2019, 161, 131–140. [Google Scholar] [CrossRef]
  149. Ferretti, R.; Bhutkar, A.; McNamara, M.C.; Lees, J.A. BMI1 induces an invasive signature in melanoma that promotes metastasis and chemoresistance. Genes Dev. 2016, 30, 18–33. [Google Scholar] [CrossRef] [Green Version]
  150. Badodi, S.; Pomella, N.; Lim, Y.M.; Brandner, S.; Morrison, G.; Pollard, S.M.; Zhang, X.; Zabet, N.R.; Marino, S. Combination of BMI1 and MAPK/ERK inhibitors is effective in medulloblastoma. Neuro-Oncology 2022, 24, 1273–1285. [Google Scholar] [CrossRef]
  151. Eberle-Singh, J.A.; Sagalovskiy, I.; Maurer, H.C.; Sastra, S.A.; Palermo, C.F.; Decker, A.R.; Kim, M.J.; Sheedy, J.; Mollin, A.; Cao, L.; et al. Effective Delivery of a Microtubule Polymerization Inhibitor Synergizes with Standard Regimens in Models of Pancreatic Ductal Adenocarcinoma. Clin. Cancer Res. 2019, 25, 5548–5560. [Google Scholar] [CrossRef]
  152. Jernigan, F.; Branstrom, A.; Baird, J.D.; Cao, L.; Dali, M.; Furia, B.; Kim, M.J.; O’Keefe, K.; Kong, R.; Laskin, O.L.; et al. Preclinical and Early Clinical Development of PTC596, a Novel Small-Molecule Tubulin-Binding Agent. Mol. Cancer Ther. 2021, 20, 1846–1857. [Google Scholar] [CrossRef] [PubMed]
  153. Strub, T.; Ghiraldini, F.G.; Carcamo, S.; Li, M.; Wroblewska, A.; Singh, R.; Goldberg, M.S.; Hasson, D.; Wang, Z.; Gallagher, S.; et al. SIRT6 haploinsufficiency induces BRAF(V600E) melanoma cell resistance to MAPK inhibitors via IGF signalling. Nat. Commun. 2018, 9, 3440. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  154. Shalem, O.; Sanjana, N.E.; Hartenian, E.; Shi, X.; Scott, D.A.; Mikkelsen, T.S.; Heckl, D.; Ebert, B.L.; Root, D.E.; Doench, J.G.; et al. Genome-scale CRISPR-Cas9 knockout screening in human cells. Science 2014, 343, 84–87. [Google Scholar] [CrossRef] [Green Version]
  155. Mattei, J.; Ballhausen, A.; Bassett, R.; Shephard, M.; Chattopadhyay, C.; Hudgens, C.; Tetzlaff, M.; Woodman, S.; Sato, T.; Patel, S.P. A phase II study of the insulin-like growth factor type I receptor inhibitor IMC-A12 in patients with metastatic uveal melanoma. Melanoma Res. 2020, 30, 574–579. [Google Scholar] [CrossRef]
  156. Smith, T.J.; Kahaly, G.J.; Ezra, D.G.; Fleming, J.C.; Dailey, R.A.; Tang, R.A.; Harris, G.J.; Antonelli, A.; Salvi, M.; Goldberg, R.A.; et al. Teprotumumab for Thyroid-Associated Ophthalmopathy. N. Engl. J. Med. 2017, 376, 1748–1761. [Google Scholar] [CrossRef]
  157. Fiorentino, F.; Mai, A.; Rotili, D. Emerging Therapeutic Potential of SIRT6 Modulators. J. Med. Chem. 2021, 64, 9732–9758. [Google Scholar] [CrossRef]
  158. Palamaris, K.; Moutafi, M.; Gakiopoulou, H.; Theocharis, S. Histone Deacetylase (HDAC) Inhibitors: A Promising Weapon to Tackle Therapy Resistance in Melanoma. Int. J. Mol. Sci. 2022, 23, 3660. [Google Scholar] [CrossRef]
  159. Yamada, T.; Hasegawa, S.; Inoue, Y.; Date, Y.; Yamamoto, N.; Mizutani, H.; Nakata, S.; Matsunaga, K.; Akamatsu, H. Wnt/beta-catenin and kit signaling sequentially regulate melanocyte stem cell differentiation in UVB-induced epidermal pigmentation. J. Investig. Dermatol. 2013, 133, 2753–2762. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  160. Larribere, L.; Hasegawa, S.; Inoue, Y.; Date, Y.; Yamamoto, N.; Mizutani, H.; Nakata, S.; Matsunaga, K.; Akamatsu, H. PI3K mediates protection against TRAIL-induced apoptosis in primary human melanocytes. Cell Death Differ. 2004, 11, 1084–1091. [Google Scholar] [CrossRef] [Green Version]
  161. Edmondson, S.R.; Russo, V.C.; McFarlane, A.C.; Wraight, C.J.; Werther, G.A. Interactions between growth hormone, insulin-like growth factor I, and basic fibroblast growth factor in melanocyte growth. J. Clin. Endocrinol. Metab. 1999, 84, 1638–1644. [Google Scholar] [CrossRef]
  162. Hoek, K.S.; Goding, C.R. Cancer stem cells versus phenotype-switching in melanoma. Pigment Cell Melanoma Res. 2010, 23, 746–759. [Google Scholar] [CrossRef] [PubMed]
  163. Verfaillie, A.; Imrichova, H.; Atak, Z.K.; 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. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  164. Hoek, K.S.; Eichhoff, O.M.; Schlegel, N.C.; Döbbeling, U.; Kobert, N.; Schaerer, L.; Hemmi, S.; Dummer, R. In vivo switching of human melanoma cells between proliferative and invasive states. Cancer Res. 2008, 68, 650–656. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  165. Tsoi, J.; Robert, L.; Paraiso, K.; Galvan, C.; Sheu, K.M.; Lay, J.; Wong, D.J.; Atefi, M.; Shirazi, R.; Wang, X.; et al. Multi-stage Differentiation Defines Melanoma Subtypes with Differential Vulnerability to Drug-Induced Iron-Dependent Oxidative Stress. Cancer Cell 2018, 33, 890–904.e5. [Google Scholar] [CrossRef] [PubMed]
  166. Wouters, J.; Kalender-Atak, Z.; Minnoye, L.; Spanier, K.I.; De Waegeneer, M.; González-Blas, C.B.; Mauduit, D.; Davie, K.; Hulselmans, G.; Najem, A.; et al. Robust gene expression programs underlie recurrent cell states and phenotype switching in melanoma. Nat. Cell Biol. 2020, 22, 986–998. [Google Scholar] [CrossRef]
  167. Rambow, F.; Marine, J.-C.; Goding, C.R. Melanoma plasticity and phenotypic diversity: Therapeutic barriers and opportunities. Genes Dev. 2019, 33, 1295–1318. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  168. Bertolotto, C.; Lesueur, F.; Giuliano, S.; Strub, T.; De Lichy, M.; Bille, K.; Dessen, P.; D’Hayer, B.; Mohamdi, H.; Remenieras, A.; et al. A SUMOylation-defective MITF germline mutation predisposes to melanoma and renal carcinoma. Nature 2011, 480, 94–98. [Google Scholar] [CrossRef]
  169. Ngeow, K.C.; Friedrichsen, H.J.; Li, L.; Zeng, Z.; Andrews, S.; Volpon, L.; Brunsdon, H.; Berridge, G.; Picaud, S.; Fischer, R.; et al. BRAF/MAPK and GSK3 signaling converges to control MITF nuclear export. Proc. Natl. Acad. Sci. USA 2018, 115, E8668–E8677. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  170. Schepsky, A.; Bruser, K.; Gunnarsson, G.J.; Goodall, J.; Hallsson, J.H.; Goding, C.R.; Steingrimsson, E.; Hecht, A. The microphthalmia-associated transcription factor Mitf interacts with beta-catenin to determine target gene expression. Mol. Cell Biol. 2006, 26, 8914–8927. [Google Scholar] [CrossRef] [Green Version]
  171. de la Serna, I.L.; Ohkawa, Y.; Higashi, C.; Dutta, C.; Osias, J.; Kommajosyula, N.; Tachibana, T.; Imbalzano, A.N. The microphthalmia-associated transcription factor requires SWI/SNF enzymes to activate melanocyte-specific genes. J. Biol. Chem. 2006, 281, 20233–20241. [Google Scholar] [CrossRef] [Green Version]
  172. Bemis, L.T.; Chen, R.; Amato, C.M.; Classen, E.H.; Robinson, S.E.; Coffey, D.G.; Erickson, P.F.; Shellman, Y.G.; Robinson, W.A. MicroRNA-137 targets microphthalmia-associated transcription factor in melanoma cell lines. Cancer Res. 2008, 68, 1362–1368. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  173. Arts, N.; Cané, S.; Hennequart, M.; Lamy, J.; Bommer, G.; Van Den Eynde, B.; De Plaen, E. microRNA-155, induced by interleukin-1ss, represses the expression of microphthalmia-associated transcription factor (MITF-M) in melanoma cells. PLoS ONE 2015, 10, e0122517. [Google Scholar] [CrossRef] [Green Version]
  174. Qian, H.; Yang, C.; Yang, Y. MicroRNA-26a inhibits the growth and invasiveness of malignant melanoma and directly targets on MITF gene. Cell Death Discov. 2017, 3, 17028. [Google Scholar] [CrossRef] [PubMed]
  175. Haflidadóttir, B.S.; Bergsteinsdóttir, K.; Praetorius, C.; Steingrímsson, E. miR-148 regulates Mitf in melanoma cells. PLoS ONE 2010, 5, e11574. [Google Scholar] [CrossRef] [PubMed]
  176. Louphrasitthiphol, P.; Siddaway, R.; Loffreda, A.; Pogenberg, V.; Friedrichsen, H.; Schepsky, A.; Zeng, Z.; Lu, M.; Strub, T.; Freter, R.; et al. Tuning Transcription Factor Availability through Acetylation-Mediated Genomic Redistribution. Mol. Cell 2020, 79, 472–487.e10. [Google Scholar] [CrossRef] [PubMed]
  177. Falletta, P.; Sanchez-Del-Campo, L.; Chauhan, J.; Effern, M.; Kenyon, A.; Kershaw, C.J.; Siddaway, R.; Lisle, R.; Freter, R.; Daniels, M.J.; et al. Translation reprogramming is an evolutionarily conserved driver of phenotypic plasticity and therapeutic resistance in melanoma. Genes Dev. 2017, 31, 18–33. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  178. Ferguson, J.; Smith, M.; Zudaire, I.; Wellbrock, C.; Arozarena, I. Glucose availability controls ATF4-mediated MITF suppression to drive melanoma cell growth. Oncotarget 2017, 8, 32946–32959. [Google Scholar] [CrossRef] [Green Version]
  179. Hartman, M.L.; Talar, B.; Noman, M.Z.; Gajos-Michniewicz, A.; Chouaib, S.; Czyz, M. Gene expression profiling identifies microphthalmia-associated transcription factor (MITF) and Dickkopf-1 (DKK1) as regulators of microenvironment-driven alterations in melanoma phenotype. PLoS ONE 2014, 9, e95157. [Google Scholar] [CrossRef] [Green Version]
  180. Landsberg, J.; Kohlmeyer, J.; Renn, M.; Bald, T.; Rogava, M.; Cron, M.; Fatho, M.; Lennerz, V.; Wölfel, T.; Hölzel, M.; et al. Melanomas resist T-cell therapy through inflammation-induced reversible dedifferentiation. Nature 2012, 490, 412–416. [Google Scholar] [CrossRef]
  181. Riesenberg, S.; Groetchen, A.; Siddaway, R.; Bald, T.; Reinhardt, J.; Smorra, D.; Kohlmeyer, J.; Renn, M.; Phung, B.; Aymans, P.; et al. MITF and c-Jun antagonism interconnects melanoma dedifferentiation with pro-inflammatory cytokine responsiveness and myeloid cell recruitment. Nat. Commun. 2015, 6, 8755. [Google Scholar] [CrossRef] [Green Version]
  182. Golan, T.; Messer, A.R.; Amitai-Lange, A.; Melamed, Z.; Ohana, R.; Bell, R.E.; Kapitansky, O.; Lerman, G.; Greenberger, S.; Khaled, M.; et al. Interactions of Melanoma Cells with Distal Keratinocytes Trigger Metastasis via Notch Signaling Inhibition of MITF. Mol. Cell 2015, 59, 664–676. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  183. Cheli, Y.; Giuliano, S.; Fenouille, N.; Allegra, M.; Hofman, V.; Hofman, P.; Bahadoran, P.; Lacour, J.-P.; Tartare-Deckert, S.; Bertolotto, C.; et al. Hypoxia and MITF control metastatic behaviour in mouse and human melanoma cells. Oncogene 2012, 31, 2461–2470. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  184. Feige, E.; Yokoyama, S.; Levy, C.; Khaled, M.; Igras, V.; Lin, R.J.; Lee, S.; Widlund, H.R.; Granter, S.R.; Kung, A.L.; et al. Hypoxia-induced transcriptional repression of the melanoma-associated oncogene MITF. Proc. Natl. Acad. Sci. USA 2011, 108, E924–E933. [Google Scholar] [CrossRef] [Green Version]
  185. Miskolczi, Z.; Smith, M.; Rowling, E.J.; Ferguson, J.; Barriuso, J.; Wellbrock, C. Collagen abundance controls melanoma phenotypes through lineage-specific microenvironment sensing. Oncogene 2018, 37, 3166–3182. [Google Scholar] [CrossRef]
  186. Reinhardt, J.; Landsberg, J.; Schmid-Burgk, J.L.; Ramis, B.B.; Bald, T.; Glodde, N.; Lopez-Ramos, D.; Young, A.; Ngiow, S.F.; Nettersheim, D.; et al. MAPK Signaling and Inflammation Link Melanoma Phenotype Switching to Induction of CD73 during Immunotherapy. Cancer Res. 2017, 77, 4697–4709. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  187. Berico, P.; Cigrang, M.; Davidson, G.; Braun, C.; Sandoz, J.; Legras, S.; Vokshi, B.H.; Slovic, N.; Peyresaubes, F.; Robles, C.M.G.; et al. CDK7 and MITF repress a transcription program involved in survival and drug tolerance in melanoma. EMBO Rep. 2021, 22, e51683. [Google Scholar] [CrossRef] [PubMed]
  188. García-Jiménez, C.; Goding, C.R. Starvation and Pseudo-Starvation as Drivers of Cancer Metastasis through Translation Reprogramming. Cell Metab. 2019, 29, 254–267. [Google Scholar] [CrossRef] [Green Version]
  189. Liu, X.; Zhang, S.-M.; McGeary, M.K.; Krykbaeva, I.; Lai, L.; Jansen, D.J.; Kales, S.C.; Simeonov, A.; Hall, M.D.; Kelly, D.P.; et al. KDM5B Promotes Drug Resistance by Regulating Melanoma-Propagating Cell Subpopulations. Mol. Cancer Ther. 2019, 18, 706–717. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  190. Orouji, E.; Federico, A.; Larribère, L.; Novak, D.; Lipka, D.B.; Assenov, Y.; Sachindra, S.; Hüser, L.; Granados, K.; Gebhardt, C.; et al. Histone methyltransferase SETDB1 contributes to melanoma tumorigenesis and serves as a new potential therapeutic target. Int. J. Cancer 2019, 145, 3462–3477. [Google Scholar] [CrossRef]
  191. Borsotti, P.; Ghilardi, C.; Ostano, P.; Silini, A.; Dossi, R.; Pinessi, D.; Foglieni, C.; Scatolini, M.; Lacal, P.M.; Ferrari, R.; et al. Thrombospondin-1 is part of a Slug-independent motility and metastatic program in cutaneous melanoma, in association with VEGFR-1 and FGF-2. Pigment Cell Melanoma Res. 2015, 28, 73–81. [Google Scholar] [CrossRef]
  192. Jayachandran, A.; Anaka, M.; Prithviraj, P.; Hudson, C.; McKeown, S.J.; Lo, P.-H.; Vella, L.J.; Goding, C.R.; Cebon, J.; Behren, A. Thrombospondin 1 promotes an aggressive phenotype through epithelial-to-mesenchymal transition in human melanoma. Oncotarget 2014, 5, 5782–5797. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  193. Jeanne, A.; Boulagnon-Rombi, C.; Devy, J.; Théret, L.; Fichel, C.; Bouland, N.; Diebold, M.-D.; Martiny, L.; Schneider, C.; Dedieu, S. Matricellular TSP-1 as a target of interest for impeding melanoma spreading: Towards a therapeutic use for TAX2 peptide. Clin. Exp. Metastasis 2016, 33, 637–649. [Google Scholar] [CrossRef]
  194. Anastasiadou, E.; Jacob, L.S.; Slack, F.J. Non-coding RNA networks in cancer. Nat. Rev. Cancer 2018, 18, 5–18. [Google Scholar] [CrossRef] [PubMed]
  195. Ramilowski, J.A.; Yip, C.W.; Agrawal, S.; Chang, J.-C.; Ciani, Y.; Kulakovskiy, I.V.; Mendez, M.; Ooi, J.L.C.; Ouyang, J.F.; Parkinson, N.; et al. Functional annotation of human long noncoding RNAs via molecular phenotyping. Genome Res. 2020, 30, 1060–1072. [Google Scholar] [CrossRef] [PubMed]
  196. Harrow, J.; Frankish, A.; Gonzalez, J.M.; Tapanari, E.; Diekhans, M.; Kokocinski, F.; Aken, B.L.; Barrell, D.; Zadissa, A.; Searle, S.; et al. GENCODE: The reference human genome annotation for The ENCODE Project. Genome Res. 2012, 22, 1760–1774. [Google Scholar] [CrossRef] [Green Version]
  197. Iyer, M.K.; Niknafs, Y.S.; Malik, R.; Singhal, U.; Sahu, A.; Hosono, Y.; Barrette, T.R.; Prensner, J.R.; Evans, J.R.; Zhao, S.; et al. The landscape of long noncoding RNAs in the human transcriptome. Nat. Genet. 2015, 47, 199–208. [Google Scholar] [CrossRef]
  198. Schmitt, A.M.; Chang, H.Y. Long Noncoding RNAs in Cancer Pathways. Cancer Cell 2016, 29, 452–463. [Google Scholar] [CrossRef] [Green Version]
  199. Melixetian, M.; Bossi, D.; Mihailovich, M.; Punzi, S.; Barozzi, I.; Marocchi, F.; Cuomo, A.; Bonaldi, T.; Testa, G.; Marine, J.; et al. Long non-coding RNA TINCR suppresses metastatic melanoma dissemination by preventing ATF4 translation. EMBO Rep. 2021, 22, e50852. [Google Scholar] [CrossRef]
  200. Sun, X.; Li, J.; Sun, Y.; Zhang, Y.; Dong, L.; Shen, C.; Yang, L.; Yang, M.; Li, Y.; Shen, G.; et al. miR-7 reverses the resistance to BRAFi in melanoma by targeting EGFR/IGF-1R/CRAF and inhibiting the MAPK and PI3K/AKT signaling pathways. Oncotarget 2016, 7, 53558–53570. [Google Scholar] [CrossRef] [Green Version]
  201. Vitiello, M.; D’Aurizio, R.; Poliseno, L. Biological role of miR-204 and miR-211 in melanoma. Oncoscience 2018, 5, 248–251. [Google Scholar] [CrossRef] [Green Version]
  202. Diaz-Martinez, M.; Benito-Jardon, L.; Teixido, J. New insights in melanoma resistance to BRAF inhibitors: A role for microRNAs. Oncotarget 2018, 9, 35374–35375. [Google Scholar] [CrossRef] [PubMed]
  203. Ennen, M.; Keime, C.; Gambi, G.; Kieny, A.; Coassolo, S.; Thibault-Carpentier, C.; Margerin-Schaller, F.; Davidson, G.; Vagne, C.; Lipsker, D.; et al. MITF-High and MITF-Low Cells and a Novel Subpopulation Expressing Genes of Both Cell States Contribute to Intra- and Intertumoral Heterogeneity of Primary Melanoma. Clin. Cancer Res. 2017, 23, 7097–7107. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  204. Titov, D.V.; Gilman, B.; He, Q.-L.; Bhat, S.; Low, W.-K.; Dang, Y.; Smeaton, M.; Demain, A.L.; Miller, P.S.; Kugel, J.F.; et al. XPB, a subunit of TFIIH, is a target of the natural product triptolide. Nat. Chem. Biol. 2011, 7, 182–188. [Google Scholar] [CrossRef] [Green Version]
  205. Nuñez, G.S.; Robles, C.M.G.; Giraudon, C.; Martínez-Leal, J.F.; Compe, E.; Coin, F.; Aviles, P.; Galmarini, C.M.; Egly, J.-M. Lurbinectedin Specifically Triggers the Degradation of Phosphorylated RNA Polymerase II and the Formation of DNA Breaks in Cancer Cells. Mol. Cancer Ther. 2016, 15, 2399–2412. [Google Scholar] [CrossRef] [PubMed]
  206. Skorupan, N.; I Ahmad, M.; Steinberg, S.M.; Trepel, J.B.; Cridebring, D.; Han, H.; Von Hoff, D.D.; Alewine, C. A phase II trial of the super-enhancer inhibitor MinnelideTM in advanced refractory adenosquamous carcinoma of the pancreas. Future Oncol. 2022, 18, 2475–2481. [Google Scholar] [CrossRef] [PubMed]
  207. Bradner, J.E.; Hnisz, D.; Young, R.A. Transcriptional Addiction in Cancer. Cell 2017, 168, 629–643. [Google Scholar] [CrossRef] [Green Version]
  208. Winter, G.E.; Buckley, D.L.; Paulk, J.; Roberts, J.M.; Souza, A.; Dhe-Paganon, S.; Bradner, J.E. DRUG DEVELOPMENT. Phthalimide conjugation as a strategy for in vivo target protein degradation. Science 2015, 348, 1376–1381. [Google Scholar] [CrossRef] [Green Version]
  209. Xiao, L.; Parolia, A.; Qiao, Y.; Bawa, P.; Eyunni, S.; Mannan, R.; Carson, S.E.; Chang, Y.; Wang, X.; Zhang, Y.; et al. Targeting SWI/SNF ATPases in enhancer-addicted prostate cancer. Nature 2022, 601, 434–439. [Google Scholar] [CrossRef]
  210. Kress, T.R.; Sabò, A.; Amati, B. MYC: Connecting selective transcriptional control to global RNA production. Nat. Rev. Cancer 2015, 15, 593–607. [Google Scholar] [CrossRef] [PubMed]
  211. Han, H.; Jain, A.D.; Truica, M.I.; Izquierdo-Ferrer, J.; Anker, J.F.; Lysy, B.; Sagar, V.; Luan, Y.; Chalmers, Z.R.; Unno, K.; et al. Small-Molecule MYC Inhibitors Suppress Tumor Growth and Enhance Immunotherapy. Cancer Cell 2019, 36, 483–497.e15. [Google Scholar] [CrossRef]
  212. Quemener, A.M.; Bachelot, L.; Forestier, A.; Donnou-Fournet, E.; Gilot, D.; Galibert, M.-D. The powerful world of antisense oligonucleotides: From bench to bedside. Wiley Interdiscip. Rev. RNA 2020, 11, e1594. [Google Scholar] [CrossRef] [PubMed]
  213. Crooke, S.T.; Wang, S.; Vickers, T.A.; Shen, W.; Liang, X.-H. Cellular uptake and trafficking of antisense oligonucleotides. Nat. Biotechnol. 2017, 35, 230–237. [Google Scholar] [CrossRef] [PubMed]
  214. Dowdy, S.F. Overcoming cellular barriers for RNA therapeutics. Nat. Biotechnol. 2017, 35, 222–229. [Google Scholar] [CrossRef] [PubMed]
  215. Geary, R.S.; Norris, D.; Yu, R.; Bennett, C.F. Pharmacokinetics, biodistribution and cell uptake of antisense oligonucleotides. Adv. Drug Deliv. Rev. 2015, 87, 46–51. [Google Scholar] [CrossRef]
  216. Ämmälä, C.; Drury, W.J.; Knerr, L.; Ahlstedt, I.; Stillemark-Billton, P.; Wennberg-Huldt, C.; Andersson, E.-M.; Valeur, E.; Jansson-Löfmark, R.; Janzén, D.; et al. Targeted delivery of antisense oligonucleotides to pancreatic beta-cells. Sci. Adv. 2018, 4, eaat3386. [Google Scholar] [CrossRef] [Green Version]
  217. Mazur, C.; Powers, B.; Zasadny, K.; Sullivan, J.M.; Dimant, H.; Kamme, F.; Hesterman, J.; Matson, J.; Oestergaard, M.; Seaman, M.; et al. Brain pharmacology of intrathecal antisense oligonucleotides revealed through multimodal imaging. JCI Insight 2019, 4, e129240. [Google Scholar] [CrossRef]
Figure 1. Viral mimicry by epigenetic regulators: A beneficial instance of cellular hypochondria. Epigenetic regulation of post-translational modifications by LSD1, PRMT7, KDM5B, ATF7IP, and SETDB1 modulates the expression of genomically integrated endogenous retroviral (ERV) elements and elicits interferon (IFN)-driven states that regulate anti-PD-1 and anti-CTLA-4 immunotherapy responses.
Figure 1. Viral mimicry by epigenetic regulators: A beneficial instance of cellular hypochondria. Epigenetic regulation of post-translational modifications by LSD1, PRMT7, KDM5B, ATF7IP, and SETDB1 modulates the expression of genomically integrated endogenous retroviral (ERV) elements and elicits interferon (IFN)-driven states that regulate anti-PD-1 and anti-CTLA-4 immunotherapy responses.
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Figure 2. Epigenetic mechanisms underlying MAPKi resistance in melanoma. Inhibition of the MAPK pathway (MAPKi) results in the senescence or apoptosis of sensitive melanoma cell states. Meanwhile, escape from senescence and intrinsic and acquired resistance can be facilitated by histone modifications induced by SETDB1, SIRT6, and KMD5B or by chromatin compaction via BMI1, which activates cell survival pathways and changes melanoma cell states.
Figure 2. Epigenetic mechanisms underlying MAPKi resistance in melanoma. Inhibition of the MAPK pathway (MAPKi) results in the senescence or apoptosis of sensitive melanoma cell states. Meanwhile, escape from senescence and intrinsic and acquired resistance can be facilitated by histone modifications induced by SETDB1, SIRT6, and KMD5B or by chromatin compaction via BMI1, which activates cell survival pathways and changes melanoma cell states.
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Figure 3. Tumor microenvironment drives phenotypic diversity in melanoma. The tumor microenvironment is characterized by the juxtaposition of heterotypic interactions between healthy tissue and melanoma cells, influencing two opposing MITF- or AP1/TEAD-dependent transcriptional programs that define distinct phenotypic states. Phenotype-switching represents a dynamic adaptation to microenvironmental variation comprising immune infiltration, inflammation, extracellular matrix (ECM), nutrient availability, oxygen levels, and melanoma-cell-autonomous mechanisms, such as de novo mutations and double-stranded RNA (dsRNA) regulation. CAS: cancer-associated stroma.
Figure 3. Tumor microenvironment drives phenotypic diversity in melanoma. The tumor microenvironment is characterized by the juxtaposition of heterotypic interactions between healthy tissue and melanoma cells, influencing two opposing MITF- or AP1/TEAD-dependent transcriptional programs that define distinct phenotypic states. Phenotype-switching represents a dynamic adaptation to microenvironmental variation comprising immune infiltration, inflammation, extracellular matrix (ECM), nutrient availability, oxygen levels, and melanoma-cell-autonomous mechanisms, such as de novo mutations and double-stranded RNA (dsRNA) regulation. CAS: cancer-associated stroma.
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Table 1. Epigenetic regulators of immune therapy through viral mimicry.
Table 1. Epigenetic regulators of immune therapy through viral mimicry.
GeneFunctionMechanism
PRMT7 [136]Protein arginine methyl transferase (PRMT)PRMT7 inhibition represses DNMTs regulating ERV expression. ERVs cause dsRNA stress, detected by increased MDA5 and RIG-I due to H3K4me3 and H4R3me2 hypomethylation of their promoters. Shifting the cell into viral mimicry, the resultant IFN expression enhances antigen presentation and improves PD-1 & CTLA-4 checkpoint blockade via increased CD8+ T cell and reduced MDSC infiltration.
LSD1 [139]Histone demethylaseLSD1 ablation increases ERV transcription and reduces core RISC complex proteins, generating cellular dsRNA stress detected by TLR3 and MDA5, inducing viral mimicry, activating IFN signaling, and increasing MHC-I and PD-L1 expression. Enhanced immunogenicity improves CD8+ T cell infiltration and boosts response to PD-1 checkpoint blockade.
SETDB1 [141]Histone methyltransferaseSETDB1 loss de-represses TEs encoding viral antigens and immunostimulatory genes. Intact viral ORFs in induced TEs facilitate generation of MHC-I peptides and trigger CD8+ T cell responses. In B16F10-GVAX melanoma, SETDB1 knockout sensitized cells to PD-1 checkpoint blockade. Rare bi-directional TE transcription prevented the generation of dsRNA and interferon signaling and thwarted viral mimicry.
KDM5B and SETDB1 [143]Histone demethylase and histone methyltransferaseKDM5B recruits and regulates SETDB1 in a proteosome-dependent manner. KDM5B loss increases bi-directional ERV transcription, accumulating dsRNA stress. MDA5 and cGAS depletion ablate IFN-I activation, MHC-I signaling, and induction of ISGs following KDM5B knockout. Increased CD8+ T cell activity following KDM5B knockout sensitized treatment-resistant YUMM1.7 melanoma cells to anti-PD-1 in vivo treatment.
ATF7IP and SETDB1 [144]SETDB1 adaptor and histone methyltransferaseATF7IP interacts with SETDB1 to repress ERV expression. ATF7IP deficiency reduces H3K9me3 deposition by SETDB1 and inhibits spliceosome activity, significantly increasing mRNA intron retention. Interferon signaling through IRF7 and IRF9 increase ERV antigen presentation, enhancing immunogenicity and facilitating clearance via elevated T cell infiltration.
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Rubanov, A.; Berico, P.; Hernando, E. Epigenetic Mechanisms Underlying Melanoma Resistance to Immune and Targeted Therapies. Cancers 2022, 14, 5858. https://doi.org/10.3390/cancers14235858

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Rubanov A, Berico P, Hernando E. Epigenetic Mechanisms Underlying Melanoma Resistance to Immune and Targeted Therapies. Cancers. 2022; 14(23):5858. https://doi.org/10.3390/cancers14235858

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Rubanov, Andrey, Pietro Berico, and Eva Hernando. 2022. "Epigenetic Mechanisms Underlying Melanoma Resistance to Immune and Targeted Therapies" Cancers 14, no. 23: 5858. https://doi.org/10.3390/cancers14235858

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