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Review

A Sequencing Overview of Malignant Peripheral Nerve Sheath Tumors: Findings and Implications for Treatment

by
Kangwen Xiao
,
Kuangying Yang
and
Angela C. Hirbe
*
Division of Oncology, Department of Internal Medicine, Siteman Cancer Center, Washington University School of Medicine, St. Louis, MO 63110, USA
*
Author to whom correspondence should be addressed.
Cancers 2025, 17(2), 180; https://doi.org/10.3390/cancers17020180
Submission received: 23 November 2024 / Revised: 6 January 2025 / Accepted: 7 January 2025 / Published: 8 January 2025
(This article belongs to the Special Issue Sarcoma: Clinical Trials and Management)
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Simple Summary

In recent decades, advancements in high-throughput sequencing technology, coupled with reduced sequencing costs, have led to a significant increase in the genomic profiling of benign and malignant peripheral nerve sheath tumors. This review synthesizes recent sequencing discoveries from multiple sequencing technologies, underscores the critical mutation events involved in tumor pathogenesis, and explores their potential therapeutic implications. By elucidating the molecular basis of these tumors, clinicians and researchers can improve patient outcomes and provide a foundation for more effective treatment strategies.

Abstract

Malignant peripheral nerve sheath tumors (MPNSTs) are rare but aggressive malignancies with a low 5-year survival rate despite current treatments. MPNSTs frequently harbor mutations in key genes such as NF1, CDKN2A, TP53, and PRC2 components (EED or SUZ12) across different disease stages. With the rapid advancement of high-throughput sequencing technologies, the molecular characteristics driving MPNST development are becoming clearer. This review summarizes recent sequencing studies on peripheral nerve sheath tumors, including plexiform neurofibromas (PNs), atypical neurofibromatous neoplasm with uncertain biologic potential (ANNUBP), and MPNSTs, highlighting key mutation events in tumor progression from the perspectives of epigenetics, transcriptomics, genomics, proteomics, and metabolomics. We also discuss the therapeutic implications of these genomic findings, focusing on preclinical and clinical trials targeting these alterations. Finally, we conclude that overcoming tumor resistance through combined targeted therapies and personalized treatments based on the molecular characteristics of MPNSTs will be a key direction for future treatment strategies.

Graphical Abstract

1. Overview of MPNSTs

The earliest MPNST in a human can be traced back to 1952, which was described as an unclassified tumor which resembles a neurofibroma in a Caucasian male [1]. In 1984, Ducatman et al. reported 16 cases of MPNSTs in children under 16, with a mean survival of only 1.8 years [2]. In 1986, a study reviewed 120 cases of MPNSTs and concluded that total resection of the tumor could improve the prognosis of the MPNST. Surgery is still the main treatment for MPNSTs today, but recurrence rates are high [3]. MPNSTs predominant locate along the main nerve bundles [4]. While MPNSTs are traditionally characterized as Schwann cell-derived tumors, this may oversimplify their heterogeneous nature. A recent study highlights the fact that low-grade MPNSTs should be identified as ANNUBP with increased proliferation [5]. The presenting symptom of MPNSTs is typically a gradually enlarging painless mass, which, as the tumor grows to a certain size, compresses adjacent tissues and becomes painful [6]. The MPNST is ranked as the sixth most common soft tissue sarcoma, consisting of 2%~10% of soft tissue sarcomas, annually [7].
The treatment for MPNSTs depends on the stage of the disease. For localized MPNSTs, complete surgical resection with or without chemotherapy and radiation remains the most effective therapy to date [8,9]. A recent retrospective analysis of an MPNST cohort showed that some patients receiving radiation therapy had a better overall survival rate, indicating that radiation therapy may be beneficial for MPNSTs [10]. However, the effect on overall survival with MPNST requires further clinical validation [11]. For those patients who have already developed metastasis, chemotherapy—typically doxorubicin and ifosfamide-based regimen—remain the main treatment, but only extends life expectancy by 1 to 2 years [12,13]. The overall prognosis remains poor, despite surgical resection, adjuvant radiation, or chemotherapy, with the five-year overall survival rate of 47.2% [14].
NF1 is one of the most common tumor predisposition syndromes and is caused by the loss of function of the NF1 tumor suppressor gene. Patients have an increased risk of developing benign tumors, as well as malignancies including soft tissue sarcoma, glioma, breast cancer, and melanoma [15]. Fifty percent of MPNSTs occur in patients with NF1, while 40% occur sporadically, and 10% occur in the setting of prior radiation [16]. The etiology of sporadic MPNSTs is not entirely understood, but the tumors are thought to arise de novo instead of from PNs and ANNUBP [17]. The pathogenesis of NF1-associated MPNST involves dysregulation of multiple genes and progression through several disease stages. In addition to the NF1 gene loss, several other key genes including CDKN2A, TP53 and EED/SUZ12 have been identified as being involved in the pathogenesis of MPNSTs [18,19,20]. In the context of NF1, the loss of the second copy of the NF1 gene along with cues from the heterozygous microenvironment leads to the development of a PN, which is a benign tumor that can cause pain, mobility dysfunction, and organ compromise, depending on the location [21]. The ANNUBP was proposed in 2017 and is an intermediate pre-malignant disease stage between PNs and MPNST [22]. It has been shown that loss of CDKN2A is correlated with ANNUBP [23] and further loss of PRC2 components, TP53, or other alterations are likely to drive the malignant transformation from ANNUBP to MPNST [24,25,26].
In the last several decades, with the rapid development of high-throughput sequencing technology and the reduction of sequencing costs, many MPNST samples have been sent for sequencing, including micro-array, RNA-sequencing (RNA-seq), methylation-sequencing, single-cell RNA sequencing, whole exome sequencing (WES) and whole genome sequencing (WGS). The complex genomic characteristics of MPNSTs are gradually being uncovered at multiple levels. Here we summarize recent MPNST-associated sequencing studies, highlighting potential targeted therapy options.

2. Sequencing Technologies and Its Application in MPNST

Researchers have utilized sequencing studies on MPNSTs with several research focuses. (1) Identification of molecular subtypes and diagnostic tools of MPNST. (2) Comparison of differential gene expression between MPNST and peripheral nerve sheath tumor (PNST). (3) Investigation of the pharmacogenomic changes following various drug treatments. (4) Identification of functional genes in MPNSTs by analyzing genetic mutations and gene-noncoding RNA interactions. The following chapters will introduce in detail the application of various sequencing technologies in MPNSTs. The summary of key findings is shown in Table 1 and Figure 1.

2.1. Microarray and RNA-Seq Analysis

Microarrays are based on hybridization of pre-designed labeled probes with target cDNA sequences [27]. To date, microarray sequencing technology is still one of the most common technologies due to its relatively low cost and straightforward pipeline and has been widely applied in MPNST samples. These datasets employ a range of platforms, including Affymetrix, Agilent, and Illumina. However, the accuracy of this technology relies heavily on the pre-designed probes, and the affinity of the probe hybridization [28]. Therefore, microarray technology is not the best fit for samples with low-abundance transcripts, and cannot distinguish isoforms or identify genetic variations. In addition, probes are often accompanied by problems such as cross-hybridization and non-specific hybridization [28].
Table 1. Recent sequencing summary of MPNST-related samples.
Table 1. Recent sequencing summary of MPNST-related samples.
Data TypeOrganismSample SettingsReference
MPNSTPNNFANNUBP
MicroarrayHumanTissue64NA15NAGSE241224 (Høland et al., 2023) [29]
MicroarrayHumanTissue610910GSE239561 (Rhodes, 2023) [30]
MicroarrayHumanTissue10NANANAGSE52390, GSE52391 (Wolf et al., 2013) [31]
DNA methylationHumanTissue10NANANA
MicroarrayHuman Tissue6NA26NAGSE41747 (Jessen et al., 2012) [32]
MicroarrayMouseTissue18NA15NA
MicroarrayHuman Tissue16NANANAGSE17118 (Lafferty-Whyte et al., 2010) [33]
MicroarrayHuman Tissue3NA3NAGSE52252 (Wang et al., 2014) [34]
MicroarrayHuman Tissue30NA8NAGSE66743 (Kolberg et al., 2015) [35]
MicroarrayHuman Tissue4NANANAGSE77203 (Yasuhiro et al., 2019) [36]
MicroarrayHuman Cell21NANANAGSE39764 (Sun et al., 2013) [37]
MicroarrayHuman Tissue3NANANAGSE35852, GSE35851 (Kelly et al., 2012) [38]
micro-RNAHumanTissue3NANANA
MicroarrayHuman Cell22NANANAGSE8717 (Mahller et al., 2007) [39]
MicroarrayHuman Cell20NANANAGSE47476 and GSE47477 (Zhang et al., 2013) [40]
micro-RNAHumanCell12NANANA
MicroarrayHuman Cell9NANANAGSE62500 (De Raedt et al., 2014) [41]
MicroarrayHuman Cell12NANANAGSE84205 (Malone et al., 2017) [42]
MicroarrayHuman PDX/Tissue11NANANAGSE60082 (Castellsagué et al., 2015) [43]
MicroarrayMouseTissue8NANANAGSE57141 (Malone et al., 2014) [44]
Bulk RNA-seq HumanPDX13NANANAsyn11638893 (Hirbe et al., 2022) [45]
WES/WGSHumanPDX16NANANA
scRNA-seq HumanPDX7 MPNST
Bulk RNA-seq HumanTissue73NA12EGAD00001008608 (Genomics of Malignant Peripheral Nerve Sheath Tumor (GeM) Consortium, 2020) [46]
WGSHumanTissue72NA23
Bulk RNA-seq HumanTissue41NANANAGSE206527 and GSE179699 (Chi et al., 2022) [47]
ATAC-seqHumanCell12NANANA
Bulk RNA-seq HumanTissue9NA8NAGSE178989, GSE179033 and GSE179041 (Wu et al., 2022) [48]
scRNA-seq HumanTissue PN vs. MPNST
scRNA-seq MouseTissue1.5-month MPNST vs. 4-months MPNST
DNA methylationHumanCell63NANANAGSE141438, GSE141435 and GSE141437 (Kochat et al., 2021) [49]
Bulk RNA-seq HumanTissue7NA3NA
Bulk RNA-seq HumanCell36NANANA
Bulk RNA-seq HumanTissue2521NANAGSE145064 (Kohlmeyer et al., 2020) [50]
Bulk RNA-seq HumanTissue1212NANAGSE212964 (Vasudevan et al., 2023) [51]
scRNA-seqHumanTissue3 MPNST vs. 3 PN
Bulk RNA-seq HumanTissue14NA34NAGSE207400, PRJNA854920 and GSe207399 (Suppiah et al., 2023) [52]
WESHumanTissue18NA16NA
scRNA-seqHumanTissueANNUBP vs. MPNST
Bulk RNA-seq HumanTissue10NANANATCGA-SARC (https://www.cancer.gov/tcga, accesed on 28 August 2024)
Bulk RNA-seq HumanTissue6NANANAGSE120685 (Woodhoo et al., 2021) [53]
Bulk RNA-seq HumanCell28NANANAGSE183308, GSE183307 (Zhang et al., 2022) [54]
scRNA-seq HumanTissue primary vs. metastasis
Bulk RNA-seq HumanTissue3NA3NAGSE270880 (Zhang et al., 2024) [55]
Bulk RNA-seq HumanCell16NANANAGSE179585, GSe179586 (Patel et al., 2022) [56]
DNA methylationHumanCell16NANANA
Bulk RNA-seq HumanCell6NA10NAGSE118185 (Wassef et al., 2019) [57]
Bulk RNA-seq HumanCell6NANANAGSE213988 (Chung et al., 2022) [58]
Bulk RNA-seq HumanCell4NANANAGSE216792
WGSHuman Tissue (blood)4623NANAsyn23651229 (Shern et al., 2020) [59]
WESHumanTissue51NANANAEGAS0000100452 (Lyskjær et al., 2020) [60]
WESHumanTissue23NANAHirbe et al., 2015 [61]
WESHumanTissue15NANANALee et al., 2014 [19]
WESHumanTissue6NANANAGodec et al., 2020 [62]
WES/WGSHumanTissue11NA2NAKinoshita et al., 2020 [63]
DNA methylationHumanTissue1NA1NAGSE21714 (Feber et al., 2011) [64]
DNA methylationHumanCell16NANANAGSE263127 (Bhunia et al., 2024) [65]
DNA methylationHumanTissue102NANANAE-MTAB-8864 www.ebi.ac.uk/biostudies/arrayexpress/studies/E-MTAB-6961, accesed on 12 December 2024)
DNA methylationHumanTissue8NANANAGSE36982 (Renner et al., 2012) [66]
micro-RNAHumanTissue199NANAGSE140987 (Wiemer, 2019) [67]
ATAC-seqHumanCell6NANANAGSE275047
NA: Not applicable; PDX: Patient-derived xenograft; ATAC-seq: Assay for Transposase-Accessible Chromatin with sequencing.
Given the limitation of microarray sequencing, RNA-seq, one of the most common next-generation sequencing technologies, has been widely applied in the field of biomedicine [68]. RNA-seq has several advantages: first, it can detect low-abundance transcripts. Second, it does not rely on designed probes or primers in advance. Thirdly, new transcripts and splice variants can also be discovered [69].
Molecular classification has become increasingly important in tumor diagnostics and treatment. For instance, in 2016, medulloblastoma was subdivided into multiple molecular subtypes, including the Sonic Hedgehog (SHH) and WNT subtypes, based on transcriptomic data [70]. This subtype-specific classification has since enabled the development of tailored therapeutic strategies, significantly improving patient prognoses [71]. Similarly, several studies have utilized computational algorithms to identify different gene expression patterns to define subtypes of MPNST. For example, Holand et al. utilized the transcriptome of the MPNST to perform clustering analysis and identified two immune-related clusters of MPNSTs which correlated with patient prognosis [29]. They predicted that EGFR, EZH2, KIF11, PLK1 and RRM2 could be potential therapeutic targets for MPNSTs. The largest MPNST sequencing study to date was performed by the GeM consortium, which suggested that genomic expression pattern could predict the prognosis of MPNST better than clinical or pathological evidence alone [72]. In 2023, Suppiah et al. identified two distinct MPNST subtypes, which were associated with activated SHH pathway and WNT pathway, respectively. They found that further targeting of the SHH pathway inhibited growth of MPNST with poor prognosis [52]. Taken together, these analyses highlight the heterogeneity of MPNSTs and revealed different gene expression signatures, which may inform the precision medicine of MPNSTs in the future.
Other studies have focused on comparison between MPNSTs and normal adjacent samples, PN, ANNUBP and benign NF, respectively. For example, Kohlmeyer et al. identified the upregulation of genes such as TWIST1, AURKA, BUB1, and NEK2 in MPNSTs compared to PN, which appears to be important for MPNST growth and proliferation [50]. The comparison of MPNSTs with ANNUBP by Mitchell et al. identified CCMB1, WNT3A, GAS1, and PROM1 as significantly upregulated genes [30]. It is plausible that these could be key genes during the transformation of ANNUBP to MPNST. Wu et al. noted the upregulation of epithelial–mesenchymal transition (EMT) and EZH2 pathways in MPNSTs and upregulation of P53 and interferon signaling in benign neurofibromas (NF) [48]. However, few recurrent DEGs or pathways are identified among these studies, highlighting the heterogeneity throughout MPNST development.
Biomarkers related to Schwann cell development have been identified as potential predictive and diagnostic tools for MPNST. Recently, Holand et al. found CDH19, ERBB3, S100B and other Schwann cell differentiation markers were downregulated in MPNSTs compared to neurofibroma, indicating dysregulation of Schwann cell development in MPNSTs [29]. Vasudevan et al. added to this by showing that compared to neurofibroma cells, Schwann cell differentiation markers S100B and SOX10 were downregulated in PRC2 mutant MPNST cells, suggesting that PRC2 loss is related to the transformation of Schwann cells into MPNSTs [51]. The above sequencing data have shown distinct gene expression signatures between other PNST and MPNSTs and identified a few gene candidates for MPNSTs. However, there is still a lack of RNA-seq-based predictors to clinically assess the risk of patients transforming from PNs to MPNST and then formulate personalized treatment plans. In addition, there is also a lack of a consistent set of genes or pathways implicated across all studies, which highlights the inherent complexity and heterogeneity of MPNSTs. Future research needs to focus on identifying common molecular pathways that can be targeted for therapy, as well as developing reliable predictive models based on RNA-seq data in a prospective manner to guide clinical decisions.
Gene expression analysis can also help assess the drug synergy effect and uncover potential mechanisms to target the MPNST. For example, Borcherding et al. treated MPNST cells with TYK2 inhibitor, and then performed RNA-seq which showed the upregulation of the MAPK pathway after treatment. Following this finding, they combined mirdametinib, a MEK inhibitor, with deucravacitinib, a TYK2 inhibitor, and these two drugs synergistically inhibited the MPNST [73]. In another example, Sun et al. found that the BMP2-SMAD1/5/8 pathway was activated in NF1-associated MPNSTs, and inhibition of this pathway reduced tumor cell proliferation and invasion, hinting at a possible therapeutic target [37]. These studies have uncovered novel therapeutic targets for MPNSTs, and guide more effective treatment strategies.
Overall, microarray and RNA-seq have greatly deepened our understanding of the pathogenesis of MPNSTs at the gene expression level. However, traditional bulk RNA-seq extracts mixed RNA from tissues or a group of cells for sequencing to obtain the average gene expression of all cells, which cannot reveal the expression specificity of individual cells, and cannot understand the interaction between tumor cells and other cells in their microenvironment and the role of each cell type. Future studies should be focused on combination with single-cell and spatial transcriptomic approaches to dissect the cellular heterogeneity within the MPNST.

2.2. Single-Cell RNA-Seq

The single-cell RNA-seq reveals the gene structure and gene expression level of individual cells in the MPNST, reflecting the heterogeneity of cells and allowing for analysis of the contribution of individual cells to the MPNST. Several studies have revealed that MPNSTs are composed predominantly of tumor cells, whereas PNs show a higher proportion of immune-rich environments. For example, Vasudevan et al. showed that MPNSTs were mainly enriched in tumor proliferation cell clusters and growth factor-stimulated tumor cell clusters, while PNs were mainly enriched in T cell and endothelial clusters, which are non-tumor cells [51]. Zhang et al. showed a higher proportion of MPNST cells and fewer immune cell clusters in metastatic MPNSTs, compared to primary MPNSTs, which suggests that metastatic MPNSTs may suppress the immune environment more effectively than even the primary tumors, leading to their aggressiveness and ability to evade immune surveillance [54].
Similar trends are observed in mice models. Wu et al. found that the microenvironment in early-tumor stages of MPNST had more antitumor characteristics, with a higher presence of antitumor macrophages, while the microenvironment in advanced tumor stage became more pro-tumorigenic, characterized by a rise in activated fibroblasts and anti-inflammatory macrophages. They also compared the PNs with MPNSTs and determined unique cell clusters including hypoxic malignant Schwann cell precursors, mesenchymal-like neural crest cells, and mesenchymal-like malignant cells in MPNST, while immature Schwann cells as well as mesenchymal-associated macrophages were enriched in PNs [48].
In another study, Suppiah et al. found that MPNSTs with worse prognosis had a larger proportion of neoplastic cells, while in MPNSTs with better prognosis and atypical NF, the proportion of immune cells are significantly higher. They also found that the known marker of Schwann cells including S100B and ERBB3 were decreased in MPNSTs with worse prognosis, while early neural crest cell markers SOX9 and OTX2 were increased in MPNSTs with better prognosis, indicating that the de-differentiation of Schwann cells is related to the progression to MPNSTs [52].
The above analysis showed that unique cell types, including malignant Schwann cell precursors and mesenchymal-like cells, have been identified in MPNSTs, while Schwann cells and macrophages dominate in PNs. These findings highlight the cellular heterogeneity of MPNSTs and their evolving tumor microenvironment as the disease progresses. However, several limitations of single-cell sequencing technologies cannot be ignored: first, the low-expression transcripts are still likely to escape the detection from sequencing. Second, it requires good practice and relatively complex training to obtain a qualified dataset. Third, it is easy to produce artifacts such as multiplets. Finally, it mainly focuses on the expression of different types of cells and ignores the spatial information and physical interaction of different cells in the tumor microenvironment. Future research should strive to improve the sensitivity of the low-abundance transcripts and simplify the pipeline of single-cell RNA-seq.

2.3. Whole Exome Sequencing (WES) and Whole Genome Sequencing (WGS)

WES and WGS have been widely used to uncover MPNST mutation patterns. NF1 germline mutation and the inactivation of this tumor suppressor gene, is known to predispose individuals to various cancers, including MPNSTs. Additional somatic mutations in NF1 can lead to biallelic inactivation, further increasing the malignancy rate of the disease [74]. In 2015, Hirbe et al. sequenced the same MPNST patient with different stages of diseases including PN, MPNST and metastasis MPNST. They found that the proportion of NF1 mutations increased with the progression of the MPNST and that the tumors became much more copy-number aberrant as they progressed to cancer [61].
PRC2 complex mutations, TP53 loss, and CDKN2A loss are other common events in MPNSTs. In 2021, Dehner et al. performed whole exome sequencing in MPNST PDX lines. They found that SUZ12 loss occurred in 62.5% of PDX lines while proportion of TP53 loss was only 12.5% [45]. Similarly, Lee et al. noticed that the proportion of PRC2 components EED/SUZ12 loss in sporadic MPNST, NF1-MPNST, and radiation-induced MPNST was 92%, 70%, and 90%, respectively. In addition, they found that somatic mutation of CDKN2A co-occurred with PRC2 loss, accounting for 81% of all MPNSTs [19]. Cortes-Ciriano et al. also showed that histone H3 on lysine 27 (H3K27me3) loss occurred in 55% of MPNSTs and biallelic inactivation of SUZ12 and EED occurred in 28% and 17% of MPNSTs, respectively. They also found that CDKN2A biallelic inactivation occurred in 63% of NF1-related MPNST and 55% of sporadic MPNST [72]. In Lorenz’s study, CDKN2A loss existed in 100% of MPNSTs (n = 15) and 100% of MPNST cell lines (n = 8) [75]. These recurrent gene mutation events also suggest that these genes are of great significance in the development of MPNSTs.
Chromosome aberrations have been found in MPNSTs, especially the chromosome 8 gain. Szymanski et al. showed extensive chromosome aberrations, including gain of 1q, 7p, 8q, 9q, and 17q and chromosome loss in 6p and 9p in MPNST [76]. Similarly, Suppiah et al. also showed that gain of SMO, 1q, 8q,13q, 17p and 18q occurred in MPNSTs with poor prognosis [52]. Dehner et al. also found that chromosome 8 gain was exclusively occurring in 87.5% of MPNSTs, while no chromosome 8 gain was observed in PN samples, indicating chromosome 8 gain may be an important genomic event in MPNST development [45].
Together, the above sequencing studies displayed multiple events that occur during the transformation from PNs to MPNSTs, including loss of NF1, CDKN2A, PRC2 components and TP53, which are considered as common genetic mutation events in MPNSTs. Many other gene structural variations are identified such as chromosome gain of 8q, 1q, and 19q. However, it remains unclear at which stage these structural variations occur. Additionally, to understand polyclone evolution during the MPNST transformation, it would require more spatial transcriptome and genome sequencing evidence.

2.4. Epigenetics Sequencing

Epigenetic alterations, including DNA methylation, histone modifications, and non-coding RNA regulation, play a critical role in the pathogenesis of MPNSTs, as well.
Few recurrent gene methylations are reported in MPNSTs. CDKN2A, however, has been identified as one of the few recurrent hypermethylated genes in MPNSTs. In 2011, Feber et al. found hypermethylation of CDKN2A in MPNSTs, which indicated that the repression of the CDKN2A might be due to the epigenetic dysregulation in some cases [64]. In addition, Renner et al. found that CpG sites of CDKN2A could reflect the different types of soft tissue sarcomas [66]. Other distinct DNA methylations in MPNSTs have been also identified. For example, several differentially methylated CpGs located in IL17 were shown to be related to MPNST progression [77]. Danielsen et al. detected hypermethylation of promoter of RASSF1A in 60% of MPNST samples, which is also correlated with poor prognosis of NF1-associated MPNST, indicating RASSF1A might be a prognostic predictor in a subset of MPNSTs [78]. These studies have implications for the epigenetic-based therapies of MPNSTs.
In addition to CDKN2A alterations, mutations in components of the PRC2 complex are common in MPNSTs. PRC2 primarily catalyzes the methylation of H3K27me3 and has been identified as an important modification in MPNSTs. For example, Kochat et al. found that PRC2 loss would reduce the repression of several enhancers, which further promotes MPNST progression [49]. In 2016, Röhrich et al. found that loss or reduction of H3K27me3 was exclusively seen in a subset of MPNSTs based on methylation clustering, which could be used as a biomarker to differentiate the cellular schwannoma and MPNST [79]. Cortes-Ciriano et al. found that global hypomethylation occurred in MPNSTs and hypermethylation of CpG islands and PRC2-component mutations occurred in MPNSTs with poor prognosis compared to MPNSTs with a better prognosis in patients with NF1 [72]. In 2022, Yan et al. performed assay for transposase-accessible chromatin (ATAC-seq) to detect the chromosome accessibility after the knockout of SUZ12. The results showed that the loss of PRC2 due to the knockout of SUZ12 led to decreased chromosome accessibility, which further impaired IFN-gamma response in MPNST cells [47]. However, a previous study showed that was no significant survival difference between MPNSTs with the loss and retaining of PRC2 (n = 100), but they did not stratify patients by NF1 status, which may account for the lack of correlation with survival [60].
Micro-RNA array sequencing has been applied in MPNSTs recently. Amirnasr et al. identified upregulated micro-RNAs miR135b and miR-889 in MPNSTs compared to PN. Subsequent functional inhibition of these two micro-RNAs led to impaired Wnt/β-catenin pathways and inhibited MPNST growth. They also established a micro-RNA expression signature that could differentiate the sporadic and NF1-associated MPNSTs, indicating that micro-RNA could potentially be used as a promising diagnostic tool [67]. Zhang et al. observed an increased expression of miR-30d after the knockdown of EZH2, which further induced the proliferation of MPNST cells and revealed that altered micro-RNA expression could be related to tumor aggressiveness [40]. Overall, these epigenetic studies suggest that PRC2 loss and CDKN2A hypermethylation are common pathways that could serve as prognostic indicators or therapeutic targets for MPNSTs. Future research should leverage a combination of high-resolution methylation profiling, ATAC-seq for chromatin accessibility, and ChIP-seq for histone modifications—to map out the full landscape of epigenetic alterations in MPNSTs.

2.5. Emerging Sequencing Technologies: Proteomics and Metabolomic Sequencing

Recently, proteomics and metabolomics sequencing are beginning to be applied in MPNST. Tsuchiya et al. performed proteomics sequencing for 23 MPNSTs and found that the MET pathway was upregulated in the recurrent or metastatic MPNST group. Further drug screens also identified the fact that one of the MET inhibitors, crizotinib, could effectively inhibit MPNST growth, highlighting the potential to use this technology for therapeutic drug discovery [80]. Jia et al. performed proteomics sequencing of MPNST samples with different prognosis (less than 2 years’ survival vs. more than 5 years’ survival). They observed that expression of decorin, a protein maintaining the stability of extracellular matrix, was significantly lower in the poor-prognosis MPNST group, highlighting the use of this technology to identify prognostic biomarkers [81].
No large metabolomic screens have been performed. However, Lemberg et al. evaluated the effect of an inhibitor of glutamine, JHU395, on MPNST growth. JHU395 could inhibit the MPNST growth in vivo and in vitro with mild toxicity. They further analyzed the metabolomic changes following JHU395 inhibition in mice and identified formylglycinamide ribonucleotide as the most significantly altered metabolite. This metabolite plays a key role in purine synthesis, suggesting that the inhibition of purine synthesis could be the mechanism by which JHU395 impedes MPNST progression [82]. More recently, they evaluated a combination therapy of pro905 and JHU395 in MPNSTs, and detected the metabolites that change after the treatment in a mouse model. Similarly, purine and pyrimidine metabolism were still the top differentially altered pathways [83]. These studies highlight the possibility of identifying combination therapies through metabolomic analyses. Future studies should focus on cell type-specific metabolic and proteomic signatures, and within the MPNST.

3. From Sequencing to Implications: Targeted Therapy

3.1. MEK Inhibitor

MEK pathway activation has been observed in MPNSTs [52,84]. Although several studies showed promising results utilizing MEK inhibitors in benign PN, single-agent MEK inhibition has not been nearly as effective in MPNST models [32,85], likely due to adaptive signaling leading to drug resistance [86]. However, a MEK inhibitor is a potentially promising partner in combination therapies. Borcherding et al. showed the synergistic anti-tumor effect of a TYK2 inhibitor and MEK inhibitor in NF1-associated MPNST in vivo and in vitro [73]. Other promising combinations have included CDK4/6 inhibition with MEK inhibition in MPNST models. Additionally, that combination treatment increased the MPNST response rate to anti-PD-L1 immunotherapy in the mice model [87]. Wang et al. identified SHP2 as a central node in MPNST pathogenesis and the combination of an SHP2 inhibitor and MEK inhibitor looked promising in vivo, in MPNST models [88]. The above studies highlight the importance of combination therapy in MPNST to overcome drug resistance. Several clinical trials are evaluating MEK inhibitor combinations in MPNSTs (NCT05107037, NCT05253131 and NCT03433183) and other trials based on the pre-clinical studies described here are in development.

3.2. Histone Deacetylase (HDAC) Inhibitors

Much evidence from RNA-seq and DNA methylation sequencing has shown that PRC2 loss is a key molecular mutation event during the MPNST transformation [19,49]. PRC2 is crucial for the methylation of H3k27me3, which is the rationale for attempting to treat MPNSTs using HDAC inhibitors. Several HDAC inhibitors have been evaluated, showing promising results in vitro and in vivo in MPNST models [89,90,91]. To date, there are several HDAC inhibitors that have been approved to treat other cancers, including romidepsin, vorinostat, belinostat and panobinostat, which are currently used for lymphoma and multiple myeloma [92]. However, there is only one completed phase II clinical trial testing the effect of HDAC inhibitor romidepsin on various sarcomas (NCT00112463), and no studies to date have focused solely on MPNSTs. These findings underscore the need for further research and clinical trials to explore the potential of HDAC inhibitors in the treatment of MPNSTs, potentially in combination with other therapies.

3.3. Other Emerging Targeted Therapies

Several DNMT inhibitors have been evaluated in MPNSTs. Patel et al. found that a DNMT1 inhibitor selectively inhibited PRC2-loss and MPNST growth and activated transcription of a portion of PRC2 target genes [56]. Similarly, decitabine, a DNA methyltransferase inhibitor, has been approved to treat myelodysplastic syndrome [93] and an ongoing phase II clinical trial is testing the effect of ASTX727, a combination of decitabine and cedazurine, in MPNSTs (NCT04872543). These studies underscore the therapeutic potential of DNMT inhibitors in MPNST, particularly in targeting MPNSTs with PRC2 loss.
The mTOR signaling pathway has also been found to be activated in MPNSTs, and increased expression of p-mTOR is related to the poor prognosis of MPNSTs [94]. Preclinical studies have implicated mTOR signaling in MPNSTs. Johasson et al. found that the single mTOR inhibitor everolimus could inhibit the sporadic and NF1-associated MPNST growth in vivo [95]. They also showed that a combination of everolimus and erlotinib, an EGFR inhibitor, could achieve an additive effect on inhibition of tumor growth. However, clinical trial results with mTOR inhibitors have not shown benefit [96,97,98]. This may be due to poor selection of partners, specificity of mTOR inhibitors, or perhaps the fact that these combinations were studied in a single preclinical model which may not be representative of MPNSTs.
In 2013, Peacock et al. found up-regulated DNA repair mechanisms in MPNSTs, which indicates that DNA repair inhibitors might be a potential treatment for MPNSTs. Poly (ADP-ribose) polymerase (PARP) is a protein involved in DNA repair, and has been studied in MPNSTs recently. Several PARP inhibitors (e.g., olaparib, rucaparib, niraparib and talazoparib) have been approved by FDA and applied in many cancers. Larsson et al. tested a combination of trabectidin and olaparib in MPNSTs and found that this combination therapy reduced tumor growth in vivo [99]. Kivlin et al. also tested olaparib in MPNST and mouse models and found that olaparib could help decrease tumor growth and metastasis [100]. Unfortunately, no clinical trials report the effects of PARP inhibition on MPNST, and we look forward to further investigation of this target in MPNST patients.

3.4. Implications for Immunotherapy

Although there is currently no approved immunotherapy regimen for MPNSTs, several preclinical studies have shown that this could be a promising treatment strategy. Holand et al. identified two subtypes of MPNSTs based on transcriptome data, named immune deficient and immune active. They found that MPNSTs from an immune-deficient group had a poor prognosis and upregulated LGR5, IGF2BP1, PROM1, which could be used as potential druggable targets [29].
PRC2 status is also correlated with immune infiltration in MPNSTs. Yan et al. compared gene expression of MPNSTs with PRC2 retaining or loss [47]. They found that adaptive immune response pathway and T-cell receptor pathway are enriched in the PRC2-retained group while spinal cord development and other differentiation pathways are enriched in the PRC2-loss group. Further, they found that immunogenic virus injection into the PRC2-loss group could enhance the tumor immune infiltration and sensitize the tumor to immune checkpoint therapy. In addition, Cortes-Ciriano et al. identified two transcriptomic subtypes of MPNSTs, which were correlated with the H3Kme27 status, and the subtype with H3K27me3 loss had decreased immune infiltration and immune checkpoint expression inferred from RNAseq data [72].
The alteration of the immune microenvironment is being explored as a therapeutic strategy. Patwardhan et al. depleted macrophages in an MPNST xenograft model using pexidartinib, which led to reduced tumor growth [101]. In addition, Somatilaka et al. were able to cause a shift in MPNSTs from immune cold- to immune hot-tumor, using stimulator of IFN genes (STING) signaling, which sensitized the MPNST to the PD-1/PD-L1 blockade treatment [102]. Future studies could be aimed at therapeutic strategies to shift this immune cold state to an immune-rich environment, to harness immunotherapies.

4. Conclusions and Implications for Clinical Practice

As sequencing technologies have advanced, there has been an explosion in the volume and variety of data from MPNST, derived at multiple biological levels. These data have led to the identification of MPNST driver genes including NF1, CDKN2A, PRC2 components and TP53, as well as numerous potential targeted therapies including MEK inhibition combinations, HDAC inhibitors, SHP2 inhibitors, TYK2 inhibitors, and CDK4/6 inhibitors, etc. Additionally, these data have begun to uncover the potential of targeting the immune system, as well.
Given the relatively low cost of clinical sequencing platforms and potential information that can be gained to guide clinical practice, obtaining molecular information on MPNST patients is potentially beneficial. For example, MTAP loss, which occurs with CDKN2A loss in at least 25% of MPNSTs, confers sensitivity to PRMT5 inhibitors which are currently in clinical trials [103]. Having this information would allow for patients to be enrolled in such a clinical trial. Additionally, MDM2 inhibitors are being utilized in clinical trials for MPNSTs. However, 25% of MPNSTs have TP53 mutations, and as such, an MDM2 inhibitor would not be beneficial in this patient population [104]. Thus, molecular data can again guide decisions for clinical trial enrollment.
Despite these advancements, significant challenges remain. First, we are only starting to understand molecular subtypes of MPNSTs. For example, 25% of MPNSTs have TP53 mutations, and approximately 65% have mutations in the PRC2 complex. However, for at least 10% of MPNSTs, the driver is unclear. Additionally, we have not had validated studies to understand whether or not there are clear transcriptomic subtypes that should guide therapy. Second, once a therapy begins, drug resistance driven by adaptive changes is bound to occur, which will certainly impact therapeutic decisions. In the coming years, development of larger cohorts in clinical trials and the integration of multi-omics data, combined with targeted therapies to overcome tumor resistance and personalized treatments, based on the molecular characteristics of MPNST, will shape the therapeutic trends of the future.

Author Contributions

Conceptualization, K.X. and A.C.H.; supervision, A.C.H.; writing—original draft, K.X., K.Y. and A.C.H.; writing—review and editing, K.X., K.Y. and A.C.H. All authors have read and agreed to the published version of the manuscript.

Funding

K.X. is funded by R01NS134815-02 awarded to ACH through NINDS. K.Y. is partially funded by a grant from the St. Louis Men’s Group Against Cancer.

Acknowledgments

The authors thank Yang Lyu and Diana Akinyi Odhiambo for their suggestions and help with the review paper.

Conflicts of Interest

A.C.H. has the following COI: Serving on the Advisory Board of Aadi Subsidiary, Inc., engaging in licensing activities with Boehringer Ingelheim GmbH, providing consulting services to Springworks Therapeutic Operating Company, PBC, receiving an honorarium from the American Physician Institute for Advanced Professional Studies, and receiving research funding for Tango. The other authors declare no conflicts of interest.

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Cancers 17 00180 g001
Figure 1. Summary of key findings from recent sequencing technologies (created by Biorender).
Figure 1. Summary of key findings from recent sequencing technologies (created by Biorender).
Cancers 17 00180 g001
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Xiao, K.; Yang, K.; Hirbe, A.C. A Sequencing Overview of Malignant Peripheral Nerve Sheath Tumors: Findings and Implications for Treatment. Cancers 2025, 17, 180. https://doi.org/10.3390/cancers17020180

AMA Style

Xiao K, Yang K, Hirbe AC. A Sequencing Overview of Malignant Peripheral Nerve Sheath Tumors: Findings and Implications for Treatment. Cancers. 2025; 17(2):180. https://doi.org/10.3390/cancers17020180

Chicago/Turabian Style

Xiao, Kangwen, Kuangying Yang, and Angela C. Hirbe. 2025. "A Sequencing Overview of Malignant Peripheral Nerve Sheath Tumors: Findings and Implications for Treatment" Cancers 17, no. 2: 180. https://doi.org/10.3390/cancers17020180

APA Style

Xiao, K., Yang, K., & Hirbe, A. C. (2025). A Sequencing Overview of Malignant Peripheral Nerve Sheath Tumors: Findings and Implications for Treatment. Cancers, 17(2), 180. https://doi.org/10.3390/cancers17020180

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