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Ruggiero, A. Intra-Tumoral Heterogeneity of Pediatric Intracranial Ependymoma. Encyclopedia. Available online: https://encyclopedia.pub/entry/19003 (accessed on 05 December 2025).
Ruggiero A. Intra-Tumoral Heterogeneity of Pediatric Intracranial Ependymoma. Encyclopedia. Available at: https://encyclopedia.pub/entry/19003. Accessed December 05, 2025.
Ruggiero, Antonio. "Intra-Tumoral Heterogeneity of Pediatric Intracranial Ependymoma" Encyclopedia, https://encyclopedia.pub/entry/19003 (accessed December 05, 2025).
Ruggiero, A. (2022, January 29). Intra-Tumoral Heterogeneity of Pediatric Intracranial Ependymoma. In Encyclopedia. https://encyclopedia.pub/entry/19003
Ruggiero, Antonio. "Intra-Tumoral Heterogeneity of Pediatric Intracranial Ependymoma." Encyclopedia. Web. 29 January, 2022.
Intra-Tumoral Heterogeneity of Pediatric Intracranial Ependymoma
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This entry is adapted from the peer-reviewed paper 10.3390/cancers13236100

Intra-tumoral heterogeneity (ITH) is a complex multifaceted phenomenon that posits major challenges for the clinical management of cancer patients. Genetic, epigenetic, and microenvironmental factors are concurrent drivers of diversity among the distinct populations of cancer cells. ITH may also be installed by cancer stem cells (CSCs), that foster unidirectional hierarchy of cellular phenotypes or, alternatively, shift dynamically between distinct cellular states. Ependymoma (EPN), a molecularly heterogeneous group of tumors, shows a specific spatiotemporal distribution that suggests a link between ependymomagenesis and alterations of the biological processes involved in embryonic brain development. In children, EPN most often arises intra-cranially and is associated with an adverse outcome. Emerging evidence shows that EPN displays large intra-patient heterogeneity. 

intra-tumoral heterogeneity ependymoma genetics epigenetics tumor microenvironment cancer stem cells

1. Introduction

Tumors are complex ecosystems composed of non-malignant and malignant cell populations [1]. The malignant populations themselves are genetically and phenotypically heterogeneous and define the so-called intra-tumoral heterogeneity (ITH) that governs tumor evolution [2] and drug resistance [3]. Although ITH is a “contemporary concept” [4], its complex nature was highlighted back in the 1970s [5]. However, the mechanistic explanations and full understanding of the origins of ITH still have a long way to go.
Tumors might be considered as an “organ system”, where the cellular subclones act as “tissue types” with distinct functions [6] and reciprocal signaling between tumor subpopulations [7][8][9] and between tumor and the surrounding microenvironment [10], with complex interactions to enhance tumor fitness and facilitate immune evasion [11], drug resistance [12], and metastasis [13].
Pediatric brain tumors (PBTs) represent the leading cause of cancer-related morbidity and mortality in children [14]. The second most common PBT is ependymoma (EPN), a group of molecularly and clinically heterogeneous entities that in children arise almost exclusively intra-cranially. Despite advances in the understanding of EPN biology, the prognosis is still grim in approximately 40% of patients because of a high degree of ITH and intrinsic chemoresistance [15]. Over the last few years, whole-genome sequencing, gene-expression profiling, and genome-wide methylation at a whole-population level have stratified EPN into nine molecular groups, four of which represent the major types of intracranial pediatric EPN and differ in demographic, clinicopathological, and (epi)genetic profiles (Figure 1) [16].
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Figure 1. A timeline of the most important molecular findings which have contributed to uncovering inter-tumor and intra-tumor heterogeneity in intracranial pediatric ependymoma.
ST-EPN-RELA (ST-RELA) and ST-EPN-YAP1 (ST-YAP1) tumors arise in the supratentorial (ST) compartment, and are distinguished by mutually exclusive recurrent zinc finger translocation associated (ZFTA)-RELA proto-oncogene, NF-kB subunit (RELA) or Yes1 associated transcriptional regulator (YAP1)-involving fusions [17], whereas PF-EPN-A (PFA) and PF-EPN-B (PFB) tumors occur in the posterior fossa (PF) [18].
To date, the majority of research and treatment decisions in EPN have been based on analyses of bulk tumors that, however, return an averaged picture of all cell populations. The advances of sequencing technologies and single-cell omics over the last decade have deepened the knowledge of the bewildering heterogeneity of the tumor genome, transcriptome, epigenome, and proteome at an unprecedented scale [19][20].

2. Clinicopathological Characteristics of Pediatric Intracranial EPN

Approximately 70% of childhood EPNs occur in the PF, whereas 20% occur in the supratentorium. PFAs arise in younger children (<5 years) and are characterized by dysregulation of numerous cancer-related networks, such as angiogenesis, receptor tyrosine kinase (RTK) signaling, and cell cycle (Table 1) [18]. PFBs frequently occur in older children (5–18 years) and display many gains and losses of entire chromosomes [16], but dysregulation of a very restricted number of pathways controlling microtubule assembly and oxidative metabolism [18] (Table 1). More than two thirds of ST-EPNs harbor alternative ZFTA–RELA fusions [17] (Table 1), that lead to constitutively active NF-κB signaling, an established driver of solid tumors [21]. In addition, ST-RELA EPNs display other subgroup-specific genomic alterations, including frequent loss of chromosome 9 and homozygous INK4a-ARF (CDKN2a) deletions [16]. YAP1 fusions, the most common being YAP1-mastermind like domain containing 1 (MAMLD1), define the other clinically relevant subgroup of ST-EPN, rare tumors with relatively stable genomes, besides recurrent rearrangements involving YAP1 gene locus on chromosome 11 [16][22]. Compared to ST-RELA, YAP1-MAMLD1 tumors differ in demographic distribution (occurring mainly in children with a median age of 1.4 years vs. 8 years of RELA EPNs, and mostly restricted to female patients), anatomical location (intra-/periventricular in YAP1 vs. cerebral in RELA) and prognosis (favorable vs. unfavorable) [23].

Table 1. Summary of the main genetic/epigenetic alterations and clinicopathological characteristics of the major groups of pediatric intra-cranial EPN.
Molecular Group ST-RELA ST-YAP1 PFA PFB References
Location ST, cerebral ST, intra-periventricular PF PF [18][23]
Age children/adolescents
median age 8 years		 young children
median age 1.4 years		 young children
median age 3 years		 all age groups
median age 30 years		 [16]
Gender         [16]
Male 65% 25% 65% 41%  
Female 35% 75% 35% 59%  
Molecular events          
Genetic chromothripsis
ZFTA-RELA fusions
CDKN2a deletion
loss of chromosome 9		 YAP1-fusions balanced genome
1q gain
6q loss
infrequent H3K27M substitution
infrequent EZHIP mutations		 chromosomal instability [16]
[16][17]
[16][24]
[25]
[26]
Epigenetic     CIMP positive
DNA hypomethylation
H3K27me3 loss
EZHIP overexpression		 CIMP negative
H3K27me3 retention		 [27]
[28]
[28][29]
[26]
Pathogenic impact NF-κB pathway
cell cycle
cell migration
MAPK pathway		 Hippo pathway angiogenesis
RTK pathways
cell cycle
cell migration
derepression of PRC2 target genes		 ciliogenesis
oxidative metabolism		 [17][22]
[16][18]
[27]
Outcome poor favorable poor favorable [16]

There is increasing evidence of ST-EPNs with alternative gene fusions and ambiguous DNA methylation-based classification [30][31][32]. Pediatric supratentorial RELA fusion-negative EPNs show other fusion events, the majority involving ZFTA as a partner gene, such as ZFTA-mastermind like transcriptional coactivator 2 (MAML2) or ZFTA-nuclear receptor coactivator 1 (NCOA1). These tumors exhibit histopathological heterogeneity, no nuclear NF-κB expression, and epigenetic proximity to the RELA methylation class in some cases [33][34].

3. CSCs as a Source of ITH

3.1. The CSC Model

The CSC model was revived about two decades ago with the isolation of a subset of functionally distinct cells from hematologic and solid malignancies that uniquely drive tumor growth [35], and has rapidly emerged to explain the versatile features of tumor populations [36]. The isolation of clonogenic neural stem cells (NSCs) from human fetal brain tissue [37] corroborated the hypothesis that brain tumors may develop from transformed NSCs or progenitor cells [38].
According to the classical paradigm, CSCs divide asymmetrically and give rise to daughter stem cells and non-stem progeny to drive unidirectional hierarchy-organized phenotypic differentiation that installs ITH (Figure 2a). More recently, accumulating evidence has revisited the traditional model into the plasticity model [39], whereby cancer cells possess the ability to bidirectionally transition between an SC and non-SC state. According to this model: (1) CSCs are not necessarily a small, slow-proliferating fraction of the bulk tumor, and (2) the functional features of both CSCs and non-CSCs are dynamically generated by combinatorial genetic and non-genetic factors. The observation that purified breast cancer [40] and glioblastoma (GBM) [41][42] stem and non-stem cells re-establish an equilibrium of mixed populations of all cellular states in cultures and in vivo is a proof of principle of the plasticity model.
Although cancers result from genetic and epigenetic events in interaction with the microenvironment, the cell(s) where these events occur are equally important determinants of tumorigenesis [43][44]. It is likely that CSCs derive from neoplastic transformation of healthy SCs, because pathways involved in the normal SC homeostasis are often hijacked and/or epigenetically altered in CSCs to bring about a “malignant reprogramming” that locks the cells into a state of self-renewal that persists beyond the timeframe of normal development (Figure 2b) [45].
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Figure 2. Intra-tumoral heterogeneity (ITH) of EPN in the cancer stem cell (CSC) perspective. (a) Schematics of the classical (on the left) and plasticity (on the right) CSC model. The classical CSC model asserts that CSCs are a rare fraction of the bulk tumor, able to self-renew and to differentiate along multiple lineages, driving unidirectional hierarchy of heterogeneous cells. CSC plasticity model proposes that cancer cells can dynamically transition from a CSC state to a non-CSC state and vice versa. Combinatorial dynamics of genetic and epigenetic alterations and distinct microenvironmental contexts engender cell stemness and plasticity, installing ITH. (b) CSC model(s) in intra-cranial ependymomagenesis. Molecular events occurring in distinct cells of origin drive ependymomagenesis in the two compartments: genomic instability, leading to chromothripsis, gene fusions, and INK4a-ARF deletions, predominate in ST-RELA, whereas epigenetic factors (e.g., CIMP phenotype, global loss of H3K27me3, EZHIP overexpression) predominate in PFA.

3.2. CSC-Driven Preclinical Models of EPN

Cell lines have been established from EPN surgical samples by selection of the CSC component in NS-promoting conditions [46][47][48][49]. NS models mimic a 3D structure, which resembles the tumor microenvironment (TME) more faithfully than 2D cultures, preserving cell variability [50]. Compared to cell lines grown as monolayers, EPN 3D cultures transplanted in the mouse brain show better fidelity to the original tumor in terms of genetic, transcriptomic, and histopathological characteristics [48][49].
Drug treatment of patient-derived EPN cell lines results in preferential depletion of a stem-like cell population with a tumor-initiating property, as shown by a decrease in NSC markers, increase in differentiation-associated markers, and reduction in tumorigenicity in ex vivo transplantation assays [46][48]. Specific targeting of BLBP by PPAR antagonists lessens cell migration and invasion and promotes chemoresistance in vitro [51]. In comparison with EPN stem-like cells, differentiated cells are less sensitive to temozolomide because of differentiation-induced upregulation of MGMT [52], although others have also reported temozolomide resistance in undifferentiated EPN SCs [48].
Clonal expansion of patient-derived cells by differential selective pressure exerted by culture conditions can help uncover genetic ITH in EPN. One mitogen-independent highly tumorigenic EPN line has been found to harbor protein-coding SEC61G–EGFR fusion genes, also found in one PFA out of 16 pediatric EPN cases by RT-PCR sequencing [53] and in glioblastoma (GBM) [54]. Similar to findings in EPN, ITH of GBM with a heterogeneous pattern of expression/amplification of RTKs is revealed by genotype selection under receptor-targeted ligand stimulation [55]. GBM SCs (GSCs) in EGF-free media retain EGFR amplification and EGFRvIII expression, which are usually lost in cells cultured in mitogen-enriched media [56]. Together, these data suggest that differential selection in vitro and in vivo may represent a complementary strategy to address ITH and its functional relevance in EPN.

4. Determinants of ITH

4.1. Genetic ITH

Besides small-scale genetic changes, genomic instability includes large-scale genomic events [57], such as chromosomal instability [58], aneuploidy, chromothripsis [59], and extrachromosomal DNA (ecDNA) [60], that involve an ample number of genes and provide massive copy-number amplification. Large-scale DNA alterations are quite unstable through mitoses and can undergo strong selective pressures, accelerating tumor evolution [12].
ecDNAs exist in almost 50% of tumors [61], and are circular-shaped elements of DNA with high chromatin accessibility [62] that contain amplified oncogenes and drug resistance genes [63]. As they lack centromeres, ecDNAs segregate randomly among daughter cells and confer massive intercellular genetic heterogeneity and improved fitness, that might result in tumor aggressiveness and chemoresistance [64][65]. Loss of ecDNA carrying EGFRvIII induces resistance to EGFRvIII inhibitors in GBM models and patients [66]. Different evolution of ecDNA at diagnosis and relapse has been reported across multiple cancers, including pediatric high-grade gliomas (pHGGs) [67]. However, the contribution of ecDNA to ITH of EPN has not been addressed yet.
Chromothripsis is a single cellular catastrophic event in which hundreds of genomic rearrangements take place at once in one or a few chromosomes [59]. Generally considered as an early mutational phenomenon occurring in a minority of neoplasms, sequencing-based analyses at high coverage depth have demonstrated that chromothripsis is pervasive in cancers, reaching a frequency of more than 50% in some entities [68]. Moreover, longitudinal analysis in paired primary and relapsed tumors has shown that chromothripsis may occur only in the primary tumor, only at relapse, or, conversely, in both events in the same patient, which suggests subclonal heterogeneity and evolution occurring through all steps of tumor progression [69]. A causative role for chromothripsis has been inferred in ST-RELA EPNs, where ZFTA–RELA fusions result from a shattering event on chromosome 11, that juxtaposes ZFTA to the NF-κB master transcription factor RELA [17]. Remarkably, among the nine EPN molecular subgroups, chromothripsis is detected exclusively in ST-RELA, where it more frequently involves chromosome 11 [16].

4.2. Epigenetic ITH

Variable phenotypes of cancer cells can also be mediated by epigenetic, transcriptional, and microenvironmental changes without concomitant genetic mutations. Non-genetic ITH is far more dynamic than genetic heterogeneity and is therefore increasingly recognized as a driving force of tumor evolution [70][71].
The term “epigenetic” describes the covalent modifications of DNA and histones that affect gene expression without intrinsic changes in the DNA sequence through modulation of the chromatin structure [70]. Epigenetic changes are inherited by offspring cells just like genetic alterations and provide an additional pool of selectable traits. An interplay between genetic and epigenetic alterations occurs in virtually all tumor types, where epigenetic lesions may precede or arise simultaneously with genetic mutations, or conversely be a consequential event [72][73]. PBTs display an overall low mutational burden, but there are a number of epigenetic dysregulations [74][75][76] that can drive tumorigenesis even in the absence of highly recurrent driver mutations, CIMP-positive PFA being a prominent example. Most of the few recurrent mutations of PBTs target epigenetic regulatory genes, such as H3.3A, ATRX, and enhancer of zeste homolog 2 (EZH2) [77].
Epigenome regulation of intercellular heterogeneous gene expression is a dynamic condition between transcriptionally active and repressive chromatin states by virtue of cell-to-cell variation in DNA methylation at enhancers and promoters, covalent histone modifications [78], nucleosome positioning [79], and chromatin accessibility [80]. Prominent alterations of DNA methylation in cancers, including high-risk PFAs, are focal gains at normally unmethylated CpG islands and promoter regions, that heritably silence hundreds of genes that counteract tumor development, outnumbering gene mutations [81][82]. Posttranslational covalent histone modifications include methylation or acetylation at histone tails, such as H3K27me3 and H3K27ac, markers of repressed and active transcription, respectively [73]. H3K27 trimethylation is mediated by the Polycomb repressive complex 2 (PRC2) via the methyltransferase activity of the PRC2 catalytic subunit EZH2 [83].
Epigenetic ITH has primarily been assessed focusing on DNA methylation, because of its stability and mitotic heritability, and is found in regulatory regions that control the transcription of associated genes, contributing to gene expression heterogeneity relevant to cell identity and disease processes [81][84][85][86]. High epigenetic heterogeneity at enhancers has been reported in ESCs [87] and during progression from normal tissues to primary tumors and to metastases with a cancer-specific pattern [88], which indicates that enhancer DNA methylation may be primed to respond to microenvironmental cues and to increase cancer cell plasticity. In temporally distinct tumor specimens, DNA methylation levels are reported to be increased, equal, or decreased in primary vs. relapsed tumors [89], maybe because of variable epigenetic clonal dynamics in different cancers. Compared to primary EPNs, relapsed EPNs display neither significant differences in DNA methylation profiles nor in H3K27me3 levels, whereas major changes occur at CpG islands that show higher methylations in relapsed ST-RELA and PFA EPNs [90].
Spatiotemporal epigenetic heterogeneity in distinct areas of the same tumor has been described in a wide range of cancers and allows for building the evolutionary history of the tumor alongside genetic heterogeneity. Comparison between phylogenetic and epigenetic trees has usually shown similar and integrated patterns, which suggests a codependency of genetic and epigenetic mechanisms in tumor progression [89][91]. In primary low-grade gliomas and matched recurrent HGG, cell cycle genes are epigenetically upregulated through promoter hypomethylation during tumor progression, in parallel with genetic mutations that affect cell cycle checkpoints [92]. Multiplatform molecular profiling of spatially distinct meningioma shows regional alterations in chromosome structure that underpin clonal transcriptomic, epigenomic, and histopathologic signatures [93]. DNA methylation and RNA sequencing of six topographically distinct samples from one ST-RELA tumor reveal significant transcriptional and epigenetic heterogeneity [94]. Remarkably, the expression of the subgroup-specific markers L1CAM, CCND1, ZFTA, and RELA is similar across the sections, whereas DNA methylation-based and gene expression variability define three geographically distinct clusters enriched in stem-like, neuronal differentiation, and mature microglia signatures that recapitulate brain development.

4.3. TME, CSCs, and EPN

The epigenome stands at the intersection of the genome and TME. Unlike genetic alterations, epigenetic modifications are reversible and less consistently transmitted through mitosis, and therefore play a major role in opportunistic adaptation to spatiotemporal fluctuations of the TME [3][95]. Whereas in healthy tissues the environment acts as the main barrier to counteract cancer initiation, in tumor tissues neoplastic cells subvert this organized architecture into a deranged tumor-sustaining milieu. These changes include matrix remodeling, development of tumor vasculature networks, recruitment of stromal and immune cells, and interactions between tumor and normal cells as well as between functionally different tumor subpopulations [10]. The complex tumor architecture creates topographical constraints, changeable blood flow [96], and heterogeneous microenvironmental conditions with a combinatorial dynamic of contextual cues that trigger a variety of signaling pathways and regulatory networks [6].
This paradigmatically occurs at the tumor core and tumor/host interface. Although region-specific driver mutations have been documented [97], contextual factors are equally important in shaping the zonal pattern, with high proliferation, signaling activities and invasion-promoting properties almost exclusively restricted to the leading edge of the tumor as opposed to a quiescent, apoptotic, and therapy-resistant phenotype predominating in the center. These distinct intrinsic signatures and phenotypes are driven by hypoxic [98] and/or acidic microenvironmental gradients [99][100] and paracrine cross-talk [101] between the distinct tumor populations.
Microenvironmental variability promotes commonly observed phenotypic cellular properties, such as stemness and epithelial-to-mesenchymal transition (EMT). There is an intricate interaction between CSCs and their microenvironment. CSCs are actively engaged in shaping their own supportive niche, but are in turn regulated by exogenous signals that affect their epigenome and cellular state [41][82] shaping tumor heterogeneity and evolution. Examples of the interconnections between EPN and TME are given below (Figure 3).
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Figure 3. Schematic of the main factors that contribute to intra-tumoral heterogeneity in ependymoma. Genetic alterations and epigenetic modifications are selected in distinct tumor microenvironments, such as the perivascular and the hypoxic niche, that create gradients of oxygen, nutrients, and acidification. Changing contextual cues affect epigenetic regulators and remodel the chromatin landscape that mediates dynamic cellular plasticity. CSCs are able to adapt bidirectionally to both normoxic and hypoxic niches, fostering tumor growth and progression. Cross-talk among the distinct tumor and normal cell populations (such as endothelial cells) mediated by soluble factors or extracellular vesicles contributes to intra-tumoral cell-to-cell diversity. Cancer cells of the same phenotype tend to cluster together in the same topographical location. Differentiated cell concentrate in perivascular niches, whereas CSCs and mesenchymal-like cells coexist in hypoxic microenvironments, suggesting cooperative interactions and interconversion between these cell states (epithelial-to-mesenchymal transition, EMT).

5. ITH of EPN: A Single-Cell Perspective

5.1. EPN Is Composed of Multiple Discrete Neoplastic Subpopulations

The four major childhood EPN subgroups dissected by scRNA-seq appear to be a composite mixture of multiple phenotypically discrete neoplastic subpopulations with divergent transcriptomic profiles. Although transcriptional signatures and their number differ across EPN subgroups, the common patterns of ITH that have been observed are mostly associated with cell cycle and neurodevelopmental programs. Contrasting the current classification paradigms, these studies have demonstrated that the relative proportions of the individual cell types dictate the molecular subgroup assignment, aggressiveness, and potential biomarkers of individual tumors, as reported in other brain tumors [102]. A high degree of ITH and enrichment for undifferentiated cell populations are associated with lower age and an unfavorable clinical outcome, as observed in ST-RELA and PFA, which might explain the profound difference in prognosis between these subtypes and their respective anatomical counterparts ST-YAP1 and PFB (Figure 4). Another commonality between ST-RELA and PFA is that cycling cells are specifically enriched in undifferentiated subpopulations, implying that progenitor subpopulations are more proliferative than more differentiated ones. Although, overall, cells separate according to the bulk tumor subgrouping, partially shared transcriptional programs are observed across all EPN molecular variants [103][104]. For instance, programs related to cell cycle, stress response, and ependymal differentiation are similar in ST-EPN and PF-EPN.
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Figure 4. Intra-tumoral heterogeneity in intracranial ependymoma by scRNA-seq. scRNA-seq of tumor cells is colored based on distinct gene signatures that define cell subpopulations. The relative frequency of each subpopulation is different in PFA tumors that are enriched for undifferentiated cells vs. PFB tumors enriched for ependymal-like cells. ST-RELA tumors also harbor high fractions of progenitor cell subpopulations, whereas a distinct ST-YAP1 gene signature is overrepresented in ST-YAP1. Some overlap between transcriptional signatures is observed across EPN groups.

5.2. The Cell of Origin and Developmental Trajectories of EPN from an scRNA-seq Perspective

Leveraging scRNA-seq and TI analyses, it emerges that the distinct PF-EPN subpopulations are arranged in a neural tri-lineage cancer hierarchy driven by immature progenitor cells at the apex, that undergo impaired differentiation along neuronal, astrocytic, and ependymal-like trajectories [104]. Of the three branches, the predominant one sees the NSC-like population differentiate into less aggressive progenies, the astro-ependymal cells, and successively to ependymal-like cells, presumably in response to developmental or differentiation stimuli (Figure 5). This axis potentially overlaps with the differentiating trajectory described in Gillen et al.’s study, whereby the stem cell population of UEC-1 develops into TECs (which express markers of further differentiation, such as oRGC genes, as well as gliogenic progenitor and astrocytic progenitor genes), and then CECs characterized by an ependymal-like signature. In response to unfavorable microenvironmental cues, such as oxygen and/or nutrient deprivation in hypoxic areas, undifferentiated UEC-1 develop along a stress-associated trajectory and undergo EMT to give rise to mesenchymal MECs.
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Figure 5. The candidate cell of origin of PF-EPNs resides in the ventricular zone (VZ) of the developing brain, whereas that of ST-EPNs is located in the subventricular zone (SVZ). In PF-EPNs, tumor subpopulations are arranged in two major distinct lineage trajectories driven by undifferentiated progenitors, that either undergo impaired differentiation towards ependymal-like cells, or transition to mesenchymal-like cells in response to cellular stresses, e.g., hypoxia. In ST-EPNs, clear developmental trajectories have not been identified yet (Reprinted with permission from Austin E. Gillen et al., (2020), Elsevier and Copyright Clearance Center, Licence Number 5200810483356, 2 December 2021).

6. Therapeutic Applications

A potentially druggable pathway in PFA EPN is EZHIP, although direct targeting of EZHIP might prove difficult to achieve, because no enzymatic activity has hitherto been identified [105]. Numerous EZH2 inhibitors are currently undergoing phase 1 and phase 2 clinical testing in different tumors [106], and might have important implications for novel treatment protocols. Specifically, an advanced trial is evaluating the effectiveness of the EZH2 inhibitor tazemetostat in pediatric patients with recurrent EPN (NCT03213665).

Compounds aimed at blocking the oncogenic NF-κB pathway [107] are potential therapeutic agents against ST-EPNs harboring ZFTA–RELA fusion that contains the NF-κB subunit encoding gene RELA. The NF-κB subunit is activated via proteosomal degradation of its inhibitor IκB, thus suggesting proteasome inhibitors as candidate drugs in ST-RELA [108].
scRNA-seq is expected to make significant breakthroughs in EPN and inform future therapeutic approaches. Since an increased proportion of differentiated cells is associated with a favorable clinical behavior in ST-RELA and PFA, differentiation-promoting agents might prove effective in these high-risk groups. Corroboratively, retinoids have demonstrated selective efficacy against EPN lines compared to other brain tumor-derived models in an in vitro drug screen [109].
In the context of PF-EPN, a druggable driver in the PF-NSC-like program is the Wnt pathway gene LGR5, a key mediator of cell proliferation and stemness features, as shown by small interfering RNA (siRNA)-mediated LGR5 knockdown that results in reduction of self-renewal [104]. In PF-Neuronal-Precursor-like cells, targetable pathways might be the epigenetic regulators HDAC2, DNMT3A, and BRD3. Indeed, the pan-HDAC inhibitor CN133 [110] and the HDAC2 inhibitor panobinostat [104], as well as the pan-BRD inhibitors JQ1 [111] and OTX012 [112], have been reported to decrease cell viability and tumor growth in patient-derived PFA cell lines.
As for ST-EPN, actionable vulnerabilities in ST-Radial-Glia-like cells are FGFR3 and IGF2, whereas in the ST-Neuronal-Precursor-like subpopulation they are CCND2 and HDAC2. FGFR3 mRNA levels are enriched in ST-RELA EPNs, and specifically in cycling and progenitor-like cell populations, mirroring FGFR3 expression in RGCs of the embryonic and adult brain [113]. Indeed, blockade of FGFR by dominant-negative and pharmacological inhibitors impairs cell survival and stemness features in ST-RELA cells [104][113] Simultaneous inhibition of CDK4/6-CCND2 (with palbociclib) and IGF2/IGF1R (with ceritinib) pathways results in combinatorial drug efficacy, highlighting that targeting distinct subpopulations may be a successful therapeutic option [104]. CDK4/6 has also been proposed as an actionable driver in PFA, because the tumor suppressor gene CDKN2A, which codes for the CDK4/6 inhibitor p16, is epigenetically silenced by H3K27 trimethylation in PFA [114][105]. A phase 1 trial is addressing the safety and tolerability of the CDK4/6 inhibitor ribociclib in children and young adults with recurrent brain tumors, including EPN (NCT03434262).
A link between the overexpression of strong growth-promoting IGF2 and members of the PLAG1/PLAG1L TF family is emerging in EPN. The PLAGL1 gene is developmentally regulated and is expressed in NSCs and developing neuroepithelial cells, with low expression in the adult brain [115][116]. Although the function of PLAGL1 in tumorigenesis is controversial, acting as either a tumor suppressor or an oncogene in a context-dependent manner, PLAG1L has been shown to foster progression of GBM [105]. In ST-RELA tumors, ZETA-RELA protein binds to PLAGL family TF motifs, indicating a possible corecruitment to drive ependymoma-related transcriptional programs [117]. PLAG1 is silenced during development by PRC2-mediated H3K27 trimethylation; however, in tumors with impaired PRC2 function, such as PFA and H3K27M mutant pHGG, PLAG1 is derepressed, leading to overexpression of its downstream targets, including IGF2 [105]. Therefore, it is conceivable to hypothesize that the PLAG1/PLAG1L-IGF2 axis might be therapeutically targeted in EPN.

References

  1. McGranahan, N.; Swanton, C. Clonal Heterogeneity and Tumor Evolution: Past, Present, and the Future. Cell 2017, 168, 613–628.
  2. McGranahan, N.; Swanton, C. Biological and Therapeutic Impact of Intratumor Heterogeneity in Cancer Evolution. Cancer Cell 2015, 27, 15–26.
  3. Junttila, M.R.; de Sauvage, F.J. Influence of Tumour Micro-Environment Heterogeneity on Therapeutic Response. Nature 2013, 501, 346–354.
  4. Welch, D.R. Tumor Heterogeneity—A “Contemporary Concept” Founded on Historical Insights and Predictions. Cancer Res. 2016, 76, 4–6.
  5. Heppner, G.H. Tumor Heterogeneity. Cancer Res. 1984, 44, 2259–2265.
  6. Hinohara, K.; Polyak, K. Intratumoral Heterogeneity: More Than Just Mutations. Trends Cell Biol. 2019, 29, 569–579.
  7. Inda, M.-M.; Bonavia, R.; Mukasa, A.; Narita, Y.; Sah, D.W.Y.; Vandenberg, S.; Brennan, C.; Johns, T.G.; Bachoo, R.; Hadwiger, P.; et al. Tumor Heterogeneity Is an Active Process Maintained by a Mutant EGFR-Induced Cytokine Circuit in Glioblastoma. Genes Dev. 2010, 24, 1731–1745.
  8. Polyak, K.; Marusyk, A. Cancer: Clonal Cooperation. Nature 2014, 508, 52–53.
  9. Ricklefs, F.; Mineo, M.; Rooj, A.K.; Nakano, I.; Charest, A.; Weissleder, R.; Breakefield, X.O.; Chiocca, E.A.; Godlewski, J.; Bronisz, A. Extracellular Vesicles from High-Grade Glioma Exchange Diverse Pro-Oncogenic Signals That Maintain Intratumoral Heterogeneity. Cancer Res. 2016, 76, 2876–2881.
  10. Broekman, M.L.; Maas, S.L.N.; Abels, E.R.; Mempel, T.R.; Krichevsky, A.M.; Breakefield, X.O. Multidimensional Communication in the Microenvirons of Glioblastoma. Nat. Rev. Neurol. 2018, 14, 482–495.
  11. Janiszewska, M.; Tabassum, D.P.; Castaño, Z.; Cristea, S.; Yamamoto, K.N.; Kingston, N.L.; Murphy, K.C.; Shu, S.; Harper, N.W.; Del Alcazar, C.G.; et al. Subclonal Cooperation Drives Metastasis by Modulating Local and Systemic Immune Microenvironments. Nat. Cell Biol. 2019, 21, 879–888.
  12. Marusyk, A.; Janiszewska, M.; Polyak, K. Intratumor Heterogeneity: The Rosetta Stone of Therapy Resistance. Cancer Cell 2020, 37, 471–484.
  13. Naffar-Abu Amara, S.; Kuiken, H.J.; Selfors, L.M.; Butler, T.; Leung, M.L.; Leung, C.T.; Kuhn, E.P.; Kolarova, T.; Hage, C.; Ganesh, K.; et al. Transient Commensal Clonal Interactions Can Drive Tumor Metastasis. Nat. Commun. 2020, 11, 5799.
  14. Ostrom, Q.T.; Gittleman, H.; Truitt, G.; Boscia, A.; Kruchko, C.; Barnholtz-Sloan, J.S. CBTRUS Statistical Report: Primary Brain and Other Central Nervous System Tumors Diagnosed in the United States in 2011–2015. Neuro Oncol. 2018, 20, iv1–iv86.
  15. Pajtler, K.W.; Mack, S.C.; Ramaswamy, V.; Smith, C.A.; Witt, H.; Smith, A.; Hansford, J.R.; von Hoff, K.; Wright, K.D.; Hwang, E.; et al. The Current Consensus on the Clinical Management of Intracranial Ependymoma and Its Distinct Molecular Variants. Acta Neuropathol. 2017, 133, 5–12.
  16. Pajtler, K.W.; Witt, H.; Sill, M.; Jones, D.T.W.; Hovestadt, V.; Kratochwil, F.; Wani, K.; Tatevossian, R.; Punchihewa, C.; Johann, P.; et al. Molecular Classification of Ependymal Tumors across All CNS Compartments, Histopathological Grades, and Age Groups. Cancer Cell 2015, 27, 728–743.
  17. Parker, M.; Mohankumar, K.M.; Punchihewa, C.; Weinlich, R.; Dalton, J.D.; Li, Y.; Lee, R.; Tatevossian, R.G.; Phoenix, T.N.; Thiruvenkatam, R.; et al. C11orf95-RELA Fusions Drive Oncogenic NF-ΚB Signalling in Ependymoma. Nature 2014, 506, 451–455.
  18. Witt, H.; Mack, S.C.; Ryzhova, M.; Bender, S.; Sill, M.; Isserlin, R.; Benner, A.; Hielscher, T.; Milde, T.; Remke, M.; et al. Delineation of Two Clinically and Molecularly Distinct Subgroups of Posterior Fossa Ependymoma. Cancer Cell 2011, 20, 143–157.
  19. Suvà, M.L.; Tirosh, I. Single-Cell RNA Sequencing in Cancer: Lessons Learned and Emerging Challenges. Mol. Cell 2019, 75, 7–12.
  20. Stuart, T.; Satija, R. Integrative Single-Cell Analysis. Nat. Rev. Genet. 2019, 20, 257–272.
  21. Taniguchi, K.; Karin, M. NF-ΚB, Inflammation, Immunity and Cancer: Coming of Age. Nat. Rev. Immunol. 2018, 18, 309–324.
  22. Pajtler, K.W.; Wei, Y.; Okonechnikov, K.; Silva, P.B.G.; Vouri, M.; Zhang, L.; Brabetz, S.; Sieber, L.; Gulley, M.; Mauermann, M.; et al. YAP1 Subgroup Supratentorial Ependymoma Requires TEAD and Nuclear Factor I-Mediated Transcriptional Programmes for Tumorigenesis. Nat. Commun. 2019, 10, 3914.
  23. Andreiuolo, F.; Varlet, P.; Tauziède-Espariat, A.; Jünger, S.T.; Dörner, E.; Dreschmann, V.; Kuchelmeister, K.; Waha, A.; Haberler, C.; Slavc, I.; et al. Childhood Supratentorial Ependymomas with YAP1-MAMLD1 Fusion: An Entity with Characteristic Clinical, Radiological, Cytogenetic and Histopathological Features. Brain Pathol. Zurich Switz. 2019, 29, 205–216.
  24. Baroni, L.V.; Sundaresan, L.; Heled, A.; Coltin, H.; Pajtler, K.W.; Lin, T.; Merchant, T.E.; McLendon, R.; Faria, C.; Buntine, M.; et al. Ultra High-Risk PFA Ependymoma Is Characterized by Loss of Chromosome 6q. Neuro Oncol. 2021, 23, 1360–1370.
  25. Gessi, M.; Capper, D.; Sahm, F.; Huang, K.; von Deimling, A.; Tippelt, S.; Fleischhack, G.; Scherbaum, D.; Alfer, J.; Juhnke, B.-O.; et al. Evidence of H3 K27M Mutations in Posterior Fossa Ependymomas. Acta Neuropathol. 2016, 132, 635–637.
  26. Pajtler, K.W.; Wen, J.; Sill, M.; Lin, T.; Orisme, W.; Tang, B.; Hübner, J.-M.; Ramaswamy, V.; Jia, S.; Dalton, J.D.; et al. Molecular Heterogeneity and CXorf67 Alterations in Posterior Fossa Group A (PFA) Ependymomas. Acta Neuropathol. 2018, 136, 211–226.
  27. Mack, S.C.; Witt, H.; Piro, R.M.; Gu, L.; Zuyderduyn, S.; Stütz, A.M.; Wang, X.; Gallo, M.; Garzia, L.; Zayne, K.; et al. Epigenomic Alterations Define Lethal CIMP-Positive Ependymomas of Infancy. Nature 2014, 506, 445–450.
  28. Bayliss, J.; Mukherjee, P.; Lu, C.; Jain, S.U.; Chung, C.; Martinez, D.; Sabari, B.; Margol, A.S.; Panwalkar, P.; Parolia, A.; et al. Lowered H3K27me3 and DNA Hypomethylation Define Poorly Prognostic Pediatric Posterior Fossa Ependymomas. Sci. Transl. Med. 2016, 8, 366ra161.
  29. Panwalkar, P.; Clark, J.; Ramaswamy, V.; Hawes, D.; Yang, F.; Dunham, C.; Yip, S.; Hukin, J.; Sun, Y.; Schipper, M.J.; et al. Immunohistochemical Analysis of H3K27me3 Demonstrates Global Reduction in Group-A Childhood Posterior Fossa Ependymoma and Is a Powerful Predictor of Outcome. Acta Neuropathol. 2017, 134, 705–714.
  30. Fukuoka, K.; Kanemura, Y.; Shofuda, T.; Fukushima, S.; Yamashita, S.; Narushima, D.; Kato, M.; Honda-Kitahara, M.; Ichikawa, H.; Kohno, T.; et al. Significance of Molecular Classification of Ependymomas: C11orf95-RELA Fusion-Negative Supratentorial Ependymomas Are a Heterogeneous Group of Tumors. Acta Neuropathol. Commun. 2018, 6, 134.
  31. Tamai, S.; Nakano, Y.; Kinoshita, M.; Sabit, H.; Nobusawa, S.; Arai, Y.; Hama, N.; Totoki, Y.; Shibata, T.; Ichimura, K.; et al. Ependymoma with C11orf95-MAML2 Fusion: Presenting with Granular Cell and Ganglion Cell Features. Brain Tumor Pathol. 2021, 38, 64–70.
  32. Zheng, T.; Ghasemi, D.R.; Okonechnikov, K.; Korshunov, A.; Sill, M.; Maass, K.K.; Benites Goncalves da Silva, P.; Ryzhova, M.; Gojo, J.; Stichel, D.; et al. Cross-Species Genomics Reveals Oncogenic Dependencies in ZFTA/C11orf95 Fusion-Positive Supratentorial Ependymomas. Cancer Discov. 2021, 11, 2230–2247.
  33. Tauziède-Espariat, A.; Siegfried, A.; Nicaise, Y.; Kergrohen, T.; Sievers, P.; Vasiljevic, A.; Roux, A.; Dezamis, E.; Benevello, C.; Machet, M.-C.; et al. Supratentorial Non-RELA, ZFTA-Fused Ependymomas: A Comprehensive Phenotype Genotype Correlation Highlighting the Number of Zinc Fingers in ZFTA-NCOA1/2 Fusions. Acta Neuropathol. Commun. 2021, 9, 135.
  34. Zschernack, V.; Jünger, S.T.; Mynarek, M.; Rutkowski, S.; Garre, M.L.; Ebinger, M.; Neu, M.; Faber, J.; Erdlenbruch, B.; Claviez, A.; et al. Supratentorial Ependymoma in Childhood: More than Just RELA or YAP. Acta Neuropathol. 2021, 141, 455–466.
  35. Reya, T.; Morrison, S.J.; Clarke, M.F.; Weissman, I.L. Stem Cells, Cancer, and Cancer Stem Cells. Nature 2001, 414, 105–111.
  36. Prasetyanti, P.R.; Medema, J.P. Intra-Tumor Heterogeneity from a Cancer Stem Cell Perspective. Mol. Cancer 2017, 16, 41.
  37. Uchida, N.; Buck, D.W.; He, D.; Reitsma, M.J.; Masek, M.; Phan, T.V.; Tsukamoto, A.S.; Gage, F.H.; Weissman, I.L. Direct Isolation of Human Central Nervous System Stem Cells. Proc. Natl. Acad. Sci. USA 2000, 97, 14720–14725.
  38. Singh, S.K.; Hawkins, C.; Clarke, I.D.; Squire, J.A.; Bayani, J.; Hide, T.; Henkelman, R.M.; Cusimano, M.D.; Dirks, P.B. Identification of Human Brain Tumour Initiating Cells. Nature 2004, 432, 396–401.
  39. Batlle, E.; Clevers, H. Cancer Stem Cells Revisited. Nat. Med. 2017, 23, 1124–1134.
  40. Gupta, P.B.; Fillmore, C.M.; Jiang, G.; Shapira, S.D.; Tao, K.; Kuperwasser, C.; Lander, E.S. Stochastic State Transitions Give Rise to Phenotypic Equilibrium in Populations of Cancer Cells. Cell 2011, 146, 633–644.
  41. Dirkse, A.; Golebiewska, A.; Buder, T.; Nazarov, P.V.; Muller, A.; Poovathingal, S.; Brons, N.H.C.; Leite, S.; Sauvageot, N.; Sarkisjan, D.; et al. Stem Cell-Associated Heterogeneity in Glioblastoma Results from Intrinsic Tumor Plasticity Shaped by the Microenvironment. Nat. Commun. 2019, 10, 1787.
  42. Wang, X.; Prager, B.C.; Wu, Q.; Kim, L.J.Y.; Gimple, R.C.; Shi, Y.; Yang, K.; Morton, A.R.; Zhou, W.; Zhu, Z.; et al. Reciprocal Signaling between Glioblastoma Stem Cells and Differentiated Tumor Cells Promotes Malignant Progression. Cell Stem Cell 2018, 22, 514–528.e5.
  43. Lytle, N.K.; Barber, A.G.; Reya, T. Stem Cell Fate in Cancer Growth, Progression and Therapy Resistance. Nat. Rev. Cancer 2018, 18, 669–680.
  44. Kondo, T. Glioblastoma-Initiating Cell Heterogeneity Generated by the Cell-of-Origin, Genetic/Epigenetic Mutation and Microenvironment. Semin. Cancer Biol. 2021.
  45. Muñoz, P.; Iliou, M.S.; Esteller, M. Epigenetic Alterations Involved in Cancer Stem Cell Reprogramming. Mol. Oncol. 2012, 6, 620–636.
  46. Servidei, T.; Meco, D.; Trivieri, N.; Patriarca, V.; Vellone, V.G.; Zannoni, G.F.; Lamorte, G.; Pallini, R.; Riccardi, R. Effects of Epidermal Growth Factor Receptor Blockade on Ependymoma Stem Cells in Vitro and in Orthotopic Mouse Models. Int. J. Cancer 2012, 131, E791–E803.
  47. Yu, L.; Baxter, P.A.; Voicu, H.; Gurusiddappa, S.; Zhao, Y.; Adesina, A.; Man, T.-K.; Shu, Q.; Zhang, Y.-J.; Zhao, X.-M.; et al. A Clinically Relevant Orthotopic Xenograft Model of Ependymoma That Maintains the Genomic Signature of the Primary Tumor and Preserves Cancer Stem Cells in Vivo. Neuro Oncol. 2010, 12, 580–594.
  48. Milde, T.; Kleber, S.; Korshunov, A.; Witt, H.; Hielscher, T.; Koch, P.; Kopp, H.-G.; Jugold, M.; Deubzer, H.E.; Oehme, I.; et al. A Novel Human High-Risk Ependymoma Stem Cell Model Reveals the Differentiation-Inducing Potential of the Histone Deacetylase Inhibitor Vorinostat. Acta Neuropathol. 2011, 122, 637–650.
  49. Amani, V.; Donson, A.M.; Lummus, S.C.; Prince, E.W.; Griesinger, A.M.; Witt, D.A.; Hankinson, T.C.; Handler, M.H.; Dorris, K.; Vibhakar, R.; et al. Characterization of 2 Novel Ependymoma Cell Lines with Chromosome 1q Gain Derived From Posterior Fossa Tumors of Childhood. J. Neuropathol. Exp. Neurol. 2017, 76, 595–604.
  50. Bian, S.; Repic, M.; Guo, Z.; Kavirayani, A.; Burkard, T.; Bagley, J.A.; Krauditsch, C.; Knoblich, J.A. Genetically Engineered Cerebral Organoids Model Brain Tumor Formation. Nat. Methods 2018, 15, 631–639.
  51. Sabnis, D.H.; Liu, J.-F.; Simmonds, L.; Blackburn, S.; Grundy, R.G.; Kerr, I.D.; Coyle, B. BLBP Is Both a Marker for Poor Prognosis and a Potential Therapeutic Target in Paediatric Ependymoma. Cancers 2021, 13, 2100.
  52. Meco, D.; Servidei, T.; Lamorte, G.; Binda, E.; Arena, V.; Riccardi, R. Ependymoma Stem Cells Are Highly Sensitive to Temozolomide in Vitro and in Orthotopic Models. Neuro Oncol. 2014, 16, 1067–1077.
  53. Servidei, T.; Meco, D.; Muto, V.; Bruselles, A.; Ciolfi, A.; Trivieri, N.; Lucchini, M.; Morosetti, R.; Mirabella, M.; Martini, M.; et al. Novel SEC61G-EGFR Fusion Gene in Pediatric Ependymomas Discovered by Clonal Expansion of Stem Cells in Absence of Exogenous Mitogens. Cancer Res. 2017, 77, 5860–5872.
  54. Brennan, C.W.; Verhaak, R.G.W.; McKenna, A.; Campos, B.; Noushmehr, H.; Salama, S.R.; Zheng, S.; Chakravarty, D.; Sanborn, J.Z.; Berman, S.H.; et al. The Somatic Genomic Landscape of Glioblastoma. Cell 2013, 155, 462–477.
  55. Szerlip, N.J.; Pedraza, A.; Chakravarty, D.; Azim, M.; McGuire, J.; Fang, Y.; Ozawa, T.; Holland, E.C.; Huse, J.T.; Jhanwar, S.; et al. Intratumoral Heterogeneity of Receptor Tyrosine Kinases EGFR and PDGFRA Amplification in Glioblastoma Defines Subpopulations with Distinct Growth Factor Response. Proc. Natl. Acad. Sci. USA 2012, 109, 3041–3046.
  56. Schulte, A.; Günther, H.S.; Martens, T.; Zapf, S.; Riethdorf, S.; Wülfing, C.; Stoupiec, M.; Westphal, M.; Lamszus, K. Glioblastoma Stem-like Cell Lines with Either Maintenance or Loss of High-Level EGFR Amplification, Generated via Modulation of Ligand Concentration. Clin. Cancer Res. Off. J. Am. Assoc. Cancer Res. 2012, 18, 1901–1913.
  57. Negrini, S.; Gorgoulis, V.G.; Halazonetis, T.D. Genomic Instability--an Evolving Hallmark of Cancer. Nat. Rev. Mol. Cell Biol. 2010, 11, 220–228.
  58. Siri, S.O.; Martino, J.; Gottifredi, V. Structural Chromosome Instability: Types, Origins, Consequences, and Therapeutic Opportunities. Cancers 2021, 13, 3056.
  59. Stephens, P.J.; Greenman, C.D.; Fu, B.; Yang, F.; Bignell, G.R.; Mudie, L.J.; Pleasance, E.D.; Lau, K.W.; Beare, D.; Stebbings, L.A.; et al. Massive Genomic Rearrangement Acquired in a Single Catastrophic Event during Cancer Development. Cell 2011, 144, 27–40.
  60. Paulsen, T.; Kumar, P.; Koseoglu, M.M.; Dutta, A. Discoveries of Extrachromosomal Circles of DNA in Normal and Tumor Cells. Trends Genet. 2018, 34, 270–278.
  61. Verhaak, R.G.W.; Bafna, V.; Mischel, P.S. Extrachromosomal Oncogene Amplification in Tumour Pathogenesis and Evolution. Nat. Rev. Cancer 2019, 19, 283–288.
  62. Wu, S.; Turner, K.M.; Nguyen, N.; Raviram, R.; Erb, M.; Santini, J.; Luebeck, J.; Rajkumar, U.; Diao, Y.; Li, B.; et al. Circular EcDNA Promotes Accessible Chromatin and High Oncogene Expression. Nature 2019, 575, 699–703.
  63. Turner, K.M.; Deshpande, V.; Beyter, D.; Koga, T.; Rusert, J.; Lee, C.; Li, B.; Arden, K.; Ren, B.; Nathanson, D.A.; et al. Extrachromosomal Oncogene Amplification Drives Tumour Evolution and Genetic Heterogeneity. Nature 2017, 543, 122–125.
  64. deCarvalho, A.C.; Kim, H.; Poisson, L.M.; Winn, M.E.; Mueller, C.; Cherba, D.; Koeman, J.; Seth, S.; Protopopov, A.; Felicella, M.; et al. Discordant Inheritance of Chromosomal and Extrachromosomal DNA Elements Contributes to Dynamic Disease Evolution in Glioblastoma. Nat. Genet. 2018, 50, 708–717.
  65. Kim, H.; Nguyen, N.-P.; Turner, K.; Wu, S.; Gujar, A.D.; Luebeck, J.; Liu, J.; Deshpande, V.; Rajkumar, U.; Namburi, S.; et al. Extrachromosomal DNA Is Associated with Oncogene Amplification and Poor Outcome across Multiple Cancers. Nat. Genet. 2020, 52, 891–897.
  66. Nathanson, D.A.; Gini, B.; Mottahedeh, J.; Visnyei, K.; Koga, T.; Gomez, G.; Eskin, A.; Hwang, K.; Wang, J.; Masui, K.; et al. Targeted Therapy Resistance Mediated by Dynamic Regulation of Extrachromosomal Mutant EGFR DNA. Science 2014, 343, 72–76.
  67. Xu, K.; Ding, L.; Chang, T.-C.; Shao, Y.; Chiang, J.; Mulder, H.; Wang, S.; Shaw, T.I.; Wen, J.; Hover, L.; et al. Structure and Evolution of Double Minutes in Diagnosis and Relapse Brain Tumors. Acta Neuropathol. 2019, 137, 123–137.
  68. Cortés-Ciriano, I.; Lee, J.J.-K.; Xi, R.; Jain, D.; Jung, Y.L.; Yang, L.; Gordenin, D.; Klimczak, L.J.; Zhang, C.-Z.; Pellman, D.S.; et al. Comprehensive Analysis of Chromothripsis in 2,658 Human Cancers Using Whole-Genome Sequencing. Nat. Genet. 2020, 52, 331–341.
  69. Voronina, N.; Wong, J.K.L.; Hübschmann, D.; Hlevnjak, M.; Uhrig, S.; Heilig, C.E.; Horak, P.; Kreutzfeldt, S.; Mock, A.; Stenzinger, A.; et al. The Landscape of Chromothripsis across Adult Cancer Types. Nat. Commun. 2020, 11, 2320.
  70. Brock, A.; Chang, H.; Huang, S. Non-Genetic Heterogeneity—A Mutation-Independent Driving Force for the Somatic Evolution of Tumours. Nat. Rev. Genet. 2009, 10, 336–342.
  71. Carja, O.; Plotkin, J.B. The Evolutionary Advantage of Heritable Phenotypic Heterogeneity. Sci. Rep. 2017, 7, 5090.
  72. Flavahan, W.A. Epigenetic Plasticity, Selection, and Tumorigenesis. Biochem. Soc. Trans. 2020, 48, 1609–1621.
  73. Shen, H.; Laird, P.W. Interplay between the Cancer Genome and Epigenome. Cell 2013, 153, 38–55.
  74. 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.
  75. Capper, D.; Jones, D.T.W.; Sill, M.; Hovestadt, V.; Schrimpf, D.; Sturm, D.; Koelsche, C.; Sahm, F.; Chavez, L.; Reuss, D.E.; et al. DNA Methylation-Based Classification of Central Nervous System Tumours. Nature 2018, 555, 469–474.
  76. Gröbner, S.N.; Worst, B.C.; Weischenfeldt, J.; Buchhalter, I.; Kleinheinz, K.; Rudneva, V.A.; Johann, P.D.; Balasubramanian, G.P.; Segura-Wang, M.; Brabetz, S.; et al. The Landscape of Genomic Alterations across Childhood Cancers. Nature 2018, 555, 321–327.
  77. Huether, R.; Dong, L.; Chen, X.; Wu, G.; Parker, M.; Wei, L.; Ma, J.; Edmonson, M.N.; Hedlund, E.K.; Rusch, M.C.; et al. The Landscape of Somatic Mutations in Epigenetic Regulators across 1,000 Paediatric Cancer Genomes. Nat. Commun. 2014, 5, 3630.
  78. Baylin, S.B.; Jones, P.A. Epigenetic Determinants of Cancer. Cold Spring Harb. Perspect. Biol. 2016, 8, a019505.
  79. Carter, B.; Zhao, K. The Epigenetic Basis of Cellular Heterogeneity. Nat. Rev. Genet. 2021, 22, 235–250.
  80. Klemm, S.L.; Shipony, Z.; Greenleaf, W.J. Chromatin Accessibility and the Regulatory Epigenome. Nat. Rev. Genet. 2019, 20, 207–220.
  81. Flavahan, W.A.; Gaskell, E.; Bernstein, B.E. Epigenetic Plasticity and the Hallmarks of Cancer. Science 2017, 357, eaal2380.
  82. Mack, S.C.; Hubert, C.G.; Miller, T.E.; Taylor, M.D.; Rich, J.N. An Epigenetic Gateway to Brain Tumor Cell Identity. Nat. Neurosci. 2016, 19, 10–19.
  83. Comet, I.; Riising, E.M.; Leblanc, B.; Helin, K. Maintaining Cell Identity: PRC2-Mediated Regulation of Transcription and Cancer. Nat. Rev. Cancer 2016, 16, 803–810.
  84. Shen, L.; Kondo, Y.; Rosner, G.L.; Xiao, L.; Hernandez, N.S.; Vilaythong, J.; Houlihan, P.S.; Krouse, R.S.; Prasad, A.R.; Einspahr, J.G.; et al. MGMT Promoter Methylation and Field Defect in Sporadic Colorectal Cancer. J. Natl. Cancer Inst. 2005, 97, 1330–1338.
  85. Hnisz, D.; Abraham, B.J.; Lee, T.I.; Lau, A.; Saint-André, V.; Sigova, A.A.; Hoke, H.A.; Young, R.A. Super-Enhancers in the Control of Cell Identity and Disease. Cell 2013, 155, 934–947.
  86. Brocks, D.; Assenov, Y.; Minner, S.; Bogatyrova, O.; Simon, R.; Koop, C.; Oakes, C.; Zucknick, M.; Lipka, D.B.; Weischenfeldt, J.; et al. Intratumor DNA Methylation Heterogeneity Reflects Clonal Evolution in Aggressive Prostate Cancer. Cell Rep. 2014, 8, 798–806.
  87. Song, Y.; van den Berg, P.R.; Markoulaki, S.; Soldner, F.; Dall’Agnese, A.; Henninger, J.E.; Drotar, J.; Rosenau, N.; Cohen, M.A.; Young, R.A.; et al. Dynamic Enhancer DNA Methylation as Basis for Transcriptional and Cellular Heterogeneity of ESCs. Mol. Cell 2019, 75, 905–920.e6.
  88. Bell, R.E.; Golan, T.; Sheinboim, D.; Malcov, H.; Amar, D.; Salamon, A.; Liron, T.; Gelfman, S.; Gabet, Y.; Shamir, R.; et al. Enhancer Methylation Dynamics Contribute to Cancer Plasticity and Patient Mortality. Genome Res. 2016, 26, 601–611.
  89. Mazor, T.; Pankov, A.; Song, J.S.; Costello, J.F. Intratumoral Heterogeneity of the Epigenome. Cancer Cell 2016, 29, 440–451.
  90. Yang, D.; Holsten, T.; Börnigen, D.; Frank, S.; Mawrin, C.; Glatzel, M.; Schüller, U. Ependymoma Relapse Goes along with a Relatively Stable Epigenome, but a Severely Altered Tumor Morphology. Brain Pathol. 2021, 31, 33–44.
  91. Guo, M.; Peng, Y.; Gao, A.; Du, C.; Herman, J.G. Epigenetic Heterogeneity in Cancer. Biomark. Res. 2019, 7, 23.
  92. Mazor, T.; Pankov, A.; Johnson, B.E.; Hong, C.; Hamilton, E.G.; Bell, R.J.A.; Smirnov, I.V.; Reis, G.F.; Phillips, J.J.; Barnes, M.J.; et al. DNA Methylation and Somatic Mutations Converge on the Cell Cycle and Define Similar Evolutionary Histories in Brain Tumors. Cancer Cell 2015, 28, 307–317.
  93. Magill, S.T.; Vasudevan, H.N.; Seo, K.; Villanueva-Meyer, J.E.; Choudhury, A.; John Liu, S.; Pekmezci, M.; Findakly, S.; Hilz, S.; Lastella, S.; et al. Multiplatform Genomic Profiling and Magnetic Resonance Imaging Identify Mechanisms Underlying Intratumor Heterogeneity in Meningioma. Nat. Commun. 2020, 11, 4803.
  94. Liu, S.J.; Magill, S.T.; Vasudevan, H.N.; Hilz, S.; Villanueva-Meyer, J.E.; Lastella, S.; Daggubati, V.; Spatz, J.; Choudhury, A.; Orr, B.A.; et al. Multiplatform Molecular Profiling Reveals Epigenomic Intratumor Heterogeneity in Ependymoma. Cell Rep. 2020, 30, 1300–1309.e5.
  95. Yuan, Y. Spatial Heterogeneity in the Tumor Microenvironment. Cold Spring Harb. Perspect. Med. 2016, 6, a026583.
  96. Gillies, R.J.; Brown, J.S.; Anderson, A.R.A.; Gatenby, R.A. Eco-Evolutionary Causes and Consequences of Temporal Changes in Intratumoural Blood Flow. Nat. Rev. Cancer 2018, 18, 576–585.
  97. Hoefflin, R.; Lahrmann, B.; Warsow, G.; Hübschmann, D.; Spath, C.; Walter, B.; Chen, X.; Hofer, L.; Macher-Goeppinger, S.; Tolstov, Y.; et al. Spatial Niche Formation but Not Malignant Progression Is a Driving Force for Intratumoural Heterogeneity. Nat. Commun. 2016, 7, 1–12.
  98. Hubert, C.G.; Rivera, M.; Spangler, L.C.; Wu, Q.; Mack, S.C.; Prager, B.C.; Couce, M.; McLendon, R.E.; Sloan, A.E.; Rich, J.N. A Three-Dimensional Organoid Culture System Derived from Human Glioblastomas Recapitulates the Hypoxic Gradients and Cancer Stem Cell Heterogeneity of Tumors Found In Vivo. Cancer Res. 2016, 76, 2465–2477.
  99. Estrella, V.; Chen, T.; Lloyd, M.; Wojtkowiak, J.; Cornnell, H.H.; Ibrahim-Hashim, A.; Bailey, K.; Balagurunathan, Y.; Rothberg, J.M.; Sloane, B.F.; et al. Acidity Generated by the Tumor Microenvironment Drives Local Invasion. Cancer Res. 2013, 73, 1524–1535.
  100. Korenchan, D.E.; Flavell, R.R. Spatiotemporal PH Heterogeneity as a Promoter of Cancer Progression and Therapeutic Resistance. Cancers 2019, 11, 1026.
  101. Bastola, S.; Pavlyukov, M.S.; Yamashita, D.; Ghosh, S.; Cho, H.; Kagaya, N.; Zhang, Z.; Minata, M.; Lee, Y.; Sadahiro, H.; et al. Glioma-Initiating Cells at Tumor Edge Gain Signals from Tumor Core Cells to Promote Their Malignancy. Nat. Commun. 2020, 11, 4660.
  102. Patel, A.P.; Tirosh, I.; Trombetta, J.J.; Shalek, A.K.; Gillespie, S.M.; Wakimoto, H.; Cahill, D.P.; Nahed, B.V.; Curry, W.T.; Martuza, R.L.; et al. Single-Cell RNA-Seq Highlights Intratumoral Heterogeneity in Primary Glioblastoma. Science 2014, 344, 1396–1401.
  103. Gillen, A.E.; Riemondy, K.A.; Amani, V.; Griesinger, A.M.; Gilani, A.; Venkataraman, S.; Madhavan, K.; Prince, E.; Sanford, B.; Hankinson, T.C.; et al. Single-Cell RNA Sequencing of Childhood Ependymoma Reveals Neoplastic Cell Subpopulations That Impact Molecular Classification and Etiology. Cell Rep. 2020, 32, 108023.
  104. Gojo, J.; Englinger, B.; Jiang, L.; Hübner, J.M.; Shaw, M.L.; Hack, O.A.; Madlener, S.; Kirchhofer, D.; Liu, I.; Pyrdol, J.; et al. Single-Cell RNA-Seq Reveals Cellular Hierarchies and Impaired Developmental Trajectories in Pediatric Ependymoma. Cancer Cell 2020, 38, 44–59.e9.
  105. Jenseit, A.; Camgöz, A.; Pfister, S.M.; Kool, M. EZHIP: A New Piece of the Puzzle towards Understanding Pediatric Posterior Fossa Ependymoma. Acta Neuropathol. 2021, 1–13.
  106. Duan, R.; Du, W.; Guo, W. EZH2: A Novel Target for Cancer Treatment. J. Hematol. Oncol. J. Hematol. Oncol. 2020, 13, 104.
  107. Medeiros, M.; Candido, M.; Valera, E.; Brassesco, M. The Multifaceted NF-KB: Are There Still Prospects of Its Inhibition for Clinical Intervention in Pediatric Central Nervous System Tumors? Cell Mol. Life Sci. 2021, 78, 6161–6200.
  108. Armstrong, T.S.; Gilbert, M.R. Clinical Trial Challenges, Design Considerations, and Outcome Measures in Rare CNS Tumors. Neuro Oncol. 2021, 23, S30–S38.
  109. Donson, A.M.; Amani, V.; Warner, E.A.; Griesinger, A.M.; Witt, D.A.; Levy, J.M.M.; Hoffman, L.M.; Hankinson, T.C.; Handler, M.H.; Vibhakar, R.; et al. Identification of FDA-Approved Oncology Drugs with Selective Potency in High-Risk Childhood Ependymoma. Mol. Cancer Ther. 2018, 17, 1984–1994.
  110. Antonelli, R.; Jiménez, C.; Riley, M.; Servidei, T.; Riccardi, R.; Soriano, A.; Roma, J.; Martínez-Saez, E.; Martini, M.; Ruggiero, A.; et al. CN133, a Novel Brain-Penetrating Histone Deacetylase Inhibitor, Hampers Tumor Growth in Patient-Derived Pediatric Posterior Fossa Ependymoma Models. Cancers 2020, 12, 1922.
  111. Mack, S.C.; Pajtler, K.W.; Chavez, L.; Okonechnikov, K.; Bertrand, K.C.; Wang, X.; Erkek, S.; Federation, A.; Song, A.; Lee, C.; et al. Therapeutic Targeting of Ependymoma as Informed by Oncogenic Enhancer Profiling. Nature 2018, 553, 101–105.
  112. Servidei, T.; Meco, D.; Martini, M.; Battaglia, A.; Granitto, A.; Buzzonetti, A.; Babini, G.; Massimi, L.; Tamburrini, G.; Scambia, G.; et al. The BET Inhibitor OTX015 Exhibits In Vitro and In Vivo Antitumor Activity in Pediatric Ependymoma Stem Cell Models. Int. J. Mol. Sci. 2021, 22, 1877.
  113. Lötsch, D.; Kirchhofer, D.; Englinger, B.; Jiang, L.; Okonechnikov, K.; Senfter, D.; Laemmerer, A.; Gabler, L.; Pirker, C.; Donson, A.M.; et al. Targeting Fibroblast Growth Factor Receptors to Combat Aggressive Ependymoma. Acta Neuropathol. 2021, 142, 339–360.
  114. Jain, S.U.; Do, T.J.; Lund, P.J.; Rashoff, A.Q.; Diehl, K.L.; Cieslik, M.; Bajic, A.; Juretic, N.; Deshmukh, S.; Venneti, S.; et al. PFA Ependymoma-Associated Protein EZHIP Inhibits PRC2 Activity through a H3 K27M-like Mechanism. Nat. Commun. 2019, 10, 2146.
  115. Sievers, P.; Henneken, S.C.; Blume, C.; Sill, M.; Schrimpf, D.; Stichel, D.; Okonechnikov, K.; Reuss, D.E.; Benzel, J.; Maaß, K.K.; et al. Recurrent Fusions in PLAGL1 Define a Distinct Subset of Pediatric-Type Supratentorial Neuroepithelial Tumors. Acta Neuropathol. 2021, 142, 827–839.
  116. Vega-Benedetti, A.F.; Saucedo, C.; Zavattari, P.; Vanni, R.; Zugaza, J.L.; Parada, L.A. PLAGL1: An Important Player in Diverse Pathological Processes. J. Appl. Genet. 2017, 58, 71–78.
  117. Arabzade, A.; Zhao, Y.; Varadharajan, S.; Chen, H.-C.; Jessa, S.; Rivas, B.; Stuckert, A.J.; Solis, M.; Kardian, A.; Tlais, D.; et al. ZFTA-RELA Dictates Oncogenic Transcriptional Programs to Drive Aggressive Supratentorial Ependymoma. Cancer Discov. 2021, 11, 2200–2215.
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Subjects: Allergy
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