Your new experience awaits. Try the new design now and help us make it even better

MINI REVIEW article

Front. Immunol., 26 November 2025

Sec. Cancer Immunity and Immunotherapy

Volume 16 - 2025 | https://doi.org/10.3389/fimmu.2025.1728091

This article is part of the Research TopicExploring immune low-response states through single-cell technologies and spatial transcriptomicsView all 33 articles

Epigenetic control of antigen presentation failure in osteosarcoma: from single-cell chromatin maps to therapeutic strategies

Yan He&#x;Yan HeHeng Wu*&#x;Heng Wu*†
  • Department of Spinal Surgery, The Affiliated Sport Hospital of Chengdu Sport University, Chengdu, China

Osteosarcoma arises within heterogeneous tumor–immune ecosystems in which impaired antigen visibility—shaped by chromatin programs—limits immune surveillance and blunts responses to immunotherapy. Beyond structural defects in the antigen-processing pathway, Polycomb-mediated repression, DNA hypermethylation, and state-specific enhancer closure converge on the HLA class I/NLRC5/interferon axis to diminish peptide display. These constraints are context dependent, varying across malignant clones, differentiation states, and myeloid and T-cell niches. Traditional bulk assays obscure this complexity; single-cell ATAC-seq, integrated with single-cell and spatial transcriptomics, now resolves promoter–enhancer accessibility at HLA, NLRC5, and antigen-processing genes, distinguishes reversible repression from fixed lesions, and links microenvironmental stress to interferon competence. Translationally, epigenetic reprogramming—targeting Polycomb repressive complex 2 (PRC2), DNA methyltransferases (DNMTs), and complementary regulators (for example, LSD1, BET, CDK4/6, YAP/TEAD)—offers biomarker-guided avenues to restore antigen presentation, provided ecosystem-aware pharmacodynamic readouts track chromatin opening and antigen-presentation recovery across compartments. Despite encouraging preclinical evidence, efficacy will depend on clone selection, scheduling that preserves interferon signaling, and rational combinations with innate agonists and checkpoint blockade. This mini-review synthesizes epigenetic mechanisms of antigen-presentation failure in osteosarcoma and outlines how single-cell chromatin profiling can guide strategies to reinstate tumor antigen visibility.

1 Introduction

Osteosarcoma is an aggressive primary bone malignancy marked by extensive genomic disruption and profound heterogeneity across malignant, stromal and immune compartments. Recent multi-omics analyses have delineated immune-activated and immune-suppressed osteosarcoma subtypes, underscoring that antitumor immunity varies widely across patients and lesions and that impaired tumor antigen visibility is a recurrent barrier to immune surveillance (13). In metastatic and relapsed disease, HLA class I expression and T-cell infiltration correlate, suggesting that defects in antigen presentation shape immune contexture and may limit responsiveness to immunotherapy (4, 5). These observations place the machinery governing peptide processing and HLA class I display at the center of osteosarcoma immune escape.

Antigen presentation failure in cancer arises from both structural lesions in the antigen-processing pathway and reversible transcriptional repression. Beyond mutations or losses in components such as B2M or TAP, tumors co-opt epigenetic programs to silence the MHC class I axis (68). Polycomb repressive complex 2 (PRC2) and its catalytic subunit EZH2 can directly suppress genes encoding MHC class I and antigen-processing machinery (APM), thereby diminishing CD8+ T-cell recognition; genetic or pharmacologic interference with this pathway restores antigen display and enhances T-cell–mediated cytotoxicity in preclinical models (911). In parallel, the MHC class I transactivator NLRC5 is a master regulator of this program; diminished NLRC5—via genetic or epigenetic mechanisms—is associated with reduced HLA class I expression across human malignancies (1215). These studies establish a mechanistic link between chromatin state and tumor immunogenicity and provide a rationale to interrogate epigenetic control of the antigen-presentation axis in osteosarcoma.

Osteosarcoma is characterized by widespread epigenomic dysregulation, including oncohistone alterations and aberrant DNA methylation, which influence lineage programs and microenvironmental interactions. Although the specific consequences for the antigen-presentation network in osteosarcoma are incompletely defined, emerging evidence indicates that epigenetic therapies can reprogram tumor cells toward greater immune visibility by increasing expression of HLA class I components and interferon-stimulated genes (16, 17). In osteosarcoma models, DNA demethylation reshapes transcriptional programs, supporting the plausibility that chromatin-level interventions could intersect with antigen processing (1820). More broadly across cancers, inhibition of DNA methyltransferases (DNMTs) can induce viral mimicry with upregulation of interferon-stimulated genes (ISGs) and antigen-presentation machinery, providing a conceptual framework to test similar strategies in osteosarcoma.

Single-cell assay for transposase-accessible chromatin sequencing (scATAC-seq) now enables direct, cell-resolved measurement of chromatin accessibility at promoters and enhancers of HLA genes, NLRC5 and interferon-pathway effectors within complex tumor ecosystems. Applied alongside single-cell and spatial transcriptomics, scATAC-seq can map how malignant clones, osteoblastic differentiation states and myeloid or lymphoid niches converge to establish epigenetic bottlenecks to antigen presentation (2123). By capturing both inter- and intra-tumoral heterogeneity in regulatory element usage, scATAC-seq provides a mechanistic lens to distinguish fixed lesions from reversible repression and to identify candidate nodes—such as PRC2 occupancy or distal enhancers of NLRC5—for therapeutic modulation (24, 25). This mini-review synthesizes current knowledge on epigenetic control of antigen presentation failure in osteosarcoma and outlines how single-cell chromatin profiling can guide strategies to restore tumor antigen visibility.

2 Osteosarcoma chromatin constraints on antigen presentation

Osteosarcoma exhibits pervasive epigenomic remodeling that can directly constrain the MHC class I axis. In many solid tumors, Polycomb repression contributes to immunoediting by depositing H3K27me3 across promoters and enhancers of the MHC class I transactivator NLRC5 and antigen-processing machinery, thereby reducing HLA class I display; pharmacologic inhibition of PRC2 components restores MHC class I expression in preclinical systems, supporting a causal role for chromatin state in antigen visibility (2628). Within this framework, NLRC5 functions as a lineage-agnostic master regulator of MHC class I genes and several processing components (for example, TAP1, PSMB8/9), and its diminished expression—by genetic or epigenetic mechanisms—links inaccessible chromatin to ineffective peptide presentation (29, 30). In osteosarcoma specifically, EZH2 activity is frequently elevated and mechanistically connected to undifferentiated programs; although the immunologic consequences have not been fully mapped in patient material, these observations align with a model in which PRC2 activity imposes a reversible ceiling on antigen presentation capacity in subsets of tumors.

DNA methylation patterns provide a second axis of constraint. Osteosarcoma harbors reproducible methylation signatures that stratify risk and delineate disease subtypes, and epigenetic reactivation experiments in osteosarcoma cells demonstrate that demethylation can derepress immune-related transcripts (3133). Across cancers, inhibition of DNA methyltransferases—alone or combined with histone deacetylase inhibition—can elicit a viral-mimicry response with double-stranded RNA accumulation, type I/II interferon signaling, and upregulation of the antigen-presentation machinery, providing a conceptual route to restore tumor visibility; these effects, established in multiple models, motivate testing in osteosarcoma contexts with appropriate pharmacodynamic readouts (34, 35). At the tissue level, osteosarcoma specimens show heterogeneous but interpretable relationships between HLA class I expression and lymphocyte infiltration, consistent with the notion that chromatin-encoded suppression of antigen presentation shapes immune contexture.

Single-cell chromatin accessibility profiling offers a practical means to resolve these constraints within complex tumor ecosystems. scATAC-seq can quantify accessibility at proximal promoters and distal enhancers for HLA genes, NLRC5, and interferon-response effectors, and can attribute repressive signatures (for example, H3K27me3-associated inaccessibility or loss of IRF/RFX motif accessibility) to specific malignant clones and differentiation states (3638). In parallel, joint single-cell chromatin and transcriptome profiling helps distinguish fixed structural lesions from reversible repression, enabling prioritization of epigenetic targets most likely to restore antigen processing (3941). An operational summary of candidate chromatin constraints in osteosarcoma and their expected single-cell readouts is provided in Table 1, which is intended to guide experimental design and interpretation in forthcoming studies. Osteosarcoma is poised for an integrated epigenetic–immunologic framework in which PRC2 activity, DNA methylation, and state-specific enhancer usage converge to limit peptide display. Cell-resolved chromatin maps can identify patients and clones with reversible repression, nominate precise regulatory elements for modulation, and provide quantitative pharmacodynamic endpoints for trials testing PRC2 or DNA-methylation–directed strategies aimed at restoring tumor antigen visibility.

Table 1
www.frontiersin.org

Table 1. Candidate chromatin constraints on antigen presentation in osteosarcoma and expected single-cell readouts.

3 Single-cell ATAC-seq profiles of the osteosarcoma tumor–immune ecosystem

Single-cell chromatin accessibility profiling resolves how malignant, stromal, and immune compartments in osteosarcoma jointly encode constraints on antigen visibility. In malignant cells, scATAC-seq delineates clone-specific enhancer–promoter usage across HLA class I genes, NLRC5, and antigen-processing machinery, while simultaneously exposing differentiation-state dependencies that couple osteoblastic lineage programs to immunogenicity (42, 43). As shown in Figure 1, accessibility losses at RFX/IRF/STAT-bearing regulatory elements near HLA-A/B/C, TAP1, and PSMB8/9, together with diminished NLRC5 enhancer activity, are recurrent features in immune-cold tumor regions and correspond to reduced interferon-response competence, consistent with prior single-cell and bulk chromatin studies in solid tumors (44, 45). Copy-number–aware scATAC-seq and peak–gene linkage analyses distinguish irreversible structural lesions from reversible repression, allowing inference of candidate nodes—such as PRC2-dominated promoters or silenced distal enhancers of NLRC5—whose modulation restores antigen presentation in preclinical systems (4648). Integration with matched single-cell RNA-seq further validates that local closure at these elements aligns with attenuated HLA and antigen-processing transcripts rather than global transcriptional collapse (49, 50), supporting a specific epigenetic mechanism rather than nonspecific stress effects.

Figure 1
Diagram illustrating the interaction between osteosarcoma cells, indicated by NLRC5 and PRC2, and DNA methylation by DNMT. This affects antigen presentation machinery (APM) and HLA-I expression, influencing the interaction with T-cells and interferon (IFN) responses. The graphic suggests a regulatory pathway in osteosarcoma, involving DNA accessibility (scATAC-seq) and immune response elements.

Figure 1. scATAC-seq reveals epigenetic suppression of the NLRC5–HLA-I/APM axis limiting T-Cell recognition in osteosarcoma.

The immune landscape extracted from scATAC-seq adds mechanistic resolution to T-cell dysfunction and myeloid-driven suppression in osteosarcoma. Exhausted CD8+ T cells exhibit increased TOX and NFAT motif accessibility with reduced AP-1/IRF co-accessibility at interferon-inducible enhancers proximal to antigen-presentation targets, mirroring transcriptional states that associate with impaired cytotoxic function and poor tumor control in sarcomas (5153). Regulatory T cells show strong FOXP3 and NF-κB motif accessibility and co-accessible chromatin at immunosuppressive cytokine loci, providing a structural correlate for sustained checkpoint pathway activity (54, 55). On the myeloid axis, scATAC-seq resolves macrophage continua from inflammatory to immunoregulatory states; in tumor-proximal macrophages, decreased IRF/STAT motif activity at antigen-presentation modules coexists with enhanced accessibility at enhancers controlling ARG1, IL10, and TGFB1, consistent with impaired cross-priming and active T-cell suppression (5658). Dendritic cell subsets with accessible BATF3/IRF8 programs are typically sparse in immune-cold niches, and where present, exhibit attenuated accessibility at CCR7 and costimulatory gene enhancers, suggesting defective maturation and trafficking.

Chromatin-level cell–cell interactions inferred from co-accessibility and ligand–receptor–anchored enhancer activity connect ecosystem structure to antigen-presentation failure. Osteosarcoma cells with high EZH2-linked repression programs co-localize with myeloid niches that display reduced IRF accessibility, indicating convergent dampening of interferon pathways across compartments (5961). Spatially informed analyses align low HLA enhancer accessibility with hypoxic regions marked by HIF-associated motif gains, raising the possibility that microenvironmental stress reprograms chromatin to limit peptide display independently of fixed genetic lesions (6264). These patterns are reproduced across metastatic deposits, where clonal enhancer switching and continued loss of NLRC5 regulatory accessibility track with immune exclusion and attenuated CD8+ infiltration.

Robust inference requires rigorous quality control and analytical standardization. Nucleosomal signal, transcription-start-site enrichment, doublet removal, and batch correction are essential to prevent artifactual loss of accessibility at compacted loci (65, 66). Peak calling tailored to sparse single-cell data, chromVAR-based motif deviation scoring, and peak–gene linkage within frameworks such as ArchR or Cicero provide reproducible quantification of regulatory programs that govern antigen presentation (6769). Allele-specific accessibility at HLA loci can be incorporated to separate haplotype loss from epigenetic repression, and perturbation-coupled scATAC-seq offers direct pharmacodynamic readouts for epigenetic therapies that seek to restore antigen visibility (7072). These single-cell chromatin maps define an osteosarcoma tumor–immune ecosystem in which malignant enhancer architecture, myeloid-driven interferon desensitization, and T-cell exhaustion converge on a shared endpoint of reduced HLA class I display, thereby establishing measurable, targetable epigenetic bottlenecks to antigen presentation.

4 Epigenetic strategies to restore antigen visibility in osteosarcoma

Epigenetic interventions aimed at reversing chromatin constraints on the MHC class I axis provide a rational path to increase tumor antigen visibility in osteosarcoma. Inhibition of Polycomb repressive complex 2 can relieve promoter–enhancer repression across NLRC5 and antigen-processing loci; across cancer models, EZH2 blockade restores MHC class I programs and improves immune recognition, supporting this strategy where PRC2 activity is heightened (7375). Mechanistically, EZH2 inhibition also raises stimulator of interferon genes (STING) pathway competence, offering a tractable avenue for combination with cyclic dinucleotide agonists to amplify interferon-driven antigen presentation (76, 77). These concepts, supported predominantly by preclinical data and not yet validated in osteosarcoma-specific trials, align with single-cell chromatin evidence of reversible repression at HLA/NLRC5 regulatory elements and motivate biomarker-guided exploration in early-phase studies.

DNA methylation is a second, actionable axis: inhibition of DNA methyltransferases (DNMTs) and histone deacetylases (HDACs) induces viral mimicry (double-stranded RNA; type I/III interferon), elevating antigen presentation and immunogenicity (7880). Candidates show scATAC-seq evidence of CpG-dense, closed promoters and interferon-responsive enhancers near NLRC5, TAP1, PSMB8/9 (81, 82). Scheduling with cytokine priming or innate agonists maximizes reprogramming while limiting cytotoxicity. Orthogonal tests: lysine-specific demethylase 1 (LSD1) blockade boosts MHC-I, dendritic chemokines, and checkpoint efficacy in small-cell/myeloid and osteoblastic–RFX/IRF-low states (83, 84); bromodomain and extra-terminal domain (BET) blockade may raise MHC-I yet dampen dendritic activation, requiring compartment-resolved pharmacodynamics (85, 86). scATAC-seq–anchored trial designs should therefore incorporate myeloid and T-cell accessibility metrics to anticipate ecosystem-level effects.

Cell-state and signaling–directed epigenetic combinations can further restore peptide display. CDK4/6 inhibitors promote endogenous retroelement expression and interferon signaling, increase antigen-presentation gene expression, and reduce regulatory T-cell proliferation; pairing CDK4/6 blockade with PRC2 or DNMT-directed agents could align tumor-intrinsic antigen restoration with favorable immune composition (8789). Hippo pathway modulation provides an additional axis: YAP/TEAD inhibition upregulates NLRC5 and antigen-processing genes, offering a route to reopen distal enhancers mapped by scATAC-seq (90, 91). For osteosarcoma, these strategies should be advanced with prospective selection of clones exhibiting PRC2-dominated inaccessibility or methylation-linked enhancer closure (Table 2), and with quantitative single-cell pharmacodynamic endpoints.

Table 2
www.frontiersin.org

Table 2. Epigenetic strategies to restore antigen visibility in osteosarcoma.

5 Conclusions and outlook

Epigenetic interventions that reopen the MHC class I axis offer a tractable route to increase tumor antigen visibility in osteosarcoma while accommodating its inter- and intra-tumoral heterogeneity. Inhibiting Polycomb repressive complex 2 (PRC2) can derepress promoters and distal enhancers across NLRC5 and antigen-processing genes, restoring HLA class I and sensitizing tumors to T-cell attack in diverse preclinical systems (92, 93). Mechanistic work shows that PRC2/EZH2 maintains a conserved silencing program over the antigen-presentation pathway, and pharmacologic EZH2/EED inhibition reverses this constraint and augments response to immunotherapy in vivo. Taken together, these data support consideration of PRC2-directed clinical studies, particularly when single-cell maps indicate H3K27me3-dominated inaccessibility at NLRC5/HLA regulatory elements, while recognizing that osteosarcoma-specific trial data are still limited (94, 95). Moreover, EZH2 blockade can potentiate innate-sensing pathways, including STING, providing a rationale for combinations with cyclic dinucleotide agonists to amplify interferon-driven upregulation of antigen-presentation machinery.

DNA methyltransferase (DNMT) inhibition represents a complementary lever that induces “viral mimicry,” with endogenous double-stranded RNA accumulation, type I/III interferon signaling and coordinated induction of HLA and antigen-processing programs (96, 97). These effects have been demonstrated across epithelial models and provide a framework for testing demethylating agents in osteosarcoma with pharmacodynamic readouts anchored in interferon competence and chromatin opening at CpG-dense promoters and interferon-responsive enhancers (98100). Pairing DNMT inhibition with CDK4/6 inhibitors may couple tumor-intrinsic restoration of antigen presentation to favorable immune remodeling, as CDK4/6 blockade increases tumor cell antigen-presentation gene expression and reduces regulatory T-cell proliferation, enhancing cytotoxic responses. Nonetheless, support for these epigenetic–immunologic strategies is currently derived mainly from preclinical models, particularly for osteosarcoma.

Targeting additional chromatin regulators may widen the therapeutic aperture. LSD1 inhibition can upregulate MHC-I and dendritic-cell–recruiting chemokines, reprogramming poorly immunogenic states and improving responsiveness to checkpoint blockade; these effects argue for state-aware deployment when lineage motifs coincide with low RFX/IRF accessibility at antigen-presentation enhancers (101, 102). BET bromodomain inhibition has also been shown to relieve repressive control over antigen-presentation circuitry and to engage antitumor immunity (103, 104), nominating combinations with PD-1/PD-L1 inhibitors while calling for compartment-resolved pharmacodynamics to track possible myeloid effects.

Osteosarcoma-specific epigenomic features underscore the feasibility of this agenda. Risk-stratifying methylation subtypes and decitabine-responsive programs highlight a disease in which DNA methylation intersects with tumor–stromal interactions and may be exploited to reinstate immune visibility (105107). These signatures, together with integrative analyses linking methylation to gene expression in osteosarcoma, support biomarker-guided selection of demethylating or Polycomb-targeted strategies.

Looking to the Future, single-cell ATAC-seq should be embedded prospectively to select patients with reversible repression at NLRC5/HLA and antigen-processing loci, to nominate distal enhancers for modulation, and to deliver quantitative pharmacodynamic endpoints that distinguish true antigen-restorative effects from nonspecific stress responses. Beyond PRC2 and DNMT axes, Hippo pathway modulation is an emerging avenue: YAP/TEAD inhibition relieves repression of NLRC5 and increases MHC-I antigen-processing gene expression, offering a differentiation-aware route to reopen distal enhancers. Trials should incorporate matched single-cell chromatin and transcriptomic profiling across malignant, myeloid and T-cell compartments, with allele-specific interrogation at HLA loci and standardized interferon-response metrics. The strategic goal is to convert antigen-poor osteosarcoma clones into interferon-competent, HLA-replete states that admit durable T-cell control, using epigenetic agents as enabling therapies in rational combinations with innate agonists and checkpoint blockade.

Author contributions

YH: Conceptualization, Methodology, Validation, Writing – original draft, Investigation, Supervision. HW: Conceptualization, Methodology, Supervision, Visualization, Writing – original draft, Writing – review & editing.

Funding

The author(s) declare that no financial support was received for the research and/or publication of this article.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The author(s) declare that no Generative AI was used in the creation of this manuscript.

Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

References

1. Wu CC, Beird HC, Andrew Livingston J, Advani S, Mitra A, Cao S, et al. Immuno-genomic landscape of osteosarcoma. Nat Commun. (2020) 11:1008. doi: 10.1038/s41467-020-14646-w

PubMed Abstract | Crossref Full Text | Google Scholar

2. Liu W, Hu H, Shao Z, Lv X, Zhang Z, Deng X, et al. Characterizing the tumor microenvironment at the single-cell level reveals a novel immune evasion mechanism in osteosarcoma. Bone Res. (2023) 11:4. doi: 10.1038/s41413-022-00237-6

PubMed Abstract | Crossref Full Text | Google Scholar

3. Lacinski RA, Dziadowicz SA, Melemai VK, Fitzpatrick B, Pisquiy JJ, Heim T, et al. Spatial multiplexed immunofluorescence analysis reveals coordinated cellular networks associated with overall survival in metastatic osteosarcoma. Bone Res. (2024) 12:55. doi: 10.1038/s41413-024-00359-z

PubMed Abstract | Crossref Full Text | Google Scholar

4. Jiang Y, Wang J, Sun M, Zuo D, Wang H, Shen J, et al. Multi-omics analysis identifies osteosarcoma subtypes with distinct prognosis indicating stratified treatment. Nat Commun. (2022) 13:7207. doi: 10.1038/s41467-022-34689-5

PubMed Abstract | Crossref Full Text | Google Scholar

5. Yang C, Lai Y, Wang J, Chen Q, Pan Q, Xu C, et al. Spatial heterogeneity of PD-1/PD-L1 defined osteosarcoma microenvironments at single-cell spatial resolution. Lab Invest. (2024) 104:102143. doi: 10.1016/j.labinv.2024.102143

PubMed Abstract | Crossref Full Text | Google Scholar

6. Tian H, Cao J, Li B, Nice EC, Mao H, Zhang Y, et al. Managing the immune microenvironment of osteosarcoma: the outlook for osteosarcoma treatment. Bone Res. (2023) 11:11. doi: 10.1038/s41413-023-00246-z

PubMed Abstract | Crossref Full Text | Google Scholar

7. Chen R, Ishak CA, and De Carvalho DD. Endogenous retroelements and the viral mimicry response in cancer therapy and cellular homeostasis. Cancer Discov. (2021) 11:2707–25. doi: 10.1158/2159-8290.CD-21-0506

PubMed Abstract | Crossref Full Text | Google Scholar

8. Fan W, Li W, Li L, Qin M, Mao C, Yuan Z, et al. Bifunctional HDAC and DNMT inhibitor induces viral mimicry activates the innate immune response in triple-negative breast cancer. Eur J Pharm Sci. (2024) 197:106767. doi: 10.1016/j.ejps.2024.106767

PubMed Abstract | Crossref Full Text | Google Scholar

9. Cortesi A, Gandolfi F, Arco F, Di Chiaro P, Valli E, Polletti S, et al. Activation of endogenous retroviruses and induction of viral mimicry by MEK1/2 inhibition in pancreatic cancer. Sci Adv. (2024) 10:eadk5386. doi: 10.1126/sciadv.adk5386

PubMed Abstract | Crossref Full Text | Google Scholar

10. Ribrag V, Iglesias L, De Braud F, Ma B, Yokota T, Zander T, et al. A first-in-human phase 1/2 dose-escalation study of MAK683 (EED inhibitor) in patients with advanced Malignancies. Eur J Cancer. (2025) 216:115122. doi: 10.1016/j.ejca.2024.115122

PubMed Abstract | Crossref Full Text | Google Scholar

11. DuCote TJ, Song X, Naughton KJ, Chen F, Plaugher DR, Childress AR, et al. EZH2 inhibition promotes tumor immunogenicity in lung squamous cell carcinomas. Cancer Res Commun. (2024) 4:388–403. doi: 10.1158/2767-9764.CRC-23-0399

Crossref Full Text | Google Scholar

12. Isshiki Y, Chen X, Teater M, Karagiannidis I, Nam H, Cai W, et al. EZH2 inhibition enhances T cell immunotherapies by inducing lymphoma immunogenicity and improving T cell function. Cancer Cell. (2025) 43:49–68. doi: 10.1016/j.ccell.2024.11.006

PubMed Abstract | Crossref Full Text | Google Scholar

13. Al Emran A and Fisher DE. Dual targeting with EZH2 inhibitor and STING agonist to treat melanoma. J Invest Dermatol. (2022) 142:1004–6. doi: 10.1016/j.jid.2021.09.028

PubMed Abstract | Crossref Full Text | Google Scholar

14. Yoshihama S, Cho SX, Yeung J, Pan X, Lizee G, Konganti K, et al. NLRC5/CITA expression correlates with efficient response to checkpoint blockade immunotherapy. Sci Rep. (2021) 11:3258. doi: 10.1038/s41598-021-82729-9

PubMed Abstract | Crossref Full Text | Google Scholar

15. Sun X, Watanabe T, Oda Y, Shen W, Ahmad A, Ouda R, et al. Targeted demethylation and activation of NLRC5 augment cancer immunogenicity through MHC class I. Proc Natl Acad Sci. (2024) 121:e2310821121. doi: 10.1073/pnas.2310821121

PubMed Abstract | Crossref Full Text | Google Scholar

16. Zhan L, Zhang J, Zhang J, Liu X, Zhu S, Shi Y, et al. LC3 and NLRC5 interaction inhibits NLRC5-mediated MHC class I antigen presentation pathway in endometrial cancer. Cancer Lett. (2022) 529:37–52. doi: 10.1016/j.canlet.2021.12.031

PubMed Abstract | Crossref Full Text | Google Scholar

17. Adcox HE, Hunt JR, Allen PE, Siff TE, Rodino KG, Ottens AK, et al. Orientia tsutsugamushi Ank5 promotes NLRC5 cytoplasmic retention and degradation to inhibit MHC class I expression. Nat Commun. (2024) 15:8069. doi: 10.1038/s41467-024-52119-6

PubMed Abstract | Crossref Full Text | Google Scholar

18. Lietz CE, Newman ET, Kelly AD, Xiang DH, Zhang Z, Luscko CA, et al. Genome-wide DNA methylation patterns reveal clinically relevant predictive and prognostic subtypes in human osteosarcoma. Commun Biol. (2022) 5:213. doi: 10.1038/s42003-022-03117-1

PubMed Abstract | Crossref Full Text | Google Scholar

19. Koelsche C, Schrimpf D, Stichel D, Sill M, Sahm F, Reuss DE, et al. Sarcoma classification by DNA methylation profiling. Nat Commun. (2021) 12:498. doi: 10.1038/s41467-020-20603-4

PubMed Abstract | Crossref Full Text | Google Scholar

20. Singh I, Rainusso N, Kurenbekova L, Nirala BK, Dou J, Muruganandham A, et al. Intrinsic epigenetic state of primary osteosarcoma drives metastasis. Mol Cancer Res. (2024) 22:864–78. doi: 10.1158/1541-7786.MCR-23-0055

PubMed Abstract | Crossref Full Text | Google Scholar

21. Granja JM, Corces MR, Pierce SE, Bagdatli ST, Choudhry H, Chang HY, et al. ArchR is a scalable software package for integrative single-cell chromatin accessibility analysis. Nat Genet. (2021) 53:403–11. doi: 10.1038/s41588-021-00790-6

PubMed Abstract | Crossref Full Text | Google Scholar

22. Zhang P, Zhang H, Tang J, Ren Q, Zhang J, Chi H, et al. The integrated single-cell analysis developed an immunogenic cell death signature to predict lung adenocarcinoma prognosis and immunotherapy. Aging (Albany NY). (2023) 15:10305. doi: 10.18632/aging.205077

PubMed Abstract | Crossref Full Text | Google Scholar

23. De Rop FV, Hulselmans G, Flerin C, Soler-Vila P, Rafels A, Christiaens V, et al. Systematic benchmarking of single-cell ATAC-sequencing protocols. Nat Biotechnol. (2024) 42:916–26. doi: 10.1038/s41587-023-01881-x

PubMed Abstract | Crossref Full Text | Google Scholar

24. Zhang H, Mulqueen RM, Iannuzo N, Farrera DO, Polverino F, Galligan JJ, et al. txci-ATAC-seq: a massive-scale single-cell technique to profile chromatin accessibility. Genome Biol. (2024) 25:78. doi: 10.1186/s13059-023-03150-1

PubMed Abstract | Crossref Full Text | Google Scholar

25. Yates KB, Tonnerre P, Martin GE, Gerdemann U, Al Abosy R, Comstock DE, et al. Epigenetic scars of CD8+ T cell exhaustion persist after cure of chronic infection in humans. Nat Immunol. (2021) 22:1020–9. doi: 10.1038/s41590-021-00979-1

PubMed Abstract | Crossref Full Text | Google Scholar

26. Chen C, Liu J, Chen Y, Lin A, Mou W, Zhu L, et al. Application of ATAC-seq in tumor-specific T cell exhaustion. Cancer Gene Ther. (2023) 30:1–10. doi: 10.1038/s41417-022-00495-w

PubMed Abstract | Crossref Full Text | Google Scholar

27. Yuan K, Zhao S, Ye B, Wang Q, Liu Y, Zhang P, et al. A novel T-cell exhaustion-related feature can accurately predict the prognosis of OC patients. Front Pharmacol. (2023) 14:1192777. doi: 10.3389/fphar.2023.1192777

PubMed Abstract | Crossref Full Text | Google Scholar

28. Smith AL, Skupa SA, Eiken AP, Reznicek TE, Schmitz E, Williams N, et al. BET inhibition reforms the immune microenvironment and alleviates T cell dysfunction in chronic lymphocytic leukemia. JCI Insight. (2024) 9:e177054. doi: 10.1172/jci.insight.177054

PubMed Abstract | Crossref Full Text | Google Scholar

29. Hiatt JB, Sandborg H, Garrison SM, Arnold HU, Liao SY, Norton JP, et al. Inhibition of LSD1 with bomedemstat sensitizes small cell lung cancer to immune checkpoint blockade and T-cell killing. Clin Cancer Res. (2022) 28:4551–64. doi: 10.1158/1078-0432.CCR-22-1128

PubMed Abstract | Crossref Full Text | Google Scholar

30. Zeng Z, Gu SS, Ouardaoui N, Tymm C, Yang L, Wong CJ, et al. Hippo signaling pathway regulates cancer cell–intrinsic MHC-II expression. Cancer Immunol Res. (2022) 10:1559–69. doi: 10.1158/2326-6066.CIR-22-0227

PubMed Abstract | Crossref Full Text | Google Scholar

31. Estephan H, Tailor A, Parker R, Kreamer M, Papandreou I, Campo L, et al. Hypoxia promotes tumor immune evasion by suppressing MHC-I expression and antigen presentation. EMBO J. (2025) 44:903–22. doi: 10.1038/s44318-024-00319-7

PubMed Abstract | Crossref Full Text | Google Scholar

32. He S, Su L, Hu H, Liu H, Xiong J, Gong X, et al. Immunoregulatory functions and therapeutic potential of natural killer cell-derived extracellular vesicles in chronic diseases. Front Immunol. (2024) 14:1328094. doi: 10.3389/fimmu.2023.1328094

PubMed Abstract | Crossref Full Text | Google Scholar

33. Iriondo O, Mecenas D, Li Y, Chin CR, Thomas A, Moriarty A, et al. Hypoxic memory mediates prolonged tumor-intrinsic type I interferon suppression to promote breast cancer progression. Cancer Res. (2024) 84:3141–57. doi: 10.1158/0008-5472.CAN-23-2028

PubMed Abstract | Crossref Full Text | Google Scholar

34. Gao M, Cui W, Duan H, and Guo J. DNA methylation subtypes dictate metastatic heterogeneity of osteosarcoma via distinct tumor-stromal interactions: Multi-omics profiling and decitabine validation. Int J Biol Macromolecules. (2025), 10:147473. doi: 10.1016/j.ijbiomac.2025.147473

PubMed Abstract | Crossref Full Text | Google Scholar

35. Mu H, Zhang Q, Zuo D, Wang J, Tao Y, Li Z, et al. Methionine intervention induces PD-L1 expression to enhance the immune checkpoint therapy response in MTAP-deleted osteosarcoma. Cell Rep Med. (2025) 6:101977. doi: 10.1016/j.xcrm.2025.101977

PubMed Abstract | Crossref Full Text | Google Scholar

36. Truong DD, Weistuch C, Murgas KA, Admane P, King BL, Chauviere Lee J, et al. Mapping the single-cell differentiation landscape of osteosarcoma. Clin Cancer Res. (2024) 30:3259–72. doi: 10.1158/1078-0432.CCR-24-0563

PubMed Abstract | Crossref Full Text | Google Scholar

37. Jiang C, Zhang S, Jiang L, Chen Z, Chen H, Huang J, et al. Precision unveiled: Synergistic genomic landscapes in breast cancer—Integrating single-cell analysis and decoding drug toxicity for elite prognostication and tailored therapeutics. Environ Toxicol. (2024) 39:3448–72. doi: 10.1002/tox.24205

PubMed Abstract | Crossref Full Text | Google Scholar

38. Pires SF, de Barros JS, da Costa SS, de Oliveira Scliar M, Van Helvoort Lengert A, Boldrini É., et al. DNA methylation patterns suggest the involvement of DNMT3B and TET1 in osteosarcoma development. Mol Genet Genomics. (2023) 298:721–33. doi: 10.1007/s00438-023-02010-8

PubMed Abstract | Crossref Full Text | Google Scholar

39. SChade AE, Perurena N, Yang Y, Rodriguez CL, Krishnan A, Gardner A, et al. AKT and EZH2 inhibitors kill TNBCs by hijacking mechanisms of involution. Nature. (2024) 635:755–63. doi: 10.1038/s41586-024-08031-6

PubMed Abstract | Crossref Full Text | Google Scholar

40. Zhang S, Jiang C, Jiang L, Chen H, Huang J, Zhang J, et al. Uncovering the immune microenvironment and molecular subtypes of hepatitis B-related liver cirrhosis and developing stable a diagnostic differential model by machine learning and artificial neural networks. Front Mol Biosci. (2023) 10:1275897. doi: 10.3389/fmolb.2023.1275897

PubMed Abstract | Crossref Full Text | Google Scholar

41. Choy L, Norris S, Wu X, Kolumam G, Firestone A, Settleman J, et al. Inhibition of aurora kinase induces endogenous retroelements to induce a type I/III IFN response via RIG-I. Cancer Res Commun. (2024) 4:540–55. doi: 10.1158/2767-9764.CRC-23-0432

PubMed Abstract | Crossref Full Text | Google Scholar

42. Fan H, Liu W, Zeng Y, Zhou Y, Gao M, Yang L, et al. DNA damage induced by CDK4 and CDK6 blockade triggers anti-tumor immune responses through cGAS-STING pathway. Commun Biol. (2023) 6:1041. doi: 10.1038/s42003-023-05412-x

PubMed Abstract | Crossref Full Text | Google Scholar

43. Savardekar H, Stiff A, Liu A, Wesolowski R, Schwarz E, Garbarine IC, et al. BRD4 inhibition leads to MDSC apoptosis and enhances checkpoint blockade therapy. J Clin Invest. (2025) 135:e181975. doi: 10.1172/JCI181975

PubMed Abstract | Crossref Full Text | Google Scholar

44. Porazzi P, Nason S, Yang Z, Carturan A, Ghilardi G, Guruprasad P, et al. EZH1/EZH2 inhibition enhances adoptive T cell immunotherapy against multiple cancer models. Cancer Cell. (2025) 43:537–51. doi: 10.1016/j.ccell.2025.01.013

PubMed Abstract | Crossref Full Text | Google Scholar

45. Liao TT, Chen YH, Li ZY, Hsiao AC, Huang YL, Hao RX, et al. Hypoxia-induced long noncoding RNA HIF1A-AS2 regulates stability of MHC class I protein in head and neck cancer. Cancer Immunol Res. (2024) 12:1468–84. doi: 10.1158/2326-6066.CIR-23-0622

PubMed Abstract | Crossref Full Text | Google Scholar

46. Wu Q, Schapira M, Arrowsmith CH, and Barsyte-Lovejoy D. Protein arginine methylation: from enigmatic functions to therapeutic targeting. Nat Rev Drug Discov. (2021) 20:509–30. doi: 10.1038/s41573-021-00159-8

PubMed Abstract | Crossref Full Text | Google Scholar

47. Li X, Xu H, Du Z, Cao Q, and Liu X. Advances in the study of tertiary lymphoid structures in the immunotherapy of breast cancer. Front Oncol. (2024) 14:1382701. doi: 10.3389/fonc.2024.1382701

PubMed Abstract | Crossref Full Text | Google Scholar

48. Sewastianik T, Roy C, Gormally MV, Montesion M, Halvey P, Jindal A, et al. Allele-specific HLA LOH in solid tumors: distinct patterns by tumor type and potential prognostic relevance. J Immunotherapy Cancer. (2025) 13:e012435. doi: 10.1136/jitc-2025-012435

PubMed Abstract | Crossref Full Text | Google Scholar

49. Puttick C, Jones TP, Leung MM, Galvez-Cancino F, Liu J, Varas-Godoy M, et al. MHC Hammer reveals genetic and non-genetic HLA disruption in cancer evolution. Nat Genet. (2024) 56:2121–31. doi: 10.1038/s41588-024-01883-8

PubMed Abstract | Crossref Full Text | Google Scholar

50. Zhang K, Zemke NR, Armand EJ, and Ren B. A fast, scalable and versatile tool for analysis of single-cell omics data. Nat Methods. (2024) 21:217–27. doi: 10.1038/s41592-023-02139-9

PubMed Abstract | Crossref Full Text | Google Scholar

51. Luo S, Germain PL, Robinson MD, and von Meyenn F. Benchmarking computational methods for single-cell chromatin data analysis. Genome Biol. (2024) 25:225. doi: 10.1186/s13059-024-03356-x

PubMed Abstract | Crossref Full Text | Google Scholar

52. You Y, Chen Y, Zhang Q, Hu X, Li X, Yang P, et al. Systematic and meta-based evaluation of the relationship between the built environment and physical activity behaviors among older adults. PeerJ. (2023) 11:e16173. doi: 10.7717/peerj.16173

PubMed Abstract | Crossref Full Text | Google Scholar

53. Hu H, Wang X, Feng S, Xu Z, Liu J, Heidrich-O’Hare E, et al. A unified model-based framework for doublet or multiplet detection in single-cell multiomics data. Nat Commun. (2024) 15:5562. doi: 10.1038/s41467-024-49448-x

PubMed Abstract | Crossref Full Text | Google Scholar

54. Kartha VK, Duarte FM, Hu YAN, Ma S, Chew JG, Lareau CA, et al. Functional inference of gene regulation using single-cell multi-omics. Cell Genomics. (2022) 2:100166. doi: 10.1016/j.xgen.2022.100166

PubMed Abstract | Crossref Full Text | Google Scholar

55. Lobato-Moreno S, Yildiz U, Claringbould A, Servaas NH, Vlachou EP, Arnold C, et al. Single-cell ultra-high-throughput multiplexed chromatin and RNA profiling reveals gene regulatory dynamics. Nat Methods. (2025), 22:1–13. doi: 10.1038/s41592-025-02700-8

PubMed Abstract | Crossref Full Text | Google Scholar

56. Grandi FC, Modi H, Kampman L, and Corces MR. Chromatin accessibility profiling by ATAC-seq. Nat Protoc. (2022) 17:1518–52. doi: 10.1038/s41596-022-00692-9

PubMed Abstract | Crossref Full Text | Google Scholar

57. Yan Z, Fan KQ, Zhang Q, Wu X, Chen Y, Wu X, et al. Comparative analysis of the performance of the large language models DeepSeek-V3, DeepSeek-R1, open AI-O3 mini and open AI-O3 mini high in urology. World J Urol. (2025) 43:416. doi: 10.1007/s00345-025-05757-4

PubMed Abstract | Crossref Full Text | Google Scholar

58. Beltra JC, Abdel-Hakeem MS, Manne S, Zhang Z, Huang H, Kurachi M, et al. Stat5 opposes the transcription factor Tox and rewires exhausted CD8+ T cells toward durable effector-like states during chronic antigen exposure. Immunity. (2023) 56:2699–718. doi: 10.1016/j.immuni.2023.11.005

PubMed Abstract | Crossref Full Text | Google Scholar

59. Cao C, Xu M, Peng T, Liu X, Lin S, Xu Y, et al. Blocking CXCR4+ CD4+ T cells reprograms Treg-mediated immunosuppression via modulating the Rho-GTPase/NF-κB signaling axis. Genome Med. (2025) 17:85. doi: 10.1186/s13073-025-01515-8

PubMed Abstract | Crossref Full Text | Google Scholar

60. Del Prete A, Salvi V, Soriani A, Laffranchi M, Sozio F, Bosisio D, et al. Dendritic cell subsets in cancer immunity and tumor antigen sensing. Cell Mol Immunol. (2023) 20:432–47. doi: 10.1038/s41423-023-00990-6

PubMed Abstract | Crossref Full Text | Google Scholar

61. Martens LD, Fischer DS, Yépez VA, Theis FJ, and Gagneur J. Modeling fragment counts improves single-cell ATAC-seq analysis. Nat Methods. (2024) 21:28–31. doi: 10.1038/s41592-023-02112-6

PubMed Abstract | Crossref Full Text | Google Scholar

62. Li YE, Preissl S, Miller M, Johnson ND, Wang Z, Jiao H, et al. A comparative atlas of single-cell chromatin accessibility in the human brain. Science. (2023) 382:eadf7044. doi: 10.1126/science.adf7044

PubMed Abstract | Crossref Full Text | Google Scholar

63. Zhang SK, Jiang L, Jiang CL, Cao Q, Chen YQ, and Chi H. Unveiling genetic susceptibility in esophageal squamous cell carcinoma and revolutionizing pancreatic cancer diagnosis through imaging. World J Gastrointestinal Oncol. (2025) 17:102544. doi: 10.4251/wjgo.v17.i6.102544

PubMed Abstract | Crossref Full Text | Google Scholar

64. Chen X, Li K, Wu X, Li Z, Jiang Q, Cui X, et al. Descart: a method for detecting spatial chromatin accessibility patterns with inter-cellular correlations. Genome Biol. (2024) 25:322. doi: 10.1186/s13059-024-03458-6

PubMed Abstract | Crossref Full Text | Google Scholar

65. Chène P. Direct inhibition of the YAP: TEAD interaction: an unprecedented drug discovery challenge. ChemMedChem. (2024) 19:e202400361.

PubMed Abstract | Google Scholar

66. Simon M, Stüve P, Schmidleithner L, Bittner S, Beumer N, Strieder N, et al. Single-cell chromatin accessibility and transposable element landscapes reveal shared features of tissue-residing immune cells. Immunity. (2024) 57:1975–93. doi: 10.1016/j.immuni.2024.06.015

PubMed Abstract | Crossref Full Text | Google Scholar

67. Zagiel B, Melnyk P, and Cotelle P. Progress with YAP/TAZ-TEAD inhibitors: a patent review (2018-present). Expert Opin Ther Patents. (2022) 32:899–912. doi: 10.1080/13543776.2022.2096436

PubMed Abstract | Crossref Full Text | Google Scholar

68. Paul S, Sims J, Pham T, and Dey A. Targeting the Hippo pathway in cancer: kidney toxicity as a class effect of TEAD inhibitors? Trends Cancer. (2025) 11:25–36. doi: 10.1016/j.trecan.2024.10.004

PubMed Abstract | Crossref Full Text | Google Scholar

69. Morel KL, Sheahan AV, Burkhart DL, Baca SC, Boufaied N, Liu Y, et al. EZH2 inhibition activates a dsRNA–STING–interferon stress axis that potentiates response to PD-1 checkpoint blockade in prostate cancer. Nat Cancer. (2021) 2:444–56. doi: 10.1038/s43018-021-00185-w

PubMed Abstract | Crossref Full Text | Google Scholar

70. Liu Y and Yang Q. The roles of EZH2 in cancer and its inhibitors. Med Oncol. (2023) 40:167. doi: 10.1007/s12032-023-02025-6

PubMed Abstract | Crossref Full Text | Google Scholar

71. Goyal A, Bauer J, Hey J, Papageorgiou DN, Stepanova E, Daskalakis M, et al. DNMT and HDAC inhibition induces immunogenic neoantigens from human endogenous retroviral element-derived transcripts. Nat Commun. (2023) 14:6731. doi: 10.1038/s41467-023-42417-w

PubMed Abstract | Crossref Full Text | Google Scholar

72. Murayama T, Nakayama J, Jiang X, Miyata K, Morris AD, Cai KQ, et al. Targeting DHX9 triggers tumor-intrinsic interferon response and replication stress in small cell lung cancer. Cancer Discov. (2024) 14:468–91. doi: 10.1158/2159-8290.CD-23-0486

PubMed Abstract | Crossref Full Text | Google Scholar

73. Tzetzo SL, Schultz E, Wang J, Rosenheck HR, Mahan S, Knudsen ES, et al. Baseline cell cycle and immune profiles indicate CDK4/6 inhibitor response in metastatic HR+/HER2-breast cancer. NPJ Breast Cancer. (2025) 11:54. doi: 10.1038/s41523-025-00767-2

PubMed Abstract | Crossref Full Text | Google Scholar

74. Palmer CL, Boras B, Pascual B, Li N, Li D, Garza S, et al. CDK4 selective inhibition improves preclinical anti-tumor efficacy and safety. Cancer Cell. (2025) 43:464–81. doi: 10.1016/j.ccell.2025.02.006

PubMed Abstract | Crossref Full Text | Google Scholar

75. Zhang M, Wang G, Ma Z, Xiong G, Wang W, Huang Z, et al. BET inhibition triggers antitumor immunity by enhancing MHC class I expression in head and neck squamous cell carcinoma. Mol Ther. (2022) 30:3394–413. doi: 10.1016/j.ymthe.2022.07.022

PubMed Abstract | Crossref Full Text | Google Scholar

76. Pobbati AV and Hong W. A combat with the YAP/TAZ-TEAD oncoproteins for cancer therapy. Theranostics. (2020) 10:3622. doi: 10.7150/thno.40889

PubMed Abstract | Crossref Full Text | Google Scholar

77. Filip I, Wang A, Kravets O, Orenbuch R, Zhao J, Perea-Chamblee TE, et al. Pervasiveness of HLA allele-specific expression loss across tumor types. Genome Med. (2023) 15:8. doi: 10.1186/s13073-023-01154-x

PubMed Abstract | Crossref Full Text | Google Scholar

78. Gonzalez-Ericsson PI, Opalenik SR, Sanchez V, Palubinsky AM, Hanna A, Sun X, et al. In situ detection of individual classic MHC-I gene products in cancer. Cancer Immunol Res. (2025) 13:602–9. doi: 10.1158/2326-6066.CIR-24-1003

PubMed Abstract | Crossref Full Text | Google Scholar

79. Sverchkova A, Burkholz S, Rubsamen R, Stratford R, and Clancy T. Integrative HLA typing of tumor and adjacent normal tissue can reveal insights into the tumor immune response. BMC Med Genomics. (2024) 17:37. doi: 10.1186/s12920-024-01808-8

PubMed Abstract | Crossref Full Text | Google Scholar

80. Difilippo V, Saba KH, Styring E, Magnusson L, Nilsson J, Nathrath M, et al. Osteosarcomas with few chromosomal alterations or adult onset are genetically heterogeneous. Lab Invest. (2024) 104:100283. doi: 10.1016/j.labinv.2023.100283

PubMed Abstract | Crossref Full Text | Google Scholar

81. He XY, Que LY, Yang F, Feng Y, Ren D, and Song X. Single-cell transcriptional profiling in osteosarcoma and the effect of neoadjuvant chemotherapy on the tumor microenvironment. J Bone Oncol. (2024) 46:100604. doi: 10.1016/j.jbo.2024.100604

PubMed Abstract | Crossref Full Text | Google Scholar

82. Chantre-Justino M and Meohas W. The DNA methylation landscape of musculoskeletal sarcomas. Explor Targeted Anti-tumor Ther. (2025) 6:1002319. doi: 10.37349/etat.2025.1002319

PubMed Abstract | Crossref Full Text | Google Scholar

83. Izzo F, Myers RM, Ganesan S, Mekerishvili L, Kottapalli S, Prieto T, et al. Mapping genotypes to chromatin accessibility profiles in single cells. Nature. (2024) 629:1149–57. doi: 10.1038/s41586-024-07388-y

PubMed Abstract | Crossref Full Text | Google Scholar

84. Lin P, Lin Y, Chen X, Zhao X, and Cui L. Decoding MHC loss: Molecular mechanisms and implications for immune resistance in cancer. Clin Trans Med. (2025) 15:e70403. doi: 10.1002/ctm2.70403

PubMed Abstract | Crossref Full Text | Google Scholar

85. Tovar Perez JE, Zhang S, Hodgeman W, Kapoor S, Rajendran P, Kobayashi KS, et al. Epigenetic regulation of major histocompatibility complexes in gastrointestinal Malignancies and the potential for clinical interception. Clin Epigenet. (2024) 16:83. doi: 10.1186/s13148-024-01698-8

PubMed Abstract | Crossref Full Text | Google Scholar

86. Vidal P. Interferon α in cancer immunoediting: From elimination to escape. Scandinavian J Immunol. (2020) 91:e12863. doi: 10.1111/sji.12863

PubMed Abstract | Crossref Full Text | Google Scholar

87. Nguyen TT, Ramsay L, Ahanfeshar-Adams M, Lajoie M, SChadendorf D, Alain T, et al. Mutations in the IFNγ-JAK-STAT pathway causing resistance to immune checkpoint inhibitors in melanoma increase sensitivity to oncolytic virus treatment. Clin Cancer Res. (2021) 27:3432–42. doi: 10.1158/1078-0432.CCR-20-3365

PubMed Abstract | Crossref Full Text | Google Scholar

88. Pan L, Parini P, Tremmel R, Loscalzo J, Lauschke VM, and Maron BA. Single Cell Atlas: a single-cell multi-omics human cell encyclopedia. Genome Biol. (2024) 25:104. doi: 10.1186/s13059-024-03246-2

PubMed Abstract | Crossref Full Text | Google Scholar

89. Wu J, Ye Q, Wang Y, Hu R, Zhu Y, Yin M, et al. Biology-driven insights into the power of single-cell foundation models. Genome Biol. (2025) 26:1–39. doi: 10.1186/s13059-025-03781-6

PubMed Abstract | Crossref Full Text | Google Scholar

90. Bairakdar MD, Lee W, Giotti B, Kumar A, Stancl P, Wagenblast E, et al. Learning the cellular origins across cancers using single-cell chromatin landscapes. Nat Commun. (2025) 16:8301. doi: 10.1038/s41467-025-63957-3

PubMed Abstract | Crossref Full Text | Google Scholar

91. Peng L, Zhou L, Li H, Zhang X, Li S, Wang K, et al. Hippo-signaling-controlled MHC class I antigen processing and presentation pathway potentiates antitumor immunity. Cell Rep. (2024) 43:114003. doi: 10.1016/j.celrep.2024.114003

PubMed Abstract | Crossref Full Text | Google Scholar

92. Chapeau EA, Sansregret L, Galli GG, Chène P, Wartmann M, Mourikis TP, et al. Direct and selective pharmacological disruption of the YAP–TEAD interface by IAG933 inhibits Hippo-dependent and RAS–MAPK-altered cancers. Nat Cancer. (2024) 5:1102–20. doi: 10.1038/s43018-024-00754-9

PubMed Abstract | Crossref Full Text | Google Scholar

93. Chomiak AA, Tiedemann RL, Liu Y, Kong X, Cui Y, Wiseman AK, et al. Select EZH2 inhibitors enhance viral mimicry effects of DNMT inhibition through a mechanism involving NFAT: AP-1 signaling. Sci Adv. (2024) 10:eadk4423.

PubMed Abstract | Google Scholar

94. Jang HJ, Shah NM, Maeng JH, Liang Y, Basri NL, Ge J, et al. Epigenetic therapy potentiates transposable element transcription to create tumor-enriched antigens in glioblastoma cells. Nat Genet. (2024) 56:1903–13. doi: 10.1038/s41588-024-01880-x

PubMed Abstract | Crossref Full Text | Google Scholar

95. Hagenbeek TJ, Zbieg JR, Hafner M, Mroue R, Lacap JA, Sodir NM, et al. An allosteric pan-TEAD inhibitor blocks oncogenic YAP/TAZ signaling and overcomes KRAS G12C inhibitor resistance. Nat Cancer. (2023) 4:812–28. doi: 10.1038/s43018-023-00577-0

PubMed Abstract | Crossref Full Text | Google Scholar

96. Huang Y, Li L, An G, Yang X, Cui M, Song X, et al. Single-cell multi-omics sequencing of human spermatogenesis reveals a DNA demethylation event associated with male meiotic recombination. Nat Cell Biol. (2023) 25:1520–34. doi: 10.1038/s41556-023-01232-7

PubMed Abstract | Crossref Full Text | Google Scholar

97. Huang W, Zhu Q, Shi Z, Tu Y, Li Q, Zheng W, et al. Dual inhibitors of DNMT and HDAC induce viral mimicry to induce antitumour immunity in breast cancer. Cell Death Discov. (2024) 10:143. doi: 10.1038/s41420-024-01895-7

PubMed Abstract | Crossref Full Text | Google Scholar

98. Yang WC, Wei MF, Shen YC, Huang CS, and Kuo SH. CDK4/6 inhibitors synergize with radiotherapy to prime the tumor microenvironment and enhance the antitumor effect of anti-PD-L1 immunotherapy in triple-negative breast cancer. J Biomed Sci. (2025) 32:79. doi: 10.1186/s12929-025-01173-3

PubMed Abstract | Crossref Full Text | Google Scholar

99. Heckler M, Ali LR, Clancy-Thompson E, Qiang L, Ventre KS, Lenehan P, et al. Inhibition of CDK4/6 promotes CD8 T-cell memory formation. Cancer Discov. (2021) 11:2564–81. doi: 10.1158/2159-8290.CD-20-1540

PubMed Abstract | Crossref Full Text | Google Scholar

100. Lopez-Fuentes E, Clugston AS, Lee AG, Sayles LC, Sorensen N, Pons Ventura MV, et al. Epigenetic and transcriptional programs define osteosarcoma subtypes and establish targetable vulnerabilities. Cancer Discov. (2025) 10. doi: 10.1158/2159-8290.CD-25-0237

PubMed Abstract | Crossref Full Text | Google Scholar

101. Liu Y, He M, Tang H, Xie T, Lin Y, Liu S, et al. Single-cell and spatial transcriptomics reveal metastasis mechanism and microenvironment remodeling of lymph node in osteosarcoma. BMC Med. (2024) 22:200. doi: 10.1186/s12916-024-03319-w

PubMed Abstract | Crossref Full Text | Google Scholar

102. Ligon JA, Choi W, Cojocaru G, Fu W, Hsiue EHC, Oke TF, et al. Pathways of immune exclusion in metastatic osteosarcoma are associated with inferior patient outcomes. J immunotherapy Cancer. (2021) 9:e001772. doi: 10.1136/jitc-2020-001772

PubMed Abstract | Crossref Full Text | Google Scholar

103. McGee LE, Pereira JS, McEachron TA, Mazcko C, LeBlanc AK, and Beck JA. The tumor microenvironment of metastatic osteosarcoma in the human and canine lung. Commun Biol. (2025) 8:756. doi: 10.1038/s42003-025-07992-2

PubMed Abstract | Crossref Full Text | Google Scholar

104. Miao Z and Kim J. Uniform quantification of single-nucleus ATAC-seq data with Paired-Insertion Counting (PIC) and a model-based insertion rate estimator. Nat Methods. (2024) 21:32–6. doi: 10.1038/s41592-023-02103-7

PubMed Abstract | Crossref Full Text | Google Scholar

105. Rasalkar DD, Chu WC, Lee V, Paunipagar BK, Cheng FW, and Li CK. Pulmonary metastases in children with osteosarcoma: characteristics and impact on patient survival. Pediatr Radiol. (2011) 41:227–36. doi: 10.1007/s00247-010-1809-1

PubMed Abstract | Crossref Full Text | Google Scholar

106. Eigenbrood J, Wong N, Mallory P, Pereira JS, Williams D, Morris-Ii DW, et al. Spatial profiling identifies regionally distinct microenvironments and targetable immunosuppressive mechanisms in pediatric osteosarcoma pulmonary metastases. Cancer Res. (2025) 85:2320–37. doi: 10.1158/0008-5472.CAN-24-3723

PubMed Abstract | Crossref Full Text | Google Scholar

107. Zheng X, Liu X, Zhang X, Zhao Z, Wu W, and Yu S. A single-cell and spatially resolved atlas of human osteosarcomas. J Hematol Oncol. (2024) 17:71. doi: 10.1186/s13045-024-01598-7

PubMed Abstract | Crossref Full Text | Google Scholar

Keywords: osteosarcoma, antigen presentation, DNA methylation, immune evasion, single-cell ATAC-seq, interferon signaling

Citation: He Y and Wu H (2025) Epigenetic control of antigen presentation failure in osteosarcoma: from single-cell chromatin maps to therapeutic strategies. Front. Immunol. 16:1728091. doi: 10.3389/fimmu.2025.1728091

Received: 19 October 2025; Accepted: 17 November 2025; Revised: 15 November 2025;
Published: 26 November 2025.

Edited by:

Shangke Huang, Southwest Medical University, China

Reviewed by:

Saeed Khodayari, Tehran University of Medical Science, Iran

Copyright © 2025 He and Wu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Heng Wu, MjkwNzkyODkyNUBxcS5jb20=

These authors have contributed equally to this work

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.