Aberrant cytoplasmic expression of UHRF1 restrains the MHC-I-mediated anti-tumor immune response

Tan, Lianmei; Yin, Tao; Xiang, Handan; Wang, Liuyang; Mudgal, Poorva; Chen, Junying; Ding, Yi; Wang, Guoping; Lim, Bryan Jian Wei; Huang, Yuqi; Huang, De; Liang, Yaosi; Alexander, Peter B.; Xiang, Kun; Wang, Ergang; Yan, Chengsong; Ma, Zhehao; Tan, Minjia; Li, Qi-Jing; Wang, Xiao-Fan

doi:10.1038/s41467-024-52902-5

Download PDF

Article
Open access
Published: 03 October 2024

Aberrant cytoplasmic expression of UHRF1 restrains the MHC-I-mediated anti-tumor immune response

Nature Communications volume 15, Article number: 8569 (2024) Cite this article

330 Accesses
9 Altmetric
Metrics details

Subjects

Abstract

Immunotherapy successfully complements traditional cancer treatment. However, primary and acquired resistance might limit efficacy. Reduced antigen presentation by MHC-I has been identified as potential resistance factor. Here we show that the epigenetic regulator ubiquitin-like with PHD and ring finger domains 1 (UHRF1), exhibits altered expression and aberrant cytosolic localization in cancerous tissues, where it promotes MHC-I ubiquitination and degradation. Cytoplasmic translocation of UHRF1 is induced by its phosphorylation on a specific serine in response to signals provided by factors present in the tumor microenvironment (TME), such as TGF-β, enabling UHRF1 to bind MHC-I. Downregulation of MHC-I results in suppression of the antigen presentation pathway to establish an immune hostile TME. UHRF1 inactivation by genetic deletion synergizes with immune checkpoint blockade (ICB) treatment and induces an anti-tumour memory response by evoking low-affinity T cells. Our study adds to the understanding of UHRF1 in cancer immune evasion and provides a potential target to synergize with immunotherapy and overcome immunotherapeutic resistance.

Tumor-intrinsic YTHDF1 drives immune evasion and resistance to immune checkpoint inhibitors via promoting MHC-I degradation

Article Open access 17 January 2023

Combination of epigenetic regulation with gene therapy-mediated immune checkpoint blockade induces anti-tumour effects and immune response in vivo

Article Open access 18 November 2021

ARIH1 activates STING-mediated T-cell activation and sensitizes tumors to immune checkpoint blockade

Article Open access 10 July 2023

Introduction

In recent years, immunotherapy has significantly improved the prognosis of patients with certain types of cancer^1,2. The anti-cancer immunity process initiates with the priming and activation of immune cells by antigen-presentation cells. Upon recirculate into the tumor, the activated immune cells recognize cognate antigens presented on MHC molecules of tumor cells, leading to tumor cell destruction³. However, a majority of patients develop primary and acquired resistance to immunotherapy, with only a subgroup experiencing long-term benefits⁴. Resistance is a multifactorial process; as a part of cancer-immune co-evolution, cancer cells develop multiple intrinsic and extrinsic mechanisms to evade anti-cancer immunity⁵. For instance, impaired MHC-I antigen processing leads to acquired resistance to immune checkpoint blockade (ICB) treatment⁶. However, the complex molecular mechanisms whereby cancer cells restrain MHC-I remain to be further explored.

The tumor microenvironment (TME) plays a critical role in cancer development and progression⁷. Aberrant features frequently observed in the TME include hypoxia, abnormal vasculature, and altered metabolism^8,9,10. Notably, dynamic intercellular communications occur within the TME⁷, resulting in biochemical cues that influence behaviors of both tumor and immune cells. Moreover, through the activities of immune-suppressive cells (such as regulatory T cells and myeloid-derived suppressor cells) and molecules (e.g., TGF-β and IL-10), the TME in solid tumors maintains an immune cold microenvironment¹¹, effectively shielding tumor cells from immune surveillance¹². Through co-evolution with tumor cells, the TME can further inhibit anti-cancer immunity via hijacking mechanisms for selective advantage for tumor growth. For instance, tumor cells can reduce antigen presentation molecules through genetic or epigenetic regulation^13,14, leading to a suppressive TME. Conversely, a suppressive TME often reduces the production of interferons, potent stimulators of antigen presentation^15,16, further constraining anti-tumor immunity. Therapeutically facilitating the cold-to-hot transition in tumors could promote adequate T cell infiltration and mitigate immune suppression¹¹, thereby enhancing the efficacy of ICB therapy¹⁷. Consequently, developing strategies to overcome TME-induced immune suppression represents a promising avenue for enhancing the effectiveness of immunotherapy.

During cancer progression, tumor cells generally become more heterogeneous¹⁸. On the other hand, hosts develop a diverse tumor-reactive T cell repertoire comprised of TCRs with varying affinities^19,20. In contrast to high-affinity T cells, those T cells targeting tumor-associated antigens (TAA) may express low-affinity TCRs as a result of thymic selection^19,21. Functionally, high-affinity T cells against tumor-specific antigens (TSA) have a stronger effector response, whereas low-affinity T cells are more ready to form memory T cells as demonstrated in the context of infection^22,23. However, MHC-I antigen presentation is often compromised in the TME^13,14, which may limit the formation of memory T cells critical for long term tumor control and recurrence. Therefore, restoring MHC-I expression on tumor cells could potentially elicit CD8+ T cell responses to endogenous low-affinity TAAs derived from poorly immunogenic tumors, thereby overcoming immunosuppression to achieve durable immunotherapy²³.

Here, we found that the epigenetic regulator UHRF1 is involved in cancer immune evasion by downregulating MHC-I expression. This process is enabled by UHRF1’s aberrant cytosolic localization, triggered by phosphorylation in response to factors in the TME. Furthermore, UHRF1 inactivation synergizes with ICB and induces anti-tumor memory responses. These findings suggest that cytoplasmic UHRF1 could be a potential therapeutic target to enhance immunotherapy efficacy and address resistance in cancer treatment.

Results

UHRF1 is overexpressed in NSCLC and promotes tumor growth in syngeneic mouse models

Alterations of epigenetic marks have been observed in multiple physiological and pathological conditions, including autoimmune disorders, aging, and cancer^24,25. UHRF1, a key epigenetic regulator, is well-documented to coordinate DNA methylation and histone modifications in the nucleus²⁶. The PHD and SRA domains are critical for UHRF1 to maintain aberrant DNA methylation in colorectal cancer (CRC) cells²⁷. Analysis of the TCGA database by TIMER 2.0^28,29,30 revealed that UHRF1 mRNA is markedly upregulated in multiple cancer types, including lung adenocarcinoma (LUAD) and lung squamous carcinoma (LUSC) (Fig. 1a). Supporting this finding, an independent analysis of LUAD cohort GSE32867³¹ demonstrated that UHRF1 mRNA levels are upregulated in tumors, when compared with adjacent nonmalignant tissues (Fig. 1b). Moreover, analysis of an online dataset comprising 17 independent cohorts with 1411 total number of patients³² revealed that high UHRF1 expression correlates with reduced survival rates in lung cancer patients (Fig. 1c). Notably, analysis of mass spectrometry data from the Gillett et al. cohort³³ demonstrated increased UHRF1 protein expression in non-small cell lung cancer (NSCLC) tumor tissues (Fig. 1d). In addition, result of the Xu et al. cohort³⁴ suggested that higher levels of UHRF1 protein expression are correlated with poor NSCLC patient prognosis (Fig. 1e), which is consistent with a previous report³⁵. Together, these findings indicate that high expression of UHRF1 correlates with the progression of NSCLC and poor prognosis of those patients.

**Fig. 1: UHRF1 is overexpressed in cancer and promotes tumor growth in syngenic mouse models.**

To further investigate a possible functional role for UHRF1 in tumor development, we took advantage of CRISPR/Cas9 gene-editing technology to knockout Uhrf1 in LG1233 cells, a murine lung adenocarcinoma cell line containing K-ras mutation and p53-loss derived from C57BL/6 mice³⁶ (Supplementary Fig. 1a). Following complete UHRF1 depletion, tumor growth was severely compromised: 1 × 10⁵ LG1233 cells failed to establish tumors in immune-competent C57BL/6 mice; when inoculating 10-fold more tumor cells, UHRF1 inactivation still exhibited a 90% inhibition rate of tumor growth and longer survival of the animals (Fig. 1f–i). Similar levels of tumor suppression were observed in LG1233 tumors expressing the ovalbumin (OVA) antigen (Supplementary Fig. 1b, c). Partial loss of UHRF1 was also sufficient to retard tumor growth in vivo, as revealed by CRISPR interference (CRISPRi) technology^37,38; moreover, tumor growth could be restored by reintroducing Uhrf1 expression (Supplementary Fig. 1d, e).

To assess the generalizability of these findings, we genetically silenced Uhrf1 in Lewis lung cancer cells (LLC) expressing the OVA antigen (Supplementary Fig. 1f). As with LG1233 cells, when transplanting LLC-OVA cells into immune-competent C57BL/6 mice, UHRF1 deficiency significantly slowed tumor growth compared with controls (Fig. 1j, l). Interestingly, UHRF1 inactivation produced only a mild decrease in tumor cell proliferation in culture (Supplementary Fig. 1g), consistent with previous reports that UHRF1 either promotes^39,40,41 or has a negligible impact on cell proliferation^42,43. The growth difference between in vitro and in vivo indicates that the host immune system might be involved in UHRF1 inactivation-induced tumor suppression. In supporting this notion, we found that UHRF1 deficiency did not impact tumor growth in immune-compromised athymic nude mice (Fig. 1k, m), which lack mature T cells⁴⁴, demonstrating the critical role of adaptive immunity in the anti-tumor effect of UHRF1 inactivation. To evaluate the broader role of UHRF1 inactivation in tumor suppression, we also silenced Uhrf1 in mouse MC38 colon cancer cells and B16 melanoma cells (Supplementary Fig. 1h, j). The absence of UHRF1 markedly inhibited the growth of both colon cancer and melanoma cells in syngeneic mouse hosts (Supplementary Fig. 1i, k). Taken together, these data demonstrate a general role for UHRF1 in promoting tumor growth across multiple cancer types, which is dependent on the evasion of the adaptive immune system by the tumor cells.

UHRF1 restrains CD8+ T cell response in the TME via down-regulation of MHC-I expression

To investigate UHRF1’s role in immune evasion by tumor cells, we assessed the status of major immune components within the TME. Importantly, flow cytometric analysis revealed increased CD8+ T and NK cells in UHRF1-deficient LLC-OVA tumors (Fig. 2a). We also detected a significant intratumoral enrichment of CD8+ T cells in Uhrf1-knockout (Fig. 2b) or Uhrf1-knockdown (Supplementary Fig. 2a) LG1233 tumors. These results suggest that CD8+ T cells may be critical for tumor control in both tumor models. To test this possibility, we performed a CD8+ T cell depletion assay in tumor-bearing immune-competent mice. Indeed, CD8+ T cell ablation completely or partially restored the growth of UHRF1-deficient tumors (Fig. 2c, d, Supplementary Fig. 2b, c), consistent with the negligible effect of UHRF1 deficiency on tumor growth in athymic nude mice (Fig. 1k, m), pointing to a critical role for CD8+ T cells in restricting UHRF1-deficient tumor growth.

**Fig. 2: UHRF1 restrains CD8+ T cell response through downregulate MHC-I expression.**

To further assess the impact of UHRF1 deficiency in tumor cells on CD8+ T cells, we assessed the cytokine production capacity of tumor-infiltrating CD8+ T cells. More IFNγ- and granzyme B (GzmB)-producing CD8+ T cells were observed in UHRF1-deficient tumors (Fig. 2e). Moreover, there were more proliferating CD8+ T cells after UHRF1 deprivation (Fig. 2e). Next, we co-cultured OT-I T cells, which recognize the OVA antigen⁴⁵, with non-targeting control or UHRF1-deficient LG1233-OVA tumor cells (Supplementary Fig. 2d). Following the co-culture period, OT-I T cells in the UHRF1-deficient group were more readily activated, as indicated by elevated surface levels of CD69 (Fig. 2f), a well-known T cell activation marker^46,47. Meanwhile, the OT-I T cells in UHRF1-deficient group secreted more effector molecules such as IFNγ, TNFα, and GzmB (Fig. 2g). In line with this, UHRF1-deficient tumor cells were more readily to be killed by OT-I cells (Supplementary Fig. 2e). Together, these data suggest enhanced T-cell activation in the absent of UHRF1.

To unbiasedly assess the signaling pathways affected by UHRF1, we performed RNA-sequencing on WT and UHRF1-deficient bulk tumor tissues. Interestingly, among the upregulated genes in UHRF1-deficient tumors, the top-ranked pathways were T cell receptor binding, peptide antigen binding, and MHC-I protein binding (Fig. 2h). Given that impaired tumor antigen processing and presentation is a principal mechanism by which tumors evade immune recognition and elimination by CD8+ cytotoxic T cells⁴⁸, we hypothesized that UHRF1 may modulate MHC-I expression, consequently affecting T cell activation. Indeed, we noticed an increased expression of H2k1 in RNA-seq data (Supplementary Fig. 2f). We further performed flow cytometry to measure the level of MHC-I molecules on tumor cell surface. The results showed that H2Kb-H2Db and HLA-ABC are significantly increased in UHRF1-deficient mouse and human tumor cells compared with non-targeting (NT) control cells (Fig. 2i, j, Supplementary Fig. 2g, h). Consequently, when the OVA peptide was loaded onto LG1233 tumor cells, UHRF1-deficient LG1233 tumor cells presented more OVA peptide on the tumor cell surface (Fig. 2k, Supplementary Fig. 2i). Furthermore, in the presence of IFNγ, which is known to enhance cell surface MHC-I expression in the TME⁴⁹, UHRF1-deficient tumor cells exhibited greater levels of OVA antigen presented on cell surface (Fig. 2k). To evaluate the role of UHRF1 on the antigen presentation of endogenous self-antigen, we expressed full-length OVA protein in LG1233 cells and detected SIINFEKL epitope presentation. As expected, more SIINFEKL epitope was presented on the UHRF1-deficient tumor cell surface (Supplementary Fig. 2j), indicating enhanced antigen processing and presentation following UHRF1 elimination. These data collectively suggest that UHRF1 may induce immune escape of tumor cells from recognition by CD8+ T cells, potentially through downregulation of MHC-I. Additionally, we also observed an enhanced type I interferon response, and increased endogenous retroviruses (ERV) gene expression (Supplementary Fig. 2k), both of which can induce innate immune response^50,51.

Cytoplasmic UHRF1 mediates MHC-I degradation in tumor cells within the TME

To systematically identify proteins interacting with MHC-I, which is widely presented on all nucleated cells⁵², we transfected HEK293T cells with Flag-HLAA, followed by affinity purification of HLAA-containing protein complexes using anti-Flag antibody and subsequent mass spectrometry analysis. Interestingly, UHRF1 was among the top interacting proteins (Fig. 3a). To confirm this finding, we co-transfected Flag-Uhrf1 and HA-H2k1 plasmids into HEK293T cells. Co-immunoprecipitation (Co-IP) assays revealed that UHRF1 interacts with H2K1 (Fig. 3b). In addition to its role as an epigenetic regulator, UHRF1 is also recognized as an E3 ligase capable of ubiquitinating and degrading nuclear substrates, with several reports substantiating this biochemical function^42,53,54,55. Given that proteolytic degradation is a well-known mechanism for restraining MHC-I expression^56,57,58, we reasoned that UHRF1 may bind to and ubiquitinate MHC-I, leading to its subsequent degradation. To test this postulation, we expressed HA-H2k1 in both UHRF1-sufficient and -deficient tumor cells. Subsequent Co-IP assays revealed that ubiquitination levels of H2K1 were significantly reduced in UHRF1-deficient cells (Fig. 3c), suggesting that UHRF1 possesses the biochemical capability to facilitate MHC-I ubiquitination and degradation.

**Fig. 3: Cytoplasmic UHRF1 mediates MHC-I degradation.**

However, UHRF1 has been defined to function primarily in the nucleus, and the known substrates of its E3 ligase are nuclear proteins^42,53,54,55. To address this intriguing question, we closely examined the expression pattern of UHRF1 protein in tumor tissue sections derived from NSCLC patients. We were surprised to find that 70% of tumor samples exhibit cytoplasmic UHRF1 staining in cancer cells in contrast to normal cells in the adjacent regions (Fig. 3d, e). To validate this highly unusual observation, we stained tissue samples from another lung cancer cohort using an independent UHRF1 antibody from a different vendor. Consistently, we observed more than 70% of patient samples from this cohort with cytoplasmic UHRF1 localization (Supplementary Fig. 3a, b). These results indicate that tumor cells not only highly express UHRF1 but also exhibit aberrant cytoplasmic localization detected in tissue samples derived from NSCLC patients. To probe this question further, we conducted immunofluorescence analysis in cultured LG1233 cells and found that UHRF1 was primarily located in the nucleus of the tumor cells; in contrast, UHRF1 was detected predominantly in the cytoplasm of LG1233 tumor tissues isolated from mice (Fig. 3f), consistent with IHC results from human cancer samples. Meanwhile, UHRF1 was consistently found in the nuclei of normal cells in mouse tissues (Fig. 3f). These observations suggest that cytoplasmic localization of UHRF1 occurs only in tumor cells grown in the TME, where UHRF1 could interact with MHC-I and cause its degradation.

Phosphorylation of UHRF1 regulates its cytoplasmic localization in response to factors in the TME, such as TGF-β

Previous studies found that phosphorylation of UHRF1 could be linked to its sequestration in the cytoplasm^59,60. To test whether the TME-induced cytoplasmic translocation of UHRF1 is regulated by phosphorylation, we generated an antibody specifically recognizing phosphorylated UHRF1 on a specific serine residue (Ser661 in human and Ser656 in mouse; hereafter termed pUHRF1), based on findings from a zebrafish study⁶⁰. We confirmed the specificity of this pUHRF1 antibody using HEK293T cells overexpressing Flag-tagged wild-type (WT) UHRF1 or Flag-tagged S661A mutant UHRF1, which is non-phosphorylable at this residue⁶⁰. Western blot analysis showed that, whereas anti-UHRF1 antibody recognized both WT UHRF1 and the S661A mutant, the anti-pUHRF1 antibody exclusively recognized WT UHRF1 (Supplementary Fig. 4a). Immunofluorescent imaging analysis with the anti-pUHRF1 antibody further revealed a robust signal in the cytoplasm of tumor samples, in contrast with weak staining in normal lung tissues (Fig. 4a). Consistently, when we overexpressed Flag-UHRF1 (S661A, resistant to phosphorylation) and Flag-UHRF1 (S661D, mimicking phosphorylation) into A549 human lung cancer cells, significantly higher cytoplasmic Flag signal was detected in Flag-UHRF1 (S661D) overexpressing cells, while Flag-UHRF1 (S661A) was mainly located in the nuclei (Supplementary Fig. 4b). These findings are consistent with previous reports showing that UHRF1 phosphorylated at Ser661 (mouse Ser656) is enriched in the cytoplasm^59,60. Since we found that UHRF1 physically interacts with the MHC-I molecule (Fig. 3a, b), we next tested whether this interaction is regulated by UHRF1 phosphorylation. To investigate this, we co-transfected HA-HLAA with either wild-type, S661A, or S661D Flag-UHRF1 and performed Co-IP analysis. This revealed that the phosphomimic S661D mutation significantly enhances the interaction between UHRF1 and MHC-I (Fig. 4b). Furthermore, immunofluorescence staining showed the co-localization of phosphorylated UHRF1 and MHC-I (Fig. 4c). Moreover, MHC-I levels on the surface of Flag-UHRF1 (S661D) overexpressing cells are reduced as compared with Flag-UHRF1 (S661A) overexpressing cells (Fig. 4d). These data suggest that UHRF1 phosphorylation promotes its interaction with MHC-I to facilitate MHC-I degradation. Functionally, we found that overexpression of Uhrf1 (S656D) in mouse LG1233 tumor cells notably enhanced tumor growth in immune-competent host, compared with the phosphorylation-resistant form Uhrf1 (S656A) (Fig. 4e, Supplementary Fig 4c), likely through more effective suppression of MHC-I expression on cell surface. To evaluate the expression profile of pUHRF1 in human lung cancer, we stained patient samples with the antibody against pUHRF1 (S661). The expression pattern of pUHRF1 (S661) was similar among patients with stage I, II and III lung cancer and colon cancer, suggesting that the pUHRF1-mediated immune evasion may be involved in both early tumor development and advanced progression (Supplementary Fig. 4f). We next explored the relationship between pUHRF1 and MHC-I in patient samples. A negative correlation between pUHRF1 and HLA-ABC expression was observed in both lung cancer and colon cancer patient samples (Fig. 4f, g, Supplementary Fig. 4d, e). Together, these results indicate that phosphorylated UHRF1 localized in the cytoplasm of tumor cells within the TME suppresses MHC-I to impair antigen presentation and curtail anti-tumor immunity.

**Fig. 4: Phosphorylated UHRF1 is localized to the cytoplasm in response to TGF-β within the TME.**

The observation that UHRF1 cytoplasmic translocation happens only when tumor cells are grown in vivo strongly indicate that factors in the TME are responsible to stimulate its phosphorylation and translocation. In this regard, a previous report indicated that UHRF1 is sequestered outside the nucleus upon TGF-β stimulation during Treg differentiation⁵⁹. Given the significant role of TGF-β in the TME across various solid tumor types^61,62, we postulated that TGF-β may facilitate UHRF1’s nuclear-to-cytoplasmic translocation. To evaluate this, we treated LG1233 cells with recombinant mouse TGF-β protein for 72 h, and found a subset of tumor cells to display UHRF1 localization in the cytoplasm (Fig. 4h), which was accompanied by an increased UHRF1 phosphorylation (Supplementary Fig. 4g). To further confirm TGF-β’s role in regulating UHRF1 cytoplasmic translocation, we employed the CRISPR-Cas9 technology to delete Tgfbr2 in LG1233 cells (Supplementary Fig. 4h)⁶³. Upon subcutaneous injection into mice, the loss of TGF-β signaling in tumor cells markedly reduced the amount of phosphorylated UHRF1 in the cytoplasm (Fig. 4i). Furthermore, Tgfbr2-KO in LG1233 tumor cells notably inhibited tumor growth in syngeneic mice compared with NT control cells (Fig. 4j). Collectively, these results suggest that TGF-β present in the TME could be one of the factors to induce cytoplasmic translocation of UHRF1, likely by initiating its phosphorylation. The phosphorylated UHRF1 then interacts with and facilitates the degradation of MHC-I, thereby promoting immune evasion and tumor growth. It is possible that other factors present within the TME could also contribute to the aberrant UHRF1 localization, either individually or in concert with TGF-β.

Inactivating UHRF1 induces memory formation via expansion of T cell clones with low-affinity TCRs and synergizes with anti-CTLA4-based ICB therapy

In our earlier study of the LG1233-OVA tumor model, UHRF1 deprivation eradicated a majority of tumors, while 100% mice died of NT control tumor progression (Supplementary Fig. 1b, c). Importantly, when these tumor-free mice were rechallenged with parental LG1233-OVA cells, 40% of them remained free of tumor initiation; in contrast, all naive mice died of tumor progression (Fig. 5a, b). This suggests that UHRF1 deprivation alone elicits a robust anti-tumor memory response, likely attributable to T cells with low-affinity TCRs^22,23. To determine whether those T cells were induced by UHRF1 deprivation, we analyzed TCR affinity against OVA alternative peptide ligands⁶⁴, using the affinity index that we generated recently (Supplementary Fig. 5a)²³. In Uhrf1-KO tumors, while the number of high-affinity OVA antigen-specific T cell clonotypes remained unchanged, we observed a significant increase in low-affinity OVA antigen-specific T cell clonotypes (Fig. 5c). To probe this further at the mechanistic level, we employed an approach to block the cytoplasmic translocation of UHRF1 by treatment of tumor-bearing mice with anti-TGF-β antibody. To start, OT-I T cells were primed with SIINFEKL (N4) or its weak avidity antigen variant peptide SIIQFEKL (Q4) from ovalbumin (OVA); their effector populations were then transferred into LG1233-OVA(N4) or (Q4) tumor-bearing mice presenting the corresponding peptide (Fig. 5d). Interestingly, high-affinity T cell-mediated therapy effectively slowed down tumor growth irrespective of anti-TGF-β treatment (Fig. 5e); however, treatment with anti-TGF-β antibody significantly increased the anti-tumor efficacy of low affinity T cells (Fig. 5f). Since inactivation of UHRF1 leads to increased expression of MHC-I on tumor cell surface, these data suggest that levels of MHC-I may determine the engagement of different spectrum of T cells with varying levels of TCR affinities, resulting with the expansion of low-affinity T cell pool critical for the formation of memory T cells in the presence of high levels of MHC-I.

**Fig. 5: Inactivating UHRF1 induces memory formation via expansion of T cell clones with low-affinity TCRs and synergizes with anti-CTLA4-based ICB therapy.**

Because tumor-intrinsic UHRF1 downregulates MHC-I to establish an immune cold TME, which is associated with resistance to ICB treatment⁶, we next investigated whether UHRF1 inactivation may synergize with ICB therapy. Although genetic inactivation of UHRF1 did not enhance the anti-tumor efficacy of PD-1 blockade (Supplementary Fig. 5b), it did synergize with anti-CTLA4 treatment to significantly prolong the survival of tumor-bearing mice (Fig. 5g), so that 50% of mice in the combinational therapy group maintained long-term tumor control (Fig. 5h). Consistent with results on the LG1233-OVA tumor model shown earlier, all tumor-free survivors were resistant to rechallenge with parental tumor cells (Fig. 5i, Supplementary Fig. 5c), suggesting that a robust anti-tumor immune memory response was induced by the combined UHRF1 inactivation and anti-CTLA4 treatment.

To further elucidate the mechanistic nature of this combinational strategy, we characterized the intratumoral TCR repertoire, which can be shaped by ICB-based therapy⁶⁵. Bulk tumor TCRβ-sequencing revealed that anti-CTLA4 monotherapy only mildly alters the TCR repertoire in NT LLC-OVA tumors (Fig. 5j), while significantly diminishing TCR similarity in Uhrf1-deficient tumors (Fig. 5k). Notably, UHRF1 inactivation alone results in a robust reduction in TCR similarity (Fig. 5l), which was even more pronounced when combined with anti-CTLA4 (Fig. 5m). Although the diversity of the entire intratumoral TCR pool was largely unaffected (Supplementary Fig. 5d–g), the size distribution of the TCR repertoire underwent significant changes. Specifically, while the top 10 most expanded T cell clonotypes comprised 16% of the entire repertoire in the control TME, in the Uhrf1-deficient TME with anti-CTLA treatment, this population increased to 25% of the entire repertoire (Fig. 5n). This suggests that the combinational therapy selectively expanded oligoclonal T cell populations within the TME. Collectively, these findings indicate that UHRF1 deficiency reshapes the intratumoral TCR repertoire landscape of tumor-infiltrating T cells and enhances the formation of memory T cells when combined with anti-CTLA4 treatment.

Discussion

UHRF1, also known as Inverted CCAAT box Binding Protein of 90 kDa (ICBP90) in humans or nuclear protein of 95 kDa (Np95) in mice, has been extensively studied as a nuclear protein regulating DNA methylation and histone modifications²⁶. By maintaining genomic integrity, UHRF1 actively participates in various physiological processes including spermatogenesis⁶⁶, embryonic development⁶⁷, interdigital tissue regression⁶⁸,and tissue repair and regeneration^39,69. Under pathogenic conditions, compelling evidence suggests a strong link between UHRF1 overactivity and tumorigenesis in multiple cancers^35,70,71,72.Although extensively studied in malignancy, UHRF1 has been mainly defined as an epigenetic regulator to repress target genes, such as the tumor suppressors RASSF1, CYGB and CDH13 in human lung adenocarcinoma cells⁷³. In rare instances, UHRF1 has been detected in the cytoplasm, during Treg cell differentiation⁵⁹ and zebrafish development⁶⁰. In this study, we unexpectedly found that UHRF1 exhibits aberrant cytoplasmic localization in tumor cells grown in vivo, as detected in both human patient samples and mouse tumor xenograft models, indicating that it acquired chromatin-independent activities conferring growth advantage for tumor cells within the TME, primarily through conferring an immune cold feature via down-regulation of MHC-I. In the meantime, we do not discount its classic role as an epigenetic regulator in this context since UHRF1’s PHD and SRA domains are important for maintaining DNA methylation levels²⁷, which has been demonstrated to affect tumor development. Structurally, it has been shown that an intramolecular interaction of UHRF1 is important for its histone association: the complex formation between the N-terminal tandem Tudor domain (TTD) and the C-terminal polybasic region (PBR) blocks UHRF1 binding to H3K9me3⁷⁴. Since the mutation at human S661 (mouse S656) is located in the PBR domain, it is possible that the S661 mutant may disrupt UHRF1’s ability to bind to histone, which needs further exploration.

In searching for factors in the TME that could induce the cytoplasmic translocation of UHRF1 via phosphorylation, we found that TGF-β, a known immune regulator⁷⁵, acts as one of such factors. It is possible that other factors in the TME could trigger the phosphorylation event, which is on a serine residue within a consensus sequence for CDK kinases, leading to the cytoplasmic translocation of this nuclear protein for the benefit of evading immune surveillance by tumor cells in the TME. The regulation of MHC-I, crucial for tumor immunity, has become a focal point of research in recent years^{56,57,58,76,77}. Our findings indicate that cytoplasmic UHRF1 is involved in promoting the degradation of MHC-I molecules through ubiquitination at the post-translational level, which is consistent with its known function as an E3 ligase^42,54,78. Considering the critical role of MHC-I and CD8+ T cells in the anti-cancer immunity process^79,80, these data suggest an additional role for UHRF1 in tumor immune surveillance, a notion supported by a recent study in which UHRF1 was identified as one of the top negative regulators of MHC-I through a targeted peptide-MHC I-guided CRISPR screen⁵⁶.

Given that nuclear UHRF1 plays a pivotal role in normal cell physiology³⁹, cytoplasmic phosphorylated UHRF1 might provide a rational target for cancer therapeutic drug development in the future to avoid systemic adverse effects. Here, we demonstrated the feasibility of this approach with genetic UHRF1 inactivation. Targeting UHRF1 combined with anti-CTLA4 ICB therapy significantly slows tumor growth and extends the survival of tumor-bearing mice. Moreover, this combinational therapy induces a strong anti-tumor memory response with long-term control over malignancy. Beyond switching an immune cold TME into a T cell-inflamed one, our TCRβ-seq data show that UHRF1 deficiency in combination with anti-CTLA4 therapy expands intratumoral oligoclonal T cell populations, indicating there are very limited numbers of tumor antigens. From this perspective, comparing with the negated additive effect of anti-PD-1 ICB, facilitating the antigen priming by CTLA4 blockade may represent a more effective way to enhance anti-tumor immune response. Known ICB resistance mechanisms include a lack of antigenic mutations, loss of tumor antigen expression, loss of HLA expression, alterations in antigen processing machinery, and constitutive PD-L1 expression⁴. In this regard, high levels of tumor-cell intrinsic UHRF1 may be used as a biomarker to predict ICB insensitivity, with targeting UHRF1 as a potential strategy to combat resistance to ICB therapy.

Based on our findings, UHRF1 inhibition could enhance anti-tumor immunity by increasing MHC-I presentation. In T cells, TGF-β signaling also induces UHRF1 phosphorylation and sequesters UHRF1 in the cytoplasm; as a result, DNA methylation of Foxp3 locus is decreased and induced regulatory T cells (iTreg) are generated⁵⁹. From this perspective, blocking UHRF1 cytoplasmic localization or its upstream TGF-β signaling may also suppress iTreg cells development, which could alleviate immune suppression. Currently, UHRF1 inhibitors targeting DNA methylation (e.g., NSC232003)⁸¹ are not yet clinically used. Since nuclear UHRF1 is very important for normal cell activities, it is not a suitable strategy to use inhibitors that can affect functions of nuclear UHRF1. Based on our results, targeting cytoplasmic UHRF1 in the future may offer a more specific and safer approach to combat cancer. In order to achieve a tumor specific UHRF1 inhibition, strategies may be considered to include 1) targeting TGF-β signaling to alter UHRF1 localization, as revealed by our study and others’ work⁵⁹; 2) preventing UHRF1 phosphorylation through CDK2 or CDK9 inhibition^42,59,60,82; 3) potentially developing peptide-based inhibitors to disrupt UHRF1-MHC-I interactions.

High-affinity antigen-directed treatments, including CAR-T and TCR-T cell therapies, have had some success against both liquid and solid tumors^83,84. However, these therapeutics can have considerable side effects, including cytokine release syndrome⁸⁵. Low-affinity T cells are more readily to form memory in the contexts of infection and cancer^22,23, and induced by UHRF1 inactivation in tumor cells to elicit anti-tumor immunity. In the context of T cell-based therapies, targeting low-affinity antigens by evoking endogenous T cells has stimulated interest in TAAs as therapeutic targets⁸⁶. From a translational point of view, evoking endogenous T cells by targeting UHRF1 would be highly desirable since depletion of tumor-intrinsic UHRF1 increases MHC-I expression and converts the tumor microenvironment from an immune cold into an immune hot state (Supplementary Fig. 6), representing a promising approach to improve the efficacy of immunotherapy.

Methods

Animals

All the animal experiments were approved by the Duke University Institutional Animal Care and Use Committee. Foxn1-null nude (athymic nude) mice (Strain: 007850) and C57BL/6 mice (Strain: 000664) were purchased from Jackson Laboratory. Thy1.1 + OT-I TCR transgenic mice were bred in house. Mice were housed under conditions at 22 °C, with a humidity between 30 and 70%, and a light cycle of 12h-12h on-off set.

Cell culture

A549, H2170, B16 and HEK293T cell lines were acquired from Duke University Cell Culture Facility, with original sourcing from ATCC. LLC-OVA cells were generated as previously described⁸⁷. LG1233 cells were provided by T.Jacks (Massachusetts Institute of Technology). MC38 cells were purchased from Kerafast. Mycoplasma contamination was tested by Universal Mycoplasma Detection Kit (ATCC, 30-1012 K). A549, B16, MC38, HEK293T, LLC-OVA and LG1233 cells were cultured in Dulbecco’s modified Eagle’s medium (DMEM) supplemented with 10% fetal bovine serum and 100 U/mL penicillin-streptomycin. H2170 cells were cultured in RPMI-1640 medium supplemented with 10% fetal bovine serum and 100 U/mL penicillin-streptomycin. All cells were cultured at 37 °C in a humidified atmosphere with 5% CO₂.

Stable cell lines

HEK293T cells with approximately 70% confluency were transfected with vectors together with lentiviral packaging vectors PAX2 and VSV-G using Lipofectamine 2000 (Thermo Fisher Scientific, 11668019). Viral particles were collected and filtered after transfection for 48 h. For infection, lentiviral supernatant was added to cultured cells with 8 µg/mL Polybrene (Sigma-Aldrich, TR-1003). After incubation for 24 h, infected cells were selected for 3 to 5 days with 2 µg/mL puromycin (Thermo Fisher Scientific, A1113803). To generate Uhrf1 knockdown cell line, shRNA targeting Uhrf1 was purchased from Sigma-Aldrich. (Mouse-shUhrf1, 5’- CTGTAGCTCCAGTGCCGTTAA-3’). To generate knockout cell lines, sgRNAs were designed based on webtool (https://chopchop.cbu.uib.no/)⁸⁸ and cloned according to Dr. Feng Zhang laboratory’s protocol^89,90. The sequences of sgRNAs used in this study are:

mouse-sgUhrf1: 5′-CATCATAATCGAAGAGTGTG-3′,

human-sgUHRF1: 5′-CGGGGCTTCTGGTACGACG-3′,

mouse-sgTgfbr2: 5′-ACCTGCAGGAGTACCTCACG-3′

To generate stable LG1233 cell line with OVA expression, wild-type OVA (N4) and its Q4 mutant were cloned into pWPT (Addgene, #12255), LG1233-OVA(N4)-T2A-GFP cells and LG1233-OVA(Q4)-T2A-Scarlet cells were sorted by FACS for 2 rounds with purity more than 95%.

Tumor models

In the mouse tumor experiments, 2 × 10⁵ LLC-OVA; 1 × 10⁵, 2 × 10⁵ or 1 × 10⁶ LG1233 (-OVA); 1 × 10⁶ MC38 and 2 × 10⁵ B16 cells were subcutaneously injected into C57BL/6 mice (6 to 8 weeks old). 2 × 10⁵ LLC-OVA cells were subcutaneously injected into athymic nude mice (6 to 8 weeks old). The specific gender of the mice, along with the number of cells injected for each experiment, are detailed in the corresponding figure legends. Both male and female mice were used in animal studies. Tumor volumes were calculated using the following formula: 0.52 × a × b², where a is the larger diameter and b is the smaller diameter of the tumor mass. The permitted maximal tumor size was 2000 mm³ by Duke University Institutional Animal Use and Care Committee. Mice were euthanized once tumor size reached 2000 mm³. In some instances, malignant tumors exhibited explosive growth, potentially exceeding 2000 mm³. Mice with oversized tumors were euthanized with CO₂ immediately upon discovery.

In vivo lymphocyte depletion and ICB therapy

Depletion of lymphocytes in C57BL/6 mice was conducted by injection of neutralizing antibodies. In brief, mice were given intraperitoneal injection of antibodies against CD8 (100 µg, twice a week, Bio X Cell, BE0223) or recommended isotype control (Bio X Cell, BE0088). All deletion assays were initiated one day before tumor cell inoculation. Specific cell deletion was confirmed by flow cytometry at experimental endpoints. For ICB therapy treatments, tumor-bearing mice were intraperitoneally injected with 200 µg anti-mouse PD-1 antibodies (Bio X Cell, BE0033-2) or IgG isotype control (Bio X Cell, BE0290); and 30 µg anti-mouse CTLA-4 antibodies (Bio X Cell, BP0164) or IgG isotype control (Bio X Cell, BP0086) on the indicated days.

Molecular cloning

The coding sequences for mouse Uhrf1 and human UHRF1 were purchased from Horizon. Mutant variants of human UHRF1 (S661A, S661D) were generated by PCR. The coding sequence for HLAA was cloned from Addgene plasmid (#85162)⁹¹. The coding sequence for H2k1 was cloned from cDNA of mouse embryonic fibroblast (MEF) cells. These coding sequences were subsequently cloned into pSIN-lentiviral vectors using the Gibson Assembly (New England Biolabs). Details of the primers used are listed in Supplementary Table 1.

Adoptive T cell transfer

For the adoptive transfer of OT-I T cells, lymph nodes and spleens from OT-I TCR transgenic mice were mechanically dissociated. Red blood cells were lysed using ACK lysing buffer (Quality Biological). After isolation, OT-I T cells were activated in vitro for 48 h with 1 µg/mL OVA N4 or Q4 peptide, then cultured with 50 U/mL hIL-2 (Peprotech, 200-02). 2 × 10⁵ LG1233-OVA (N4 or Q4) tumor cells were subcutaneously injected into male C57BL/6 mice. On day 2, each tumor-bearing mouse received 2 mg cyclophosphamide (Sigma-Aldrich, C7397) for lymphodepletion before T cell transfer. On day 4, mice were intravenously injected with 1 × 10⁶ OT-I T cells per mouse. 10 days after inoculation, mice were treated with isotype control antibody (10 mg/kg, Bio X cell, BE0083) or anti-TGF-β (10 mg/kg, Bio X Cell, BE0057) as previously described⁹². Briefly, antibodies were administered 3 times a week for 3 weeks. The first dose was delivered intravenously, and subsequent doses were given intraperitoneally.

Western blots

Total protein was extracted and lysed in RIPA buffer (Thermo Fisher Scientific) with 1% protease and phosphatase inhibitor cocktail (Thermo Fisher Scientific), and protein concentrations were determined using the BCA protein assay kit (Thermo Fisher Scientific). Equal amounts of protein in the lysates were boiled in SDS loading buffer. Protein samples were loaded and fractionated on a 4–12% SDS-PAGE gel and then transferred to a polyvinylidene difluoride membrane, which was subsequently blocked in 5% skim milk in Tris-buffered saline with 0.1% Tween-20 (TBST buffer). Primary antibodies against total proteins or phosphorylated forms of protein were applied, followed by secondary antibodies (Thermo Fisher Scientific) incubation and visualized using an enhanced chemiluminescent horseradish peroxidase substrate (Thermo Fisher Scientific). A protein ladder (Thermo Fisher Scientific) was used to determine molecular mass in the western blot analysis. The western blot primary antibodies, their source and dilution information as follows. Anti-UHRF1(Santa Cruz Biotechnology, sc-373750, 1:1000); Anti-Flag (Sigma, F1804, 1:5000); Anti-HA (Sigma, H3663, 1:2000); Anti-TGFBR2 (Santa Cruz Biotechnology, sc-17791, 1:500), Anti-β-Actin (Proteintech, 66009-1-Ig, 1: 5000), anti-Ubiquitin (Cell Signaling Technology, 43124, 1:1000), GAPDH (Santa Cruz Biotechnology, sc-365062, 1:5000). The polyclonal antibody recognizing human phosphorylated Ser-661(mouse Ser-656) in UHRF1 was generated by LifeTein, LLC, as previously reported⁶⁰. The antigen used was a synthetic phosphoserine peptide corresponding to residues 654-659 of human UHRF1, denoted as CGPSRAG(pS)PRRTSKKT-KLH.

Immunoprecipitation

Cells were transfected with the indicated constructs for 24 h, washed briefly, and then lysed on ice using lysis buffer (Cell Signaling Technology, 9806) for 1 h. Lysates containing 1 mg of total protein were precleared with magnetic Protein G Dynabeads (Thermo Fisher, 10003D) and incubated overnight at 4 °C with either 2 μg of the indicated antibodies or with an isotype control. The immunoprecipitates were collected with magnets, and the pellets were washed with lysis buffer and stored in 2× sample buffer prior to western blot analysis. Mass spectrometry analysis was conducted at the Proteomics and Metabolomics Core Facility at Duke University. IP samples were analyzed by LC-MS using an Orbitrap Fusion Lumos mass spectrometer. Database searches were performed against the SwissProt Homo sapiens database. A total of 2 samples were analyzed, including one IgG control and one Flag-HLAA pull down sample.

Flow cytometry

For in vitro experiments, cultured cancer cells were resuspended and stained with APC anti-mouse H-2Kb/H-2Db (28-8-6) (Biolegend, 114614, 1:100), FITC anti-human HLA-ABC (W6/32) (Biolegend, 311404, 1:100) and APC anti-mouse H-2Kb bound to SIINFEKL (25-D1.16) (Biolegend, 141605, 1:100) at 4 °C for 30 min in the dark. After washing with PBS, the surface expression of MHC-I or the MHC-I bound endogenous OVA peptide was analyzed on an BD FACS Canto flow cytometer using BD FACS DIVA SOFTWARE (Duke University Flow Cytometry Shared Facility), and data were analyzed using FlowJo. To analyze tumor-infiltrating lymphocytes, murine tumor tissues were mechanically dissociated and digested with 1 mg/mL collegenase type I (GIBCO, 17100-017) by incubating at 37 °C for 60 min. The cells were filtered with a 70 µm cell strainer. Red blood cells were removed with ACK lysis buffer. Single cell suspensions were stained with NearIR-LIVE/DEAD fixable dead cell dye (Invitrogen, 1:2000), followed by incubation with the following cell surface antibodies for 30 min at 4 °C in the dark. After washing with PBS, cells were then subjected to flow cytometry analysis as described above. The following antibodies were used to identify immune cell populations in TME: FITC anti-mouse CD45 (30-F11) (Biolegend, 103108, 1:100), PE anti-mouse TCR vβ8.3 (1B3.3) (BioLegend, 156304, 1:100), PE/Cyanine7 anti-mouse CD4 (RM4-5) (Biolegend, 100528, 1:100), PerCP/Cyanine5.5 anti-mouse CD8β (YTS156.7.7) (Biolegend, 126610, 1:100), APC anti-mouse NK-1.1 (PK136) (Biolegend, 108710, 1:100), PE/Cyanine7 anti-mouse CD69 (H1.2F3) (BioLegend, Cat. 104511, 1:100). For intracellular staining, the Intracellular Fixation & Permeabilization Buffer Set (eBioscience™, 88-8824-00) was used to fix and permeabilize cells, followed by staining with Pacific Blue anti-mouse IFN-γ (XMG1.2) (Biolegend, 505818, 1:100), PE/Cyanine7 anti-human/mouse Granzyme B (QA16A02) (Biolegend, 372214, 1:100), APC anti-mouse TNFα (MP6-XT22) (BioLegend, 506308, 1:100), Pacific Blue anti-mouse Ki-67 (16A8) (BioLegend, 652421, 1:100). Flow cytometry gating strategies are shown in Supplementary Fig. 7.

Immunofluorescence

For immunofluorescence analysis, frozen tumor samples were sectioned into 8 µm slices. Cultured cells were seeded on a sterile glass coverslip and maintained in complete medium. LG1233 cells were treated with or without 200 ng/mL recombinant mouse TGF-β1 protein (R&D, 7666-MB-005/CF) for 72 h prior to IF staining. Briefly, tumor tissues or culture cells were fixed with 4% paraformaldehyde (PFA) for 15 min at room temperature. After fixation, samples were permeabilized using 0.1% Triton X-100 in PBS for 10 min and then blocked with 5% BSA for 1 h at room temperature. The cells or tissue sections were incubated with the primary antibodies against UHRF1 (Santa Cruz Biotechnology, sc-373750, 1:200), pUHRF1 (LifeTein, customized, 1:200), anti-HLA-ABC (Proteintech, Cat. 15240-1-AP, 1:200) and Flag (Sigma, F1804, 1:200) at 4 °C overnight. Subsequently, samples were incubated with fluorescently-labeled secondary antibodies for 1 h at room temperature. Tissues or cells were counterstained with DAPI. Finally, slides were mounted using ProLong Diamond antifade mountant (ThermoFisher, P36965) and images were captured with a Leica SP5 inverted confocal microscope.

Immunohistochemistry

Lung cancer tissue arrays in Fig. 3 were obtained from Shanghai Outdo Biotechnology Company Ltd. Lung cancer tissue arrays in Fig. 4 and colon cancer tissue arrays in Supplementary Fig 4 were purchased from TissueArray.com. Lung cancer tissue sections in Supplementary Fig 3 were obtained from West China Hospital, Chengdu, China, which was approved by the ethics committees of West China Hospital, Chengdu, China. Briefly, slides were heated until tissue transparency was achieved, followed by sequential xylene and ethanol washes, and a rinse in water. Antigen retrieval was performed using Rodent Decloaker buffer (Bio Care Medical) in a pressure cooker set to 95 °C for 20 min. Next, 3% hydrogen peroxide solution (VWR, BDH7690-1) was used to block endogenous peroxidases and subsequently washed with 0.1% TBS-T (Tween-20) wash buffer. Slides were then blocked with 5% BSA for 1 h at room temperature. Primary anti-UHRF1 (Santa Cruz Biotechnology, sc-373750, 1:100), anti-UHRF1 (Abnova, H00029128-M01, 1:100), anti-pUHRF1 (LifeTein, LLC, customized, 1:100) and anti-HLA-ABC (Proteintech, 15240-1-AP, 1:1000) antibodies were diluted accordingly in the blocking buffer and incubated overnight at 4 °C. Next day, secondary antibodies were used to incubate at room temperature for 1 h. After wash 4 times with TBST buffer, DAB chromogen (DAKO, K3468) was added to slides and monitoring for brown color development. After the desired staining intensity was achieved, slides were rinsed with distilled water. Hematoxylin was applied and slides were then treated with a bluing reagent, dehydrated, and mounted using Cytoseal (Thermo Scientific, 8312-4). The images were captured by using Zeiss Axio Imager Z2. QuPath was used for quantification, images were analyzed using the H-DAB staining option, with cytoplasmic DAB thresholds set at >0.3. A minimum of 100 cells per slide were analyzed.

T-cell activation assay

CD8+ T cells were isolated from lymph nodes of OT-I mice. OT-I T cells were co-cultured with sgNT or sgUhrf1 LG1233-OVA cells at a ratio of 1:1. 16 h after co-culture, T cells were stained with anti-CD69-PE/Cy7 (Biolegend,105512, H1.2F3, 1:100). For intracellular staining, T cells were fixed and permeabilized, followed by staining with Pacific Blue anti-mouse IFN-γ(XMG1.2) (Biolegend, 505818, 1:100), PE/Cyanine7 anti-human/mouse Granzyme B (QA16A02) (Biolegend, 372214, 1:100), APC anti-mouse TNFα (MP6-XT22) (BioLegend, 506308, 1:100). Cells were acquired by flow cytometry analysis.

LDH release assay

Cytotoxicity was assessed using the LDH release assay (Promega, G1780) according to the manufacturer’s instructions. Briefly, LG1233-OVA target cells were seeded at a density of 6000 cells/well in a 96-well plate. OT-1 T cells were added at effector-to-target (E:T) ratio of 1:1 and the co-cultures were incubated for 8 h. Subsequently, the culture supernatants were carefully harvested to avoid disturbing the cell pellet. LDH release, indicative of target cell lysis, was quantified using the enzymatic assay provided in the kit. Cytotoxicity percentage was calculated using % Cytotoxicity = [(Experimental−Effector Spontaneous−Target Spontaneous)/(Target Maximum−Target Spontaneous)] × 100%, where Experimental represents LDH activity in wells with both effector and target cells, Effector Spontaneous is the background LDH release from effector cells alone, Target Spontaneous from target cells alone, and Target Maximum from target cells lysed with detergent to determine maximum LDH release. All assays were performed in quadruplicate to ensure reproducibility and statistical reliability.

Antigen presentation assay

sgNT and sgUhrf1 LG1233 cells were treated with 5 ng/mL recombinant murine IFNγ protein (Peprotech, 315-05) for 16 h. After treatment, the cells were collected and incubated with 1 µg/mL OVA(SIINFEKL) peptide in a 37 °C water bath for 1 h. Subsequently, the cells were washed twice with PBS and stained with APC-conjugated anti-mouse SIINFEKL bound H-2Kb (25-D1.16) (Biolegend, 141605, 1:100) at 4 °C for 1 h. After wash with PBS, the samples were proceeded for flow cytometry analysis.

RNA-seq

1 × 10⁶ sgNT or sgUhrf1 LG1233-OVA cells were subcutaneously injected into female C57BL/6 mice. Tumor tissues were collected on day 27. Total RNA was extracted using ZYMO RESEARCH kit (Direct-zol RNA miniprep, cat. No. R2052) and dissolved in RNase-free water. Library construction and RNA sequencing were performed by Novogene. Briefly, mRNA was purified, and first strand cDNA was synthesized using random hexamer primers followed by the second stand cDNA synthesis. After end repair, A-tailing, adapter ligation, size selection, amplification, and purification, quantified libraries were pooled and sequenced on Illumina platform (NovaSeq PE150).

For RNA-seq data analysis, fastp v0.20.157 was used for read trimming and STAR v2.7.5a58 for mapping to the Gencode GRCm38 genome. Quality filtering was applied with Samtools v1.1859. Gene counts were derived using featureCounts v2.0.160 and analyzed with DESeq2 v1.36.061, filtering for genes with >5 reads in >3 samples. P values were adjusted using the Benjamini-Hochberg method. Significant DEGs (adjusted p < 0.05) underwent hierarchical clustering with the R package pheatmap v1.0.12 and volcano plot visualization with EnhancedVolcano v1.14.0. GO enrichment was performed with R package clusterProfiler v4.4.462.

TCRβ sequencing

For LG1233-OVA tumor model, 1 × 10⁶ LG1233-OVA sgNT or sgUhrf1 cancer cells were inoculated into female C57BL/6 mice. Tumors were collected on day 27. For LLC-OVA tumor model, 2 × 10⁵ shNT or shUhrf1 LLC-OVA cells were inoculated into male C57BL/6 mice and subsequently treated with either 30 µg IgG or CTLA4 antibodies on days 7, 10 and 14. Tumors were collected on day 17. Total RNA was extracted from the bulk tumor tissues for TCR library construction using the Direct-zol RNA MiniPrep kit (Zymo reseach, 11-331). In brief, 1 µg of purified RNA was converted into cDNA, followed by amplification of the CDR3 region of rearranged TCRB loci using a multiplex PCR system. A set of forward primers, each tailored for specific TCR Vβ segments, and a reverse primer for the TCRB constant region to produce amplicons spanning the entire CDR3 region. The PCR products were run on a 2.5% agarose gels, targeted bands were purified using QIAquick Gel Extraction kit, and then sent for sequencing at on an Illumina platform at Novogene (NovaSeq PE150). Primers for TCRβ sequencing are provided in Supplementary Table 1. For data analysis, raw sequencing data was preprocessed and aligned to TCR gene segments using MiXCR software⁹³. A two-step assembly was applied to create an extensive profile of TCR clonotypes, focusing on the CDR3 region. Clonotypes were expanded, error-corrected, and quantified. Diversity and Similarity within TCR repertoires were measured using Shannon entropy and Jaccard indices, respectively, based on the analysis of amino acid sequences. The diversity and similarity analysis were conducted using R immunarch package.

Statistics and reproducibility

No statistical method was used to predetermine sample size. Experimental sample size was chosen based on commonly accepted standards. We excluded one outlier sample in Fig. 5c by performing Grubbs’ test which calculated with GraphPad. Mice were randomly assigned to each group, other experiments were not randomized. The investigators were not blinded to allocation during experiments and outcome assessment. Statistical analysis was conducted using GraphPad Prism 8 software. P values less than 0.05 were considered statistically significant and are presented in the figures by the value. Two-way ANOVA was used for multiple comparison in tumor growth experiments. Log-rank tests were used for survival analyses. Two-tailed Student’s t-tests or Wilcox tests were used to compare between two groups. The correlation coefficient (r) and P value were obtained from Pearson correlation analysis. Chi-square test was used for comparison of the different percentage of expanded clones in different groups.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

RNA and TCRβ sequencing data have been deposited in the Gene Expression Omnibus (GEO) under accession number GSE272880 and GSE261146 respectively. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE⁹⁴ partner repository with the dataset identifier PXD051193. The human tumor and adjacent data were generated from Tumor IMmune Estimation Resource (TIMER) (https://cistrome.shinyapps.io/timer/), source data from the TCGA Research Network (http://cancergenome.nih.gov/). The correlation data between UHRF1 expression and survival rate in lung cancer patients (Fig. 1c) was downloaded from http://kmplot.com website³². This study did not generate any unique code or algorithm. The algorithms used for all analysis in this study are public available. Source data are provided with this paper.

References

Ferris, R. L. et al. Nivolumab for recurrent squamous-cell carcinoma of the head and neck. N. Engl. J. Med. 375, 1856–1867 (2016).
Article PubMed PubMed Central Google Scholar
McDermott, D. F. et al. Atezolizumab, an anti-programmed death-ligand 1 antibody, in metastatic renal cell carcinoma: long-term safety, clinical activity, and immune correlates from a phase ia study. J. Clin. Oncol. 34, 833–842 (2016).
Article CAS PubMed Google Scholar
Chen, D. S. & Mellman, I. Oncology meets immunology: the cancer-immunity cycle. Immunity 39, 1–10 (2013).
Article PubMed Google Scholar
Sharma, P., Hu-Lieskovan, S., Wargo, J. A. & Ribas, A. Primary, adaptive, and acquired resistance to cancer immunotherapy. Cell 168, 707–723 (2017).
Article CAS PubMed PubMed Central Google Scholar
Bai, R. et al. Mechanisms of cancer resistance to immunotherapy. Front. Oncol. 10, 1290 (2020).
Article PubMed PubMed Central Google Scholar
Gettinger, S. et al. Impaired HLA Class I antigen processing and presentation as a mechanism of acquired resistance to immune checkpoint inhibitors in lung cancer. Cancer Discov. 7, 1420–1435 (2017).
Article CAS PubMed PubMed Central Google Scholar
de Visser, K. E. & Joyce, J. A. The evolving tumor microenvironment: from cancer initiation to metastatic outgrowth. Cancer Cell 41, 374–403 (2023).
Article PubMed Google Scholar
Martinez-Reyes, I. & Chandel, N. S. Cancer metabolism: looking forward. Nat. Rev. Cancer 21, 669–680 (2021).
Article CAS PubMed Google Scholar
Weis, S. M. & Cheresh, D. A. Tumor angiogenesis: molecular pathways and therapeutic targets. Nat. Med. 17, 1359–1370 (2011).
Article CAS PubMed Google Scholar
Harris, A. L. Hypoxia-a key regulatory factor in tumour growth. Nat. Rev. Cancer 2, 38–47 (2002).
Article CAS PubMed Google Scholar
Duan, Q., Zhang, H., Zheng, J. & Zhang, L. Turning cold into hot: firing up the tumor microenvironment. Trends Cancer 6, 605–618 (2020).
Article CAS PubMed Google Scholar
Joyce, J. A. & Fearon, D. T. T cell exclusion, immune privilege, and the tumor microenvironment. Science 348, 74–80 (2015).
Article ADS CAS PubMed Google Scholar
Cai, L. et al. Defective HLA class I antigen processing machinery in cancer. Cancer Immunol. Immunother. 67, 999–1009 (2018).
Article CAS PubMed PubMed Central Google Scholar
Dhatchinamoorthy, K., Colbert, J. D. & Rock, K. L. Cancer immune evasion through loss of MHC class I antigen presentation. Front. Immunol. 12, 636568 (2021).
Article CAS PubMed PubMed Central Google Scholar
Fruh, K. & Yang, Y. Antigen presentation by MHC class I and its regulation by interferon gamma. Curr. Opin. Immunol. 11, 76–81 (1999).
Article CAS PubMed Google Scholar
Grasso, C. S. et al. Conserved interferon-gamma signaling drives clinical response to immune checkpoint blockade therapy in melanoma. Cancer Cell 38, 500–515.e503 (2020).
Article CAS PubMed PubMed Central Google Scholar
Gide, T. N. et al. Distinct immune cell populations define response to anti-PD-1 monotherapy and anti-PD-1/anti-CTLA-4 combined therapy. Cancer Cell 35, 238–255.e236 (2019).
Article CAS PubMed Google Scholar
Dagogo-Jack, I. & Shaw, A. T. Tumour heterogeneity and resistance to cancer therapies. Nat. Rev. Clin. Oncol. 15, 81–94 (2018).
Article CAS PubMed Google Scholar
Aleksic, M. et al. Different affinity windows for virus and cancer-specific T-cell receptors: implications for therapeutic strategies. Eur. J. Immunol. 42, 3174–3179 (2012).
Article CAS PubMed PubMed Central Google Scholar
Stone, J. D., Harris, D. T. & Kranz, D. M. TCR affinity for p/MHC formed by tumor antigens that are self-proteins: impact on efficacy and toxicity. Curr. Opin. Immunol. 33, 16–22 (2015).
Article CAS PubMed PubMed Central Google Scholar
Yu, W. et al. Clonal deletion prunes but does not eliminate self-specific alphabeta CD8(+) T lymphocytes. Immunity 42, 929–941 (2015).
Article CAS PubMed PubMed Central Google Scholar
Knudson, K. M., Goplen, N. P., Cunningham, C. A., Daniels, M. A. & Teixeiro, E. Low-affinity T cells are programmed to maintain normal primary responses but are impaired in their recall to low-affinity ligands. Cell Rep. 4, 554–565 (2013).
Article CAS PubMed Google Scholar
Yin, T. et al. Breaking NGF–TrkA immunosuppression in melanoma sensitizes immunotherapy for durable memory T cell protection. Nat. Immunol. 25, 268–281 (2024).
Article CAS PubMed Google Scholar
Sharma, S., Kelly, T. K. & Jones, P. A. Epigenetics in cancer. Carcinogenesis 31, 27–36 (2010).
Article CAS PubMed Google Scholar
Grolleau-Julius, A., Ray, D. & Yung, R. L. The role of epigenetics in aging and autoimmunity. Clin. Rev. Allergy Immunol. 39, 42–50 (2010).
Article CAS PubMed PubMed Central Google Scholar
Nishiyama, A. et al. Uhrf1-dependent H3K23 ubiquitylation couples maintenance DNA methylation and replication. Nature 502, 249–253 (2013).
Article ADS CAS PubMed Google Scholar
Kong, X. et al. Defining UHRF1 domains that support maintenance of human colon cancer DNA methylation and oncogenic properties. Cancer Cell 35, 633–648.e637 (2019).
Article CAS PubMed PubMed Central Google Scholar
Li, T. et al. TIMER2.0 for analysis of tumor-infiltrating immune cells. Nucleic Acids Res. 48, W509–W514 (2020).
Article CAS PubMed PubMed Central Google Scholar
Li, T. et al. TIMER: a web server for comprehensive analysis of tumor-infiltrating immune cells. Cancer Res. 77, e108–e110 (2017).
Article ADS CAS PubMed PubMed Central Google Scholar
Li, B. et al. Comprehensive analyses of tumor immunity: implications for cancer immunotherapy. Genome Biol. 17, 174 (2016).
Article PubMed PubMed Central Google Scholar
Gao, J. et al. Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci. Signal 6, pl1 (2013).
Article PubMed PubMed Central Google Scholar
Gyorffy, B. Transcriptome-level discovery of survival-associated biomarkers and therapy targets in non-small-cell lung cancer. Br. J. Pharmacol. 181, 362–374 (2023).
Gillette, M. A. et al. Proteogenomic characterization reveals therapeutic vulnerabilities in lung adenocarcinoma. Cell 182, 200–225.e235 (2020).
Article CAS PubMed PubMed Central Google Scholar
Xu, J. Y. et al. Integrative proteomic characterization of human lung adenocarcinoma. Cell 182, 245–261.e217 (2020).
Article CAS PubMed Google Scholar
Unoki, M. et al. UHRF1 is a novel diagnostic marker of lung cancer. Br J Cancer 103, 217–222 (2010).
Article CAS PubMed PubMed Central Google Scholar
Dimitrova, N. et al. Stromal expression of miR-143/145 promotes neoangiogenesis in lung cancer development. Cancer Discov. 6, 188–201 (2016).
Article CAS PubMed Google Scholar
Larson, M. H. et al. CRISPR interference (CRISPRi) for sequence-specific control of gene expression. Nat. Protoc. 8, 2180–2196 (2013).
Article CAS PubMed PubMed Central Google Scholar
Alerasool, N., Segal, D., Lee, H. & Taipale, M. An efficient KRAB domain for CRISPRi applications in human cells. Nat Methods 17, 1093–1096 (2020).
Article CAS PubMed Google Scholar
Xiang, H. et al. UHRF1 is required for basal stem cell proliferation in response to airway injury. Cell Discov. 3, 17019 (2017).
Article CAS PubMed PubMed Central Google Scholar
Tien, A. L. et al. UHRF1 depletion causes a G2/M arrest, activation of DNA damage response and apoptosis. Biochem. J. 435, 175–185 (2011).
Article CAS PubMed Google Scholar
Li, Q., Chu, Z. & Geng, S. UHRF1 knockdown attenuates cell growth, migration, and invasion in cutaneous squamous cell carcinoma. Cancer Invest. 39, 84–97 (2021).
Article PubMed Google Scholar
Zhang, H. et al. A cell cycle-dependent BRCA1-UHRF1 cascade regulates DNA double-strand break repair pathway choice. Nat. Commun. 7, 10201 (2016).
Article ADS CAS PubMed PubMed Central Google Scholar
Tian, Y. et al. UHRF1 contributes to DNA damage repair as a lesion recognition factor and nuclease scaffold. Cell Rep. 10, 1957–1966 (2015).
Article CAS PubMed PubMed Central Google Scholar
Cordier, A. C. & Haumont, S. M. Development of thymus, parathyroids, and ultimo-branchial bodies in NMRI and nude mice. Am. J. Anat. 157, 227–263 (1980).
Article CAS PubMed Google Scholar
Clarke, S. R. et al. Characterization of the ovalbumin-specific TCR transgenic line OT-I: MHC elements for positive and negative selection. Immunol. Cell Biol. 78, 110–117 (2000).
Article CAS PubMed Google Scholar
Cibrian, D. & Sanchez-Madrid, F. CD69: from activation marker to metabolic gatekeeper. Eur. J. Immunol. 47, 946–953 (2017).
Article CAS PubMed PubMed Central Google Scholar
Simms, P. E. & Ellis, T. M. Utility of flow cytometric detection of CD69 expression as a rapid method for determining poly- and oligoclonal lymphocyte activation. Clin. Diagn. Lab Immunol. 3, 301–304 (1996).
Article CAS PubMed PubMed Central Google Scholar
Morrison, B. J., Steel, J. C. & Morris, J. C. Reduction of MHC-I expression limits T-lymphocyte-mediated killing of Cancer-initiating cells. BMC Cancer 18, 469 (2018).
Article PubMed PubMed Central Google Scholar
Zhou, F. Molecular mechanisms of IFN-gamma to up-regulate MHC class I antigen processing and presentation. Int. Rev. Immunol. 28, 239–260 (2009).
Article CAS PubMed Google Scholar
Goel, S. et al. CDK4/6 inhibition triggers anti-tumour immunity. Nature 548, 471–475 (2017).
Article ADS CAS PubMed PubMed Central Google Scholar
Magnani, E. et al. uhrf1 and dnmt1 loss induces an immune response in zebrafish livers due to viral mimicry by transposable elements. Front. Immunol. 12, 627926 (2021).
Article CAS PubMed PubMed Central Google Scholar
Lerner, E. C. et al. CD8(+) T cells maintain killing of MHC-I-negative tumor cells through the NKG2D-NKG2DL axis. Nat. Cancer 4, 1258–1272 (2023).
Article CAS PubMed PubMed Central Google Scholar
Ubiquitin ligase activity of UHRF1. https://doi.org/10.1091/mbc.E05- (2005).
Ma, J. et al. Ubiquitin E3 ligase UHRF1 regulates p53 ubiquitination and p53-dependent cell apoptosis in clear cell renal cell carcinoma. Biochem. Biophys. Res. Commun. 464, 147–153 (2015).
Article CAS PubMed Google Scholar
Foster, B. M. et al. Critical role of the UBL domain in stimulating the E3 ubiquitin ligase activity of UHRF1 toward chromatin. Mol. Cell 72, 739–752.e739 (2018).
Article CAS PubMed PubMed Central Google Scholar
Chen, X. et al. A membrane-associated MHC-I inhibitory axis for cancer immune evasion. Cell 186, 3903–3920.e21 (2023).
Article CAS PubMed PubMed Central Google Scholar
Liu, X. et al. Inhibition of PCSK9 potentiates immune checkpoint therapy for cancer. Nature 588, 693–698 (2020).
Article ADS CAS PubMed PubMed Central Google Scholar
Yamamoto, K. et al. Autophagy promotes immune evasion of pancreatic cancer by degrading MHC-I Supplementary. Nature 581, 100–105 (2020).
Article ADS CAS PubMed PubMed Central Google Scholar
Sun, X., Cui, Y., Feng, H., Liu, H. & Liu, X. TGF-beta signaling controls Foxp3 methylation and T reg cell differentiation by modulating Uhrf1 activity. J Exp Med 216, 2819–2837 (2019).
Article CAS PubMed PubMed Central Google Scholar
Chu, J. et al. UHRF1 phosphorylation by cyclin A2/cyclin-dependent kinase 2 is required for zebrafish embryogenesis. Mol. Biol. Cell 23, 59–70 (2012).
Article CAS PubMed PubMed Central Google Scholar
Pickup, M., Novitskiy, S. & Moses, H. L. The roles of TGFbeta in the tumour microenvironment. Nat. Rev. Cancer 13, 788–799 (2013).
Article CAS PubMed PubMed Central Google Scholar
Derynck, R., Turley, S. J. & Akhurst, R. J. TGFbeta biology in cancer progression and immunotherapy. Nat. Rev. Clin. Oncol. 18, 9–34 (2021).
Article PubMed Google Scholar
Fix, S. M. et al. CRISPR-mediated TGFBR2 knockout renders human ovarian cancer tumor-infiltrating lymphocytes resistant to TGF-beta signaling. J. Immunother. Cancer 10, e003750 (2022).
Article PubMed PubMed Central Google Scholar
Zehn, D., Lee, S. Y. & Bevan, M. J. Complete but curtailed T-cell response to very low-affinity antigen. Nature 458, 211–214 (2009).
Article ADS CAS PubMed PubMed Central Google Scholar
Rudqvist, N. P. et al. Radiotherapy and CTLA-4 blockade shape the TCR repertoire of tumor-infiltrating T cells. Cancer Immunol. Res. 6, 139–150 (2018).
Article CAS PubMed Google Scholar
Wu, Y. et al. UHRF1 establishes crosstalk between somatic and germ cells in male reproduction. Cell Death Dis. 13, 377 (2022).
Article CAS PubMed PubMed Central Google Scholar
Sharif, J. et al. The SRA protein Np95 mediates epigenetic inheritance by recruiting Dnmt1 to methylated DNA. Nature 450, 908–912 (2007).
Article ADS CAS PubMed Google Scholar
Sanchez-Fernandez, C., Lorda-Diez, C. I., Garcia-Porrero, J. A., Montero, J. A. & Hurle, J. M. UHRF genes regulate programmed interdigital tissue regression and chondrogenesis in the embryonic limb. Cell Death Dis. 10, 347 (2019).
Article PubMed PubMed Central Google Scholar
Sakai, H. et al. Uhrf1 governs the proliferation and differentiation of muscle satellite cells. iScience 25, 103928 (2022).
Article ADS CAS PubMed PubMed Central Google Scholar
Mudbhary, R. et al. UHRF1 overexpression drives DNA hypomethylation and hepatocellular carcinoma. Cancer Cell 25, 196–209 (2014).
Article CAS PubMed PubMed Central Google Scholar
Zhou, L. et al. Regulation of UHRF1 by miR-146a/b modulates gastric cancer invasion and metastasis. FASEB J. 27, 4929–4939 (2013).
Article CAS PubMed Google Scholar
Wang, F. et al. UHRF1 promotes cell growth and metastasis through repression of p16(ink(4)a) in colorectal cancer. Ann. Surg. Oncol. 19, 2753–2762 (2012).
Article PubMed Google Scholar
Daskalos, A. et al. UHRF1-mediated tumor suppressor gene inactivation in nonsmall cell lung cancer. Cancer 117, 1027–1037 (2011).
Article CAS PubMed Google Scholar
Gao, L. et al. An intramolecular interaction of UHRF1 reveals dual control for its histone association. Structure 26, 304–311.e303 (2018).
Article CAS PubMed PubMed Central Google Scholar
Nixon, B. G., Gao, S., Wang, X. & Li, M. O. TGFbeta control of immune responses in cancer: a holistic immuno-oncology perspective. Nat. Rev. Immunol. 23, 346–362 (2023).
Article CAS PubMed Google Scholar
Gu, S. S. et al. Therapeutically increasing MHC-I expression potentiates immune checkpoint blockade. Cancer Discov. 11, 1524–1541 (2021).
Article CAS PubMed PubMed Central Google Scholar
Burr, M. L. et al. An evolutionarily conserved function of polycomb silences the MHC class I antigen presentation pathway and enables immune evasion in cancer. Cancer Cell 36, 385–401.e388 (2019).
Article CAS PubMed PubMed Central Google Scholar
Du, Z. et al. DNMT1 stability is regulated by proteins coordinating deubiquitination and acetylation-driven ubiquitination. Sci. Signal 3, ra80 (2010).
Article PubMed PubMed Central Google Scholar
Mellman, I., Chen, D. S., Powles, T. & Turley, S. J. The cancer-immunity cycle: Indication, genotype, and immunotype. Immunity 56, 2188–2205 (2023).
Article CAS PubMed Google Scholar
Giles, J. R., Globig, A. M., Kaech, S. M. & Wherry, E. J. CD8(+) T cells in the cancer-immunity cycle. Immunity 56, 2231–2253 (2023).
Article CAS PubMed Google Scholar
Myrianthopoulos, V. et al. Tandem virtual screening targeting the SRA domain of UHRF1 identifies a novel chemical tool modulating DNA methylation. Eur. J. Med. Chem. 114, 390–396 (2016).
Article CAS PubMed Google Scholar
Johnson, J. L. et al. An atlas of substrate specificities for the human serine/threonine kinome. Nature 613, 759–766 (2023).
Article ADS CAS PubMed PubMed Central Google Scholar
Hong, M., Clubb, J. D. & Chen, Y. Y. Engineering CAR-T cells for next-generation cancer therapy. Cancer Cell 38, 473–488 (2020).
Article CAS PubMed Google Scholar
Shafer, P., Kelly, L. M. & Hoyos, V. Cancer therapy with TCR-engineered T cells: current strategies, challenges, and prospects. Front. Immunol. 13, 835762 (2022).
Article CAS PubMed PubMed Central Google Scholar
Brudno, J. N. & Kochenderfer, J. N. Recent advances in CAR T-cell toxicity: mechanisms, manifestations and management. Blood Rev. 34, 45–55 (2019).
Article CAS PubMed Google Scholar
Hollingsworth, R. E. & Jansen, K. Turning the corner on therapeutic cancer vaccines. NPJ Vaccines 4, 7 (2019).
Article PubMed PubMed Central Google Scholar
Hopewell, E. L. et al. Lung tumor NF-kappaB signaling promotes T cell-mediated immune surveillance. J. Clin. Invest. 123, 2509–2522 (2013).
Article CAS PubMed PubMed Central Google Scholar
Labun, K. et al. CHOPCHOP v3: expanding the CRISPR web toolbox beyond genome editing. Nucleic Acids Res. 47, W171–W174 (2019).
Article CAS PubMed PubMed Central Google Scholar
Shalem, O. et al. Genome-scale CRISPR-Cas9 knockout screening in human cells. Science 343, 84–87 (2014).
Article ADS CAS PubMed Google Scholar
Sanjana, N. E., Shalem, O. & Zhang, F. Improved vectors and genome-wide libraries for CRISPR screening. Nat. Methods 11, 783–784 (2014).
Article CAS PubMed PubMed Central Google Scholar
Walchli, S. et al. Invariant chain as a vehicle to load antigenic peptides on human MHC class I for cytotoxic T-cell activation. Eur. J. Immunol. 44, 774–784 (2014).
Article PubMed Google Scholar
Mariathasan, S. et al. TGFbeta attenuates tumour response to PD-L1 blockade by contributing to exclusion of T cells. Nature 554, 544–548 (2018).
Article ADS CAS PubMed PubMed Central Google Scholar
Bolotin, D. A. et al. MiXCR: software for comprehensive adaptive immunity profiling. Nat. Methods 12, 380–381 (2015).
Article CAS PubMed Google Scholar
Perez-Riverol, Y. et al. The PRIDE database resources in 2022: a hub for mass spectrometry-based proteomics evidences. Nucleic Acids Res. 50, D543–D552 (2022).
Article CAS PubMed Google Scholar

Download references

Acknowledgements

The authors thank Dr. Guohong Li at Wuhan University for providing assistance during manuscript revision. We thank Dr. Yongsheng Wang at West China Hospital for help with IHC staining for human lung cancer tissues. We thank the staff of the Duke University Flow Cytometry Shared Resource, Duke Light Microscopy Core Facility, and the Proteomics and Metabolomics Core Facility for help with data acquisition. We thank Dr. Zhao Zhang from Duke University for assistance with drawing illustrations. Schematics in Fig.5d (BioRender.com/u82q392) and Supplementary Figs. 2d (BioRender.com/e87x601), 2i (BioRender.com/k25b929), 5a (BioRender.com/o76j936), 5c (BioRender.com/j18n557) and 6 (BioRender.com/y71m947) were created in BioRender. Tan, L. (2024) with modifications and released under a Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International license. This work was supported by R01-CA233205 from the NIH to X.-F.W. and Q.-J.L. Q.-J.L. is supported by core research grants provided to the IMCB and SIgN by the BMRC, A*STAR and National Research Foundation (NRF) Singapore under the NRF Investigatorship (NRFI09-0016).

Author information

These authors contributed equally: Lianmei Tan, Tao Yin, Handan Xiang.

Authors and Affiliations

Department of Pharmacology and Cancer Biology, Duke University School of Medicine, Durham, NC, USA
Lianmei Tan, Tao Yin, Handan Xiang, Junying Chen, Yi Ding, De Huang, Yaosi Liang, Peter B. Alexander, Kun Xiang, Ergang Wang, Chengsong Yan, Zhehao Ma & Xiao-Fan Wang
Department of Immunology, Duke University School of Medicine, Durham, NC, USA
Tao Yin, Guoping Wang, Bryan Jian Wei Lim & Qi-Jing Li
Department of Molecular Genetics and Microbiology, Duke University School of Medicine, Durham, NC, USA
Liuyang Wang
TCRCure Biopharma, Durham, NC, USA
Poorva Mudgal
State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
Yuqi Huang & Minjia Tan
Institute of Molecular and Cell Biology (IMCB), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
Qi-Jing Li
Singapore Immunology Network (SIgN), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
Qi-Jing Li

Authors

Lianmei Tan
View author publications
You can also search for this author in PubMed Google Scholar
Tao Yin
View author publications
You can also search for this author in PubMed Google Scholar
Handan Xiang
View author publications
You can also search for this author in PubMed Google Scholar
Liuyang Wang
View author publications
You can also search for this author in PubMed Google Scholar
Poorva Mudgal
View author publications
You can also search for this author in PubMed Google Scholar
Junying Chen
View author publications
You can also search for this author in PubMed Google Scholar
Yi Ding
View author publications
You can also search for this author in PubMed Google Scholar
Guoping Wang
View author publications
You can also search for this author in PubMed Google Scholar
Bryan Jian Wei Lim
View author publications
You can also search for this author in PubMed Google Scholar
Yuqi Huang
View author publications
You can also search for this author in PubMed Google Scholar
De Huang
View author publications
You can also search for this author in PubMed Google Scholar
Yaosi Liang
View author publications
You can also search for this author in PubMed Google Scholar
Peter B. Alexander
View author publications
You can also search for this author in PubMed Google Scholar
Kun Xiang
View author publications
You can also search for this author in PubMed Google Scholar
Ergang Wang
View author publications
You can also search for this author in PubMed Google Scholar
Chengsong Yan
View author publications
You can also search for this author in PubMed Google Scholar
Zhehao Ma
View author publications
You can also search for this author in PubMed Google Scholar
Minjia Tan
View author publications
You can also search for this author in PubMed Google Scholar
Qi-Jing Li
View author publications
You can also search for this author in PubMed Google Scholar
Xiao-Fan Wang
View author publications
You can also search for this author in PubMed Google Scholar

Contributions

Conceptualization, L.T., T.Y., H.X., Q.-J.L., and X.-F.W.; experimental study, L.T., T.Y., H.X., J.C., Y.D., G.W., B.J.W.L., D.H., Y.L., K.X., C.Y., Z.M.; bioinformatics analysis, L.W., P.M., E.W., Y.H., M.T.; writing and editing, L.T., T.Y., P.B.A., Q.-J.L. and X.-F.W.; supervision, Q.-J.L. and X.-F.W.

Corresponding authors

Correspondence to Qi-Jing Li or Xiao-Fan Wang.

Ethics declarations

Competing interests

Q.-J.L. is a scientific co-founder and shareholder of TCRCure Biopharma and Hervor Therapeutics. The other authors declare no competing interests.

Peer review

Peer review information

Nature Communications thanks Iannis Aifantis, Cheng Cheng Zhang and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. A peer review file is available.

Additional information

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

Supplementary information

Supplementary Information

Peer Review File

Reporting Summary

Source data

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

Reprints and permissions

About this article

Cite this article

Tan, L., Yin, T., Xiang, H. et al. Aberrant cytoplasmic expression of UHRF1 restrains the MHC-I-mediated anti-tumor immune response. Nat Commun 15, 8569 (2024). https://doi.org/10.1038/s41467-024-52902-5

Download citation

Received: 26 March 2024
Accepted: 24 September 2024
Published: 03 October 2024
DOI: https://doi.org/10.1038/s41467-024-52902-5

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Subjects

Abstract

Similar content being viewed by others

Tumor-intrinsic YTHDF1 drives immune evasion and resistance to immune checkpoint inhibitors via promoting MHC-I degradation

Combination of epigenetic regulation with gene therapy-mediated immune checkpoint blockade induces anti-tumour effects and immune response in vivo

ARIH1 activates STING-mediated T-cell activation and sensitizes tumors to immune checkpoint blockade

Introduction

Results

UHRF1 is overexpressed in NSCLC and promotes tumor growth in syngeneic mouse models

UHRF1 restrains CD8+ T cell response in the TME via down-regulation of MHC-I expression

Cytoplasmic UHRF1 mediates MHC-I degradation in tumor cells within the TME

Phosphorylation of UHRF1 regulates its cytoplasmic localization in response to factors in the TME, such as TGF-β

Inactivating UHRF1 induces memory formation via expansion of T cell clones with low-affinity TCRs and synergizes with anti-CTLA4-based ICB therapy

Discussion

Methods

Animals

Cell culture

Stable cell lines

Tumor models

In vivo lymphocyte depletion and ICB therapy

Molecular cloning

Adoptive T cell transfer

Western blots

Immunoprecipitation

Flow cytometry

Immunofluorescence

Immunohistochemistry

T-cell activation assay

LDH release assay

Antigen presentation assay

RNA-seq

TCRβ sequencing

Statistics and reproducibility

Reporting summary

Data availability

References

Acknowledgements

Author information

Authors and Affiliations

Contributions

Corresponding authors

Ethics declarations

Competing interests

Peer review

Peer review information

Additional information

Supplementary information

Supplementary Information

Peer Review File

Reporting Summary

Source data

Source data

Rights and permissions

About this article

Cite this article

Share this article

Comments

Search

Quick links