CRY1 fuels resistance to T cell-based immunotherapy in NANOGhigh cancers

CRY1 fuels resistance to T cell-based immunotherapy in NANOGhigh cancers | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article CRY1 fuels resistance to T cell-based immunotherapy in NANOGhigh cancers Tae Woo Kim, Se Jin Oh, Seon Rang Woo, Jun Hyeok Ahn, Min Kyu Son, and 9 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5658722/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Cancer immunotherapies, including immune checkpoint blockade (ICB), have marked a significant breakthrough in cancer treatment but their clinical efficacy is limited in immune-resistant tumors. Previously, we found that immunotherapy-mediated immune selection enriches immune-resistant tumors with both tumor-intrinsic and -extrinsic refractory phenotypes via the transcriptional induction of HDAC1 by NANOG. Here, we identify CRY1 as a critical transcriptional target of NANOG that stabilizes Cyclin A and MCL1 to promote cancer stem cell-like property and resistance to cytotoxic T cell-mediated killing in NANOG high tumor cells through HDAC1-mediated epigenetic silencing of APC3 and TRIM17. Additionally, CRY1 downregulates CXCL10 via HDAC1-mediated repression, thereby suppressing T cell infiltration. Importantly, CRY1 inhibition synergizes with PD-1 blockade and adoptive T cell transfer in reducing tumor growth by converting immune-resistant tumors into immune-sensitive tumors. Collectively, these findings highlight CRY1 as a critical mediator of the NANOG/HDAC1 axis in the multiple refractory properties of immune-resistant tumors and suggest CRY1 as a potential therapeutic target. Biological sciences/Cancer/Cancer therapy/Cancer therapeutic resistance Biological sciences/Cancer/Cancer therapy/Cancer immunotherapy Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Cancer immunotherapies, such as immune checkpoint blockade (ICB) and adoptive T cell transfer (ACT), have significantly improved clinical outcomes of patients having various malignancies, including melanoma 1 , 2 . However, many cancer patients do not achieve a durable response to immunotherapy, and resistance to these treatments remains a major clinical challenge. Several mechanisms of resistance to immunotherapy have been identified and can be broadly categorized as either tumor cell-intrinsic or tumor cell-extrinsic refractoriness 3 , 4 , 5 , 6 . Tumor cell-intrinsic mechanisms involve factors that hinder the recognition or destruction of tumor cells by cytotoxic T lymphocytes (CTLs), such as loss of antigen presentation and major histocompatibility complex (MHC) class I, defects in the interferon gamma (IFNγ) pathway, and resistance to apoptosis 7 , 8 , 9 , 10 . On the other hand, tumor cell-extrinsic mechanisms typically involve features of the tumor microenvironment (TME) that characterize 'immune-refractory' or 'cold' tumors, including poor infiltration of CTLs and natural killer (NK) cells as well as the accumulation of suppressive myeloid cells 11 , 12 . Because addressing only intrinsic mechanisms or only extrinsic mechanisms may be insufficient to overcome resistance, identifying and targeting common pathways that regulate this multiple immune-refractoriness network is essential to improving the efficacy of immunotherapies. Emerging evidence suggests that tumor-intrinsic signaling not only promotes tumor progression but also disrupts key processes essential for effective anti-tumor immunity, such as antigen processing and presentation, T cell-mediated killing, and T cell infiltration into tumors 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 . In this regard, we have identified the embryonic transcription factor NANOG as a critical intrinsic factor that drives cancer stem cell (CSC)-like properties, renders tumor cells resistant to T cell cytotoxicity and impedes the trafficking of cytotoxic T cells to tumors through HDAC1-dependent regulation of MCL1 and CXCL10, respectively 22 . Importantly, HDAC1 inhibition in tumors restricts NANOG-driven refractory signaling, leading to increased T cell recruitment, sensitizing tumors to CTLs, and enhancing the effectiveness of PD-1 blockade in multiple mouse tumor models 22 . These findings suggest that the NANOG/HDAC1 axis may be a common pathway driving both intrinsic and extrinsic refractoriness to PD-1 blockade. Although HDAC1 inhibition shows promise in overcoming multiple refractoriness to immunotherapy, it can affect not only cancer cells but also other cell types in the TME, where HDAC1 is broadly expressed. This could lead to widespread epigenetic changes, potentially causing significant side effects 23 , 24 . Therefore, it is crucial to identify tumor-selective targets that can inhibit the NANOG/HDAC1 axis to improve clinical outcomes. The circadian rhythm is an important regulatory system that maintains the homeostasis in normal cells and tissues, such as cell proliferation, survival, DNA repair, metabolism, and inflammation 25 , 26 . To date, 14 circadian factors that interact to form a network with multiple feedback loops at the transcriptional and translational levels have been identified. Alterations in circadian-related genes can fundamentally disrupt basic cellular functions, which in turn increases the risk of diseases such as cancer 27 , 28 , 29 . While many types of cancer cells still exhibit circadian clock oscillations, several studies have revealed dysregulation of core clock genes, including PER1 , PER2 , PER3 , CRY1 , CRY2 , BMAL1 , and ClOCK , in certain human cancers 30 , 31 , 32 . Emerging evidence also suggests that circadian clocks significantly influence the TME and the T cell-mediated antitumor immune response, thereby affecting the efficacy of cancer immunotherapy 33 , 34 . Among these core clock genes, cryptochrome 1 (CRY1) is a key circadian clock repressor and has been reported to be overexpressed in various cancers 35 , 36 , 37 . Interestingly, recent studies have shown that CRY1 regulates pluripotent programs, including self-renewal capacity and metabolism, and induces resistance to genotoxic agents by regulating the DNA damage response, thereby promoting tumor growth 38 , 39 , 40 . Importantly, although CRY1 is widely expressed in normal cells, studies indicate that Cry1 deficiency does not affect embryonic development and Cry1 knockout mice remain completely healthy without any noticeable phenotypes 41 , 42 . Although the importance of CRY1 as a tumor-selective therapeutic target is growing, the potential relationship between CRY1's functions and resistance to immunotherapy has not been fully explored. We have previously developed highly ICB-resistant cell lines Yumm2.1 P3 and B16 P3 from the ICB-susceptible parental cell lines Yumm2.1 P0 and B16F0 P0, respectively 22 , 43 . These P3 tumors are also resistant to CTL-mediated killing and exhibit non-T cell-inflamed immune phenotypes within the TME 22 , 43 , suggesting that both tumor-intrinsic and -extrinsic mechanisms drive the immune-refractory characteristics of these PD-1 blockade-resistant tumor models. We have also specifically generated CTL-resistant CaSki P3 cells from the CTL-susceptible parental CaSki P0 cells 44 . Furthermore, we have demonstrated that overexpressing NANOG in immune-susceptible P0 tumor cells can replicate the multiple refractory properties of immunotherapy-resistant P3 cells by promoting CSC-like traits, increasing resistance to CTL-mediated killing, and reducing T cell infiltration 16 , 22 , 45 . Using these and other preclinical models as well as human cancer datasets in this study, we demonstrate a critical role of CRY1 at the intersection of the NANOG/HDAC1 axis and the multiple refractory properties of immune-resistant tumors. Mechanistically, NANOG-induced transcription of CRY1 results in HDAC1-mediated epigenetic silencing of the E3 ubiquitin ligases APC3 and TRIM17, which in turn stabilize Cyclin A and MCL1 proteins and promote CSC-like properties and resistance to CTL-mediated killing, respectively. Additionally, CRY1 represses CXCL10 expression via HDAC1-mediated silencing, thereby suppressing T cell infiltration in NANOG high tumors. Furthermore, we show that CRY1 inhibition sensitizes tumors to T cell-based immunotherapy by making tumors more susceptible to CTL and shifting the TME from immune-refractory to immune-favorable. These findings provide proof-of-concept that targeting CRY1 could be a promising therapeutic strategy to overcome resistance to T cell-based therapies. Results CRY1 expression in tumor cells contributes to PD-1 blockade resistance in a CD8 + T cell-dependent manner To explore the relationship between circadian factors and ICB-resistance, we first measured the expression levels of core circadian genes, including Clock , Bmal1 , Cry1 , Cry2 , Per1 , Per2 , and Per3 , in two independent preclinical models. Among these genes, the Cry1 mRNA level was significantly elevated in both Yumm2.1 P3 and B16 P3 cells compared to their respective P0 counterparts (Fig. 1 a). The elevated CRY1 expression in P3 cells than P0 cells was further confirmed by CRY1 protein levels (Fig. 1 b). To assess CRY1's role in the ICB-refractory phenotypes of P3 tumor cells, we intravenously administered Yumm2.1 P3 or B16 P3 tumor-bearing mice with chitosan nanoparticles (CNPs) carrying either Cry1 - or GFP -targeting siRNA in combination with an anti-PD-1 antibody (Fib. 1c and Supplementary Fig. 1). While anti-PD-1 therapy alone had no effect on the tumor growth, the combination of anti-PD-1 antibody and Cry1 -targeting siRNA CNPs significantly retarded the tumor growth (Fig. 1 d, e). To confirm the role of CD8 + CTL in the observed therapeutic effect, we depleted CD8 + T cells using an anti-CD8 antibody and found that the therapeutic benefits of the siCry1 and anti-PD-1 combination were significantly reduced (Fig. 1 f). These findings suggested that CRY1 expression in tumor cells contributed to PD-1 blockade resistance in a CD8 + T cell-dependent manner. CRY1 promotes PD-1 blockade resistance by regulating both tumor-intrinsic and extrinsic mechanisms To investigate the role of CRY1 upregulation in the immune-refractory phenotypes, we silenced CRY1 in Yumm2.1 and B16 P3 cells. Interestingly, Cry1 -silenced P3 cells were more susceptible to CTL-induced apoptosis compared to control P3 cells, without significant changes in the MHC class I expression (Fig. 1 g and Supplementary Fig. 2). Silencing Cry1 also increased the sensitivity of P3 cells to granzyme B (GrB), a key factor in CTL-mediated apoptosis (Fig. 1 h), suggesting that CRY1 protects PD-1 blockade-resistant P3 tumor cells from CTL-mediated killing independent of T cell recognition. Additionally, a transwell-based chemotaxis assay revealed enhanced T cell migration when incubated with conditioned media from siCry1 -silenced P3 cells compared to that from control cells (Fig. 1 i), indicating that CRY1 may inhibit T cell infiltration by reducing the secretion of chemotactic factors. These findings suggested that CRY1 contributed to PD-1 blockade resistance by regulating both tumor-intrinsic and extrinsic mechanisms. CTL-mediated immune selection drives CRY1 upregulation in tumor cells Given the essential role of tumor antigen-specific CTLs in the anti-tumor effects of anti-PD-1 therapy, we investigated whether CRY1 upregulation was also present in CTL-resistant human tumor cells. Interestingly, the CRY1 expression was upregulated in CTL-resistant CaSki P3 cells compared to CTL-susceptible CaSki P0 cells, while expression levels of other core circadian genes remained unchanged (Fig. 2 a, b). Additionally, CRY1 knockdown in CaSki P3 cells sensitized the cells to the cognate CTLs and GrB, and enhanced T cell migration (Fig. 2 c- 2 f). Our results confirmed that CRY1 expression was upregulated not only in PD-1 blockade-resistant tumor cells but also in CTL-resistant human tumor cells, suggesting that CTL-mediated immune selection was a key mechanism driving CRY1 upregulation following PD-1 blockade-mediated immune selection. NANOG directly upregulates CRY1 transcription to promote CSC-like and immune-refractory phenotypes We sought to elucidate the mechanism responsible for CRY1 upregulation in tumor cells resistant to T cell-based immunotherapy. Given that NANOG upregulation has been observed in Yumm2.1 P3, B16 P3, and CaSki P3 cells 16 , 22 , 43 , we hypothesized that NANOG might be responsible for the transcriptional upregulation of CRY1. Notably, silencing NANOG in CaSki P3 cells reduced both CRY1 protein and mRNA levels (Fig. 3 a, b) whereas overexpressing NANOG in CaSki P0 cells increased both CRY1 protein and mRNA levels (Fig. 3 c, d), indicating that NANOG regulated the CRY1 expression. Importantly, NANOG's regulation of the CRY1 expression relied on its transcriptional activity, as overexpressing a transcriptionally inactive NANOG mutant (NANOG MT) 45 did not affect the CRY1 expression (Fig. 3 e, f). We identified a potential NANOG-binding site in the CRY1 promoter, suggesting that NANOG might directly activate the CRY1 transcription (Fig. 3 g). Using the luciferase reporter assay, we found that expressing the wild-type NANOG (NANOG WT), but not a NANOG MT (Fig. 3 h), significantly increased the CRY1 promoter activity and that mutating the NANOG-binding site in the CRY1 promoter abolished the promoter activation by NANOG WT (Fig. 3 h). Chromatin immunoprecipitation (ChIP) assays verified NANOG's direct binding to the CRY1 regulatory region (Fig. 3 i) and showed a higher NANOG occupancy in CaSki P3 cells compared to CaSki P0 cells (Fig. 3 j). Importantly, we found that the NANOG signature ( NANOG sig.), a more reliable indicator of the NANOG expression in tumor cells 16 , 22 , was positively correlated with the CRY1 expression across multiple tumor types in the TCGA cohort (Fig. 3 k). These findings demonstrated that NANOG directly upregulated the CRY1 transcription by binding to its promoter, and that the NANOG/CRY1 axis was conserved across multiple human cancer types. We then explored whether CRY1 was critical for NANOG-driven phenotypes and found that CRY1 knockdown in NANOG-overexpressing CaSki cells reduced CSC-like traits, increased sensitivity to GrB, and enhanced T cell infiltration (Supplementary Fig. 3). These results suggested that CRY1 played a key role in the CSC-like and immune-refractory characteristics driven by NANOG. CRY1 confers CSC-like property and resistance to CTL killing in NANOG high tumor cells through HDAC1-mediated epigenetic repression of APC3 and TRIM17. Although we previously demonstrated that NANOG upregulation of Cyclin A and MCL1 was AKT-dependent 16 , 22 , 45 , CRY1 knockdown did not affect AKT phosphorylation in NANOG-overexpressing CaSki cells (Fig. 4 a). Moreover, CRY1 knockdown significantly reduced Cyclin A and MCL1 protein levels without altering their transcript levels (Fig. 4 a, b). A cycloheximide-chase assay revealed a reduced half-life of Cyclin A and MCL1 proteins in CRY1 -silenced compared to control NANOG-overexpressing CaSki cells (Fig. 4 c). Additionally, treatment with MG132 prevented the CRY1 knockdown-induced reduction of Cyclin A and MCL1, suggesting proteasome-mediated degradation of these proteins (Fig. 4 d). These findings indicated that CRY1 regulated the protein stability of Cyclin A and MCL1 in an AKT-independent manner. It has been reported that the Cyclin A and MCL1 protein levels were tightly controlled by proteasomal degradation through their ubiquitination by various E3 ubiquitin ligases and their cofactors 46 , 47 . We hypothesized that the CRY1-mediated accumulation of Cyclin A and MCL1 might result from repressive effects of CRY1 on the expression of genes involved in their degradation. To test this, we compared the expression of genes responsible for the negative regulation of Cyclin A and MCL1 in control and NANOG-overexpressing CaSki cells. Among the candidates, we found that APC3 and TRIM17 were downregulated in NANOG-overexpressing compared to control CaSki cells (Supplementary Fig. 4) and that their expressions were restored following CRY1 knockdown (Fig. 4 e). These results suggested that NANOG downregulated APC3 and TRIM17 in a CRY1-dependent manner. To confirm that APC3 and TRIM17 negatively regulated Cyclin A and MCL1, we silenced APC3 or TRIM17 in control and CRY1 -knockdown CaSki NANOG cells (Supplementary Fig. 5). The reduction of Cyclin A and MCL1 levels observed in NANOG-overexpressing CaSki cells after CRY1 knockdown was inhibited by APC3 or TRIM17 knockdown (Fig. 4 f, g). Consistently, the reduced sphere-forming capacity and increased susceptibility to GrB following CRY1 knockdown in NANOG-overexpressing CaSki cells were also reversed by APC3 or TRIM17 -knockdown (Fig. 4 h, i). These findings suggested that CRY1-mediated stabilization of Cyclin A and MCL1 was critical for NANOG-driven CSC-like properties and resistance to CTL-mediated killing. Previous studies have shown that HDAC1 is a key element in NANOG-mediated transcriptional repression 17 , and that CRYs can repress target gene expression by recruiting a repressive complex, including HDAC1, to the promoters of these genes 48 , 49 . Based on these, we hypothesized that CRY1 might epigenetically repress the expression of APC3 and TRIM17 by regulating HDAC1 recruitment at their regulatory sites. Interestingly, CRY1 co-precipitated with HDAC1 (Fig. 4 j), but this interaction was disrupted following the treatment with KS15 (Fig. 4 k), a CRY1 inhibitor that blocks its physiological interactions with other proteins 50 . Previously, we have identified H3K27 deacetylation as a potential epigenetic marker linked to the NANOG/HDAC1 axis 17 . ChIP-qPCR analysis confirmed that NANOG overexpression reduced the AcH3K27 occupancies at the promoter regions of APC3 and TRIM17 (Fig. 4 l). In line with these findings, CRY1 and HDAC1 were more enriched at these gene promoters in NANOG-overexpressing CaSki cells decreased HDAC1 occupancies and increased AcH3K27 levels at the promoter regions of APC3 and TRIM17 (Fig. 4 l). Taken together, our results suggested that CRY1 downregulated APC3 and TRIM17 expression through HDAC1-mediated epigenetic repression, leading to the stabilization of Cyclin A and MCL1, thereby promoting CSC-like properties and resistance to CTL-mediated killing in NANOG high tumor cells. CRY1 inhibits T cell infiltration by repressing CXCL10 expression through HDAC1-mediated epigenetic silencing in NANOG high tumors. Given NANOG impairing T cell recruitment by suppressing the CXCL10 production 22 and CRY1’s role in NANOG-mediated suppression of T cell infiltration, we hypothesized that CRY1 might contribute to CXCL10 repression in NANOG-overexpressing cells. Indeed, CRY1 knockdown significantly increased CXCL10 protein and transcript levels (Fig. 5 a, b). Consistent with this, ChIP-qPCR analysis showed that NANOG overexpression reduced the AcH3K27 occupancy at the CXCL10 promoter, which was reversed by CRY1 knockdown (Fig. 5 c). Additionally, HDAC1 was more enriched at the CXCL10 promoter in NANOG-overexpressing compared to control CaSki cells, but CRY1 knockdown reduced the HDAC1 occupancy at this site (Fig. 5 c). To evaluate the role of CXCL10 in the suppression of T cell infiltration mediated by the NANOG/CRY1 axis, we performed antibody-mediated neutralization of CXCL10 in NANOG-overexpressing and control CaSki cells. The increased T cell infiltration observed with CRY1 knockdown was completely reversed by CXCL10 neutralization (Fig. 5 d). Therefore, our results suggested that CRY1 repressed CXCL10 expression through HDAC1-mediated epigenetic silencing, thereby inhibiting T cell infiltration. CRY1 activity is associated with the multiple immune-refractory phenotypes to T cell-based immunotherapy Based on our observations, we further hypothesized that CRY1 might contribute to poor responses to T cell-based immunotherapies, including PD-1 blockade, in cancer patients. To investigate the potential link between CRY1 activity and immune-refractory phenotypes in patients resistant to PD-1 blockade, we first defined the CRY1 activity to provide a more reliable indicator of CRY1 function in tumors. Because CRY1 acts as a transcriptional repressor, we focused on CRY1-bound genes 40 and filtered for those downregulated in non-responders (NR; patients with stable or progressive disease) compared to responders (R; patients with complete or partial response) to PD-1 blockade in an integrated transcriptomic dataset of melanoma patients 51 , 52 , 53 , 54 , 55 . We refined the CRY1 activity as a score by multiplying the average expression of CRY1-responsive genes by a negative number (Fig. 6 a). Notably, the CRY1 activity score was significantly higher in NR than in R (Fig. 6 b). Additionally, we found that melanoma patients with high CRY1 activity scores had significantly worse overall survival rates (Fig. 6 c). These findings suggested that elevated CRY1 activity might contribute to resistance to PD-1 blockade, leading to poor clinical outcomes in melanoma patients. We next investigated whether CRY1 contributed to multiple immune-refractory features in tumors, leading to resistance to immunotherapy in cancer patients. Tumors with either immune-favorable or immune-refractory features can be predicted by evaluating the expression signature scores of eight gene sets, which serve as indicators of stemness, resistance to T cell-mediated anti-tumor responses, and poor T cell infiltration, collectively referred to as multiple immune-refractoriness (MIR) 56 , 57 , 58 , 59 . Interestingly, we found that the CRY1 activity score was strongly associated with gene signatures representing stemness, resistance to T cell-mediated anti-tumor responses, and poor T cell infiltration in an integrated dataset of melanoma patients treated with PD-1 blockade (Fig. 6 d, e). These results were further validated across multiple tumor types in the TCGA cohort (Fig. 6 f). Together, our findings suggested that CRY1 was linked to multiple immune-refractory phenotypes in various types of tumors, and that inhibiting CRY1 activity in tumor cells could potentially overcome resistance to T cell-based immunotherapies, including PD-1 blockade, by shifting the tumor's immune status from immune-refractory to immune-favorable. The NANOG/CRY1 axis is conserved across multiple types of NANOG tumor cells Having explored the molecular mechanism by which the NANOG/CRY1 axis contributes to multiple immune-refractory phenotypes to immunotherapy, we assessed its clinical relevance in human cancer patients. We found that patients with high levels (H) of both the NANOG signature ( NANOG sig.) and CRY1 activity score (CRY1 as.) had a stronger association with MIR compared to those with low levels (L) of both (Fig. 7 a). This suggested that the NANOG/CRY1 axis was closely linked to immunotherapy resistance and serves as an important prognostic factor across multiple cancer types. To verify the functional roles of the NANOG/CRY1 axis in various human cancer cell types, we selected an additional ACT-resistant tumor model (MDA-MB-231 P3) and two NANOG-upregulated cancer cell lines (CUMC6 and MKN28) 17 , 45 , 60 . Compared to CaSki P0 cells, the three selected tumor cells expressed higher levels of both NANOG and CRY1 (Supplementary Fig. 6a). Notably, CRY1 knockdown significantly reduced Cyclin A and MCL1 levels and increased CXCL10 levels in these tumor cells (Supplementary Fig. 6b), indicating that the NANOG/CRY1 axis was conserved across all the cells tested. To evaluate the clinical potential of targeting NANOG high tumor cells with a CRY1 inhibitor, we assessed the viability of siGFP - or siNANOG -transfected tumor cells after in vitro treatment with the CRY1 inhibitor KS15. We found that NANOG knockdown reduced the sensitivity to KS15 in all tested tumor cells (Fig. 7 b), suggesting that NANOG is a key mediator determining the susceptibility to CRY1 inhibition. We next evaluated the expression of effectors involved in the multiple immune-refractory phenotypes mediated by the NANOG/CRY1 axis. CRY1 inhibition with KS15 significantly reduced Cyclin A and MCL1 levels and increased CXCL10 levels compared to the control (Fig. 7 c). Consistently, in all tested tumor cells, Consistently, in all tested tumor cells, CRY1 inhibition resulted in reduced CSC-like properties, increased sensitivity to GrB, and enhanced T cell infiltration compared to the control (Fig. 7 d- 7 f). Together, these results demonstrated that the biochemical and functional properties of the NANOG/CRY1 axis were well conserved across various cancer cell types, and that CRY1 was an actionable target for controlling NANOG high immunotherapy-resistant tumor cells. CRY1 inhibition reverses resistance to T cell-based immunotherapy Based on our in vitro observations, we hypothesized that in vivo administration of KS15 could overcome resistance to T cell-based immunotherapy. To test this, we treated mice bearing PD-1 blockade-resistant Yumm2.1 P3 tumor with a combination of an anti-PD-1 antibody and KS15 (Fig. 8 a). While anti-PD-1 antibody alone had no effect on the tumor growth, its combination with KS15 significantly inhibited the tumor growth without affecting the body weight (Fig. 8 b- 8 d) and extended the survival of tumor-bearing mice (Fig. 8 e). Additionally, these results were also reproduced in mice bearing B16F10, a model of innate PD-1 blockade resistance with high NANOG expression 22 (Supplementary Fig. 7). Our data suggested that CRY1 inhibition with KS15 could overcome the resistance of NANOG high tumors to PD-1 blockade. We further investigated whether CRY1 inhibition could shift the immune status in the TME from immune-refractory to immune-favorable. The combination treatment groups showed significantly higher numbers of CD8 + T cells and tumor reactive CD8 + T cells expressing GrB compared to the other treatment groups (Fig. 8 f, g). Additionally, the percentage of apoptotic tumor cells was higher in the combination treatment group than groups treated with either one alone (Fig. 8 h). Together, these findings demonstrated that CRY1 inhibition with KS15 could overcome the resistance of NANOG high tumors to PD-1 blockade by shifting the immune phenotype from non-T cell inflamed to T cell inflamed. Next, we expended the preclinical therapeutic potential of CRY1 inhibition to ACT. To assess this, NOD/SCID mice bearing ACT-resistant MDA-MB-231 P3 tumors were treated with MART1-specific CTLs with or without KS15 treatment (Supplementary Fig. 8a). While treatment with CTLs alone had no effect on the tumor growth, the combination of CTLs and KS15 significantly inhibited tumor growth without affecting the body weight (Supplementary Fig. 8b-8d) and improved the survival compared to other groups (Supplementary Fig. 8e). These findings indicate that targeting CRY1 with KS15 could be a promising combinatorial strategy to enhance the response to various T cell-based immunotherapies, such as PD-1 blockade and ACT, for controlling multiple refractory phenotypes of NANOG high tumors. Discussion Cancer immunotherapy has shown remarkable clinical efficacies across various cancer types; however, resistance to this promising approach remains a significant clinical challenge. While mechanisms of resistance are typically categorized into cancer cell-intrinsic and extrinsic factors, the pathways governing these mechanisms often overlap 3 , 4 . Thus, identifying common pathways that regulate both cancer cell-intrinsic and extrinsic resistance is crucial for advancing the effectiveness of immunotherapy. In this study, using mouse preclinical models resistant to PD-1 blockade and ACT, we demonstrate that CRY1 functions as a common factor contributing to multiple immune resistance networks by simultaneously restricting CTL-mediated killing of tumor cells and T cell infiltration into tumors. CRY1 upregulation has been reported in various tumors 35 , 36 , 37 ; however, the regulatory mechanisms governing CRY1 expression, particularly during CTL-mediated immune selection, remain largely unexplored. In this respect, we noted previously that the immune pressure from antigen-specific CTLs promoted the acquisition of NANOG, a key transcription factor that drives the emergence of a stem-like cancer cell state and immune-refractory characteristics 22 , 45 . In this study, we have identified CRY1 as a novel transcriptional target of NANOG, suggesting that CRY1 upregulation may result from the selection of NANOG high immune-resistant tumor cells during T cell-based immunotherapy, including PD-1 blockade and ACT. Indeed, CRY1 expression shows a positive correlation with the NANOG signature across various tumor types in the TCGA cohort, supporting that the molecular axis observed in vitro also exists in cancer patients. Importantly, our results show that CRY1 is a crucial component of several NANOG-dependent phenotypes. Notably, CSC-like and multiple immune-refractory phenotypes in NANOG-overexpressing P0 cells are nearly abolished following CRY1 depletion. These findings provide insights into the direct link between NANOG-induced immune-refractory phenotypes and CRY1 dysregulation in immune-resistant tumor cells. Although circadian clock dysregulation has been associated with both antitumor immunity and tumorigenesis, the mechanisms linking CRY1 to immunotherapeutic resistance remain poorly understood. Typically, CRY1 forms a heterodimer with PER proteins, translocates into the nucleus, and inhibits CLOCK/BMAL1 activity, establishing a negative feedback loop that regulates circadian rhythms in mammals 61 . Beyond its role in the regulation of core clock genes, CRY1 also acts as a transcriptional repressor impacting processes like DNA damage response and glucocorticoid signaling 38 , 62 . Previously, we highlighted HDAC1’s role in NANOG-dependent phenotypes, such as CSC-like properties and immune refractoriness 17 , 22 . This study focuses on CRY1’s role in HDAC1-mediated transcriptional repression to elucidate how the NANOG/CRY1 axis drives multiple immune-refractory phenotypes. We show that CRY1 supports HDAC1-mediated silencing of APC3 and TRIM17, which stabilizes Cyclin A and MCL1, to promote CSC-like property and resistance to CTL-mediated killing. CRY1 also represses CXCL10 expression via HDAC1, limiting T cell infiltration in NANOG-high tumor cells. Importantly, we have identified HDAC1 as a novel CRY1 binding partner and found that CRY1 inhibition restores AcH3K27 levels at target gene promoters, indicating that CRY1 modulates gene expression via HDAC1 binding. While further studies are needed to clarify the precise mechanisms by which CRY1 regulates HDAC1-driven epigenetic events, this connection points to promising therapeutic targets for addressing immune-resistant cancer. Currently, programmed death-ligand 1 (PD-L1), microsatellite instability (MSI), and tumor mutational burden (TMB) are the three validated biomarkers for predicting the response to immunotherapy. However, relying on a single biomarker is still insufficient for optimal patient selection. Given the substantial and growing use of these therapies, identifying new predictive biomarkers is essential to improve treatment outcomes through optimal patient selection 63 . Here, by analyzing integrated transcriptome data from melanoma patients classified as responders and non-responders to PD-1 blockade, we show that the CRY1 activity score, based on the expression of genes bound by CRY1, significantly correlates with response to anti-PD-1 therapy and survival in cancer patients. This suggests that CRY1 activity score levels could serve as a predictive marker for clinical outcomes of PD-1 blockade. Importantly, the CRY1 activity score is strongly associated with the MIR gene signature, which includes stemness, resistance to T cell-mediated anti-tumor responses, and poor T cell infiltration. Furthermore, in multiple tumor types within the TCGA cohort, a strong correlation with the MIR status is observed in tumors exhibiting high levels of both the CRY1 activity and the NANOG signature, rather than with either marker alone. Thus, we propose that the CRY1 activity score, particularly in immunotherapy-resistant tumors with high NANOG levels, may serve as a promising predictive marker. This provides a framework for selecting patients who may benefit from combination strategies involving T cell-based therapies and CRY1-targeting agents. Given the critical role of the CRY1/HDAC1 interaction in driving immune-refractory phenotypes through the NANOG/CRY1 axis, disrupting this interaction may be an effective strategy to overcome immunotherapy resistance. CRYs have a conserved N-terminal photolyase region and a variable C-terminal tail, which is crucial for their nuclear localization and interaction with core clock proteins 64 . KS15, a small molecule with the 2-ethoxypropanoic acid scaffold, binds to CRY1's C-terminal region 50 and affects CRY1's interactions with its binding partners, restoring CLOCK/BMAL1-driven transcription suppressed by CRY1 50 . In this study, we show that CRY1 inhibition with KS15 disrupts the CRY1/HDAC1 interaction, thereby reducing CSC-like properties, increasing sensitivity to GrB, and enhancing T cell infiltration across various cancer types. Importantly, KS15 treatment synergizes with T cell-based immunotherapies, such as anti-PD-1 therapy and ACT, to reverse multiple immune-refractoriness in immune-resistant tumors. These findings provide rational for the combination of CRY1 inhibition with T cell-mediated immunotherapy. Our previous study suggests that HDAC1 inhibitors could help overcome immunotherapy resistance 17 , 22 ; however, their broad impact on the tumor microenvironment poses limitations 65 , 66 . Our current findings indicate that multiple immune-refractory properties associated with the NANOG/HDAC1 axis can be regulated by CRY1, suggesting that targeting CRY1 could address the limitations of HDAC1 inhibition. There are several CRY1-targeting drugs under development for clinical application to cure prevalent circadian rhythm and sleep disorders, including delayed sleep phase disorder (DSPD) symptoms 67 , 68 . Therefore, targeting CRY1 with small molecules could be a promising strategy to enhance the immunotherapy efficacy without severe side effects and improve clinical outcomes for cancer patients with immune-resistant tumors. In summary, our findings identify CRY1 as a key transcriptional target of NANOG and reveal CRY1’s pivotal role at the intersection of the NANOG/HDAC1 axis and multiple refractory properties in immune-resistant tumors. Although CRY1 enhances NANOG-driven aggressive traits, it also represents a potential vulnerability if selectively targeted. Thus, our data suggest that CRY1 inhibition could be a promising strategy to combat NANOG high immune-resistant tumors, especially in the context of T cell-based cancer therapy. Methods Mice Six- to eight-week-old NOD/SCID or C57BL/6 mice were purchased from Central Lab. Animal Inc. (Seoul, Korea). All mice were maintained and handled under the protocol approved by the Korea University Institutional Animal Care and Use Committee (KOREA-2022-0005). All animal procedures were performed in accordance with recommendations for the proper use and care of laboratory animals. Cell lines B16F0 (ATCC, CRL-6322), B16F10 (ATCC, CRL-6475), CaSki (ATCC, CRL-1550), HEK293 (ATCC, CRL-1573), MDA-MB-231 (ATCC, CRM-HTB-26), and MKN28 (JCRB Cell Bank, 0253) cell lines were purchased from the American Type Culture Collection (ATCC, Manassas, VA, USA) or the Japanese Collection of Research Bioresources Cell Bank (JCRB Cell Bank, Osaka, JPN). CUMC6 cell lines were obtained from Catholic University Medical College (Seoul, KOR). Yumm2.1 cell lines were kindly provided by Marcus W. Bosenberg of Yale University 69 . All cell lines were obtained between 2010 and 2022 and tested for mycoplasma using the Mycoplasma Detection Kit (Thermo Fisher Scientific, San Jose, CA, USA). The identities of the cell line were confirmed by short tandem repeat (STR) profiling by IDEXX Laboratories Inc. and were used within six months for testing. Generation and maintenance of the PD-1 blockade-resistant Yumm2.1 P3 22 and B16 P3 43 , and ACT-resistant CaSki P3 44 and MDA-MB-231 P3 17 cell lines have been previously described. The CaSki NANOG or HEK293 NANOG cell lines have also been previously described 17 . All cells were grown at 37℃ in a 5% CO 2 incubator with a humidified chamber. Chemical reagents The following chemical reagents were used in this study: Cycloheximide (Sigma-Aldrich, St. Louis, MO, USA, 01810), MG132 (Sigma-Aldrich, M8699), and KS15 (Selleckchem, Houston, TX, USA, HY-115672). The chemical reagents we used were dissolved in dimethyl sulfoxide (DMSO). siRNA constructs The following synthetic small interfering RNAs (siRNAs) were produced by Bioneer (Daejeon, KOR): Non-specific GFP (green fluorescent protein), 5’-GCAUCAAGGUGAACUUCAA-3’ (sense), 5’-UUGAAGUUCACCUUGAUGC-3’ (antisense); mouse Cry1 no. 1, 5’-CUCUGUCUGAUGACCAUGA-3’ (sense), 5’-UCAUGGUCAUCAGACAGAG-3’ (antisense); mouse Cry1 no. 2, 5’-GACAGUCAGCAGACUCACU-3’ (sense), 5’-AGUGAGUCUGCUGACUGUC-3’ (antisense); human CRY1 no. 1, 5’-CAGUGUAGUAAACACACUU-3’ (sense), 5’-AAGUGUGUUUACUACACUG-3’ (antisense) ; human CRY1 no. 2, 5’-CUCUGUCUGAUGACCAUGA-3’ (sense), 5’-UCAUGGUCAUCAGACAGAG-3’ (antisense) ; human NANOG , 5’-GCAACCAGACCUGGAACAA-3’ (sense), 5’-UUGUUCCAGGUCUGGUUGC-3’ (antisense); human APC3 no. 1, 5’-CGACUCUUUACUAGUGACA-3’ (sense), 5’-UGUCACUAGUAAAGAGUCG-3’ (antisense); human APC3 no. 2, 5’-ACAGAUCAUGGGAACAGAU-3’ (sense), 5’-AUCUGUUCCCAUGAUCUGU-3’ (antisense); human TRIM17 no. 1, 5’-CAGAGUUCCCGGACAGAUU-3’ (sense), 5’-AAUCUGUCCGGGAACUCUG-3’ (antisense); human TRIM17 no. 2, 5’-GAUCACCAGGACAGGGAAU-3’ (sense), 5’-AUUCCCUGUCCUGGUGAUC-3’ (antisense). For in vitro delivery, the cells were transfected with 100 pmol of siRNAs using Lipofectamine 2000 (Invitrogen, San Jose, CA, USA, 11668027) according to the manufacturer’s instructions. DNA constructs and site-directed mutagenesis The pMSCV- FLAG-NANOG WT and pMSCV- FLAG-NANOG MT plasmids have been previously described. To generate the pGL3- CRY1 promoter, the promoter region of the CRY1 gene was isolated by PCR from genomic DNA extracted from CaSki cells using the following primer set, 5’- CCCCTCGAGCAATTCAACCAATAAGAATT-3’ (forward) and 5’- TTTAAGCTTACTACACTGGCTCGGAGGGG-3’ (reverse). The PCR products were digested with XhoⅠ and HindⅢ and subcloned into the XhoⅠ/HindⅢ restriction sites of the pGL3-Basic vector (Promega, Madison, WI, USA, E1751). To generate mutations in the NANOG binding site of the CRY1 promoter region, we used the pGL3-CRY1 promoter plasmid as a template and replaced the conserved NANOG binding sequence AATGA with ATATA. Site-directed Mutagenesis was performed using a QuickChange XL Site-directed Mutagenesis Kit (Stratagene, San Diego, CA, USA, 200516) according to the manufacturer’s instructions. Mutations were verified by DNA sequencing. Real-time quantitative RT-PCR Total RNA from the cells was purified using RNeasy Micro Kit (Qiagen, Valencia, CA, USA, 74004) and cDNA was synthesized by reverse transcriptase (RT) using an iScript cDNA synthesis kit (Bio-Rad, Hercules, CA, USA, 1708891) according to the manufacturer’s recommended protocol. Real-time PCR was performed using IQ SYBR Green Super mix (Bio-Rad, 1708880) with the specific primers on a CFX96 real-time PCR detection system. Fold-change was calculated relative to the expression level of mRNA in the control cells. qPCR primers were purchased from Bioneer: mouse Clock , 5’-CCAAAGGCCAGCAGTGGATA-3’ (forward), 5’-TTGTCAGCAGCTGTCTCAGG-3’ (reverse); mouse Bmal1 , 5’-AATGAGCCAGACAACGAGGG-3’ (forward), 5’-GCTGTCGCCCTCTGATCTAC-3’ (reverse); mouse Cry1 , 5’-AACATTCCAGGGAAAGGTCCTG-3’ (forward), 5’-CTGCATCTCGTTCCTTCCCAA-3’ (forward), 5’-CTGCATCTCGTTCCTTCCCAA-3’ (reverse); mouse Cry2 , 5’-CCCTTCCTGTGTGGAAGACC-3’ (forward), 5’-CTCTGGGGTTGGCAACTCTG-3’; mouse Per1 , 5’-CCCAGGATGTGGGTGTCTTC-3’ (forward), 5’-GACCTCCTCTGATTCGGCAG-3’; mouse Per2 , 5’-ACGCAATGGGAAGGAGCTG-3’ (forward), 5’-CAGACTGCTCACTGCAGCC-3’ (reverse); mouse Per3 , 5’-TCCAGAGCATGGAACAGCAG-3’ (forward), 5’-TCTGTCTTCACAGGCGACAC-3’ (reverse); mouse β-actin , 5’-GATATCGCTGCGCTGGTCG-3’ (forward), 5’-CATTCCCACCATCACACCCT-3’ (reverse); human CRY1 , 5’-TGGGAATGGAGGCTTCATGG-3’ (forward), 5’-ACGTTTCCCACCACTGAGAC-3’ (reverse); human CDC20 , 5’-GACCGCTATATCCCCCATCG-3’ (forward) and 5’- GGCGTCTGGCTGTTTTCAGA-3’ (reverse); human APC3 , 5’- CTGCCCAACTCTTGCACAAC-3’ (forward) and 5’-TTGTGTCCTGGGGTGTTTCC-3’ (reverse); human CUL1 , 5’-GCCGTCAGAGTTGGAACGTA-3’ (forward) and 5’-TGTCGACGCCTGCAAAGTAT-3’ (reverse); human RBX1 , 5’- TGTCAAGCTAACCAGGCGTC − 3’ (forward) and 5’- AGCGAGAGATGCAGTGGAAG − 3’ (reverse); human SKP1 , 5’- CACCCACCACAAGGATGACC-3’ (forward) and 5’- TCTTGGTCCCAAACAGGGAT-3’ (reverse); human HUWE1 , 5’- GGAGAAGAAGGGCAGGATGC-3’ (forward) and 5’- GTGAGGTACGGAACAAGGCA-3’ (reverse); human BTRC , 5’-TGACCTCTGATGGCATGCTG-3’ (forward) and 5’- ACTGTCCCCATCCTCTTCGT-3’ (reverse); human FBW7 , 5’-GTTTGGTCAGCAGTCACAGGCA-3’ (forward) and 5’-CCACACTTTGAGTGTCCGATCTG-3’ (reverse); human TRIM17 , 5’- TCCCGGACAGATTGAAGTGC − 3’ (forward) and 5’- AGGGGTAAGCCACAAATCGG − 3’ (reverse); human β-ACTIN , 5’-CGACAGGATGCAGAAGGAG-3’ (forward) and 5’-TAGAAGCATTTGCGGTGGAC-3’ (reverse). All real-time quantitative PCR experiments were performed triplicate and quantification cycle (Cq) values were determined using Bio-Rad CFX96 Manager 3.0 software. Relative quantification of the mRNA levels was performed using the comparative Ct method with β-actin as the reference gene. Western blot analysis Lysate extracted from a total of 1 x 10 5 cells was used to perform Western blots analysis. Primary antibodies against mouse CRY1 (1:3000; Santa Cruz Biotechnology, Dallas, TX, USA, sc-5953), human CRY1 (1:3000; Bethyl Laboratories, Montgomery, TX, USA, A302-614A), human NANOG (1:3000; Bethyl Laboratories, A300-379A), FLAG (1:5000; Medical & Biological Laboratories, Nagoya, JPN, M185-3L), pAKT (T308) (1:3000; Cell Signaling Technology, Danvers, MA, USA, D25E6), AKT1 (1:3000; Cell Signaling, 9272), Cyclin A (1:3000; Santa Cruz Biotechnology, sc-239), MCL1 (1:3000; Santa Cruz Biotechnology, sc-819), APC3 (1:3000; Proteintech, Rosemont, IL, USA, 10918-1-AP), TRIM17 (1:3000; Proteintech, 13663-1-AP), HDAC1 (1:3000; Cell signaling Technology, 5356S), CXCL10 (1:1000, Invitrogen, 10H11L3), and β-actin (1:5000; MBL, Nagoya, JPN, M177-3) were used. Western blotting was followed by incubation with the appropriate secondary antibodies conjugated to horseradish peroxidase (HRP), anti-rabbit IgG-HRP (1:5000; Enzo, Farmingdale, NY, USA, ADI-SAB-300-J), and anti-mouse IgG-HRP (1:5000; Enzo, ADI-SAB-100-J). The immunoreactive bands were developed with the chemiluminescence ECL Detection System (GE Healthcare, Chicago, IL, USA), and signals were detected using a luminescent image analyzer (LAS-4000 Mini, Fujifilm, JPN). Quantitative ChIP (qChIP) assays The ChIP kit (Millipore, Burlington, MA, USA, 17–295) was employed according to the manufacturer’s instructions and the ChIP assay was performed as described previously 17 . Briefly, 1 x 10 7 cells (per assay) were bathed in 1% formaldehyde at 25℃ for 10 min for cross-linking of proteins and DNA and then lysed in sodium dodecyl sulfate buffer containing protease inhibitors. DNA was sheared by sonication using a Sonic Dismembrator Model 500 (Fisher Scientific, Pittsburgh, PA, USA). Immunoprecipitation was carried out by incubating with 1 µg of antibodies against FLAG (MBL, M185-3L), NANOG (Bethyl Laboratories, A300-379A), CRY1 (Bethyl Laboratories, A300-614A), HDAC1 (Cell signaling Technology, 5356S), AcH3K27 (Cell signaling Technology, 8173) or rabbit IgG (Millipore, PP64) for 16 hr and then the immunoprecipitated DNA was quantified by real-time qPCR using the following primer set: CRY1 , 5’-AAACAGCAAAGGTTAAGAGACAAA-3’ (forward) and 5’-GGCCATGGCATCCCTTAGAT-3’ (reverse); APC3 , 5’-TCCCATTTTTCCTCCCTTCACT-3’ (forward), 5’-AGCAGTGTAACAGAGAACGCT-3’ (reverse); TRIM17 , 5’-CAGAGCATTGGTCAGGGAGG-3’ (forward), 5’-ACAGAGGAGGGCTAGGACTG-3’ (reverse); CXCL10, 5’-CAGCCAGCAGGTTTTGCTAAG-3’ (forward), 5’-AGAAAACGTGGGCTAGTGT-3’ (reverse). Each sample was assayed in triplicate, and the amount of precipitated DNA was calculated at the percentage of the input sample. Tumor sphere-forming assay Cells were plated at 1 x 10 3 cells per well in six-well, super-low adherence vessels (Corning, Lowell, MA, USA, CLS3471) containing serum-free DMEM-F12 (Thermo Fisher Scientific, 12634010) supplemented with epidermal growth factor (20 ng/mL), basic fibroblast growth factor (20 ng/mL), and 1x B27. Medium was replaced every 3 days to replenish nutrients. Colonies more than 50 µm in diameter were counted under a microscope. CTL-mediated apoptosis assay Tumor cells were labeled with 10 µM CFSE (Molecular Probes, Eugene, OR, USA, 11524217) in DMEM supplemented with 0.1% FBS. The CFSE-labeled Yumm2.1 or CaSki cells were pulsed with OVA or MART1 peptide (10 µg/mL) for 1 h, respectively. The CFSE-labeled Yumm2.1 or CaSki cells were mixed with cognate OVA- or MART1-specific CD8 + CTLs at 1:1 ratio and incubated for 4 h at 37℃. In addition, the CFSE-labeled B16 cells were mixed with cognate GP100-specific CD8 + CTLs at the same condition as above. Cells were stained for active caspase-3 (BD biosciences, Franklin Lakes, NJ, USA, 560626) as an index of apoptosis and examined by flow cytometry as shown gating strategy in Supplementary Fig. 9. Granzyme B-mediated apoptosis assay Recombinant human granzyme B (Enzo, BML-SE238-5000) was mixed with BioPorter Reagent Sigma-Aldrich, BPQ24) at 25℃ for 5 min. The tumor cells were mixed with BioPorter-granzyme B complexes for 4 h at 37℃. Next, the cells were stained for active caspase-3 as index of apoptosis and examined by flow cytometry. Trypan blue exclusion assay To determine cell viability, a trypan blue exclusion assay was performed. Briefly, cells were seeded at 1 x 10 5 cells per well in 12-well plates 1 day prior to the assay. The treatments were added at the concentrations indicated in the figures. After 72 h, the cells were detached and stained with 0.4% trypan blue. Unstained cells were counted using a hemocytometer. The data are expressed as the percentage of unstained cells compared with the control cells not exposed to the chemical reagents. Luciferase assay To determine the promoter activity of CRY1 , luciferase assay was performed. Briefly, the reporter construct, pGL3 basic, pGL3- CRY1 WT, or pGL3- CRY1 MT together with pCMV-β-Gal, an internal control for transfection efficiency, were co-transfected into HEK293 cells using Lipofectamine 2000. After 24 h, cells were washed with phosphate-buffered saline (PBS) and lysed with Cell Culture Lysis Reagent (Promega, E1500). Luciferase activity was measured with a Turner Biosystems TD-20/20 luminometer after addition of 40 µL of luciferase assay reagent (Promega, E1500). Relative luciferase activity was normalized with the β-galactosidase activity in the cell lysate and calculated as an average of three independent experiments. Immunoprecipitation CaSki NANOG cells were lysed in NP40 lysis buffer (50 mM Tris-HCL, pH 8.0, 5 mM EDTA, 150 mM NaCl, 1% NP40, 1 mM PMSF) containing protease inhibitor. Immunoprecipitation was carried out by incubation with 1 µg of anti-CRY1 antibody (Bethyl Laboratories, A300-614A) or rabbit IgG (Millipore, PP64) for 16 h. The bound proteins were eluted by boiling in SDS sample buffer and were immunoblotted using anti-HDAC1 antibody (Cell signaling Technology, 5356S). Bioinformatic analyses from published clinical database To determine the clinical relevance of the NANOG-CRY1 axis in human cancer patients, we utilized a standard processing pipeline Gene Expression Profiling Interactive Analysis, version 2 (GEPIA2) ( http://gepia2.cancer-pku.cn ). The expression correlation between NANOG sig. and CRY1 was detected by the “Correlation Analysis” tool of GEPIA2, based on the datasets of the TCGA. To investigate the clinical relevance in patients treated with PD-1 blockade, we analyzed six published melanoma datasets, Riaz et al., (GEO accession number: GSE91061), Gide et al., (BioProject accession number: PRJEB23709), a published MGH cohort (GEO accession number: GSE115821), Hugo et al., (GEO accession number GSE78220), Liu et al., (dbGaP accession number: phs000452.v3.p1) and TCGA-SKCM, in which patients were treated with PD-1 blockade and pre-treatment biopsy samples were subject to RNA sequencing. Raw sequencing reads were quality-checked using FastQC 70 and trimmed using Trimmomatic to remove low-quality bases and adapter sequences 71 . The cleaned reads were then aligned to the human reference genome (GRCh38) using STAR aligner 72 . Gene-level read counts were obtained using HTSeq-count 73 . The count matrices from different cohorts were combined into a single dataset. DESeq2 was used for data normalization and to account for differences in sequencing depth across samples. The merged count data were transformed using the variance stabilizing transformation (VST) function in DESeq2 to prepare for batch correction 70 , 74 . To mitigate batch effects arising from different experimental conditions and cohorts, we applied the ComBat function from the sva package to the VST-transformed data 75 , 76 . This step helped to remove unwanted variation while preserving biological differences of interest. In addition, gene expression data for human cancer patients profiled by TCGA were collected from the Firehose BROAD GDAC data repository ( https://gdac.broadinsitute.org ). Clinical data were also retrieved from the same source. The stemness, T cell-mediated anti-tumor response, and T cell infiltration gene expression signatures were previously defined 56 , 57 , 58 , 59 . We used the single-sample gene set enrichment analysis (GSEA) algorithm, implemented in R package’s gene set variation analysis (GSVA), to calculate the stemness, T cell-mediated anti-tumor response, and T cell infiltration signature scores for each sample. The default parameters from the GSVA package were used. To obtain the refined signature score for resistance to T cell-mediated anti-tumor response, we multiplied the T cell-mediated anti-tumor response signature score by a negative number. The poor T cell infiltration signature score was obtained using the same method. Spearman’s correlation was used to quantify the association between CRY1 activity score, stemness, resistance to T cell-mediated anti-tumor response, and poor T cell infiltration scores individually for each tumor type. The association between CRY1 activity score and survival was evaluated by Cox regression and Kaplan-Meier analyses. The 50th percentile was used as cutoff thresholds. Tumor treatment experiments To characterize the in vivo resistance to PD-1 blockade conferred by CRY1, C57BL/6 mice were inoculated subcutaneously with 1 x 10 5 Yumm2.1 P3 cells or 1 x 10 5 B16F10 cells per mouse. Nine days following the tumor challenge, DMSO- or KS15 (0.01 mg/kg)-loaded chitosan hydrogel was administered via intratumoral injection for a day before anti-PD-1 (BioXcell, Lebanon, NH, USA) or isotype antibody control that was administrated via intraperitoneal injection every 3 days at a dose of 100 µg per mouse in accordance with the schedule described in Supplementary Fig. 9. This treatment regimen was repeated for 2 cycles. To characterized the in vivo resistance to adoptive CTL transfer conferred by CRY1, NOD/SCID mice were inoculated subcutaneously with 1 x 10 5 MDA-MB-231 P3 cells per mice. Six days following the tumor challenge, DMSO- or KS15 (0.01 mg/kg)-loaded chitosan hydrogel was administered via intratumoral injection for a day before adoptive transfer with MART-1-specitic CTLs in accordance with the schedule described in Supplementary Fig. 10a. This treatment regimen was repeated for 3 cycles. Mice were handled and monitored for tumor burden and survival under the protocol permitted by the Korea University Institutional Animal Care and Use Committee (KUIACUC-2022-0005). Tumor size was measured before the tumors were smaller than, or at about 10% of mice body weight, the maximal tumor size/burden permitted by KUIACUC. In some cases, this limit has been reached on the last day of tumor size measurement and the mice were immediately euthanized. Tumor digestion, cell isolation, and flow cytometric analysis To analyze the immune cells in tumor, treated mice were euthanized on day 18 following tumor inoculation and the tumors were harvested. The tumors were dissected into fragments by cutting and digested by collagenase (0.5 mg/mL, Millipore) and DNase (1 µg/mL, Millipore) at 37℃ for 45 min. The digested samples were then filtered through a 70 µm cell strainer and washed with PBS buffer. The cell pellets incubated with red blood cell (RBC) lysis buffer to lyse the RBCs. The cell suspensions were stained for the intracellular and extracellular protein markers of interest, and the stained samples were assessed on a flow cytometer (BD biosciences) along with CellQuest Pro software. The following staining antibodies used: anti-CD3, anti-CD8, anti-granzyme B, and anti-active caspase-3 (all from BD biosciences). Statistics All data shown are representative of at least three separate experiments. Statistical differences were calculated by either Student’s t-test (two-tailed, unpaired), one-way ANOVA, or two-way ANOVA using GraphPad Prism software version 10. Results with two-tailed p values of < 0.05 were considered statistically significant. Declarations Conflict of interest statement: The authors have declared that no conflicts of interest exist. Data availability Transcriptomic data from patients with melanoma classified as responders or nonresponders to PD-1 blockade are available in the NCBI’s Gene Expression Omnibus (GEO) database (Riaz et al. cohort; GSE91061), (MGH cohort; GSE115821), (Hugo et al. cohort; GSE78220); the European Nucleotide Archive (ENA) (Gide et al. cohort; PRJEB23709); and the Database of Genotypes and Phenotypes (dbGap) (Liu et al. cohort; phs000452.v3.p1). Transcriptomic data from TCGA were deposited in the Firehose BROAD GDAC data repository portal ( https://gdac.broadinsitute.org/ ). The gating strategy is provided in Supplementary Fig. 9. The raw images for the immunoblots are provided in Supplementary Fig. 10. The remaining data are available within the article and Supplementary information. Source data are provided with this paper. Competing interests The authors declare no competing interest. Author contributions Study concept and design: S.J.O., S.R.W., K.-H.S., and T.W.K.; acquisition of data: S.J.O., S.R.W., J.H.A., M.K.S., H.-J.L., and E.H.C.; analysis and interpretation of the data: S.J.O., S.R.W., K.-H.S., and T.W.K.; technical or other material support: K.-M.L., Y.J.P., Y.J.S., C. Y., G.H.S., J.-W.J.; writing and review of the manuscript: S.J.O., S.R.W., K.-H.S., and T.W.K. Acknowledgements This work was funded by the National Research Foundation of Korea (NRF-2022R1A4A2000827, to T.W. Kim; and RS-2023-00280965, to T.W. Kim; NRF-2022R1A4A5032702, to K.-H. Song; and RS-2024-00457721, to K.-M. Lee). References Huang AC, Zappasodi R (2022) A decade of checkpoint blockade immunotherapy in melanoma: understanding the molecular basis for immune sensitivity and resistance. 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Medicine","correspondingAuthor":false,"prefix":"","firstName":"Kwon-Ho","middleName":"","lastName":"Song","suffix":""}],"badges":[],"createdAt":"2024-12-17 06:20:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5658722/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5658722/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":73271959,"identity":"36a08351-2a20-4b58-a2a1-c9c51ba825be","added_by":"auto","created_at":"2025-01-08 11:08:12","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":543959,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCRY1 repression enhances the response to PD-1 blockade by reducing immune-refractory properties.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea\u003c/strong\u003e mRNA levels of core circadian factors in parental (P0) and PD-1 blockade-resistant (P3) tumor cells was determined by qRT-PCR. \u003cstrong\u003eb\u003c/strong\u003e The CRY1 protein level in P0 and P3 cells was determined by Western blot. \u003cstrong\u003ec\u003c/strong\u003e Protein levels of CRY1 and β-actin in P3 cells transfected with indicated siRNA determined by Western blot. \u003cstrong\u003ed\u003c/strong\u003e and \u003cstrong\u003ee\u003c/strong\u003e Tumor growth (\u003cstrong\u003ed\u003c/strong\u003e) and tumor mass (\u003cstrong\u003ee\u003c/strong\u003e) of Yumm2.1 P3 (top) or B16 P3 (bottom) tumor-bearing mice administrated with indicated reagents. \u003cstrong\u003ef\u003c/strong\u003e The growth of tumors in mice administered with indicated reagents and inoculated with a control IgG or a CD8 depletion antibody. \u003cstrong\u003eg\u003c/strong\u003e and \u003cstrong\u003eh\u003c/strong\u003e Flow cytometry analysis of the frequency of apoptotic (active caspase-3\u003csup\u003e+\u003c/sup\u003e) cells in indicated cells treated with indicated siRNAs after with or without co-culture with tumor-specific CTLs at a 1:1 ratio (\u003cstrong\u003eg\u003c/strong\u003e) or intracellular delivery of GrB (\u003cstrong\u003eh\u003c/strong\u003e) for 4 h. \u003cstrong\u003ei\u003c/strong\u003e Transwell-based T cell chemotaxis assay by using conditioned media from indicated cells treated with indicated reagents. β-actin was included as an internal loading control. Th numbers below each lane in blot images indicates the protein level relative to that in P0 (\u003cstrong\u003eb\u003c/strong\u003e) or cells transfected with siGFP (\u003cstrong\u003ec\u003c/strong\u003e). All experiments were performed in triplicate. For the \u003cem\u003ein vivo\u003c/em\u003e experiments, 5 mice from each group were used. The p-values were calculated by two-tailed Student’s \u003cem\u003et\u003c/em\u003e test. (\u003cstrong\u003ea)\u003c/strong\u003e, one-way ANOVA (\u003cstrong\u003ee\u003c/strong\u003e and \u003cstrong\u003ei)\u003c/strong\u003e, or two-way ANOVA (\u003cstrong\u003ed\u003c/strong\u003e, and \u003cstrong\u003ef\u003c/strong\u003e-\u003cstrong\u003eh).\u003c/strong\u003e Data represent the mean ±SD. Source data are provided as a Source Data file.\u0026nbsp;\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-5658722/v1/0b765e91d644b2e29e56d758.png"},{"id":73272005,"identity":"3965e4b4-b7c2-4cae-bdab-a6c26112f446","added_by":"auto","created_at":"2025-01-08 11:08:14","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":188890,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCRY1 is required for multiple immune-refractory properties of ACT-resistant tumor cells.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea \u003c/strong\u003emRNA levels of core circadian factors in CaSki P0 and CaSki P3 cells was determined by qRT-PCR.\u003cstrong\u003e b\u003c/strong\u003e CRY1 and β-actin proteins detected by Western blot. \u003cstrong\u003ec\u003c/strong\u003e-\u003cstrong\u003ef\u003c/strong\u003e CaSki P3 cells were transfected with siRNA targeting \u003cem\u003eGFP\u003c/em\u003e or \u003cem\u003eCRY1 \u003c/em\u003eas indicated. Levels of indicated proteins detected by Western blot (\u003cstrong\u003ec\u003c/strong\u003e) and the frequency of apoptotic (active caspase-3\u003csup\u003e+\u003c/sup\u003e) cells with or without co-culture with tumor-specific CTLs at a 1:1 ratio (\u003cstrong\u003ed\u003c/strong\u003e) or intracellular delivery of GrB (\u003cstrong\u003ee\u003c/strong\u003e) for 4 h determined by flow cytometry anaylsis, and Transwell-based T cell chemotaxis assay using conditioned media from indicated cells (\u003cstrong\u003ef\u003c/strong\u003e). β-actin was included as an internal loading control and the number below each lane in blot images indicates the protein level relative to that in P0 cells (\u003cstrong\u003eb\u003c/strong\u003e) or cells transfected with \u003cem\u003esiGFP\u003c/em\u003e (\u003cstrong\u003ec\u003c/strong\u003e). All experiments were performed in triplicate. The p-values were calculated by two-tailed Student’s \u003cem\u003et\u003c/em\u003e test. (\u003cstrong\u003ea)\u003c/strong\u003e, one-way ANOVA (\u003cstrong\u003ef)\u003c/strong\u003e, or two-way ANOVA (\u003cstrong\u003ed\u003c/strong\u003e and \u003cstrong\u003ee\u003c/strong\u003e). Data represent the mean ±SD. Source data are provided as a Source Data file.\u0026nbsp;\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-5658722/v1/4d37c0376a115d096d53fec2.png"},{"id":73271943,"identity":"367e7d64-8eb7-415b-b299-fbca34752913","added_by":"auto","created_at":"2025-01-08 11:08:10","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":403080,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNANOG directly regulates CRY1 through promoter occupancy.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea\u003c/strong\u003e and \u003cstrong\u003eb\u003c/strong\u003e CaSki P3 cells were transfected with siRNA-targeting \u003cem\u003eGFP\u003c/em\u003e or \u003cem\u003eNANOG\u003c/em\u003e then levels of NANOG and CRY1 protein were analyzed by Western blot (\u003cstrong\u003ea\u003c/strong\u003e) and the \u003cem\u003eCRY1\u003c/em\u003e mRNA expression was analyzed by qRT-PCR (\u003cstrong\u003eb\u003c/strong\u003e). \u003cstrong\u003ec\u003c/strong\u003e and \u003cstrong\u003ed\u003c/strong\u003e CaSki P0 cells were stably transfected with empty vector (\u003cem\u003eno insert\u003c/em\u003e) or NANOG-expressing vector (\u003cem\u003eNANOG) \u003c/em\u003ethen levels of NANOG and CRY1 protein were detected by Western blot (\u003cstrong\u003ec\u003c/strong\u003e) and the \u003cem\u003eCRY1\u003c/em\u003e mRNA expression was analyzed by qRT-PCR (\u003cstrong\u003ed\u003c/strong\u003e). \u003cstrong\u003ee\u003c/strong\u003e and \u003cstrong\u003ef\u003c/strong\u003e HEK293 cells were stably transfected with a vector without insert (\u003cem\u003eno insert\u003c/em\u003e) or expressing \u003cem\u003eNANOG\u003c/em\u003e wild type (\u003cem\u003eNANOG\u003c/em\u003e WT) or \u003cem\u003eNANOG\u003c/em\u003e mutant (\u003cem\u003eNANOG\u003c/em\u003e MT) then levels of NANOG and CRY1 protein were analyzed by Western blot (\u003cstrong\u003ee\u003c/strong\u003e) and the \u003cem\u003eCRY1\u003c/em\u003e mRNA expression was analyzed by qRT-PCR (\u003cstrong\u003ef\u003c/strong\u003e). \u003cstrong\u003eg\u003c/strong\u003e Diagram illustrating the \u003cem\u003eCRY1\u003c/em\u003e promoter region, -1250 to +100 from the transcription starting site (+1), containing the NANOG binding element, sequences of the wild type and mutant NANOG binding element in reporters, and locations of primers (arrows) used for ChIP. \u003cstrong\u003eh\u003c/strong\u003e Luciferase activity in HEK293 cells transfected with \u003cem\u003eno insert\u003c/em\u003e, \u003cem\u003eNANOG\u003c/em\u003e WT or \u003cem\u003eNANOG\u003c/em\u003e MT plasmids together with pGL3-\u003cem\u003eCRY1\u003c/em\u003e WT or MT plasmid. \u003cstrong\u003ei\u003c/strong\u003eFLAG-NANOG on the \u003cem\u003eCRY1\u003c/em\u003e promoter region in HEK293 cells transfected with \u003cem\u003eno insert\u003c/em\u003e or \u003cem\u003eFLAG-NANOG\u003c/em\u003e WT assessed by qChIP-PCR analysis. The value represents the percentage relative to the input. \u003cstrong\u003ej\u003c/strong\u003e Relative binding of NANOG on the \u003cem\u003eCRY1\u003c/em\u003e promoter in CaSki P0 and P3 cells analyzed by ChIP. \u003cstrong\u003ek\u003c/strong\u003e The correlation between the \u003cem\u003eNANOG\u003c/em\u003e sig. and \u003cem\u003eCRY1 \u003c/em\u003eexpression in indicated TCGA cohorts analyzed by the “Correlation Analysis” tool of GEPIA2. β-actin was included as an internal loading control. Numbers below blot images indicate the expression as measured by fold change \u003cstrong\u003ea\u003c/strong\u003e, \u003cstrong\u003ec\u003c/strong\u003e and \u003cstrong\u003ee\u003c/strong\u003e. All experiments were performed in triplicate. The p-values were calculated using two-tailed Student’s \u003cem\u003et\u003c/em\u003etest. (\u003cstrong\u003eb, d\u003c/strong\u003e, \u003cstrong\u003ej\u003c/strong\u003e and \u003cstrong\u003ek\u003c/strong\u003e), one-way ANOVA (\u003cstrong\u003ef\u003c/strong\u003e), or two-way ANOVA (\u003cstrong\u003eh\u003c/strong\u003e and \u003cstrong\u003ei\u003c/strong\u003e). Data represent the mean ±SD. Source data are provided as a Source Data file.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-5658722/v1/a22def8ddd9df162db85dd90.png"},{"id":73271960,"identity":"a1b62901-d423-474e-9303-fc10f93065b9","added_by":"auto","created_at":"2025-01-08 11:08:12","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":603743,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCRY1 confers stem-like properties and resistance to CTL-mediated killing through epigenetic repression of APC3 and TRIM17 by HDAC1.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea\u003c/strong\u003e-\u003cstrong\u003el\u003c/strong\u003e CaSki \u003cem\u003eNANOG\u003c/em\u003e cells were transfected with siRNA-targeting \u003cem\u003eGFP\u003c/em\u003e or \u003cem\u003eCRY1. \u003c/em\u003e\u003cstrong\u003ea\u003c/strong\u003e The levels of CRY1, pAKT, AKT, Cyclin A, and MCL1 proteins measured by Western blot. \u003cstrong\u003eb\u003c/strong\u003e \u003cem\u003eCyclin A\u003c/em\u003e and \u003cem\u003eMCL1\u003c/em\u003e mRNA expression analyzed by qRT-PCR. \u003cstrong\u003ec\u003c/strong\u003e Western blot for indicated proteins in cell lysates from cells treated with cycloheximide (CHX) for the indicated times (left) and graphs (right) representing the means ±SD of three quantified data, after normalization to the corresponding β-actin level. \u003cstrong\u003ed\u003c/strong\u003e Indicated proteins in lysates from cells with or without MG132 (10 μM, for 8 h) treatment detected by Western blot. \u003cstrong\u003ee\u003c/strong\u003e \u003cem\u003eAPC3, RBX1,\u003c/em\u003e and \u003cem\u003eTRIM17 \u003c/em\u003emRNA expression was measured by qRT-PCR. \u003cstrong\u003ef\u003c/strong\u003e-\u003cstrong\u003ei\u003c/strong\u003e The cells were transfected with indicated siRNA. \u003cstrong\u003ef\u003c/strong\u003e and \u003cstrong\u003eg \u003c/strong\u003eThe levels of indicated proteins were analyzed by Western blot. \u003cstrong\u003eh\u003c/strong\u003e Sphere-forming capacity of the cells in low-density suspension culture. \u003cstrong\u003ei\u003c/strong\u003e Flow cytometry analysis of the frequency of apoptotic (active caspase-3\u003csup\u003e+\u003c/sup\u003e) cells after intracellular delivery of GrB for 4 h. \u003cstrong\u003ej\u003c/strong\u003e and \u003cstrong\u003ek\u003c/strong\u003e Cell lysates were immunoprecipitated with an anti-CRY1 antibody, followed by western blot using anti-CRY1 and anti-HDAC1 antibodies. \u003cstrong\u003el\u003c/strong\u003e Relative occupancy of CRY1, HDAC1, and AcH3K27 in the \u003cem\u003eAPC3\u003c/em\u003e or \u003cem\u003eTRIM17 \u003c/em\u003epromoter was assessed by qChIP-PCR analysis. The ChIP data values represent ratios relative to the input. β-actin was included as an internal loading control. Numbers below blot images indicate the expression as measured by fold change \u003cstrong\u003ea\u003c/strong\u003e, \u003cstrong\u003ec\u003c/strong\u003e,\u003cstrong\u003e d\u003c/strong\u003e, \u003cstrong\u003ef,\u003c/strong\u003e and \u003cstrong\u003eg\u003c/strong\u003e. All experiments were performed in triplicate. The p-values were calculated by two-tailed Student’s \u003cem\u003et\u003c/em\u003e test. (\u003cstrong\u003eb \u003c/strong\u003eand \u003cstrong\u003ee\u003c/strong\u003e), one-way ANOVA (\u003cstrong\u003eh \u003c/strong\u003eand \u003cstrong\u003el\u003c/strong\u003e), or two-way ANOVA (\u003cstrong\u003ei\u003c/strong\u003e). Data represent the mean ±SD. Source data are provided as a Source Data file.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-5658722/v1/03df5dc271ea817291995700.png"},{"id":73271971,"identity":"916831fe-3ae6-416f-8459-c653b9318d57","added_by":"auto","created_at":"2025-01-08 11:08:13","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":174915,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCRY1 reduces T cell infiltration through HDAC1-mediated CXCL10 repression.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea\u003c/strong\u003e-\u003cstrong\u003ed\u003c/strong\u003eCaSki \u003cem\u003eNANOG\u003c/em\u003e cells were transfected with siRNA-targeting \u003cem\u003eGFP\u003c/em\u003e or \u003cem\u003eCRY1.\u003c/em\u003e \u003cstrong\u003ea\u003c/strong\u003e The levels of CRY1, and CXCL10 proteins were detected by Western blot. β-actin was included as an internal loading control. Numbers below blot images indicate the expression as measured by fold change.\u003cstrong\u003eb\u003c/strong\u003e The \u003cem\u003eCXCL10\u003c/em\u003e mRNA expression was analyzed by qRT-PCR. \u003cstrong\u003ec\u003c/strong\u003e Relative occupancy of CRY1, HDAC1, and AcH3K27 in the \u003cem\u003eCXCL10 \u003c/em\u003epromoter was assessed by qChIP-PCR analysis. The ChIP data values represent ratios relative to the input. \u003cstrong\u003ed\u003c/strong\u003e Transwell-based T cell chemotaxis assay by using conditioned media from indicated cells were treated with IgG or anti-CXCL10 antibodies. All experiments were performed in triplicate. The p-values were calculated using two-tailed Student’s \u003cem\u003et\u003c/em\u003e test. (\u003cstrong\u003eb\u003c/strong\u003e) or one-way ANOVA (\u003cstrong\u003ec \u003c/strong\u003eand \u003cstrong\u003ed\u003c/strong\u003e). Data represent the mean ±SD. Source data are provided as a Source Data file.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-5658722/v1/3d540bba2a308ebb3bab6926.png"},{"id":73271973,"identity":"53950690-cb46-4d33-8334-b6ed0f703495","added_by":"auto","created_at":"2025-01-08 11:08:13","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":509723,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe CRY1 activity is associated with the multiple immune-refractoriness in patients with cancer\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea\u003c/strong\u003e Venn diagram showing the overlap between genes downregulated in non-responders compared to responders to PD-1 blockade in integrated transcriptomic data set of melanoma patients and genes bound by CRY1. \u003cstrong\u003eb\u003c/strong\u003e Comparison of CRY1 activity scores between R (n = 96) and NR (n = 149) patients. The p-values were determined by two-tailed Student’s \u003cem\u003et\u003c/em\u003e test.\u003cstrong\u003e c\u003c/strong\u003e Kaplan-Meier analysis of overall survival (calculated as months to death or months to last follow-up) and median cutoff values for CRY1 activity score levels (CRY1 activity score\u003csup\u003ehigh\u003c/sup\u003e \u0026gt; median; CRY1 activity score\u003csup\u003eLow\u003c/sup\u003e \u0026lt; median, \u003cem\u003eP\u003c/em\u003e = 0.0072). The p-values were determined by Gehan-Breslow-Wilcoxon test. \u003cstrong\u003ed\u003c/strong\u003e and \u003cstrong\u003ee\u003c/strong\u003e Pearson’s correlation between the CRY1 activity score and indicated gene signatures in integrated melanoma patients. \u003cstrong\u003ef\u003c/strong\u003e Correlation plot of the CRY1 activity score and indicated gene signatures in indicated tumor types in the TCGA dataset. Correlation and two-tailed \u003cem\u003eP\u003c/em\u003e values were assessed using the Pearson’s correlation coefficient and the unpaired Student’s \u003cem\u003et\u003c/em\u003e test. Source data are provided as a Source Data file.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-5658722/v1/d472025d01f28b6fff18d086.png"},{"id":73271964,"identity":"7168fb22-9ded-447a-ae93-40037583a074","added_by":"auto","created_at":"2025-01-08 11:08:13","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":779787,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe NANOG/CRY1 axis is strongly associated with multiple immune-refractoriness across multiple cancer types\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea\u003c/strong\u003e Comparisons of levels of the MIR gene signature according to combinations of \u003cem\u003eNANOG\u003c/em\u003e and CRY1 activity score signature expression in pan-tumor types. \u003cstrong\u003eb\u003c/strong\u003e The viability of indicated tumor cells transfected with \u003cem\u003esiGFP\u003c/em\u003e or \u003cem\u003esiNANOG\u003c/em\u003e for 16 hours then treated with the indicated concentration of KS15 for 48 h measured by counting live cells using trypan blue. \u003cstrong\u003ec\u003c/strong\u003e-\u003cstrong\u003ef \u003c/strong\u003eMDA-MB-231 P3 (top), CUMC6 (middle), or MKN28 (bottom) cells were treated with DMSO or KS15. \u003cstrong\u003ec\u003c/strong\u003e Levels of Cyclin A, MCL1, and CXCL10 were analyzed by Western blot. β-actin was included as an internal loading control. Numbers below blot images indicate the expression as measured by fold change. \u003cstrong\u003ed\u003c/strong\u003e Sphere-forming capacity of the cells in low-density suspension culture. \u003cstrong\u003ee\u003c/strong\u003e Flow cytometry analysis of the frequency of apoptotic (active caspase-3\u003csup\u003e+\u003c/sup\u003e) cells after intracellular delivery of GrB for 4 h. \u003cstrong\u003ef\u003c/strong\u003e Transwell-based T cell chemotaxis assay by using tumor cells-derived conditioned media. All experiments were performed in triplicate. The p-values were calculated using two-tailed Student’s \u003cem\u003et\u003c/em\u003e test. (\u003cstrong\u003ed\u003c/strong\u003e and \u003cstrong\u003ef\u003c/strong\u003e), one-way ANOVA (\u003cstrong\u003ea\u003c/strong\u003e), or two-way ANOVA (\u003cstrong\u003ee\u003c/strong\u003e). Data represent the mean ±SD. Source data are provided as a Source Data file.\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-5658722/v1/5426ffb5582c7d236ef125eb.png"},{"id":73271975,"identity":"1b7017df-83f8-47b0-a349-0d500d78e23f","added_by":"auto","created_at":"2025-01-08 11:08:14","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":301722,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCRY1 inhibition renders tumors susceptible to PD-1 blockade-mediated anti-tumor response\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea \u003c/strong\u003eSchematic of the therapy regiment for C57BL/6 mice implanted with Yumm2.1 P3 tumor cells were treated with indicated reagents. \u003cstrong\u003eb\u003c/strong\u003e Tumor growth, \u003cstrong\u003ec\u003c/strong\u003e the mouse body weight, \u003cstrong\u003ed\u003c/strong\u003e tumor mass at 23 days after challenge, and \u003cstrong\u003ee\u003c/strong\u003e survival of mice. \u003cstrong\u003ef\u003c/strong\u003e Flow cytometry profiles of tumor-infiltrating CD3\u003csup\u003e+\u003c/sup\u003e and CD8\u003csup\u003e+\u003c/sup\u003e T cells. \u003cstrong\u003eg\u003c/strong\u003e The ratio of GrB\u003csup\u003e+\u003c/sup\u003e to tumor-infiltrating CD3\u003csup\u003e+\u003c/sup\u003e CD8\u003csup\u003e+\u003c/sup\u003e T cells. \u003cstrong\u003eh\u003c/strong\u003e Frequency of apoptotic cells in the tumors. For in vivo experiments, five mice from each group were used. Results in the graphs represent 3 independent experiments performed in triplicate. The p-values were calculated using one-way ANOVA (\u003cstrong\u003ec\u003c/strong\u003e, \u003cstrong\u003ed\u003c/strong\u003e and \u003cstrong\u003ef\u003c/strong\u003e-\u003cstrong\u003eh\u003c/strong\u003e),\u003cstrong\u003e \u003c/strong\u003etwo-way ANOVA (\u003cstrong\u003eb\u003c/strong\u003e)\u003cstrong\u003e, \u003c/strong\u003eor Gehan-Breslow-wilcoxon test (\u003cstrong\u003ee\u003c/strong\u003e)\u003cstrong\u003e. \u003c/strong\u003eData represent the mean ±SD. Source data are provided as a Source Data file.\u003c/p\u003e","description":"","filename":"image8.png","url":"https://assets-eu.researchsquare.com/files/rs-5658722/v1/19403ee3cb30c5d921aa1d56.png"},{"id":104808778,"identity":"92d6e6e2-a3b0-48f2-8ebe-d41e68e97aab","added_by":"auto","created_at":"2026-03-17 12:39:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5249863,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5658722/v1/106bdd63-d2b6-4d45-9460-1a13363eb7cb.pdf"},{"id":73271952,"identity":"8d35fedc-89bf-44c4-bd4e-d204394e23bf","added_by":"auto","created_at":"2025-01-08 11:08:12","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1650466,"visible":true,"origin":"","legend":"Supplementary Figures","description":"","filename":"OhetalSupplementaryinformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-5658722/v1/b41571d287cda71deb9930ee.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"CRY1 fuels resistance to T cell-based immunotherapy in NANOGhigh cancers","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCancer immunotherapies, such as immune checkpoint blockade (ICB) and adoptive T cell transfer (ACT), have significantly improved clinical outcomes of patients having various malignancies, including melanoma\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. However, many cancer patients do not achieve a durable response to immunotherapy, and resistance to these treatments remains a major clinical challenge. Several mechanisms of resistance to immunotherapy have been identified and can be broadly categorized as either tumor cell-intrinsic or tumor cell-extrinsic refractoriness\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Tumor cell-intrinsic mechanisms involve factors that hinder the recognition or destruction of tumor cells by cytotoxic T lymphocytes (CTLs), such as loss of antigen presentation and major histocompatibility complex (MHC) class I, defects in the interferon gamma (IFNγ) pathway, and resistance to apoptosis\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. On the other hand, tumor cell-extrinsic mechanisms typically involve features of the tumor microenvironment (TME) that characterize 'immune-refractory' or 'cold' tumors, including poor infiltration of CTLs and natural killer (NK) cells as well as the accumulation of suppressive myeloid cells\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Because addressing only intrinsic mechanisms or only extrinsic mechanisms may be insufficient to overcome resistance, identifying and targeting common pathways that regulate this multiple immune-refractoriness network is essential to improving the efficacy of immunotherapies.\u003c/p\u003e \u003cp\u003eEmerging evidence suggests that tumor-intrinsic signaling not only promotes tumor progression but also disrupts key processes essential for effective anti-tumor immunity, such as antigen processing and presentation, T cell-mediated killing, and T cell infiltration into tumors\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. In this regard, we have identified the embryonic transcription factor NANOG as a critical intrinsic factor that drives cancer stem cell (CSC)-like properties, renders tumor cells resistant to T cell cytotoxicity and impedes the trafficking of cytotoxic T cells to tumors through HDAC1-dependent regulation of MCL1 and CXCL10, respectively\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Importantly, HDAC1 inhibition in tumors restricts NANOG-driven refractory signaling, leading to increased T cell recruitment, sensitizing tumors to CTLs, and enhancing the effectiveness of PD-1 blockade in multiple mouse tumor models\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. These findings suggest that the NANOG/HDAC1 axis may be a common pathway driving both intrinsic and extrinsic refractoriness to PD-1 blockade. Although HDAC1 inhibition shows promise in overcoming multiple refractoriness to immunotherapy, it can affect not only cancer cells but also other cell types in the TME, where HDAC1 is broadly expressed. This could lead to widespread epigenetic changes, potentially causing significant side effects\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Therefore, it is crucial to identify tumor-selective targets that can inhibit the NANOG/HDAC1 axis to improve clinical outcomes.\u003c/p\u003e \u003cp\u003eThe circadian rhythm is an important regulatory system that maintains the homeostasis in normal cells and tissues, such as cell proliferation, survival, DNA repair, metabolism, and inflammation \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. To date, 14 circadian factors that interact to form a network with multiple feedback loops at the transcriptional and translational levels have been identified. Alterations in circadian-related genes can fundamentally disrupt basic cellular functions, which in turn increases the risk of diseases such as cancer \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. While many types of cancer cells still exhibit circadian clock oscillations, several studies have revealed dysregulation of core clock genes, including \u003cem\u003ePER1\u003c/em\u003e, \u003cem\u003ePER2\u003c/em\u003e, \u003cem\u003ePER3\u003c/em\u003e, \u003cem\u003eCRY1\u003c/em\u003e, \u003cem\u003eCRY2\u003c/em\u003e, \u003cem\u003eBMAL1\u003c/em\u003e, and \u003cem\u003eClOCK\u003c/em\u003e, in certain human cancers\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Emerging evidence also suggests that circadian clocks significantly influence the TME and the T cell-mediated antitumor immune response, thereby affecting the efficacy of cancer immunotherapy\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Among these core clock genes, cryptochrome 1 (CRY1) is a key circadian clock repressor and has been reported to be overexpressed in various cancers\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. Interestingly, recent studies have shown that CRY1 regulates pluripotent programs, including self-renewal capacity and metabolism, and induces resistance to genotoxic agents by regulating the DNA damage response, thereby promoting tumor growth\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. Importantly, although CRY1 is widely expressed in normal cells, studies indicate that \u003cem\u003eCry1\u003c/em\u003e deficiency does not affect embryonic development and \u003cem\u003eCry1\u003c/em\u003e knockout mice remain completely healthy without any noticeable phenotypes\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. Although the importance of CRY1 as a tumor-selective therapeutic target is growing, the potential relationship between CRY1's functions and resistance to immunotherapy has not been fully explored.\u003c/p\u003e \u003cp\u003eWe have previously developed highly ICB-resistant cell lines Yumm2.1 P3 and B16 P3 from the ICB-susceptible parental cell lines Yumm2.1 P0 and B16F0 P0, respectively\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. These P3 tumors are also resistant to CTL-mediated killing and exhibit non-T cell-inflamed immune phenotypes within the TME\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e, suggesting that both tumor-intrinsic and -extrinsic mechanisms drive the immune-refractory characteristics of these PD-1 blockade-resistant tumor models. We have also specifically generated CTL-resistant CaSki P3 cells from the CTL-susceptible parental CaSki P0 cells\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. Furthermore, we have demonstrated that overexpressing NANOG in immune-susceptible P0 tumor cells can replicate the multiple refractory properties of immunotherapy-resistant P3 cells by promoting CSC-like traits, increasing resistance to CTL-mediated killing, and reducing T cell infiltration\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. Using these and other preclinical models as well as human cancer datasets in this study, we demonstrate a critical role of CRY1 at the intersection of the NANOG/HDAC1 axis and the multiple refractory properties of immune-resistant tumors. Mechanistically, NANOG-induced transcription of CRY1 results in HDAC1-mediated epigenetic silencing of the E3 ubiquitin ligases APC3 and TRIM17, which in turn stabilize Cyclin A and MCL1 proteins and promote CSC-like properties and resistance to CTL-mediated killing, respectively. Additionally, CRY1 represses CXCL10 expression via HDAC1-mediated silencing, thereby suppressing T cell infiltration in NANOG\u003csup\u003ehigh\u003c/sup\u003e tumors. Furthermore, we show that CRY1 inhibition sensitizes tumors to T cell-based immunotherapy by making tumors more susceptible to CTL and shifting the TME from immune-refractory to immune-favorable. These findings provide proof-of-concept that targeting CRY1 could be a promising therapeutic strategy to overcome resistance to T cell-based therapies.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e \u003cb\u003eCRY1 expression in tumor cells contributes to PD-1 blockade resistance in a CD8\u003c/b\u003e \u003csup\u003e \u003cb\u003e+\u003c/b\u003e \u003c/sup\u003e \u003cb\u003eT cell-dependent manner\u003c/b\u003e\u003c/p\u003e \u003cp\u003eTo explore the relationship between circadian factors and ICB-resistance, we first measured the expression levels of core circadian genes, including \u003cem\u003eClock\u003c/em\u003e, \u003cem\u003eBmal1\u003c/em\u003e, \u003cem\u003eCry1\u003c/em\u003e, \u003cem\u003eCry2\u003c/em\u003e, \u003cem\u003ePer1\u003c/em\u003e, \u003cem\u003ePer2\u003c/em\u003e, and \u003cem\u003ePer3\u003c/em\u003e, in two independent preclinical models. Among these genes, the \u003cem\u003eCry1\u003c/em\u003e mRNA level was significantly elevated in both Yumm2.1 P3 and B16 P3 cells compared to their respective P0 counterparts (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). The elevated CRY1 expression in P3 cells than P0 cells was further confirmed by CRY1 protein levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). To assess CRY1's role in the ICB-refractory phenotypes of P3 tumor cells, we intravenously administered Yumm2.1 P3 or B16 P3 tumor-bearing mice with chitosan nanoparticles (CNPs) carrying either \u003cem\u003eCry1\u003c/em\u003e- or \u003cem\u003eGFP\u003c/em\u003e-targeting siRNA in combination with an anti-PD-1 antibody (Fib. 1c and Supplementary Fig.\u0026nbsp;1). While anti-PD-1 therapy alone had no effect on the tumor growth, the combination of anti-PD-1 antibody and \u003cem\u003eCry1\u003c/em\u003e-targeting siRNA CNPs significantly retarded the tumor growth (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed, e). To confirm the role of CD8\u003csup\u003e+\u003c/sup\u003e CTL in the observed therapeutic effect, we depleted CD8\u003csup\u003e+\u003c/sup\u003e T cells using an anti-CD8 antibody and found that the therapeutic benefits of the \u003cem\u003esiCry1\u003c/em\u003e and anti-PD-1 combination were significantly reduced (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ef). These findings suggested that CRY1 expression in tumor cells contributed to PD-1 blockade resistance in a CD8\u003csup\u003e+\u003c/sup\u003e T cell-dependent manner.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eCRY1 promotes PD-1 blockade resistance by regulating both tumor-intrinsic and extrinsic mechanisms\u003c/h2\u003e \u003cp\u003eTo investigate the role of CRY1 upregulation in the immune-refractory phenotypes, we silenced CRY1 in Yumm2.1 and B16 P3 cells. Interestingly, \u003cem\u003eCry1\u003c/em\u003e-silenced P3 cells were more susceptible to CTL-induced apoptosis compared to control P3 cells, without significant changes in the MHC class I expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eg and Supplementary Fig.\u0026nbsp;2). Silencing \u003cem\u003eCry1\u003c/em\u003e also increased the sensitivity of P3 cells to granzyme B (GrB), a key factor in CTL-mediated apoptosis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eh), suggesting that CRY1 protects PD-1 blockade-resistant P3 tumor cells from CTL-mediated killing independent of T cell recognition. Additionally, a transwell-based chemotaxis assay revealed enhanced T cell migration when incubated with conditioned media from \u003cem\u003esiCry1\u003c/em\u003e-silenced P3 cells compared to that from control cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ei), indicating that CRY1 may inhibit T cell infiltration by reducing the secretion of chemotactic factors. These findings suggested that CRY1 contributed to PD-1 blockade resistance by regulating both tumor-intrinsic and extrinsic mechanisms.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eCTL-mediated immune selection drives CRY1 upregulation in tumor cells\u003c/h3\u003e\n\u003cp\u003eGiven the essential role of tumor antigen-specific CTLs in the anti-tumor effects of anti-PD-1 therapy, we investigated whether CRY1 upregulation was also present in CTL-resistant human tumor cells. Interestingly, the CRY1 expression was upregulated in CTL-resistant CaSki P3 cells compared to CTL-susceptible CaSki P0 cells, while expression levels of other core circadian genes remained unchanged (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea, b). Additionally, \u003cem\u003eCRY1\u003c/em\u003e knockdown in CaSki P3 cells sensitized the cells to the cognate CTLs and GrB, and enhanced T cell migration (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec-\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ef). Our results confirmed that CRY1 expression was upregulated not only in PD-1 blockade-resistant tumor cells but also in CTL-resistant human tumor cells, suggesting that CTL-mediated immune selection was a key mechanism driving CRY1 upregulation following PD-1 blockade-mediated immune selection.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eNANOG directly upregulates CRY1 transcription to promote CSC-like and immune-refractory phenotypes\u003c/h3\u003e\n\u003cp\u003eWe sought to elucidate the mechanism responsible for CRY1 upregulation in tumor cells resistant to T cell-based immunotherapy. Given that NANOG upregulation has been observed in Yumm2.1 P3, B16 P3, and CaSki P3 cells\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e, we hypothesized that NANOG might be responsible for the transcriptional upregulation of CRY1. Notably, silencing NANOG in CaSki P3 cells reduced both CRY1 protein and mRNA levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea, b) whereas overexpressing NANOG in CaSki P0 cells increased both CRY1 protein and mRNA levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec, d), indicating that NANOG regulated the CRY1 expression. Importantly, NANOG's regulation of the CRY1 expression relied on its transcriptional activity, as overexpressing a transcriptionally inactive NANOG mutant (NANOG MT)\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e did not affect the CRY1 expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ee, f). We identified a potential NANOG-binding site in the \u003cem\u003eCRY1\u003c/em\u003e promoter, suggesting that NANOG might directly activate the \u003cem\u003eCRY1\u003c/em\u003e transcription (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eg). Using the luciferase reporter assay, we found that expressing the wild-type NANOG (NANOG WT), but not a NANOG MT (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eh), significantly increased the CRY1 promoter activity and that mutating the NANOG-binding site in the \u003cem\u003eCRY1\u003c/em\u003e promoter abolished the promoter activation by NANOG WT (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eh). Chromatin immunoprecipitation (ChIP) assays verified NANOG's direct binding to the \u003cem\u003eCRY1\u003c/em\u003e regulatory region (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ei) and showed a higher NANOG occupancy in CaSki P3 cells compared to CaSki P0 cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ej). Importantly, we found that the \u003cem\u003eNANOG\u003c/em\u003e signature (\u003cem\u003eNANOG\u003c/em\u003e sig.), a more reliable indicator of the \u003cem\u003eNANOG\u003c/em\u003e expression in tumor cells\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e, was positively correlated with the \u003cem\u003eCRY1\u003c/em\u003e expression across multiple tumor types in the TCGA cohort (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ek). These findings demonstrated that NANOG directly upregulated the CRY1 transcription by binding to its promoter, and that the NANOG/CRY1 axis was conserved across multiple human cancer types.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe then explored whether CRY1 was critical for NANOG-driven phenotypes and found that \u003cem\u003eCRY1\u003c/em\u003e knockdown in NANOG-overexpressing CaSki cells reduced CSC-like traits, increased sensitivity to GrB, and enhanced T cell infiltration (Supplementary Fig.\u0026nbsp;3). These results suggested that CRY1 played a key role in the CSC-like and immune-refractory characteristics driven by NANOG.\u003c/p\u003e \u003cp\u003e \u003cb\u003eCRY1 confers CSC-like property and resistance to CTL killing in NANOG\u003c/b\u003e \u003csup\u003e \u003cb\u003ehigh\u003c/b\u003e \u003c/sup\u003e \u003cb\u003etumor cells through HDAC1-mediated epigenetic repression of APC3 and TRIM17.\u003c/b\u003e\u003c/p\u003e \u003cp\u003eAlthough we previously demonstrated that NANOG upregulation of Cyclin A and MCL1 was AKT-dependent\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e, \u003cem\u003eCRY1\u003c/em\u003e knockdown did not affect AKT phosphorylation in NANOG-overexpressing CaSki cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). Moreover, \u003cem\u003eCRY1\u003c/em\u003e knockdown significantly reduced Cyclin A and MCL1 protein levels without altering their transcript levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea, b). A cycloheximide-chase assay revealed a reduced half-life of Cyclin A and MCL1 proteins in \u003cem\u003eCRY1\u003c/em\u003e-silenced compared to control NANOG-overexpressing CaSki cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec). Additionally, treatment with MG132 prevented the \u003cem\u003eCRY1\u003c/em\u003e knockdown-induced reduction of Cyclin A and MCL1, suggesting proteasome-mediated degradation of these proteins (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed). These findings indicated that CRY1 regulated the protein stability of Cyclin A and MCL1 in an AKT-independent manner.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIt has been reported that the Cyclin A and MCL1 protein levels were tightly controlled by proteasomal degradation through their ubiquitination by various E3 ubiquitin ligases and their cofactors \u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. We hypothesized that the CRY1-mediated accumulation of Cyclin A and MCL1 might result from repressive effects of CRY1 on the expression of genes involved in their degradation. To test this, we compared the expression of genes responsible for the negative regulation of Cyclin A and MCL1 in control and NANOG-overexpressing CaSki cells. Among the candidates, we found that \u003cem\u003eAPC3\u003c/em\u003e and \u003cem\u003eTRIM17\u003c/em\u003e were downregulated in NANOG-overexpressing compared to control CaSki cells (Supplementary Fig.\u0026nbsp;4) and that their expressions were restored following CRY1 knockdown (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ee). These results suggested that NANOG downregulated \u003cem\u003eAPC3\u003c/em\u003e and \u003cem\u003eTRIM17\u003c/em\u003e in a CRY1-dependent manner. To confirm that APC3 and TRIM17 negatively regulated Cyclin A and MCL1, we silenced APC3 or TRIM17 in control and \u003cem\u003eCRY1\u003c/em\u003e-knockdown CaSki \u003cem\u003eNANOG\u003c/em\u003e cells (Supplementary Fig.\u0026nbsp;5). The reduction of Cyclin A and MCL1 levels observed in NANOG-overexpressing CaSki cells after CRY1 knockdown was inhibited by \u003cem\u003eAPC3\u003c/em\u003e or \u003cem\u003eTRIM17\u003c/em\u003e knockdown (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ef, g). Consistently, the reduced sphere-forming capacity and increased susceptibility to GrB following \u003cem\u003eCRY1\u003c/em\u003e knockdown in NANOG-overexpressing CaSki cells were also reversed by \u003cem\u003eAPC3\u003c/em\u003e or \u003cem\u003eTRIM17\u003c/em\u003e-knockdown (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eh, i). These findings suggested that CRY1-mediated stabilization of Cyclin A and MCL1 was critical for NANOG-driven CSC-like properties and resistance to CTL-mediated killing.\u003c/p\u003e \u003cp\u003ePrevious studies have shown that HDAC1 is a key element in NANOG-mediated transcriptional repression\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e, and that CRYs can repress target gene expression by recruiting a repressive complex, including HDAC1, to the promoters of these genes \u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e. Based on these, we hypothesized that CRY1 might epigenetically repress the expression of APC3 and TRIM17 by regulating HDAC1 recruitment at their regulatory sites. Interestingly, CRY1 co-precipitated with HDAC1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ej), but this interaction was disrupted following the treatment with KS15 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ek), a CRY1 inhibitor that blocks its physiological interactions with other proteins\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e. Previously, we have identified H3K27 deacetylation as a potential epigenetic marker linked to the NANOG/HDAC1 axis\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. ChIP-qPCR analysis confirmed that NANOG overexpression reduced the AcH3K27 occupancies at the promoter regions of \u003cem\u003eAPC3\u003c/em\u003e and \u003cem\u003eTRIM17\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003el). In line with these findings, CRY1 and HDAC1 were more enriched at these gene promoters in NANOG-overexpressing CaSki cells decreased HDAC1 occupancies and increased AcH3K27 levels at the promoter regions of \u003cem\u003eAPC3\u003c/em\u003e and \u003cem\u003eTRIM17\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003el). Taken together, our results suggested that CRY1 downregulated \u003cem\u003eAPC3\u003c/em\u003e and \u003cem\u003eTRIM17\u003c/em\u003e expression through HDAC1-mediated epigenetic repression, leading to the stabilization of Cyclin A and MCL1, thereby promoting CSC-like properties and resistance to CTL-mediated killing in NANOG\u003csup\u003ehigh\u003c/sup\u003e tumor cells.\u003c/p\u003e \u003cp\u003e \u003cb\u003eCRY1 inhibits T cell infiltration by repressing CXCL10 expression through HDAC1-mediated epigenetic silencing in NANOG\u003c/b\u003e \u003csup\u003e \u003cb\u003ehigh\u003c/b\u003e \u003c/sup\u003e \u003cb\u003etumors.\u003c/b\u003e\u003c/p\u003e \u003cp\u003eGiven NANOG impairing T cell recruitment by suppressing the CXCL10 production \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e and CRY1\u0026rsquo;s role in NANOG-mediated suppression of T cell infiltration, we hypothesized that CRY1 might contribute to CXCL10 repression in NANOG-overexpressing cells. Indeed, \u003cem\u003eCRY1\u003c/em\u003e knockdown significantly increased CXCL10 protein and transcript levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea, b). Consistent with this, ChIP-qPCR analysis showed that NANOG overexpression reduced the AcH3K27 occupancy at the \u003cem\u003eCXCL10\u003c/em\u003e promoter, which was reversed by \u003cem\u003eCRY1\u003c/em\u003e knockdown (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec). Additionally, HDAC1 was more enriched at the \u003cem\u003eCXCL10\u003c/em\u003e promoter in NANOG-overexpressing compared to control CaSki cells, but \u003cem\u003eCRY1\u003c/em\u003e knockdown reduced the HDAC1 occupancy at this site (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec). To evaluate the role of CXCL10 in the suppression of T cell infiltration mediated by the NANOG/CRY1 axis, we performed antibody-mediated neutralization of CXCL10 in NANOG-overexpressing and control CaSki cells. The increased T cell infiltration observed with CRY1 knockdown was completely reversed by CXCL10 neutralization (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ed). Therefore, our results suggested that CRY1 repressed CXCL10 expression through HDAC1-mediated epigenetic silencing, thereby inhibiting T cell infiltration.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eCRY1 activity is associated with the multiple immune-refractory phenotypes to T cell-based immunotherapy\u003c/h3\u003e\n\u003cp\u003eBased on our observations, we further hypothesized that CRY1 might contribute to poor responses to T cell-based immunotherapies, including PD-1 blockade, in cancer patients. To investigate the potential link between CRY1 activity and immune-refractory phenotypes in patients resistant to PD-1 blockade, we first defined the CRY1 activity to provide a more reliable indicator of CRY1 function in tumors. Because CRY1 acts as a transcriptional repressor, we focused on CRY1-bound genes \u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e and filtered for those downregulated in non-responders (NR; patients with stable or progressive disease) compared to responders (R; patients with complete or partial response) to PD-1 blockade in an integrated transcriptomic dataset of melanoma patients\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e. We refined the CRY1 activity as a score by multiplying the average expression of CRY1-responsive genes by a negative number (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea). Notably, the CRY1 activity score was significantly higher in NR than in R (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb). Additionally, we found that melanoma patients with high CRY1 activity scores had significantly worse overall survival rates (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ec). These findings suggested that elevated CRY1 activity might contribute to resistance to PD-1 blockade, leading to poor clinical outcomes in melanoma patients.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe next investigated whether CRY1 contributed to multiple immune-refractory features in tumors, leading to resistance to immunotherapy in cancer patients. Tumors with either immune-favorable or immune-refractory features can be predicted by evaluating the expression signature scores of eight gene sets, which serve as indicators of stemness, resistance to T cell-mediated anti-tumor responses, and poor T cell infiltration, collectively referred to as multiple immune-refractoriness (MIR)\u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e. Interestingly, we found that the CRY1 activity score was strongly associated with gene signatures representing stemness, resistance to T cell-mediated anti-tumor responses, and poor T cell infiltration in an integrated dataset of melanoma patients treated with PD-1 blockade (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ed, e). These results were further validated across multiple tumor types in the TCGA cohort (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ef). Together, our findings suggested that CRY1 was linked to multiple immune-refractory phenotypes in various types of tumors, and that inhibiting CRY1 activity in tumor cells could potentially overcome resistance to T cell-based immunotherapies, including PD-1 blockade, by shifting the tumor's immune status from immune-refractory to immune-favorable.\u003c/p\u003e\n\u003ch3\u003eThe NANOG/CRY1 axis is conserved across multiple types of NANOG tumor cells\u003c/h3\u003e\n\u003cp\u003eHaving explored the molecular mechanism by which the NANOG/CRY1 axis contributes to multiple immune-refractory phenotypes to immunotherapy, we assessed its clinical relevance in human cancer patients. We found that patients with high levels (H) of both the \u003cem\u003eNANOG\u003c/em\u003e signature (\u003cem\u003eNANOG\u003c/em\u003e sig.) and CRY1 activity score (CRY1 as.) had a stronger association with MIR compared to those with low levels (L) of both (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ea). This suggested that the NANOG/CRY1 axis was closely linked to immunotherapy resistance and serves as an important prognostic factor across multiple cancer types.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo verify the functional roles of the NANOG/CRY1 axis in various human cancer cell types, we selected an additional ACT-resistant tumor model (MDA-MB-231 P3) and two NANOG-upregulated cancer cell lines (CUMC6 and MKN28)\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e. Compared to CaSki P0 cells, the three selected tumor cells expressed higher levels of both NANOG and CRY1 (Supplementary Fig.\u0026nbsp;6a). Notably, \u003cem\u003eCRY1\u003c/em\u003e knockdown significantly reduced Cyclin A and MCL1 levels and increased CXCL10 levels in these tumor cells (Supplementary Fig.\u0026nbsp;6b), indicating that the NANOG/CRY1 axis was conserved across all the cells tested. To evaluate the clinical potential of targeting NANOG\u003csup\u003ehigh\u003c/sup\u003e tumor cells with a CRY1 inhibitor, we assessed the viability of \u003cem\u003esiGFP\u003c/em\u003e- or \u003cem\u003esiNANOG\u003c/em\u003e-transfected tumor cells after \u003cem\u003ein vitro\u003c/em\u003e treatment with the CRY1 inhibitor KS15. We found that \u003cem\u003eNANOG\u003c/em\u003e knockdown reduced the sensitivity to KS15 in all tested tumor cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eb), suggesting that NANOG is a key mediator determining the susceptibility to CRY1 inhibition.\u003c/p\u003e \u003cp\u003eWe next evaluated the expression of effectors involved in the multiple immune-refractory phenotypes mediated by the NANOG/CRY1 axis. CRY1 inhibition with KS15 significantly reduced Cyclin A and MCL1 levels and increased CXCL10 levels compared to the control (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ec). Consistently, in all tested tumor cells, Consistently, in all tested tumor cells, CRY1 inhibition resulted in reduced CSC-like properties, increased sensitivity to GrB, and enhanced T cell infiltration compared to the control (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ed-\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ef). Together, these results demonstrated that the biochemical and functional properties of the NANOG/CRY1 axis were well conserved across various cancer cell types, and that CRY1 was an actionable target for controlling NANOG\u003csup\u003ehigh\u003c/sup\u003e immunotherapy-resistant tumor cells.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eCRY1 inhibition reverses resistance to T cell-based immunotherapy\u003c/h2\u003e \u003cp\u003eBased on our \u003cem\u003ein vitro\u003c/em\u003e observations, we hypothesized that \u003cem\u003ein vivo\u003c/em\u003e administration of KS15 could overcome resistance to T cell-based immunotherapy. To test this, we treated mice bearing PD-1 blockade-resistant Yumm2.1 P3 tumor with a combination of an anti-PD-1 antibody and KS15 (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ea). While anti-PD-1 antibody alone had no effect on the tumor growth, its combination with KS15 significantly inhibited the tumor growth without affecting the body weight (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eb-\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ed) and extended the survival of tumor-bearing mice (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ee). Additionally, these results were also reproduced in mice bearing B16F10, a model of innate PD-1 blockade resistance with high NANOG expression\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e (Supplementary Fig.\u0026nbsp;7). Our data suggested that CRY1 inhibition with KS15 could overcome the resistance of NANOG\u003csup\u003ehigh\u003c/sup\u003e tumors to PD-1 blockade. We further investigated whether CRY1 inhibition could shift the immune status in the TME from immune-refractory to immune-favorable. The combination treatment groups showed significantly higher numbers of CD8\u003csup\u003e+\u003c/sup\u003e T cells and tumor reactive CD8\u003csup\u003e+\u003c/sup\u003e T cells expressing GrB compared to the other treatment groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ef, g). Additionally, the percentage of apoptotic tumor cells was higher in the combination treatment group than groups treated with either one alone (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eh). Together, these findings demonstrated that CRY1 inhibition with KS15 could overcome the resistance of NANOG\u003csup\u003ehigh\u003c/sup\u003e tumors to PD-1 blockade by shifting the immune phenotype from non-T cell inflamed to T cell inflamed.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eNext, we expended the preclinical therapeutic potential of CRY1 inhibition to ACT. To assess this, NOD/SCID mice bearing ACT-resistant MDA-MB-231 P3 tumors were treated with MART1-specific CTLs with or without KS15 treatment (Supplementary Fig.\u0026nbsp;8a). While treatment with CTLs alone had no effect on the tumor growth, the combination of CTLs and KS15 significantly inhibited tumor growth without affecting the body weight (Supplementary Fig.\u0026nbsp;8b-8d) and improved the survival compared to other groups (Supplementary Fig.\u0026nbsp;8e). These findings indicate that targeting CRY1 with KS15 could be a promising combinatorial strategy to enhance the response to various T cell-based immunotherapies, such as PD-1 blockade and ACT, for controlling multiple refractory phenotypes of NANOG\u003csup\u003ehigh\u003c/sup\u003e tumors.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eCancer immunotherapy has shown remarkable clinical efficacies across various cancer types; however, resistance to this promising approach remains a significant clinical challenge. While mechanisms of resistance are typically categorized into cancer cell-intrinsic and extrinsic factors, the pathways governing these mechanisms often overlap\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Thus, identifying common pathways that regulate both cancer cell-intrinsic and extrinsic resistance is crucial for advancing the effectiveness of immunotherapy. In this study, using mouse preclinical models resistant to PD-1 blockade and ACT, we demonstrate that CRY1 functions as a common factor contributing to multiple immune resistance networks by simultaneously restricting CTL-mediated killing of tumor cells and T cell infiltration into tumors.\u003c/p\u003e \u003cp\u003eCRY1 upregulation has been reported in various tumors\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e; however, the regulatory mechanisms governing CRY1 expression, particularly during CTL-mediated immune selection, remain largely unexplored. In this respect, we noted previously that the immune pressure from antigen-specific CTLs promoted the acquisition of NANOG, a key transcription factor that drives the emergence of a stem-like cancer cell state and immune-refractory characteristics \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. In this study, we have identified CRY1 as a novel transcriptional target of NANOG, suggesting that CRY1 upregulation may result from the selection of NANOG\u003csup\u003ehigh\u003c/sup\u003e immune-resistant tumor cells during T cell-based immunotherapy, including PD-1 blockade and ACT. Indeed, \u003cem\u003eCRY1\u003c/em\u003e expression shows a positive correlation with the \u003cem\u003eNANOG\u003c/em\u003e signature across various tumor types in the TCGA cohort, supporting that the molecular axis observed \u003cem\u003ein vitro\u003c/em\u003e also exists in cancer patients. Importantly, our results show that CRY1 is a crucial component of several NANOG-dependent phenotypes. Notably, CSC-like and multiple immune-refractory phenotypes in NANOG-overexpressing P0 cells are nearly abolished following CRY1 depletion. These findings provide insights into the direct link between NANOG-induced immune-refractory phenotypes and CRY1 dysregulation in immune-resistant tumor cells.\u003c/p\u003e \u003cp\u003eAlthough circadian clock dysregulation has been associated with both antitumor immunity and tumorigenesis, the mechanisms linking CRY1 to immunotherapeutic resistance remain poorly understood. Typically, CRY1 forms a heterodimer with PER proteins, translocates into the nucleus, and inhibits CLOCK/BMAL1 activity, establishing a negative feedback loop that regulates circadian rhythms in mammals \u003csup\u003e\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e. Beyond its role in the regulation of core clock genes, CRY1 also acts as a transcriptional repressor impacting processes like DNA damage response and glucocorticoid signaling\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e. Previously, we highlighted HDAC1\u0026rsquo;s role in NANOG-dependent phenotypes, such as CSC-like properties and immune refractoriness\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. This study focuses on CRY1\u0026rsquo;s role in HDAC1-mediated transcriptional repression to elucidate how the NANOG/CRY1 axis drives multiple immune-refractory phenotypes. We show that CRY1 supports HDAC1-mediated silencing of APC3 and TRIM17, which stabilizes Cyclin A and MCL1, to promote CSC-like property and resistance to CTL-mediated killing. CRY1 also represses CXCL10 expression via HDAC1, limiting T cell infiltration in NANOG-high tumor cells. Importantly, we have identified HDAC1 as a novel CRY1 binding partner and found that CRY1 inhibition restores AcH3K27 levels at target gene promoters, indicating that CRY1 modulates gene expression via HDAC1 binding. While further studies are needed to clarify the precise mechanisms by which CRY1 regulates HDAC1-driven epigenetic events, this connection points to promising therapeutic targets for addressing immune-resistant cancer.\u003c/p\u003e \u003cp\u003eCurrently, programmed death-ligand 1 (PD-L1), microsatellite instability (MSI), and tumor mutational burden (TMB) are the three validated biomarkers for predicting the response to immunotherapy. However, relying on a single biomarker is still insufficient for optimal patient selection. Given the substantial and growing use of these therapies, identifying new predictive biomarkers is essential to improve treatment outcomes through optimal patient selection\u003csup\u003e\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e. Here, by analyzing integrated transcriptome data from melanoma patients classified as responders and non-responders to PD-1 blockade, we show that the CRY1 activity score, based on the expression of genes bound by CRY1, significantly correlates with response to anti-PD-1 therapy and survival in cancer patients. This suggests that CRY1 activity score levels could serve as a predictive marker for clinical outcomes of PD-1 blockade. Importantly, the CRY1 activity score is strongly associated with the MIR gene signature, which includes stemness, resistance to T cell-mediated anti-tumor responses, and poor T cell infiltration. Furthermore, in multiple tumor types within the TCGA cohort, a strong correlation with the MIR status is observed in tumors exhibiting high levels of both the CRY1 activity and the \u003cem\u003eNANOG\u003c/em\u003e signature, rather than with either marker alone. Thus, we propose that the CRY1 activity score, particularly in immunotherapy-resistant tumors with high NANOG levels, may serve as a promising predictive marker. This provides a framework for selecting patients who may benefit from combination strategies involving T cell-based therapies and CRY1-targeting agents.\u003c/p\u003e \u003cp\u003eGiven the critical role of the CRY1/HDAC1 interaction in driving immune-refractory phenotypes through the NANOG/CRY1 axis, disrupting this interaction may be an effective strategy to overcome immunotherapy resistance. CRYs have a conserved N-terminal photolyase region and a variable C-terminal tail, which is crucial for their nuclear localization and interaction with core clock proteins \u003csup\u003e\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e. KS15, a small molecule with the 2-ethoxypropanoic acid scaffold, binds to CRY1's C-terminal region \u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e and affects CRY1's interactions with its binding partners, restoring CLOCK/BMAL1-driven transcription suppressed by CRY1 \u003csup\u003e50\u003c/sup\u003e. In this study, we show that CRY1 inhibition with KS15 disrupts the CRY1/HDAC1 interaction, thereby reducing CSC-like properties, increasing sensitivity to GrB, and enhancing T cell infiltration across various cancer types. Importantly, KS15 treatment synergizes with T cell-based immunotherapies, such as anti-PD-1 therapy and ACT, to reverse multiple immune-refractoriness in immune-resistant tumors. These findings provide rational for the combination of CRY1 inhibition with T cell-mediated immunotherapy. Our previous study suggests that HDAC1 inhibitors could help overcome immunotherapy resistance\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e; however, their broad impact on the tumor microenvironment poses limitations\u003csup\u003e\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u003c/sup\u003e. Our current findings indicate that multiple immune-refractory properties associated with the NANOG/HDAC1 axis can be regulated by CRY1, suggesting that targeting CRY1 could address the limitations of HDAC1 inhibition. There are several CRY1-targeting drugs under development for clinical application to cure prevalent circadian rhythm and sleep disorders, including delayed sleep phase disorder (DSPD) symptoms\u003csup\u003e\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u003c/sup\u003e. Therefore, targeting CRY1 with small molecules could be a promising strategy to enhance the immunotherapy efficacy without severe side effects and improve clinical outcomes for cancer patients with immune-resistant tumors.\u003c/p\u003e \u003cp\u003eIn summary, our findings identify CRY1 as a key transcriptional target of NANOG and reveal CRY1\u0026rsquo;s pivotal role at the intersection of the NANOG/HDAC1 axis and multiple refractory properties in immune-resistant tumors. Although CRY1 enhances NANOG-driven aggressive traits, it also represents a potential vulnerability if selectively targeted. Thus, our data suggest that CRY1 inhibition could be a promising strategy to combat NANOG\u003csup\u003ehigh\u003c/sup\u003e immune-resistant tumors, especially in the context of T cell-based cancer therapy.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eMice\u003c/h2\u003e \u003cp\u003eSix- to eight-week-old NOD/SCID or C57BL/6 mice were purchased from Central Lab. Animal Inc. (Seoul, Korea). All mice were maintained and handled under the protocol approved by the Korea University Institutional Animal Care and Use Committee (KOREA-2022-0005). All animal procedures were performed in accordance with recommendations for the proper use and care of laboratory animals.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eCell lines\u003c/h2\u003e \u003cp\u003eB16F0 (ATCC, CRL-6322), B16F10 (ATCC, CRL-6475), CaSki (ATCC, CRL-1550), HEK293 (ATCC, CRL-1573), MDA-MB-231 (ATCC, CRM-HTB-26), and MKN28 (JCRB Cell Bank, 0253) cell lines were purchased from the American Type Culture Collection (ATCC, Manassas, VA, USA) or the Japanese Collection of Research Bioresources Cell Bank (JCRB Cell Bank, Osaka, JPN). CUMC6 cell lines were obtained from Catholic University Medical College (Seoul, KOR). Yumm2.1 cell lines were kindly provided by Marcus W. Bosenberg of Yale University \u003csup\u003e\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u003c/sup\u003e. All cell lines were obtained between 2010 and 2022 and tested for mycoplasma using the Mycoplasma Detection Kit (Thermo Fisher Scientific, San Jose, CA, USA). The identities of the cell line were confirmed by short tandem repeat (STR) profiling by IDEXX Laboratories Inc. and were used within six months for testing. Generation and maintenance of the PD-1 blockade-resistant Yumm2.1 P3 \u003csup\u003e22\u003c/sup\u003e and B16 P3 \u003csup\u003e43\u003c/sup\u003e, and ACT-resistant CaSki P3 \u003csup\u003e44\u003c/sup\u003e and MDA-MB-231 P3 \u003csup\u003e17\u003c/sup\u003e cell lines have been previously described. The CaSki \u003cem\u003eNANOG\u003c/em\u003e or HEK293 \u003cem\u003eNANOG\u003c/em\u003e cell lines have also been previously described \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. All cells were grown at 37℃ in a 5% CO\u003csub\u003e2\u003c/sub\u003e incubator with a humidified chamber.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eChemical reagents\u003c/h2\u003e \u003cp\u003eThe following chemical reagents were used in this study: Cycloheximide (Sigma-Aldrich, St. Louis, MO, USA, 01810), MG132 (Sigma-Aldrich, M8699), and KS15 (Selleckchem, Houston, TX, USA, HY-115672). The chemical reagents we used were dissolved in dimethyl sulfoxide (DMSO).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003esiRNA constructs\u003c/h2\u003e \u003cp\u003eThe following synthetic small interfering RNAs (siRNAs) were produced by Bioneer (Daejeon, KOR): Non-specific GFP (green fluorescent protein), 5\u0026rsquo;-GCAUCAAGGUGAACUUCAA-3\u0026rsquo; (sense), 5\u0026rsquo;-UUGAAGUUCACCUUGAUGC-3\u0026rsquo; (antisense); mouse \u003cem\u003eCry1\u003c/em\u003e no. 1, 5\u0026rsquo;-CUCUGUCUGAUGACCAUGA-3\u0026rsquo; (sense), 5\u0026rsquo;-UCAUGGUCAUCAGACAGAG-3\u0026rsquo; (antisense); mouse \u003cem\u003eCry1\u003c/em\u003e no. 2, 5\u0026rsquo;-GACAGUCAGCAGACUCACU-3\u0026rsquo; (sense), 5\u0026rsquo;-AGUGAGUCUGCUGACUGUC-3\u0026rsquo; (antisense); human \u003cem\u003eCRY1\u003c/em\u003e no. 1, 5\u0026rsquo;-CAGUGUAGUAAACACACUU-3\u0026rsquo; (sense), 5\u0026rsquo;-AAGUGUGUUUACUACACUG-3\u0026rsquo; (antisense) ; human \u003cem\u003eCRY1\u003c/em\u003e no. 2, 5\u0026rsquo;-CUCUGUCUGAUGACCAUGA-3\u0026rsquo; (sense), 5\u0026rsquo;-UCAUGGUCAUCAGACAGAG-3\u0026rsquo; (antisense) ; human \u003cem\u003eNANOG\u003c/em\u003e, 5\u0026rsquo;-GCAACCAGACCUGGAACAA-3\u0026rsquo; (sense), 5\u0026rsquo;-UUGUUCCAGGUCUGGUUGC-3\u0026rsquo; (antisense); human \u003cem\u003eAPC3\u003c/em\u003e no. 1, 5\u0026rsquo;-CGACUCUUUACUAGUGACA-3\u0026rsquo; (sense), 5\u0026rsquo;-UGUCACUAGUAAAGAGUCG-3\u0026rsquo; (antisense); human \u003cem\u003eAPC3\u003c/em\u003e no. 2, 5\u0026rsquo;-ACAGAUCAUGGGAACAGAU-3\u0026rsquo; (sense), 5\u0026rsquo;-AUCUGUUCCCAUGAUCUGU-3\u0026rsquo; (antisense); human \u003cem\u003eTRIM17\u003c/em\u003e no. 1, 5\u0026rsquo;-CAGAGUUCCCGGACAGAUU-3\u0026rsquo; (sense), 5\u0026rsquo;-AAUCUGUCCGGGAACUCUG-3\u0026rsquo; (antisense); human \u003cem\u003eTRIM17\u003c/em\u003e no. 2, 5\u0026rsquo;-GAUCACCAGGACAGGGAAU-3\u0026rsquo; (sense), 5\u0026rsquo;-AUUCCCUGUCCUGGUGAUC-3\u0026rsquo; (antisense). For in vitro delivery, the cells were transfected with 100 pmol of siRNAs using Lipofectamine 2000 (Invitrogen, San Jose, CA, USA, 11668027) according to the manufacturer\u0026rsquo;s instructions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eDNA constructs and site-directed mutagenesis\u003c/h2\u003e \u003cp\u003eThe pMSCV-\u003cem\u003eFLAG-NANOG\u003c/em\u003e WT and pMSCV-\u003cem\u003eFLAG-NANOG\u003c/em\u003e MT plasmids have been previously described. To generate the pGL3-\u003cem\u003eCRY1\u003c/em\u003e promoter, the promoter region of the \u003cem\u003eCRY1\u003c/em\u003e gene was isolated by PCR from genomic DNA extracted from CaSki cells using the following primer set, 5\u0026rsquo;- CCCCTCGAGCAATTCAACCAATAAGAATT-3\u0026rsquo; (forward) and 5\u0026rsquo;- TTTAAGCTTACTACACTGGCTCGGAGGGG-3\u0026rsquo; (reverse). The PCR products were digested with XhoⅠ and HindⅢ and subcloned into the XhoⅠ/HindⅢ restriction sites of the pGL3-Basic vector (Promega, Madison, WI, USA, E1751). To generate mutations in the NANOG binding site of the CRY1 promoter region, we used the pGL3-CRY1 promoter plasmid as a template and replaced the conserved NANOG binding sequence AATGA with ATATA. Site-directed Mutagenesis was performed using a QuickChange XL Site-directed Mutagenesis Kit (Stratagene, San Diego, CA, USA, 200516) according to the manufacturer\u0026rsquo;s instructions. Mutations were verified by DNA sequencing.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eReal-time quantitative RT-PCR\u003c/h2\u003e \u003cp\u003eTotal RNA from the cells was purified using RNeasy Micro Kit (Qiagen, Valencia, CA, USA, 74004) and cDNA was synthesized by reverse transcriptase (RT) using an iScript cDNA synthesis kit (Bio-Rad, Hercules, CA, USA, 1708891) according to the manufacturer\u0026rsquo;s recommended protocol. Real-time PCR was performed using IQ SYBR Green Super mix (Bio-Rad, 1708880) with the specific primers on a CFX96 real-time PCR detection system. Fold-change was calculated relative to the expression level of mRNA in the control cells. qPCR primers were purchased from Bioneer: mouse \u003cem\u003eClock\u003c/em\u003e, 5\u0026rsquo;-CCAAAGGCCAGCAGTGGATA-3\u0026rsquo; (forward), 5\u0026rsquo;-TTGTCAGCAGCTGTCTCAGG-3\u0026rsquo; (reverse); mouse \u003cem\u003eBmal1\u003c/em\u003e, 5\u0026rsquo;-AATGAGCCAGACAACGAGGG-3\u0026rsquo; (forward), 5\u0026rsquo;-GCTGTCGCCCTCTGATCTAC-3\u0026rsquo; (reverse); mouse \u003cem\u003eCry1\u003c/em\u003e, 5\u0026rsquo;-AACATTCCAGGGAAAGGTCCTG-3\u0026rsquo; (forward), 5\u0026rsquo;-CTGCATCTCGTTCCTTCCCAA-3\u0026rsquo; (forward), 5\u0026rsquo;-CTGCATCTCGTTCCTTCCCAA-3\u0026rsquo; (reverse); mouse \u003cem\u003eCry2\u003c/em\u003e, 5\u0026rsquo;-CCCTTCCTGTGTGGAAGACC-3\u0026rsquo; (forward), 5\u0026rsquo;-CTCTGGGGTTGGCAACTCTG-3\u0026rsquo;; mouse \u003cem\u003ePer1\u003c/em\u003e, 5\u0026rsquo;-CCCAGGATGTGGGTGTCTTC-3\u0026rsquo; (forward), 5\u0026rsquo;-GACCTCCTCTGATTCGGCAG-3\u0026rsquo;; mouse \u003cem\u003ePer2\u003c/em\u003e, 5\u0026rsquo;-ACGCAATGGGAAGGAGCTG-3\u0026rsquo; (forward), 5\u0026rsquo;-CAGACTGCTCACTGCAGCC-3\u0026rsquo; (reverse); mouse \u003cem\u003ePer3\u003c/em\u003e, 5\u0026rsquo;-TCCAGAGCATGGAACAGCAG-3\u0026rsquo; (forward), 5\u0026rsquo;-TCTGTCTTCACAGGCGACAC-3\u0026rsquo; (reverse); mouse \u003cem\u003eβ-actin\u003c/em\u003e, 5\u0026rsquo;-GATATCGCTGCGCTGGTCG-3\u0026rsquo; (forward), 5\u0026rsquo;-CATTCCCACCATCACACCCT-3\u0026rsquo; (reverse); human \u003cem\u003eCRY1\u003c/em\u003e, 5\u0026rsquo;-TGGGAATGGAGGCTTCATGG-3\u0026rsquo; (forward), 5\u0026rsquo;-ACGTTTCCCACCACTGAGAC-3\u0026rsquo; (reverse); human \u003cem\u003eCDC20\u003c/em\u003e, 5\u0026rsquo;-GACCGCTATATCCCCCATCG-3\u0026rsquo; (forward) and 5\u0026rsquo;- GGCGTCTGGCTGTTTTCAGA-3\u0026rsquo; (reverse); human \u003cem\u003eAPC3\u003c/em\u003e, 5\u0026rsquo;- CTGCCCAACTCTTGCACAAC-3\u0026rsquo; (forward) and 5\u0026rsquo;-TTGTGTCCTGGGGTGTTTCC-3\u0026rsquo; (reverse); human \u003cem\u003eCUL1\u003c/em\u003e, 5\u0026rsquo;-GCCGTCAGAGTTGGAACGTA-3\u0026rsquo; (forward) and 5\u0026rsquo;-TGTCGACGCCTGCAAAGTAT-3\u0026rsquo; (reverse); human \u003cem\u003eRBX1\u003c/em\u003e, 5\u0026rsquo;- TGTCAAGCTAACCAGGCGTC \u0026minus;\u0026thinsp;3\u0026rsquo; (forward) and 5\u0026rsquo;- AGCGAGAGATGCAGTGGAAG \u0026minus;\u0026thinsp;3\u0026rsquo; (reverse); human \u003cem\u003eSKP1\u003c/em\u003e, 5\u0026rsquo;- CACCCACCACAAGGATGACC-3\u0026rsquo; (forward) and 5\u0026rsquo;- TCTTGGTCCCAAACAGGGAT-3\u0026rsquo; (reverse); human \u003cem\u003eHUWE1\u003c/em\u003e, 5\u0026rsquo;- GGAGAAGAAGGGCAGGATGC-3\u0026rsquo; (forward) and 5\u0026rsquo;- GTGAGGTACGGAACAAGGCA-3\u0026rsquo; (reverse); human \u003cem\u003eBTRC\u003c/em\u003e, 5\u0026rsquo;-TGACCTCTGATGGCATGCTG-3\u0026rsquo; (forward) and 5\u0026rsquo;- ACTGTCCCCATCCTCTTCGT-3\u0026rsquo; (reverse); human \u003cem\u003eFBW7\u003c/em\u003e, 5\u0026rsquo;-GTTTGGTCAGCAGTCACAGGCA-3\u0026rsquo; (forward) and 5\u0026rsquo;-CCACACTTTGAGTGTCCGATCTG-3\u0026rsquo; (reverse); human \u003cem\u003eTRIM17\u003c/em\u003e, 5\u0026rsquo;- TCCCGGACAGATTGAAGTGC \u0026minus;\u0026thinsp;3\u0026rsquo; (forward) and 5\u0026rsquo;- AGGGGTAAGCCACAAATCGG \u0026minus;\u0026thinsp;3\u0026rsquo; (reverse); human \u003cem\u003eβ-ACTIN\u003c/em\u003e, 5\u0026rsquo;-CGACAGGATGCAGAAGGAG-3\u0026rsquo; (forward) and 5\u0026rsquo;-TAGAAGCATTTGCGGTGGAC-3\u0026rsquo; (reverse). All real-time quantitative PCR experiments were performed triplicate and quantification cycle (Cq) values were determined using Bio-Rad CFX96 Manager 3.0 software. Relative quantification of the mRNA levels was performed using the comparative Ct method with β-actin as the reference gene.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eWestern blot analysis\u003c/h2\u003e \u003cp\u003eLysate extracted from a total of 1 x 10\u003csup\u003e5\u003c/sup\u003e cells was used to perform Western blots analysis. Primary antibodies against mouse CRY1 (1:3000; Santa Cruz Biotechnology, Dallas, TX, USA, sc-5953), human CRY1 (1:3000; Bethyl Laboratories, Montgomery, TX, USA, A302-614A), human NANOG (1:3000; Bethyl Laboratories, A300-379A), FLAG (1:5000; Medical \u0026amp; Biological Laboratories, Nagoya, JPN, M185-3L), pAKT (T308) (1:3000; Cell Signaling Technology, Danvers, MA, USA, D25E6), AKT1 (1:3000; Cell Signaling, 9272), Cyclin A (1:3000; Santa Cruz Biotechnology, sc-239), MCL1 (1:3000; Santa Cruz Biotechnology, sc-819), APC3 (1:3000; Proteintech, Rosemont, IL, USA, 10918-1-AP), TRIM17 (1:3000; Proteintech, 13663-1-AP), HDAC1 (1:3000; Cell signaling Technology, 5356S), CXCL10 (1:1000, Invitrogen, 10H11L3), and β-actin (1:5000; MBL, Nagoya, JPN, M177-3) were used. Western blotting was followed by incubation with the appropriate secondary antibodies conjugated to horseradish peroxidase (HRP), anti-rabbit IgG-HRP (1:5000; Enzo, Farmingdale, NY, USA, ADI-SAB-300-J), and anti-mouse IgG-HRP (1:5000; Enzo, ADI-SAB-100-J). The immunoreactive bands were developed with the chemiluminescence ECL Detection System (GE Healthcare, Chicago, IL, USA), and signals were detected using a luminescent image analyzer (LAS-4000 Mini, Fujifilm, JPN).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eQuantitative ChIP (qChIP) assays\u003c/h2\u003e \u003cp\u003eThe ChIP kit (Millipore, Burlington, MA, USA, 17\u0026ndash;295) was employed according to the manufacturer\u0026rsquo;s instructions and the ChIP assay was performed as described previously\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Briefly, 1 x 10\u003csup\u003e7\u003c/sup\u003e cells (per assay) were bathed in 1% formaldehyde at 25℃ for 10 min for cross-linking of proteins and DNA and then lysed in sodium dodecyl sulfate buffer containing protease inhibitors. DNA was sheared by sonication using a Sonic Dismembrator Model 500 (Fisher Scientific, Pittsburgh, PA, USA). Immunoprecipitation was carried out by incubating with 1 \u0026micro;g of antibodies against FLAG (MBL, M185-3L), NANOG (Bethyl Laboratories, A300-379A), CRY1 (Bethyl Laboratories, A300-614A), HDAC1 (Cell signaling Technology, 5356S), AcH3K27 (Cell signaling Technology, 8173) or rabbit IgG (Millipore, PP64) for 16 hr and then the immunoprecipitated DNA was quantified by real-time qPCR using the following primer set: \u003cem\u003eCRY1\u003c/em\u003e, 5\u0026rsquo;-AAACAGCAAAGGTTAAGAGACAAA-3\u0026rsquo; (forward) and 5\u0026rsquo;-GGCCATGGCATCCCTTAGAT-3\u0026rsquo; (reverse); \u003cem\u003eAPC3\u003c/em\u003e, 5\u0026rsquo;-TCCCATTTTTCCTCCCTTCACT-3\u0026rsquo; (forward), 5\u0026rsquo;-AGCAGTGTAACAGAGAACGCT-3\u0026rsquo; (reverse); \u003cem\u003eTRIM17\u003c/em\u003e, 5\u0026rsquo;-CAGAGCATTGGTCAGGGAGG-3\u0026rsquo; (forward), 5\u0026rsquo;-ACAGAGGAGGGCTAGGACTG-3\u0026rsquo; (reverse); CXCL10, 5\u0026rsquo;-CAGCCAGCAGGTTTTGCTAAG-3\u0026rsquo; (forward), 5\u0026rsquo;-AGAAAACGTGGGCTAGTGT-3\u0026rsquo; (reverse). Each sample was assayed in triplicate, and the amount of precipitated DNA was calculated at the percentage of the input sample.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eTumor sphere-forming assay\u003c/h2\u003e \u003cp\u003eCells were plated at 1 x 10\u003csup\u003e3\u003c/sup\u003e cells per well in six-well, super-low adherence vessels (Corning, Lowell, MA, USA, CLS3471) containing serum-free DMEM-F12 (Thermo Fisher Scientific, 12634010) supplemented with epidermal growth factor (20 ng/mL), basic fibroblast growth factor (20 ng/mL), and 1x B27. Medium was replaced every 3 days to replenish nutrients. Colonies more than 50 \u0026micro;m in diameter were counted under a microscope.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eCTL-mediated apoptosis assay\u003c/h2\u003e \u003cp\u003eTumor cells were labeled with 10 \u0026micro;M CFSE (Molecular Probes, Eugene, OR, USA, 11524217) in DMEM supplemented with 0.1% FBS. The CFSE-labeled Yumm2.1 or CaSki cells were pulsed with OVA or MART1 peptide (10 \u0026micro;g/mL) for 1 h, respectively. The CFSE-labeled Yumm2.1 or CaSki cells were mixed with cognate OVA- or MART1-specific CD8\u003csup\u003e+\u003c/sup\u003e CTLs at 1:1 ratio and incubated for 4 h at 37℃. In addition, the CFSE-labeled B16 cells were mixed with cognate GP100-specific CD8\u003csup\u003e+\u003c/sup\u003e CTLs at the same condition as above. Cells were stained for active caspase-3 (BD biosciences, Franklin Lakes, NJ, USA, 560626) as an index of apoptosis and examined by flow cytometry as shown gating strategy in Supplementary Fig.\u0026nbsp;9.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eGranzyme B-mediated apoptosis assay\u003c/h2\u003e \u003cp\u003eRecombinant human granzyme B (Enzo, BML-SE238-5000) was mixed with BioPorter Reagent Sigma-Aldrich, BPQ24) at 25℃ for 5 min. The tumor cells were mixed with BioPorter-granzyme B complexes for 4 h at 37℃. Next, the cells were stained for active caspase-3 as index of apoptosis and examined by flow cytometry.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eTrypan blue exclusion assay\u003c/h2\u003e \u003cp\u003eTo determine cell viability, a trypan blue exclusion assay was performed. Briefly, cells were seeded at 1 x 10\u003csup\u003e5\u003c/sup\u003e cells per well in 12-well plates 1 day prior to the assay. The treatments were added at the concentrations indicated in the figures. After 72 h, the cells were detached and stained with 0.4% trypan blue. Unstained cells were counted using a hemocytometer. The data are expressed as the percentage of unstained cells compared with the control cells not exposed to the chemical reagents.\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eLuciferase assay\u003c/h2\u003e \u003cp\u003eTo determine the promoter activity of \u003cem\u003eCRY1\u003c/em\u003e, luciferase assay was performed. Briefly, the reporter construct, pGL3 basic, pGL3-\u003cem\u003eCRY1\u003c/em\u003e WT, or pGL3-\u003cem\u003eCRY1\u003c/em\u003e MT together with pCMV-β-Gal, an internal control for transfection efficiency, were co-transfected into HEK293 cells using Lipofectamine 2000. After 24 h, cells were washed with phosphate-buffered saline (PBS) and lysed with Cell Culture Lysis Reagent (Promega, E1500). Luciferase activity was measured with a Turner Biosystems TD-20/20 luminometer after addition of 40 \u0026micro;L of luciferase assay reagent (Promega, E1500). Relative luciferase activity was normalized with the β-galactosidase activity in the cell lysate and calculated as an average of three independent experiments.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eImmunoprecipitation\u003c/h2\u003e \u003cp\u003eCaSki \u003cem\u003eNANOG\u003c/em\u003e cells were lysed in NP40 lysis buffer (50 mM Tris-HCL, pH 8.0, 5 mM EDTA, 150 mM NaCl, 1% NP40, 1 mM PMSF) containing protease inhibitor. Immunoprecipitation was carried out by incubation with 1 \u0026micro;g of anti-CRY1 antibody (Bethyl Laboratories, A300-614A) or rabbit IgG (Millipore, PP64) for 16 h. The bound proteins were eluted by boiling in SDS sample buffer and were immunoblotted using anti-HDAC1 antibody (Cell signaling Technology, 5356S).\u003c/p\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003eBioinformatic analyses from published clinical database\u003c/h2\u003e \u003cp\u003eTo determine the clinical relevance of the NANOG-CRY1 axis in human cancer patients, we utilized a standard processing pipeline Gene Expression Profiling Interactive Analysis, version 2 (GEPIA2) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://gepia2.cancer-pku.cn\u003c/span\u003e\u003cspan address=\"http://gepia2.cancer-pku.cn\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The expression correlation between NANOG sig. and \u003cem\u003eCRY1\u003c/em\u003e was detected by the \u0026ldquo;Correlation Analysis\u0026rdquo; tool of GEPIA2, based on the datasets of the TCGA. To investigate the clinical relevance in patients treated with PD-1 blockade, we analyzed six published melanoma datasets, Riaz et al., (GEO accession number: GSE91061), Gide et al., (BioProject accession number: PRJEB23709), a published MGH cohort (GEO accession number: GSE115821), Hugo et al., (GEO accession number GSE78220), Liu et al., (dbGaP accession number: phs000452.v3.p1) and TCGA-SKCM, in which patients were treated with PD-1 blockade and pre-treatment biopsy samples were subject to RNA sequencing. Raw sequencing reads were quality-checked using FastQC\u003csup\u003e\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e\u003c/sup\u003e and trimmed using Trimmomatic to remove low-quality bases and adapter sequences\u003csup\u003e\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u003c/sup\u003e. The cleaned reads were then aligned to the human reference genome (GRCh38) using STAR aligner\u003csup\u003e\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e\u003c/sup\u003e. Gene-level read counts were obtained using HTSeq-count\u003csup\u003e\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e\u003c/sup\u003e. The count matrices from different cohorts were combined into a single dataset. DESeq2 was used for data normalization and to account for differences in sequencing depth across samples. The merged count data were transformed using the variance stabilizing transformation (VST) function in DESeq2 to prepare for batch correction\u003csup\u003e\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e\u003c/sup\u003e. To mitigate batch effects arising from different experimental conditions and cohorts, we applied the ComBat function from the sva package to the VST-transformed data\u003csup\u003e\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e, \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e\u003c/sup\u003e. This step helped to remove unwanted variation while preserving biological differences of interest. In addition, gene expression data for human cancer patients profiled by TCGA were collected from the Firehose BROAD GDAC data repository (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gdac.broadinsitute.org\u003c/span\u003e\u003cspan address=\"https://gdac.broadinsitute.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Clinical data were also retrieved from the same source.\u003c/p\u003e \u003cp\u003eThe stemness, T cell-mediated anti-tumor response, and T cell infiltration gene expression signatures were previously defined\u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e. We used the single-sample gene set enrichment analysis (GSEA) algorithm, implemented in R package\u0026rsquo;s gene set variation analysis (GSVA), to calculate the stemness, T cell-mediated anti-tumor response, and T cell infiltration signature scores for each sample. The default parameters from the GSVA package were used. To obtain the refined signature score for resistance to T cell-mediated anti-tumor response, we multiplied the T cell-mediated anti-tumor response signature score by a negative number. The poor T cell infiltration signature score was obtained using the same method. Spearman\u0026rsquo;s correlation was used to quantify the association between CRY1 activity score, stemness, resistance to T cell-mediated anti-tumor response, and poor T cell infiltration scores individually for each tumor type. The association between CRY1 activity score and survival was evaluated by Cox regression and Kaplan-Meier analyses. The 50th percentile was used as cutoff thresholds.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003eTumor treatment experiments\u003c/h2\u003e \u003cp\u003eTo characterize the in vivo resistance to PD-1 blockade conferred by CRY1, C57BL/6 mice were inoculated subcutaneously with 1 x 10\u003csup\u003e5\u003c/sup\u003e Yumm2.1 P3 cells or 1 x 10\u003csup\u003e5\u003c/sup\u003e B16F10 cells per mouse. Nine days following the tumor challenge, DMSO- or KS15 (0.01 mg/kg)-loaded chitosan hydrogel was administered via intratumoral injection for a day before anti-PD-1 (BioXcell, Lebanon, NH, USA) or isotype antibody control that was administrated via intraperitoneal injection every 3 days at a dose of 100 \u0026micro;g per mouse in accordance with the schedule described in Supplementary Fig.\u0026nbsp;9. This treatment regimen was repeated for 2 cycles. To characterized the in vivo resistance to adoptive CTL transfer conferred by CRY1, NOD/SCID mice were inoculated subcutaneously with 1 x 10\u003csup\u003e5\u003c/sup\u003e MDA-MB-231 P3 cells per mice. Six days following the tumor challenge, DMSO- or KS15 (0.01 mg/kg)-loaded chitosan hydrogel was administered via intratumoral injection for a day before adoptive transfer with MART-1-specitic CTLs in accordance with the schedule described in Supplementary Fig.\u0026nbsp;10a. This treatment regimen was repeated for 3 cycles. Mice were handled and monitored for tumor burden and survival under the protocol permitted by the Korea University Institutional Animal Care and Use Committee (KUIACUC-2022-0005). Tumor size was measured before the tumors were smaller than, or at about 10% of mice body weight, the maximal tumor size/burden permitted by KUIACUC. In some cases, this limit has been reached on the last day of tumor size measurement and the mice were immediately euthanized.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section3\"\u003e \u003ch2\u003eTumor digestion, cell isolation, and flow cytometric analysis\u003c/h2\u003e \u003cp\u003eTo analyze the immune cells in tumor, treated mice were euthanized on day 18 following tumor inoculation and the tumors were harvested. The tumors were dissected into fragments by cutting and digested by collagenase (0.5 mg/mL, Millipore) and DNase (1 \u0026micro;g/mL, Millipore) at 37℃ for 45 min. The digested samples were then filtered through a 70 \u0026micro;m cell strainer and washed with PBS buffer. The cell pellets incubated with red blood cell (RBC) lysis buffer to lyse the RBCs. The cell suspensions were stained for the intracellular and extracellular protein markers of interest, and the stained samples were assessed on a flow cytometer (BD biosciences) along with CellQuest Pro software. The following staining antibodies used: anti-CD3, anti-CD8, anti-granzyme B, and anti-active caspase-3 (all from BD biosciences).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003eStatistics\u003c/h2\u003e \u003cp\u003eAll data shown are representative of at least three separate experiments. Statistical differences were calculated by either Student\u0026rsquo;s t-test (two-tailed, unpaired), one-way ANOVA, or two-way ANOVA using GraphPad Prism software version 10. Results with two-tailed p values of \u0026lt;\u0026thinsp;0.05 were considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflict of interest statement:\u0026nbsp;\u003c/strong\u003eThe authors have declared that no conflicts of interest exist.\u003c/p\u003e \u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003ch2\u003eData availability\u003c/h2\u003e \u003cp\u003eTranscriptomic data from patients with melanoma classified as responders or nonresponders to PD-1 blockade are available in the NCBI\u0026rsquo;s Gene Expression Omnibus (GEO) database (Riaz et al. cohort; GSE91061), (MGH cohort; GSE115821), (Hugo et al. cohort; GSE78220); the European Nucleotide Archive (ENA) (Gide et al. cohort; PRJEB23709); and the Database of Genotypes and Phenotypes (dbGap) (Liu et al. cohort; phs000452.v3.p1). Transcriptomic data from TCGA were deposited in the Firehose BROAD GDAC data repository portal (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gdac.broadinsitute.org/\u003c/span\u003e\u003cspan address=\"https://gdac.broadinsitute.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The gating strategy is provided in Supplementary Fig.\u0026nbsp;9. The raw images for the immunoblots are provided in Supplementary Fig.\u0026nbsp;10. The remaining data are available within the article and Supplementary information. Source data are provided with this paper.\u003c/p\u003e \u003c/div\u003e\n\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interest.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor contributions\u003c/h2\u003e \u003cp\u003eStudy concept and design: S.J.O., S.R.W., K.-H.S., and T.W.K.; acquisition of data: S.J.O., S.R.W., J.H.A., M.K.S., H.-J.L., and E.H.C.; analysis and interpretation of the data: S.J.O., S.R.W., K.-H.S., and T.W.K.; technical or other material support: K.-M.L., Y.J.P., Y.J.S., C. Y., G.H.S., J.-W.J.; writing and review of the manuscript: S.J.O., S.R.W., K.-H.S., and T.W.K.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eThis work was funded by the National Research Foundation of Korea (NRF-2022R1A4A2000827, to T.W. Kim; and RS-2023-00280965, to T.W. Kim; NRF-2022R1A4A5032702, to K.-H. Song; and RS-2024-00457721, to K.-M. Lee).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHuang AC, Zappasodi R (2022) A decade of checkpoint blockade immunotherapy in melanoma: understanding the molecular basis for immune sensitivity and resistance. Nat Immunol 23:660\u0026ndash;670\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang Y, Zhang Z (2020) The history and advances in cancer immunotherapy: understanding the characteristics of tumor-infiltrating immune cells and their therapeutic implications. Cell Mol Immunol 17:807\u0026ndash;821\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSharma P, Hu-Lieskovan S, Wargo JA, Ribas A, Primary (2017) Adaptive, and Acquired Resistance to Cancer Immunotherapy. Cell 168:707\u0026ndash;723\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eO'Donnell JS, Teng MWL, Smyth MJ (2019) Cancer immunoediting and resistance to T cell-based immunotherapy. 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Bioinformatics 28:882\u0026ndash;883\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-5658722/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5658722/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCancer immunotherapies, including immune checkpoint blockade (ICB), have marked a significant breakthrough in cancer treatment but their clinical efficacy is limited in immune-resistant tumors. Previously, we found that immunotherapy-mediated immune selection enriches immune-resistant tumors with both tumor-intrinsic and -extrinsic refractory phenotypes via the transcriptional induction of HDAC1 by NANOG. Here, we identify CRY1 as a critical transcriptional target of NANOG that stabilizes Cyclin A and MCL1 to promote cancer stem cell-like property and resistance to cytotoxic T cell-mediated killing in NANOG\u003csup\u003ehigh\u003c/sup\u003e tumor cells through HDAC1-mediated epigenetic silencing of APC3 and TRIM17. Additionally, CRY1 downregulates CXCL10 via HDAC1-mediated repression, thereby suppressing T cell infiltration. Importantly, CRY1 inhibition synergizes with PD-1 blockade and adoptive T cell transfer in reducing tumor growth by converting immune-resistant tumors into immune-sensitive tumors. Collectively, these findings highlight CRY1 as a critical mediator of the NANOG/HDAC1 axis in the multiple refractory properties of immune-resistant tumors and suggest CRY1 as a potential therapeutic target.\u003c/p\u003e","manuscriptTitle":"CRY1 fuels resistance to T cell-based immunotherapy in NANOGhigh cancers","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-08 11:08:05","doi":"10.21203/rs.3.rs-5658722/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"46df7c97-6600-4723-8237-8fbb2c117978","owner":[],"postedDate":"January 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":42507505,"name":"Biological sciences/Cancer/Cancer therapy/Cancer therapeutic resistance"},{"id":42507506,"name":"Biological sciences/Cancer/Cancer therapy/Cancer immunotherapy"}],"tags":[],"updatedAt":"2026-03-17T10:35:54+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-08 11:08:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5658722","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5658722","identity":"rs-5658722","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}