mTORC1 Suppression by Trp53 Mutation Drives Resistance to Immune Checkpoint Blockade

mTORC1 Suppression by Trp53 Mutation Drives Resistance to Immune Checkpoint Blockade | 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 mTORC1 Suppression by Trp53 Mutation Drives Resistance to Immune Checkpoint Blockade Binfeng Lu, Alireza Labani-Motlagh, Yang Li, David Shihong Gao, and 10 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8214123/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract p53 is a critical tumor suppressor gene that inhibits cancer development by regulating cell cycle arrest, apoptosis, DNA repair, and metabolism. However, recent studies examining TP53 mutations in cancer immunotherapy have yielded inconsistent results, likely due to differences in t umor m utational b urden (TMB) and the context-dependent roles of specific p53 mutants. In this study, we assessed the function of G242V and S258I Trp53 mutations in MC38 cells in the context of immunotherapy by generating Trp53 deletion and observed significantly enhanced responses to anti-PD-1 therapy. Trp53 -null tumors showed increased CD8 + T cell infiltration and clonal expansion, along with reduced regulatory T (Treg) cells. Mechanistically, Trp53 deletion downregulated mTORC1 inhibitor genes, leading to elevated mTORC1 signaling and diminished autophagy, which sensitized tumor cells to IFN-γ and TNF-α-induced apoptosis. Besides mouse cells, we confirmed the human p53 mutants regulate the same sets of mTORC1 inhibitor genes in a human colorectal cancer cell line. Our findings demonstrate that certain p53 mutants, despite losing other canonical functions, retain wild-type p53’s ability to suppress mTORC1 and enhance autophagy, thereby inhibiting responses to immunotherapy. Biological sciences/Immunology/Cell death and immune response Biological sciences/Cancer/Tumour-suppressor proteins Immune checkpoint inhibitor p53 mTOR autophagy PD-1 T cells IFN-γ TNF-α cancer immunotherapy tumor suppressor Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction The p53 tumor suppressor protein, encoded by the TP53 gene, is a critical regulator of cellular processes and plays a fundamental role in cancer prevention. Often referred to as the "guardian of the genome," p53 is involved in multiple central cellular functions, including transcription, DNA repair, genomic stability, cell cycle control, and apoptosis( 1 , 2 ). The importance of p53 in tumor suppression is underscored by its frequent inactivation in human cancers, with mutations in TP53 observed in more than half of all sporadic tumors( 2 , 3 ). While the classical functions of p53 in cell cycle arrest, senescence, apoptosis, and metabolism have been well-established, recent research has unveiled its involvement in regulating tumor immune responses ( 2 , 4 ). Recent studies on the role of TP53 mutations in cancer immunotherapy have yielded complex and sometimes contradictory results. p53 activation in cancer cells can induce immune activating signals such as tumor necrosis factor-related apoptosis-inducing ligand (TRAIL), death receptor 5 (DR5), toll-like receptors (TLRs), and the cyclic GMP–AMP synthase (cGAS)–stimulator of IFN genes (STING) pathway ( 5 ). P53 mutants were shown to suppress antitumor immunity by interfering with the function of the cytoplasmic DNA sensing machinery, cGAS-STING-TBK1-IRF3( 6 ). Conversely, the activation of p53 in cancer cells can suppress immunity via increasing PDL1 levels( 5 ). In addition, TP53 mutations have been found to be associated with increased lymphocytes in a large-scale pan-cancer genomics and transcriptomic study ( 7 ). In human non-small cell lung cancer (NSCLC) patients, TP53 mutations are significantly associated with levels of immune checkpoint molecules, activated T-effector genes, and IFNG signature genes ( 8 , 9 ). NSCLC patients with co-occurring TP53/KRAS mutations showed improved clinical benefits after treatment with PD-1 inhibitors ( 9 ). In lung cancer patients treated with PD-1 inhibitors, TP53 mutations have been associated with better overall response rates and overall survival ( 10 – 12 ). For relapsed/refractory (R/R) acute myeloid leukemia (AML) patients treated with Flotetuzumab, an investigational CD123xCD3 bispecific dual-affinity retargeting antibody (DART) molecule, higher expression of immune markers such as IFNG, FOXP3, and immune checkpoints was observed in primary bone marrow samples with TP53 mutations when compared to those with wild-type TP53 . Moreover, TP53 mutations and deletions are associated with clinical response to Flotetuzumab immunotherapy in AML ( 13 ). In a neoadjuvant setting, a recent phase 2 clinical trial examined the combination of anti-CTLA4 and PD-1 monoclonal antibodies (mAbs) for operable NSCLC. The trial revealed that resected tumors with TP53 alterations exhibited a higher median pathological regression compared to tumors with wild-type P53( 14 ). The enhanced immune response observed against TP53 -mutated cancers is thought to be attributed to an increased t otal m utational b urden (TMB). This hypothesis aligns with the well-established role of p53 as a critical guardian of genomic stability. The discrepancies in these findings highlight the nuanced impact of TP53 mutations on immunotherapy response, which likely depends on factors such as the specific mutation type, cancer type, and broader genetic context such as TMB. This complexity underscores the need for further research to fully elucidate the relationship between TP53 mutations and immunotherapy outcomes. To investigate the role of p53 mutants in immunotherapy, we utilized MC38, a mouse colorectal cancer cell line known for its responsiveness to immunotherapy and reported to harbor Trp53 mutations( 15 ). Initially, we confirmed the presence of Trp53 mutations in the parental MC38 cells. We then explored the tumor-intrinsic effects of p53 mutants by generating Trp53 -deficient MC38 cells and evaluating their impact on the immunogenicity of the cell line. Additionally, we compared the influence of mutant p53 and Trp53 deletion on the efficacy of PD-1 blockade immunotherapy in a murine tumor immune therapy model. To gain mechanistic insights into the immune-modulatory effects of mutant p53 in vivo, we performed s ingle- c ell RNA seq uencing (scRNA-seq) and immunological analyses of both parental MC38 and Trp53 -deficient MC38 tumor cells, as well as their associated TMEs. Lastly, we elucidated the molecular mechanisms by which p53 mutants regulate antitumor immunity in both mouse and human colorectal cancer cells. Results The Trp53 mutations conferred resistance to cancer immunotherapy. To investigate the role of p53 in i mmune c heckpoint i nhibitor (ICI) therapy, we utilized MC38, a mouse colorectal cancer model known for its responsiveness to ICI treatment. Previous studies reported that MC38 harbors two mutated alleles of the Trp53 gene( 15 ). We characterized the Trp53 genomic region in MC38 cells and confirmed the presence of two mutations: G242V and S258I. Notably, the G242V mutation corresponds to the human TP53 hotspot mutation G245S, frequently observed in human cancers. Additionally, the TP53 G245S mutation is a missense mutation known to disrupt the structure of p53 and its ability to bind DNA( 16 ). The S258I mutation, on the other hand, is located at a splice site. This mutation has been shown to result in aberrant splicing, effectively creating a null allele of Trp53 ( 17 ). We definitively tested whether and how mutant p53 affects the efficacy of ICI immunotherapy using a loss-of-function approach in the transplant mouse model of MC38 colon adenocarcinoma. Using CRISPR-Cas9, we targeted for deletion a region of exon 4 in the Trp53 gene that encodes in the proline-rich domain and a part of the DNA-binding domain. Deletion of this region resulted in the elimination of p53 protein expression in vitro and Trp53 mRNA expression in vivo (Fig. 1 A). These data show that we have generated a Trp53 -null MC38 cell line, which we hereafter term Trp53 KO cells, along with their unedited controls, Trp53 CON cells. We next tested the response of Trp53 CON and Trp53 KO cells to anti-PD-1 therapy in vivo (Fig. 1 B). As early as day 9 – a time point at which anti-PD-1 therapy does not yet affect the growth of WT tumors – treated Trp53 KO tumors already had a significantly smaller volume compared to both untreated and treated Trp53 CON MC38 tumors. These differences remained at later time points, with 1/3 of Trp53 KO tumors undergoing complete remission (Fig. 1 C). Interestingly, untreated Trp53 KO tumors also grew slower than untreated and treated Trp53 CON MC38 tumors, though faster than their treated counterparts. Our results provide functional evidence that the p53 mutant in MC38 cells inhibits the efficacy of cancer immunotherapy. We next determined whether tumor cell-intrinsic or -extrinsic immune response changes were responsible for the reduction in tumor growth. Trp53 CON and Trp53 KO MC38 cells were inoculated into immunodeficient RAG1 −/− (lack B and T cells) and NSG (lack B, T, and NK cells) mice (Fig. 1 D). We found no difference in tumor growth between the Trp53 CON and Trp53 KO cells in either immunodeficient mice strains (Fig. 1 E-F). These data show that alterations to the immune response, and not cell-intrinsic changes induced by Trp53 deficiency in tumor cells, are required for the reduction of tumor growth and improved efficacy of checkpoint immunotherapy. Characterization of the immune responses in Trp53 CON and Trp53 KO tumors during anti-PD-1 therapy We next characterized how the Trp53 deletion in MC38 tumor cells alters the immune response. To this end, we performed single-cell RNA and paired single-cell TCR sequencing (scRNAseq and scTCRseq) of anti-PD-1 treated Trp53 CON and Trp53 KO tumors on day 11 (Fig. 2 A). We observed a decrease in monocytic myeloid-derived suppressor cells (mMDSCs) and a slight increase in neutrophils in Trp53 KO tumors compared to Trp53 CON tumors. Notably, TNK cells, encompassing all T cells, NK cells, and ILCs, were predominantly increased in Trp53 KO tumors relative to Trp53 CON tumors (Fig. 2 A). Although there is no striking difference in the total DC population, both the cDC1 and cDC2 populations decreased, while mature DCs increased in Trp53 KO tumors, suggesting an increased DC maturation in these tumors (Fig. 2 B). In the monocyte/macrophage compartment, we observed an increase in macrophage 1 (Mac1) and macrophage 2 (Mac2) populations, suggesting an enhanced monocyte-to-macrophage differentiation program (Fig. 2 C). Within the T cell population, we found that CD8 + T cells increased in Trp53 KO tumors while Treg cells decreased (Fig. 2 D). Paired scTCR-seq enabled us to analyze clonal expansion in T cells. We found that the average clonal size of expanded clones was significantly larger in CD8⁺ T cells from Trp53 KO tumors than those from Trp53 CON tumors (Fig. 2 E). We also analyzed differential gene expression in CD8⁺ T cells from Trp53 KO and Trp53 CON tumors. CD8⁺ T cells from Trp53 KO tumors exhibited higher expression levels of checkpoint molecules, including Lag3 , Tim3 , and Tigit , as well as costimulatory molecules such as 4-1bb , Gitr , and Icos . Additionally, they expressed higher levels of chemokine receptors ( Cxcr6 ), effector molecules ( Gzmb ), signaling molecules involved in the TCR complex, and transcription factors that drive effector T cell function, including Nfkb1 , Runx3 , Irf8 , and Batf (Fig. 2 F). In contrast, CD8⁺ T cells from Trp53 CON tumors predominantly expressed early activation markers such as Cd69 and Fos , along with chronic inflammation-associated genes, particularly interferon (IFN)-induced genes (Fig. 2 G). Collectively, our analysis reveals that CD8⁺ T cells in Trp53 KO tumors undergo greater clonal expansion and exhibit a more activated phenotype, which aligns with the increased susceptibility of Trp53 KO tumors to anit-PD1 ICI treatment. We validated our characterization of the immune response by performing multicolor flow cytometry (Fig. 3 A). We found that treated Trp53 KO tumors had increased CD8 + T cells that expressed an intense type 1 signature of IFN-g and GzmB, compared to both untreated and treated Trp53 CON tumors (Fig. 3 B-D, Supplemental Fig. 1 ). In addition, we also observed an increase in conventional CD4 + T cells and a decrease in Treg cells in the treated Trp53 KO tumors compared to both untreated and treated Trp53 CON tumors (Fig. 3 E-F). Additionally, we observed a decrease in mMDSCs and a trend toward an increase in gMDSCs in Trp53 KO tumors compared to Trp53 CON tumors (Fig. 3 G-H, Supplemental Fig. 2 ). Our profiling of the immune response consistently showed that Trp53 deletion in tumor cells and anti-PD-1 therapy drastically alter the immune response in vivo , primarily augmenting T cell responses. Trp53 deletion leads to altered oncogenic programs and increased immune signature genes in tumor cells. The alteration of tumor growth prompted us to investigate how Trp53 deletion affects tumor cells during cancer immunotherapy compared with Trp53 CON MC38 in vivo. We conducted a comprehensive analysis of differentially expressed genes (DEGs) in Trp53 CON and Trp53 KO MC38 cells in the scRNAseq data. G ene s et e nrichment a nalysis (GSEA) using gene ontology, hallmark, and reactome datasets showed that Trp53 CON cells exhibited upregulation of processes linked to e pithelial- m esenchymal t ransition (EMT) and hypoxia, such as extracellular matrix organization, cell adhesion, coagulation, hypoxia response, collagen formation, and KRAS signaling downregulation (Fig. 4 A and Supplemental Table 1 ). In addition, we utilized the D atabase for A nnotation, V isualization, and I ntegrated Discovery (DAVID)( 18 , 19 ) to functionally annotate the pathways, focusing on cancer cell-intrinsic programs that were differentially regulated based on the identified DEGs (Fig. 4 B and Supplemental Table 2 ). The upregulated pathways in Trp53 CON MC38 cells can be categorized into transcriptional regulation by TP53, stromal responses, anti-viral responses, stress responses, Wnt signaling, and autophagy/metabolic regulation. The "Transcriptional Regulation by Trp53 " pathway was significantly upregulated, reflecting residual canonical P53 function in MC38 cells, with genes such as Btg2 , Sesn3 , Ubb , Zfp385a , Trp53 , Ddit4 , Tnks1bp1 , and Cox6a2 . Stromal pathways were also prominently activated on Trp53 CON MC38 cells, including angiogenesis ( Acvrl1, Xbp1, Ecscsr, Fn1, Hspg2, Rhob, Eng ), collagen-containing extracellular matrix ( Scara3, Sparc, Fn1, Lamc1, Col1a1, Thbs2, Col5a1, Col5a2 , and Serpinh1 ), platelet degranulation ( Islr, Sparc, Fn1, Aldoa , and Clu ), signaling by Notch3 ( Ncstn, Ubb , and Wwp2 ), and the TGF-beta receptor signaling signature ( Acvrl1, Jun, Cited1, Trp53 , and Ccl2 ), emphasizing tissue remodeling and immune modulation by the mutant p53. Anti-viral mechanisms were significantly upregulated, with pathways like antiviral defense ( Mavs, Apobec3, Rsad2, Ddit4, Isg15 , and Ifit1 ), double-stranded DNA binding ( Nr5a1, Egr1, Jun, Aim2, Jund, Fosb , and Fos ), IFN-stimulated genes ( Mavs, Isg15, Usp18 , and Hspa1b ), and RNA binding ( Apobec3, Rbpms, Msi2 , and Ifit1 ) collectively indicating heightened nucleic acid sensing and antiviral responses in Trp53 CON MC38 cells. Stress pathways, such as unfolded protein binding ( Serpinh1, Clu, Cryab , and Hspa1b ) and cellular senescence ( Jun, Ubb, Trp53 , and Fos ), reflect adaptations to cellular damage. The Wnt signaling pathway exhibited mixed regulation, with genes promoting Wnt signaling ( Rspo3, Rspo4, Wls, Csnk2a1, Csnk2a2 , and Ccnd1 ) and genes inhibiting it ( Hic1, Tle5 ). Autophagy and metabolic pathways were highly induced, including mitochondrial function ( Cyb5b, Pink1, Mavs, Ubb, Bnip3, Ddit4 , and Mgarp ), cellular response to hypoxia ( Pink1, Bnip3, Trp53 , and Hif1a ), positive regulation of macroautophagy ( Pink1, Sesn3, Bnip3 , and Hif1a ), and insulin-like growth factor I binding ( Igfbp5, Igfbp4 , and Igfbp6 ), highlighting mitochondrial dynamics, hypoxia adaptation, and enhanced autophagic processes. Together, these results reveal a complex response network involving transcriptional regulation, stromal remodeling, immune defense, stress adaptation, and metabolic reprogramming. In Trp53 KO cells, GSEA analysis revealed that pathways associated with increased protein synthesis and proliferation were significantly upregulated, including ribosome biogenesis, MYC targets, eukaryotic translation initiation, RNA metabolism, E2F targets, mRNA splicing, and cell cycle processes (Fig. 4 A and Supplemental Table 1 ). DAVID analysis showed a similar pattern of changes (Fig. 4 B and Supplemental Table 2 ). The upregulated pathways in Trp53 KO MC38 cells span multiple major categories, reflecting diverse biological processes ( Fig. 4 B). Tumor promotion signaling pathways were also prominent, including "EGFR tyrosine kinase inhibitor resistance," "ErbB signaling," "Hippo signaling," and multiple "Wnt signaling" pathways. These are driven by genes such as Nras, Plcγ2, Prkca, Wnt5a, Sox9 , and Csnk1e , which regulate cell proliferation, differentiation, and oncogenic signaling. Angiogenesis-related pathways such as "Angiogenesis" and "PDGF signaling" were highlighted, with genes like Pdgfa, Prkca , and Vegfa indicating enhanced vascularization and extracellular matrix remodeling. In the mTOR category, pathways such as "mTOR signaling," "PI3K-Akt signaling," and "Amino acid regulation of mTORC1" were enriched, involving genes like Nras, Lamtor3, Prkca , and Eif4e , reflecting metabolic regulation and cellular growth. Autophagy pathways, including "Macroautophagy" and "Cellular response to starvation," were driven by genes like Gabarapl2, Lamtor3 , and Tomm20 , emphasizing cellular recycling and stress responses. Protein translation promotion was observed through pathways such as "rRNA processing," "cytoplasmic translation," "mRNA processing," and "Eukaryotic translation initiation," involving genes like Rpl31, Rpl34, Rps27 , and Eif4e . These pathways highlight robust translational activity. Gene transcription regulation pathways, including "Transcription by RNA polymerase II," "RNA Pol II CTD phosphorylation," and "Transcription initiation and promoter clearance," involved genes such as Polr2c, Gtf2h3 , and Gtf2h5 . Lastly, DNA replication pathways, such as "Cell Cycle," "DNA Replication," "Mitotic Prometaphase," and "Chromosome segregation," featured genes like Cdt1, Mcm3, Cdk1 , and Cenpe , reflecting cell division and genomic stability. Together, these pathways suggest enhanced mTOR signaling, reduced autophagy, and metabolic alterations that collectively promote cancer cell proliferation in Trp53 KO MC38 cells. In addition to cancer cell gene programs, we analyzed immune gene programs differentially expressed in Trp53 CON and Trp53 KO MC38 cells in vivo. Notably, the type 1 immune response program, which includes genes such as Cxcl9, Gbp2, Gbp3, Cxcl10, Gbp7, Stat1, Irf1, Gbp4 , and Cd274 , was significantly enriched (Fig. 4 C). In contrast, Trp53 CON cells exhibited higher expression of chemokines that attract myeloid cells, enhanced interferon signatures, and increased levels of TGF-β–related genes (Fig. 4 C). These findings align with the observation that Trp53 KO MC38 tumors exhibit increased sensitivity to PD-1 blockade. p53 deficiency in MC38 cells increased sensitivity to TNF-α/IFN-γ-induced cancer cell death. CD8 + T cells promote antitumor immunity through several mechanisms, including cytokine-triggered cell death by releasing inflammatory cytokines such as IFN- γ and tumor necrosis factor alpha (TNF-α), as well as perforin-dependent tumor cell killing ( 20 ). Several recent studies showed that resistance to IFN- γ and TNF-α-mediated cancer cell death is a major mechanism of immune evasion ( 21 – 24 ). Because we found that Trp53 deletion in MC38 cells led to increased sensitivity to PD-1-blockade immunotherapy in vivo, we aimed to determine whether the p53 mutant regulates IFN- γ and TNF-α-induced cancer cell death in vitro. To this end, we incubated MC38 (p53 CON control clones) and Trp53 KO -MC38 cells (two independently generated clones #6 and #16) with IFN- γ , TNF-α, or IFN- γ plus TNF-α. The combination of IFN- γ and TNF-α reduced live control MC38 (p53 MUT ) cells (Fig. 5 A). Further analysis demonstrated an increase in apoptosis, as determined by Western blot of active caspase 3 and caspase 8, in Trp53 KO MC38 cells treated with IFN- γ and TNF-α when compared to control Trp53 CON MC38 cells (Fig. 5 B). Notably, the protein level of pro-Caspase-8 was also increased in Trp53 KO MC38 cells compared to Trp53 CON MC38 cells (Fig. 5 B). These data indicate that mutant p53 inhibits IFN- γ and TNF-α-induced tumor cell death. Trp53 deletion in MC38 cells increased mTOR activation and diminished autophagy. Autophagy is a vital cellular process that plays a key role in suppressing cancer cell apoptosis( 25 , 26 ). Notably, autophagy has also been shown to inhibit TNF-α-induced apoptosis, serving as a mechanism of cancer immune evasion( 21 , 22 , 27 ). Since mTORC1 is a well-established inhibitor of autophagy, p53’s role in suppressing mTOR signaling is particularly relevant( 28 – 30 ). To further investigate this relationship, we examined mTORC1 activity and autophagy in Trp53 KO and Trp53 CON MC38 cells. Our analysis revealed that Trp53 KO MC38 cells exhibit significantly higher mTORC1 activity compared to Trp53 CON MC38 cells as measured by levels of phosphorylated p70 S6 Kinase (Fig. 6 A). To assess autophagy levels, we measured p62 protein, a widely used marker of autophagic activity. p62 levels were significantly elevated in Trp53 KO MC38 cells relative to Trp53 CON MC38 cells (Fig. 6 A), suggesting lower autophagy activity in Trp53 KO cells. In addition, we found that inhibiting autophagy potentiates TNFα/IFNγ-induced cell death in multiple murine cancer cell lines (Fig. 6 B). These findings suggest that mutant Trp53 suppresses mTORC1 activity, leading to enhanced autophagy and ultimately inhibiting apoptosis. Consistent with the notion, scRNA-seq analysis revealed that Trp53 KO cells exhibit reduced expression of genes involved in mTORC1 inhibition, including Ddit4, Sesn2, Sesn3, Castor1 , and Castor2 ( 29 , 31 – 33 ) (Fig. 4 A, B, Fig. 6 C). Given that CASTOR1 is a known negative regulator of mTORC1 activity( 34 ), we focused on its expression. RT-qPCR analysis confirmed that Castor1 expression was significantly reduced in Trp53 KO MC38 cells compared to Trp53 CON MC38 cells (Fig. 6 D). These data suggest that the Trp53 mutation in MC38 cells enhances the expression of mTORC1 inhibitor genes, which consequently inhibits mTORC1 signaling and promotes autophagy. To determine whether TP53 mutations similarly impact these genes in human cells, we analyzed a recently published scRNA-seq dataset derived from HCT116 (a human colorectal cancer cell line) engineered to harbor all known TP53 hotspot mutations( 17 ). The typical TP53 target gene CDKN1A is downregulated in all cells harboring various TP53 mutations (Fig. 6 E). Conversely, mTORC1 inhibitor genes, including DDIT4 , SESN3 and CASTOR1 , displayed variable expression patterns in TP53 mutants: their expression remained unaffected in some mutants but was completely absent in others (Fig. 6 E). Notably, in the G245S mutant, SESN3 and CASTOR1 expression was modestly reduced, whereas DDIT4 expression remained largely unaffected (Fig. 6 E). Collectively, these results demonstrate certain p53 mutants, including G245S (and its murine counterpart G242V), despite losing other canonical functions, retain some levels of the wild-type p53’s ability to suppress mTORC1 and enhance autophagy, thereby inhibiting responses to immunotherapy. Discussion In this study, we demonstrated that deleting mutant Trp53 in MC38 cells significantly enhances the efficacy of PD-1 immune checkpoint blockade (ICB) therapy. This improved response is driven by increased CD8⁺ T cell clonal expansion and reduced Treg infiltration within the TME. Mechanistically, Trp53 deletion leads to hyperactivation of mTOR signaling and suppression of autophagy, sensitizing tumor cells to cytokine-induced apoptosis. These findings reveal a novel mechanism linking Trp53 mutations to immunotherapy outcomes, providing fresh mechanistic insights into how Trp53 status influences immune responses in cancer. A substantial body of research, especially in lung cancer, indicates that TP53 mutations are strongly correlated with increased expression of immune checkpoint molecules, activation of T-effector genes, and enrichment of IFNγ signature genes ( 7 ). These findings are corroborated by multiple studies ( 8 , 9 ). Notably, non-small cell lung cancer (NSCLC) patients harboring TP53 mutations exhibit improved clinical outcomes, including prolonged survival, in response to PD-1 inhibitor treatment ( 9 – 12 ). However, a major confounding factor in these studies is the possibility that Trp53 mutations indirectly affect t umor m utational b urden (TMB) and neoantigen load, rather than exerting a direct impact on immune surveillance( 35 ). In contrast, some p53 mutants, including P142L, P152Q, A161V, C174Y, R175H, R248W, R249S, R273H, and R280K, have been reported to interact with TBK1 and inhibit STING activation( 6 ), and potentially inhibit cancer immunotherapy. In addition, another study demonstrates that Trp53 loss, within the genetic context of Kras mutation and TMB, promotes immune resistance in an autochthonous mouse lung cancer model ( 36 ). This observation aligns with the well-established role of wild-type P53 in stimulating innate immune responses ( 37 ). Our study uncovers distinct and direct mechanisms of the G242V mutant (corresponding to the human TP53 hotspot mutation G245S), including suppression of mTOR signaling and induction of autophagy. We also showed that this property of G245S is shared with some but not all p53 mutants. Biochemical, structural, and in vivo studies have demonstrated that the G245S TP53 mutation retains greater residual p53 function compared to other mutations, such as R248Q. Specifically, G245S preserves approximately 25% of wild-type p53 transcriptional activity for select promoters, such as p21, while R248Q exhibits negligible transcriptional activity and fails to induce canonical targets like PUMA and caspase-3 ( 38 ). Structurally, G245S induces localized destabilization in the DNA-binding domain’s L3 loop, leading to a reduction of DNA affinity by approximately 15-fold. In contrast, R248Q disrupts both DNA minor-groove binding and global domain stability( 16 , 39 ). In vivo, G245S/- mice display delayed tumor onset, whereas R248Q/- mice exhibit accelerated tumorigenesis, expanded stem cell pools, and reduced survival( 38 ). Despite its weakened oncogenic activity, the G245S p53 mutant, through its preserved capacity to suppress mTORC1, hinders the efficacy of cancer immunotherapy. Interestingly, a recent study demonstrated that the mouse R172H mutation—a well-characterized missense mutation in the p53 DNA-binding domain classified as a structural mutant that impairs p53’s tumor suppressor function and exhibits both dominant-negative and gain-of-function (GOF) effects—inhibits the antitumor immune response by upregulating the CXCL1–neutrophil axis ( 40 ). Collectively, multiple in vitro and mouse studies demonstrate the critical role of p53 mutants in inhibiting antitumor immunity, albeit through diverse mechanisms. Under stress conditions, p53 generally inhibits mTORC1 activity through multiple mechanisms( 29 , 31 – 33 ). One key pathway involves p53-mediated activation of AMPK, which inhibits mTORC1 via the TSC1/2 complex. Additionally, p53 can also decrease S6K1 activity and promote 4E-BP1 dephosphorylation, further limiting mTORC1 signaling. P53 has also been shown to induce genes encoding proteins that regulate mTORC1 activation. p53 induces PTEN transcription, which in turn inhibits the PI3K/AKT pathway, leading to mTORC1 suppression. Moreover, DDIT4, a transcriptional target of P53, suppresses mTORC1 through the same TSC1/2-dependent mechanism. The SESTRIN (SESN1–3) family, also transcriptionally regulated by p53, plays a crucial role in suppressing both mTORC1 and mTORC2( 41 ). In our experimental system, we found no evidence of p53-mediated Pten regulation. However, we observed a significant upregulation of Sesn2 , Sesn3 , and Ddit4 in MC38 cells, specifically dependent on the Trp53 G245S mutation. Furthermore, we identified CASTOR1, a known mTORC1 inhibitor, as an additional p53 G245S -regulated gene in these cells. Consistent with these findings, we demonstrated that wild-type p53 promotes the expression of these mTORC1 regulatory genes in human HCT116 cells. Notably, different p53 mutants exhibited varying degrees of functional deficiency in this context. Specifically, the p53 G245S mutant retained some of its ability to induce the expression of these mTORC1 regulatory genes. These results align with recent studies reporting that p53 deletion in human lung and colon cancer cell lines, including HCT116, leads to increased mTORC1 activation. Moreover, several common p53 hotspot mutants (R175H, R248W, and R273H) failed to suppress mTORC1 activation in a p53-null background, underscoring the functional heterogeneity of p53 mutants in regulating mTORC1 activity ( 42 ). Our study demonstrates a new mechanism by which p53 mutants promote cancer immunotherapy resistance through the modulation of mTORC1 activation and autophagy inhibition. This observation highlights the potential of targeting the P53/mTORC1 axis to improve cancer immunotherapy outcomes. Materials and methods Mice Mice were maintained in a specific-pathogen-free animal facility in accordance with an animal protocol approved by the Institutional Animal Care and Use Committee of the University of Pittsburgh. WT (stock #000664) and RAG1 -/- (B6.129S7- Rag1 tm1Mom /J, 002216) mice on the C57Bl/6J background, as well as NSG (NOD.Cg- Prkdc scid Il2rg tm1Wjl /SzJ, 005557; RRID:BCBC_4611) were purchased from the Jackson laboratory. Male and female mice aged 6 to 12 weeks were used in the study. The sample size for each group was calculated based on a power analysis to ensure adequate statistical power. Tissue culture MC38 colon adenocarcinoma cells (Cat# YC-A002, RRID:CVCL_B288) were maintained in Dubecco’s Modified Eagle Medium, high glucose supplemented with 10% fetal bovine serum in the presence of benzylpenicillin (100 U/mL), streptomycin (100 mg/mL), and 2 mmol/L L-glutamine and 1% penicillin-streptomycin. CT26 colon cancer (Cat# YC-A002, (RRID:CVCL_7254), and 4T1 breast carcinoma cells (RRID:CVCL_0125) were all cultured in RPMI-1640 (#11875-093, Gibco) supplemented with 10% FBS and 1% penicillin-streptomycin. EMT6 breast carcinoma cells (ATCC Cat# CRL-2755, RRID:CVCL_1923) were cultured in Waymouth’s medium (#11220-035, Gibco) supplemented with 15% FBS and 1% penicillin-streptomycin. The cells were frequently checked for mycoplasma using Universal Mycoplasma Detection kit (#30-1012k, ATCC). Verification of G242V and S258I mutations in MC38 cells Mutations in Trp53 were confirmed through PCR amplification and Sanger sequencing. Genomic DNA was extracted from MC38 wild-type and clonal lines (#6 and #16) using a tissue DNA extraction kit with heat lysis at 95°C and stabilization buffer treatment. To verify the G242V mutation, the region encompassing exon 7 was amplified using the primers mP53 Forward (5’ GCTATAGCCAGCCATTCCC 3’) and mP53 Reverse (5’ ACCATCCAATCCAATCGGACA 3’). PCR was performed using OneTaq® Quick-Load® 2X Master Mix under the following thermal conditions: 95°C for 5 min; 35 cycles of 95°C for 30 sec, 58°C for 30 sec, 72°C for 1 min; and a final extension at 72°C for 5 min. PCR products were purified using a PCR purification kit and sequenced using the forward sequencing primer TGGTAGGTTAGGTTAGCCTGT. The G242V mutation was confirmed by identifying a G-to-T nucleotide substitution in exon 7. Similarly, the S258I mutation was validated for having a T-to-G substitution resulting in a serine-to-isoleucine amino acid change. Generation of Trp53 deletion in MC38 cells Trp53 knockout was performed using CRISPR-Cas9. Briefly, single-guide RNA (sgRNA) was designed using online CRISPR Design Tool ( https://crispr.cos.uni-heidelberg.de ) and cloned into plasmid lentiCRISPRv2GFP (Addgene, catalog no. 82416). The sgRNA sequences were designed to delete exon 4 of mouse Trp53 . They are gRNA1: ACAGCCATCACCTCACTGCA, gRNA2: ACACTCGGAGGGCTTCACTT. The plasmids were transfected into the MC38 cell line using Lipofectamine 2000 (Thermo Fisher Scientific, catalog no. 11668030). Transfected cells were sorted and single cells cloned, and mutant cells were identified using genomic DNA PCR and confirmed by Western blot analysis. The genomic target sequences used for targeting screening were AGGAAATCAGGAACTAACTCTCTGCTCTT (forward) and TGCACATAACAGACTTGGCTGTCC (reverse). Tumor models Mice were shaved and inoculated intradermally with Trp53 CON and Trp53 KO MC38 cells at 10 6 cells/50uL PBS/tumor. One tumor was inoculated per mouse. Tumor length (L) and width (W) were measured using a digital caliper. Tumor volume was calculated using the formula: L x W x W x 0.5. Tumors were treated with anti-PD-1 purchased from BioxCell InVivo MAb anti-mouse PD-1 (catalog #BE0146). Antibodies were aliquoted and diluted in PBS to the required concentration to inject each mice intraperitoneally with 100ug in 100uL. Mice were treated on days 5, 9, 13, and 17, and tumors were allowed to grow up beyond the last treatment. Tissue processing Mice were euthanized and tumors immediately dissected. Excess skin, hair, fat, and connective tissue were removed. Tumors were transferred into 6-well plates containing 0.25mg/mL LiberaseTL (Roche, 5401020001) and 0.33mg/mL DNase (Sigma-Aldrich DN25-10MG) in RPMI, minced with scissors, and digested in a tissue culture incubator for 30min without agitation. Digestion was quenched with 3mL RPMI. Samples were strained through 70-uM strainers and pushed through using a pestle, then strained through 30uM nylon mesh. Samples were pelleted and resuspended in 2% FBS in HANKS. Flow cytometry Staining was performed in 96-well V-bottom plates. For cytokine analysis, cells were first stimulated using the Leukocyte Activation Cocktail, with BD GolgiPlug (BD Biosciences 550583) in a tissue culture incubator for 4 hours. Cells were initially stained in 0.1% Ghost Dye Violet 510 (Tonbo Biosciences, 13-0870-T500) in PBS on ice for 30min. They were then stained in antibody cocktails in 2% FBS in HANKS on ice for 5 min. Cells were then filtered through 30uM nylon mesh. Samples were run on a Cytek Aurora. The following antibodies were used: ArgI (eBioscience, catalog #46-3697-82, clone A1exF5), CD4 (BD Biosciences, 612844, RM4-5; RRID:AB_2870166), CD8a (BD Biosciences, 566096, 53 − 6.7; RRID:AB_2739500), CD11b (BD Biosciences, 564443, M1/70; RRID:AB_2738811), CD11c (Biolegend, 117312, N418; RRID:AB_389328), CD24 (BioLegend, 101822, M1/69; RRID:AB_756048), CD45 (BioLegend, 103130, c30-F11; RRID:AB_893339), CD44 (BioLegend, 103005, IM7; RRID:AB_312956), CD62L(BD Biosciences, 560514; RRID:AB_10611861), CD80 (BioLegend,104724, 16-10A1; RRID:AB_2075999), CD86 (BioLegend, 105043, GL-1; RRID:AB_2566722), CD206 (BioLegend, 141708, C068C2; RRID:AB_10900231), F4/80 (BioLegend, 123130, BM8; RRID:AB_2293450), Gr-1, Ly-6C (BioLegend, 128037, HK1.4; RRID:AB_2562630), Ly-6G (BioLegend, 127628, 1A8; RRID:AB_2562567), MHCII, PD-1 (BD Biosciences, 551892, J43; RRID:AB_394284), Tim-3 (BioLegend, 119721, RMT3-23; RRID:AB_2616907), LAG-3 (BioLegend, 125212, C9B7W; RRID:AB_2561517), ST2 (eBioscience, 46-9333-82, RMST2-33), CD103 (BD Biosciences, 565849, M290; RRID:AB_2739377), TCF1 (Cell Signaling Technology, 14456, C63D9; RRID:AB_2798483), Foxp3 (BioLegend, 126410, MF-14; RRID:AB_2105047), GzmB (BioLegend, 372204, QA16A02; RRID:AB_2687028), Ki-67 (BioLegend, 652410, 16A8; RRID:AB_2562141), and IFN-γ (BioLegend, 505826, clone XMG1.2; RRID:AB_2295770). All antibodies were used at 1:100 dilution. scRNAseq Dissected and trimmed tumors were washed in pre-cooled RNase-free H 2 O and transferred into MACS Tissue storage solution (Miltenyi Biotec, 130-100-008). Samples were shipped on ice overnight to Novogene. Samples were processed according to the standard 10X pipeline. Three tumors from separate mice were prepared separately for each condition. The sample with the highest best cell viability and concentration for each condition was chosen. From the two samples, 10000 cells (target 7000 cells in output) per sample were loaded onto two separate lanes of a Chromium Chip in a Chromium Controller (10X Genomics) with 5’ chemistry for gene expression and TCR analysis. Libraries were sequenced on an Illumina Novaseq 6000 S4 with 30000 reads/cell (~ 200M reads total, 60Gb data output). Cellranger 2 was used to align fastq files, which were aligned to the mm10 reference genome and generate barcode, feature, and count matrices. Initial downstream analysis was performed using Seurat and Scanpy. Custom code was used for further analyses. Cell death stimulation and autophagy inhibition MC38 and Trp53 KO MC38 clones (each 8x10 4 cells per well) were seeded in 24-well plates for 24h before treatment with murine IFNγ (50ng/ml, #315-05, Pepro Tech), murine TNFα (50ng/ml, #315-01A, Pepro Tech), their combination, or left untreated. The cytokines were added into each well, and the cells were incubated at 37°C for 24h and 48h prior to counting with a TC20 cell counter (Bio-Rad). For autophagy inhibition, MC38, B16, 4T1, EMT6 cells (8x10 4 per well), and CT26 cells (10 5 per well) were seeded in 24-well plates. Next day, the cells were treated with 50ng/ml of both IFNγ and TNFα as well as 6µM and/or 10µM of EAD1 (#S8576, Selleck Chemicals). The cells were then incubated at 37°C for 24h and 48h prior to counting with a TC20 cell counter (Bio-Rad). Western blot analysis Trp53 CON and Trp53 KO MC38 clone were treated with both IFNγ and TNFα or left untreated and incubated for 24h, 36h, and 48h at 37°C. The harvested cells were lysed with lysis buffer and ran into Mini-Protean TGX pre-cast gels (#4561085, Bio-Rad) prior to transferring to PVDF membranes using a Trans-Blot Turbo (#1704150, Bio-Rad). The membranes were then blocked with 6% non-fat milk and subjected to primary antibodies for overnight incubation. The mouse primary antibodies used for the experiments include caspase 3 (#9662; RRID:AB_331439), caspase 8 (#4927; RRID:AB_2068301), cleaved caspase 3 (#9664; RRID:AB_2070042), cleaved caspase 8 (#8592; RRID:AB_10891784), p62 (#39749; RRID:AB_2799160), phosphorylated P70S6K (#9205; RRID:AB_330944) and beta-actin (#4970; RRID:AB_2223172). The membranes were incubated with secondary antibody, mouse anti rabbit IgG (#93702; RRID:AB_2800208) after and before washing steps. All antibodies were purchased from Cell Signaling (Danvers, MA, USA). All washing steps were conducted with TBST at room temperature. At the end, an equal amount of SuperSignal West Pico Plus (#34580, Thermo Scientific) was added to the membranes to visualize the bands in a ChemiDoc imaging system (Bio-Rad). The bands were quantified with Image Lab version 6.1 (Bio-Rad). Quantitative RT-PCR The cells were treated with mouse IFNγ (50ng/ml), TNFα (50ng/ml), their combination or left untreated for 24h. Total RNA was isolated from the cells using RNeasy Plus Mini kit (#74134, Qiagen). Complementary DNA was synthesized using qScript cDNA Synthesis kit (#95047, Quanta bio). Quantitative RT-PCR was performed using PowerUp SYBR Green Master Mix (#A25742, Applied Biosystems) to detect Castor1 gene with the following primers: CAGAACCGCTTTTGTGTCCTCAC (forward), GGAGAAAGCGAAGAACGGAATGG (reverse). GAPDH was utilized as endogenous control using AGGTCGGTGTGAACGGATTTG (forward) and GGGGTCGTTGATGGCAACA (reverse) primers. The MicroAmp Optical 96-well Reaction plates (#4306737, Applied Biosystems) were run in a QuantStudio 5 (Applied Biosystems). Statistical analyses GraphPad Prism 6.0 software package (GraphPad Software, Inc.; RRID:SCR_002798) was used for the analysis. Statistical analysis was done using the Student t test (two-tailed), ordinary one-way ANOVA, or the log-rank survival analysis. A P value of < 0.05 was considered to be statistically significant. No statistical correction was used. Data availability The generated data is available upon request from the corresponding author. Public scRNA-seq data were retrieved from EMBL BioStudies ( https://www.ebi.ac.uk/biostudies/ ), under accession number E-MTAB-13904. scRNAseq data will be uploaded to Gene Expression Omnibus upon publication (RRID:SCR_005012). Contributions Conceptualization: B.L., Y.L., D.S.G. A.L.M. 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Gating strategy for T cells. Live CD45 + single cells were further gated on CD4 and CD8 markers. Treg populations were gated as Foxp3 + CD4 + T cells. IFNG and GzmB expression were further gated in CD8 + T cells. Supplemental Figure 2. Gating strategy for MDSC. Live CD45 + single cells were further gated on monocytic and neutrophilic MDSC markers Ly6c and Ly6g as indicated. Cite Share Download PDF Status: Under Review Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Pittsburgh","correspondingAuthor":false,"prefix":"","firstName":"Xinghua","middleName":"","lastName":"Lu","suffix":""}],"badges":[],"createdAt":"2025-11-26 14:45:34","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8214123/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8214123/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":98390572,"identity":"e2e70ba7-6ab5-43ff-b76d-7a38ce1307cc","added_by":"auto","created_at":"2025-12-17 09:25:16","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":616492,"visible":true,"origin":"","legend":"","description":"","filename":"Figure1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8214123/v1/04fcd857d73d7a8f92e85d38.pdf"},{"id":98390584,"identity":"f928f9c8-717f-4256-8bd2-1af5b2b02cae","added_by":"auto","created_at":"2025-12-17 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09:25:16","extension":"csv","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":9706,"visible":true,"origin":"","legend":"","description":"","filename":"supplementaltable1.csv","url":"https://assets-eu.researchsquare.com/files/rs-8214123/v1/ad8d1ce7a5ac23409af0057b.csv"},{"id":98441490,"identity":"d3059ff4-ff4e-4afc-83da-e2c2bf4b7872","added_by":"auto","created_at":"2025-12-17 17:05:29","extension":"csv","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":3806,"visible":true,"origin":"","legend":"","description":"","filename":"supplementaltable2.csv","url":"https://assets-eu.researchsquare.com/files/rs-8214123/v1/853c8a9fe5076a49c5ad0445.csv"},{"id":98441604,"identity":"2c870702-034b-418f-84ff-7aecd626b4b6","added_by":"auto","created_at":"2025-12-17 17:05:38","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":412935,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eTrp53\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e deletion in tumor cells increases response to cancer immunotherapy. \u003c/strong\u003e(A)\u003cem\u003e Trp53\u003c/em\u003e protein and mRNA levels in\u003cem\u003e Trp53\u003c/em\u003e “knock-out” (\u003cem\u003eTrp53\u003c/em\u003e\u003csup\u003eKO\u003c/sup\u003e) and control (\u003cem\u003eTrp53\u003c/em\u003e\u003csup\u003eCON\u003c/sup\u003e) MC38 tumor cells were assessed using Western blot analysis of in vitro cultured cells and scRNA-seq on isolated tumors, respectively. (B) Schematic drawing of anti-PD1 monoclonal antibody (mAb) treatment in\u003cem\u003e Trp53\u003c/em\u003e\u003csup\u003eCON\u003c/sup\u003e and\u003cem\u003e Trp53\u003c/em\u003e\u003csup\u003eKO\u003c/sup\u003e tumors. (C)\u003cem\u003e Trp53\u003c/em\u003e\u003csup\u003eCON\u003c/sup\u003e and\u003cem\u003e Trp53\u003c/em\u003e\u003csup\u003eKO\u003c/sup\u003e tumor cells were inoculated into C57BL/6J mice (n=5), treated with anti-PD-1 therapy, and tumor growth was measured. (D-F)\u003cem\u003e Trp53\u003c/em\u003e\u003csup\u003eCON\u003c/sup\u003e and\u003cem\u003e Trp53\u003c/em\u003e\u003csup\u003eKO\u003c/sup\u003e tumor cells were inoculated into NSG or RAG1-/- mice (n=5), and tumor growth was measured. **** p\u0026lt;0.0001 ** p\u0026lt;0.01, two-way ANOVA.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-8214123/v1/106d3b5d04b270a77fe7ff63.png"},{"id":98390585,"identity":"e8a1dc76-a2ee-432c-b093-fea992d0cbcd","added_by":"auto","created_at":"2025-12-17 09:25:17","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1104669,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eTrp53\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e deletion led to enhanced adaptive antitumor immune responses in the TME.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) \u003cem\u003eTrp53\u003c/em\u003e\u003csup\u003eCON\u003c/sup\u003e (CON) and \u003cem\u003eTrp53\u003c/em\u003e\u003csup\u003eKO\u003c/sup\u003e (KO) tumors were treated with anti-PD1 mAb. Ten days post-tumor cell inoculation, paired scRNAseq and scTCRseq were performed. The left panel shows a Uniform Manifold Approximation and Projection (UMAP) plot illustrating the diverse cell populations within the TME. The right panel presents the relative proportions of these identified cell types. (B) The left panel shows a UMAP plot illustrating the DC subsets. The right panel presents the relative proportions of DC subsets. (C) The left panel shows a UMAP plot illustrating the monocyte and macrophage subsets. The right panel presents the relative proportions of monocyte and macrophage. (D) The left panel shows a UMAP plot illustrating the T cell lineages. The right panel presents the relative proportions of T cell lineages. (E) Clonal expansion of CD8\u003csup\u003e+\u003c/sup\u003e T cells in the TME. (F) Dot plot showing genes with elevated expression in CD8\u003csup\u003e+\u003c/sup\u003e T cells from \u003cem\u003eTrp53\u003c/em\u003e\u003csup\u003eKO\u003c/sup\u003e tumors, for comparison with \u003cem\u003eTrp53\u003c/em\u003e\u003csup\u003eCON\u003c/sup\u003e. (G) Dot plot showing genes with elevated expression in CD8\u003csup\u003e+\u003c/sup\u003e T cells from \u003cem\u003eTrp53\u003c/em\u003e\u003csup\u003eCON\u003c/sup\u003e tumors. * p\u0026lt;0.05, by Student’s \u003cem\u003et\u003c/em\u003e-test.\u003c/p\u003e","description":"","filename":"Figure21119.png","url":"https://assets-eu.researchsquare.com/files/rs-8214123/v1/f21d68ac0fe069e74f06cd38.png"},{"id":98390570,"identity":"72a4fbe5-f2ef-4101-89fa-abc411dfabb8","added_by":"auto","created_at":"2025-12-17 09:25:16","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":368189,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLoss of\u003c/strong\u003e\u003cem\u003e\u003cstrong\u003e Trp53\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e in tumor cells remodels the immune compartment during anti-PD-1 therapy. \u003c/strong\u003e(A) Anti-PD1 mAb treatment was administered to\u003cem\u003e Trp53\u003c/em\u003e\u003csup\u003eCON\u003c/sup\u003e and\u003cem\u003e Trp53\u003c/em\u003e\u003csup\u003eKO\u003c/sup\u003e tumors, as depicted in the schematic. Ten days after tumor inoculation, tumors were harvested, and single-cell suspensions were prepared for flow cytometry analysis. (B) Percentages of CD8\u003csup\u003e+\u003c/sup\u003e TIL in live lymphocytes are shown. (C) Percentages of IFN-γ-producing CD8\u003csup\u003e+\u003c/sup\u003e TIL in CD8\u003csup\u003e+\u003c/sup\u003e T cells. (D) Percentages of GZMB-producing CD8\u003csup\u003e+\u003c/sup\u003e TIL in CD8\u003csup\u003e+\u003c/sup\u003e T cells. (E) Percentages of conventional CD4\u003csup\u003e+\u003c/sup\u003e T cells among total CD4\u003csup\u003e+\u003c/sup\u003e TILs are shown. (F) Percentages of Tregs in CD4\u003csup\u003e+\u003c/sup\u003e T cells. (G) Percentages of granulocytic myeloid-derived suppressor cells (gMDSC). (H) Percentages of monocytic myeloid-derived suppressor cells (mMDSC). **** p\u0026lt;0.0001, *** p\u0026lt;0.005, ** p\u0026lt;0.01, * p\u0026lt;0.05, two-way ANOVA.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-8214123/v1/00042ac4cc55849cb242838c.png"},{"id":98440359,"identity":"04a1b1f1-d56e-4def-8eb7-de67523b3a1c","added_by":"auto","created_at":"2025-12-17 17:03:46","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":748698,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eTrp53\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e Deletion Alters Tumor Metabolism and Immunogenicity.\u003c/strong\u003e Tumor cell counts from total tumor scRNAseqs were analyzed. (A) Gene Set Enrichment Analysis (GSEA) illustrating enriched pathways in \u003cem\u003eTrp53\u003c/em\u003e\u003csup\u003eCON\u003c/sup\u003e (CON, grey) and \u003cem\u003eTrp53\u003c/em\u003e\u003csup\u003eKO\u003c/sup\u003e (KO, red) tumor cells. (B) DAVID functional annotation analysis showing enriched pathways in\u003cem\u003e Trp53\u003c/em\u003e\u003csup\u003eCON\u003c/sup\u003e (grey) and\u003cem\u003e Trp53\u003c/em\u003e\u003csup\u003eKO\u003c/sup\u003e (red) tumor cells. (C) Dot plots depicting differentially expressed immune-related genes in\u003cem\u003e Trp53\u003c/em\u003e\u003csup\u003eCON\u003c/sup\u003e and\u003cem\u003e Trp53\u003c/em\u003e\u003csup\u003eKO\u003c/sup\u003e tumor cells.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-8214123/v1/7b8c00194c1d2ade14d06971.png"},{"id":98439671,"identity":"a470551f-4562-415b-8ee0-1c3b1012e27a","added_by":"auto","created_at":"2025-12-17 17:02:31","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":406054,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eTrp53\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e deletion led to increased cytokine-induced cell death.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Induction of cell death in\u003cem\u003e Trp53\u003c/em\u003e\u003csup\u003eCON\u003c/sup\u003e and\u003cem\u003e Trp53\u003c/em\u003e\u003csup\u003eKO\u003c/sup\u003e MC38 clones after 24 and 48 hours of treatment with IFNγ, TNFα, or their combined application. Statistical significance was assessed using two-way ANOVA (*p \u0026lt; 0.05, **p \u0026lt; 0.01, ****p \u0026lt; 0.0001, ns = not significant, n=3). (B) Changes in pro- and cleaved caspase-8 protein and cleaved caspase-3 levels in\u003cem\u003e Trp53\u003c/em\u003e\u003csup\u003eCON\u003c/sup\u003e and\u003cem\u003e Trp53\u003c/em\u003e\u003csup\u003eKO\u003c/sup\u003e MC38 cells following 24, 36, and 48 hours of treatment with combined murine IFNγ and TNFα, or in untreated control cells. β-actin was used as a loading control. The lower panel displays densitometric quantification of the Western blot bands. Ctrl = untreated control.\u003c/p\u003e","description":"","filename":"Figure51119.png","url":"https://assets-eu.researchsquare.com/files/rs-8214123/v1/806f65f70abe2a36fff3aa10.png"},{"id":98440999,"identity":"13b5beb7-81cc-45dc-b63a-91e409e607ef","added_by":"auto","created_at":"2025-12-17 17:04:44","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1337244,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eTrp53\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e deletion led to increased mTORC1 activation and reduced autophagy.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Changes in phosphorylated p70S6K and p62 protein expression in \u003cem\u003eTrp53\u003c/em\u003e\u003csup\u003eCON\u003c/sup\u003e and\u003cem\u003e Trp53\u003c/em\u003e\u003csup\u003eKO\u003c/sup\u003e MC38 cells following 24, 36, and 48 hours of treatment with murine IFNγ and TNFα. β-actin was used as a loading control. The lower panel displays densitometric quantification of the Western blot bands. Ctrl = untreated control. (B) Assessment of autophagy inhibition (EAD1, 6 µM or 10 µM) on IFNγ and TNFα-induced cell death in murine cell lines (MC38, CT26, B16, 4T1, EMT6) at 24 hours (left panel) and 48 hours (right panel). Statistical significance was determined by one-way ANOVA (*p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001, ****p \u0026lt; 0.0001, ns = not significant, n=3). (C) scRNAseq data showing expression of \u003cem\u003eDdit4, Sesn2, Sesn3, Castor1,\u003c/em\u003e and \u003cem\u003eCastor2\u003c/em\u003e genes in\u003cem\u003e Trp53\u003c/em\u003e\u003csup\u003eCON\u003c/sup\u003e and\u003cem\u003e Trp53\u003c/em\u003e\u003csup\u003eKO\u003c/sup\u003e MC38 cells isolated in vivo. (D) Fold change in Castor1 mRNA levels in\u003cem\u003e Trp53\u003c/em\u003e\u003csup\u003eCON\u003c/sup\u003e and\u003cem\u003e Trp53\u003c/em\u003e\u003csup\u003eKO\u003c/sup\u003e MC38 cells treated with IFNγ, TNFα, their combination, or untreated (control) for 24 hours. Statistical significance was determined by one-way ANOVA (****p \u0026lt; 0.0001, n=3). (E) Dot plots showing expression of \u003cem\u003eCDKN1A\u003c/em\u003e, \u003cem\u003eDDIT4, SESN3, \u003c/em\u003eand\u003cem\u003e CASTOR1\u003c/em\u003e in various\u003cem\u003e \u003c/em\u003eWT, \u003cem\u003eTP53\u003c/em\u003e mutated and truncated HCT116 cells.\u003c/p\u003e","description":"","filename":"Figure61119.png","url":"https://assets-eu.researchsquare.com/files/rs-8214123/v1/a9ff41a1b57671d5b123d136.png"},{"id":98445845,"identity":"0802c638-e83e-46ef-a458-442c8f07fc2e","added_by":"auto","created_at":"2025-12-17 17:21:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5056956,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8214123/v1/f947ae64-8410-451f-a37c-62b76c5b5760.pdf"},{"id":98390568,"identity":"bdd23d13-47ea-4c4b-8269-53d7f692f70f","added_by":"auto","created_at":"2025-12-17 09:25:16","extension":"csv","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":9706,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"supplementaltable1.csv","url":"https://assets-eu.researchsquare.com/files/rs-8214123/v1/0c33ca57715bf40cff981499.csv"},{"id":98440326,"identity":"718c1e3d-25f2-4494-ab23-49bb897acc83","added_by":"auto","created_at":"2025-12-17 17:03:43","extension":"csv","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":3806,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"supplementaltable2.csv","url":"https://assets-eu.researchsquare.com/files/rs-8214123/v1/5ca6fa655f9aae5f8c724765.csv"},{"id":98390583,"identity":"2ee29005-3d1e-41b2-9d5b-2f0e19978e46","added_by":"auto","created_at":"2025-12-17 09:25:17","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":425023,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplemental Figure 1. Gating strategy for T cells. \u003c/strong\u003eLive CD45\u003csup\u003e+\u003c/sup\u003e single cells were further gated on CD4 and CD8 markers. Treg populations were gated as Foxp3\u003csup\u003e+\u003c/sup\u003e CD4\u003csup\u003e+\u003c/sup\u003e T cells. IFNG and GzmB expression were further gated in CD8\u003csup\u003e+\u003c/sup\u003e T cells.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplemental Figure 2. Gating strategy for MDSC.\u003c/strong\u003e Live CD45\u003csup\u003e+\u003c/sup\u003e single cells were further gated on monocytic and neutrophilic MDSC markers Ly6c and Ly6g as indicated.\u003c/p\u003e","description":"","filename":"Supplementalfigures.docx","url":"https://assets-eu.researchsquare.com/files/rs-8214123/v1/82d31ccc383921143bf4bdbb.docx"}],"financialInterests":"There is no duality of interest","formattedTitle":"mTORC1 Suppression by Trp53 Mutation Drives Resistance to Immune Checkpoint Blockade","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe p53 tumor suppressor protein, encoded by the \u003cem\u003eTP53\u003c/em\u003e gene, is a critical regulator of cellular processes and plays a fundamental role in cancer prevention. Often referred to as the \"guardian of the genome,\" p53 is involved in multiple central cellular functions, including transcription, DNA repair, genomic stability, cell cycle control, and apoptosis(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). The importance of p53 in tumor suppression is underscored by its frequent inactivation in human cancers, with mutations in \u003cem\u003eTP53\u003c/em\u003e observed in more than half of all sporadic tumors(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). While the classical functions of p53 in cell cycle arrest, senescence, apoptosis, and metabolism have been well-established, recent research has unveiled its involvement in regulating tumor immune responses (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRecent studies on the role of \u003cem\u003eTP53\u003c/em\u003e mutations in cancer immunotherapy have yielded complex and sometimes contradictory results. p53 activation in cancer cells can induce immune activating signals such as tumor necrosis factor-related apoptosis-inducing ligand (TRAIL), death receptor 5 (DR5), toll-like receptors (TLRs), and the cyclic GMP\u0026ndash;AMP synthase (cGAS)\u0026ndash;stimulator of IFN genes (STING) pathway (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). P53 mutants were shown to suppress antitumor immunity by interfering with the function of the cytoplasmic DNA sensing machinery, cGAS-STING-TBK1-IRF3(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Conversely, the activation of p53 in cancer cells can suppress immunity via increasing PDL1 levels(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). In addition, \u003cem\u003eTP53\u003c/em\u003e mutations have been found to be associated with increased lymphocytes in a large-scale pan-cancer genomics and transcriptomic study (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). In human non-small cell lung cancer (NSCLC) patients, \u003cem\u003eTP53\u003c/em\u003e mutations are significantly associated with levels of immune checkpoint molecules, activated T-effector genes, and IFNG signature genes (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). NSCLC patients with co-occurring \u003cem\u003eTP53/KRAS\u003c/em\u003e mutations showed improved clinical benefits after treatment with PD-1 inhibitors (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). In lung cancer patients treated with PD-1 inhibitors, \u003cem\u003eTP53\u003c/em\u003e mutations have been associated with better overall response rates and overall survival (\u003cspan additionalcitationids=\"CR11\" citationid=\"CR29\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). For relapsed/refractory (R/R) acute myeloid leukemia (AML) patients treated with Flotetuzumab, an investigational CD123xCD3 bispecific dual-affinity retargeting antibody (DART) molecule, higher expression of immune markers such as IFNG, FOXP3, and immune checkpoints was observed in primary bone marrow samples with \u003cem\u003eTP53\u003c/em\u003e mutations when compared to those with wild-type \u003cem\u003eTP53\u003c/em\u003e. Moreover, \u003cem\u003eTP53\u003c/em\u003e mutations and deletions are associated with clinical response to Flotetuzumab immunotherapy in AML (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). In a neoadjuvant setting, a recent phase 2 clinical trial examined the combination of anti-CTLA4 and PD-1 monoclonal antibodies (mAbs) for operable NSCLC. The trial revealed that resected tumors with \u003cem\u003eTP53\u003c/em\u003e alterations exhibited a higher median pathological regression compared to tumors with wild-type P53(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). The enhanced immune response observed against \u003cem\u003eTP53\u003c/em\u003e-mutated cancers is thought to be attributed to an increased \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003et\u003c/span\u003eotal \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003em\u003c/span\u003eutational \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eb\u003c/span\u003eurden (TMB). This hypothesis aligns with the well-established role of p53 as a critical guardian of genomic stability. The discrepancies in these findings highlight the nuanced impact of \u003cem\u003eTP53\u003c/em\u003e mutations on immunotherapy response, which likely depends on factors such as the specific mutation type, cancer type, and broader genetic context such as TMB. This complexity underscores the need for further research to fully elucidate the relationship between \u003cem\u003eTP53\u003c/em\u003e mutations and immunotherapy outcomes.\u003c/p\u003e \u003cp\u003eTo investigate the role of p53 mutants in immunotherapy, we utilized MC38, a mouse colorectal cancer cell line known for its responsiveness to immunotherapy and reported to harbor \u003cem\u003eTrp53\u003c/em\u003e mutations(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Initially, we confirmed the presence of \u003cem\u003eTrp53\u003c/em\u003e mutations in the parental MC38 cells. We then explored the tumor-intrinsic effects of p53 mutants by generating \u003cem\u003eTrp53\u003c/em\u003e-deficient MC38 cells and evaluating their impact on the immunogenicity of the cell line. Additionally, we compared the influence of mutant p53 and \u003cem\u003eTrp53\u003c/em\u003e deletion on the efficacy of PD-1 blockade immunotherapy in a murine tumor immune therapy model. To gain mechanistic insights into the immune-modulatory effects of mutant p53 in vivo, we performed \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003es\u003c/span\u003eingle-\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ec\u003c/span\u003eell RNA \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eseq\u003c/span\u003euencing (scRNA-seq) and immunological analyses of both parental MC38 and \u003cem\u003eTrp53\u003c/em\u003e-deficient MC38 tumor cells, as well as their associated TMEs. Lastly, we elucidated the molecular mechanisms by which p53 mutants regulate antitumor immunity in both mouse and human colorectal cancer cells.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e \u003cb\u003eThe\u003c/b\u003e \u003cb\u003eTrp53\u003c/b\u003e \u003cb\u003emutations conferred resistance to cancer immunotherapy.\u003c/b\u003e\u003c/p\u003e \u003cp\u003eTo investigate the role of p53 in \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ei\u003c/span\u003emmune \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ec\u003c/span\u003eheckpoint \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ei\u003c/span\u003enhibitor (ICI) therapy, we utilized MC38, a mouse colorectal cancer model known for its responsiveness to ICI treatment. Previous studies reported that MC38 harbors two mutated alleles of the \u003cem\u003eTrp53\u003c/em\u003e gene(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). We characterized the \u003cem\u003eTrp53\u003c/em\u003e genomic region in MC38 cells and confirmed the presence of two mutations: G242V and S258I. Notably, the G242V mutation corresponds to the human \u003cem\u003eTP53\u003c/em\u003e hotspot mutation G245S, frequently observed in human cancers. Additionally, the \u003cem\u003eTP53\u003c/em\u003e G245S mutation is a missense mutation known to disrupt the structure of p53 and its ability to bind DNA(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). The S258I mutation, on the other hand, is located at a splice site. This mutation has been shown to result in aberrant splicing, effectively creating a null allele of \u003cem\u003eTrp53\u003c/em\u003e(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWe definitively tested whether and how mutant p53 affects the efficacy of ICI immunotherapy using a loss-of-function approach in the transplant mouse model of MC38 colon adenocarcinoma. Using CRISPR-Cas9, we targeted for deletion a region of exon 4 in the \u003cem\u003eTrp53\u003c/em\u003e gene that encodes in the proline-rich domain and a part of the DNA-binding domain. Deletion of this region resulted in the elimination of p53 protein expression in vitro and \u003cem\u003eTrp53\u003c/em\u003e mRNA expression \u003cem\u003ein vivo\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). These data show that we have generated a \u003cem\u003eTrp53\u003c/em\u003e-null MC38 cell line, which we hereafter term \u003cem\u003eTrp53\u003c/em\u003e\u003csup\u003eKO\u003c/sup\u003e cells, along with their unedited controls, \u003cem\u003eTrp53\u003c/em\u003e\u003csup\u003eCON\u003c/sup\u003e cells.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe next tested the response of \u003cem\u003eTrp53\u003c/em\u003e\u003csup\u003eCON\u003c/sup\u003e and \u003cem\u003eTrp53\u003c/em\u003e\u003csup\u003eKO\u003c/sup\u003e cells to anti-PD-1 therapy \u003cem\u003ein vivo\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). As early as day 9 \u0026ndash; a time point at which anti-PD-1 therapy does not yet affect the growth of WT tumors \u0026ndash; treated \u003cem\u003eTrp53\u003c/em\u003e\u003csup\u003eKO\u003c/sup\u003e tumors already had a significantly smaller volume compared to both untreated and treated \u003cem\u003eTrp53\u003c/em\u003e\u003csup\u003eCON\u003c/sup\u003e MC38 tumors. These differences remained at later time points, with 1/3 of \u003cem\u003eTrp53\u003c/em\u003e\u003csup\u003eKO\u003c/sup\u003e tumors undergoing complete remission (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). Interestingly, untreated \u003cem\u003eTrp53\u003c/em\u003e\u003csup\u003eKO\u003c/sup\u003e tumors also grew slower than untreated and treated \u003cem\u003eTrp53\u003c/em\u003e\u003csup\u003eCON\u003c/sup\u003e MC38 tumors, though faster than their treated counterparts. Our results provide functional evidence that the p53 mutant in MC38 cells inhibits the efficacy of cancer immunotherapy.\u003c/p\u003e \u003cp\u003eWe next determined whether tumor cell-intrinsic or -extrinsic immune response changes were responsible for the reduction in tumor growth. \u003cem\u003eTrp53\u003c/em\u003e\u003csup\u003eCON\u003c/sup\u003e and \u003cem\u003eTrp53\u003c/em\u003e\u003csup\u003eKO\u003c/sup\u003e MC38 cells were inoculated into immunodeficient RAG1\u003csup\u003e\u0026minus;/\u0026minus;\u003c/sup\u003e (lack B and T cells) and NSG (lack B, T, and NK cells) mice (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). We found no difference in tumor growth between the \u003cem\u003eTrp53\u003c/em\u003e\u003csup\u003eCON\u003c/sup\u003e and \u003cem\u003eTrp53\u003c/em\u003e\u003csup\u003eKO\u003c/sup\u003e cells in either immunodeficient mice strains (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE-F). These data show that alterations to the immune response, and not cell-intrinsic changes induced by \u003cem\u003eTrp53\u003c/em\u003e deficiency in tumor cells, are required for the reduction of tumor growth and improved efficacy of checkpoint immunotherapy.\u003c/p\u003e \u003cp\u003e \u003cb\u003eCharacterization of the immune responses in\u003c/b\u003e \u003cb\u003eTrp53\u003c/b\u003e\u003csup\u003e\u003cb\u003eCON\u003c/b\u003e\u003c/sup\u003e \u003cb\u003eand\u003c/b\u003e \u003cb\u003eTrp53\u003c/b\u003e\u003csup\u003e\u003cb\u003eKO\u003c/b\u003e\u003c/sup\u003e \u003cb\u003etumors during anti-PD-1 therapy\u003c/b\u003e\u003c/p\u003e \u003cp\u003eWe next characterized how the \u003cem\u003eTrp53\u003c/em\u003e deletion in MC38 tumor cells alters the immune response. To this end, we performed single-cell RNA and paired single-cell TCR sequencing (scRNAseq and scTCRseq) of anti-PD-1 treated \u003cem\u003eTrp53\u003c/em\u003e\u003csup\u003eCON\u003c/sup\u003e and \u003cem\u003eTrp53\u003c/em\u003e\u003csup\u003eKO\u003c/sup\u003e tumors on day 11 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). We observed a decrease in monocytic myeloid-derived suppressor cells (mMDSCs) and a slight increase in neutrophils in \u003cem\u003eTrp53\u003c/em\u003e\u003csup\u003eKO\u003c/sup\u003e tumors compared to \u003cem\u003eTrp53\u003c/em\u003e\u003csup\u003eCON\u003c/sup\u003e tumors. Notably, TNK cells, encompassing all T cells, NK cells, and ILCs, were predominantly increased in \u003cem\u003eTrp53\u003c/em\u003e\u003csup\u003eKO\u003c/sup\u003e tumors relative to \u003cem\u003eTrp53\u003c/em\u003e\u003csup\u003eCON\u003c/sup\u003e tumors (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Although there is no striking difference in the total DC population, both the cDC1 and cDC2 populations decreased, while mature DCs increased in \u003cem\u003eTrp53\u003c/em\u003e\u003csup\u003eKO\u003c/sup\u003e tumors, suggesting an increased DC maturation in these tumors (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). In the monocyte/macrophage compartment, we observed an increase in macrophage 1 (Mac1) and macrophage 2 (Mac2) populations, suggesting an enhanced monocyte-to-macrophage differentiation program (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). Within the T cell population, we found that CD8\u003csup\u003e+\u003c/sup\u003e T cells increased in \u003cem\u003eTrp53\u003c/em\u003e\u003csup\u003eKO\u003c/sup\u003e tumors while Treg cells decreased (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). Paired scTCR-seq enabled us to analyze clonal expansion in T cells. We found that the average clonal size of expanded clones was significantly larger in CD8⁺ T cells from \u003cem\u003eTrp53\u003c/em\u003e\u003csup\u003eKO\u003c/sup\u003e tumors than those from \u003cem\u003eTrp53\u003c/em\u003e\u003csup\u003eCON\u003c/sup\u003e tumors (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE). We also analyzed differential gene expression in CD8⁺ T cells from \u003cem\u003eTrp53\u003c/em\u003e\u003csup\u003eKO\u003c/sup\u003e and \u003cem\u003eTrp53\u003c/em\u003e\u003csup\u003eCON\u003c/sup\u003e tumors. CD8⁺ T cells from \u003cem\u003eTrp53\u003c/em\u003e\u003csup\u003eKO\u003c/sup\u003e tumors exhibited higher expression levels of checkpoint molecules, including \u003cem\u003eLag3\u003c/em\u003e, \u003cem\u003eTim3\u003c/em\u003e, and \u003cem\u003eTigit\u003c/em\u003e, as well as costimulatory molecules such as \u003cem\u003e4-1bb\u003c/em\u003e, \u003cem\u003eGitr\u003c/em\u003e, and \u003cem\u003eIcos\u003c/em\u003e. Additionally, they expressed higher levels of chemokine receptors (\u003cem\u003eCxcr6\u003c/em\u003e), effector molecules (\u003cem\u003eGzmb\u003c/em\u003e), signaling molecules involved in the TCR complex, and transcription factors that drive effector T cell function, including \u003cem\u003eNfkb1\u003c/em\u003e, \u003cem\u003eRunx3\u003c/em\u003e, \u003cem\u003eIrf8\u003c/em\u003e, and \u003cem\u003eBatf\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF). In contrast, CD8⁺ T cells from \u003cem\u003eTrp53\u003c/em\u003e\u003csup\u003eCON\u003c/sup\u003e tumors predominantly expressed early activation markers such as \u003cem\u003eCd69\u003c/em\u003e and \u003cem\u003eFos\u003c/em\u003e, along with chronic inflammation-associated genes, particularly interferon (IFN)-induced genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eG). Collectively, our analysis reveals that CD8⁺ T cells in \u003cem\u003eTrp53\u003c/em\u003e\u003csup\u003eKO\u003c/sup\u003e tumors undergo greater clonal expansion and exhibit a more activated phenotype, which aligns with the increased susceptibility of \u003cem\u003eTrp53\u003c/em\u003e\u003csup\u003eKO\u003c/sup\u003e tumors to anit-PD1 ICI treatment.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe validated our characterization of the immune response by performing multicolor flow cytometry (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). We found that treated \u003cem\u003eTrp53\u003c/em\u003e\u003csup\u003eKO\u003c/sup\u003e tumors had increased CD8\u003csup\u003e+\u003c/sup\u003e T cells that expressed an intense type 1 signature of IFN-g and GzmB, compared to both untreated and treated \u003cem\u003eTrp53\u003c/em\u003e\u003csup\u003eCON\u003c/sup\u003e tumors (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB-D, \u003cb\u003eSupplemental Fig.\u0026nbsp;1\u003c/b\u003e). In addition, we also observed an increase in conventional CD4\u003csup\u003e+\u003c/sup\u003e T cells and a decrease in Treg cells in the treated \u003cem\u003eTrp53\u003c/em\u003e\u003csup\u003eKO\u003c/sup\u003e tumors compared to both untreated and treated \u003cem\u003eTrp53\u003c/em\u003e\u003csup\u003eCON\u003c/sup\u003e tumors (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE-F). Additionally, we observed a decrease in mMDSCs and a trend toward an increase in gMDSCs in \u003cem\u003eTrp53\u003c/em\u003e\u003csup\u003eKO\u003c/sup\u003e tumors compared to \u003cem\u003eTrp53\u003c/em\u003e\u003csup\u003eCON\u003c/sup\u003e tumors (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eG-H, \u003cb\u003eSupplemental Fig.\u0026nbsp;2\u003c/b\u003e). Our profiling of the immune response consistently showed that \u003cem\u003eTrp53\u003c/em\u003e deletion in tumor cells and anti-PD-1 therapy drastically alter the immune response \u003cem\u003ein vivo\u003c/em\u003e, primarily augmenting T cell responses.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eTrp53\u003c/b\u003e \u003cb\u003edeletion leads to altered oncogenic programs and increased immune signature genes in tumor cells.\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe alteration of tumor growth prompted us to investigate how \u003cem\u003eTrp53\u003c/em\u003e deletion affects tumor cells during cancer immunotherapy compared with \u003cem\u003eTrp53\u003c/em\u003e\u003csup\u003eCON\u003c/sup\u003e MC38 in vivo. We conducted a comprehensive analysis of differentially expressed genes (DEGs) in \u003cem\u003eTrp53\u003c/em\u003e\u003csup\u003eCON\u003c/sup\u003e and \u003cem\u003eTrp53\u003c/em\u003e\u003csup\u003eKO\u003c/sup\u003e MC38 cells in the scRNAseq data. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eG\u003c/span\u003eene \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003es\u003c/span\u003eet \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ee\u003c/span\u003enrichment \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ea\u003c/span\u003enalysis (GSEA) using gene ontology, hallmark, and reactome datasets showed that \u003cem\u003eTrp53\u003c/em\u003e\u003csup\u003eCON\u003c/sup\u003e cells exhibited upregulation of processes linked to \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ee\u003c/span\u003epithelial-\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003em\u003c/span\u003eesenchymal \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003et\u003c/span\u003eransition (EMT) and hypoxia, such as extracellular matrix organization, cell adhesion, coagulation, hypoxia response, collagen formation, and KRAS signaling downregulation (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA and \u003cb\u003eSupplemental Table\u0026nbsp;1\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn addition, we utilized the \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eD\u003c/span\u003eatabase for \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eA\u003c/span\u003ennotation, \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eV\u003c/span\u003eisualization, and \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eI\u003c/span\u003entegrated Discovery (DAVID)(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e) to functionally annotate the pathways, focusing on cancer cell-intrinsic programs that were differentially regulated based on the identified DEGs (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB and \u003cb\u003eSupplemental Table\u0026nbsp;2\u003c/b\u003e). The upregulated pathways in \u003cem\u003eTrp53\u003c/em\u003e\u003csup\u003eCON\u003c/sup\u003e MC38 cells can be categorized into transcriptional regulation by TP53, stromal responses, anti-viral responses, stress responses, Wnt signaling, and autophagy/metabolic regulation. The \"Transcriptional Regulation by \u003cem\u003eTrp53\u003c/em\u003e\" pathway was significantly upregulated, reflecting residual canonical P53 function in MC38 cells, with genes such as \u003cem\u003eBtg2\u003c/em\u003e, \u003cem\u003eSesn3\u003c/em\u003e, \u003cem\u003eUbb\u003c/em\u003e, \u003cem\u003eZfp385a\u003c/em\u003e, \u003cem\u003eTrp53\u003c/em\u003e, \u003cem\u003eDdit4\u003c/em\u003e, \u003cem\u003eTnks1bp1\u003c/em\u003e, and \u003cem\u003eCox6a2\u003c/em\u003e. Stromal pathways were also prominently activated on \u003cem\u003eTrp53\u003c/em\u003e\u003csup\u003eCON\u003c/sup\u003e MC38 cells, including angiogenesis (\u003cem\u003eAcvrl1, Xbp1, Ecscsr, Fn1, Hspg2, Rhob, Eng\u003c/em\u003e), collagen-containing extracellular matrix (\u003cem\u003eScara3, Sparc, Fn1, Lamc1, Col1a1, Thbs2, Col5a1, Col5a2\u003c/em\u003e, and \u003cem\u003eSerpinh1\u003c/em\u003e), platelet degranulation (\u003cem\u003eIslr, Sparc, Fn1, Aldoa\u003c/em\u003e, and \u003cem\u003eClu\u003c/em\u003e), signaling by Notch3 (\u003cem\u003eNcstn, Ubb\u003c/em\u003e, and \u003cem\u003eWwp2\u003c/em\u003e), and the TGF-beta receptor signaling signature (\u003cem\u003eAcvrl1, Jun, Cited1, Trp53\u003c/em\u003e, and \u003cem\u003eCcl2\u003c/em\u003e), emphasizing tissue remodeling and immune modulation by the mutant p53. Anti-viral mechanisms were significantly upregulated, with pathways like antiviral defense (\u003cem\u003eMavs, Apobec3, Rsad2, Ddit4, Isg15\u003c/em\u003e, and \u003cem\u003eIfit1\u003c/em\u003e), double-stranded DNA binding (\u003cem\u003eNr5a1, Egr1, Jun, Aim2, Jund, Fosb\u003c/em\u003e, and \u003cem\u003eFos\u003c/em\u003e), IFN-stimulated genes (\u003cem\u003eMavs, Isg15, Usp18\u003c/em\u003e, and \u003cem\u003eHspa1b\u003c/em\u003e), and RNA binding (\u003cem\u003eApobec3, Rbpms, Msi2\u003c/em\u003e, and \u003cem\u003eIfit1\u003c/em\u003e) collectively indicating heightened nucleic acid sensing and antiviral responses in \u003cem\u003eTrp53\u003c/em\u003e\u003csup\u003eCON\u003c/sup\u003e MC38 cells. Stress pathways, such as unfolded protein binding (\u003cem\u003eSerpinh1, Clu, Cryab\u003c/em\u003e, and \u003cem\u003eHspa1b\u003c/em\u003e) and cellular senescence (\u003cem\u003eJun, Ubb, Trp53\u003c/em\u003e, and \u003cem\u003eFos\u003c/em\u003e), reflect adaptations to cellular damage. The Wnt signaling pathway exhibited mixed regulation, with genes promoting Wnt signaling (\u003cem\u003eRspo3, Rspo4, Wls, Csnk2a1, Csnk2a2\u003c/em\u003e, and \u003cem\u003eCcnd1\u003c/em\u003e) and genes inhibiting it (\u003cem\u003eHic1, Tle5\u003c/em\u003e). Autophagy and metabolic pathways were highly induced, including mitochondrial function (\u003cem\u003eCyb5b, Pink1, Mavs, Ubb, Bnip3, Ddit4\u003c/em\u003e, and \u003cem\u003eMgarp\u003c/em\u003e), cellular response to hypoxia (\u003cem\u003ePink1, Bnip3, Trp53\u003c/em\u003e, and \u003cem\u003eHif1a\u003c/em\u003e), positive regulation of macroautophagy (\u003cem\u003ePink1, Sesn3, Bnip3\u003c/em\u003e, and \u003cem\u003eHif1a\u003c/em\u003e), and insulin-like growth factor I binding (\u003cem\u003eIgfbp5, Igfbp4\u003c/em\u003e, and \u003cem\u003eIgfbp6\u003c/em\u003e), highlighting mitochondrial dynamics, hypoxia adaptation, and enhanced autophagic processes. Together, these results reveal a complex response network involving transcriptional regulation, stromal remodeling, immune defense, stress adaptation, and metabolic reprogramming.\u003c/p\u003e \u003cp\u003eIn \u003cem\u003eTrp53\u003c/em\u003e\u003csup\u003eKO\u003c/sup\u003e cells, GSEA analysis revealed that pathways associated with increased protein synthesis and proliferation were significantly upregulated, including ribosome biogenesis, MYC targets, eukaryotic translation initiation, RNA metabolism, E2F targets, mRNA splicing, and cell cycle processes (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA and \u003cb\u003eSupplemental Table\u0026nbsp;1\u003c/b\u003e). DAVID analysis showed a similar pattern of changes (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB and \u003cb\u003eSupplemental Table\u0026nbsp;2\u003c/b\u003e). The upregulated pathways in \u003cem\u003eTrp53\u003c/em\u003e\u003csup\u003eKO\u003c/sup\u003e MC38 cells span multiple major categories, reflecting diverse biological processes \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Tumor promotion signaling pathways were also prominent, including \"EGFR tyrosine kinase inhibitor resistance,\" \"ErbB signaling,\" \"Hippo signaling,\" and multiple \"Wnt signaling\" pathways. These are driven by genes such as \u003cem\u003eNras, Plcγ2, Prkca, Wnt5a, Sox9\u003c/em\u003e, and \u003cem\u003eCsnk1e\u003c/em\u003e, which regulate cell proliferation, differentiation, and oncogenic signaling. Angiogenesis-related pathways such as \"Angiogenesis\" and \"PDGF signaling\" were highlighted, with genes like \u003cem\u003ePdgfa, Prkca\u003c/em\u003e, and \u003cem\u003eVegfa\u003c/em\u003e indicating enhanced vascularization and extracellular matrix remodeling. In the mTOR category, pathways such as \"mTOR signaling,\" \"PI3K-Akt signaling,\" and \"Amino acid regulation of mTORC1\" were enriched, involving genes like \u003cem\u003eNras, Lamtor3, Prkca\u003c/em\u003e, and \u003cem\u003eEif4e\u003c/em\u003e, reflecting metabolic regulation and cellular growth. Autophagy pathways, including \"Macroautophagy\" and \"Cellular response to starvation,\" were driven by genes like \u003cem\u003eGabarapl2, Lamtor3\u003c/em\u003e, and \u003cem\u003eTomm20\u003c/em\u003e, emphasizing cellular recycling and stress responses. Protein translation promotion was observed through pathways such as \"rRNA processing,\" \"cytoplasmic translation,\" \"mRNA processing,\" and \"Eukaryotic translation initiation,\" involving genes like \u003cem\u003eRpl31, Rpl34, Rps27\u003c/em\u003e, and \u003cem\u003eEif4e\u003c/em\u003e. These pathways highlight robust translational activity. Gene transcription regulation pathways, including \"Transcription by RNA polymerase II,\" \"RNA Pol II CTD phosphorylation,\" and \"Transcription initiation and promoter clearance,\" involved genes such as \u003cem\u003ePolr2c, Gtf2h3\u003c/em\u003e, and \u003cem\u003eGtf2h5\u003c/em\u003e. Lastly, DNA replication pathways, such as \"Cell Cycle,\" \"DNA Replication,\" \"Mitotic Prometaphase,\" and \"Chromosome segregation,\" featured genes like \u003cem\u003eCdt1, Mcm3, Cdk1\u003c/em\u003e, and \u003cem\u003eCenpe\u003c/em\u003e, reflecting cell division and genomic stability. Together, these pathways suggest enhanced mTOR signaling, reduced autophagy, and metabolic alterations that collectively promote cancer cell proliferation in \u003cem\u003eTrp53\u003c/em\u003e\u003csup\u003eKO\u003c/sup\u003e MC38 cells.\u003c/p\u003e \u003cp\u003eIn addition to cancer cell gene programs, we analyzed immune gene programs differentially expressed in \u003cem\u003eTrp53\u003c/em\u003e\u003csup\u003eCON\u003c/sup\u003e and \u003cem\u003eTrp53\u003c/em\u003e\u003csup\u003eKO\u003c/sup\u003e MC38 cells in vivo. Notably, the type 1 immune response program, which includes genes such as \u003cem\u003eCxcl9, Gbp2, Gbp3, Cxcl10, Gbp7, Stat1, Irf1, Gbp4\u003c/em\u003e, and \u003cem\u003eCd274\u003c/em\u003e, was significantly enriched (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). In contrast, \u003cem\u003eTrp53\u003c/em\u003e\u003csup\u003eCON\u003c/sup\u003e cells exhibited higher expression of chemokines that attract myeloid cells, enhanced interferon signatures, and increased levels of TGF-β\u0026ndash;related genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). These findings align with the observation that \u003cem\u003eTrp53\u003c/em\u003e\u003csup\u003eKO\u003c/sup\u003e MC38 tumors exhibit increased sensitivity to PD-1 blockade.\u003c/p\u003e \u003cp\u003e \u003cb\u003ep53 deficiency in MC38 cells increased sensitivity to TNF-α/IFN-γ-induced cancer cell death.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eCD8\u003csup\u003e+\u003c/sup\u003e T cells promote antitumor immunity through several mechanisms, including cytokine-triggered cell death by releasing inflammatory cytokines such as IFN-\u003cb\u003eγ\u003c/b\u003e and tumor necrosis factor alpha (TNF-α), as well as perforin-dependent tumor cell killing (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Several recent studies showed that resistance to IFN-\u003cb\u003eγ\u003c/b\u003e and TNF-α-mediated cancer cell death is a major mechanism of immune evasion (\u003cspan additionalcitationids=\"CR22 CR23\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Because we found that \u003cem\u003eTrp53\u003c/em\u003e deletion in MC38 cells led to increased sensitivity to PD-1-blockade immunotherapy in vivo, we aimed to determine whether the p53 mutant regulates IFN-\u003cb\u003eγ\u003c/b\u003e and TNF-α-induced cancer cell death in vitro. To this end, we incubated MC38 (p53\u003csup\u003eCON\u003c/sup\u003e control clones) and \u003cem\u003eTrp53\u003c/em\u003e\u003csup\u003eKO\u003c/sup\u003e-MC38 cells (two independently generated clones #6 and #16) with IFN-\u003cb\u003eγ\u003c/b\u003e, TNF-α, or IFN-\u003cb\u003eγ\u003c/b\u003e plus TNF-α. The combination of IFN-\u003cb\u003eγ\u003c/b\u003e and TNF-α reduced live control MC38 (p53\u003csup\u003eMUT\u003c/sup\u003e) cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). Further analysis demonstrated an increase in apoptosis, as determined by Western blot of active caspase 3 and caspase 8, in \u003cem\u003eTrp53\u003c/em\u003e\u003csup\u003eKO\u003c/sup\u003e MC38 cells treated with IFN-\u003cb\u003eγ\u003c/b\u003e and TNF-α when compared to control \u003cem\u003eTrp53\u003c/em\u003e\u003csup\u003eCON\u003c/sup\u003e MC38 cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). Notably, the protein level of pro-Caspase-8 was also increased in \u003cem\u003eTrp53\u003c/em\u003e\u003csup\u003eKO\u003c/sup\u003e MC38 cells compared to \u003cem\u003eTrp53\u003c/em\u003e\u003csup\u003eCON\u003c/sup\u003e MC38 cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). These data indicate that mutant p53 inhibits IFN-\u003cb\u003eγ\u003c/b\u003e and TNF-α-induced tumor cell death.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eTrp53\u003c/b\u003e \u003cb\u003edeletion in MC38 cells increased mTOR activation and diminished autophagy.\u003c/b\u003e\u003c/p\u003e \u003cp\u003eAutophagy is a vital cellular process that plays a key role in suppressing cancer cell apoptosis(\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Notably, autophagy has also been shown to inhibit TNF-α-induced apoptosis, serving as a mechanism of cancer immune evasion(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Since mTORC1 is a well-established inhibitor of autophagy, p53\u0026rsquo;s role in suppressing mTOR signaling is particularly relevant(\u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e30\u003c/span\u003e). To further investigate this relationship, we examined mTORC1 activity and autophagy in \u003cem\u003eTrp53\u003c/em\u003e\u003csup\u003eKO\u003c/sup\u003e and \u003cem\u003eTrp53\u003c/em\u003e\u003csup\u003eCON\u003c/sup\u003e MC38 cells. Our analysis revealed that \u003cem\u003eTrp53\u003c/em\u003e\u003csup\u003eKO\u003c/sup\u003e MC38 cells exhibit significantly higher mTORC1 activity compared to \u003cem\u003eTrp53\u003c/em\u003e\u003csup\u003eCON\u003c/sup\u003e MC38 cells as measured by levels of phosphorylated p70 S6 Kinase (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). To assess autophagy levels, we measured p62 protein, a widely used marker of autophagic activity. p62 levels were significantly elevated in \u003cem\u003eTrp53\u003c/em\u003e\u003csup\u003eKO\u003c/sup\u003e MC38 cells relative to \u003cem\u003eTrp53\u003c/em\u003e\u003csup\u003eCON\u003c/sup\u003e MC38 cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA), suggesting lower autophagy activity in \u003cem\u003eTrp53\u003c/em\u003e\u003csup\u003eKO\u003c/sup\u003e cells. In addition, we found that inhibiting autophagy potentiates TNFα/IFNγ-induced cell death in multiple murine cancer cell lines (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). These findings suggest that mutant \u003cem\u003eTrp53\u003c/em\u003e suppresses mTORC1 activity, leading to enhanced autophagy and ultimately inhibiting apoptosis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eConsistent with the notion, scRNA-seq analysis revealed that \u003cem\u003eTrp53\u003c/em\u003e\u003csup\u003eKO\u003c/sup\u003e cells exhibit reduced expression of genes involved in mTORC1 inhibition, including \u003cem\u003eDdit4, Sesn2, Sesn3, Castor1\u003c/em\u003e, and \u003cem\u003eCastor2\u003c/em\u003e(\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan additionalcitationids=\"CR32\" citationid=\"CR32\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e33\u003c/span\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, B, Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). Given that CASTOR1 is a known negative regulator of mTORC1 activity(\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e34\u003c/span\u003e), we focused on its expression. RT-qPCR analysis confirmed that \u003cem\u003eCastor1\u003c/em\u003e expression was significantly reduced in \u003cem\u003eTrp53\u003c/em\u003e\u003csup\u003eKO\u003c/sup\u003e MC38 cells compared to \u003cem\u003eTrp53\u003c/em\u003e\u003csup\u003eCON\u003c/sup\u003e MC38 cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD). These data suggest that the Trp53 mutation in MC38 cells enhances the expression of mTORC1 inhibitor genes, which consequently inhibits mTORC1 signaling and promotes autophagy.\u003c/p\u003e \u003cp\u003eTo determine whether \u003cem\u003eTP53\u003c/em\u003e mutations similarly impact these genes in human cells, we analyzed a recently published scRNA-seq dataset derived from HCT116 (a human colorectal cancer cell line) engineered to harbor all known TP53 hotspot mutations(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). The typical TP53 target gene \u003cem\u003eCDKN1A\u003c/em\u003e is downregulated in all cells harboring various \u003cem\u003eTP53\u003c/em\u003e mutations (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE). Conversely, mTORC1 inhibitor genes, including \u003cem\u003eDDIT4\u003c/em\u003e, \u003cem\u003eSESN3\u003c/em\u003e and \u003cem\u003eCASTOR1\u003c/em\u003e, displayed variable expression patterns in \u003cem\u003eTP53\u003c/em\u003e mutants: their expression remained unaffected in some mutants but was completely absent in others (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE). Notably, in the \u003cem\u003eG245S\u003c/em\u003e mutant, \u003cem\u003eSESN3\u003c/em\u003e and \u003cem\u003eCASTOR1\u003c/em\u003e expression was modestly reduced, whereas DDIT4 expression remained largely unaffected (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE). Collectively, these results demonstrate certain p53 mutants, including G245S (and its murine counterpart G242V), despite losing other canonical functions, retain some levels of the wild-type p53\u0026rsquo;s ability to suppress mTORC1 and enhance autophagy, thereby inhibiting responses to immunotherapy.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we demonstrated that deleting mutant \u003cem\u003eTrp53\u003c/em\u003e in MC38 cells significantly enhances the efficacy of PD-1 immune checkpoint blockade (ICB) therapy. This improved response is driven by increased CD8⁺ T cell clonal expansion and reduced Treg infiltration within the TME. Mechanistically, \u003cem\u003eTrp53\u003c/em\u003e deletion leads to hyperactivation of mTOR signaling and suppression of autophagy, sensitizing tumor cells to cytokine-induced apoptosis. These findings reveal a novel mechanism linking \u003cem\u003eTrp53\u003c/em\u003e mutations to immunotherapy outcomes, providing fresh mechanistic insights into how \u003cem\u003eTrp53\u003c/em\u003e status influences immune responses in cancer.\u003c/p\u003e \u003cp\u003eA substantial body of research, especially in lung cancer, indicates that \u003cem\u003eTP53\u003c/em\u003e mutations are strongly correlated with increased expression of immune checkpoint molecules, activation of T-effector genes, and enrichment of IFNγ signature genes (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). These findings are corroborated by multiple studies (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Notably, non-small cell lung cancer (NSCLC) patients harboring \u003cem\u003eTP53\u003c/em\u003e mutations exhibit improved clinical outcomes, including prolonged survival, in response to PD-1 inhibitor treatment (\u003cspan additionalcitationids=\"CR10 CR11\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). However, a major confounding factor in these studies is the possibility that \u003cem\u003eTrp53\u003c/em\u003e mutations indirectly affect \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003et\u003c/span\u003eumor \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003em\u003c/span\u003eutational \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eb\u003c/span\u003eurden (TMB) and neoantigen load, rather than exerting a direct impact on immune surveillance(\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e35\u003c/span\u003e). In contrast, some p53 mutants, including P142L, P152Q, A161V, C174Y, R175H, R248W, R249S, R273H, and R280K, have been reported to interact with TBK1 and inhibit STING activation(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e), and potentially inhibit cancer immunotherapy. In addition, another study demonstrates that \u003cem\u003eTrp53\u003c/em\u003e loss, within the genetic context of Kras mutation and TMB, promotes immune resistance in an autochthonous mouse lung cancer model (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e36\u003c/span\u003e). This observation aligns with the well-established role of wild-type P53 in stimulating innate immune responses (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e37\u003c/span\u003e). Our study uncovers distinct and direct mechanisms of the G242V mutant (corresponding to the human TP53 hotspot mutation G245S), including suppression of mTOR signaling and induction of autophagy. We also showed that this property of G245S is shared with some but not all p53 mutants. Biochemical, structural, and in vivo studies have demonstrated that the G245S \u003cem\u003eTP53\u003c/em\u003e mutation retains greater residual p53 function compared to other mutations, such as R248Q. Specifically, G245S preserves approximately 25% of wild-type p53 transcriptional activity for select promoters, such as p21, while R248Q exhibits negligible transcriptional activity and fails to induce canonical targets like \u003cem\u003ePUMA\u003c/em\u003e and \u003cem\u003ecaspase-3\u003c/em\u003e(\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e38\u003c/span\u003e). Structurally, G245S induces localized destabilization in the DNA-binding domain\u0026rsquo;s L3 loop, leading to a reduction of DNA affinity by approximately 15-fold. In contrast, R248Q disrupts both DNA minor-groove binding and global domain stability(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e39\u003c/span\u003e). In vivo, G245S/- mice display delayed tumor onset, whereas R248Q/- mice exhibit accelerated tumorigenesis, expanded stem cell pools, and reduced survival(\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e38\u003c/span\u003e). Despite its weakened oncogenic activity, the G245S p53 mutant, through its preserved capacity to suppress mTORC1, hinders the efficacy of cancer immunotherapy. Interestingly, a recent study demonstrated that the mouse R172H mutation\u0026mdash;a well-characterized missense mutation in the p53 DNA-binding domain classified as a structural mutant that impairs p53\u0026rsquo;s tumor suppressor function and exhibits both dominant-negative and gain-of-function (GOF) effects\u0026mdash;inhibits the antitumor immune response by upregulating the CXCL1\u0026ndash;neutrophil axis (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e40\u003c/span\u003e). Collectively, multiple in vitro and mouse studies demonstrate the critical role of p53 mutants in inhibiting antitumor immunity, albeit through diverse mechanisms.\u003c/p\u003e \u003cp\u003eUnder stress conditions, p53 generally inhibits mTORC1 activity through multiple mechanisms(\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan additionalcitationids=\"CR32\" citationid=\"CR32\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e33\u003c/span\u003e). One key pathway involves p53-mediated activation of AMPK, which inhibits mTORC1 via the TSC1/2 complex. Additionally, p53 can also decrease S6K1 activity and promote 4E-BP1 dephosphorylation, further limiting mTORC1 signaling. P53 has also been shown to induce genes encoding proteins that regulate mTORC1 activation. p53 induces PTEN transcription, which in turn inhibits the PI3K/AKT pathway, leading to mTORC1 suppression.\u003c/p\u003e \u003cp\u003eMoreover, DDIT4, a transcriptional target of P53, suppresses mTORC1 through the same TSC1/2-dependent mechanism. The SESTRIN (SESN1\u0026ndash;3) family, also transcriptionally regulated by p53, plays a crucial role in suppressing both mTORC1 and mTORC2(\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e41\u003c/span\u003e). In our experimental system, we found no evidence of p53-mediated Pten regulation. However, we observed a significant upregulation of \u003cem\u003eSesn2\u003c/em\u003e, \u003cem\u003eSesn3\u003c/em\u003e, and \u003cem\u003eDdit4\u003c/em\u003e in MC38 cells, specifically dependent on the \u003cem\u003eTrp53\u003c/em\u003e\u003csup\u003eG245S\u003c/sup\u003e mutation. Furthermore, we identified CASTOR1, a known mTORC1 inhibitor, as an additional p53\u003csup\u003eG245S\u003c/sup\u003e-regulated gene in these cells. Consistent with these findings, we demonstrated that wild-type p53 promotes the expression of these mTORC1 regulatory genes in human HCT116 cells. Notably, different p53 mutants exhibited varying degrees of functional deficiency in this context. Specifically, the p53\u003csup\u003eG245S\u003c/sup\u003e mutant retained some of its ability to induce the expression of these mTORC1 regulatory genes. These results align with recent studies reporting that p53 deletion in human lung and colon cancer cell lines, including HCT116, leads to increased mTORC1 activation. Moreover, several common p53 hotspot mutants (R175H, R248W, and R273H) failed to suppress mTORC1 activation in a p53-null background, underscoring the functional heterogeneity of p53 mutants in regulating mTORC1 activity (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e42\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOur study demonstrates a new mechanism by which p53 mutants promote cancer immunotherapy resistance through the modulation of mTORC1 activation and autophagy inhibition. This observation highlights the potential of targeting the P53/mTORC1 axis to improve cancer immunotherapy outcomes.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eMice\u003c/h2\u003e \u003cp\u003e Mice were maintained in a specific-pathogen-free animal facility in accordance with an animal protocol approved by the Institutional Animal Care and Use Committee of the University of Pittsburgh. WT (stock #000664) and RAG1\u003csup\u003e-/-\u003c/sup\u003e (B6.129S7-\u003cem\u003eRag1\u003c/em\u003e\u003csup\u003e\u003cem\u003etm1Mom\u003c/em\u003e\u003c/sup\u003e/J, 002216) mice on the C57Bl/6J background, as well as NSG (NOD.Cg-\u003cem\u003ePrkdc\u003c/em\u003e\u003csup\u003e\u003cem\u003escid\u003c/em\u003e\u003c/sup\u003e\u003cem\u003eIl2rg\u003c/em\u003e\u003csup\u003e\u003cem\u003etm1Wjl\u003c/em\u003e\u003c/sup\u003e/SzJ, 005557; RRID:BCBC_4611) were purchased from the Jackson laboratory. Male and female mice aged 6 to 12 weeks were used in the study. The sample size for each group was calculated based on a power analysis to ensure adequate statistical power.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eTissue culture\u003c/h3\u003e\n\u003cp\u003eMC38 colon adenocarcinoma cells (Cat# YC-A002, RRID:CVCL_B288) were maintained in Dubecco\u0026rsquo;s Modified Eagle Medium, high glucose supplemented with 10% fetal bovine serum in the presence of benzylpenicillin (100 U/mL), streptomycin (100 mg/mL), and 2 mmol/L L-glutamine and 1% penicillin-streptomycin. CT26 colon cancer (Cat# YC-A002, (RRID:CVCL_7254), and 4T1 breast carcinoma cells (RRID:CVCL_0125) were all cultured in RPMI-1640 (#11875-093, Gibco) supplemented with 10% FBS and 1% penicillin-streptomycin. EMT6 breast carcinoma cells (ATCC Cat# CRL-2755, RRID:CVCL_1923) were cultured in Waymouth\u0026rsquo;s medium (#11220-035, Gibco) supplemented with 15% FBS and 1% penicillin-streptomycin. The cells were frequently checked for mycoplasma using Universal Mycoplasma Detection kit (#30-1012k, ATCC).\u003c/p\u003e\n\u003ch3\u003eVerification of G242V and S258I mutations in MC38 cells\u003c/h3\u003e\n\u003cp\u003eMutations in \u003cem\u003eTrp53\u003c/em\u003e were confirmed through PCR amplification and Sanger sequencing. Genomic DNA was extracted from MC38 wild-type and clonal lines (#6 and #16) using a tissue DNA extraction kit with heat lysis at 95\u0026deg;C and stabilization buffer treatment. To verify the G242V mutation, the region encompassing exon 7 was amplified using the primers mP53 Forward (5\u0026rsquo; GCTATAGCCAGCCATTCCC 3\u0026rsquo;) and mP53 Reverse (5\u0026rsquo; ACCATCCAATCCAATCGGACA 3\u0026rsquo;). PCR was performed using OneTaq\u0026reg; Quick-Load\u0026reg; 2X Master Mix under the following thermal conditions: 95\u0026deg;C for 5 min; 35 cycles of 95\u0026deg;C for 30 sec, 58\u0026deg;C for 30 sec, 72\u0026deg;C for 1 min; and a final extension at 72\u0026deg;C for 5 min. PCR products were purified using a PCR purification kit and sequenced using the forward sequencing primer TGGTAGGTTAGGTTAGCCTGT. The G242V mutation was confirmed by identifying a G-to-T nucleotide substitution in exon 7. Similarly, the S258I mutation was validated for having a T-to-G substitution resulting in a serine-to-isoleucine amino acid change.\u003c/p\u003e \u003cp\u003e \u003cb\u003eGeneration of\u003c/b\u003e \u003cb\u003eTrp53\u003c/b\u003e \u003cb\u003edeletion in MC38 cells\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003cem\u003eTrp53\u003c/em\u003e knockout was performed using CRISPR-Cas9. Briefly, single-guide RNA (sgRNA) was designed using online CRISPR Design Tool (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://crispr.cos.uni-heidelberg.de\u003c/span\u003e\u003cspan address=\"https://crispr.cos.uni-heidelberg.de\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and cloned into plasmid lentiCRISPRv2GFP (Addgene, catalog no. 82416). The sgRNA sequences were designed to delete exon 4 of mouse \u003cem\u003eTrp53\u003c/em\u003e. They are gRNA1: ACAGCCATCACCTCACTGCA, gRNA2: ACACTCGGAGGGCTTCACTT. The plasmids were transfected into the MC38 cell line using Lipofectamine 2000 (Thermo Fisher Scientific, catalog no. 11668030). Transfected cells were sorted and single cells cloned, and mutant cells were identified using genomic DNA PCR and confirmed by Western blot analysis. The genomic target sequences used for targeting screening were AGGAAATCAGGAACTAACTCTCTGCTCTT (forward) and TGCACATAACAGACTTGGCTGTCC (reverse).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eTumor models\u003c/h2\u003e \u003cp\u003eMice were shaved and inoculated intradermally with \u003cem\u003eTrp53\u003c/em\u003e\u003csup\u003eCON\u003c/sup\u003e and \u003cem\u003eTrp53\u003c/em\u003e\u003csup\u003eKO\u003c/sup\u003e MC38 cells at 10\u003csup\u003e6\u003c/sup\u003e cells/50uL PBS/tumor. One tumor was inoculated per mouse. Tumor length (L) and width (W) were measured using a digital caliper. Tumor volume was calculated using the formula: L x W x W x 0.5. Tumors were treated with anti-PD-1 purchased from BioxCell \u003cem\u003eInVivo\u003c/em\u003eMAb anti-mouse PD-1 (catalog #BE0146). Antibodies were aliquoted and diluted in PBS to the required concentration to inject each mice intraperitoneally with 100ug in 100uL. Mice were treated on days 5, 9, 13, and 17, and tumors were allowed to grow up beyond the last treatment.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eTissue processing\u003c/h3\u003e\n\u003cp\u003eMice were euthanized and tumors immediately dissected. Excess skin, hair, fat, and connective tissue were removed. Tumors were transferred into 6-well plates containing 0.25mg/mL LiberaseTL (Roche, 5401020001) and 0.33mg/mL DNase (Sigma-Aldrich DN25-10MG) in RPMI, minced with scissors, and digested in a tissue culture incubator for 30min without agitation. Digestion was quenched with 3mL RPMI. Samples were strained through 70-uM strainers and pushed through using a pestle, then strained through 30uM nylon mesh. Samples were pelleted and resuspended in 2% FBS in HANKS.\u003c/p\u003e\n\u003ch3\u003eFlow cytometry\u003c/h3\u003e\n\u003cp\u003eStaining was performed in 96-well V-bottom plates. For cytokine analysis, cells were first stimulated using the Leukocyte Activation Cocktail, with BD GolgiPlug (BD Biosciences 550583) in a tissue culture incubator for 4 hours. Cells were initially stained in 0.1% Ghost Dye Violet 510 (Tonbo Biosciences, 13-0870-T500) in PBS on ice for 30min. They were then stained in antibody cocktails in 2% FBS in HANKS on ice for 5 min. Cells were then filtered through 30uM nylon mesh. Samples were run on a Cytek Aurora.\u003c/p\u003e \u003cp\u003eThe following antibodies were used: ArgI (eBioscience, catalog #46-3697-82, clone A1exF5), CD4 (BD Biosciences, 612844, RM4-5; RRID:AB_2870166), CD8a (BD Biosciences, 566096, 53\u0026thinsp;\u0026minus;\u0026thinsp;6.7; RRID:AB_2739500), CD11b (BD Biosciences, 564443, M1/70; RRID:AB_2738811), CD11c (Biolegend, 117312, N418; RRID:AB_389328), CD24 (BioLegend, 101822, M1/69; RRID:AB_756048), CD45 (BioLegend, 103130, c30-F11; RRID:AB_893339), CD44 (BioLegend, 103005, IM7; RRID:AB_312956), CD62L(BD Biosciences, 560514; RRID:AB_10611861), CD80 (BioLegend,104724, 16-10A1; RRID:AB_2075999), CD86 (BioLegend, 105043, GL-1; RRID:AB_2566722), CD206 (BioLegend, 141708, C068C2; RRID:AB_10900231), F4/80 (BioLegend, 123130, BM8; RRID:AB_2293450), Gr-1, Ly-6C (BioLegend, 128037, HK1.4; RRID:AB_2562630), Ly-6G (BioLegend, 127628, 1A8; RRID:AB_2562567), MHCII, PD-1 (BD Biosciences, 551892, J43; RRID:AB_394284), Tim-3 (BioLegend, 119721, RMT3-23; RRID:AB_2616907), LAG-3 (BioLegend, 125212, C9B7W; RRID:AB_2561517), ST2 (eBioscience, 46-9333-82, RMST2-33), CD103 (BD Biosciences, 565849, M290; RRID:AB_2739377), TCF1 (Cell Signaling Technology, 14456, C63D9; RRID:AB_2798483), Foxp3 (BioLegend, 126410, MF-14; RRID:AB_2105047), GzmB (BioLegend, 372204, QA16A02; RRID:AB_2687028), Ki-67 (BioLegend, 652410, 16A8; RRID:AB_2562141), and IFN-γ (BioLegend, 505826, clone XMG1.2; RRID:AB_2295770). All antibodies were used at 1:100 dilution.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003escRNAseq\u003c/h2\u003e \u003cp\u003eDissected and trimmed tumors were washed in pre-cooled RNase-free H\u003csub\u003e2\u003c/sub\u003eO and transferred into MACS Tissue storage solution (Miltenyi Biotec, 130-100-008). Samples were shipped on ice overnight to Novogene. Samples were processed according to the standard 10X pipeline. Three tumors from separate mice were prepared separately for each condition. The sample with the highest best cell viability and concentration for each condition was chosen. From the two samples, 10000 cells (target 7000 cells in output) per sample were loaded onto two separate lanes of a Chromium Chip in a Chromium Controller (10X Genomics) with 5\u0026rsquo; chemistry for gene expression and TCR analysis. Libraries were sequenced on an Illumina Novaseq 6000 S4 with 30000 reads/cell (~\u0026thinsp;200M reads total, 60Gb data output).\u003c/p\u003e \u003cp\u003eCellranger 2 was used to align fastq files, which were aligned to the mm10 reference genome and generate barcode, feature, and count matrices. Initial downstream analysis was performed using Seurat and Scanpy. Custom code was used for further analyses.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eCell death stimulation and autophagy inhibition\u003c/h2\u003e \u003cp\u003eMC38 and \u003cem\u003eTrp53\u003c/em\u003e\u003csup\u003eKO\u003c/sup\u003e MC38 clones (each 8x10\u003csup\u003e4\u003c/sup\u003e cells per well) were seeded in 24-well plates for 24h before treatment with murine IFNγ (50ng/ml, #315-05, Pepro Tech), murine TNFα (50ng/ml, #315-01A, Pepro Tech), their combination, or left untreated. The cytokines were added into each well, and the cells were incubated at 37\u0026deg;C for 24h and 48h prior to counting with a TC20 cell counter (Bio-Rad). For autophagy inhibition, MC38, B16, 4T1, EMT6 cells (8x10\u003csup\u003e4\u003c/sup\u003e per well), and CT26 cells (10\u003csup\u003e5\u003c/sup\u003e per well) were seeded in 24-well plates. Next day, the cells were treated with 50ng/ml of both IFNγ and TNFα as well as 6\u0026micro;M and/or 10\u0026micro;M of EAD1 (#S8576, Selleck Chemicals). The cells were then incubated at 37\u0026deg;C for 24h and 48h prior to counting with a TC20 cell counter (Bio-Rad).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eWestern blot analysis\u003c/h2\u003e \u003cp\u003e \u003cem\u003eTrp53\u003c/em\u003e \u003csup\u003eCON\u003c/sup\u003e and \u003cem\u003eTrp53\u003c/em\u003e\u003csup\u003eKO\u003c/sup\u003e MC38 clone were treated with both IFNγ and TNFα or left untreated and incubated for 24h, 36h, and 48h at 37\u0026deg;C. The harvested cells were lysed with lysis buffer and ran into Mini-Protean TGX pre-cast gels (#4561085, Bio-Rad) prior to transferring to PVDF membranes using a Trans-Blot Turbo (#1704150, Bio-Rad). The membranes were then blocked with 6% non-fat milk and subjected to primary antibodies for overnight incubation. The mouse primary antibodies used for the experiments include caspase 3 (#9662; RRID:AB_331439), caspase 8 (#4927; RRID:AB_2068301), cleaved caspase 3 (#9664; RRID:AB_2070042), cleaved caspase 8 (#8592; RRID:AB_10891784), p62 (#39749; RRID:AB_2799160), phosphorylated P70S6K (#9205; RRID:AB_330944) and beta-actin (#4970; RRID:AB_2223172). The membranes were incubated with secondary antibody, mouse anti rabbit IgG (#93702; RRID:AB_2800208) after and before washing steps. All antibodies were purchased from Cell Signaling (Danvers, MA, USA). All washing steps were conducted with TBST at room temperature. At the end, an equal amount of SuperSignal West Pico Plus (#34580, Thermo Scientific) was added to the membranes to visualize the bands in a ChemiDoc imaging system (Bio-Rad). The bands were quantified with Image Lab version 6.1 (Bio-Rad).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eQuantitative RT-PCR\u003c/h2\u003e \u003cp\u003eThe cells were treated with mouse IFNγ (50ng/ml), TNFα (50ng/ml), their combination or left untreated for 24h. Total RNA was isolated from the cells using RNeasy Plus Mini kit (#74134, Qiagen). Complementary DNA was synthesized using qScript cDNA Synthesis kit (#95047, Quanta bio). Quantitative RT-PCR was performed using PowerUp SYBR Green Master Mix (#A25742, Applied Biosystems) to detect \u003cem\u003eCastor1\u003c/em\u003e gene with the following primers: CAGAACCGCTTTTGTGTCCTCAC (forward), GGAGAAAGCGAAGAACGGAATGG (reverse). GAPDH was utilized as endogenous control using AGGTCGGTGTGAACGGATTTG (forward) and GGGGTCGTTGATGGCAACA (reverse) primers. The MicroAmp Optical 96-well Reaction plates (#4306737, Applied Biosystems) were run in a QuantStudio 5 (Applied Biosystems).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analyses\u003c/h2\u003e \u003cp\u003eGraphPad Prism 6.0 software package (GraphPad Software, Inc.; RRID:SCR_002798) was used for the analysis. Statistical analysis was done using the Student \u003cem\u003et\u003c/em\u003e test (two-tailed), ordinary one-way ANOVA, or the log-rank survival analysis. A P value of \u0026lt;\u0026thinsp;0.05 was considered to be statistically significant. No statistical correction was used.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eData availability\u003c/h2\u003e \u003cp\u003eThe generated data is available upon request from the corresponding author. Public scRNA-seq data were retrieved from EMBL BioStudies (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ebi.ac.uk/biostudies/\u003c/span\u003e\u003cspan address=\"https://www.ebi.ac.uk/biostudies/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), under accession number E-MTAB-13904. scRNAseq data will be uploaded to Gene Expression Omnibus upon publication (RRID:SCR_005012).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eContributions\u003c/h2\u003e \u003cp\u003eConceptualization: B.L., Y.L., D.S.G. A.L.M. Experimentation and analysis: A.L.M., Y.L., D.S.G., L.Y., F.G., R.S., J.X., J.S., Z.K., M.L., V.C., E.L. Computational analysis: D.S.G., B.L., X.L.\u003c/p\u003e \u003cp\u003eWriting: D.S.G. and B.L. Supervision: B.L.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors claim no conflicts of interest related to this study. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank Ariel Aptekmann for bioinformatics support, Tara Lozy for statistical analysis assistance, and Hui Wang for technical assistance. This work was funded by NIH/NCI grant R01CA239716-01A1.\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eHarris CC. Structure and function of the p53 tumor suppressor gene: clues for rational cancer therapeutic strategies. J Natl Cancer Inst \u003cstrong\u003e1996\u003c/strong\u003e;88(20):1442-55 doi 10.1093/jnci/88.20.1442.\u003c/li\u003e\n\u003cli\u003eBieging KT, Mello SS, Attardi LD. 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Mol Cancer Res \u003cstrong\u003e2016\u003c/strong\u003e;14(1):66-77 doi 10.1158/1541-7786.MCR-15-0159.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"cell-death-and-disease","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"cddis","sideBox":"Learn more about [Cell Death \u0026 Disease](http://www.nature.com/cddis/)","snPcode":"41419","submissionUrl":"https://mts-cddis.nature.com/cgi-bin/main.plex","title":"Cell Death \u0026 Disease","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Immune checkpoint inhibitor, p53, mTOR, autophagy, PD-1, T cells, IFN-γ, TNF-α, cancer immunotherapy, tumor suppressor","lastPublishedDoi":"10.21203/rs.3.rs-8214123/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8214123/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ep53 is a critical tumor suppressor gene that inhibits cancer development by regulating cell cycle arrest, apoptosis, DNA repair, and metabolism. However, recent studies examining \u003cem\u003eTP53\u003c/em\u003e mutations in cancer immunotherapy have yielded inconsistent results, likely due to differences in \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003et\u003c/span\u003eumor \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003em\u003c/span\u003eutational \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eb\u003c/span\u003eurden (TMB) and the context-dependent roles of specific p53 mutants. In this study, we assessed the function of G242V and S258I \u003cem\u003eTrp53\u003c/em\u003e mutations in MC38 cells in the context of immunotherapy by generating \u003cem\u003eTrp53\u003c/em\u003e deletion and observed significantly enhanced responses to anti-PD-1 therapy. \u003cem\u003eTrp53\u003c/em\u003e-null tumors showed increased CD8\u003csup\u003e+\u003c/sup\u003e T cell infiltration and clonal expansion, along with reduced regulatory T (Treg) cells. Mechanistically, \u003cem\u003eTrp53\u003c/em\u003e deletion downregulated mTORC1 inhibitor genes, leading to elevated mTORC1 signaling and diminished autophagy, which sensitized tumor cells to IFN-γ and TNF-α-induced apoptosis. Besides mouse cells, we confirmed the human p53 mutants regulate the same sets of mTORC1 inhibitor genes in a human colorectal cancer cell line. Our findings demonstrate that certain p53 mutants, despite losing other canonical functions, retain wild-type p53\u0026rsquo;s ability to suppress mTORC1 and enhance autophagy, thereby inhibiting responses to immunotherapy.\u003c/p\u003e","manuscriptTitle":"mTORC1 Suppression by Trp53 Mutation Drives Resistance to Immune Checkpoint Blockade","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-17 09:25:00","doi":"10.21203/rs.3.rs-8214123/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"cell-death-and-disease","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"cddis","sideBox":"Learn more about [Cell Death \u0026 Disease](http://www.nature.com/cddis/)","snPcode":"41419","submissionUrl":"https://mts-cddis.nature.com/cgi-bin/main.plex","title":"Cell Death \u0026 Disease","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"41cf2996-6406-45f4-9b27-a0a22990a6c5","owner":[],"postedDate":"December 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":58885885,"name":"Biological sciences/Immunology/Cell death and immune response"},{"id":58885886,"name":"Biological sciences/Cancer/Tumour-suppressor proteins"}],"tags":[],"updatedAt":"2025-12-17T09:25:00+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-17 09:25:00","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8214123","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8214123","identity":"rs-8214123","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}