RNF19B confers tumor resistance to CAR T cells

RNF19B confers tumor resistance to CAR T cells | 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 RNF19B confers tumor resistance to CAR T cells Roger Geiger, Ian Vogel, Andrea Casagranda, Margherita Cattaneo, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7847831/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 Chimeric antigen receptor (CAR) T cells have transformed cancer therapy, yet many tumors remain refractory. To uncover broadly acting mechanisms of resistance, we performed genome-wide CRISPR activation screens across diverse cancer cell types. These screens converged on RNF19B, an E3 ubiquitin ligase whose high expression correlates with poor patient survival and confers robust CAR-T resistance in mouse xenograft models. Mechanistically, RNF19B destabilizes the interferon-γ receptor subunit IFNGR1, blunting interferon-γ signaling, and simultaneously induces CAMKK2, which mediates resistance through an independent pathway. Pharmacologic inhibition of CAMKK2 synergized with CAR-T therapy in different xenograft mouse models. Our findings identify RNF19B as a previously unrecognized, dual-pathway mediator of CAR-T resistance and reveal CAMKK2 inhibition as a potential strategy to enhance CAR-T efficacy. Biological sciences/Immunology/Immune evasion Biological sciences/Immunology/Tumour immunology/Immunosurveillance Biological sciences/Cancer/Cancer therapy/Cancer immunotherapy Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction CAR-T cells emerged as a transformative cell-based approach, with the potential to target malignant cells independent of conventional antigenic constraints 1 – 6 . However, currently 10–20% of patients with B-cell malignancies, for which CAR-T cell therapies have been FDA-approved, fail to respond 7 . Additionally, 30–50% that initially enter remission relapse within one year 7 ; thus, many patients do not achieve durable clinical benefit. Furthermore, the efficacy of CAR-T cells against solid tumors has proven challenging 8 , 9 . Therapeutic responses can be limited due to declining persistence of T cells or emergence of T cell dysfunction pre- and post-infusion 10 , 11 . Alternatively, malignant cells can develop escape mechanisms, including downregulation of their target antigen 12 – 15 . Beyond antigen loss, cancer cells may acquire resistance, for which the underlying molecular mechanisms are incompletely understood 16 – 19 . As CAR-T cells become a standard line of treatment in oncology, it is crucial to define key vulnerabilities in cancer cells and exploit them to further augment CAR-T cell efficacy, especially for solid tumors. Functional genetic screens have the potential to comprehensively profile mechanisms underpinning resistance to current immunotherapies 20 . Several studies employed CRISPR screens in both human and murine cancer cells to interrogate and identify genetic determinants driving immune evasion to CD8 T cells or CAR-T cells. These screens identified well-established processes governing resistance to immune cells, including interferon-gamma (IFN-γ) signaling and antigen presentation 21 – 27 , TNF and cell death pathways 26 , 28 , 29 , 18 , 19 , 30 , epigenetic regulation 31 – 33 , autophagy 24 , 29 as well as individual genetic determinants 30 , 34 . In this study, we performed genome-wide CRISPR activation (CRISPRa) screens in cancer cells of diverse etiology identified a subset of genes that modulated CAR-T cell sensitivity. Among these were several well-known regulators of cell death pathways, as well as the E3 ubiquitin ligase RNF19B, which conferred resistance to CAR-T cells in vitro and in vivo . Furthermore, its expression is associated with poor survival in cancer patients. RNF19B attenuates IFN-γ signaling by destabilizing IFNGR1 and increases CAMKK2 abundance, a kinase we confirmed as a druggable target in synergy with CAR-T cells in multiple xenograft mouse models. Taken together, our focused examination uncovered resistance mechanisms to CAR-T cells and lays the foundation for combination strategies to augment the efficacy and broaden the applicability of CAR-T cell therapies. Results CRISPRa Screens identify RNF19B as a Multi-Cancer Resistance Factor to CAR-T Cells To systematically interrogate genetic determinants of cancer cell susceptibility and resistance to CAR-T cells, we performed genome-wide CRISPR activation (CRISPRa) screens 35 in human cell lines from mantle cell lymphoma (MCL, JeKo-1), hepatocellular carcinoma (HCC, Huh-7) and glioblastoma (GBM, U-343MG) in the presence and absence of cognate CAR-T cells (anti-CD19 36 , anti-GPC3 37 and anti-IL13Rα2 38,39 , respectively) (Fig. 1 A). Cells were then split into two arms: an alone condition or co-culture with cognate CAR-T cells seeded at a relatively low effector-to-target ratio (E:T = 1:20 − 1:50) 19,23,24,31 , allowing for a gradual and progressive selection of cancer cells over the course of 5–8 days. After co-cultures reached approximately 50% cytotoxicity, we extracted genomic DNA and compared sgRNA representations via amplicon sequencing and determined their gene-level enrichment (screen hits = FDR 2). To identify genes conferring resistance or susceptibility to CAR-T cells, we compared sgRNA representations in alone versus co-culture conditions. Collectively, the screens revealed 264 genes that affect cancer cell vulnerability to CAR-T cells (JeKo-1 = 41, Huh-7 = 71, U-343MG = 175) (Fig. 1 B and Supplementary Table 1). Notably, cognate antigens were not among the screen hits, indicating antigen density was not a limiting factor (Supplementary Table 1). Many screen hits mapped to cell-death signaling pathways and other established mediators of resistance to T-cell-mediated cytotoxicity, including Fas ( FAS ), Fas ligand ( FASLG ), interferon-β ( IFNB1 ), TRAIL ( TNFSF10 ), and SERPINB9 40 (Fig. 1 B). The intersection among screen hits revealed only two genes that were shared and consistent across all three cell types: CFLAR and RNF19B (Fig. 1 C,D). Of these, CFLAR (protein: CFLIP), is a well-characterized, cell-intrinsic negative regulator of cell death by inhibiting both apoptotic and necroptotic programmed cell death pathways, including against T-cell mediated killing 18 , 40 – 42 . Conversely, RNF19B (Ring Finger Protein 19B) is less well-characterized and previously undescribed for its role in cancer or resistance to T-cell mediated cytotoxicity. To further validate our screen results, we generated distinct subset libraries for Huh-7 and U-343MG cells comprised of sgRNAs targeting respective screen hits, which displayed largely concordant effects and validated RNF19B and CFLAR as top-ranked resistance factors (Supplementary Fig. 1B and Supplementary Table 2). Next, we performed arrayed transduction of RNF19B and NT-CTRL sgRNAs in CRISPRa-competent Huh-7, JeKo-1 and U-343MG cells and confirmed on-target RNF19 B mRNA upregulation by qRT-PCR (Supplementary Fig. 2A). While RNF19B -OE did not affect proliferation or viability of cancer cells cultured alone (Supplementary Fig. 2B), it conferred resistance to CAR-T mediated killing, with a 3 to 5-fold increase in the number of live cancer cells after 4–5 days of co-culture (Fig. 2 A). To include a model where cancer cells are recognized by T cells through peptide-MHC, we used A-375 NY-ESO-1 + [HLA-A*02-restricted] human melanoma cells recognized by 1G4-TCR T cells 43 . Indeed, RNF19B -OE A-375 cells were more resistant to 1G4-TCR T cells (Fig. 2 A), indicating that RNF19B confers resistance to T cells independent of mode of recognition. As an orthogonal approach, we employed CRISPR inhibition (CRISPRi) in Daudi Burkitt’s lymphoma and U-251MG malignant glioblastoma cells that endogenously express high levels of RNF19B according to The Human Protein Atlas (proteinatlas.org/). Indeed, RNF19B knockdown (-KD) in CRISPRi-competent Daudi and U-251MG cells led to a decrease in RNF19B expression (Supplementary Fig. 2C) and were more susceptible to CAR-T cells (Fig. 2 B). RNF19B confers resistance in vivo and is negatively prognostic in cancer patients We next assessed whether RNF19B mediates resistance to CAR-T cells in vivo . We engrafted RNF19B -OE and NT-CTRL JeKo-1 cells intravenously (i.v.) in immunodeficient NOD-scid gamma (NSG) mice 44 . After nine days, human CD4 and CD8 αCD19 CAR-T cells were injected i.v. into tumor bearing mice. In the absence of αCD19 CAR-T cells, RNF19B-OE and NT-CTRL JeKo-1 tumors grew at equivalent kinetics. While NT-CTRL JeKo-1 cells were effectively controlled by CAR-T cells, RNF19B-OE rendered JeKo-1 cells refractory, leading to increased tumor burden and diminished mouse survival (Fig. 2 C). To further explore the potential clinical relevance of RNF19B, we searched the GEPIA portal 45 for analyzing The Cancer Genome Atlas (TCGA) to assess the correlation between RNF19B transcript levels (quartile group cutoff) with overall survival. Notably, in patients with Liver Hepatocellular Carcinoma (LIHC) and Glioblastoma multiforme (GBM), high RNF19B transcript levels were strongly negatively prognostic (Fig. 2 D). In addition, RNF19B was also negatively prognostic on aggregate among all cancer types, corroborating its potential role in promoting cancer progression (Supplementary Fig. 2D) 45 . RNF19B blunts IFN-γ responsiveness Having established RNF19B confers resistance to CAR-T cells, we sought to characterize its mechanistic function. To exclude the possibility that resistance was caused by antigen loss, we confirmed the presence of the target antigens GPC3, CD19 and IL13Rα2 in the respective cancer cells by flow cytometry (Supplementary Fig. 2E). Furthermore, we analyzed CAR-T cells by flow cytometry following co-culture with RNF19B -OE cells and observed no defect in T cell activation nor effector function, suggesting that RNF19B causes resistance through a cancer cell-intrinsic mechanism (Supplementary Fig. 2F). Thus, we concluded its mechanism was not due to its effect on proliferation, antigen escape or inhibition of CAR-T cell effector function. RNF19B is an E3-ubiquitin ligase and likely involved in protein trafficking and degradation 46 ; therefore, we used liquid chromatography-coupled, high-resolution mass-spectrometry (LC-MS/MS) to globally profile protein abundances. RNF19B-OE and NT-CTRL Huh-7, JeKo-1, and U-343MG cells were cultured alone or under cognate CAR-T cell pressure. Viable cancer cells were sorted after 4–5 days and subjected to LC-MS/MS. T cell lineage markers CD3 and CD8 were not detected in the cancer cell proteomes, confirming sorting purity (Supplementary Table 3). The proteomic signatures we identified in CAR T-cell-exposed cancer cells were also found in CART19-treated ALL patient samples 18 and were significantly reduced in non-responders (NR) versus complete responders (CR) (Supplementary Fig. 3A-3D), indicating our proteomics data captured clinically relevant signatures. Principal component analysis (PCA) revealed largely overlapping proteomes between NT-CTRL and RNF19B-OE cells alone; however, a separation was evident under cognate CAR-T cell pressure (Fig. 2 E and Supplementary Fig. 2G). Moreover, the global proteomics response to CAR-T cell pressure was dampened in RNF19B-OE cells (Fig. 2 F and Supplementary Fig. 2H). A module of pro-inflammatory proteins up in NT-CTRL cells under CAR-T cell pressure was absent in RNF19B-OE cells, resembling cells cultured alone (Fig. 2 G and Supplementary Fig. 2I). GSEA of this module revealed a distinct profile marked by a reduction in the abundance of IFN stimulated genes (Fig. 3 A and Supplementary Fig. 4A). ISGs are induced by either type I or type II interferons. To assess the role of RNF19B in these pathways, we treated RNF19B -OE and NT-CTRL cells with recombinant IFN-β (type I) and IFN-γ (type II). We performed global proteomic profiling by mass spectrometry and quantified the induction of hallmark ISGs, PD-L1 and MHC-I, by flow cytometry. Notably, RNF19B -OE selectively blunted the IFN-γ-mediated ISG induction evident as early as 6 hours post-treatment (Fig. 3 B-D and Supplementary Fig. 4B), whereas the response to IFN-β remained unaltered (Supplementary Fig. 4C). Because both type I (IFN-β) and type II (IFN-γ) signaling pathways converge on a JAK1–STAT1 cascade, these results suggest that RNF19B interferes upstream of JAK-STAT, most likely at the level of the IFN-γ receptor complex. RNF19B downregulates IFNGR1 The IFN-γ receptor is a heterodimer composed of IFNGR1 and IFNGR2 47 . IFNGR1 harbors multiple cytosolic lysine residues that serve as ubiquitination sites, with E3 ubiquitin ligases known to control its stability 48 – 51 . Consistent with this, RNF19B -OE markedly reduced IFNGR1 abundance both at the cell surface and in total cell lysates (Fig. 3 E and Supplementary Fig. 4D). Conversely, CRISPRi-mediated knockdown of RNF19B in U-251MG cells led to increased surface IFNGR1 levels (Supplementary Fig. 4E). Together, these data indicate that RNF19B regulates IFNGR1 stability, thereby attenuating IFN-γ signaling. To pinpoint the lysine residue(s) responsible for RNF19B-mediated IFNGR1 destabilization, we generated IFNGR1-knockout (IFNGR1-KO) cells and reconstituted them with either wild-type (WT) IFNGR1 or single lysine-to-arginine mutants at residues K279, K285, and K299 48–50 . Only the IFNGR1 K285R mutant restored receptor levels (Fig. 3 F) and reverted the resistance phenotype (Fig. 3 G and Supplementary Fig. 4F) in RNF19B-OE cells similar to those of NT-CTRL cells. These findings identified that blunted IFN-γ responsiveness and CAR-T resistance in RNF19B-OE cells are linked to IFNGR1 stability via residue K285. RNF19B Resistance is Linked to CAMKK2 To investigate whether impaired IFN-γ responsiveness conferred by RNF19B fully accounts for the resistance phenotype, we over-expressed RNF19B in IFNGR1-KO cells and subjected them to CAR-T cell pressure (Fig. 3 H and Supplementary Fig. 4G). We confirmed that in NT-CTRL cells, IFNGR1-KO led to increased resistance to CAR-T cells, consistent with impaired IFN-γ signaling. However, RNF19B-OE further enhanced resistance in IFNGR1-KO cells, indicating that RNF19B mediates resistance to CAR-T cells through additional mechanisms. To explore alternative mechanisms contributing to RNF19B-mediated resistance, we compared the proteomes of NT-CTRL and RNF19B-OE cancer cells, which identified calcium/calmodulin-dependent kinase kinase 2 (CAMKK2) as the only protein differentially abundant in RNF19B-OE cells across JeKo-1, Huh-7, and U-343MG cell types (Fig. 4 A). Moreover, CAMKK2 protein abundance was consistently elevated in RNF19B-OE cells under CAR-T cell pressure (Fig. 4 B). To test whether CAMKK2 contributes to the refractory phenotype of RNF19B-OE cells, we used STO-609, a CAMKK2 inhibitor 52 – 54 . Strikingly, STO-609 reverted the resistance state of RNF19B-OE cells to CAR-T cell pressure, achieving an almost equal live target cell count to NT-CTRL cells at 15 µM in all three cell types (Fig. 4 C-E). The effect of STO-609 on RNF19B-OE cells was dependent on the synergistic activity of CAR-T cells, as the compound alone was not toxic and only had a slightly cytostatic effect at the highest concentrations tested, which was equal between NT-CTRL and RNF19B-OE cancer cells (Supplementary Fig. 5A-D). Notably, the addition of STO-609 abolished the added resistance of RNF19B-OE in IFNGR1-KO cells (Supplementary Fig. 4F). To further investigate the association between RNF19B and CAMKK2, we used CRISPRi to target endogenous RNF19B expression in cancer cells. We confirmed that RNF19B-KD in Daudi, HepG2, and U-2521MG cells led to a concomitant decrease in CAMKK2 protein abundance across the three cell types (Fig. 4 G). We then subjected U-251MG cells to IL13RA2-CAR T-cell pressure at increasing concentrations of STO-609 (Fig. 4 H). Strikingly, RNF19B-KD U-251MG cells exhibited no sensitivity in response to STO-609, whereas NT-CTRL U-251MG displayed increased susceptibility to CAR-T cells at increasing concentrations of STO-609. To assess the potential clinical relevance of CAMMK2, we used the GEPIA portal for analyzing TCGA, as had been done previously for RNF19B. Indeed, high transcript levels of CAMKK2 (quartile group cutoff) for HCC and GBM were significantly correlated with decreased overall survival (Supplementary Fig. 5E). Taken together, our data demonstrated that the abundance of CAMKK2 is tightly associated with RNF19B expression and contributes to their increased resistance to CAR-T cells. CAMKK2 Inhibition Synergizes with CAR-T Cells in vivo To explore the potential translational relevance of our findings, we examined if STO-609 treatment synergizes with CAR-T cells in vivo . To this aim, we engrafted JeKo-1 cells i.v. in NSG mice followed by treatment with αCD19 CAR-T cells. Immediately following CAR-T cell treatment, we initiated either DMSO:PBS (1:1 v/v) vehicle control or 30 µM/Kg twice a day (BID) STO-609 for a 1-week treatment regimen (Fig. 5 A). While STO-609 treatment had no effect alone, the combination of STO-609 with CAR-T cells provided a significant improvement in tumor control and mouse survival (Supplementary Fig. 5B-D), demonstrating the potential benefit of combination therapy. Next, we wanted to test if STO-609 could synergize with CAR-T cells in a solid tumor model where the effect of CAR-T cell treatment alone is insufficient for tumor control. Hence, we engrafted Huh-7 cells subcutaneously in the right flank of NSG mice (Fig. 5 E). After tumors became palpable, we injected αGPC3 CAR-T cells and commenced a thrice-weekly treatment regimen of STO-609 or vehicle control. Only the synergistic effect of STO-609 treatment in combination with αGPC3 CAR-T cells led to a reduction in Huh-7 tumor growth and concomitant improvement in mouse survival, whereas αGPC3 CAR-T cells nor STO-609 treatments alone displayed tumor control (Fig. 5 F-H). Taken together, CAMKK2 inhibition can be leveraged to synergize with CAR-T cell therapies. Discussion Our genome-wide CRISPRa screens across multiple tumor contexts identify RNF19B as a common, tumor-intrinsic mediator of resistance to CAR-T cells in vitro and in vivo . In independent datasets, high RNF19B expression associates with inferior survival, including in hepatocellular carcinoma and glioblastoma, and has been proposed as a prognostic marker that also predicts aspects of immunotherapy response in HCC 55 . Immunohistochemistry further indicates predominantly cytoplasmic RNF19B staining in malignant and stromal compartments with low signal in benign tissues, supporting the therapeutic tractability of tumor-cell targeting 55 . RNF19B, also referred to as NKLAM (Natural Killer Lytic-Associated Molecule), is an E3 ubiquitin-protein ligase originally described for its role in innate immunity 46 . It contributes to the anti-tumoral cytotoxic activity of natural killer (NK) cells 56 , 57 and modulates cytokine production of macrophages 58 , 59 . Accordingly, while RNF19B overexpression makes it an attractive pharmaceutical target, strategies should ideally preserve RNF19B function in immune cells 46 , 60 . Mechanistically, RNF19B blunts IFN-γ responsiveness in cancer cells by reducing IFNGR1 abundance via lysine residue K285. This receptor-level control aligns with other E3 ubiquitin ligases, including STUB1 49 , RNF149 50 and FBXW7 51 , that converge on IFNGR1 stability. RNF19B-mediated regulation of IFNGR1 as a mechanism of resistance aligns with our observations that IFN-γ signaling was the prevailing response to CAR-T cell pressure, highlighting that ISGs are part of a general response program that promotes susceptibility applicable to CAR-T cells. Desensitization and reduced responsiveness to type II IFN (IFN-γ) is a well-known tumor intrinsic resistance and evasion mechanism that affects the efficacy of immune-checkpoint blockade and CAR-T cell therapies and serves as a predictive marker of response to immunotherapy 61 – 69 . RNF19B-driven resistance is not solely explained by IFN-γ pathway dampening. We also find that CAMKK2 protein abundance tightly associates with RNF19B expression through an as-yet undefined mechanism. Inhibiting CaMKK2 with STO-609 reverses the resistance phenotype of RNF19B-overexpressing cancer cells and abrogates residual resistance even in IFNGR1-deficient cells, indicating a parallel, targetable axis that persists when IFN-γ sensing is compromised. Importantly, CaMKK2 inhibition using STO-609 synergizes with CAR-T cells. STO-609 did not impair T-cell function in our assays, and treated mice displayed no toxicity. Next-generation pharmaceuticals targeting CAMKK2 with improved selectivity and pharmacokinetics may provide translationally suitable inhibitors for combinations with engineered T-cell therapies 70 . These findings integrate with prior work implicating CAMKK2 in both tumor-intrinsic and microenvironmental control. In glioblastoma, neuronal CAMKK2 deletion converts tumor-associated macrophages from a disease-associated microglia-like state to an antigen-presenting, stimulatory phenotype improving responsiveness to checkpoint blockade 71 . Beyond central nervous system (CNS), CAMKK2 contributes to immunosuppressive myeloid programming 72 , while in prostate cancer CAMKK2 acts downstream of androgen receptor signaling to support metabolic adaptation, invasion, and growth 73 . These context-specific roles make CAMKK2 an attractive point of intervention. In conclusion, these data position RNF19B as a common resistance driver through IFNGR1 down-modulation and a CAMKK2-linked program and nominate CAMKK2 inhibition as a rational combinatorial strategy to restore CAR-T efficacy across diverse tumor settings. Declarations All animal procedures were approved by the Ticino Cantonal Commission for Animal Welfare and conducted in full compliance with the Swiss Animal Welfare Legislation, specifically the Animal Welfare Ordinance and the Animal Experimentation Ordinance. Experiments were carried out under the oversight of the institutional animal care committee. Author contributions Conceptualization: I.V., A.C., and R.G. Methodology: I.V., A.C., and R.G. Investigation: I.V., A.C., M.C., L.G.M., M.P., C.B., and E.S. Resources: R.G. Writing: I.V., A.C., and R.G. Visualization: I.V. and A.C. Supervision: R.G. Funding acquisition: R.G. Acknowledgements We thank David Jarrossay for cell sorting and Dr. Magnus Essand for the IL13RA2 CAR-construct used throughout this investigation. R.G. is supported in part by the European Research Council (803150), by Swiss Cancer Research (KFS-4593-08-2018), and by the San Salvatore Foundation. Data availability The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD068450. Methods Molecular Cloning Single sgRNAs were generated via Golden Gate Assembly. Briefly, complementary ssDNA oligos were annealed with overhangs for Golden Gate Assembly and subsequently cloned into the pXPR_502 vector (Addgene #96923) for CRISPRa, in the CROPseq-Guide-Puro vector (Addgene #86708) for CRISPRi or in the PX458 vector (Addgene #48138) for IFNGR1-KO. Afterwards, plasmids were expanded in DH10-beta chemically competent bacteria and purified using a MiniPrep Kit (Machery-Nagel). The human CRISPRa Calabrese library was ordered from Addgene and expended via transformation of Endura ElectroCompetent Cells (Lucigen, catalog no. 60242-2). After transformation, Endura cells were grown in a shaking incubator for 16 hours at 30°C in the presence of ampicillin. Plasmids were isolated using the MidiPrep Kit (Machery-Nagel) and sequenced via NGS to determine library representation. IFNGR1 K279R, K285R and K299R mutants were generated by overlapping PCRs from the human IFNGR1 WT cDNA (ENST00000367739, ordered via Twist Bioscience) using DNA primers carrying the nucleotide bases to generate the K to R codon mutants and ligated with Gibson assembly. Lentivirus production Human embryonic kidney (HEK) 293T cells were cultured in complete DMEM supplemented with 10% FBS, 1 mM sodium pyruvate (Fisher Scientific), and 1 x MEM nonessential amino acids (Fisher Scientific) at approximately 40% confluency 24 hours prior to transfection. Second generation lentiviral vectors were produced per T75 culture flask using 20 µg of transfer vector, 15 µg of psPAX2 (Addgene #12260), 5 µg of pMD2.G (Addgene #12259) in 2 mL of OPTI-MEM, after which 90 µL of PEI MAX™ reagent was added, mixed by gentle inversion, and incubated for 10 minutes at room temperature. Media was carefully replaced with cOPTI-MEM without detaching HEK293T cells, after which the transfection mix was added dropwise to HEK293T cells. After 6–12 hours, transfection medium was replaced with 12 mL of cOPTI-MEM containing 1 x ViralBoost (Alstem Bio, catalog no. VB100). Lentiviral supernatant was harvested 24 hours after transfection, centrifuged at 500 x g for 5 min at 4°C, filtered through a 0.45 µM filter and concentrated using homemade Lenti-X concentrator. Viral supernatants were slowly added to Lenti-X and incubated overnight at 4 degrees Celsius. After centrifugation at 1,500 x g for 45 minutes at 4 degrees Celsius, concentrated lentivirus was resuspended in 1/50th the original volume in PBS. Virus particles were subsequently aliquoted and frozen immediately at − 80°C. Isolation, culture of human T cells and transduction Peripheral blood from healthy donors was obtained from the Swiss Blood Donation Center of Basel and Lugano and used in compliance with the Federal Office of Public Health (authorization no. CE3428). PBMCs were isolated by Ficoll gradient centrifugation. CD8 + T cells were enriched with magnetic microbeads (Miltenyi Biotec) according to the manufacturer’s recommendations. Cells were subsequently cultured in RPMI-1640 medium supplemented with 2 mM glutamine, 1% (v/v) non-essential amino acids, 1% (v/v) sodium pyruvate, penicillin (50 U/mL), streptomycin (50 µg/mL; all from Invitrogen) and 10% (v/v) fetal bovine serum (Gibco). Unless otherwise indicated, primary human CD8 + T cells were activated with plate-bound anti-CD3 (5 µg/mL, clone TR66) and anti-CD28 (1 µg/mL, clone CD28.2, BD Biosciences) for 48 h in 96-well Nunc Maxisorb plates. After 48 h of activation, cells were expanded to be maintained between 0.4-1x10 6 /mL and cultured in IL-2-containing medium (50 U/mL). For T cell transduction, 24 hours after activation, T cells were infected with 2–5% (v/v) concentrated lentivirus and spinoculated at 800 x g for 45 minutes at 32°C. CRISPRa screens 3 days following transduction of the Calabrese library lentiviral constructs, Huh-7, JeKo-1, and U-343MG cells were selected with puromycin (1–4 ug/mL) for a minimum of 4 days. Subsequently, Huh-7 and U-343MG cells underwent sequential rounds of co-culture of CAR-T cells generated from two independent healthy donors, while JeKo-1 cells underwent a single round of CAR-T cell selection. 20x10 6 cells were resuspended in 1.6 mL of lysis buffer (1% SDS, 50 mM Tris, pH 8, 10 mM EDTA). 16 µL of NaCl (5M) was added, and the sample was incubated on a heat block overnight at 66°C. The next morning, 8 µL of RNAse A (10mg/mL) was added, and the sample was vortexed briefly, and incubated at 37°C for 1 hour. Next, 8 µL of Proteinase K (20mg/mL) was added, the sample was vortexed briefly, and incubated at 55°C for 1 hour. 400 µL of Phenol:Chloroform:Isoamyl Alcohol (25:24:1) was mixed with an equal volume of sample, shaken vigorously and centrifuged at maximum speed at room temperature for 5 minutes. The aqueous phase was transferred to eppendorf tubes and then 40 µL of Sodium Acetate (3M), 1 µL GlycoBlue, and 600 µL of room temperature isopropanol was added. The sample was then vortexed and stored at -80°C for 30 minutes or until the sample had frozen solid. Next the sample was centrifuged at maximum speed at 4°C for 30 minutes, the pellet was washed with fresh 70% room temperature Ethanol and allowed to air dry for 15 minutes. Pellets were then resuspended in DNA elution buffer (Machery-Nagel) and placed on the heat block at 65°C for 1 hour to completely dissolve the genomic DNA. For sgRNA amplification and barcoding from genomic DNA, a nested PCR1 and PCR2 strategy was adopted. PCR1 amplified virally integrated sgRNA cassette from genomic DNA using a P5 stagger primers mix and a P7 common primer. PCR2 amplified PCR1 products using P7 indexing primers and P5 common primer while also adding the Illumina adapters. In PCR1, 1–3 µg of genomic DNA was added per 50 µL reaction. Each reaction tube contained 25 µL of Q5® High-Fidelity 2X Master Mix (NEB), 2.5 µL of 10 µM forward primers, 2.5 µL of 10 µM reverse primers, and H 2 O up to 50 µL. PCR1 reaction products were pooled from each experimental condition (16 reactions per condition). In PCR2, 10 µL of 1:100 diluted PCR1 product was added to 25 µL reaction for a total of 8 reactions per condition. Each reaction tube contained 12.5 µL of Q5® High-Fidelity 2X Master Mix (NEB), 1.25 µL of 10 µM forward primers, 1.25 µL of 10 µM reverse primers, and H2O up to 25 µL. Up to 2 µg of each sample were loaded on a 2% agarose gel, and the band between 368–373 base pairs was extracted using a DNA agarose gel recovery kit (Machery-Nagel) and SPRI purified using magnetic beads. The concentration of each sample was then measured using the Qubit dsDNA high sensitivity assay kit (Thermo Fisher Scientific). Samples were then sequenced on an Illumina NextSeq 4000 using 10–30% PhiX. CRISPRa screen analysis Raw reads of FASTQ files were aligned to our custom TF library using MAGeCK (version v0.5.9.2) with the default arguments to generate a normalized read count table 74 . The test function was then performed by grouping initial representation immediately following puromycin selection, control cells cultured alone in parallel to the surviving cells post CAR-T cell challenge. Screen hits were classified as having a z-scaled log 2 -fold change (LFC) > 2 and a FDR < 0.05. In vitro CAR-T cell cytotoxicity assays Cancer cells were seeded at 1-2x10 5 /mL in complete RPMI medium containing 50 IU/mL interleukin-2 (IL-2) one day prior to co-culture. The following day, cognate CAR-T cells were added at 1:4 E:T ratios, unless otherwise indicated. For STO-609 / DMSO control, cells were treated as indicated followed immediately by the addition of cognate CAR-T cells at a 1:4 E:T ratio. Absolute number of viable cancer cells was determined by FACS analysis. Briefly, adherent cells were detached with trypsin-EDTA following by staining with anti-CD8 or anti-CD3 antibodies and Sytox viability dyes prior to being resuspend in MACS buffer and a constant volume was acquired for each well without a limit to the number of recorded events. Reverse Transcription-quantitative PCR RNA was isolated using TRIzol™ reagent (Thermo Fisher Scientific) according to manufacturer’s instructions. Briefly, cell pellets were resuspended in TRIzol™ reagent and incubated for 5 minutes before adding 200 µL of chloroform. Tubes were spinned at 15k x g for 15 minutes at 4°C and after the aqueous phase was transferred to new tubes, 500 µL of isopropanol was added and incubated for 10 minutes. After centrifugation at 15k x g for 15 minutes at 4°C, RNA pellet was washed with 70% ethanol in DNAse/RNAse-free water, dried and resuspended in DNAse/RNAse-free water. RNA was reverse transcribed into cDNA using the Maxima H Minus Reverse Transcriptase (Thermo Fisher Scientific) according to manufacturer’s instructions. Briefly, each reaction contained up to 500 ng of RNA diluted in a total of 7.5 µL of DNAse/RNAse-free water, 1 µL of Oligo(dT) 15 500 µg/mL (Promega), 0.5 µL of random hexamers 500 µg/mL (Promega), 1 uL of dNTPs 10 mM (Carl ROTH), 4 µL of 5x Retro Transcriptase buffer, 0.5 µL of RiboLock RNAse Inhibitor 40 U/µL (Thermo Fisher Scientific), 1 µL of Maxima H Minus Reverse Transcriptase and 2 µL of DTT 0.1 M (Thermo Fisher Scientific) for a total of 20 µL and 15 minutes at 50°C followed by 5 minutes at 85°C. cDNA samples were diluted 5 fold and qPCR reaction was run using the Perfecta SYBR Green Fast Mix™ (Quantabio) following manufacture’s instructions. Briefly, each reaction contained up to 10ng of cDNA template, 5 µL of Perfecta SYBR Green Fast Mix™ 2x, 0.3 µL of forward primer and 0.3 µL of reverse primer for a total volume of 10 µL and run on a QuantStudio™ 3 Real-Time PCR System. cDNA samples were probed for the expression of GAPDH and RNF19B with the following primers: GAPDH : Forward: 5’ – AATCCCATCACCATCTTCCA – 3’ Reverse: 5’ – TGGACTCCACGACGTACTCA – 3’ RNF19B : Forward: 5’ – AGACACAGCCAGTCTTGGTGCA – 3’ Reverse: 5’ – GCTGATAGTGGCTTGGTTTGGC – 3’ Live cell imaging Cancer cells were seeded at 1-2x10 5 cells/mL in complete RPMI medium containing 50 IU/mL interleukin-2 (IL-2) one day prior to co-culture. The following morning, cognate CAR-T cells were added at a 1:4 E:T ratio. Plates were loaded onto an ImageXpress (Molecular Devices) live cell imaging microscope to acquire transmitted light and green fluorescence every 3 hours for 4 days. IFN-γ and IFN-β treatment and IFN Response Score Cancer cells were seeded 1-day prior subjecting them to human recombinant IFN-γ (LubioScience) at 40 ng/mL for 24 hours (for kinetic of ISG induction, at indicated timepoints), recombinant IFN-β (Peprotech) at 500 U/mL for 24 hours at indicated concentrations for 24h. Cells were washed with PBS and detached before being processed for LC-MS/MS or stained with αPD-L1-APC (Biolegend) and αHLA-A/B/C-FITC (Thermo Fisher Scientific) antibodies and analyzed at flow-cytometer. For flow cytometry, the IFN response was calculated as follows: for each marker, the gMFI of untreated cells was subtracted to obtain background corrected values. Then, values were normalized by dividing by the mean gMFI of NT-CTRL cells. For each replicate, the response scores of the PD-L1-APC and HLA-A/B/C-FITC were summed and divided by 2 to generate a composite response score. For LC-MS/MS data, heatmap and hierarchical clustering were performed by first computing the mean protein abundance across three replicates per condition for each protein that showed increased expression in NT-CTRL + IFN compared to NT-CTRL cells alone. Then, z-score normalization was applied across the condition-specific means for each protein. Sample preparation for proteome analysis Samples were processed as described by previously 75 . Briefly, cell pellets were washed with PBS, lysed in 8 M urea, 50 mM ammonium bicarbonate (ABC) and then sonicated at 4°C for 15 min (Bioruptor, Diagenode, 30s on, 30s off, high mode). Proteins were reduced with 10 mM dithiothreitol for 20 minutes at room temperature and alkylation was performed in the dark for 30 min by adding 55 mM iodoacetamide. A two-step proteolytic digestion was performed. First, samples were digested at 21°C (room temperature) with LysC (Wako Fujifilm, 1:100, w/w) for 2 h. Then, they were diluted 1:4 with 50 mM ABC and digested with trypsin (Promega, 1:100, w/w) at 21°C overnight. The resulting peptide mixtures were acidified and loaded on C18 StageTips 76 . Peptides were eluted with 80% acetonitrile (ACN), 0.5% acetic acid, dried using a SpeedVac centrifuge (Savant, Concentrator plus, SC 110 A), and resuspended in 2% ACN, 0.1% trifluoroacetic acid and 0.5% acetic acid. LC–MS/MS for analysis of proteomes Peptides were separated on an EASY-nLC 1200 HPLC system (Thermo Fisher Scientific) coupled online to a Q Exactive mass HF spectrometer via a nanoelectrospray source (Thermo Fisher Scientific) 77 . Peptides were loaded in buffer A (0.1% formic acid) on in-house-packed columns (75 µm inner diameter, 50 cm length and 1.9-µm C18 particles from Dr. Maisch GmbH). Peptides were eluted with a nonlinear 180-min gradient of 5%–60% buffer B (80% ACN, 0.1% formic acid) at a flow rate of 250 nl min − 1 and a column temperature of 50°C. The Q Exactive was operated in a data-dependent mode with a survey scan range of 300–1,650 m / z and a resolution of 60,000 at m / z 200. Up to 10 most abundant isotope patterns with a charge 2 to 5 were isolated with a 1.8-Th-wide isolation window and subjected to higher-energy C-trap dissociation (HCD) at a normalized collision energy of 27. Fragmentation spectra were acquired with a resolution of 15,000 at m / z 200. Dynamic exclusion of sequenced peptides was set to 30 s to reduce the number of repeated sequences. Thresholds for the ion injection time and ion target values were set to 20 ms and 3 × 10 6 for the survey scans and 55 ms and 1 × 10 5 for the MS/MS scans, respectively. Data were acquired using the Xcalibur software (Thermo Fisher Scientific). For the Huh-7_EMT experiment, peptides were separated on a nanoElute2 HPLC system (Bruker) coupled via a nanoelectrospray source (Captive spray source, Bruker) to a timsTOF HT mass spectrometer (Bruker). Peptides were loaded in buffer A on an in-house-packed column (75 µm inner diameter, 25 cm length and 1.9-µm C18 particles) kept at 50°C and eluted over a 60-min linear gradient of 2 to 35% ACN/0.1% formic acid at a 300 nl/min flow rate. The mass spectrometer was operated in a data-independent (DIA)-PASEF mode 78 with accumulation and ramp times of 100 ms, covering with 21 mass steps (25 Da wide) and 1 mobility window, a mass range from 475 to 1000 Da and a mobility range from 0.85 to 1.27 Vs cm-2, with an estimated cycle time of 0.95 s. Data were acquired using the Bruker Compass Hystar software. Analysis of proteomics data MaxQuant software (version 1.6.7.0) was used to analyze DDA MS raw files 79 . MS/MS spectra were searched against the human Uniprot FASTA database (June 2019) and a common contaminants database (247 entries) by the Andromeda search engine 80 . Cysteine carbamidomethylation was set as a fixed modification, and N-terminal acetylation and methionine oxidation were set as variable modifications. Enzyme specificity was set to rypsin/P with a maximum of two missed cleavages and a minimum peptide length of seven amino acids. A false discovery rate of 1% was required for peptides and proteins. Peptide identification was performed with an allowed precursor mass deviation of up to 4.5 ppm and an allowed fragment mass deviation of 20 ppm. Nonlinear retention time alignment of all measured samples was performed in MaxQuant. Peptide identifications were matched across different replicates within a matching time window of 0.7 min and an alignment time window of 20 min. A minimum ratio count of 1 was required for valid quantification events via MaxQuant’s Label Free Quantification algorithm (MaxLFQ). Data were filtered for common contaminants and reverse hits, and peptides identified only by side modification were excluded from further analysis. DIA raw files were analyzed using DIA-NN version 1.8.1 with default settings searching against a deep learning-based predicted library generated from the human Uniprot database (February 2024). For library generation, the FASTA sequences were digested with trypsin/P with 1 missed cleavage, enabling N-terminal methionine excision and cysteine carbamidomethylation as a fixed modification. Precursor and fragment mass tolerance were determined automatically for each run ranging from 10 to 20 ppm. The ‘report.pg_matrix.tsv’ table was used for further analysis. In vivo tumor models NSG mice were bred in house. Mice were maintained under specific pathogen-free (SPF) conditions in the animal facility of the Institute for Research in Biomedicine. A maximum of five mice per cage were housed in ventilated cages under standardized conditions (20 ± 2°C, 55 ± 8% relative humidity, 12 h light/dark cycle). Food and water were available ad libitum, and mice were examined daily. Female mice were used between 6 and 10 weeks of age. Mice were treated in accordance with the Ticino Cantonal Commission for Animal Welfare, which is in accordance with the Animal Welfare Ordinance and the Animal Experimentation Ordinance from the Swiss Animal Welfare Legislation. Maximal tumour endpoints (2 cm in largest diameter) were not exceeded (cantonal authorization number 34613). JeKo-1-Luciferase cells were maintained in RPMI (Gibco) supplemented with 10% FBS (Gibco), 1 x GlutaMax (Gibco), 25 mmol/L HEPES (Gibco) and penicillin–streptomycin. For in vivo experiments, 1x10 6 cells were injected intravenously in 100 µL PBS in the right flank of 8–12 week-old NSG mice. For adoptive transfer of CTL019 CAR-T cells, mice were injected intravenously ( i.v. ) in 100 µL PBS as soon as tumors were measurable. STO-609 was administered twice per day (BID) for 7 days at (30 µM/kg body weight) via intraperitoneal ( i.p. ) injection. DMSO:PBS (1:1 v/v) vehicle was used as a control. Tumor volume was measured weekly via bioluminescence (BLI). Mice were euthanized when tumor volumes reached 1e 10 photons/s or reached other humane endpoints including loss of 15% of their initial body weight. Huh-7 cells were maintained in DMEM (Gibco) supplemented with 10% FBS (Gibco), 1× GlutaMax (Gibco), 25 mmol/L HEPES (Gibco) and penicillin–streptomycin. For in vivo experiments, 1×10 6 cells were injected subcutaneously in 100 µL PBS in the right flank of 8–12 week-old NSG mice. For adoptive transfer of GPC3 CAR-T cells, mice were injected intravenously ( i.v. ) with 1×10 6 αGPC3 CAR-T cells in 100 µL PBS as soon as tumors were measurable. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7847831","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":532143850,"identity":"422cdf84-8428-4fef-994b-cdf69b062d20","order_by":0,"name":"Roger 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1","display":"","copyAsset":false,"role":"figure","size":462291,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGenome-wide CRISPRa screens identify cancer cell resistance factors to CAR-T cells\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A)\u0026nbsp; Schematic of CRISPRa screening workflow.\u003c/p\u003e\n\u003cp\u003e(B)\u0026nbsp; Volcano plots showing resistance factors (red) and susceptibility factors (blue) under CAR-T cell pressure (screen hit: |z-scaled Log\u003csub\u003e2\u003c/sub\u003eFC| \u0026gt;2 \u0026amp; FDR \u0026lt; 0.05).\u003c/p\u003e\n\u003cp\u003e(C)\u0026nbsp; Venn diagram showing overlap of resistance factors for Huh-7, U-343MG and JeKo-1 cell CRISPRa screens.\u003c/p\u003e\n\u003cp\u003e(D)\u0026nbsp; Enrichment of individual RNF19B and CFLAR sgRNAs in Huh-7, JeKo-1 and U-343MG cells for their effect under CAR-T cell pressure (red bars) overlaid on the distribution of all 113,238 sgRNAs in the library (grey). False discovery rate (FDR) values are shown to the right.\u003c/p\u003e","description":"","filename":"FiguresMain71.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7847831/v1/f6607a1b64b1805d79151951.jpg"},{"id":94203078,"identity":"6d4129ba-0a0a-4033-ae65-6252fb57273c","added_by":"auto","created_at":"2025-10-23 14:11:59","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":445732,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRNF19B is a resistance factor under CAR-T cell pressure\u003c/strong\u003e\u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e(A)\u0026nbsp; Arrayed validation of RNF19B-OE under CAR/TCR-T cell pressure; box plot showing Huh-7, JeKo-1, U-343MG and A-375 RNF19B-OE live cell count normalized to NT-CTRL after 4 to 5 days of co-culture. All values are mean ± s.d. with \u003cem\u003en\u003c/em\u003e=4 for Huh-7 and JeKo-1, \u003cem\u003en\u003c/em\u003e=6 for U-343MG and \u003cem\u003en\u003c/em\u003e=5 for A-375. Two-tailed\u0026nbsp;\u003cem\u003et\u003c/em\u003e\u0026nbsp;test was performed.\u003c/p\u003e\n\u003cp\u003e(B)\u0026nbsp; Bar plot showing live cell counts normalized to NT-CTRL of Daudi and U-251MG RNF19B-KD cells after 4 to 5 days of co-culture. All values are mean ± s.d. with \u003cem\u003en\u003c/em\u003e=4. Two-tailed\u0026nbsp;\u003cem\u003et\u003c/em\u003e\u0026nbsp;test was performed.\u003c/p\u003e\n\u003cp\u003e(C)\u0026nbsp; Kaplan-Meier (KM) curves of mouse survival probability for all treatment groups. NT-CTRL (\u003cem\u003en\u003c/em\u003e=13), RNF19B-OE (\u003cem\u003en\u003c/em\u003e=8), NT-CTRL + CTL019 (\u003cem\u003en\u003c/em\u003e=16), RNF19B-OE + CTL019 (n=10). All values are mean ± s.e.m. Two-tailed \u003cem\u003et \u003c/em\u003etest was performed. Logrank (Mantel-Cox) test was performed between CTL019-treated groups.\u003c/p\u003e\n\u003cp\u003e(D)\u0026nbsp; Overall survival of patients with high vs low \u003cem\u003eRNF19B\u003c/em\u003e transcript levels (quartile group cutoff) for GBM and LIHC (\u003cem\u003en\u003c/em\u003e = 132/131)\u003csup\u003e45\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e(E)\u0026nbsp; PCA of proteomes from Huh-7 cells (NT-CTRL and RNF19B-OE) at steady-state and under CAR-T cell pressure. \u003cem\u003en\u003c/em\u003e=3.\u003c/p\u003e\n\u003cp\u003e(F)\u0026nbsp; Bar plot showing the proteomic response score to CAR-T cell pressure in Huh-7 cells. The score was calculated as the average of z-scaled protein abundances of proteins of increased abundance in NT-CTRL cells under CAR-T cell pressure compared to cells at steady-state. All values are mean ± s.d. with \u003cem\u003en\u003c/em\u003e=3. Two-tailed\u0026nbsp;\u003cem\u003et\u003c/em\u003e\u0026nbsp;tests were performed with NT-CTRL + CAR-T as reference group.\u003c/p\u003e\n\u003cp\u003e(G) Heatmap showing cluster of upregulated proteins in NT-CTRL as compared to RNF19B-OE Huh-7 cells in response to co-culture with CAR-T cells. Hierarchical clustering was performed on individual replicates and shown in the dendrogram. A subset of proteins is shown to the right. \u003cem\u003en\u003c/em\u003e=3.\u003c/p\u003e","description":"","filename":"FiguresMain72.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7847831/v1/aa755536a0b74f7a82e495f7.jpg"},{"id":94203085,"identity":"d381a6e0-40b7-4078-8075-c60949793f7d","added_by":"auto","created_at":"2025-10-23 14:11:59","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":534734,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRNF19B abrogates responsiveness to IFN-γ\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A)\u0026nbsp; GSEA of proteins downregulated in RNF19B-OE Huh-7 cells under αGPC3 CAR-T pressure compared to NT-CTRL cells.\u003c/p\u003e\n\u003cp\u003e(B)\u0026nbsp; Proteomic profile of U-343MG upon exposure to recombinant IFN-γ. Heatmap and hierarchical clustering were performed based on proteins upregulated in NT-CTRL + IFN-γ \u003cem\u003evs\u003c/em\u003e NT-CTRL untreated. \u003cem\u003en=3\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003e(C)\u0026nbsp; IFN\u003cstrong\u003e-\u003c/strong\u003eγ response in Huh-7, U-343MG and JeKo-1 cells, measured by surface upregulation of HLA-A/B/C and PD-L1. All values are mean ± s.d. with \u003cem\u003en\u003c/em\u003e=4. Two-tailed\u0026nbsp;\u003cem\u003et\u003c/em\u003e\u0026nbsp;test was performed.\u003c/p\u003e\n\u003cp\u003e(D)\u0026nbsp; Kinetic (t=0h, 3h, 6h, 12h, 24h) of IFN\u003cstrong\u003e-\u003c/strong\u003eγ response in Huh-7 cells, measured by surface upregulation of HLA-A/B/C and PD-L1. All values are mean ± s.d. with \u003cem\u003en\u003c/em\u003e=3. Two-tailed\u0026nbsp;\u003cem\u003et\u003c/em\u003e\u0026nbsp;test was performed for each time point.\u003c/p\u003e\n\u003cp\u003e(E)\u0026nbsp; Relative surface expression of IFNGR1 (gMFI) in Huh-7, U-343MG and JeKo-1 comparing NT-CTRL and RNF19B-OE cells, adjusted for IFNGR1-KO gMFI for each cell type. All values are mean ± s.d. with \u003cem\u003en\u003c/em\u003e=3. Two-tailed\u0026nbsp;\u003cem\u003et\u003c/em\u003e\u0026nbsp;test was performed.\u003c/p\u003e\n\u003cp\u003e(F)\u0026nbsp; Relative surface expression of IFNGR1 (gMFI) in U-343MG IFNGR1-KO cells reconstituted with IFNGR1\u003csup\u003eWT\u003c/sup\u003e, IFNGR1\u003csup\u003eK279R\u003c/sup\u003e, IFNGR1\u003csup\u003eK285R\u003c/sup\u003e or IFNGR1\u003csup\u003eK299R\u003c/sup\u003e, comparing NT-CTRL and RNF19B-OE cells. Values are normalized relative to the respective variant and adjusted for IFNGR1-KO gMFI. All values are mean ± s.d. with \u003cem\u003en\u003c/em\u003e=3. Two-tailed\u0026nbsp;\u003cem\u003et\u003c/em\u003e\u0026nbsp;test was performed.\u003c/p\u003e\n\u003cp\u003e(G) Relative count of viable Huh-7 IFNGR1-KO cells reconstituted with IFNGR1\u003csup\u003eWT\u003c/sup\u003e, IFNGR1\u003csup\u003eK279R\u003c/sup\u003e, IFNGR1\u003csup\u003eK285R\u003c/sup\u003e or IFNGR1\u003csup\u003eK299R\u003c/sup\u003e under αGPC3 CAR-T pressure, comparing NT-CTRL and RNF19B-OE cells after 4 days of co-culture. Values are normalized to the respective variant. All values are mean ± s.d. with \u003cem\u003en\u003c/em\u003e=6 except for IFNGR1-WT NT-CTRL (\u003cem\u003en\u003c/em\u003e=5). Two-tailed\u0026nbsp;\u003cem\u003et\u003c/em\u003e\u0026nbsp;test were performed.\u003c/p\u003e\n\u003cp\u003e(H)\u0026nbsp; Progression of absolute counts of viable IFNGR1-KO NT-CTRL and IFNGR1-KO RNF19B-OE JeKo-1 cells under αCD19 CAR-T pressure, compared to JeKo-1 IFNGR1-WT NT-CTRL cells as reference. All values are mean ± s.d. with \u003cem\u003en\u003c/em\u003e=3. Two-tailed\u0026nbsp;\u003cem\u003et\u003c/em\u003e\u0026nbsp;test were performed with IFNGR1-KO NT-CTRL + CAR-T as reference group.\u003c/p\u003e","description":"","filename":"FiguresMain73.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7847831/v1/4c99ea20ab4b00034a7e4499.jpg"},{"id":94203084,"identity":"ef0188a7-71fa-4c43-a6b1-76ec93d46457","added_by":"auto","created_at":"2025-10-23 14:11:59","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":521628,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRNF19B resistance is linked to CAMKK2\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A)\u0026nbsp; Number of unique and shared differentially abundant proteins (|Log\u003csub\u003e2\u003c/sub\u003eFC| \u0026gt;1 \u0026amp; p-value \u0026lt; 0.05) comparing NT-CTRL \u003cem\u003evs\u003c/em\u003e RNF19B-OE in Huh-7, JeKo-1 and U-343MG cells at steady-state.\u003c/p\u003e\n\u003cp\u003e(B)\u0026nbsp; Boxplot of protein abundance (LFQ intensities) of CAMKK2 detected in Huh-7, JeKo-1 and U-343MG proteomes. \u003cem\u003eN\u003c/em\u003e=3.\u003c/p\u003e\n\u003cp\u003e(C)\u0026nbsp; Absolute counts of viable NT-CTRL or RNF19B-OE Huh-7 cells after 4 days of cognate CAR-T cell pressure at increasing concentrations of STO-609. For each condition, the fitted line (\u003cem\u003en\u003c/em\u003e=4) is shown with 95% confidence intervals. F-test for comparing slopes was performed (top-right) and two-way ANOVA with Tukey’s multiple comparisons test were performed for each concentration tested.\u003c/p\u003e\n\u003cp\u003e(D)\u0026nbsp; Absolute counts of viable NT-CTRL or RNF19B-OE JeKo-1 cells after 4 days of cognate CAR-T cell pressure at increasing concentrations of STO-609. For each condition, the fitted line is shown with 95% confidence intervals. F-test for comparing slopes was performed (top-right) and two-way ANOVA with Tukey’s multiple comparisons test were performed for each concentration tested.\u003c/p\u003e\n\u003cp\u003e(E)\u0026nbsp; Absolute counts of viable NT-CTRL or RNF19B-OE U-343MG cells after 4 days of cognate CAR-T cell pressure at increasing concentrations of STO-609. For each condition, the fitted line is shown with 95% confidence intervals. F-test for comparing slopes was performed (top-right) and two-way ANOVA with Tukey’s multiple comparisons test were performed for each concentration tested.\u003c/p\u003e\n\u003cp\u003e(F)\u0026nbsp; Relative number of live Huh-7 IFNGR1-KO and IFNGR1-WT cells under αGPC3 CAR-T pressure treated with DMSO control or 10 μM STO-609. All values are mean ± s.d. with \u003cem\u003en\u003c/em\u003e=6 for untreated samples, except for IFNGR1-WT NT-CTRL (\u003cem\u003en\u003c/em\u003e=5), and for STO-609-treated samples. (\u003cem\u003en\u003c/em\u003e=4). Two-tailed\u0026nbsp;\u003cem\u003et\u003c/em\u003e\u0026nbsp;test were performed.\u003c/p\u003e\n\u003cp\u003e(G) Boxplot of protein abundance (LFQ intensities) of CAMKK2 detected in Daudi, HepG2 and U-251MG proteomes of NT-CTRL \u003cem\u003evs\u003c/em\u003e RNF19B-KD cells. \u003cem\u003eN\u003c/em\u003e=3.\u003c/p\u003e\n\u003cp\u003e(H)\u0026nbsp; Absolute counts of viable NT-CTRL or RNF19B-KD U-251MG cells after 4 days of cognate CAR-T cell pressure at increasing concentrations of STO-609. For each condition, the fitted line (\u003cem\u003en\u003c/em\u003e=4) is shown with 95% confidence intervals. F-test for comparing slopes was performed (top-right) and two-way ANOVA with Tukey’s multiple comparisons test was performed for each concentration tested.\u003c/p\u003e","description":"","filename":"FiguresMain74.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7847831/v1/8538fe07808c18885c3215f5.jpg"},{"id":94203086,"identity":"177b967a-3e5a-4082-84d2-122249cdbf7a","added_by":"auto","created_at":"2025-10-23 14:11:59","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":297049,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e. Inhibition of CAMKK2 reverts RNF19B-OE resistance and synergizes with CAR-T cell \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003ein vivo\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e(A)\u0026nbsp; Schematic of the \u003cem\u003ein vivo\u003c/em\u003e orthotopic JeKo-1 Mantle Cell Lymphoma (MCL) xenograft study design. Treatment groups (mice per group): DMSO (\u003cem\u003en\u003c/em\u003e=13), STO-609 (\u003cem\u003en\u003c/em\u003e=6), DMSO+CTL019 (\u003cem\u003en\u003c/em\u003e=16), STO-609+CTL019 (\u003cem\u003en\u003c/em\u003e=8).\u003c/p\u003e\n\u003cp\u003e(B)\u0026nbsp; Disease progression was monitored by bioluminescence imaging (BLI) each week following tumor cell injection (mean ± s.e.m.).\u003c/p\u003e\n\u003cp\u003e(C)\u0026nbsp; BLI intensity (photons per second) at week 6. Data are mean ± s.e.m. One-way ANOVA followed by Tukey’s multiple-comparisons test was performed.\u003c/p\u003e\n\u003cp\u003e(D)\u0026nbsp; Kaplan-Meier (KM) curves of survival probability for all treatment groups. Logrank (Mantel-Cox) test was performed between STO-609 treated groups.\u003c/p\u003e\n\u003cp\u003e(E)\u0026nbsp; Schematic of the \u003cem\u003ein vivo\u003c/em\u003e subcutaneous Huh7 hepatocellular carcinoma xenograft study design. Treatment groups (mice per group): vehicle (DMSO; n = 5), STO-609 (n = 5), DMSO + CAR T cells (n = 6), STO-609 + CAR T cells (n = 7).\u003c/p\u003e\n\u003cp\u003e(F)\u0026nbsp; Tumor growth over time. Data are mean ± s.e.m.\u003c/p\u003e\n\u003cp\u003e(G) Tumor size (mm\u003csup\u003e3\u003c/sup\u003e) at week 5 post-injection. All values are mean ± s.e.m.; one-way ANOVA followed by Tukey’s multiple-comparisons test was performed.\u003c/p\u003e\n\u003cp\u003e(H)\u0026nbsp; Kaplan-Meier (KM) curves of survival probability for all treatment groups. Logrank (Mantel-Cox) test was performed across groups.\u003c/p\u003e","description":"","filename":"FiguresMain75.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7847831/v1/88c9b319a37dc6a0bcd8014a.jpg"},{"id":94205257,"identity":"23658a59-e8e0-44e9-a946-49f857fa0406","added_by":"auto","created_at":"2025-10-23 14:36:00","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3051971,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7847831/v1/f575175e-01eb-4120-950c-7566630133cc.pdf"},{"id":94203591,"identity":"6261546f-9417-4c20-8c2c-847ba9150285","added_by":"auto","created_at":"2025-10-23 14:19:59","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":67601,"visible":true,"origin":"","legend":"Suppl. Table 2","description":"","filename":"SupplementaryTable2SubsetValidations.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7847831/v1/fa5b622d57c5e10e9ad28a65.xlsx"},{"id":94203592,"identity":"d1128b4e-5d74-43eb-b30c-30639636869a","added_by":"auto","created_at":"2025-10-23 14:19:59","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":55795,"visible":true,"origin":"","legend":"Suppl. Table 4","description":"","filename":"SupplementaryTable4DNASequences.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7847831/v1/d83d13c29ffd33aab966a084.xlsx"},{"id":94203080,"identity":"bdfbaf49-61fe-4ac2-a286-38d26bfe5f7e","added_by":"auto","created_at":"2025-10-23 14:11:59","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":1666682,"visible":true,"origin":"","legend":"Reporting Summary","description":"","filename":"nrreportingsummaryRNF.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7847831/v1/e33b3b4ae435fbdb4c066ab9.pdf"},{"id":94203087,"identity":"d99c961a-c00c-4d2c-9d2d-3be4ff855567","added_by":"auto","created_at":"2025-10-23 14:11:59","extension":"pdf","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":1682357,"visible":true,"origin":"","legend":"Editorial checklist","description":"","filename":"nreditorialpolicychecklistRNF.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7847831/v1/0e71ec61870a407397b776de.pdf"},{"id":94203092,"identity":"f6890494-6a5b-49c7-8d03-d2700b862388","added_by":"auto","created_at":"2025-10-23 14:11:59","extension":"xlsx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":5654901,"visible":true,"origin":"","legend":"Suppl Table 1","description":"","filename":"SupplementaryTable1GWCRISPRaScreens.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7847831/v1/072857edaa882d0e6227a92e.xlsx"},{"id":94203097,"identity":"fe0d6001-5990-45fd-b4f1-99e18e4c8bd8","added_by":"auto","created_at":"2025-10-23 14:11:59","extension":"xlsx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":7421883,"visible":true,"origin":"","legend":"Suppl. Table 3","description":"","filename":"SupplementaryTable3Proteomics.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7847831/v1/b68ea8e673a69670530d7730.xlsx"},{"id":94203597,"identity":"82539cde-93b1-4dc4-971b-52538d82d029","added_by":"auto","created_at":"2025-10-23 14:19:59","extension":"pdf","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":16154279,"visible":true,"origin":"","legend":"Supplementary Figures","description":"","filename":"FiguresSupplemental1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7847831/v1/8695a55511c693e441d56347.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"RNF19B confers tumor resistance to CAR T cells","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCAR-T cells emerged as a transformative cell-based approach, with the potential to target malignant cells independent of conventional antigenic constraints\u003csup\u003e\u003cspan additionalcitationids=\"CR2 CR3 CR4 CR5\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. However, currently 10\u0026ndash;20% of patients with B-cell malignancies, for which CAR-T cell therapies have been FDA-approved, fail to respond\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Additionally, 30\u0026ndash;50% that initially enter remission relapse within one year\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e; thus, many patients do not achieve durable clinical benefit. Furthermore, the efficacy of CAR-T cells against solid tumors has proven challenging\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eTherapeutic responses can be limited due to declining persistence of T cells or emergence of T cell dysfunction pre- and post-infusion\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Alternatively, malignant cells can develop escape mechanisms, including downregulation of their target antigen\u003csup\u003e\u003cspan additionalcitationids=\"CR13 CR14\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Beyond antigen loss, cancer cells may acquire resistance, for which the underlying molecular mechanisms are incompletely understood\u003csup\u003e\u003cspan additionalcitationids=\"CR17 CR18\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. As CAR-T cells become a standard line of treatment in oncology, it is crucial to define key vulnerabilities in cancer cells and exploit them to further augment CAR-T cell efficacy, especially for solid tumors.\u003c/p\u003e\u003cp\u003eFunctional genetic screens have the potential to comprehensively profile mechanisms underpinning resistance to current immunotherapies\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Several studies employed CRISPR screens in both human and murine cancer cells to interrogate and identify genetic determinants driving immune evasion to CD8 T cells or CAR-T cells. These screens identified well-established processes governing resistance to immune cells, including interferon-gamma (IFN-γ) signaling and antigen presentation\u003csup\u003e\u003cspan additionalcitationids=\"CR22 CR23 CR24 CR25 CR26\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e, TNF and cell death pathways\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e, epigenetic regulation\u003csup\u003e\u003cspan additionalcitationids=\"CR32\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e, autophagy\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e as well as individual genetic determinants\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eIn this study, we performed genome-wide CRISPR activation (CRISPRa) screens in cancer cells of diverse etiology identified a subset of genes that modulated CAR-T cell sensitivity. Among these were several well-known regulators of cell death pathways, as well as the E3 ubiquitin ligase RNF19B, which conferred resistance to CAR-T cells \u003cem\u003ein vitro\u003c/em\u003e and \u003cem\u003ein vivo\u003c/em\u003e. Furthermore, its expression is associated with poor survival in cancer patients. RNF19B attenuates IFN-γ signaling by destabilizing IFNGR1 and increases CAMKK2 abundance, a kinase we confirmed as a druggable target in synergy with CAR-T cells in multiple xenograft mouse models. Taken together, our focused examination uncovered resistance mechanisms to CAR-T cells and lays the foundation for combination strategies to augment the efficacy and broaden the applicability of CAR-T cell therapies.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eCRISPRa Screens identify RNF19B as a Multi-Cancer Resistance Factor to CAR-T Cells\u003c/h2\u003e\u003cp\u003eTo systematically interrogate genetic determinants of cancer cell susceptibility and resistance to CAR-T cells, we performed genome-wide CRISPR activation (CRISPRa) screens\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e in human cell lines from mantle cell lymphoma (MCL, JeKo-1), hepatocellular carcinoma (HCC, Huh-7) and glioblastoma (GBM, U-343MG) in the presence and absence of cognate CAR-T cells (anti-CD19\u003csup\u003e36\u003c/sup\u003e, anti-GPC3\u003csup\u003e37\u003c/sup\u003e and anti-IL13Rα2\u003csup\u003e38,39\u003c/sup\u003e, respectively) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Cells were then split into two arms: an alone condition or co-culture with cognate CAR-T cells seeded at a relatively low effector-to-target ratio (E:T\u0026thinsp;=\u0026thinsp;1:20\u0026thinsp;\u0026minus;\u0026thinsp;1:50)\u003csup\u003e19,23,24,31\u003c/sup\u003e, allowing for a gradual and progressive selection of cancer cells over the course of 5\u0026ndash;8 days. After co-cultures reached approximately 50% cytotoxicity, we extracted genomic DNA and compared sgRNA representations via amplicon sequencing and determined their gene-level enrichment (screen hits\u0026thinsp;=\u0026thinsp;FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05 \u0026amp; | z-scaled log\u003csub\u003e2\u003c/sub\u003e(FC) | \u0026gt;2).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTo identify genes conferring resistance or susceptibility to CAR-T cells, we compared sgRNA representations in alone \u003cem\u003eversus\u003c/em\u003e co-culture conditions. Collectively, the screens revealed 264 genes that affect cancer cell vulnerability to CAR-T cells (JeKo-1\u0026thinsp;=\u0026thinsp;41, Huh-7\u0026thinsp;=\u0026thinsp;71, U-343MG\u0026thinsp;=\u0026thinsp;175) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB and Supplementary Table\u0026nbsp;1). Notably, cognate antigens were not among the screen hits, indicating antigen density was not a limiting factor (Supplementary Table\u0026nbsp;1). Many screen hits mapped to cell-death signaling pathways and other established mediators of resistance to T-cell-mediated cytotoxicity, including Fas (\u003cem\u003eFAS\u003c/em\u003e), Fas ligand (\u003cem\u003eFASLG\u003c/em\u003e), interferon-β (\u003cem\u003eIFNB1\u003c/em\u003e), TRAIL (\u003cem\u003eTNFSF10\u003c/em\u003e), and \u003cem\u003eSERPINB9\u003c/em\u003e\u003csup\u003e40\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). The intersection among screen hits revealed only two genes that were shared and consistent across all three cell types: \u003cem\u003eCFLAR\u003c/em\u003e and \u003cem\u003eRNF19B\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC,D). Of these, \u003cem\u003eCFLAR\u003c/em\u003e (protein: CFLIP), is a well-characterized, cell-intrinsic negative regulator of cell death by inhibiting both apoptotic and necroptotic programmed cell death pathways, including against T-cell mediated killing\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan additionalcitationids=\"CR41\" citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. Conversely, \u003cem\u003eRNF19B\u003c/em\u003e (Ring Finger Protein 19B) is less well-characterized and previously undescribed for its role in cancer or resistance to T-cell mediated cytotoxicity. To further validate our screen results, we generated distinct subset libraries for Huh-7 and U-343MG cells comprised of sgRNAs targeting respective screen hits, which displayed largely concordant effects and validated RNF19B and CFLAR as top-ranked resistance factors (Supplementary Fig.\u0026nbsp;1B and Supplementary Table\u0026nbsp;2).\u003c/p\u003e\u003cp\u003eNext, we performed arrayed transduction of RNF19B and NT-CTRL sgRNAs in CRISPRa-competent Huh-7, JeKo-1 and U-343MG cells and confirmed on-target \u003cem\u003eRNF19\u003c/em\u003eB mRNA upregulation by qRT-PCR (Supplementary Fig.\u0026nbsp;2A). While \u003cem\u003eRNF19B\u003c/em\u003e-OE did not affect proliferation or viability of cancer cells cultured alone (Supplementary Fig.\u0026nbsp;2B), it conferred resistance to CAR-T mediated killing, with a 3 to 5-fold increase in the number of live cancer cells after 4\u0026ndash;5 days of co-culture (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). To include a model where cancer cells are recognized by T cells through peptide-MHC, we used A-375 NY-ESO-1\u003csup\u003e+\u003c/sup\u003e [HLA-A*02-restricted] human melanoma cells recognized by 1G4-TCR T cells\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. Indeed, \u003cem\u003eRNF19B\u003c/em\u003e-OE A-375 cells were more resistant to 1G4-TCR T cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA), indicating that RNF19B confers resistance to T cells independent of mode of recognition. As an orthogonal approach, we employed CRISPR inhibition (CRISPRi) in Daudi Burkitt\u0026rsquo;s lymphoma and U-251MG malignant glioblastoma cells that endogenously express high levels of \u003cem\u003eRNF19B\u003c/em\u003e according to The Human Protein Atlas (proteinatlas.org/). Indeed, RNF19B knockdown (-KD) in CRISPRi-competent Daudi and U-251MG cells led to a decrease in \u003cem\u003eRNF19B\u003c/em\u003e expression (Supplementary Fig.\u0026nbsp;2C) and were more susceptible to CAR-T cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eRNF19B confers resistance in vivo and is negatively prognostic in cancer patients\u003c/h3\u003e\n\u003cp\u003eWe next assessed whether RNF19B mediates resistance to CAR-T cells \u003cem\u003ein vivo\u003c/em\u003e. We engrafted \u003cem\u003eRNF19B\u003c/em\u003e-OE and NT-CTRL JeKo-1 cells intravenously (i.v.) in immunodeficient NOD-scid gamma (NSG) mice\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. After nine days, human CD4 and CD8 αCD19 CAR-T cells were injected i.v. into tumor bearing mice. In the absence of αCD19 CAR-T cells, RNF19B-OE and NT-CTRL JeKo-1 tumors grew at equivalent kinetics. While NT-CTRL JeKo-1 cells were effectively controlled by CAR-T cells, RNF19B-OE rendered JeKo-1 cells refractory, leading to increased tumor burden and diminished mouse survival (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC).\u003c/p\u003e\u003cp\u003eTo further explore the potential clinical relevance of RNF19B, we searched the GEPIA portal\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e for analyzing The Cancer Genome Atlas (TCGA) to assess the correlation between \u003cem\u003eRNF19B\u003c/em\u003e transcript levels (quartile group cutoff) with overall survival. Notably, in patients with Liver Hepatocellular Carcinoma (LIHC) and Glioblastoma multiforme (GBM), high \u003cem\u003eRNF19B\u003c/em\u003e transcript levels were strongly negatively prognostic (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). In addition, \u003cem\u003eRNF19B\u003c/em\u003e was also negatively prognostic on aggregate among all cancer types, corroborating its potential role in promoting cancer progression (Supplementary Fig.\u0026nbsp;2D)\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003ch3\u003eRNF19B blunts IFN-γ responsiveness\u003c/h3\u003e\n\u003cp\u003eHaving established RNF19B confers resistance to CAR-T cells, we sought to characterize its mechanistic function. To exclude the possibility that resistance was caused by antigen loss, we confirmed the presence of the target antigens GPC3, CD19 and IL13Rα2 in the respective cancer cells by flow cytometry (Supplementary Fig.\u0026nbsp;2E). Furthermore, we analyzed CAR-T cells by flow cytometry following co-culture with \u003cem\u003eRNF19B\u003c/em\u003e-OE cells and observed no defect in T cell activation nor effector function, suggesting that RNF19B causes resistance through a cancer cell-intrinsic mechanism (Supplementary Fig.\u0026nbsp;2F). Thus, we concluded its mechanism was not due to its effect on proliferation, antigen escape or inhibition of CAR-T cell effector function.\u003c/p\u003e\u003cp\u003eRNF19B is an E3-ubiquitin ligase and likely involved in protein trafficking and degradation\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e; therefore, we used liquid chromatography-coupled, high-resolution mass-spectrometry (LC-MS/MS) to globally profile protein abundances. RNF19B-OE and NT-CTRL Huh-7, JeKo-1, and U-343MG cells were cultured alone or under cognate CAR-T cell pressure. Viable cancer cells were sorted after 4\u0026ndash;5 days and subjected to LC-MS/MS. T cell lineage markers CD3 and CD8 were not detected in the cancer cell proteomes, confirming sorting purity (Supplementary Table\u0026nbsp;3). The proteomic signatures we identified in CAR T-cell-exposed cancer cells were also found in CART19-treated ALL patient samples\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e and were significantly reduced in non-responders (NR) versus complete responders (CR) (Supplementary Fig.\u0026nbsp;3A-3D), indicating our proteomics data captured clinically relevant signatures.\u003c/p\u003e\u003cp\u003ePrincipal component analysis (PCA) revealed largely overlapping proteomes between NT-CTRL and RNF19B-OE cells alone; however, a separation was evident under cognate CAR-T cell pressure (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE and Supplementary Fig.\u0026nbsp;2G). Moreover, the global proteomics response to CAR-T cell pressure was dampened in RNF19B-OE cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF and Supplementary Fig.\u0026nbsp;2H). A module of pro-inflammatory proteins up in NT-CTRL cells under CAR-T cell pressure was absent in RNF19B-OE cells, resembling cells cultured alone (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eG and Supplementary Fig.\u0026nbsp;2I).\u003c/p\u003e\u003cp\u003eGSEA of this module revealed a distinct profile marked by a reduction in the abundance of IFN stimulated genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA and Supplementary Fig.\u0026nbsp;4A). ISGs are induced by either type I or type II interferons. To assess the role of RNF19B in these pathways, we treated \u003cem\u003eRNF19B\u003c/em\u003e-OE and NT-CTRL cells with recombinant IFN-β (type I) and IFN-γ (type II). We performed global proteomic profiling by mass spectrometry and quantified the induction of hallmark ISGs, PD-L1 and MHC-I, by flow cytometry. Notably, \u003cem\u003eRNF19B\u003c/em\u003e-OE selectively blunted the IFN-γ-mediated ISG induction evident as early as 6 hours post-treatment (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB-D and Supplementary Fig.\u0026nbsp;4B), whereas the response to IFN-β remained unaltered (Supplementary Fig.\u0026nbsp;4C). Because both type I (IFN-β) and type II (IFN-γ) signaling pathways converge on a JAK1\u0026ndash;STAT1 cascade, these results suggest that RNF19B interferes upstream of JAK-STAT, most likely at the level of the IFN-γ receptor complex.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003e\u003c/h3\u003e\n\u003cdiv class=\"Heading\"\u003e\u003cem\u003eRNF19B downregulates IFNGR1\u003c/em\u003e\u003c/div\u003e\u003cp\u003eThe IFN-γ receptor is a heterodimer composed of IFNGR1 and IFNGR2\u003csup\u003e47\u003c/sup\u003e. IFNGR1 harbors multiple cytosolic lysine residues that serve as ubiquitination sites, with E3 ubiquitin ligases known to control its stability\u003csup\u003e\u003cspan additionalcitationids=\"CR49 CR50\" citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. Consistent with this, \u003cem\u003eRNF19B\u003c/em\u003e-OE markedly reduced IFNGR1 abundance both at the cell surface and in total cell lysates (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE and Supplementary Fig.\u0026nbsp;4D). Conversely, CRISPRi-mediated knockdown of RNF19B in U-251MG cells led to increased surface IFNGR1 levels (Supplementary Fig.\u0026nbsp;4E). Together, these data indicate that RNF19B regulates IFNGR1 stability, thereby attenuating IFN-γ signaling.\u003c/p\u003e\u003cp\u003eTo pinpoint the lysine residue(s) responsible for RNF19B-mediated IFNGR1 destabilization, we generated IFNGR1-knockout (IFNGR1-KO) cells and reconstituted them with either wild-type (WT) IFNGR1 or single lysine-to-arginine mutants at residues K279, K285, and K299\u003csup\u003e48\u0026ndash;50\u003c/sup\u003e. Only the IFNGR1\u003csup\u003eK285R\u003c/sup\u003e mutant restored receptor levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF) and reverted the resistance phenotype (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eG and Supplementary Fig.\u0026nbsp;4F) in RNF19B-OE cells similar to those of NT-CTRL cells. These findings identified that blunted IFN-γ responsiveness and CAR-T resistance in RNF19B-OE cells are linked to IFNGR1 stability via residue K285.\u003c/p\u003e\n\u003ch3\u003eRNF19B Resistance is Linked to CAMKK2\u003c/h3\u003e\n\u003cp\u003eTo investigate whether impaired IFN-γ responsiveness conferred by RNF19B fully accounts for the resistance phenotype, we over-expressed RNF19B in IFNGR1-KO cells and subjected them to CAR-T cell pressure (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eH and Supplementary Fig.\u0026nbsp;4G). We confirmed that in NT-CTRL cells, IFNGR1-KO led to increased resistance to CAR-T cells, consistent with impaired IFN-γ signaling. However, RNF19B-OE further enhanced resistance in IFNGR1-KO cells, indicating that RNF19B mediates resistance to CAR-T cells through additional mechanisms.\u003c/p\u003e\u003cp\u003eTo explore alternative mechanisms contributing to RNF19B-mediated resistance, we compared the proteomes of NT-CTRL and RNF19B-OE cancer cells, which identified calcium/calmodulin-dependent kinase kinase 2 (CAMKK2) as the only protein differentially abundant in RNF19B-OE cells across JeKo-1, Huh-7, and U-343MG cell types (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Moreover, CAMKK2 protein abundance was consistently elevated in RNF19B-OE cells under CAR-T cell pressure (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). To test whether CAMKK2 contributes to the refractory phenotype of RNF19B-OE cells, we used STO-609, a CAMKK2 inhibitor\u003csup\u003e\u003cspan additionalcitationids=\"CR53\" citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e. Strikingly, STO-609 reverted the resistance state of RNF19B-OE cells to CAR-T cell pressure, achieving an almost equal live target cell count to NT-CTRL cells at 15 \u0026micro;M in all three cell types (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC-E). The effect of STO-609 on RNF19B-OE cells was dependent on the synergistic activity of CAR-T cells, as the compound alone was not toxic and only had a slightly cytostatic effect at the highest concentrations tested, which was equal between NT-CTRL and RNF19B-OE cancer cells (Supplementary Fig.\u0026nbsp;5A-D). Notably, the addition of STO-609 abolished the added resistance of RNF19B-OE in IFNGR1-KO cells (Supplementary Fig.\u0026nbsp;4F).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTo further investigate the association between RNF19B and CAMKK2, we used CRISPRi to target endogenous RNF19B expression in cancer cells. We confirmed that RNF19B-KD in Daudi, HepG2, and U-2521MG cells led to a concomitant decrease in CAMKK2 protein abundance across the three cell types (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eG). We then subjected U-251MG cells to IL13RA2-CAR T-cell pressure at increasing concentrations of STO-609 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eH). Strikingly, RNF19B-KD U-251MG cells exhibited no sensitivity in response to STO-609, whereas NT-CTRL U-251MG displayed increased susceptibility to CAR-T cells at increasing concentrations of STO-609. To assess the potential clinical relevance of CAMMK2, we used the GEPIA portal for analyzing TCGA, as had been done previously for RNF19B. Indeed, high transcript levels of CAMKK2 (quartile group cutoff) for HCC and GBM were significantly correlated with decreased overall survival (Supplementary Fig.\u0026nbsp;5E). Taken together, our data demonstrated that the abundance of CAMKK2 is tightly associated with RNF19B expression and contributes to their increased resistance to CAR-T cells.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eCAMKK2 Inhibition Synergizes with CAR-T Cells in vivo\u003c/h2\u003e\u003cp\u003eTo explore the potential translational relevance of our findings, we examined if STO-609 treatment synergizes with CAR-T cells \u003cem\u003ein vivo\u003c/em\u003e. To this aim, we engrafted JeKo-1 cells i.v. in NSG mice followed by treatment with αCD19 CAR-T cells. Immediately following CAR-T cell treatment, we initiated either DMSO:PBS (1:1 v/v) vehicle control or 30 \u0026micro;M/Kg twice a day (BID) STO-609 for a 1-week treatment regimen (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). While STO-609 treatment had no effect alone, the combination of STO-609 with CAR-T cells provided a significant improvement in tumor control and mouse survival (Supplementary Fig.\u0026nbsp;5B-D), demonstrating the potential benefit of combination therapy.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eNext, we wanted to test if STO-609 could synergize with CAR-T cells in a solid tumor model where the effect of CAR-T cell treatment alone is insufficient for tumor control. Hence, we engrafted Huh-7 cells subcutaneously in the right flank of NSG mice (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE). After tumors became palpable, we injected αGPC3 CAR-T cells and commenced a thrice-weekly treatment regimen of STO-609 or vehicle control. Only the synergistic effect of STO-609 treatment in combination with αGPC3 CAR-T cells led to a reduction in Huh-7 tumor growth and concomitant improvement in mouse survival, whereas αGPC3 CAR-T cells nor STO-609 treatments alone displayed tumor control (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eF-H). Taken together, CAMKK2 inhibition can be leveraged to synergize with CAR-T cell therapies.\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur genome-wide CRISPRa screens across multiple tumor contexts identify RNF19B as a common, tumor-intrinsic mediator of resistance to CAR-T cells \u003cem\u003ein vitro\u003c/em\u003e and \u003cem\u003ein vivo\u003c/em\u003e. In independent datasets, high RNF19B expression associates with inferior survival, including in hepatocellular carcinoma and glioblastoma, and has been proposed as a prognostic marker that also predicts aspects of immunotherapy response in HCC\u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e. Immunohistochemistry further indicates predominantly cytoplasmic RNF19B staining in malignant and stromal compartments with low signal in benign tissues, supporting the therapeutic tractability of tumor-cell targeting\u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eRNF19B, also referred to as NKLAM (Natural Killer Lytic-Associated Molecule), is an E3 ubiquitin-protein ligase originally described for its role in innate immunity\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. It contributes to the anti-tumoral cytotoxic activity of natural killer (NK) cells\u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e,\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e and modulates cytokine production of macrophages\u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e,\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e. Accordingly, while RNF19B overexpression makes it an attractive pharmaceutical target, strategies should ideally preserve RNF19B function in immune cells\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e,\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eMechanistically, RNF19B blunts IFN-γ responsiveness in cancer cells by reducing IFNGR1 abundance via lysine residue K285. This receptor-level control aligns with other E3 ubiquitin ligases, including STUB1\u003csup\u003e49\u003c/sup\u003e, RNF149\u003csup\u003e50\u003c/sup\u003e and FBXW7\u003csup\u003e51\u003c/sup\u003e, that converge on IFNGR1 stability. RNF19B-mediated regulation of IFNGR1 as a mechanism of resistance aligns with our observations that IFN-γ signaling was the prevailing response to CAR-T cell pressure, highlighting that ISGs are part of a general response program that promotes susceptibility applicable to CAR-T cells. Desensitization and reduced responsiveness to type II IFN (IFN-γ) is a well-known tumor intrinsic resistance and evasion mechanism that affects the efficacy of immune-checkpoint blockade and CAR-T cell therapies and serves as a predictive marker of response to immunotherapy\u003csup\u003e\u003cspan additionalcitationids=\"CR62 CR63 CR64 CR65 CR66 CR67 CR68\" citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eRNF19B-driven resistance is not solely explained by IFN-γ pathway dampening. We also find that CAMKK2 protein abundance tightly associates with RNF19B expression through an as-yet undefined mechanism. Inhibiting CaMKK2 with STO-609 reverses the resistance phenotype of RNF19B-overexpressing cancer cells and abrogates residual resistance even in IFNGR1-deficient cells, indicating a parallel, targetable axis that persists when IFN-γ sensing is compromised. Importantly, CaMKK2 inhibition using STO-609 synergizes with CAR-T cells. STO-609 did not impair T-cell function in our assays, and treated mice displayed no toxicity. Next-generation pharmaceuticals targeting CAMKK2 with improved selectivity and pharmacokinetics may provide translationally suitable inhibitors for combinations with engineered T-cell therapies\u003csup\u003e\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThese findings integrate with prior work implicating CAMKK2 in both tumor-intrinsic and microenvironmental control. In glioblastoma, neuronal CAMKK2 deletion converts tumor-associated macrophages from a disease-associated microglia-like state to an antigen-presenting, stimulatory phenotype improving responsiveness to checkpoint blockade\u003csup\u003e\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u003c/sup\u003e. Beyond central nervous system (CNS), CAMKK2 contributes to immunosuppressive myeloid programming\u003csup\u003e\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e\u003c/sup\u003e, while in prostate cancer CAMKK2 acts downstream of androgen receptor signaling to support metabolic adaptation, invasion, and growth\u003csup\u003e\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e\u003c/sup\u003e. These context-specific roles make CAMKK2 an attractive point of intervention.\u003c/p\u003e\u003cp\u003eIn conclusion, these data position RNF19B as a common resistance driver through IFNGR1 down-modulation and a CAMKK2-linked program and nominate CAMKK2 inhibition as a rational combinatorial strategy to restore CAR-T efficacy across diverse tumor settings.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAll animal procedures were approved by the Ticino Cantonal Commission for Animal Welfare and conducted in full compliance with the Swiss Animal Welfare Legislation, specifically the Animal Welfare Ordinance and the Animal Experimentation Ordinance. Experiments were carried out under the oversight of the institutional animal care committee.\u003c/p\u003e\u003ch2\u003eAuthor contributions\u003c/h2\u003e\u003cp\u003eConceptualization: I.V., A.C., and R.G. Methodology: I.V., A.C., and R.G. Investigation: I.V., A.C., M.C., L.G.M., M.P., C.B., and E.S. Resources: R.G. Writing: I.V., A.C., and R.G. Visualization: I.V. and A.C. Supervision: R.G. Funding acquisition: R.G.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e\u003cp\u003eWe thank David Jarrossay for cell sorting and Dr. Magnus Essand for the IL13RA2 CAR-construct used throughout this investigation. R.G. is supported in part by the European Research Council (803150), by Swiss Cancer Research (KFS-4593-08-2018), and by the San Salvatore Foundation.\u003c/p\u003e\u003ch2\u003eData availability\u003c/h2\u003e\u003cp\u003eThe mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD068450.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eMolecular Cloning\u003c/p\u003e\u003cp\u003eSingle sgRNAs were generated via Golden Gate Assembly. Briefly, complementary ssDNA oligos were annealed with overhangs for Golden Gate Assembly and subsequently cloned into the pXPR_502 vector (Addgene #96923) for CRISPRa, in the CROPseq-Guide-Puro vector (Addgene #86708) for CRISPRi or in the PX458 vector (Addgene #48138) for IFNGR1-KO. Afterwards, plasmids were expanded in DH10-beta chemically competent bacteria and purified using a MiniPrep Kit (Machery-Nagel).\u003c/p\u003e\u003cp\u003eThe human CRISPRa Calabrese library was ordered from Addgene and expended via transformation of Endura ElectroCompetent Cells (Lucigen, catalog no. 60242-2). After transformation, Endura cells were grown in a shaking incubator for 16 hours at 30\u0026deg;C in the presence of ampicillin. Plasmids were isolated using the MidiPrep Kit (Machery-Nagel) and sequenced via NGS to determine library representation.\u003c/p\u003e\u003cp\u003eIFNGR1 K279R, K285R and K299R mutants were generated by overlapping PCRs from the human IFNGR1 WT cDNA (ENST00000367739, ordered via Twist Bioscience) using DNA primers carrying the nucleotide bases to generate the K to R codon mutants and ligated with Gibson assembly.\u003c/p\u003e\u003cp\u003eLentivirus production\u003c/p\u003e\u003cp\u003eHuman embryonic kidney (HEK) 293T cells were cultured in complete DMEM supplemented with 10% FBS, 1 mM sodium pyruvate (Fisher Scientific), and 1 x MEM nonessential amino acids (Fisher Scientific) at approximately 40% confluency 24 hours prior to transfection. Second generation lentiviral vectors were produced per T75 culture flask using 20 \u0026micro;g of transfer vector, 15 \u0026micro;g of psPAX2 (Addgene #12260), 5 \u0026micro;g of pMD2.G (Addgene #12259) in 2 mL of OPTI-MEM, after which 90 \u0026micro;L of PEI MAX\u0026trade; reagent was added, mixed by gentle inversion, and incubated for 10 minutes at room temperature. Media was carefully replaced with cOPTI-MEM without detaching HEK293T cells, after which the transfection mix was added dropwise to HEK293T cells. After 6\u0026ndash;12 hours, transfection medium was replaced with 12 mL of cOPTI-MEM containing 1 x ViralBoost (Alstem Bio, catalog no. VB100). Lentiviral supernatant was harvested 24 hours after transfection, centrifuged at 500 x g for 5 min at 4\u0026deg;C, filtered through a 0.45 \u0026micro;M filter and concentrated using homemade Lenti-X concentrator. Viral supernatants were slowly added to Lenti-X and incubated overnight at 4 degrees Celsius. After centrifugation at 1,500 x g for 45 minutes at 4 degrees Celsius, concentrated lentivirus was resuspended in 1/50th the original volume in PBS. Virus particles were subsequently aliquoted and frozen immediately at \u0026minus;\u0026thinsp;80\u0026deg;C.\u003c/p\u003e\u003cp\u003eIsolation, culture of human T cells and transduction\u003c/p\u003e\u003cp\u003ePeripheral blood from healthy donors was obtained from the Swiss Blood Donation Center of Basel and Lugano and used in compliance with the Federal Office of Public Health (authorization no. CE3428). PBMCs were isolated by Ficoll gradient centrifugation. CD8\u003csup\u003e+\u003c/sup\u003e T cells were enriched with magnetic microbeads (Miltenyi Biotec) according to the manufacturer\u0026rsquo;s recommendations. Cells were subsequently cultured in RPMI-1640 medium supplemented with 2 mM glutamine, 1% (v/v) non-essential amino acids, 1% (v/v) sodium pyruvate, penicillin (50 U/mL), streptomycin (50 \u0026micro;g/mL; all from Invitrogen) and 10% (v/v) fetal bovine serum (Gibco). Unless otherwise indicated, primary human CD8\u003csup\u003e+\u003c/sup\u003e T cells were activated with plate-bound anti-CD3 (5 \u0026micro;g/mL, clone TR66) and anti-CD28 (1 \u0026micro;g/mL, clone CD28.2, BD Biosciences) for 48 h in 96-well Nunc Maxisorb plates. After 48 h of activation, cells were expanded to be maintained between 0.4-1x10\u003csup\u003e6\u003c/sup\u003e /mL and cultured in IL-2-containing medium (50 U/mL). For T cell transduction, 24 hours after activation, T cells were infected with 2\u0026ndash;5% (v/v) concentrated lentivirus and spinoculated at 800 x g for 45 minutes at 32\u0026deg;C.\u003c/p\u003e\u003cp\u003eCRISPRa screens\u003c/p\u003e\u003cp\u003e3 days following transduction of the Calabrese library lentiviral constructs, Huh-7, JeKo-1, and U-343MG cells were selected with puromycin (1\u0026ndash;4 ug/mL) for a minimum of 4 days. Subsequently, Huh-7 and U-343MG cells underwent sequential rounds of co-culture of CAR-T cells generated from two independent healthy donors, while JeKo-1 cells underwent a single round of CAR-T cell selection.\u003c/p\u003e\u003cp\u003e20x10\u003csup\u003e6\u003c/sup\u003e cells were resuspended in 1.6 mL of lysis buffer (1% SDS, 50 mM Tris, pH 8, 10 mM EDTA). 16 \u0026micro;L of NaCl (5M) was added, and the sample was incubated on a heat block overnight at 66\u0026deg;C. The next morning, 8 \u0026micro;L of RNAse A (10mg/mL) was added, and the sample was vortexed briefly, and incubated at 37\u0026deg;C for 1 hour. Next, 8 \u0026micro;L of Proteinase K (20mg/mL) was added, the sample was vortexed briefly, and incubated at 55\u0026deg;C for 1 hour. 400 \u0026micro;L of Phenol:Chloroform:Isoamyl Alcohol (25:24:1) was mixed with an equal volume of sample, shaken vigorously and centrifuged at maximum speed at room temperature for 5 minutes. The aqueous phase was transferred to eppendorf tubes and then 40 \u0026micro;L of Sodium Acetate (3M), 1 \u0026micro;L GlycoBlue, and 600 \u0026micro;L of room temperature isopropanol was added. The sample was then vortexed and stored at -80\u0026deg;C for 30 minutes or until the sample had frozen solid. Next the sample was centrifuged at maximum speed at 4\u0026deg;C for 30 minutes, the pellet was washed with fresh 70% room temperature Ethanol and allowed to air dry for 15 minutes. Pellets were then resuspended in DNA elution buffer (Machery-Nagel) and placed on the heat block at 65\u0026deg;C for 1 hour to completely dissolve the genomic DNA.\u003c/p\u003e\u003cp\u003eFor sgRNA amplification and barcoding from genomic DNA, a nested PCR1 and PCR2 strategy was adopted. PCR1 amplified virally integrated sgRNA cassette from genomic DNA using a P5 stagger primers mix and a P7 common primer. PCR2 amplified PCR1 products using P7 indexing primers and P5 common primer while also adding the Illumina adapters. In PCR1, 1\u0026ndash;3 \u0026micro;g of genomic DNA was added per 50 \u0026micro;L reaction. Each reaction tube contained 25 \u0026micro;L of Q5\u0026reg; High-Fidelity 2X Master Mix (NEB), 2.5 \u0026micro;L of 10 \u0026micro;M forward primers, 2.5 \u0026micro;L of 10 \u0026micro;M reverse primers, and H\u003csub\u003e2\u003c/sub\u003eO up to 50 \u0026micro;L. PCR1 reaction products were pooled from each experimental condition (16 reactions per condition). In PCR2, 10 \u0026micro;L of 1:100 diluted PCR1 product was added to 25 \u0026micro;L reaction for a total of 8 reactions per condition. Each reaction tube contained 12.5 \u0026micro;L of Q5\u0026reg; High-Fidelity 2X Master Mix (NEB), 1.25 \u0026micro;L of 10 \u0026micro;M forward primers, 1.25 \u0026micro;L of 10 \u0026micro;M reverse primers, and H2O up to 25 \u0026micro;L.\u003c/p\u003e\u003cp\u003eUp to 2 \u0026micro;g of each sample were loaded on a 2% agarose gel, and the band between 368\u0026ndash;373 base pairs was extracted using a DNA agarose gel recovery kit (Machery-Nagel) and SPRI purified using magnetic beads. The concentration of each sample was then measured using the Qubit dsDNA high sensitivity assay kit (Thermo Fisher Scientific). Samples were then sequenced on an Illumina NextSeq 4000 using 10\u0026ndash;30% PhiX.\u003c/p\u003e\u003cp\u003eCRISPRa screen analysis\u003c/p\u003e\u003cp\u003eRaw reads of FASTQ files were aligned to our custom TF library using MAGeCK (version v0.5.9.2) with the default arguments to generate a normalized read count table\u003csup\u003e\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e\u003c/sup\u003e. The test function was then performed by grouping initial representation immediately following puromycin selection, control cells cultured alone in parallel to the surviving cells post CAR-T cell challenge. Screen hits were classified as having a z-scaled log\u003csub\u003e2\u003c/sub\u003e-fold change (LFC)\u0026thinsp;\u0026gt;\u0026thinsp;2 and a FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003cp\u003e\u003cem\u003eIn vitro\u003c/em\u003e CAR-T cell cytotoxicity assays\u003c/p\u003e\u003cp\u003eCancer cells were seeded at 1-2x10\u003csup\u003e5\u003c/sup\u003e/mL in complete RPMI medium containing 50 IU/mL interleukin-2 (IL-2) one day prior to co-culture. The following day, cognate CAR-T cells were added at 1:4 E:T ratios, unless otherwise indicated. For STO-609 / DMSO control, cells were treated as indicated followed immediately by the addition of cognate CAR-T cells at a 1:4 E:T ratio. Absolute number of viable cancer cells was determined by FACS analysis. Briefly, adherent cells were detached with trypsin-EDTA following by staining with anti-CD8 or anti-CD3 antibodies and Sytox viability dyes prior to being resuspend in MACS buffer and a constant volume was acquired for each well without a limit to the number of recorded events.\u003c/p\u003e\u003cp\u003eReverse Transcription-quantitative PCR\u003c/p\u003e\u003cp\u003eRNA was isolated using TRIzol\u0026trade; reagent (Thermo Fisher Scientific) according to manufacturer\u0026rsquo;s instructions. Briefly, cell pellets were resuspended in TRIzol\u0026trade; reagent and incubated for 5 minutes before adding 200 \u0026micro;L of chloroform. Tubes were spinned at 15k x g for 15 minutes at 4\u0026deg;C and after the aqueous phase was transferred to new tubes, 500 \u0026micro;L of isopropanol was added and incubated for 10 minutes. After centrifugation at 15k x g for 15 minutes at 4\u0026deg;C, RNA pellet was washed with 70% ethanol in DNAse/RNAse-free water, dried and resuspended in DNAse/RNAse-free water. RNA was reverse transcribed into cDNA using the Maxima H Minus Reverse Transcriptase (Thermo Fisher Scientific) according to manufacturer\u0026rsquo;s instructions. Briefly, each reaction contained up to 500 ng of RNA diluted in a total of 7.5 \u0026micro;L of DNAse/RNAse-free water, 1 \u0026micro;L of Oligo(dT)\u003csub\u003e15\u003c/sub\u003e 500 \u0026micro;g/mL (Promega), 0.5 \u0026micro;L of random hexamers 500 \u0026micro;g/mL (Promega), 1 uL of dNTPs 10 mM (Carl ROTH), 4 \u0026micro;L of 5x Retro Transcriptase buffer, 0.5 \u0026micro;L of RiboLock RNAse Inhibitor 40 U/\u0026micro;L (Thermo Fisher Scientific), 1 \u0026micro;L of Maxima H Minus Reverse Transcriptase and 2 \u0026micro;L of DTT 0.1 M (Thermo Fisher Scientific) for a total of 20 \u0026micro;L and 15 minutes at 50\u0026deg;C followed by 5 minutes at 85\u0026deg;C. cDNA samples were diluted 5 fold and qPCR reaction was run using the Perfecta SYBR Green Fast Mix\u0026trade; (Quantabio) following manufacture\u0026rsquo;s instructions. Briefly, each reaction contained up to 10ng of cDNA template, 5 \u0026micro;L of Perfecta SYBR Green Fast Mix\u0026trade; 2x, 0.3 \u0026micro;L of forward primer and 0.3 \u0026micro;L of reverse primer for a total volume of 10 \u0026micro;L and run on a QuantStudio\u0026trade; 3 Real-Time PCR System. cDNA samples were probed for the expression of \u003cem\u003eGAPDH\u003c/em\u003e and \u003cem\u003eRNF19B\u003c/em\u003e with the following primers:\u003c/p\u003e\u003cp\u003e\u003cem\u003eGAPDH\u003c/em\u003e:\u003c/p\u003e\u003cp\u003eForward: 5\u0026rsquo; \u0026ndash; AATCCCATCACCATCTTCCA \u0026ndash; 3\u0026rsquo;\u003c/p\u003e\u003cp\u003eReverse: 5\u0026rsquo; \u0026ndash; TGGACTCCACGACGTACTCA \u0026ndash; 3\u0026rsquo;\u003c/p\u003e\u003cp\u003e\u003cem\u003eRNF19B\u003c/em\u003e:\u003c/p\u003e\u003cp\u003eForward: 5\u0026rsquo; \u0026ndash; AGACACAGCCAGTCTTGGTGCA \u0026ndash; 3\u0026rsquo;\u003c/p\u003e\u003cp\u003eReverse: 5\u0026rsquo; \u0026ndash; GCTGATAGTGGCTTGGTTTGGC \u0026ndash; 3\u0026rsquo;\u003c/p\u003e\u003cp\u003eLive cell imaging\u003c/p\u003e\u003cp\u003eCancer cells were seeded at 1-2x10\u003csup\u003e5\u003c/sup\u003e cells/mL in complete RPMI medium containing 50 IU/mL interleukin-2 (IL-2) one day prior to co-culture. The following morning, cognate CAR-T cells were added at a 1:4 E:T ratio. Plates were loaded onto an ImageXpress (Molecular Devices) live cell imaging microscope to acquire transmitted light and green fluorescence every 3 hours for 4 days.\u003c/p\u003e\u003cp\u003eIFN-γ and IFN-β treatment and IFN Response Score\u003c/p\u003e\u003cp\u003eCancer cells were seeded 1-day prior subjecting them to human recombinant IFN-γ (LubioScience) at 40 ng/mL for 24 hours (for kinetic of ISG induction, at indicated timepoints), recombinant IFN-β (Peprotech) at 500 U/mL for 24 hours at indicated concentrations for 24h. Cells were washed with PBS and detached before being processed for LC-MS/MS or stained with αPD-L1-APC (Biolegend) and αHLA-A/B/C-FITC (Thermo Fisher Scientific) antibodies and analyzed at flow-cytometer. For flow cytometry, the IFN response was calculated as follows: for each marker, the gMFI of untreated cells was subtracted to obtain background corrected values. Then, values were normalized by dividing by the mean gMFI of NT-CTRL cells. For each replicate, the response scores of the PD-L1-APC and HLA-A/B/C-FITC were summed and divided by 2 to generate a composite response score. For LC-MS/MS data, heatmap and hierarchical clustering were performed by first computing the mean protein abundance across three replicates per condition for each protein that showed increased expression in NT-CTRL\u0026thinsp;+\u0026thinsp;IFN compared to NT-CTRL cells alone. Then, z-score normalization was applied across the condition-specific means for each protein.\u003c/p\u003e\u003cp\u003eSample preparation for proteome analysis\u003c/p\u003e\u003cp\u003eSamples were processed as described by previously\u003csup\u003e\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e\u003c/sup\u003e. Briefly, cell pellets were washed with PBS, lysed in 8 M urea, 50 mM ammonium bicarbonate (ABC) and then sonicated at 4\u0026deg;C for 15 min (Bioruptor, Diagenode, 30s on, 30s off, high mode). Proteins were reduced with 10 mM dithiothreitol for 20 minutes at room temperature and alkylation was performed in the dark for 30 min by adding 55 mM iodoacetamide. A two-step proteolytic digestion was performed. First, samples were digested at 21\u0026deg;C (room temperature) with LysC (Wako Fujifilm, 1:100, w/w) for 2 h. Then, they were diluted 1:4 with 50 mM ABC and digested with trypsin (Promega, 1:100, w/w) at 21\u0026deg;C overnight. The resulting peptide mixtures were acidified and loaded on C18 StageTips\u003csup\u003e\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e\u003c/sup\u003e. Peptides were eluted with 80% acetonitrile (ACN), 0.5% acetic acid, dried using a SpeedVac centrifuge (Savant, Concentrator plus, SC 110 A), and resuspended in 2% ACN, 0.1% trifluoroacetic acid and 0.5% acetic acid.\u003c/p\u003e\u003cp\u003eLC\u0026ndash;MS/MS for analysis of proteomes\u003c/p\u003e\u003cp\u003ePeptides were separated on an EASY-nLC 1200 HPLC system (Thermo Fisher Scientific) coupled online to a Q Exactive mass HF spectrometer via a nanoelectrospray source (Thermo Fisher Scientific)\u003csup\u003e\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e\u003c/sup\u003e. Peptides were loaded in buffer A (0.1% formic acid) on in-house-packed columns (75 \u0026micro;m inner diameter, 50 cm length and 1.9-\u0026micro;m C18 particles from Dr. Maisch GmbH). Peptides were eluted with a nonlinear 180-min gradient of 5%\u0026ndash;60% buffer B (80% ACN, 0.1% formic acid) at a flow rate of 250 nl min\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and a column temperature of 50\u0026deg;C. The Q Exactive was operated in a data-dependent mode with a survey scan range of 300\u0026ndash;1,650 \u003cem\u003em\u003c/em\u003e/\u003cem\u003ez\u003c/em\u003e and a resolution of 60,000 at \u003cem\u003em\u003c/em\u003e/\u003cem\u003ez\u003c/em\u003e 200. Up to 10 most abundant isotope patterns with a charge 2 to 5 were isolated with a 1.8-Th-wide isolation window and subjected to higher-energy C-trap dissociation (HCD) at a normalized collision energy of 27. Fragmentation spectra were acquired with a resolution of 15,000 at \u003cem\u003em\u003c/em\u003e/\u003cem\u003ez\u003c/em\u003e 200. Dynamic exclusion of sequenced peptides was set to 30 s to reduce the number of repeated sequences. Thresholds for the ion injection time and ion target values were set to 20 ms and 3 \u0026times; 10\u003csup\u003e6\u003c/sup\u003e for the survey scans and 55 ms and 1 \u0026times; 10\u003csup\u003e5\u003c/sup\u003e for the MS/MS scans, respectively. Data were acquired using the Xcalibur software (Thermo Fisher Scientific). For the Huh-7_EMT experiment, peptides were separated on a nanoElute2 HPLC system (Bruker) coupled via a nanoelectrospray source (Captive spray source, Bruker) to a timsTOF HT mass spectrometer (Bruker). Peptides were loaded in buffer A on an in-house-packed column (75 \u0026micro;m inner diameter, 25 cm length and 1.9-\u0026micro;m C18 particles) kept at 50\u0026deg;C and eluted over a 60-min linear gradient of 2 to 35% ACN/0.1% formic acid at a 300 nl/min flow rate. The mass spectrometer was operated in a data-independent (DIA)-PASEF mode\u003csup\u003e\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e\u003c/sup\u003e with accumulation and ramp times of 100 ms, covering with 21 mass steps (25 Da wide) and 1 mobility window, a mass range from 475 to 1000 Da and a mobility range from 0.85 to 1.27 Vs cm-2, with an estimated cycle time of 0.95 s. Data were acquired using the Bruker Compass Hystar software.\u003c/p\u003e\u003cp\u003eAnalysis of proteomics data\u003c/p\u003e\u003cp\u003eMaxQuant software (version 1.6.7.0) was used to analyze DDA MS raw files\u003csup\u003e\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e\u003c/sup\u003e. MS/MS spectra were searched against the human Uniprot FASTA database (June 2019) and a common contaminants database (247 entries) by the Andromeda search engine\u003csup\u003e\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e\u003c/sup\u003e. Cysteine carbamidomethylation was set as a fixed modification, and N-terminal acetylation and methionine oxidation were set as variable modifications. Enzyme specificity was set to rypsin/P with a maximum of two missed cleavages and a minimum peptide length of seven amino acids. A false discovery rate of 1% was required for peptides and proteins. Peptide identification was performed with an allowed precursor mass deviation of up to 4.5 ppm and an allowed fragment mass deviation of 20 ppm. Nonlinear retention time alignment of all measured samples was performed in MaxQuant. Peptide identifications were matched across different replicates within a matching time window of 0.7 min and an alignment time window of 20 min. A minimum ratio count of 1 was required for valid quantification events via MaxQuant\u0026rsquo;s Label Free Quantification algorithm (MaxLFQ). Data were filtered for common contaminants and reverse hits, and peptides identified only by side modification were excluded from further analysis. DIA raw files were analyzed using DIA-NN version 1.8.1 with default settings searching against a deep learning-based predicted library generated from the human Uniprot database (February 2024). For library generation, the FASTA sequences were digested with trypsin/P with 1 missed cleavage, enabling N-terminal methionine excision and cysteine carbamidomethylation as a fixed modification. Precursor and fragment mass tolerance were determined automatically for each run ranging from 10 to 20 ppm. The \u0026lsquo;report.pg_matrix.tsv\u0026rsquo; table was used for further analysis.\u003c/p\u003e\u003cp\u003e\u003cem\u003eIn vivo\u003c/em\u003e tumor models\u003c/p\u003e\u003cp\u003eNSG mice were bred in house. Mice were maintained under specific pathogen-free (SPF) conditions in the animal facility of the Institute for Research in Biomedicine. A maximum of five mice per cage were housed in ventilated cages under standardized conditions (20\u0026thinsp;\u0026plusmn;\u0026thinsp;2\u0026deg;C, 55\u0026thinsp;\u0026plusmn;\u0026thinsp;8% relative humidity, 12 h light/dark cycle). Food and water were available ad libitum, and mice were examined daily. Female mice were used between 6 and 10 weeks of age. Mice were treated in accordance with the Ticino Cantonal Commission for Animal Welfare, which is in accordance with the Animal Welfare Ordinance and the Animal Experimentation Ordinance from the Swiss Animal Welfare Legislation. Maximal tumour endpoints (2 cm in largest diameter) were not exceeded (cantonal authorization number 34613).\u003c/p\u003e\u003cp\u003eJeKo-1-Luciferase cells were maintained in RPMI (Gibco) supplemented with 10% FBS (Gibco), 1 x GlutaMax (Gibco), 25 mmol/L HEPES (Gibco) and penicillin\u0026ndash;streptomycin. For \u003cem\u003ein vivo\u003c/em\u003e experiments, 1x10\u003csup\u003e6\u003c/sup\u003e cells were injected intravenously in 100 \u0026micro;L PBS in the right flank of 8\u0026ndash;12 week-old NSG mice. For adoptive transfer of CTL019 CAR-T cells, mice were injected intravenously (\u003cem\u003ei.v.\u003c/em\u003e) in 100 \u0026micro;L PBS as soon as tumors were measurable. STO-609 was administered twice per day (BID) for 7 days at (30 \u0026micro;M/kg body weight) via intraperitoneal (\u003cem\u003ei.p.\u003c/em\u003e) injection. DMSO:PBS (1:1 v/v) vehicle was used as a control. Tumor volume was measured weekly via bioluminescence (BLI). Mice were euthanized when tumor volumes reached 1e\u003csup\u003e10\u003c/sup\u003e photons/s or reached other humane endpoints including loss of 15% of their initial body weight.\u003c/p\u003e\u003cp\u003eHuh-7 cells were maintained in DMEM (Gibco) supplemented with 10% FBS (Gibco), 1\u0026times; GlutaMax (Gibco), 25 mmol/L HEPES (Gibco) and penicillin\u0026ndash;streptomycin. For \u003cem\u003ein vivo\u003c/em\u003e experiments, 1\u0026times;10\u003csup\u003e6\u003c/sup\u003e cells were injected subcutaneously in 100 \u0026micro;L PBS in the right flank of 8\u0026ndash;12 week-old NSG mice. For adoptive transfer of GPC3 CAR-T cells, mice were injected intravenously (\u003cem\u003ei.v.\u003c/em\u003e) with 1\u0026times;10\u003csup\u003e6\u003c/sup\u003e αGPC3 CAR-T cells in 100 \u0026micro;L PBS as soon as tumors were measurable. STO-609 was administered thrice weekly (30 \u0026micro;M/kg body weight) via intraperitoneal (\u003cem\u003ei.p.\u003c/em\u003e) injection. DMSO:PBS (1:1 v/v) vehicle was used as a control. Tumor volume was measured three times per week by caliper measurements. Mice were euthanized when tumor volumes reached 1,500 mm\u003csup\u003e3\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eStatistical analysis\u003c/p\u003e\u003cp\u003eStatistical analyses were performed in the R programming environment (version 4.2.2) or Prism v.10 (GraphPad). Significance is indicated as ns\u0026thinsp;\u0026gt;\u0026thinsp;0.05, *\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ***\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001. All graphic data are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD (unless otherwise indicated). Statistical analysis of the data from 2 groups was performed using Student\u0026rsquo;s t test.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSchuster SJ et al (2019) Tisagenlecleucel in Adult Relapsed or Refractory Diffuse Large B-Cell Lymphoma. N Engl J Med 380:45\u0026ndash;56\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMaude SL et al (2018) Tisagenlecleucel in Children and Young Adults with B-Cell Lymphoblastic Leukemia. N Engl J Med 378:439\u0026ndash;448\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePasquini MC et al (2020) Real-world evidence of tisagenlecleucel for pediatric acute lymphoblastic leukemia and non-Hodgkin lymphoma. Blood Adv 4:5414\u0026ndash;5424\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMelenhorst JJ et al (2022) Decade-long leukaemia remissions with persistence of CD4\u0026thinsp;+\u0026thinsp;CAR T cells. 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J Proteome Res 10:1794\u0026ndash;1805\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7847831/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7847831/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eChimeric antigen receptor (CAR) T cells have transformed cancer therapy, yet many tumors remain refractory. To uncover broadly acting mechanisms of resistance, we performed genome-wide CRISPR activation screens across diverse cancer cell types. These screens converged on RNF19B, an E3 ubiquitin ligase whose high expression correlates with poor patient survival and confers robust CAR-T resistance in mouse xenograft models. Mechanistically, RNF19B destabilizes the interferon-γ receptor subunit IFNGR1, blunting interferon-γ signaling, and simultaneously induces CAMKK2, which mediates resistance through an independent pathway. Pharmacologic inhibition of CAMKK2 synergized with CAR-T therapy in different xenograft mouse models. Our findings identify RNF19B as a previously unrecognized, dual-pathway mediator of CAR-T resistance and reveal CAMKK2 inhibition as a potential strategy to enhance CAR-T efficacy.\u003c/p\u003e","manuscriptTitle":"RNF19B confers tumor resistance to CAR T cells","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-23 14:11:54","doi":"10.21203/rs.3.rs-7847831/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-communications","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"NCOMMS","sideBox":"Learn more about [Nature Communications](http://www.nature.com/ncomms/)","snPcode":"","submissionUrl":"https://mts-ncomms.nature.com/","title":"Nature Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Communications","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"e3f433fb-16be-45e0-8b6c-886861b1b8b7","owner":[],"postedDate":"October 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":56564451,"name":"Biological sciences/Immunology/Immune evasion"},{"id":56564452,"name":"Biological sciences/Immunology/Tumour immunology/Immunosurveillance"},{"id":56564453,"name":"Biological sciences/Cancer/Cancer therapy/Cancer immunotherapy"}],"tags":[],"updatedAt":"2025-12-24T12:35:15+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-23 14:11:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7847831","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7847831","identity":"rs-7847831","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}