AHR Activation Drives Cancer Cell-Intrinsic MHC-II expression in Human Melanoma

AHR Activation Drives Cancer Cell-Intrinsic MHC-II expression in Human Melanoma | 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 Research Article AHR Activation Drives Cancer Cell-Intrinsic MHC-II expression in Human Melanoma Yiteng Jin, Wenjin Zheng, Rui Zhang, Sen Hou, Ce Luo, Pengfei Ren, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7796457/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 20 Feb, 2026 Read the published version in Journal of Experimental & Clinical Cancer Research → Version 1 posted 9 You are reading this latest preprint version Abstract MHC-II molecules are traditionally restricted to professional antigen-presenting cells (pAPCs), but increasing evidence highlights their expression in cancer cells, where they are associated with enhanced immune infiltration and improved clinical outcomes. However, the mechanisms governing cancer cell-intrinsic MHC-II expression remain poorly understood. Here, through genome-wide CRISPR-Cas9 screening in human melanoma cells, we identify the aryl hydrocarbon receptor (AHR) and its dimerization partner (ARNT) as critical, ligand-responsive regulators of MHC-II expression. Our analyses reveal that AHR–ARNT promotes transcription of the MHC-II transactivator CIITA through direct binding to its promoter II (pII), independently of IFN-γ signaling. Clinically, an AHR–ARNT loss-of-function signature correlates with reduced immune infiltration, poor response to immunotherapy, and inferior survival across cancer types. Together, our findings uncover a previously unrecognized, tumor-intrinsic regulatory axis of MHC-II expression and suggest that targeting the AHR–ARNT pathway may enhance tumor immunogenicity and improve responses to immunotherapy. MHC-II CIITA AHR‒ARNT tumor immunity cancer immunotherapy Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 INTRODUCTION Cancer immunotherapy has significantly reshaped the treatment paradigm for various malignancies by harnessing the immune system’s inherent capacity to recognize and eliminate tumor cells. Among these approaches, immune checkpoint blockade (ICB) has demonstrated the potential for durable responses and prolonged survival in patients. 1 Traditionally, major histocompatibility complex class I (MHC-I) molecules have long been the primary focus, due to their essential role in presenting intracellular antigens to cytotoxic CD8 + T cells, enabling targeted immune surveillance and elimination of malignant cells. 2 Growing evidence highlights a critical yet often overlooked role for MHC class II (MHC-II) molecules in orchestrating effective anti-tumor immunity. MHC-II molecules present exogenous or processed endogenous antigens to CD4 + helper T cells, which play a central role in coordinating adaptive immune responses, including the priming of CD8 + cytotoxic T cells, activation of B cell-mediated antibody production, and maintenance of long-term immunological memory. 3 Under physiological conditions, MHC-II expression is largely confined to professional antigen-presenting cells (pAPCs), such as dendritic cells, macrophages, and B cells. Nevertheless, accumulating evidence indicates that MHC-II can also be expressed by non-hematopoietic cells, including various epithelial and cancer cell types. 3 – 6 Within the tumor microenvironment (TME), cancer cell-intrinsic MHC-II expression can be induced by cytokines such as IFN-γ. 3,7 Notably, recent large-scale transcriptomic analyses, including data from the Cancer Cell Line Encyclopedia (CCLE) 8 and an independent study of 675 cancer cell lines, 9 have revealed constitutive MHC-II expression in multiple human solid tumor cell lines, particularly in melanomas and subsets of lung cancers, even in the absence of external stimulation. 10 While some normal non-hematopoietic tissues, such as skin, breast, lung, and kidney tissues, also express MHC-II under specific conditions, 11 the underlying mechanisms underlying constitutive MHC-II expression in human non-hematopoietic cancer cells remain poorly understood. Intriguingly, this phenomenon appears to be species-specific: whereas numerous human tumor cell lines robustly express MHC-II, most murine non-hematopoietic cancer models, including commonly used lines such as B16F10 melanoma and MC38 colon carcinoma, exhibit minimal to no endogenous MHC-II expression. 12 The expression of MHC-II genes is primarily regulated at the transcriptional level by the MHC class II transactivator ( CIITA ), a non-DNA-binding coactivator that integrates upstream signaling cues to drive MHC-II gene expression. CIITA transcription is governed by multiple distinct promoters, pI, pIII, and pIV, which are preferentially utilized in dendritic cells, B cells, and IFN-γ–stimulated cells, respectively, and are conserved across species. 13 In contrast, promoter II (pII) appears to be unique to human cells and has been detected in a limited number of cell types, with its physiological relevance and regulatory function remaining incompletely defined. 13 , 14 The use of specific CIITA promoters is both cell-type-specific and context-dependent, contributing to the heterogeneity of MHC-II expression across immune and non-immune cell populations. Notably, in human melanoma cells, aberrant CIITA transcription can arise from non-canonical promoters such as pIII and pIV, which may account for the observed constitutive MHC-II expression even in the absence of inflammatory stimuli such as IFN-γ. 15,16 Cancer cell-intrinsic MHC-II expression has been associated with increased immune cell infiltration, enhanced responses to immune checkpoint blockade (ICB), and improved clinical outcomes. 10 In murine models of non-small cell lung cancer (NSCLC), cancer cell-intrinsic MHC-II expression correlates with higher infiltration and activation of both CD4 + and CD8 + T cells, elevated cytokine production, and enhanced sensitivity to anti-PD-1 therapy. 5 Similarly, in lung adenocarcinoma patients, higher MHC-II expression is linked to increased overall infiltration of CD4 + and CD8 + T cells, as well as closer spatial proximity between immune and cancer cells. 4 In melanoma, cancer cell-intrinsic MHC-II serves as a predictive biomarker for clinical responses to anti-PD-1 and anti-PD-L1 therapies. 17 , 18 Comparable associations have also been reported in breast cancers, particularly in triple-negative breast cancer (TNBC) and HER2-negative subtypes, where tumor MHC-II expression correlates with lymphocyte infiltration and therapeutic benefit from anti-PD-1 blockade. 19 – 22 Collectively, these findings suggest that tumor-intrinsic MHC-II not only reflects an immunologically active tumor microenvironment but may also serve as a functional mediator of therapeutic response. Despite the strong correlations between cancer cell-intrinsic MHC-II expression and favorable immunological and clinical outcomes, the mechanisms that regulate MHC-II expression in cancer cells remain largely unexplored. Elucidating these regulatory pathways is essential for developing therapeutic strategies that harness the antigen-presenting capacity of tumor cells to enhance antitumor immunity. While prior studies have identified both transcriptional and post-translational mechanisms by which cancer cells suppress MHC-II expression and evade immune detection, 23–26 the intrinsic molecular pathways that actively promote or sustain MHC-II expression in tumors are largely unknown. The aryl hydrocarbon receptor (AHR) is a ligand-activated transcription factor that, in the absence of stimulation, resides in the cytoplasm in complex with chaperone proteins. 27 Upon ligand binding, AHR undergoes conformational changes, translocates into the nucleus, and heterodimerizes with the aryl hydrocarbon receptor nuclear translocator (ARNT). The resulting AHR–ARNT complex binds to specific DNA sequences known as AHR response elements (AHREs), thereby regulating the transcription of a broad range of target genes. One of the well-characterized roles of AHR is in mediating responses to environmental toxins, including dioxins and polycyclic aromatic hydrocarbons, through the induction of detoxifying enzymes such as members of the cytochrome P450 family. 28 Beyond xenobiotic metabolism, AHR has emerged as a multifaceted regulator of immune function, influencing T cell differentiation, epithelial homeostasis, and cellular proliferation and development. 29 – 31 Despite these diverse roles, the potential involvement of the AHR–ARNT complex in regulating MHC-II expression remains unknown. In this study, we aimed to identify intrinsic regulators of MHC-II expression in human melanoma cells. Through genome-wide CRISPR-Cas9 screening combined with functional and mechanistic validation, we identified the AHR and its obligate dimerization partner ARNT as key positive regulators of cancer cell-intrinsic MHC-II expression. Genetic ablation of either AHR or ARNT markedly reduced surface MHC-II levels, while ectopic expression of these factors enhanced MHC-II expression, independently of IFN-γ signaling. Reintroduction of AHR or ARNT into their respective knockout cells rescued MHC-II expression, confirming their functional necessity. Integrated transcriptomic, chromatin accessibility, and chromatin immunoprecipitation analyses revealed that the AHR–ARNT complex binds directly to the pII promoter of CIITA to initiate its transcription, and concurrently enhances the transcriptional activity of the pIII and pIV promoters. Together, these findings uncover a previously unrecognized regulatory axis for enhancing tumor immunogenicity and improving the efficacy of cancer immunotherapy. MATERIALS AND METHODS Cell lines A375 (RRID: CVCL_0132), WM115 (RRID: CVCL_0040), SKMEL2 (RRID: CVCL_0069), B16F10 (RRID: CVCL_0159), LLC (RRID: CVCL_4358), MC38 (RRID: CVCL_B288), and HEK293T (RRID: CVCL_0063) cells were cultured in DMEM supplemented with 10% fetal bovine serum, 100 μg/mL penicillin and 100 U/ml streptomycin at 37 °C in 5% CO 2 . Viral packaging For viral production, 293T cells were plated in 15-cm culture dishes and transfected once they reached approximately 80% confluency, typically 12–18 hours later. For lentiviral packaging, transfection mixtures were prepared containing 20 μg of either sgRNA or target plasmid, 13.5 μg of psPAX2 (RRID: Addgene_12260), 6.5 μg of pMD2.G (RRID: Addgene_12259), and 120 μL of PEI (Polysciences #24765-100) in Opti-MEM (Gibco, #11058021). Plasmid DNA and PEI were each pre-incubated in Opti-MEM for 5 minutes before being combined. The resulting transfection complexes were allowed to incubate at room temperature for 30 minutes before being added to the cells. Six hours after transfection, the medium was replaced with fresh growth medium. Viral supernatants were collected 48 hours later, passed through 0.45 μm filters, aliquoted into 1 mL portions, and stored at −80 °C for future use. Genome-wide CRISPR screening for MHC-II A375-Cas9 and WM115-Cas9 were generated by transfection with lentivirus encoding Cas9-Blast (Addgene #52962) and selected with 6 μg/mL and 12 μg/mL blasticidin (InvivoGen #ant-bl-05) separately for 10 days. These two cell lines were independently transduced with Brunello lentivirus library (Addgene #73178) at an infection rate around 20%. sgRNA coverage was maintained throughout the experiments at > 500 copies of each sgRNA (~ 40 million cells for the 77,441 sgRNAs). After 48 hours of transfection, cells that had been transduced were selected using 0.7 μg/ml (A375) or 0.6 μg/ml (WM115) of puromycin (InvivoGen #ant-pr-1) for 3 days. 10 days after viral transduction, the selected WM115 cells were treated with 100 ng/mL IFN-γ (Novoprotein #C014). After 13 days of transduction, the cells were collected and divided into an experimental group and a control group, each with three replicates. Each replicate contained ~ 56 million cells (~ 700× in sgRNA coverage). The experimental groups were stained with the FITC-conjugated anti-HLA-DR antibody LN3 (Biolegend #327006), incubated on ice and protected from light for 20 min, washed with PBS, and resuspended with 2 mL PBS plus 5% fetal bovine serum prior to sorting for the lowest and highest ~ 10% populations, yielding ~ 2–4 million cells for each final population. The genomic DNA of the sorted samples and the control group was extracted using the NucleoSpin Blood XL kit (MACHEREY-NAGEL #740950.50), following the manufacturer’s instructions. Amplification of the sgRNA cassettes by PCR was performed according to the broad GPP protocol (https://portals.broadinstitute.org/gpp/public/resources/protocols). Data analysis for CRISPR screens MaGeCK (Model-based Analysis of Genome-wide CRISPR-Cas9 Knockout) was employed to process and analyze the CRISPR screen data. 32 FASTQ reads from the CRISPR screen trimmed and mapped to the corresponding library using MAGeCK “count” function to quantify sgRNA read counts. The MAGeCK “test” module was then used to calculate log2 fold changes and p-values of both the sgRNAs and genes. Custom R (v4.4.1) scripts were used to visualize the data. Generation of KO cell lines The sgRNA sequences used to generate KO cell lines were listed in Table S1. Each sgRNA was cloned into lentiGuide-Puro backbone (Addgene #52963) , with successful sgRNA insertion confirmed by Sanger sequencing. Lentiviral particles were produced as described above. The virus was used to infect the A375, WM115 and SKMEL2 cells. Following 72-hour infection, puromycin (0.7 μg/mL for A375, 0.6 μg/mL for WM115, and 0.5 μg/mL for SKMEL2) was added to the culture for selection of stable KO cell lines. Generation of overexpression cell lines A375 cells were lysed using TRIzol (Invitrogen, # 15596026), and cDNA was synthesized from total mRNA with Evo M-MLV Plus 1st Strand cDNA Synthesis Kit (AGBio #AG11615). The CDS sequences of AHR and ARNT were then amplified using corresponding primers and cloned into pHAGE vectors with RFP reporter. Lentiviral particles were produced as described above. The virus was used to infect the A375, WM115 and SKMEL2 cells. Following 5 days of viral transduction, the transduced cells were FACS sorted according to RFP signaling. In vitro AHR agonist and antagonist treatment experiments Tumor cells (0.1 million) were plated in 12-well plates per well and incubated for 24 h, 48 h, or 72 h with complete medium containing 0.1% DMSO, 1 μM FICZ, 2.5 μM GNF351, or 100 ng/mL IFN-γ. Cells were stained with APC-conjugated anti-HLA-DR/DP/DQ antibody Tü39 (Biolegend #361714) in FACS buffer (PBS supplemented with 5% FBS), incubated on ice and protected from light for 20 min, washed with PBS buffer and then were stained with DAPI in PBS to distinguish live and dead cells and analyzed by Beckman CytoFLEX S. Western blot Whole-cell lysates were solubilized in cell lysis buffer (Beyotime #P0013). Protein concentrations were determined using the BCA Protein Assay Kit (Solarbio #PC0020), and 20 μg of total protein was loaded per lane onto SDS-PAGE gels. Proteins were transferred to Immobilon PVDF membranes (Millipore). Membranes were blocked in TBST containing 5% non-fat milk for 1 h at room temperature, then incubated overnight at 4 °C with primary antibodies diluted 1:1000 in primary antibody dilution buffer (Solarbio, #A1810). The following primary antibodies were used: AHR (clone D5S6H) Rabbit mAb (CST #83200, RRID: AB_2800011), ARNT (clone D28F3) Rabbit mAb (CST #5537, RRID: AB_10694232), HLA-DRA Rabbit mAb (Boster #A01195), β-Tubulin (clone C66) Mouse mAb (Abmart #M20005, RRID: AB_2920648) and β-Actin (clone 13E5) Rabbit mAb (CST #4970, RRID: AB_2223172). After washing, membranes were incubated with HRP-conjugated secondary antibody anti-rabbit IgG (CST #7074, RRID: AB_2099233, 1:10000 dilution) or anti-mouse IgG (CST #7076, RRID: AB_330924, 1:10000 dilution) for 1 h at room temperature. Blots were visualized using M5 HiPer ECL Western HRP Substrate (Mei5 Biotechnology #MF074-01), and chemiluminescence signals were captured using a ChemiDoc™ Imaging System (Bio-Rad Laboratories). Flow Cytometry Adherent cells were dissociated into single-cell suspensions and stained with appropriate fluorochrome-conjugated antibodies. Flow cytometric analysis was performed on Beckman CytoFLEX S flow cytometer, and cell sorting was conducted using BD Aria Fusion cell sorter. Data were analyzed with FlowJo software (BD Biosciences, RRID: SCR_008520). All flow cytometry antibodies were purchased from BioLegend, including: PE anti-human CD274 (PD-L1, clone 29E.2A3) Antibody (Biolegend #329706, RRID: AB_940368), FITC anti-human HLA-DR (clone LN3) Antibody (Biolegend #327005, RRID: AB_893577), APC anti-human HLA-DR, DP, DQ (clone Tü39) Antibody (Biolegend #361714, RRID: AB_2750316), PE anti-human HLA-A,B,C (clone W6/32) Antibody (Biolegend #311406, RRID: AB_314875), FITC anti-mouse I-A/I-E (clone M5/114.15.2) Antibody (Biolegend #107605, RRID:AB_313320), PE Mouse IgG2b, κ Isotype Ctrl (clone MPC-11) Antibody (Biolegend #400311, RRID: AB_2894969), and FITC Mouse IgG2b, κ Isotype Ctrl (clone 27-35) Antibody (Biolegend #402207, RRID: AB_3097051). RNA-seq Control (non-targeting control gRNA), AHR KO and ARNT KO A375 cells, and DMSO and FICZ treated WM115 cells were cultured in 6-well plates in triplicate. A minimum of 1×10 6 cells per sample were collected across all groups. The cells were washed with PBS and lysed using TRIzol (Invitrogen #15596026). Using 1 μg of total RNA, RNA libraries for RNA-seq were prepared using VAHTS Universal V6 RNA-Seq Library Prep Kit for Illumina (Vazyme #NR604-01/02) according to manufacturer's protocols followed by Illumina sequencing. The reads were aligned to the human reference genome hg38 using STAR (RRID: SCR_004463). Feature count was used to map aligned reads to genes and generate a gene count matrix. ATAC-seq Control (non-targeting control gRNA), AHR KO and ARNT KO A375 cells were cultured in 6-well plates in triplicate. For each sample, 1 million cells were collected, washed with PBS, resuspended with 20 μL PBS, and lysed with lysis buffer (10 mM Tris-HCl pH 7.4, 10 mM NaCl, 3 mM MgCl 2 , 0.5% NP-40). The ATAC library for each sample was then prepared using TruePrep DNA Library Prep Kit V2 for Illumina (Vazyme #TD501) with TruePrep Index Kit V2 for Illumina (Vazyme #TD202) according to manufacturer's protocols followed by Illumina sequencing. The FASTQ reads were trimmed and aligned to the hg38 reference genome using Bowtie 2 (RRID: SCR_016368), followed by proper filtering using SAMtools (RRID: SCR_002105) pipeline. BigWig coverage tracks were generated using deepTools (RRID: SCR_016366) bamCoverage (v3.5.3) with 10 bp bin size and RPGC normalization (effective genome size: 2,862,010,428). chrX and chrM were excluded from normalization, and reads were extended to the estimated fragment size. ChIP-seq SKMEL2 cells stably expressing 3×HA-RFP, 3×HA-AHR-RFP and 3×HA-ARNT-RFP were cultured in 15 cm dishes separately. For each group, a total of 4×10⁷ cells were harvested and resuspended in 2 mL serum-free DMEM. For cross-linking, 1% formaldehyde solution (prepared freshly) was added, and cells were incubated at room temperature (RT) for 8 min. Cross-linking was quenched by adding 0.125 M glycine and incubating at RT for 5 min. Cells were then washed once with ice cold PBS. After fixation, pellets were flash frozen and stored at -80 °C or processed immediately for sonication. Each pellet was lysed in lysis buffer (0.1% SDS, 1% Triton X-100, 0.1% Na-Deoxycholate, 0.25% sarcosyl, 50 mM HEPES-KOH pH 7.5, 1 mM EDTA, 140 mM NaCl, 1× protease inhibitor cocktail). Samples were incubated on ice for 10–15 min, then aliquoted into thin-walled 0.5 mL PCR tubes. Sonication was performed using a Qsonica Q800R (50% amplitude, 30 s on / 30 s off, for 20 cycles) to achieve a mean DNA fragment size of 300 bp. Post-sonication, samples were centrifuged at 12,000×g for 5 min at 4 °C, and the supernatant was collected and kept on ice or stored at −80 °C. Samples were diluted 1:10 in dilution buffer (1% Triton X-100, 140 mM NaCl, 50 mM HEPES-KOH pH 7.5 and 1 mM EDTA) supplemented with 5 M NaCl to reach 150 mM NaCl, and incubated rotating with antibody and Protein A/G magnetic beads (Invitrogen), to bind the HA Tag (clone 1F5C6) Monoclonal antibody (Proteintech #66006-2-Ig, RRID: AB_2881490) and associated chromatin, for 3 h at 4 °C. After washing seven times with high salt buffer (0.1% SDS,1%Triton X-100, 20 mM Tris pH 7.9, 2 mM EDTA, 500 mM NaCl) and twice with TE buffer (10 mM Tris, 1 mM EDTA, pH 8.0), samples were eluted from the beads for 15 min at 65 °C in elution buffer (1% SDS, 200 mM NaCl). Eluates were treated with RNase A at 37 °C for 30 min and reverse cross-linked overnight by heating at 65 °C with Proteinase K. Samples were extracted with an equal volume of phenol:chloroform, vortexed, centrifuged at 12,000×g for 10 min. The aqueous phase (~ 400 μL) was transferred to a new tube and mixed with 1.6 μL GlycoBlue (250×), 40 μL 3 M sodium acetate pH 5.2, and 800 μL ice-cold ethanol. DNA was precipitated at −20 °C overnight. Samples were centrifuged at 12,000×g for 15 min at 4 °C, washed twice with 75% ethanol, and resuspended in 20 μL nuclease-free water. The ChIP library for each sample was then prepared using VAHTS Universal Pro DNA Library Prep Kit for lllumina (Vazyme #ND608) with VAHTS Multiplex Oligos Set 4 for Illumina (Vazyme #N321) and VAHTS DNA Clean Beads (Vazyme #N411) according to manufacturer's protocols followed by Illumina sequencing. The FASTQ reads were trimmed and aligned to the hg38 reference genome using Bowtie 2 (RRID: SCR_016368), followed by proper filtering using SAMtools (RRID: SCR_002105) pipeline. BigWig coverage tracks were generated using deepTools (RRID: SCR_016366) bamCoverage (v3.5.3) with 10 bp bin size and RPGC normalization (effective genome size: 2,862,010,428). chrX and chrM were excluded from normalization, and reads were extended to the estimated fragment size. MACS2 (RRID: SCR_013291) was used to call peaks and MEME-ChIP (SCR_001783) was used for motif enrichment analysis. ChIP-qPCR ChIP-qPCR analysis was conducted using ChIP DNA to detect whether AHR and ARNT protein binds to the AHRE region of the promoter pII of the CIITA gene. qPCR amplification was performed in technical triplicates using iTaq Universal SYBR ® Green Supermix (BIO-RAD #1725121) on a Bio-Rad CFX96 Touch Real-Time PCR Detection System. Fold enrichment between the control and overexpression groups was calculated. The primer sequences used in ChIP-qPCR were listed in Table S2. Dual luciferase assay The pGL4.10-pII-luc2 plasmid, in which the transcription of firefly luciferase was driven by the promoter pII of the CIITA gene, was constructed. This plasmid was co-transfected into 293T cells with the pGL4.74-TK-hRluc plasmid and one of the following: the AHR-overexpression plasmid, the ARNT-overexpression plasmid, the control plasmid or H 2 O, for the dual-luciferase reporter assay. RT-qPCR A375, WM115 and SKMEL2 cells pre-treated with 0.1% DMSO, 1 μM FICZ, 2.5 μM GNF351, or 100 ng/mL IFN-γ for 72 h, were lysed and total RNA was extracted using RNAsimple Total RNA kit (TIANGEN, Cat# DP419) according to the manufacturer’s instructions. Subsequent reverse transcription was performed with 4 μg of total RNA as template using Evo M-MLV Plus 1st Strand cDNA Synthesis Kit (AGBio #AG11615). qPCR amplification was performed in technical triplicates using iTaq Universal SYBR ® Green Supermix (BIO-RAD #1725121) on a Bio-Rad CFX96 Touch Real-Time PCR Detection System. CIITA products were amplified using the following primers specific for, respectively, CIITA-pI, CIITA-pII, CIITA-pIII, CIITA-pIV and CIITA-exon 2 (reverse) (See Table S3). As a positive control we amplified CYP1A1 products of all samples. The relative expression levels of target genes were normalized to the endogenous reference gene GAPDH using the comparative Ct (ΔΔCt) method, and log2 fold changes between the drug-treated and DMSO control groups were calculated. Analysis of TCGA cohorts Transcriptome data and clinical data were obtained from the TCGA Data Portal (https://www.cancer.gov/tcga). We chose all 33 cancer types with transcriptome data available for cancer samples. Only those samples in the clinical category of “primary tumor” and “metastatic” were used for this study. Statistical analysis Statistical analyses were performed via GraphPad Prism 9 software (RRID: SCR_002798), applying unpaired Student’s t-test, one-way ANOVA or two-way ANOVA test were used as indicated (*p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001). Group sizes for in vitro experiments were set based on prior experience of the experimental variability. Differential gene expression (DEG) analyses For bulk RNA-seq data, the R package DESeq2 (v1.34.0) was used to identify differentially expressed genes between AHR or ARNT KO versus non-targeting control A375 cells, and FICZ treated versus DMSO control WM115 cells. Genes with Benjamini–Hochberg-adjusted p value 0.5 were considered differentially expressed genes. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses KEGG pathway enrichment analysis was conducted on differentially regulated genes using the enrichKEGG function of the R package clusterProfiler (v4.12.6). AHR–ARNT -KO signature To study the clinical relevance of AHR‒ARNT, we established an AHR‒ARNT -KO signature by extracting the 199 upregulated and 101 downregulated genes and taking the normalized DESeq2 Wald statistics as weights. The weights of genes are normalized to −1 to 1 range by the equation , where indicates the weight of the -th gene, and represents the mean Wald statistic of the -th gene across the AHR KO vs NTC and ARNT KO vs NTC comparisons obtained from DESeq2. For each input expression profile, we computed an AHR‒ARNT -KO signature score to estimate the AHR‒ARNT -KO level by calculating the weighted sum expression of the signature genes following the equation , where denotes the signature score, and denotes the expression level of the -th gene. Finally, we evaluated the association of AHR‒ARNT deficiency with immune infiltration, response to ICB, and patient outcome. Materials availability Cell lines generated in this study will be provided by the lead contact under a material transfer agreement. Data and code availability Sequencing data has been deposited in the NCBI Gene Expression Omnibus (GEO): GSE305296. Transcriptome data and clinical data for TCGA cohorts were obtained from the TCGA Data Portal (https://portal.gdc.cancer.gov/). No custom programs were developed specifically for this manuscript. RESULTS Genome-wide CRISPR screens reveal AHR and ARNT as regulators of cancer cell-intrinsic MHC-II expression. To systematically identify genes that regulate cancer cell-intrinsic MHC-II expression, we conducted genome-wide CRISPR-Cas9 loss-of-function screens in two patient-derived melanoma cell lines, A375 and WM115, both of which exhibit constitutive surface expression of MHC-II molecules ( Supplementary Fig. 1A ). Cas9-expressing derivatives of these cell lines were transduced with the Brunello genome-wide gRNA library 33 (Fig. 1 A). Given the intermediate basal level of HLA-DR surface expression in WM115 cells, we pre-treated them with IFN-γ for 72 hours prior to screening to enhance the dynamic range for detection. Using flow cytometry and an HLA-DR-specific antibody, we isolated the top and bottom 10% of cells based on surface MHC-II expression and subsequently quantified sgRNA abundance via deep sequencing (Fig. 1 B). The screen successfully recovered nearly all known regulators of MHC-II expression and antigen presentation. Genes encoding MHC-II structural components (e.g., HLA-DRA , HLA-DRB1 ), the invariant chain ( CD74 ), the MHC-II master regulator CIITA , and the RFX family transcriptional co-activators ( RFXANK , RFX5 , RFXAP ) were all significantly enriched in the MHC-II low populations (Fig. 1 C-D and Supplementary Fig. 1B-E ). In the IFN-γ-treated WM115 screen, multiple components of the IFN-γ signaling pathway, including IFNGR1 , IFNGR2 , JAK1 , JAK2 , and STAT1 , also showed strong enrichment in the MHC-II low population ( Supplementary Fig. 1B-C and 1E ), further validating the screening accuracy. In addition to these established regulators, we identified several candidate genes not previously implicated in MHC-II expression. Among the top-ranked hits, AHR was enriched as the only receptor, other than IFN-γ receptors, consistently enriched in the MHC-II low populations of both A375 and WM115 cells (Fig. 1 C-D and Supplementary Fig. 1B-E ). Notably, its dimerization partner ARNT was also among the top hits in the A375 screen. The co-enrichment of AHR and ARNT strongly suggested a critical role for the AHR–ARNT complex in promoting MHC-II expression in melanoma cells. Supporting this hypothesis, integrative analysis of the CCLE transcriptomic and proteomic datasets revealed significant positive correlations between AHR expression and MHC-II expression across multiple cancer types, especially in skin cancers (Fig. 1 E-F and Supplementary Fig. 1F-G ). The AHR–ARNT complex is essential for intrinsic MHC-II expression in human melanoma cells To validate the role of AHR and ARNT in regulating cancer cell-intrinsic MHC-II expression, we generated AHR and ARNT knockout (KO) A375 cell lines using CRISPR-Cas9 (Fig. 1 G). Loss of either gene resulted in a marked repression in surface HLA-DR expression ( Supplementary Fig. 1H-I ). Notably, this reduction was reversible, as treatment with IFN-γ restored HLA-DR expression in both KO lines ( Supplementary Fig. 1H-I ), indicating that AHR and ARNT regulate MHC-II expression through an IFN-γ–independent manner. Beyond HLA-DR, we observed a broad suppression of MHC-II molecules, including HLA-DP and HLA-DQ, in AHR and or ARNT KO cells, and these were similarly rescued by IFN-γ treatment (Fig. 1 H-I and Supplementary Fig. 1J ). By contrast, MHC-I surface expression (HLA-A/B/C) remained unchanged following AHR or ARNT deletion ( Supplementary Fig. 1K ), suggesting that the AHR–ARNT complex selectively regulates MHC-II expression. These findings were further supported by experiments in WM115 cells, where AHR or ARNT deletion produced comparable downregulation of surface MHC-II expression ( Supplementary Fig. 1L-M ), reinforcing the functional relevance of this regulatory axis. To test whether AHR and ARNT are not only necessary but also sufficient to drive MHC-II expression, we overexpressed each factor individually in three human melanoma cell lines, A375, WM115, and SKMEL2 (Fig. 2 A). Overexpression of either AHR or ARNT led to significant upregulation of MHC-II surface expression in all three cell lines (Fig. 2 B-C). Notably, this upregulation occurred independently of IFN-γ stimulation (Fig. 2 B-C), reinforcing that the AHR–ARNT complex is capable of activating MHC-II expression independently of canonical inflammatory cues. To confirm the specificity of this regulatory effect, we reintroduced AHR and ARNT cDNAs, engineered to include three nonconsecutive synonymous mutations in the corresponding sgRNAs targeting sequence and the adjacent PAM sequence, into their respective knockout A375 cells ( Supplementary Fig. 2A ). Re-expression of AHR and ARNT fully rescued MHC-II expression to levels comparable to non-targeting control cells (Fig. 2 D-F), again in the absence of IFN-γ. These results confirm the functional requirement and sufficiency of the AHR–ARNT complex in promoting cancer cell-intrinsic MHC-II expression. Interestingly, this regulatory mechanism appears to be species-specific. Unlike the human cell lines, the murine melanoma cell line B16F10, colon carcinoma cell line MC38, and lung carcinoma cell line LLC did not express MHC-II at baseline ( Supplementary Fig. 2B ). Furthermore, overexpression of AHR and ARNT in these mouse lines failed to induce MHC-II expression ( Supplementary Fig. 2B ). This discrepancy may reflect fundamental differences in the structure and downstream signaling of the AHR–ARNT pathway between humans and mice 34 , including species-specific variation in ligand affinity, transcriptional targets, and chromatin context. 35 – 40 In addition, the pII promoter of CIITA is not conserved in mice, 13 providing a potential mechanistic explanation for the lack of functional conservation. Together, these findings establish AHR–ARNT as a selective and critical regulator of MHC-II expression in human melanoma cells, with species-specific constraints that limit its activity in murine models. Ligand-dependent AHR activation induces surface MHC-II expression in human melanoma cells To further investigate the functional role of the AHR–ARNT complex in regulating MHC-II expression, we treated human melanoma cells with the potent AHR agonist FICZ and the selective antagonist GNF351. In the absence of IFN-γ stimulation, FICZ treatment significantly increased surface MHC-II expression in WM115 and SKMEL2 cells (Fig. 3 A). In contrast, A375 cells, characterized by high basal MHC-II expression, did not exhibit further upregulation upon FICZ treatment (Fig. 3 A), possibly due to saturation of the regulatory pathway. GNF351 treatment led to a marked decrease in MHC-II expression across all three human melanoma lines, including SKMEL2, which expresses relatively low basal levels of MHC-II (Fig. 3 A). In line with prior genetic experiments, treatment with FICZ failed to induce MHC-II expression in B16F10, MC38 and LLC ( Supplementary Fig. 2C ), underscoring the non-conservative nature of AHR–ARNT-mediated MHC-II regulation in humans and mice. We next examined whether the magnitude of AHR-mediated regulation was dependent on the duration of ligand exposure. Time-course analysis revealed a progressive increase or decrease in surface MHC-II expression levels following treatment with FICZ or GNF351, respectively, with greater changes observed after longer treatment (Fig. 3 B-C). These results indicate that cancer cell-intrinsic MHC-II expression could be modified through pharmacologic modulation of AHR activity, and that both the direction and magnitude of this regulation are time-dependent. To evaluate the persistence of these regulatory effects, we measured surface MHC-II levels following withdrawal of FICZ or GNF351. In A375 cells, GNF351 withdrawal was followed by a further decline in surface MHC-II levels after 72 hours (Fig. 3 D-E). Similar patterns were observed in WM115 and SKMEL2 cell lines (Fig. 3 F-G). We observed that six days post-withdrawal, MHC-II expression levels showed the most pronounced upregulation or downregulation in the FICZ-treated and GNF351-treated groups, respectively (Fig. 3 F-G). Thereafter, MHC-II levels gradually returned to baseline (Fig. 3 F-G). These findings suggest that AHR modulation elicits lasting, but reversible, effects on MHC-II levels. This observation is potentially due to delayed transcriptional feedback or intracellular persistence of ligand metabolites. To confirm that FICZ-induced MHC-II upregulation is AHR‒ARNT-dependent, we treated AHR and ARNT KO cells with FICZ. While slight increases in surface MHC-II were observed in AHR and ARNT KO cells, expression levels remained significantly lower than those in FICZ-treated non-targeting controls (Fig. 3 H-I), supporting the requirement for intact AHR–ARNT signaling. The modest residual effect may be attributable to incomplete knockout efficiency. Together, these results confirm that the AHR–ARNT complex mediates ligand-responsive, reversible regulation of MHC-II expression in human melanoma cells. AHR–ARNT regulates MHC-II expression through transcriptional control of CIITA Given the ligand-responsiveness of MHC-II regulation by AHR–ARNT complex, we next sought to investigate the underlying molecular mechanisms. We performed transcriptomic profiling (RNA-seq) of A375 cells following CRISPR-mediated KO of either AHR or ARNT , and compared them to non-targeting controls. Differential gene expression analysis revealed broad transcriptional changes in both KO lines (Fig. 4 A-B), with substantial overlap between the AHR and ARNT KO groups (Fig. 4 C-E). This high concordance between AHR and ARNT KO profiles supports the cooperative function of this heterodimer in gene regulation. Notably, CIITA was consistently downregulated due to AHR - and ARNT -deficiency (Fig. 4 C), suggesting the transcriptional control of CIITA by the AHR–ARNT complex. Gene set enrichment analysis revealed that commonly downregulated genes were significantly enriched for pathways related to antigen processing and presentation (Fig. 4 F). Gene sets related to cellular stress and signaling response, especially the AP-1 complex ( FOS , FOSB , JUN , JUNB , JUND ), were significantly enriched among genes commonly upregulated in AHR - and ARNT -deficient cells compared to control cells (Fig. 4 G-H), suggesting that AHR and ARNT potentially suppressed the biological processes, such as cell growth, proliferation, and oncogenesis via AP-1 activation. To further determine whether AHR activation was sufficient to induce MHC-II-related gene expression, we treated WM115 cells with FICZ and performed RNA-Seq. Consistent with our findings from the KO models, FICZ treatment upregulated multiple MHC-II genes and CIITA (Fig. 4 I and 4 K). KEGG pathway analysis confirmed significant enrichment of the antigen processing and presentation pathway (Fig. 4 J-K). Collectively, our findings demonstrate that the AHR–ARNT complex regulates MHC-II expression through transcriptional control of CIITA . The AHR–ARNT complex binds promoter II of CIITA to drive transcription of type II, III and IV isoforms To elucidate how the AHR–ARNT complex regulates MHC-II expression at the transcriptional level, we analyzed CIITA mRNA isoforms in A375 and WM115 cells using RNA-seq. In A375 cells, the RNA-seq results showed that CIITA transcripts originated predominantly from the type III 5’ end (Fig. 5 A), consistent with prior reports describing abnormal usage of the B cell-specific promoter pIII in A375 melanoma cells 15 . Ablation of either AHR or ARNT significantly reduced type III transcript abundance (Fig. 5 A), suggesting a potential regulatory interaction between AHR–ARNT and pIII. In contrast, in WM115 cells treated with the AHR agonist FICZ, RNA-seq revealed transcription initiation from the type IV 5’ end (Fig. 5 B), indicating promoter usage that differs by context and activation state. To further investigate the regulatory mechanism, we performed ATAC-seq on non-targeting control and AHR - or ARNT -knockout A375 cells. Consistent with the RNA-seq data, ATAC-seq showed that chromatin accessibility at the HLA-DRA promoter was reduced in AHR and ARNT KO cells (Fig. 5 C), but accessibility at the CIITA locus remained unchanged (Fig. 5 D), with three promoters (pII, pIII, and pIV) accessible regardless of AHR/ARNT status (Fig. 5 D). These findings suggest that AHR–ARNT does not control MHC-II expression by altering chromatin accessibility at CIITA, but rather by directly recruiting transcriptional machinery to specific promoter regions. To test this, we overexpressed 3xHA-tagged AHR and ARNT in SKMEL2 cells and performed HA ChIP-seq. As expected, the canonical AHR–ARNT target gene CYP1A1 exhibited strong enrichment at its promoter (Fig. 5 E). Importantly, we also observed direct binding of AHR and ARNT to pII of CIITA (Fig. 5 F), indicating this promoter as a functional AHR–ARNT target. Motif enrichment analysis using MEME-ChIP 41 revealed that the AHRE (Aryl Hydrocarbon Response Element) core motif (5'-GCGTG-3') was the most significantly enriched motif in the ChIP peaks for both factors (Fig. 5 G). Notably, this motif is present in the pII region of CIITA. We further validated AHR–ARNT binding to pII using ChIP-qPCR in HA-AHR/ARNT-overexpressing SKMEL2 cells (Fig. 5 H), confirming direct occupancy at this site. To assess the functional activity of this binding site, we PCR cloned the enriched pII region into a dual-luciferase reporter construct. Co-transfection with AHR and ARNT significantly increased reporter activity, confirming its role as a transcriptionally active AHR–ARNT-responsive element (Fig. 5 I). Finally, RT-qPCR using isoform-specific primers revealed that AHR activation by FICZ induced type II, III and IV CIITA mRNA isoforms, whereas AHR inhibition by GNF351 suppressed expression of these isoforms ( Supplementary Fig. 3A-C ), especially in SKMEL2 cells ( Supplementary Fig. 3C ). Taken together, these results demonstrate that ligand-activated AHR translocates to the nucleus, dimerizes with ARNT, and directly binds to pII of CIITA to drive transcription of type II, III and IV isoforms, thereby promoting downstream MHC-II expression (Fig. 5 J). AHR–ARNT -KO signature negatively correlates with clinical benefits in multiple cancer cohorts To evaluate the clinical relevance of the AHR–ARNT regulatory axis in human tumors, we developed a gene expression signature reflective of AHR–ARNT functional loss by identifying genes consistently dysregulated upon loss of either AHR or ARNT. A composite score was then calculated for each tumor sample based on the weighted expression of this gene set, enabling estimation of AHR–ARNT pathway activity in large clinical datasets. We first assessed the association between the AHR–ARNT -KO signature and immune cell infiltration using bulk RNA-seq data from the TCGA (The Cancer Genome Atlas). Across multiple TCGA cohorts, higher AHR–ARNT -KO signature scores were significantly correlated with reduced infiltration of CD4⁺ T and CD8⁺ T cells and B cells, and with increased infiltration of cancer-associated fibroblasts, as estimated by EPIC 42 (Fig. 6 A). To explore potential implications for immunotherapy responsiveness, we applied the TIDE algorithm 43 to the TCGA datasets. In 24 cancer types, including skin cutaneous melanoma (SKCM) and lung adenocarcinoma (LUAD), patients predicted to respond to immune checkpoint blockade (ICB) exhibited significantly lower AHR–ARNT -KO signature scores than predicted non-responders (Fig. 6 B-C). These findings suggest that intact AHR–ARNT activity may contribute to an immunologically active tumor microenvironment that is more amenable to ICB therapy. We next examined the relationship between the AHR–ARNT -KO signature, MHC-II expression, and patient survival. For each TCGA cancer type, patient samples were stratified into four groups based on high or low MHC-II expression level and high or low AHR–ARNT -KO signature scores. In both SKCM and LUAD cohorts, patients with high MHC-II expression and low AHR–ARNT -KO signature scores had significantly improved overall survival compared to those with low MHC-II expression and high AHR–ARNT -KO signature scores (Fig. 6 D-E). These associations reinforce the importance of AHR–ARNT-mediated regulation of MHC-II in shaping the tumor immune landscape and influencing patient prognosis. DISSCUSSION The regulation of MHC-II expression in cancer cells is increasingly recognized as a critical determinant of TME and responsiveness to immunotherapy. 3–6,17−20 Although antigen presentation by MHC-II is canonically restricted to pAPCs, accumulating evidence has demonstrated that cancer cell-intrinsic MHC-II expression correlates with improved patient prognosis and enhanced response to ICB. Recent studies have identified several negative regulators of MHC-II expression, including FBXO11 and PRMT1, which promote degradation of CIITA through ubiquitination and methylation respectively, as well as the CtBP complex, which represses MHC-II transcription. 25 , 26 However, these regulators act downstream of CIITA, the master transcriptional activator of MHC-II, and require its presence to exert their effects. In contrast, the upstream mechanisms that drive constitutive MHC-II and CIITA in non-hematopoietic tumors such as melanoma remain poorly defined. In this study, we employed genome-wide CRISPR screening in human melanoma cell lines (A375 and WM115) and uncovered a previously unrecognized regulatory axis involving the AHR and its obligate dimerization partner ARNT. Together, the AHR–ARNT complex functions as a key positive regulator of cancer cell-intrinsic MHC-II expression. Notably, this regulation occurs independently of IFN-γ signaling, highlighting its intrinsic nature within tumor cells and revealing a parallel, non-inflammatory pathway through which tumor immunogenicity can be modulated. The significant reduction in MHC-II expression following KO of AHR or ARNT underscores the essential role of this complex in maintaining an immune-permissive tumor phenotype. Importantly, overexpression of AHR or ARNT was sufficient to induce MHC-II upregulation even in the absence of IFN-γ, suggesting that tumor cells can autonomously activate antigen presentation pathways. While MHC-II traditionally presents exogenous peptides processed by pAPCs, several alternative pathways enable endogenous antigen loading onto MHC-II molecules. 44 – 48 These mechanisms suggest that cancer cells have a route to present tumor-associated antigens (TAAs) or tumor-specific antigens (TSAs) on their own MHC-II molecules, thereby contributing to direct CD4 + T cell activation. 49 – 56 Thus, the AHR–ARNT-driven intrinsic regulation of MHC-II allows new opportunities for therapeutic intervention, whereby tumors may be pharmacologically or genetically modified to enhance their antigen-presenting capabilities and improve T cell-mediated anti-tumor immunity. Our results reveal that the regulation of the AHR and ARNT pathway is both ligand-responsive and temporally dynamic. Treatment with FICZ, a potent AHR agonist, significantly increased surface MHC-II expression in multiple melanoma lines, while AHR inhibition with GNF351 led to its repression. Importantly, the delayed peak in MHC-II expression following treatment withdrawal suggests that careful timing may be necessary to maximize the immunomodulatory benefits of AHR-targeting agents. Collectively, these findings position the AHR–ARNT complex as a tractable target for modulating MHC-II expression and augmenting anti-tumor immunity through noncanonical, tumor-intrinsic pathways. Mechanistically, we demonstrate that the AHR–ARNT complex directly binds to promoter II (pII) of CIITA , thereby enhancing its transcriptional activity. This establishes a direct link between environmental sensing via AHR ligands and the activation of antigen presentation machinery in tumor cells. The presence of a canonical AHRE within the CIITA pII, together with robust ChIP-seq/ChIP-qPCR validation, reinforces the specificity of this regulation. In A375 cells, AHR or ARNT deficiency led to reduced abundance of type III and IV mRNA of CIITA , suggesting that AHR–ARNT may influence CIITA transcription through both direct and indirect mechanisms, potentially involving promoter switching or epigenetic modulation. Moreover, transcriptomic analysis revealed that AHR–ARNT regulates additional transcriptional factors, including components of the AP-1 complex, implying a broader transcriptional remodeling that may influence tumor cell behavior beyond antigen presentation. As we continue to delineate the molecular pathways governing MHC-II regulation, it will be critical to explore potential combinatorial strategies that integrate AHR pathway modulation with approaches aimed at enhancing T cell activation and persistence. Insights from this study lay the foundation for developing novel therapies that enhance tumor immunogenicity and improve patient outcomes in cancer immunotherapy. Our findings also underscore the complex, context-dependent role of AHR in cancer. While AHR activation has previously been implicated in both tumor-promoting and tumor-suppressive processes, 29,34,57 our study reveals a novel immune-enhancing function through upregulation of MHC-II expression. This duality underscores the necessity for a nuanced comprehension of AHR signaling across various tumor types, disease stages, and immune microenvironments. Future studies should carefully delineate the conditions under which AHR modulation promotes versus impairs anti-tumor immunity, with particular attention to its interactions with other immunoregulatory pathways. In addition, the observed species-specific differences between human and murine tumor cells highlight an important limitation in current preclinical models. While AHR/ARNT overexpression or FICZ treatment robustly induced MHC-II expression in human melanoma cells, these interventions failed to elicit similar responses in murine models—likely due to the absence of a murine homolog of CIITA pII. This finding emphasizes the need for human-relevant systems and improved mouse models that recapitulate human MHC-II regulatory architecture when studying antigen presentation and its impact on immune responses, T cell infiltration, and immunotherapy outcomes. In clinical datasets, higher inferred AHR–ARNT activity, based on an AHR–ARNT loss-of-function signature, was associated with increased immune cell infiltration, better response to ICB, and improved overall survival across multiple cancer types. These findings suggest that the AHR–ARNT pathway may serve not only as a functional modulator but also as a predictive biomarker of immunotherapy responsiveness. Furthermore, these findings align with the established role of MHC-II in promoting durable antitumor immunity. Pharmacologically enhancing cancer cell-intrinsic MHC-II expression via AHR agonists could therefore strengthen and prolong T cell-mediated responses. Further studies are warranted to explore the combinatorial potential of AHR activation with immune checkpoint inhibitors and to dissect the interplay between AHR–ARNT and other immunomodulatory pathways. In summary, our study identifies the AHR–ARNT complex as a central, ligand-responsive regulator of CIITA and MHC-II expression in human melanoma cells. By enabling tumor cells to adopt an antigen-presenting phenotype through transcriptional activation of CIITA , this pathway enhances their immunogenicity and potential visibility to the immune system. The reversible and tunable nature of this mechanism presents a compelling opportunity for therapeutic intervention, particularly in combination with existing immunotherapies. These findings highlight the broader significance of tumor-intrinsic regulatory programs in shaping antitumor immunity and lay a foundation for future AHR-targeted strategies in cancer immunity. Declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Funding This work was supported by the National Natural Science Foundation of China (32470664, 12374203, 92374116), the Major Project of WIUCAS (WIUCASQD2021013), Beijing Natural Science Foundation (L248043), Noncommunicable Chronic Diseases-National Science and Technology Major Project (2024ZD0520600), Sichuan Science and Technology Program (2024YFFK0064), and the Frontier Innovation Fund of Peking University Chengdu Academy for Advanced Interdisciplinary Biotechnologies. Authors' contributions Y.J., D.P., Ch.L., and Z.Z. conceived the project. Y.J., W.Z., and Z.Z. carried out the investigation. Y.J., R.Z., and S.H. developed the methodology. Y.J. and W.Z. validated the results. Y.J., Ce.L., and P.R. performed the bioinformatic data analysis. Y.J. and Z.Z. curated the data and wrote the manuscript. Ch.L. and Z.Z. acquired funding and supervised the project. Acknowledgements We thank the National Center for Protein Sciences at Peking University, the behavioral laboratory, the Protein Preparation and Identification Core, and the Optical Imaging Core Facility for experimental platforms. Part of the analysis was performed on the High-Performance Computing Platform of the Center for Life Sciences at Peking University. We thank the Center for Quantitative Biology at Peking University for its experimental platforms. References Waldman, A. D., Fritz, J. M. & Lenardo, M. J. A guide to cancer immunotherapy: from T cell basic science to clinical practice. Nature Reviews Immunology 20 , 651-668, doi:10.1038/s41577-020-0306-5 (2020). Garrido, F., Aptsiauri, N., Doorduijn, E. M., Garcia Lora, A. M. & van Hall, T. The urgent need to recover MHC class I in cancers for effective immunotherapy. Curr Opin Immunol 39 , 44-51, doi:10.1016/j.coi.2015.12.007 (2016). Axelrod, M. L., Cook, R. S., Johnson, D. B. & Balko, J. M. Biological Consequences of MHC-II Expression by Tumor Cells in Cancer. Clinical Cancer Research 25 , 2392-2402, doi:10.1158/1078-0432.CCR-18-3200 (2019). Johnson, A. M. et al. Cancer Cell-Specific Major Histocompatibility Complex II Expression as a Determinant of the Immune Infiltrate Organization and Function in the NSCLC Tumor Microenvironment. Journal of Thoracic Oncology 16 , 1694-1704, doi:10.1016/j.jtho.2021.05.004 (2021). Johnson, A. M. et al. Cancer Cell–Intrinsic Expression of MHC Class II Regulates the Immune Microenvironment and Response to Anti–PD-1 Therapy in Lung Adenocarcinoma. The Journal of Immunology 204 , 2295-2307, doi:10.4049/jimmunol.1900778 (2020). Løvig, T. et al. Strong HLA-DR expression in microsatellite stable carcinomas of the large bowel is associated with good prognosis. Br J Cancer 87 , 756-762, doi:10.1038/sj.bjc.6600507 (2002). Steimle, V., Siegrist, C.-A., Mottet, A., Lisowska-Grospierre, B. & Mach, B. Regulation of MHC Class II Expression by Interferon-γ Mediated by the Transactivator Gene CIITA. Science 265 , 106-109, doi:10.1126/science.8016643 (1994). Barretina, J. et al. The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature 483 , 603-607, doi:10.1038/nature11003 (2012). Klijn, C. et al. A comprehensive transcriptional portrait of human cancer cell lines. Nature Biotechnology 33 , 306-312, doi:10.1038/nbt.3080 (2015). Zeng, Z. et al. Hippo signaling pathway regulates cancer cell-intrinsic MHC-II expression. Cancer Immunology Research , CIR-22-0227, doi:10.1158/2326-6066.CIR-22-0227 (2022). Zeng, Z. et al. TISMO: syngeneic mouse tumor database to model tumor immunity and immunotherapy response. Nucleic acids research 50 , D1391-D1397 (2022). Walter, W., Lingnau, K., Schmitt, E., Loos, M. & Maeurer, M. J. MHC class II antigen presentation pathway in murine tumours: tumour evasion from immunosurveillance? Br J Cancer 83 , 1192-1201, doi:10.1054/bjoc.2000.1415 (2000). Muhlethaler-Mottet, A., Otten, L. A., Steimle, V. & Mach, B. Expression of MHC class II molecules in different cellular and functional compartments is controlled by differential usage of multiple promoters of the transactivator CIITA. Embo j 16 , 2851-2860, doi:10.1093/emboj/16.10.2851 (1997). Reith, W., LeibundGut-Landmann, S. & Waldburger, J.-M. Regulation of MHC class II gene expression by the class II transactivator. Nature Reviews Immunology 5 , 793-806, doi:10.1038/nri1708 (2005). Deffrennes, V. et al. Constitutive Expression of MHC Class II Genes in Melanoma Cell Lines Results from the Transcription of Class II Transactivator Abnormally Initiated from Its B Cell-Specific Promoter1. The Journal of Immunology 167 , 98-106, doi:10.4049/jimmunol.167.1.98 (2001). Goodwin, B. L. et al. Varying functions of specific major histocompatibility class II transactivator promoter III and IV elements in melanoma cell lines. Cell Growth Differ 12 , 327-335 (2001). Johnson, D. B. et al. Melanoma-specific MHC-II expression represents a tumour-autonomous phenotype and predicts response to anti-PD-1/PD-L1 therapy. Nature Communications 7 , 10582, doi:10.1038/ncomms10582 (2016). Rodig, S. J. et al. MHC proteins confer differential sensitivity to CTLA-4 and PD-1 blockade in untreated metastatic melanoma. Science Translational Medicine 10 , eaar3342, doi:10.1126/scitranslmed.aar3342 (2018). Park, I. A. et al. Expression of the MHC class II in triple-negative breast cancer is associated with tumor-infiltrating lymphocytes and interferon signaling. PLoS One 12 , e0182786, doi:10.1371/journal.pone.0182786 (2017). Forero, A. et al. Expression of the MHC Class II Pathway in Triple-Negative Breast Cancer Tumor Cells Is Associated with a Good Prognosis and Infiltrating Lymphocytes. Cancer Immunology Research 4 , 390-399, doi:10.1158/2326-6066.CIR-15-0243 (2016). Loi, S. et al. RAS/MAPK Activation Is Associated with Reduced Tumor-Infiltrating Lymphocytes in Triple-Negative Breast Cancer: Therapeutic Cooperation Between MEK and PD-1/PD-L1 Immune Checkpoint Inhibitors. Clinical Cancer Research 22 , 1499-1509, doi:10.1158/1078-0432.CCR-15-1125 (2016). Gonzalez-Ericsson, P. I. et al. Tumor-Specific Major Histocompatibility-II Expression Predicts Benefit to Anti-PD-1/L1 Therapy in Patients With HER2-Negative Primary Breast Cancer. Clin Cancer Res 27 , 5299-5306, doi:10.1158/1078-0432.Ccr-21-0607 (2021). Balasubramanian, A., John, T. & Asselin-Labat, M.-L. Regulation of the antigen presentation machinery in cancer and its implication for immune surveillance. Biochemical Society Transactions 50 , 825-837, doi:10.1042/BST20210961 (2022). Seliger, B., Kloor, M. & Ferrone, S. HLA class II antigen-processing pathway in tumors: Molecular defects and clinical relevance. OncoImmunology 6 , e1171447, doi:10.1080/2162402X.2016.1171447 (2017). Chan, K. L. et al. Inhibition of the CtBP complex and FBXO11 enhances MHC class II expression and anti-cancer immune responses. Cancer Cell 40 , 1190-1206.e1199, doi:10.1016/j.ccell.2022.09.007 (2022). Fan, Z. et al. Protein arginine methyltransferase 1 (PRMT1) represses MHC II transcription in macrophages by methylating CIITA. Scientific Reports 7 , 40531, doi:10.1038/srep40531 (2017). Rothhammer, V. & Quintana, F. J. The aryl hydrocarbon receptor: an environmental sensor integrating immune responses in health and disease. Nature Reviews Immunology 19 , 184-197, doi:10.1038/s41577-019-0125-8 (2019). Larigot, L., Juricek, L., Dairou, J. & Coumoul, X. AhR signaling pathways and regulatory functions. Biochimie Open 7 , 1-9, doi:https://doi.org/10.1016/j.biopen.2018.05.001 (2018). Gutiérrez-Vázquez, C. & Quintana, F. J. Regulation of the Immune Response by the Aryl Hydrocarbon Receptor. Immunity 48 , 19-33, doi:10.1016/j.immuni.2017.12.012 (2018). Law, C. et al. Interferon subverts an AHR–JUN axis to promote CXCL13+ T cells in lupus. Nature , doi:10.1038/s41586-024-07627-2 (2024). Di Meglio, P. et al. Activation of the aryl hydrocarbon receptor dampens the severity of inflammatory skin conditions. Immunity 40 , 989-1001, doi:10.1016/j.immuni.2014.04.019 (2014). Li, W. et al. MAGeCK enables robust identification of essential genes from genome-scale CRISPR/Cas9 knockout screens. Genome Biol 15 , 554, doi:10.1186/s13059-014-0554-4 (2014). Doench, J. G. et al. Optimized sgRNA design to maximize activity and minimize off-target effects of CRISPR-Cas9. Nature Biotechnology 34 , 184-191, doi:10.1038/nbt.3437 (2016). Murray, I. A., Patterson, A. D. & Perdew, G. H. Aryl hydrocarbon receptor ligands in cancer: friend and foe. Nature Reviews Cancer 14 , 801-814, doi:10.1038/nrc3846 (2014). Flaveny, C. A., Murray, I. A. & Perdew, G. H. Differential gene regulation by the human and mouse aryl hydrocarbon receptor. Toxicol Sci 114 , 217-225, doi:10.1093/toxsci/kfp308 (2010). Forgacs, A. L., Dere, E., Angrish, M. M. & Zacharewski, T. R. Comparative Analysis of Temporal and Dose-Dependent TCDD-Elicited Gene Expression in Human, Mouse, and Rat Primary Hepatocytes. Toxicological Sciences 133 , 54-66, doi:10.1093/toxsci/kft028 (2013). Black, M. B. et al. Cross-species comparisons of transcriptomic alterations in human and rat primary hepatocytes exposed to 2,3,7,8-tetrachlorodibenzo-p-dioxin. Toxicol Sci 127 , 199-215, doi:10.1093/toxsci/kfs069 (2012). Flaveny, C., Reen, R. K., Kusnadi, A. & Perdew, G. H. The mouse and human Ah receptor differ in recognition of LXXLL motifs. Arch Biochem Biophys 471 , 215-223, doi:10.1016/j.abb.2008.01.014 (2008). Ramadoss, P. & Perdew, G. H. Use of 2-azido-3-[125I]iodo-7,8-dibromodibenzo-p-dioxin as a probe to determine the relative ligand affinity of human versus mouse aryl hydrocarbon receptor in cultured cells. Mol Pharmacol 66 , 129-136, doi:10.1124/mol.66.1.129 (2004). Flaveny, C. A., Murray, I. A., Chiaro, C. R. & Perdew, G. H. Ligand selectivity and gene regulation by the human aryl hydrocarbon receptor in transgenic mice. Mol Pharmacol 75 , 1412-1420, doi:10.1124/mol.109.054825 (2009). Bailey, T. L., Johnson, J., Grant, C. E. & Noble, W. S. The MEME Suite. Nucleic Acids Research 43 , W39-W49, doi:10.1093/nar/gkv416 (2015). Racle, J., de Jonge, K., Baumgaertner, P., Speiser, D. E. & Gfeller, D. Simultaneous enumeration of cancer and immune cell types from bulk tumor gene expression data. Elife 6 , doi:10.7554/eLife.26476 (2017). Jiang, P. et al. Signatures of T cell dysfunction and exclusion predict cancer immunotherapy response. Nature Medicine 24 , 1550-1558, doi:10.1038/s41591-018-0136-1 (2018). Paludan, C. et al. Endogenous MHC class II processing of a viral nuclear antigen after autophagy. Science 307 , 593-596, doi:10.1126/science.1104904 (2005). Nuchtern, J. G., Biddison, W. E. & Klausner, R. D. Class II MHC molecules can use the endogenous pathway of antigen presentation. Nature 343 , 74-76, doi:10.1038/343074a0 (1990). Riedel, A. et al. Endogenous presentation of a nuclear antigen on MHC class II by autophagy in the absence of CRM1-mediated nuclear export. Eur J Immunol 38 , 2090-2095, doi:10.1002/eji.200737900 (2008). Leung, C. S. K. Endogenous Antigen Presentation of MHC Class II Epitopes through Non-Autophagic Pathways. Frontiers in Immunology 6 (2015). Crotzer, V. L. & Blum, J. S. Autophagy and its role in MHC-mediated antigen presentation. J Immunol 182 , 3335-3341, doi:10.4049/jimmunol.0803458 (2009). van Tuyn, J. et al. Oncogene-Expressing Senescent Melanocytes Up-Regulate MHC Class II, a Candidate Melanoma Suppressor Function. Journal of Investigative Dermatology 137 , 2197-2207, doi:10.1016/j.jid.2017.05.030 (2017). Chornoguz, O., Gapeev, A., O'Neill, M. C. & Ostrand-Rosenberg, S. Major histocompatibility complex class II+ invariant chain negative breast cancer cells present unique peptides that activate tumor-specific T cells from breast cancer patients. Mol Cell Proteomics 11 , 1457-1467, doi:10.1074/mcp.M112.019232 (2012). Alspach, E. et al. MHC-II neoantigens shape tumour immunity and response to immunotherapy. Nature 574 , 696-701, doi:10.1038/s41586-019-1671-8 (2019). Armstrong, T. D., Clements, V. K., Martin, B. K., Ting, J. P. & Ostrand-Rosenberg, S. Major histocompatibility complex class II-transfected tumor cells present endogenous antigen and are potent inducers of tumor-specific immunity. Proc Natl Acad Sci U S A 94 , 6886-6891, doi:10.1073/pnas.94.13.6886 (1997). Armstrong, T. D., Clements, V. K. & Ostrand-Rosenberg, S. Class II-transfected tumor cells directly present endogenous antigen to CD4+ T cells in vitro and are APCs for tumor-encoded antigens in vivo. J Immunother 21 , 218-224, doi:10.1097/00002371-199805000-00008 (1998). Armstrong, T. D., Clements, V. K. & Ostrand-Rosenberg, S. MHC class II-transfected tumor cells directly present antigen to tumor-specific CD4+ T lymphocytes. J Immunol 160 , 661-666 (1998). Abelin, J. G. et al. Defining HLA-II Ligand Processing and Binding Rules with Mass Spectrometry Enhances Cancer Epitope Prediction. Immunity 51 , 766-779.e717, doi:10.1016/j.immuni.2019.08.012 (2019). Hos, B. J. et al. Cancer-specific T helper shared and neo-epitopes uncovered by expression of the MHC class II master regulator CIITA. Cell Rep 41 , 111485, doi:10.1016/j.celrep.2022.111485 (2022). Dean, J. W. & Zhou, L. Cell-intrinsic view of the aryl hydrocarbon receptor in tumor immunity. Trends in Immunology 43 , 245-258, doi:https://doi.org/10.1016/j.it.2022.01.008 (2022). Additional Declarations No competing interests reported. Supplementary Files SUPPLEMENTARYFIGURESANDFIGURELEGENDS.docx Cite Share Download PDF Status: Published Journal Publication published 20 Feb, 2026 Read the published version in Journal of Experimental & Clinical Cancer Research → Version 1 posted Editorial decision: Revision requested 17 Nov, 2025 Reviews received at journal 17 Nov, 2025 Reviews received at journal 11 Nov, 2025 Reviewers agreed at journal 28 Oct, 2025 Reviewers agreed at journal 27 Oct, 2025 Reviewers invited by journal 08 Oct, 2025 Editor assigned by journal 07 Oct, 2025 Submission checks completed at journal 07 Oct, 2025 First submitted to journal 07 Oct, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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1","display":"","copyAsset":false,"role":"figure","size":453206,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGenome-wide CRISPR screen identifies \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eAHR\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e and \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eARNT\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e as positive regulators of MHC-II expression.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eA\u003c/strong\u003e) Schematic overview of the genome-wide CRISPR-Cas9 screen in human melanoma cells to identify regulators of MHC-II surface protein level.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eB\u003c/strong\u003e) Representative flow cytometry histograms from three independent experiments showing sorting of HLA-DR\u003csup\u003elow\u003c/sup\u003e and HLA-DR\u003csup\u003ehigh\u003c/sup\u003e cell populations.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eC\u003c/strong\u003e) Scatterplot of sgRNAs enriched (red) or depleted (blue) in the HLA-DR\u003csup\u003elow\u003c/sup\u003e population compared to the HLA-DR\u003csup\u003ehigh \u003c/sup\u003epopulation. Top 20 sgRNAs were highlighted.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eD\u003c/strong\u003e) Log\u003csub\u003e2\u003c/sub\u003e fold change values of individual sgRNAs plotted in (\u003cstrong\u003eC\u003c/strong\u003e), showing the effect of gene perturbations on HLA-DR expression.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eE-F\u003c/strong\u003e) Scatterplot showing the Pearson correlation between \u003cem\u003eAHR\u003c/em\u003e expression and MHC-II expression across skin cancer cell lines in the Cancer Cell Line Encyclopedia (CCLE) at the mRNA level (\u003cstrong\u003eE\u003c/strong\u003e) and protein level (\u003cstrong\u003eF\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eG\u003c/strong\u003e) Western blot analysis confirming loss of AHR and ARNT protein in A375-Cas9 cells following CRISPR-mediated knockout.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eH\u003c/strong\u003e) Flow cytometry analysis of surface MHC-II (HLA-DR/DP/DQ) expression in A375-Cas9 cells transduced with sgRNAs targeting \u003cem\u003eAHR\u003c/em\u003e, \u003cem\u003eARNT\u003c/em\u003e, or control sgRNAs, with or without IFN-γ treatment (100 ng/mL, 72 h). Representative plots from three independent experiments.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eI\u003c/strong\u003e) Mean geometric mean fluorescence intensity (gMFI) of surface MHC-II (HLA-DR/DP/DQ) expression in \u003cem\u003eAHR\u003c/em\u003e- and \u003cem\u003eARNT\u003c/em\u003e-deficient A375 cells, with or without IFN-γ treatment (100 ng/mL, 72 h), across six independent experiments (normalized to unstained controls).\u003c/p\u003e\n\u003cp\u003eData are represented as mean ± SD (\u003cstrong\u003eI\u003c/strong\u003e). Statistical analysis by two-way ANOVA (\u003cstrong\u003eI\u003c/strong\u003e); *p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001, ****p \u0026lt; 0.0001.\u003cbr\u003e\n\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7796457/v1/36b4feac54f40cfd2189344f.png"},{"id":94017013,"identity":"cfc1fc86-0726-4348-b736-90939aa99feb","added_by":"auto","created_at":"2025-10-21 11:25:36","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":313975,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAHR and ARNT regulate cancer cell-intrinsic MHC-II levels.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eA\u003c/strong\u003e) Western blot analysis confirming AHR or ARNT overexpression (OE) in A375, WM115, and SKMEL2 melanoma cell lines. Protein levels of AHR, ARNT, and HLA-DRA were assessed.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eB\u003c/strong\u003e) Flow cytometry analysis of surface MHC-II (HLA-DR/DP/DQ) expression in A375, WM115, and SKMEL2 cells overexpressing AHR or ARNT. Representative plots from three independent experiments. vec, empty vector control.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eC\u003c/strong\u003e) Mean gMFI of surface MHC-II (HLA-DR/DP/DQ) expression in AHR- or ARNT-overexpressing A375, WM115, and SKMEL2 cells across three independent experiments (normalized to unstained controls). vec, empty vector control.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eD\u003c/strong\u003e) Western blot analysis of AHR, ARNT, and HLA-DRA protein levels in AHR or ARNT reconstituted A375 cells generated by reintroducing AHR or ARNT into respective KO cells.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eE\u003c/strong\u003e) Flow cytometry analysis of surface MHC-II (HLA-DR/DP/DQ) expression in AHR- or ARNT-reconstituted A375 cells. Representative plots from three independent experiments. vec, empty vector control.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eF\u003c/strong\u003e) Mean gMFI of surface MHC-II (HLA-DR/DP/DQ) expression in AHR- or ARNT-reconstituted A375 cells across three independent experiments (normalized to unstained controls). vec, empty vector control.\u003c/p\u003e\n\u003cp\u003eData are represented as mean ± SD (\u003cstrong\u003eC and F\u003c/strong\u003e). Statistical analysis by one-way ANOVA (\u003cstrong\u003eC\u003c/strong\u003e) and unpaired Student’s t-test (\u003cstrong\u003eF\u003c/strong\u003e); *p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001, ****p \u0026lt; 0.0001.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7796457/v1/853326dded4ba46279e1ec72.png"},{"id":94017120,"identity":"e941e962-a7b4-4a46-9266-a8e6593bbfa0","added_by":"auto","created_at":"2025-10-21 11:33:37","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":395798,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLigand-mediated activation of AHR regulates cancer cell-intrinsic MHC-II level.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eA\u003c/strong\u003e) Flow cytometry analysis of surface MHC-II (HLA-DR/DP/DQ) expression in A375, WM115, and SKMEL2 melanoma cells following 72-hour treatment with 2.5 μM GNF351 (AHR antagonist), 1 μM FICZ (AHR agonist), 100 ng/mL IFN-γ, or 0.1% DMSO (vehicle control). Representative plots from three independent experiments.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eB-C\u003c/strong\u003e) Time-course analysis of MHC-II (HLA-DR/DP/DQ) surface expression in A375, WM115, and SKMEL2 cells following treatment with GNF351 (2.5 μM) or FICZ (1μM) for 24, 48, or 72 hours. Representative flow cytometry plots from three independent experiments (\u003cstrong\u003eB\u003c/strong\u003e); quantification of normalized gMFI across three independent experiments (normalized to unstained controls) (\u003cstrong\u003eC\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eD-E\u003c/strong\u003e) MHC-II (HLA-DR/DP/DQ) expression in A375 cells at 72 hours post drug withdrawal following prior exposure to GNF351 (2.5 μM) or FICZ (1 μM) for 24, 48, or 72 hours. Representative flow cytometry plots from three independent experiments (\u003cstrong\u003eD\u003c/strong\u003e); quantification of normalized gMFI across three independent experiments (normalized to unstained controls) (\u003cstrong\u003eE\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eF-G\u003c/strong\u003e) Surface MHC-II (HLA-DR/DP/DQ) expression in WM115 (\u003cstrong\u003eF\u003c/strong\u003e) and SKMEL2 (\u003cstrong\u003eG\u003c/strong\u003e) cells at multiple timepoints post drug withdrawal, following treatment as in (\u003cstrong\u003eD-E\u003c/strong\u003e). gMFI values across three independent experiments are normalized to unstained controls.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eH-I\u003c/strong\u003e) Surface MHC-II (HLA-DR/DP/DQ) expression in AHR or ARNT KO WM115 (\u003cstrong\u003eH\u003c/strong\u003e) and SKMEL2 (\u003cstrong\u003eI\u003c/strong\u003e) after 72-hour treatment with 1 μM FICZ or DMSO control. gMFI values across three independent experiments are normalized to unstained controls.\u003c/p\u003e\n\u003cp\u003eData are represented as mean ± SD (\u003cstrong\u003eC and E-I\u003c/strong\u003e). Statistical analysis by one-way ANOVA (\u003cstrong\u003eC and E\u003c/strong\u003e); *p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001, ****p \u0026lt; 0.0001.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7796457/v1/31f89a9362b4392057c4ffd0.png"},{"id":94017017,"identity":"8a95d379-ed16-4890-b292-deccc4a2adac","added_by":"auto","created_at":"2025-10-21 11:25:37","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":586916,"visible":true,"origin":"","legend":"\u003cp\u003e(\u003cstrong\u003eK\u003c/strong\u003e) Heatmap of differentially expressed genes in WM115 cells with FICZ versus DMSO control, with MHC-II-related genes annotated.\u003cbr\u003e\n\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAHR\u003c/strong\u003e\u003cem\u003e\u003cstrong\u003e \u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003esignaling modulates MHC-II expression via transcriptional regulation of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eCIITA\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eA-B\u003c/strong\u003e) Scatterplots showing differentially expressed genes in A375 cells with CRSIPR-mediated knockouts of \u003cem\u003eAHR\u003c/em\u003e (\u003cstrong\u003eA\u003c/strong\u003e) or \u003cem\u003eARNT\u003c/em\u003e (\u003cstrong\u003eB\u003c/strong\u003e) compared to non-targeting control cells. Significantly downregulated (blue) and upregulated (red) genes are defined by adjusted p \u0026lt; 0.05 and |log\u003csub\u003e2\u003c/sub\u003e fold change| \u0026gt; 1.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eC\u003c/strong\u003e) Correlation analysis of gene expression changes between AHR KO and ARNT KO groups. Each point represents a gene; commonly upregulated (red) and downregulated (blue) genes are highlighted. PCC, Pearson correlation coefficient. Statistical analysis by t-test.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eD-E\u003c/strong\u003e) Venn diagrams showing the overlap of significantly downregulated (\u003cstrong\u003eD\u003c/strong\u003e) and upregulated genes (\u003cstrong\u003eE\u003c/strong\u003e) between \u003cem\u003eAHR\u003c/em\u003e KO and \u003cem\u003eARNT\u003c/em\u003e KO groups.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eF-G\u003c/strong\u003e) KEGG pathway enrichment analysis of common significantly downregulated genes (\u003cstrong\u003eF\u003c/strong\u003e) and upregulated genes (\u003cstrong\u003eG\u003c/strong\u003e) in \u003cem\u003eAHR\u003c/em\u003e KO and \u003cem\u003eARNT\u003c/em\u003e KO groups. Statistical analysis by the hypergeometric test.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eH\u003c/strong\u003e) Heatmap showing the common significantly downregulated and upregulated genes in \u003cem\u003eAHR\u003c/em\u003e KO, \u003cem\u003eARNT\u003c/em\u003e KO, and non-targeting control samples of A375, with MHC-II genes and cellular stress and signaling response genes indicated.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eI\u003c/strong\u003e) Scatterplot of significantly upregulated (red) and downregulated (blue) genes based on mean log\u003csub\u003e2\u003c/sub\u003e fold change and adjusted p-value of gene counts in the group treated with 1 μM FICZ for 72 h compared to the 0.1% DMSO control group of WM115.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eJ\u003c/strong\u003e) KEGG analysis of significantly upregulated genes in the FICZ-treated group compared to the DMSO control group of WM115. Statistical analysis by the hypergeometric test.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7796457/v1/cb88d953b6ea6a60934ec963.png"},{"id":94017018,"identity":"11a9e8c1-8612-4b74-ba42-a254ef92325f","added_by":"auto","created_at":"2025-10-21 11:25:37","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":280729,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAHR‒ARNT complex binds \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eCIITA\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e promoter II to drive MHC-II transcription.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eA-B\u003c/strong\u003e) Genome browser tracks showing gene \u003cem\u003eCIITA\u003c/em\u003e mRNA expression (RNA-Seq) across the \u003cem\u003eCIITA\u003c/em\u003elocus in A375 with sgNTC, sg\u003cem\u003eAHR\u003c/em\u003e, or sg\u003cem\u003eARNT\u003c/em\u003e (\u003cstrong\u003eA\u003c/strong\u003e) and in WM115 cells treated with 1 μM FICZ or 0.1% DMSO for 72 h (\u003cstrong\u003eB\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eC-D\u003c/strong\u003e) ATAC-seq tracks showing chromatin accessibility at the \u003cem\u003eHLA-DRA\u003c/em\u003e (\u003cstrong\u003eC\u003c/strong\u003e) and \u003cem\u003eCIITA\u003c/em\u003e(\u003cstrong\u003eD\u003c/strong\u003e) loci in A375 cells with sgNTC, sg\u003cem\u003eAHR\u003c/em\u003e, and sg\u003cem\u003eARNT\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eE-F\u003c/strong\u003e) ChIP-seq tracks showing AHR or ARNT binding across the \u003cem\u003eCYP1A1\u003c/em\u003e (\u003cstrong\u003eE\u003c/strong\u003e) and \u003cem\u003eCIITA\u003c/em\u003e(\u003cstrong\u003eF\u003c/strong\u003e) loci in SKMEL2 cells expressing empty vector control, AHR overexpression, or ARNT overexpression constructs. vec, empty vector control.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eG\u003c/strong\u003e) Enrichment analysis of transcription factor motifs in AHR- and ARNT-binding regions identified by ChIP-seq. Top-enriched motifs include canonical AHR response elements.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eH\u003c/strong\u003e) ChIP-qPCR quantification of AHR or ARNT occupancy at the CIITA promoter II (pII) region in SKMEL2 cells expressing empty vector control, AHR overexpression, or ARNT overexpression constructs. Enrichment is shown relative to input and normalized to empty vector control. vec, empty vector control.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eI\u003c/strong\u003e) Luciferase reporter assay using a construct containing the \u003cem\u003eCIITAs\u003c/em\u003e pII co-transfected with Renilla luciferase control into 293T cells expressing empty vector control, AHR overexpression, or ARNT overexpression constructs. vec, empty vector control.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eJ\u003c/strong\u003e) Schematic model illustrating transcriptional activation of \u003cem\u003eCIITA\u003c/em\u003e via pII by ligand-activated AHR–ARNT complex.\u003c/p\u003e\n\u003cp\u003eData are represented as mean ± SD (\u003cstrong\u003eH-I\u003c/strong\u003e). Each dot represents one technical replicate from triplicate wells (\u003cstrong\u003eH-I\u003c/strong\u003e). Statistical analysis by one-way ANOVA (\u003cstrong\u003eH-I\u003c/strong\u003e); *p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001, ****p \u0026lt; 0.0001.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7796457/v1/b50fb4df60de31a124ec487f.png"},{"id":94017020,"identity":"0263e5d0-cf43-4211-87a4-9eba2c04a2d8","added_by":"auto","created_at":"2025-10-21 11:25:37","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":265392,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eClinical relevance of targeting the AHR pathway to improve patient prognosis.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eA\u003c/strong\u003e) Heatmap showing Spearman correlation coefficients between infiltrating immune cell levels and \u003cem\u003eAHR\u003c/em\u003e‒\u003cem\u003eARNT\u003c/em\u003e-KO signature scores across cancer types in TCGA cohorts.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eB\u003c/strong\u003e) Heatmap of Spearman correlations between Tumor Immune Dysfunction and Exclusion (TIDE) scores and \u003cem\u003eAHR\u003c/em\u003e‒\u003cem\u003eARNT\u003c/em\u003e-KO signature scores across TCGA cohorts.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eC\u003c/strong\u003e) Distribution of \u003cem\u003eAHR\u003c/em\u003e‒\u003cem\u003eARNT\u003c/em\u003e-KO signature scores predicted by TIDE for the non-responders (SKCM, n=381; LUAD, n=378) and responders (SKCM, n=92; LUAD, n=198) in the TCGA melanoma (SKCM) and lung adenocarcinoma (LUAD) cohorts.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eD\u003c/strong\u003e) Heatmap showing associations between overall survival and \u003cem\u003eAHR\u003c/em\u003e‒\u003cem\u003eARNT\u003c/em\u003e-KO signature levels in TCGA cohorts. Cox proportional hazards model were used to compute z-scores and p-values; the MHC-II\u003csup\u003ehigh\u003c/sup\u003e/\u003cem\u003eAHR\u003c/em\u003e‒\u003cem\u003eARNT\u003c/em\u003e-KO signature\u003csup\u003elow\u003c/sup\u003e group is used as reference.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eE\u003c/strong\u003e) Kaplan-Meier survival analysis of SKCM and LUAD patients from TCGA, stratified by MHC-II expression and \u003cem\u003eAHR\u003c/em\u003e‒\u003cem\u003eARNT\u003c/em\u003e-KO signature scores.\u003c/p\u003e\n\u003cp\u003eStatistical analysis by unpaired Student’s t-test (\u003cstrong\u003ec\u003c/strong\u003e); *p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001, ****p \u0026lt; 0.0001.\u003cbr\u003e\n\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7796457/v1/192f0a315402f4885bda9b19.png"},{"id":103251149,"identity":"8d3bb208-c936-4a9b-879d-3756da091b88","added_by":"auto","created_at":"2026-02-23 16:05:17","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3517037,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7796457/v1/5cc1e3df-8f2d-436c-88b2-e28a92d846d9.pdf"},{"id":94017015,"identity":"d9c42bae-e387-4263-983b-11b18cd209a1","added_by":"auto","created_at":"2025-10-21 11:25:36","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":755712,"visible":true,"origin":"","legend":"","description":"","filename":"SUPPLEMENTARYFIGURESANDFIGURELEGENDS.docx","url":"https://assets-eu.researchsquare.com/files/rs-7796457/v1/f81f71208598ae0d2b3051c0.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"AHR Activation Drives Cancer Cell-Intrinsic MHC-II expression in Human Melanoma","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eCancer immunotherapy has significantly reshaped the treatment paradigm for various malignancies by harnessing the immune system\u0026rsquo;s inherent capacity to recognize and eliminate tumor cells. Among these approaches, immune checkpoint blockade (ICB) has demonstrated the potential for durable responses and prolonged survival in patients.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e Traditionally, major histocompatibility complex class I (MHC-I) molecules have long been the primary focus, due to their essential role in presenting intracellular antigens to cytotoxic CD8\u0026thinsp;+\u0026thinsp;T cells, enabling targeted immune surveillance and elimination of malignant cells.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e Growing evidence highlights a critical yet often overlooked role for MHC class II (MHC-II) molecules in orchestrating effective anti-tumor immunity.\u003c/p\u003e\u003cp\u003eMHC-II molecules present exogenous or processed endogenous antigens to CD4\u0026thinsp;+\u0026thinsp;helper T cells, which play a central role in coordinating adaptive immune responses, including the priming of CD8\u0026thinsp;+\u0026thinsp;cytotoxic T cells, activation of B cell-mediated antibody production, and maintenance of long-term immunological memory.\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e Under physiological conditions, MHC-II expression is largely confined to professional antigen-presenting cells (pAPCs), such as dendritic cells, macrophages, and B cells. Nevertheless, accumulating evidence indicates that MHC-II can also be expressed by non-hematopoietic cells, including various epithelial and cancer cell types.\u003csup\u003e\u003cspan additionalcitationids=\"CR4 CR5\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e Within the tumor microenvironment (TME), cancer cell-intrinsic MHC-II expression can be induced by cytokines such as IFN-γ.\u003csup\u003e3,7\u003c/sup\u003e Notably, recent large-scale transcriptomic analyses, including data from the Cancer Cell Line Encyclopedia (CCLE)\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e and an independent study of 675 cancer cell lines,\u003csup\u003e9\u003c/sup\u003e have revealed constitutive MHC-II expression in multiple human solid tumor cell lines, particularly in melanomas and subsets of lung cancers, even in the absence of external stimulation.\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e While some normal non-hematopoietic tissues, such as skin, breast, lung, and kidney tissues, also express MHC-II under specific conditions,\u003csup\u003e11\u003c/sup\u003e the underlying mechanisms underlying constitutive MHC-II expression in human non-hematopoietic cancer cells remain poorly understood. Intriguingly, this phenomenon appears to be species-specific: whereas numerous human tumor cell lines robustly express MHC-II, most murine non-hematopoietic cancer models, including commonly used lines such as B16F10 melanoma and MC38 colon carcinoma, exhibit minimal to no endogenous MHC-II expression.\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eThe expression of MHC-II genes is primarily regulated at the transcriptional level by the MHC class II transactivator (\u003cem\u003eCIITA\u003c/em\u003e), a non-DNA-binding coactivator that integrates upstream signaling cues to drive MHC-II gene expression. CIITA transcription is governed by multiple distinct promoters, pI, pIII, and pIV, which are preferentially utilized in dendritic cells, B cells, and IFN-γ\u0026ndash;stimulated cells, respectively, and are conserved across species.\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e In contrast, promoter II (pII) appears to be unique to human cells and has been detected in a limited number of cell types, with its physiological relevance and regulatory function remaining incompletely defined.\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e The use of specific CIITA promoters is both cell-type-specific and context-dependent, contributing to the heterogeneity of MHC-II expression across immune and non-immune cell populations. Notably, in human melanoma cells, aberrant CIITA transcription can arise from non-canonical promoters such as pIII and pIV, which may account for the observed constitutive MHC-II expression even in the absence of inflammatory stimuli such as IFN-γ.\u003csup\u003e15,16\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eCancer cell-intrinsic MHC-II expression has been associated with increased immune cell infiltration, enhanced responses to immune checkpoint blockade (ICB), and improved clinical outcomes.\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e In murine models of non-small cell lung cancer (NSCLC), cancer cell-intrinsic MHC-II expression correlates with higher infiltration and activation of both CD4\u0026thinsp;+\u0026thinsp;and CD8\u0026thinsp;+\u0026thinsp;T cells, elevated cytokine production, and enhanced sensitivity to anti-PD-1 therapy.\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e Similarly, in lung adenocarcinoma patients, higher MHC-II expression is linked to increased overall infiltration of CD4\u0026thinsp;+\u0026thinsp;and CD8\u0026thinsp;+\u0026thinsp;T cells, as well as closer spatial proximity between immune and cancer cells.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e In melanoma, cancer cell-intrinsic MHC-II serves as a predictive biomarker for clinical responses to anti-PD-1 and anti-PD-L1 therapies.\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e Comparable associations have also been reported in breast cancers, particularly in triple-negative breast cancer (TNBC) and HER2-negative subtypes, where tumor MHC-II expression correlates with lymphocyte infiltration and therapeutic benefit from anti-PD-1 blockade.\u003csup\u003e\u003cspan additionalcitationids=\"CR20 CR21\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e Collectively, these findings suggest that tumor-intrinsic MHC-II not only reflects an immunologically active tumor microenvironment but may also serve as a functional mediator of therapeutic response.\u003c/p\u003e\u003cp\u003eDespite the strong correlations between cancer cell-intrinsic MHC-II expression and favorable immunological and clinical outcomes, the mechanisms that regulate MHC-II expression in cancer cells remain largely unexplored. Elucidating these regulatory pathways is essential for developing therapeutic strategies that harness the antigen-presenting capacity of tumor cells to enhance antitumor immunity. While prior studies have identified both transcriptional and post-translational mechanisms by which cancer cells suppress MHC-II expression and evade immune detection,\u003csup\u003e23\u0026ndash;26\u003c/sup\u003e the intrinsic molecular pathways that actively promote or sustain MHC-II expression in tumors are largely unknown.\u003c/p\u003e\u003cp\u003eThe aryl hydrocarbon receptor (AHR) is a ligand-activated transcription factor that, in the absence of stimulation, resides in the cytoplasm in complex with chaperone proteins.\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e Upon ligand binding, AHR undergoes conformational changes, translocates into the nucleus, and heterodimerizes with the aryl hydrocarbon receptor nuclear translocator (ARNT). The resulting AHR\u0026ndash;ARNT complex binds to specific DNA sequences known as AHR response elements (AHREs), thereby regulating the transcription of a broad range of target genes. One of the well-characterized roles of AHR is in mediating responses to environmental toxins, including dioxins and polycyclic aromatic hydrocarbons, through the induction of detoxifying enzymes such as members of the cytochrome P450 family.\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e Beyond xenobiotic metabolism, AHR has emerged as a multifaceted regulator of immune function, influencing T cell differentiation, epithelial homeostasis, and cellular proliferation and development.\u003csup\u003e\u003cspan additionalcitationids=\"CR30\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e Despite these diverse roles, the potential involvement of the AHR\u0026ndash;ARNT complex in regulating MHC-II expression remains unknown.\u003c/p\u003e\u003cp\u003eIn this study, we aimed to identify intrinsic regulators of MHC-II expression in human melanoma cells. Through genome-wide CRISPR-Cas9 screening combined with functional and mechanistic validation, we identified the AHR and its obligate dimerization partner ARNT as key positive regulators of cancer cell-intrinsic MHC-II expression. Genetic ablation of either \u003cem\u003eAHR\u003c/em\u003e or \u003cem\u003eARNT\u003c/em\u003e markedly reduced surface MHC-II levels, while ectopic expression of these factors enhanced MHC-II expression, independently of IFN-γ signaling. Reintroduction of \u003cem\u003eAHR\u003c/em\u003e or \u003cem\u003eARNT\u003c/em\u003e into their respective knockout cells rescued MHC-II expression, confirming their functional necessity. Integrated transcriptomic, chromatin accessibility, and chromatin immunoprecipitation analyses revealed that the AHR\u0026ndash;ARNT complex binds directly to the pII promoter of \u003cem\u003eCIITA\u003c/em\u003e to initiate its transcription, and concurrently enhances the transcriptional activity of the pIII and pIV promoters. Together, these findings uncover a previously unrecognized regulatory axis for enhancing tumor immunogenicity and improving the efficacy of cancer immunotherapy.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cp\u003e\u003cstrong\u003eCell lines\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA375 (RRID: CVCL_0132), WM115 (RRID: CVCL_0040), SKMEL2 (RRID: CVCL_0069), B16F10 (RRID: CVCL_0159), LLC (RRID: CVCL_4358), MC38 (RRID: CVCL_B288), and HEK293T (RRID: CVCL_0063) cells were cultured in DMEM supplemented with 10% fetal bovine serum, 100 \u0026mu;g/mL penicillin and 100 U/ml streptomycin at 37 \u0026deg;C in 5% CO\u003csub\u003e2\u003c/sub\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eViral packaging\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor viral production, 293T cells were plated in 15-cm culture dishes and transfected once they reached approximately 80% confluency, typically 12\u0026ndash;18 hours later. For lentiviral packaging, transfection mixtures were prepared containing 20 \u0026mu;g of either sgRNA or target plasmid, 13.5 \u0026mu;g of psPAX2 (RRID: Addgene_12260), 6.5 \u0026mu;g of pMD2.G (RRID: Addgene_12259), and 120 \u0026mu;L of PEI (Polysciences #24765-100) in Opti-MEM (Gibco, #11058021). Plasmid DNA and PEI were each pre-incubated in Opti-MEM for 5 minutes before being combined. The resulting transfection complexes were allowed to incubate at room temperature for 30 minutes before being added to the cells. Six hours after transfection, the medium was replaced with fresh growth medium. Viral supernatants were collected 48 hours later, passed through 0.45 \u0026mu;m filters, aliquoted into 1 mL portions, and stored at \u0026minus;80 \u0026deg;C for future use.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGenome-wide CRISPR screening for MHC-II\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA375-Cas9 and WM115-Cas9 were generated by transfection with lentivirus encoding Cas9-Blast (Addgene #52962) and selected with 6 \u0026mu;g/mL and 12 \u0026mu;g/mL blasticidin (InvivoGen #ant-bl-05) separately for 10 days. These two cell lines were independently transduced with Brunello lentivirus library (Addgene #73178) at an infection rate around 20%. sgRNA coverage was maintained throughout the experiments at \u0026gt; 500 copies of each sgRNA (~ 40 million cells for the 77,441 sgRNAs). After 48 hours of transfection, cells that had been transduced were selected using 0.7 \u0026mu;g/ml (A375) or 0.6 \u0026mu;g/ml (WM115) of puromycin (InvivoGen #ant-pr-1) for 3 days. 10 days after viral transduction, the selected WM115 cells were treated with 100 ng/mL IFN-\u0026gamma; (Novoprotein #C014). After 13 days of transduction, the cells were collected and divided into an experimental group and a control group, each with three replicates. Each replicate contained ~ 56 million cells (~ 700\u0026times; in sgRNA coverage). The experimental groups were stained with the FITC-conjugated anti-HLA-DR antibody LN3 (Biolegend #327006), incubated on ice and protected from light for 20 min, washed with PBS, and resuspended with 2 mL PBS plus 5% fetal bovine serum prior to sorting for the lowest and highest ~ 10% populations, yielding ~ 2\u0026ndash;4 million cells for each final population. The genomic DNA of the sorted samples and the control group was extracted using the NucleoSpin Blood XL kit (MACHEREY-NAGEL #740950.50), following the manufacturer\u0026rsquo;s instructions. Amplification of the sgRNA cassettes by PCR was performed according to the broad GPP protocol (https://portals.broadinstitute.org/gpp/public/resources/protocols).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData analysis for CRISPR screens\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMaGeCK (Model-based Analysis of Genome-wide CRISPR-Cas9 Knockout) was employed to process and analyze the CRISPR screen data.\u003csup\u003e32\u003c/sup\u003e FASTQ reads from the CRISPR screen trimmed and mapped to the corresponding library using MAGeCK \u0026ldquo;count\u0026rdquo; function to quantify sgRNA read counts. The MAGeCK \u0026ldquo;test\u0026rdquo; module was then used to calculate log2 fold changes and p-values of both the sgRNAs and genes. Custom R (v4.4.1) scripts were used to visualize the data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGeneration of KO cell lines\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe sgRNA sequences used to generate KO cell lines were listed in Table S1. Each sgRNA was cloned into lentiGuide-Puro backbone (Addgene #52963) , with successful sgRNA insertion confirmed by Sanger sequencing. Lentiviral particles were produced as described above. The virus was used to infect the A375, WM115 and SKMEL2 cells. Following 72-hour infection, puromycin (0.7 \u0026mu;g/mL for A375, 0.6 \u0026mu;g/mL for WM115, and 0.5 \u0026mu;g/mL for SKMEL2) was added to the culture for selection of stable KO cell lines.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGeneration of overexpression cell lines\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA375 cells were lysed using TRIzol (Invitrogen, # 15596026), and cDNA was synthesized from total mRNA with Evo M-MLV Plus 1st Strand cDNA Synthesis Kit (AGBio #AG11615). The CDS sequences of \u003cem\u003eAHR\u003c/em\u003e and \u003cem\u003eARNT\u003c/em\u003e were then amplified using corresponding primers and cloned into pHAGE vectors with RFP reporter. Lentiviral particles were produced as described above. The virus was used to infect the A375, WM115 and SKMEL2 cells. Following 5 days of viral transduction, the transduced cells were FACS sorted according to RFP signaling.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eIn vitro\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;AHR agonist and antagonist treatment experiments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTumor cells (0.1 million) were plated in 12-well plates per well and incubated for 24 h, 48 h, or 72 h with complete medium containing 0.1% DMSO, 1 \u0026mu;M FICZ, 2.5 \u0026mu;M GNF351, or 100 ng/mL IFN-\u0026gamma;. Cells were stained with APC-conjugated anti-HLA-DR/DP/DQ antibody T\u0026uuml;39 (Biolegend #361714) in FACS buffer (PBS supplemented with 5% FBS), incubated on ice and protected from light for 20 min, washed with PBS buffer and then were stained with DAPI in PBS to distinguish live and dead cells and analyzed by Beckman CytoFLEX S.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWestern blot\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWhole-cell lysates were solubilized in cell lysis buffer (Beyotime #P0013). Protein concentrations were determined using the BCA Protein Assay Kit (Solarbio #PC0020), and 20 \u0026mu;g of total protein was loaded per lane onto SDS-PAGE gels. Proteins were transferred to Immobilon PVDF membranes (Millipore). Membranes were blocked in TBST containing 5% non-fat milk for 1 h at room temperature, then incubated overnight at 4 \u0026deg;C with primary antibodies diluted 1:1000 in primary antibody dilution buffer (Solarbio, #A1810). The following primary antibodies were used: AHR (clone D5S6H) Rabbit mAb (CST #83200, RRID: AB_2800011), ARNT (clone D28F3) Rabbit mAb (CST #5537, RRID: AB_10694232), HLA-DRA Rabbit mAb (Boster #A01195), \u0026beta;-Tubulin (clone C66) Mouse mAb (Abmart #M20005, RRID: AB_2920648) and \u0026beta;-Actin (clone 13E5) Rabbit mAb (CST #4970, RRID: AB_2223172). After washing, membranes were incubated with HRP-conjugated secondary antibody anti-rabbit IgG (CST #7074, RRID: AB_2099233, 1:10000 dilution) or anti-mouse IgG (CST #7076, RRID: AB_330924, 1:10000 dilution) for 1 h at room temperature. Blots were visualized using M5 HiPer ECL Western HRP Substrate (Mei5 Biotechnology #MF074-01), and chemiluminescence signals were captured using a ChemiDoc\u0026trade; Imaging System (Bio-Rad Laboratories).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFlow Cytometry\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAdherent cells were dissociated into single-cell suspensions and stained with appropriate fluorochrome-conjugated antibodies. Flow cytometric analysis was performed on Beckman CytoFLEX S flow cytometer, and cell sorting was conducted using BD Aria Fusion cell sorter. Data were analyzed with FlowJo software (BD Biosciences, RRID: SCR_008520). All flow cytometry antibodies were purchased from BioLegend, including: PE anti-human CD274 (PD-L1, clone 29E.2A3) Antibody (Biolegend #329706, RRID: AB_940368), FITC anti-human HLA-DR (clone LN3) Antibody (Biolegend #327005, RRID: AB_893577), APC anti-human HLA-DR, DP, DQ (clone T\u0026uuml;39) Antibody (Biolegend #361714, RRID: AB_2750316), PE anti-human HLA-A,B,C (clone W6/32) Antibody (Biolegend #311406, RRID: AB_314875), FITC anti-mouse I-A/I-E (clone M5/114.15.2) Antibody (Biolegend #107605, RRID:AB_313320), PE Mouse IgG2b, \u0026kappa; Isotype Ctrl (clone MPC-11) Antibody (Biolegend #400311, RRID: AB_2894969), and FITC Mouse IgG2b, \u0026kappa; Isotype Ctrl (clone 27-35) Antibody (Biolegend #402207, RRID: AB_3097051).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRNA-seq\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eControl (non-targeting control gRNA), \u003cem\u003eAHR\u003c/em\u003e KO and \u003cem\u003eARNT\u003c/em\u003e KO A375 cells, and DMSO and FICZ treated WM115 cells were cultured in 6-well plates in triplicate. A minimum of 1\u0026times;10\u003csup\u003e6\u003c/sup\u003e cells per sample were collected across all groups. The cells were washed with PBS and lysed using TRIzol (Invitrogen #15596026). Using 1 \u0026mu;g of total RNA, RNA libraries for RNA-seq were prepared using VAHTS Universal V6 RNA-Seq Library Prep Kit for Illumina (Vazyme #NR604-01/02) according to manufacturer\u0026apos;s protocols followed by Illumina sequencing. The reads were aligned to the human reference genome hg38 using STAR (RRID: SCR_004463). Feature count was used to map aligned reads to genes and generate a gene count matrix.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eATAC-seq\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eControl (non-targeting control gRNA), \u003cem\u003eAHR\u003c/em\u003e KO and \u003cem\u003eARNT\u003c/em\u003e KO A375 cells were cultured in 6-well plates in triplicate. For each sample, 1 million cells were collected, washed with PBS, resuspended with 20 \u0026mu;L PBS, and lysed with lysis buffer (10 mM Tris-HCl pH 7.4, 10 mM NaCl, 3 mM MgCl\u003csub\u003e2\u003c/sub\u003e, 0.5% NP-40). The ATAC library for each sample was then prepared using TruePrep DNA Library Prep Kit V2 for Illumina (Vazyme #TD501) with TruePrep Index Kit V2 for Illumina (Vazyme #TD202) according to manufacturer\u0026apos;s protocols followed by Illumina sequencing. The FASTQ reads were trimmed and aligned to the hg38 reference genome using Bowtie 2 (RRID: SCR_016368), followed by proper filtering using SAMtools (RRID: SCR_002105) pipeline. BigWig coverage tracks were generated using deepTools (RRID: SCR_016366) bamCoverage (v3.5.3) with 10 bp bin size and RPGC normalization (effective genome size: 2,862,010,428). chrX and chrM were excluded from normalization, and reads were extended to the estimated fragment size.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eChIP-seq\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSKMEL2 cells stably expressing 3\u0026times;HA-RFP, 3\u0026times;HA-AHR-RFP and 3\u0026times;HA-ARNT-RFP were cultured in 15 cm dishes separately. For each group, a total of 4\u0026times;10⁷ cells were harvested and resuspended in 2 mL serum-free DMEM. For cross-linking, 1% formaldehyde solution (prepared freshly) was added, and cells were incubated at room temperature (RT) for 8 min. Cross-linking was quenched by adding 0.125 M glycine and incubating at RT for 5 min. Cells were then washed once with ice cold PBS. After fixation, pellets were flash frozen and stored at -80 \u0026deg;C or processed immediately for sonication.\u003c/p\u003e\n\u003cp\u003eEach pellet was lysed in lysis buffer (0.1% SDS, 1% Triton X-100, 0.1% Na-Deoxycholate, 0.25% sarcosyl, 50 mM HEPES-KOH pH 7.5, 1 mM EDTA, 140 mM NaCl, 1\u0026times; protease inhibitor cocktail). Samples were incubated on ice for 10\u0026ndash;15 min, then aliquoted into thin-walled 0.5 mL PCR tubes. Sonication was performed using a Qsonica Q800R (50% amplitude, 30 s on / 30 s off, for 20 cycles) to achieve a mean DNA fragment size of 300 bp. Post-sonication, samples were centrifuged at 12,000\u0026times;g for 5 min at 4 \u0026deg;C, and the supernatant was collected and kept on ice or stored at \u0026minus;80 \u0026deg;C. Samples were diluted 1:10 in dilution buffer (1% Triton X-100, 140 mM NaCl, 50 mM HEPES-KOH pH 7.5 and 1 mM EDTA) supplemented with 5 M NaCl to reach 150 mM NaCl, and incubated rotating with antibody and Protein A/G magnetic beads (Invitrogen), to bind the HA Tag (clone 1F5C6) Monoclonal antibody (Proteintech #66006-2-Ig, RRID: AB_2881490) and associated chromatin, for 3 h at 4 \u0026deg;C.\u003c/p\u003e\n\u003cp\u003eAfter washing seven times with high salt buffer (0.1% SDS,1%Triton X-100, 20 mM Tris pH 7.9, 2 mM EDTA, 500 mM NaCl) and twice with TE buffer (10 mM Tris, 1 mM EDTA, pH 8.0), samples were eluted from the beads for 15 min at 65 \u0026deg;C in elution buffer (1% SDS, 200 mM NaCl). Eluates were treated with RNase A at 37 \u0026deg;C for 30 min and reverse cross-linked overnight by heating at 65 \u0026deg;C with Proteinase K. Samples were extracted with an equal volume of phenol:chloroform, vortexed, centrifuged at 12,000\u0026times;g for 10 min. The aqueous phase (~ 400 \u0026mu;L) was transferred to a new tube and mixed with 1.6 \u0026mu;L GlycoBlue (250\u0026times;), 40 \u0026mu;L 3 M sodium acetate pH 5.2, and 800 \u0026mu;L ice-cold ethanol. DNA was precipitated at \u0026minus;20 \u0026deg;C overnight. Samples were centrifuged at 12,000\u0026times;g for 15 min at 4 \u0026deg;C, washed twice with 75% ethanol, and resuspended in 20 \u0026mu;L nuclease-free water. The ChIP library for each sample was then prepared using VAHTS Universal Pro DNA Library Prep Kit for lllumina (Vazyme #ND608) with VAHTS Multiplex Oligos Set 4 for Illumina (Vazyme #N321) and VAHTS DNA Clean Beads (Vazyme #N411) according to manufacturer\u0026apos;s protocols followed by Illumina sequencing. The FASTQ reads were trimmed and aligned to the hg38 reference genome using Bowtie 2 (RRID: SCR_016368), followed by proper filtering using SAMtools (RRID: SCR_002105) pipeline. BigWig coverage tracks were generated using deepTools (RRID: SCR_016366) bamCoverage (v3.5.3) with 10 bp bin size and RPGC normalization (effective genome size: 2,862,010,428). chrX and chrM were excluded from normalization, and reads were extended to the estimated fragment size. MACS2 (RRID: SCR_013291) was used to call peaks and MEME-ChIP (SCR_001783) was used for motif enrichment analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eChIP-qPCR\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eChIP-qPCR analysis was conducted using ChIP DNA to detect whether AHR and ARNT protein binds to the AHRE region of the promoter pII of the \u003cem\u003eCIITA\u003c/em\u003e gene. qPCR amplification was performed in technical triplicates using iTaq Universal SYBR\u003csup\u003e\u0026reg;\u003c/sup\u003e Green Supermix (BIO-RAD #1725121) on a Bio-Rad CFX96 Touch Real-Time PCR Detection System. Fold enrichment between the control and overexpression groups was calculated. The primer sequences used in ChIP-qPCR were listed in Table S2.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDual luciferase assay\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe pGL4.10-pII-luc2 plasmid, in which the transcription of firefly luciferase was driven by the promoter pII of the \u003cem\u003eCIITA\u003c/em\u003e gene, was constructed. This plasmid was co-transfected into 293T cells with the pGL4.74-TK-hRluc plasmid and one of the following: the AHR-overexpression plasmid, the ARNT-overexpression plasmid, the control plasmid or H\u003csub\u003e2\u003c/sub\u003eO, for the dual-luciferase reporter assay.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRT-qPCR\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA375, WM115 and SKMEL2 cells pre-treated with 0.1% DMSO, 1 \u0026mu;M FICZ, 2.5 \u0026mu;M GNF351, or 100 ng/mL IFN-\u0026gamma; for 72 h, were lysed and total RNA was extracted using RNAsimple Total RNA kit (TIANGEN, Cat# DP419) according to the manufacturer\u0026rsquo;s instructions. Subsequent reverse transcription was performed with 4 \u0026mu;g of total RNA as template using Evo M-MLV Plus 1st Strand cDNA Synthesis Kit (AGBio #AG11615). qPCR amplification was performed in technical triplicates using iTaq Universal SYBR\u003csup\u003e\u0026reg;\u003c/sup\u003e Green Supermix (BIO-RAD #1725121) on a Bio-Rad CFX96 Touch Real-Time PCR Detection System. \u003cem\u003eCIITA\u003c/em\u003e products were amplified using the following primers specific for, respectively, CIITA-pI, CIITA-pII, CIITA-pIII, CIITA-pIV and CIITA-exon 2 (reverse) (See Table S3). As a positive control we amplified \u003cem\u003eCYP1A1\u003c/em\u003e products of all samples. The relative expression levels of target genes were normalized to the endogenous reference gene \u003cem\u003eGAPDH\u003c/em\u003e using the comparative Ct (\u0026Delta;\u0026Delta;Ct) method, and log2 fold changes between the drug-treated and DMSO control groups were calculated.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnalysis of TCGA cohorts\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTranscriptome data and clinical data were obtained from the TCGA Data Portal (https://www.cancer.gov/tcga). We chose all 33 cancer types with transcriptome data available for cancer samples. Only those samples in the clinical category of \u0026ldquo;primary tumor\u0026rdquo; and \u0026ldquo;metastatic\u0026rdquo; were used for this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStatistical analyses were performed via GraphPad Prism 9 software (RRID: SCR_002798), applying unpaired Student\u0026rsquo;s t-test, one-way ANOVA or two-way ANOVA test were used as indicated (*p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001, ****p \u0026lt; 0.0001). Group sizes for in vitro experiments were set based on prior experience of the experimental variability.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDifferential gene expression (DEG) analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor bulk RNA-seq data, the R package DESeq2 (v1.34.0) was used to identify differentially expressed genes between \u003cem\u003eAHR\u003c/em\u003e or \u003cem\u003eARNT\u003c/em\u003e KO versus non-targeting control A375 cells, and FICZ treated versus DMSO control WM115 cells. Genes with Benjamini\u0026ndash;Hochberg-adjusted p value \u0026lt; 0.0001 (for A375 cells) or 0.01 (for WM115 cells) and the absolute log\u003csub\u003e2\u003c/sub\u003e(fold change) \u0026gt; 0.5 were considered differentially expressed genes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eKyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKEGG pathway enrichment analysis was conducted on differentially regulated genes using the enrichKEGG function of the R package clusterProfiler (v4.12.6).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAHR\u0026ndash;ARNT\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e-KO signature\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo study the clinical relevance of AHR‒ARNT, we established an \u003cem\u003eAHR‒ARNT\u003c/em\u003e-KO signature by extracting the 199 upregulated and 101 downregulated genes and taking the normalized DESeq2 Wald statistics as weights. The weights of genes are normalized to \u0026minus;1 to 1 range by the equation \u0026nbsp;, where \u0026nbsp; indicates the weight of the \u0026nbsp;-th gene, and \u0026nbsp; represents the mean Wald statistic of the \u0026nbsp;-th gene across the \u003cem\u003eAHR\u003c/em\u003e KO vs NTC and \u003cem\u003eARNT\u003c/em\u003e KO vs NTC comparisons obtained from DESeq2. For each input expression profile, we computed an \u003cem\u003eAHR‒ARNT\u003c/em\u003e-KO signature score to estimate the \u003cem\u003eAHR‒ARNT\u003c/em\u003e-KO level by calculating the weighted sum expression of the signature genes following the equation \u0026nbsp;, where \u0026nbsp; denotes the signature score, and \u0026nbsp; denotes the expression level of the \u0026nbsp;-th gene. Finally, we evaluated the association of \u003cem\u003eAHR‒ARNT\u003c/em\u003e deficiency with immune infiltration, response to ICB, and patient outcome.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMaterials availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCell lines generated in this study will be provided by the lead contact under a material transfer agreement.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData and code availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSequencing data has been deposited in the NCBI Gene Expression Omnibus (GEO): GSE305296. Transcriptome data and clinical data for TCGA cohorts were obtained from the TCGA Data Portal (https://portal.gdc.cancer.gov/). No custom programs were developed specifically for this manuscript.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003e\u003cb\u003eGenome-wide CRISPR screens reveal AHR and ARNT as regulators of cancer cell-intrinsic MHC-II expression.\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo systematically identify genes that regulate cancer cell-intrinsic MHC-II expression, we conducted genome-wide CRISPR-Cas9 loss-of-function screens in two patient-derived melanoma cell lines, A375 and WM115, both of which exhibit constitutive surface expression of MHC-II molecules (\u003cb\u003eSupplementary Fig.\u0026nbsp;1A\u003c/b\u003e). Cas9-expressing derivatives of these cell lines were transduced with the Brunello genome-wide gRNA library\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Given the intermediate basal level of HLA-DR surface expression in WM115 cells, we pre-treated them with IFN-γ for 72 hours prior to screening to enhance the dynamic range for detection. Using flow cytometry and an HLA-DR-specific antibody, we isolated the top and bottom 10% of cells based on surface MHC-II expression and subsequently quantified sgRNA abundance via deep sequencing (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe screen successfully recovered nearly all known regulators of MHC-II expression and antigen presentation. Genes encoding MHC-II structural components (e.g., \u003cem\u003eHLA-DRA\u003c/em\u003e, \u003cem\u003eHLA-DRB1\u003c/em\u003e), the invariant chain (\u003cem\u003eCD74\u003c/em\u003e), the MHC-II master regulator \u003cem\u003eCIITA\u003c/em\u003e, and the RFX family transcriptional co-activators (\u003cem\u003eRFXANK\u003c/em\u003e, \u003cem\u003eRFX5\u003c/em\u003e, \u003cem\u003eRFXAP\u003c/em\u003e) were all significantly enriched in the MHC-II\u003csup\u003elow\u003c/sup\u003e populations (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC-D \u003cb\u003eand Supplementary Fig.\u0026nbsp;1B-E\u003c/b\u003e). In the IFN-γ-treated WM115 screen, multiple components of the IFN-γ signaling pathway, including \u003cem\u003eIFNGR1\u003c/em\u003e, \u003cem\u003eIFNGR2\u003c/em\u003e, \u003cem\u003eJAK1\u003c/em\u003e, \u003cem\u003eJAK2\u003c/em\u003e, and \u003cem\u003eSTAT1\u003c/em\u003e, also showed strong enrichment in the MHC-II\u003csup\u003elow\u003c/sup\u003e population (\u003cb\u003eSupplementary Fig.\u0026nbsp;1B-C and 1E\u003c/b\u003e), further validating the screening accuracy.\u003c/p\u003e\u003cp\u003eIn addition to these established regulators, we identified several candidate genes not previously implicated in MHC-II expression. Among the top-ranked hits, \u003cem\u003eAHR\u003c/em\u003e was enriched as the only receptor, other than IFN-γ receptors, consistently enriched in the MHC-II\u003csup\u003elow\u003c/sup\u003e populations of both A375 and WM115 cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC-D \u003cb\u003eand Supplementary Fig.\u0026nbsp;1B-E\u003c/b\u003e). Notably, its dimerization partner \u003cem\u003eARNT\u003c/em\u003e was also among the top hits in the A375 screen. The co-enrichment of \u003cem\u003eAHR\u003c/em\u003e and \u003cem\u003eARNT\u003c/em\u003e strongly suggested a critical role for the AHR\u0026ndash;ARNT complex in promoting MHC-II expression in melanoma cells. Supporting this hypothesis, integrative analysis of the CCLE transcriptomic and proteomic datasets revealed significant positive correlations between \u003cem\u003eAHR\u003c/em\u003e expression and MHC-II expression across multiple cancer types, especially in skin cancers (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE-F \u003cb\u003eand Supplementary Fig.\u0026nbsp;1F-G\u003c/b\u003e).\u003c/p\u003e\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\u003ch2\u003eThe AHR\u0026ndash;ARNT complex is essential for intrinsic MHC-II expression in human melanoma cells\u003c/h2\u003e\u003cp\u003eTo validate the role of AHR and ARNT in regulating cancer cell-intrinsic MHC-II expression, we generated \u003cem\u003eAHR\u003c/em\u003e and \u003cem\u003eARNT\u003c/em\u003e knockout (KO) A375 cell lines using CRISPR-Cas9 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eG). Loss of either gene resulted in a marked repression in surface HLA-DR expression (\u003cb\u003eSupplementary Fig.\u0026nbsp;1H-I\u003c/b\u003e). Notably, this reduction was reversible, as treatment with IFN-γ restored HLA-DR expression in both KO lines (\u003cb\u003eSupplementary Fig.\u0026nbsp;1H-I\u003c/b\u003e), indicating that AHR and ARNT regulate MHC-II expression through an IFN-γ\u0026ndash;independent manner. Beyond HLA-DR, we observed a broad suppression of MHC-II molecules, including HLA-DP and HLA-DQ, in \u003cem\u003eAHR\u003c/em\u003e and or \u003cem\u003eARNT\u003c/em\u003e KO cells, and these were similarly rescued by IFN-γ treatment (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eH-I \u003cb\u003eand Supplementary Fig.\u0026nbsp;1J\u003c/b\u003e). By contrast, MHC-I surface expression (HLA-A/B/C) remained unchanged following \u003cem\u003eAHR\u003c/em\u003e or \u003cem\u003eARNT\u003c/em\u003e deletion (\u003cb\u003eSupplementary Fig.\u0026nbsp;1K\u003c/b\u003e), suggesting that the AHR\u0026ndash;ARNT complex selectively regulates MHC-II expression. These findings were further supported by experiments in WM115 cells, where AHR or ARNT deletion produced comparable downregulation of surface MHC-II expression (\u003cb\u003eSupplementary Fig.\u0026nbsp;1L-M\u003c/b\u003e), reinforcing the functional relevance of this regulatory axis.\u003c/p\u003e\u003cp\u003eTo test whether AHR and ARNT are not only necessary but also sufficient to drive MHC-II expression, we overexpressed each factor individually in three human melanoma cell lines, A375, WM115, and SKMEL2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Overexpression of either AHR or ARNT led to significant upregulation of MHC-II surface expression in all three cell lines (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB-C). Notably, this upregulation occurred independently of IFN-γ stimulation (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB-C), reinforcing that the AHR\u0026ndash;ARNT complex is capable of activating MHC-II expression independently of canonical inflammatory cues. To confirm the specificity of this regulatory effect, we reintroduced \u003cem\u003eAHR\u003c/em\u003e and \u003cem\u003eARNT\u003c/em\u003e cDNAs, engineered to include three nonconsecutive synonymous mutations in the corresponding sgRNAs targeting sequence and the adjacent PAM sequence, into their respective knockout A375 cells (\u003cb\u003eSupplementary Fig.\u0026nbsp;2A\u003c/b\u003e). Re-expression of AHR and ARNT fully rescued MHC-II expression to levels comparable to non-targeting control cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD-F), again in the absence of IFN-γ. These results confirm the functional requirement and sufficiency of the AHR\u0026ndash;ARNT complex in promoting cancer cell-intrinsic MHC-II expression.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eInterestingly, this regulatory mechanism appears to be species-specific. Unlike the human cell lines, the murine melanoma cell line B16F10, colon carcinoma cell line MC38, and lung carcinoma cell line LLC did not express MHC-II at baseline (\u003cb\u003eSupplementary Fig.\u0026nbsp;2B\u003c/b\u003e). Furthermore, overexpression of AHR and ARNT in these mouse lines failed to induce MHC-II expression (\u003cb\u003eSupplementary Fig.\u0026nbsp;2B\u003c/b\u003e). This discrepancy may reflect fundamental differences in the structure and downstream signaling of the AHR\u0026ndash;ARNT pathway between humans and mice\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e, including species-specific variation in ligand affinity, transcriptional targets, and chromatin context.\u003csup\u003e\u003cspan additionalcitationids=\"CR36 CR37 CR38 CR39\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e In addition, the pII promoter of CIITA is not conserved in mice,\u003csup\u003e13\u003c/sup\u003e providing a potential mechanistic explanation for the lack of functional conservation. Together, these findings establish AHR\u0026ndash;ARNT as a selective and critical regulator of MHC-II expression in human melanoma cells, with species-specific constraints that limit its activity in murine models.\u003c/p\u003e\u003cdiv id=\"Sec25\" class=\"Section3\"\u003e\u003ch2\u003eLigand-dependent AHR activation induces surface MHC-II expression in human melanoma cells\u003c/h2\u003e\u003cp\u003eTo further investigate the functional role of the AHR\u0026ndash;ARNT complex in regulating MHC-II expression, we treated human melanoma cells with the potent AHR agonist FICZ and the selective antagonist GNF351. In the absence of IFN-γ stimulation, FICZ treatment significantly increased surface MHC-II expression in WM115 and SKMEL2 cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). In contrast, A375 cells, characterized by high basal MHC-II expression, did not exhibit further upregulation upon FICZ treatment (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA), possibly due to saturation of the regulatory pathway. GNF351 treatment led to a marked decrease in MHC-II expression across all three human melanoma lines, including SKMEL2, which expresses relatively low basal levels of MHC-II (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). In line with prior genetic experiments, treatment with FICZ failed to induce MHC-II expression in B16F10, MC38 and LLC (\u003cb\u003eSupplementary Fig.\u0026nbsp;2C\u003c/b\u003e), underscoring the non-conservative nature of AHR\u0026ndash;ARNT-mediated MHC-II regulation in humans and mice.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eWe next examined whether the magnitude of AHR-mediated regulation was dependent on the duration of ligand exposure. Time-course analysis revealed a progressive increase or decrease in surface MHC-II expression levels following treatment with FICZ or GNF351, respectively, with greater changes observed after longer treatment (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB-C). These results indicate that cancer cell-intrinsic MHC-II expression could be modified through pharmacologic modulation of AHR activity, and that both the direction and magnitude of this regulation are time-dependent.\u003c/p\u003e\u003cp\u003eTo evaluate the persistence of these regulatory effects, we measured surface MHC-II levels following withdrawal of FICZ or GNF351. In A375 cells, GNF351 withdrawal was followed by a further decline in surface MHC-II levels after 72 hours (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD-E). Similar patterns were observed in WM115 and SKMEL2 cell lines (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF-G). We observed that six days post-withdrawal, MHC-II expression levels showed the most pronounced upregulation or downregulation in the FICZ-treated and GNF351-treated groups, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF-G). Thereafter, MHC-II levels gradually returned to baseline (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF-G). These findings suggest that AHR modulation elicits lasting, but reversible, effects on MHC-II levels. This observation is potentially due to delayed transcriptional feedback or intracellular persistence of ligand metabolites.\u003c/p\u003e\u003cp\u003eTo confirm that FICZ-induced MHC-II upregulation is AHR‒ARNT-dependent, we treated \u003cem\u003eAHR\u003c/em\u003e and \u003cem\u003eARNT\u003c/em\u003e KO cells with FICZ. While slight increases in surface MHC-II were observed in \u003cem\u003eAHR\u003c/em\u003e and \u003cem\u003eARNT\u003c/em\u003e KO cells, expression levels remained significantly lower than those in FICZ-treated non-targeting controls (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eH-I), supporting the requirement for intact AHR\u0026ndash;ARNT signaling. The modest residual effect may be attributable to incomplete knockout efficiency. Together, these results confirm that the AHR\u0026ndash;ARNT complex mediates ligand-responsive, reversible regulation of MHC-II expression in human melanoma cells.\u003c/p\u003e\u003cp\u003e\u003cb\u003eAHR\u0026ndash;ARNT regulates MHC-II expression through transcriptional control of\u003c/b\u003e \u003cb\u003eCIITA\u003c/b\u003e\u003c/p\u003e\u003cp\u003eGiven the ligand-responsiveness of MHC-II regulation by AHR\u0026ndash;ARNT complex, we next sought to investigate the underlying molecular mechanisms. We performed transcriptomic profiling (RNA-seq) of A375 cells following CRISPR-mediated KO of either \u003cem\u003eAHR\u003c/em\u003e or \u003cem\u003eARNT\u003c/em\u003e, and compared them to non-targeting controls. Differential gene expression analysis revealed broad transcriptional changes in both KO lines (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA-B), with substantial overlap between the \u003cem\u003eAHR\u003c/em\u003e and \u003cem\u003eARNT\u003c/em\u003e KO groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC-E). This high concordance between \u003cem\u003eAHR\u003c/em\u003e and \u003cem\u003eARNT\u003c/em\u003e KO profiles supports the cooperative function of this heterodimer in gene regulation. Notably, \u003cem\u003eCIITA\u003c/em\u003e was consistently downregulated due to \u003cem\u003eAHR\u003c/em\u003e- and \u003cem\u003eARNT\u003c/em\u003e-deficiency (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC), suggesting the transcriptional control of \u003cem\u003eCIITA\u003c/em\u003e by the AHR\u0026ndash;ARNT complex.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eGene set enrichment analysis revealed that commonly downregulated genes were significantly enriched for pathways related to antigen processing and presentation (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF). Gene sets related to cellular stress and signaling response, especially the AP-1 complex (\u003cem\u003eFOS\u003c/em\u003e, \u003cem\u003eFOSB\u003c/em\u003e, \u003cem\u003eJUN\u003c/em\u003e, \u003cem\u003eJUNB\u003c/em\u003e, \u003cem\u003eJUND\u003c/em\u003e), were significantly enriched among genes commonly upregulated in \u003cem\u003eAHR\u003c/em\u003e- and \u003cem\u003eARNT\u003c/em\u003e-deficient cells compared to control cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eG-H), suggesting that AHR and ARNT potentially suppressed the biological processes, such as cell growth, proliferation, and oncogenesis via AP-1 activation. To further determine whether AHR activation was sufficient to induce MHC-II-related gene expression, we treated WM115 cells with FICZ and performed RNA-Seq.\u0026nbsp;Consistent with our findings from the KO models, FICZ treatment upregulated multiple MHC-II genes and \u003cem\u003eCIITA\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eI and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eK). KEGG pathway analysis confirmed significant enrichment of the antigen processing and presentation pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eJ-K). Collectively, our findings demonstrate that the AHR\u0026ndash;ARNT complex regulates MHC-II expression through transcriptional control of \u003cem\u003eCIITA\u003c/em\u003e.\u003c/p\u003e\u003cp\u003e\u003cb\u003eThe AHR\u0026ndash;ARNT complex binds promoter II of\u003c/b\u003e \u003cb\u003eCIITA\u003c/b\u003e \u003cb\u003eto drive transcription of type II, III and IV isoforms\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo elucidate how the AHR\u0026ndash;ARNT complex regulates MHC-II expression at the transcriptional level, we analyzed \u003cem\u003eCIITA\u003c/em\u003e mRNA isoforms in A375 and WM115 cells using RNA-seq.\u0026nbsp;In A375 cells, the RNA-seq results showed that \u003cem\u003eCIITA\u003c/em\u003e transcripts originated predominantly from the type III 5\u0026rsquo; end (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA), consistent with prior reports describing abnormal usage of the B cell-specific promoter pIII in A375 melanoma cells\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Ablation of either \u003cem\u003eAHR\u003c/em\u003e or \u003cem\u003eARNT\u003c/em\u003e significantly reduced type III transcript abundance (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA), suggesting a potential regulatory interaction between AHR\u0026ndash;ARNT and pIII. In contrast, in WM115 cells treated with the AHR agonist FICZ, RNA-seq revealed transcription initiation from the type IV 5\u0026rsquo; end (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB), indicating promoter usage that differs by context and activation state.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTo further investigate the regulatory mechanism, we performed ATAC-seq on non-targeting control and \u003cem\u003eAHR\u003c/em\u003e- or \u003cem\u003eARNT\u003c/em\u003e-knockout A375 cells. Consistent with the RNA-seq data, ATAC-seq showed that chromatin accessibility at the HLA-DRA promoter was reduced in \u003cem\u003eAHR\u003c/em\u003e and \u003cem\u003eARNT\u003c/em\u003e KO cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC), but accessibility at the \u003cem\u003eCIITA\u003c/em\u003e locus remained unchanged (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD), with three promoters (pII, pIII, and pIV) accessible regardless of \u003cem\u003eAHR/ARNT\u003c/em\u003e status (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). These findings suggest that AHR\u0026ndash;ARNT does not control MHC-II expression by altering chromatin accessibility at CIITA, but rather by directly recruiting transcriptional machinery to specific promoter regions. To test this, we overexpressed 3xHA-tagged AHR and ARNT in SKMEL2 cells and performed HA ChIP-seq.\u0026nbsp;As expected, the canonical AHR\u0026ndash;ARNT target gene \u003cem\u003eCYP1A1\u003c/em\u003e exhibited strong enrichment at its promoter (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE). Importantly, we also observed direct binding of AHR and ARNT to pII of \u003cem\u003eCIITA\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eF), indicating this promoter as a functional AHR\u0026ndash;ARNT target.\u003c/p\u003e\u003cp\u003eMotif enrichment analysis using MEME-ChIP\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e revealed that the AHRE (Aryl Hydrocarbon Response Element) core motif (5'-GCGTG-3') was the most significantly enriched motif in the ChIP peaks for both factors (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eG). Notably, this motif is present in the pII region of CIITA. We further validated AHR\u0026ndash;ARNT binding to pII using ChIP-qPCR in HA-AHR/ARNT-overexpressing SKMEL2 cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eH), confirming direct occupancy at this site. To assess the functional activity of this binding site, we PCR cloned the enriched pII region into a dual-luciferase reporter construct. Co-transfection with AHR and ARNT significantly increased reporter activity, confirming its role as a transcriptionally active AHR\u0026ndash;ARNT-responsive element (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eI). Finally, RT-qPCR using isoform-specific primers revealed that AHR activation by FICZ induced type II, III and IV \u003cem\u003eCIITA\u003c/em\u003e mRNA isoforms, whereas AHR inhibition by GNF351 suppressed expression of these isoforms (\u003cb\u003eSupplementary Fig.\u0026nbsp;3A-C\u003c/b\u003e), especially in SKMEL2 cells (\u003cb\u003eSupplementary Fig.\u0026nbsp;3C\u003c/b\u003e). Taken together, these results demonstrate that ligand-activated AHR translocates to the nucleus, dimerizes with ARNT, and directly binds to pII of \u003cem\u003eCIITA\u003c/em\u003e to drive transcription of type II, III and IV isoforms, thereby promoting downstream MHC-II expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eJ).\u003c/p\u003e\u003cp\u003e\u003cb\u003eAHR\u0026ndash;ARNT\u003c/b\u003e\u003cb\u003e-KO signature negatively correlates with clinical benefits in multiple cancer cohorts\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo evaluate the clinical relevance of the AHR\u0026ndash;ARNT regulatory axis in human tumors, we developed a gene expression signature reflective of AHR\u0026ndash;ARNT functional loss by identifying genes consistently dysregulated upon loss of either AHR or ARNT. A composite score was then calculated for each tumor sample based on the weighted expression of this gene set, enabling estimation of AHR\u0026ndash;ARNT pathway activity in large clinical datasets.\u003c/p\u003e\u003cp\u003eWe first assessed the association between the \u003cem\u003eAHR\u0026ndash;ARNT\u003c/em\u003e-KO signature and immune cell infiltration using bulk RNA-seq data from the TCGA (The Cancer Genome Atlas). Across multiple TCGA cohorts, higher \u003cem\u003eAHR\u0026ndash;ARNT\u003c/em\u003e-KO signature scores were significantly correlated with reduced infiltration of CD4⁺ T and CD8⁺ T cells and B cells, and with increased infiltration of cancer-associated fibroblasts, as estimated by EPIC\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). To explore potential implications for immunotherapy responsiveness, we applied the TIDE algorithm\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e to the TCGA datasets. In 24 cancer types, including skin cutaneous melanoma (SKCM) and lung adenocarcinoma (LUAD), patients predicted to respond to immune checkpoint blockade (ICB) exhibited significantly lower \u003cem\u003eAHR\u0026ndash;ARNT\u003c/em\u003e-KO signature scores than predicted non-responders (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB-C). These findings suggest that intact AHR\u0026ndash;ARNT activity may contribute to an immunologically active tumor microenvironment that is more amenable to ICB therapy.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eWe next examined the relationship between the \u003cem\u003eAHR\u0026ndash;ARNT\u003c/em\u003e-KO signature, MHC-II expression, and patient survival. For each TCGA cancer type, patient samples were stratified into four groups based on high or low MHC-II expression level and high or low \u003cem\u003eAHR\u0026ndash;ARNT\u003c/em\u003e-KO signature scores. In both SKCM and LUAD cohorts, patients with high MHC-II expression and low \u003cem\u003eAHR\u0026ndash;ARNT\u003c/em\u003e-KO signature scores had significantly improved overall survival compared to those with low MHC-II expression and high \u003cem\u003eAHR\u0026ndash;ARNT\u003c/em\u003e-KO signature scores (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD-E). These associations reinforce the importance of AHR\u0026ndash;ARNT-mediated regulation of MHC-II in shaping the tumor immune landscape and influencing patient prognosis.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"DISSCUSSION","content":"\u003cp\u003eThe regulation of MHC-II expression in cancer cells is increasingly recognized as a critical determinant of TME and responsiveness to immunotherapy.\u003csup\u003e3\u0026ndash;6,17\u0026minus;20\u003c/sup\u003e Although antigen presentation by MHC-II is canonically restricted to pAPCs, accumulating evidence has demonstrated that cancer cell-intrinsic MHC-II expression correlates with improved patient prognosis and enhanced response to ICB. Recent studies have identified several negative regulators of MHC-II expression, including FBXO11 and PRMT1, which promote degradation of CIITA through ubiquitination and methylation respectively, as well as the CtBP complex, which represses MHC-II transcription.\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e However, these regulators act downstream of CIITA, the master transcriptional activator of MHC-II, and require its presence to exert their effects. In contrast, the upstream mechanisms that drive constitutive MHC-II and CIITA in non-hematopoietic tumors such as melanoma remain poorly defined. In this study, we employed genome-wide CRISPR screening in human melanoma cell lines (A375 and WM115) and uncovered a previously unrecognized regulatory axis involving the AHR and its obligate dimerization partner ARNT. Together, the AHR\u0026ndash;ARNT complex functions as a key positive regulator of cancer cell-intrinsic MHC-II expression. Notably, this regulation occurs independently of IFN-γ signaling, highlighting its intrinsic nature within tumor cells and revealing a parallel, non-inflammatory pathway through which tumor immunogenicity can be modulated.\u003c/p\u003e\u003cp\u003eThe significant reduction in MHC-II expression following KO of \u003cem\u003eAHR\u003c/em\u003e or \u003cem\u003eARNT\u003c/em\u003e underscores the essential role of this complex in maintaining an immune-permissive tumor phenotype. Importantly, overexpression of AHR or ARNT was sufficient to induce MHC-II upregulation even in the absence of IFN-γ, suggesting that tumor cells can autonomously activate antigen presentation pathways.\u003c/p\u003e\u003cp\u003eWhile MHC-II traditionally presents exogenous peptides processed by pAPCs, several alternative pathways enable endogenous antigen loading onto MHC-II molecules.\u003csup\u003e\u003cspan additionalcitationids=\"CR45 CR46 CR47\" citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e These mechanisms suggest that cancer cells have a route to present tumor-associated antigens (TAAs) or tumor-specific antigens (TSAs) on their own MHC-II molecules, thereby contributing to direct CD4\u0026thinsp;+\u0026thinsp;T cell activation.\u003csup\u003e\u003cspan additionalcitationids=\"CR50 CR51 CR52 CR53 CR54 CR55\" citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e Thus, the AHR\u0026ndash;ARNT-driven intrinsic regulation of MHC-II allows new opportunities for therapeutic intervention, whereby tumors may be pharmacologically or genetically modified to enhance their antigen-presenting capabilities and improve T cell-mediated anti-tumor immunity.\u003c/p\u003e\u003cp\u003eOur results reveal that the regulation of the AHR and ARNT pathway is both ligand-responsive and temporally dynamic. Treatment with FICZ, a potent AHR agonist, significantly increased surface MHC-II expression in multiple melanoma lines, while AHR inhibition with GNF351 led to its repression. Importantly, the delayed peak in MHC-II expression following treatment withdrawal suggests that careful timing may be necessary to maximize the immunomodulatory benefits of AHR-targeting agents. Collectively, these findings position the AHR\u0026ndash;ARNT complex as a tractable target for modulating MHC-II expression and augmenting anti-tumor immunity through noncanonical, tumor-intrinsic pathways.\u003c/p\u003e\u003cp\u003eMechanistically, we demonstrate that the AHR\u0026ndash;ARNT complex directly binds to promoter II (pII) of \u003cem\u003eCIITA\u003c/em\u003e, thereby enhancing its transcriptional activity. This establishes a direct link between environmental sensing via AHR ligands and the activation of antigen presentation machinery in tumor cells. The presence of a canonical AHRE within the \u003cem\u003eCIITA\u003c/em\u003e pII, together with robust ChIP-seq/ChIP-qPCR validation, reinforces the specificity of this regulation. In A375 cells, AHR or ARNT deficiency led to reduced abundance of type III and IV mRNA of \u003cem\u003eCIITA\u003c/em\u003e, suggesting that AHR\u0026ndash;ARNT may influence \u003cem\u003eCIITA\u003c/em\u003e transcription through both direct and indirect mechanisms, potentially involving promoter switching or epigenetic modulation. Moreover, transcriptomic analysis revealed that AHR\u0026ndash;ARNT regulates additional transcriptional factors, including components of the AP-1 complex, implying a broader transcriptional remodeling that may influence tumor cell behavior beyond antigen presentation. As we continue to delineate the molecular pathways governing MHC-II regulation, it will be critical to explore potential combinatorial strategies that integrate AHR pathway modulation with approaches aimed at enhancing T cell activation and persistence. Insights from this study lay the foundation for developing novel therapies that enhance tumor immunogenicity and improve patient outcomes in cancer immunotherapy.\u003c/p\u003e\u003cp\u003eOur findings also underscore the complex, context-dependent role of AHR in cancer. While AHR activation has previously been implicated in both tumor-promoting and tumor-suppressive processes,\u003csup\u003e29,34,57\u003c/sup\u003e our study reveals a novel immune-enhancing function through upregulation of MHC-II expression. This duality underscores the necessity for a nuanced comprehension of AHR signaling across various tumor types, disease stages, and immune microenvironments. Future studies should carefully delineate the conditions under which AHR modulation promotes versus impairs anti-tumor immunity, with particular attention to its interactions with other immunoregulatory pathways.\u003c/p\u003e\u003cp\u003eIn addition, the observed species-specific differences between human and murine tumor cells highlight an important limitation in current preclinical models. While AHR/ARNT overexpression or FICZ treatment robustly induced MHC-II expression in human melanoma cells, these interventions failed to elicit similar responses in murine models\u0026mdash;likely due to the absence of a murine homolog of \u003cem\u003eCIITA\u003c/em\u003e pII. This finding emphasizes the need for human-relevant systems and improved mouse models that recapitulate human MHC-II regulatory architecture when studying antigen presentation and its impact on immune responses, T cell infiltration, and immunotherapy outcomes.\u003c/p\u003e\u003cp\u003eIn clinical datasets, higher inferred AHR\u0026ndash;ARNT activity, based on an AHR\u0026ndash;ARNT loss-of-function signature, was associated with increased immune cell infiltration, better response to ICB, and improved overall survival across multiple cancer types. These findings suggest that the AHR\u0026ndash;ARNT pathway may serve not only as a functional modulator but also as a predictive biomarker of immunotherapy responsiveness. Furthermore, these findings align with the established role of MHC-II in promoting durable antitumor immunity. Pharmacologically enhancing cancer cell-intrinsic MHC-II expression via AHR agonists could therefore strengthen and prolong T cell-mediated responses. Further studies are warranted to explore the combinatorial potential of AHR activation with immune checkpoint inhibitors and to dissect the interplay between AHR\u0026ndash;ARNT and other immunomodulatory pathways.\u003c/p\u003e\u003cp\u003eIn summary, our study identifies the AHR\u0026ndash;ARNT complex as a central, ligand-responsive regulator of \u003cem\u003eCIITA\u003c/em\u003e and MHC-II expression in human melanoma cells. By enabling tumor cells to adopt an antigen-presenting phenotype through transcriptional activation of \u003cem\u003eCIITA\u003c/em\u003e, this pathway enhances their immunogenicity and potential visibility to the immune system. The reversible and tunable nature of this mechanism presents a compelling opportunity for therapeutic intervention, particularly in combination with existing immunotherapies. These findings highlight the broader significance of tumor-intrinsic regulatory programs in shaping antitumor immunity and lay a foundation for future AHR-targeted strategies in cancer immunity.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Natural Science Foundation of China (32470664, 12374203, 92374116), the Major Project of WIUCAS (WIUCASQD2021013), Beijing Natural Science Foundation (L248043), Noncommunicable Chronic Diseases-National Science and Technology Major Project (2024ZD0520600), Sichuan Science and Technology Program (2024YFFK0064), and the Frontier Innovation Fund of Peking University Chengdu Academy for Advanced Interdisciplinary Biotechnologies.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eY.J., D.P., Ch.L., and Z.Z. conceived the project. Y.J., W.Z., and Z.Z. carried out the investigation. Y.J., R.Z., and S.H. developed the methodology. Y.J. and W.Z. validated the results. Y.J., Ce.L., and P.R. performed the bioinformatic data analysis. Y.J. and Z.Z. curated the data and wrote the manuscript. Ch.L. and Z.Z. acquired funding and supervised the project.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the National Center for Protein Sciences at Peking University, the behavioral laboratory, the Protein Preparation and Identification Core, and the Optical Imaging Core Facility for experimental platforms. Part of the analysis was performed on the High-Performance Computing Platform of the Center for Life Sciences at Peking University. We thank the Center for Quantitative Biology at Peking University for its experimental platforms.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWaldman, A. D., Fritz, J. M. \u0026amp; Lenardo, M. J. A guide to cancer immunotherapy: from T cell basic science to clinical practice. \u003cem\u003eNature Reviews Immunology\u003c/em\u003e \u003cstrong\u003e20\u003c/strong\u003e, 651-668, doi:10.1038/s41577-020-0306-5 (2020).\u003c/li\u003e\n\u003cli\u003eGarrido, F., Aptsiauri, N., Doorduijn, E. M., Garcia Lora, A. M. \u0026amp; van Hall, T. The urgent need to recover MHC class I in cancers for effective immunotherapy. \u003cem\u003eCurr Opin Immunol\u003c/em\u003e \u003cstrong\u003e39\u003c/strong\u003e, 44-51, doi:10.1016/j.coi.2015.12.007 (2016).\u003c/li\u003e\n\u003cli\u003eAxelrod, M. L., Cook, R. S., Johnson, D. B. \u0026amp; Balko, J. M. Biological Consequences of MHC-II Expression by Tumor Cells in Cancer. \u003cem\u003eClinical Cancer Research\u003c/em\u003e \u003cstrong\u003e25\u003c/strong\u003e, 2392-2402, doi:10.1158/1078-0432.CCR-18-3200 (2019).\u003c/li\u003e\n\u003cli\u003eJohnson, A. M.\u003cem\u003e et al.\u003c/em\u003e Cancer Cell-Specific Major Histocompatibility Complex II Expression as a Determinant of the Immune Infiltrate Organization and Function in the NSCLC Tumor Microenvironment. \u003cem\u003eJournal of Thoracic Oncology\u003c/em\u003e \u003cstrong\u003e16\u003c/strong\u003e, 1694-1704, doi:10.1016/j.jtho.2021.05.004 (2021).\u003c/li\u003e\n\u003cli\u003eJohnson, A. M.\u003cem\u003e et al.\u003c/em\u003e Cancer Cell\u0026ndash;Intrinsic Expression of MHC Class II Regulates the Immune Microenvironment and Response to Anti\u0026ndash;PD-1 Therapy in Lung Adenocarcinoma. \u003cem\u003eThe Journal of Immunology\u003c/em\u003e \u003cstrong\u003e204\u003c/strong\u003e, 2295-2307, doi:10.4049/jimmunol.1900778 (2020).\u003c/li\u003e\n\u003cli\u003eL\u0026oslash;vig, T.\u003cem\u003e et al.\u003c/em\u003e Strong HLA-DR expression in microsatellite stable carcinomas of the large bowel is associated with good prognosis. \u003cem\u003eBr J Cancer\u003c/em\u003e \u003cstrong\u003e87\u003c/strong\u003e, 756-762, doi:10.1038/sj.bjc.6600507 (2002).\u003c/li\u003e\n\u003cli\u003eSteimle, V., Siegrist, C.-A., Mottet, A., Lisowska-Grospierre, B. \u0026amp; Mach, B. Regulation of MHC Class II Expression by Interferon-\u0026gamma; Mediated by the Transactivator Gene CIITA. \u003cem\u003eScience\u003c/em\u003e \u003cstrong\u003e265\u003c/strong\u003e, 106-109, doi:10.1126/science.8016643 (1994).\u003c/li\u003e\n\u003cli\u003eBarretina, J.\u003cem\u003e et al.\u003c/em\u003e The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. \u003cem\u003eNature\u003c/em\u003e \u003cstrong\u003e483\u003c/strong\u003e, 603-607, doi:10.1038/nature11003 (2012).\u003c/li\u003e\n\u003cli\u003eKlijn, C.\u003cem\u003e et al.\u003c/em\u003e A comprehensive transcriptional portrait of human cancer cell lines. \u003cem\u003eNature Biotechnology\u003c/em\u003e \u003cstrong\u003e33\u003c/strong\u003e, 306-312, doi:10.1038/nbt.3080 (2015).\u003c/li\u003e\n\u003cli\u003eZeng, Z.\u003cem\u003e et al.\u003c/em\u003e Hippo signaling pathway regulates cancer cell-intrinsic MHC-II expression. \u003cem\u003eCancer Immunology Research\u003c/em\u003e, CIR-22-0227, doi:10.1158/2326-6066.CIR-22-0227 (2022).\u003c/li\u003e\n\u003cli\u003eZeng, Z.\u003cem\u003e et al.\u003c/em\u003e TISMO: syngeneic mouse tumor database to model tumor immunity and immunotherapy response. \u003cem\u003eNucleic acids research\u003c/em\u003e \u003cstrong\u003e50\u003c/strong\u003e, D1391-D1397 (2022).\u003c/li\u003e\n\u003cli\u003eWalter, W., Lingnau, K., Schmitt, E., Loos, M. \u0026amp; Maeurer, M. J. MHC class II antigen presentation pathway in murine tumours: tumour evasion from immunosurveillance? \u003cem\u003eBr J Cancer\u003c/em\u003e \u003cstrong\u003e83\u003c/strong\u003e, 1192-1201, doi:10.1054/bjoc.2000.1415 (2000).\u003c/li\u003e\n\u003cli\u003eMuhlethaler-Mottet, A., Otten, L. A., Steimle, V. \u0026amp; Mach, B. Expression of MHC class II molecules in different cellular and functional compartments is controlled by differential usage of multiple promoters of the transactivator CIITA. \u003cem\u003eEmbo j\u003c/em\u003e \u003cstrong\u003e16\u003c/strong\u003e, 2851-2860, doi:10.1093/emboj/16.10.2851 (1997).\u003c/li\u003e\n\u003cli\u003eReith, W., LeibundGut-Landmann, S. \u0026amp; Waldburger, J.-M. Regulation of MHC class II gene expression by the class II transactivator. \u003cem\u003eNature Reviews Immunology\u003c/em\u003e \u003cstrong\u003e5\u003c/strong\u003e, 793-806, doi:10.1038/nri1708 (2005).\u003c/li\u003e\n\u003cli\u003eDeffrennes, V.\u003cem\u003e et al.\u003c/em\u003e Constitutive Expression of MHC Class II Genes in Melanoma Cell Lines Results from the Transcription of Class II Transactivator Abnormally Initiated from Its B Cell-Specific Promoter1. \u003cem\u003eThe Journal of Immunology\u003c/em\u003e \u003cstrong\u003e167\u003c/strong\u003e, 98-106, doi:10.4049/jimmunol.167.1.98 (2001).\u003c/li\u003e\n\u003cli\u003eGoodwin, B. L.\u003cem\u003e et al.\u003c/em\u003e Varying functions of specific major histocompatibility class II transactivator promoter III and IV elements in melanoma cell lines. \u003cem\u003eCell Growth Differ\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e, 327-335 (2001).\u003c/li\u003e\n\u003cli\u003eJohnson, D. B.\u003cem\u003e et al.\u003c/em\u003e Melanoma-specific MHC-II expression represents a tumour-autonomous phenotype and predicts response to anti-PD-1/PD-L1 therapy. \u003cem\u003eNature Communications\u003c/em\u003e \u003cstrong\u003e7\u003c/strong\u003e, 10582, doi:10.1038/ncomms10582 (2016).\u003c/li\u003e\n\u003cli\u003eRodig, S. J.\u003cem\u003e et al.\u003c/em\u003e MHC proteins confer differential sensitivity to CTLA-4 and PD-1 blockade in untreated metastatic melanoma. \u003cem\u003eScience Translational Medicine\u003c/em\u003e \u003cstrong\u003e10\u003c/strong\u003e, eaar3342, doi:10.1126/scitranslmed.aar3342 (2018).\u003c/li\u003e\n\u003cli\u003ePark, I. A.\u003cem\u003e et al.\u003c/em\u003e Expression of the MHC class II in triple-negative breast cancer is associated with tumor-infiltrating lymphocytes and interferon signaling. \u003cem\u003ePLoS One\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e, e0182786, doi:10.1371/journal.pone.0182786 (2017).\u003c/li\u003e\n\u003cli\u003eForero, A.\u003cem\u003e et al.\u003c/em\u003e Expression of the MHC Class II Pathway in Triple-Negative Breast Cancer Tumor Cells Is Associated with a Good Prognosis and Infiltrating Lymphocytes. \u003cem\u003eCancer Immunology Research\u003c/em\u003e \u003cstrong\u003e4\u003c/strong\u003e, 390-399, doi:10.1158/2326-6066.CIR-15-0243 (2016).\u003c/li\u003e\n\u003cli\u003eLoi, S.\u003cem\u003e et al.\u003c/em\u003e RAS/MAPK Activation Is Associated with Reduced Tumor-Infiltrating Lymphocytes in Triple-Negative Breast Cancer: Therapeutic Cooperation Between MEK and PD-1/PD-L1 Immune Checkpoint Inhibitors. \u003cem\u003eClinical Cancer Research\u003c/em\u003e \u003cstrong\u003e22\u003c/strong\u003e, 1499-1509, doi:10.1158/1078-0432.CCR-15-1125 (2016).\u003c/li\u003e\n\u003cli\u003eGonzalez-Ericsson, P. I.\u003cem\u003e et al.\u003c/em\u003e Tumor-Specific Major Histocompatibility-II Expression Predicts Benefit to Anti-PD-1/L1 Therapy in Patients With HER2-Negative Primary Breast Cancer. \u003cem\u003eClin Cancer Res\u003c/em\u003e \u003cstrong\u003e27\u003c/strong\u003e, 5299-5306, doi:10.1158/1078-0432.Ccr-21-0607 (2021).\u003c/li\u003e\n\u003cli\u003eBalasubramanian, A., John, T. \u0026amp; Asselin-Labat, M.-L. Regulation of the antigen presentation machinery in cancer and its implication for immune surveillance. \u003cem\u003eBiochemical Society Transactions\u003c/em\u003e \u003cstrong\u003e50\u003c/strong\u003e, 825-837, doi:10.1042/BST20210961 (2022).\u003c/li\u003e\n\u003cli\u003eSeliger, B., Kloor, M. \u0026amp; Ferrone, S. HLA class II antigen-processing pathway in tumors: Molecular defects and clinical relevance. \u003cem\u003eOncoImmunology\u003c/em\u003e \u003cstrong\u003e6\u003c/strong\u003e, e1171447, doi:10.1080/2162402X.2016.1171447 (2017).\u003c/li\u003e\n\u003cli\u003eChan, K. L.\u003cem\u003e et al.\u003c/em\u003e Inhibition of the CtBP complex and FBXO11 enhances MHC class II expression and anti-cancer immune responses. \u003cem\u003eCancer Cell\u003c/em\u003e \u003cstrong\u003e40\u003c/strong\u003e, 1190-1206.e1199, doi:10.1016/j.ccell.2022.09.007 (2022).\u003c/li\u003e\n\u003cli\u003eFan, Z.\u003cem\u003e et al.\u003c/em\u003e Protein arginine methyltransferase 1 (PRMT1) represses MHC II transcription in macrophages by methylating CIITA. \u003cem\u003eScientific Reports\u003c/em\u003e \u003cstrong\u003e7\u003c/strong\u003e, 40531, doi:10.1038/srep40531 (2017).\u003c/li\u003e\n\u003cli\u003eRothhammer, V. \u0026amp; Quintana, F. J. The aryl hydrocarbon receptor: an environmental sensor integrating immune responses in health and disease. \u003cem\u003eNature Reviews Immunology\u003c/em\u003e \u003cstrong\u003e19\u003c/strong\u003e, 184-197, doi:10.1038/s41577-019-0125-8 (2019).\u003c/li\u003e\n\u003cli\u003eLarigot, L., Juricek, L., Dairou, J. \u0026amp; Coumoul, X. AhR signaling pathways and regulatory functions. \u003cem\u003eBiochimie Open\u003c/em\u003e \u003cstrong\u003e7\u003c/strong\u003e, 1-9, doi:https://doi.org/10.1016/j.biopen.2018.05.001 (2018).\u003c/li\u003e\n\u003cli\u003eGuti\u0026eacute;rrez-V\u0026aacute;zquez, C. \u0026amp; Quintana, F. J. Regulation of the Immune Response by the Aryl Hydrocarbon Receptor. \u003cem\u003eImmunity\u003c/em\u003e \u003cstrong\u003e48\u003c/strong\u003e, 19-33, doi:10.1016/j.immuni.2017.12.012 (2018).\u003c/li\u003e\n\u003cli\u003eLaw, C.\u003cem\u003e et al.\u003c/em\u003e Interferon subverts an AHR\u0026ndash;JUN axis to promote CXCL13+ T cells in lupus. \u003cem\u003eNature\u003c/em\u003e, doi:10.1038/s41586-024-07627-2 (2024).\u003c/li\u003e\n\u003cli\u003eDi Meglio, P.\u003cem\u003e et al.\u003c/em\u003e Activation of the aryl hydrocarbon receptor dampens the severity of inflammatory skin conditions. \u003cem\u003eImmunity\u003c/em\u003e \u003cstrong\u003e40\u003c/strong\u003e, 989-1001, doi:10.1016/j.immuni.2014.04.019 (2014).\u003c/li\u003e\n\u003cli\u003eLi, W.\u003cem\u003e et al.\u003c/em\u003e MAGeCK enables robust identification of essential genes from genome-scale CRISPR/Cas9 knockout screens. \u003cem\u003eGenome Biol\u003c/em\u003e \u003cstrong\u003e15\u003c/strong\u003e, 554, doi:10.1186/s13059-014-0554-4 (2014).\u003c/li\u003e\n\u003cli\u003eDoench, J. G.\u003cem\u003e et al.\u003c/em\u003e Optimized sgRNA design to maximize activity and minimize off-target effects of CRISPR-Cas9. \u003cem\u003eNature Biotechnology\u003c/em\u003e \u003cstrong\u003e34\u003c/strong\u003e, 184-191, doi:10.1038/nbt.3437 (2016).\u003c/li\u003e\n\u003cli\u003eMurray, I. A., Patterson, A. D. \u0026amp; Perdew, G. H. Aryl hydrocarbon receptor ligands in cancer: friend and foe. \u003cem\u003eNature Reviews Cancer\u003c/em\u003e \u003cstrong\u003e14\u003c/strong\u003e, 801-814, doi:10.1038/nrc3846 (2014).\u003c/li\u003e\n\u003cli\u003eFlaveny, C. A., Murray, I. A. \u0026amp; Perdew, G. H. Differential gene regulation by the human and mouse aryl hydrocarbon receptor. \u003cem\u003eToxicol Sci\u003c/em\u003e \u003cstrong\u003e114\u003c/strong\u003e, 217-225, doi:10.1093/toxsci/kfp308 (2010).\u003c/li\u003e\n\u003cli\u003eForgacs, A. L., Dere, E., Angrish, M. M. \u0026amp; Zacharewski, T. R. Comparative Analysis of Temporal and Dose-Dependent TCDD-Elicited Gene Expression in Human, Mouse, and Rat Primary Hepatocytes. \u003cem\u003eToxicological Sciences\u003c/em\u003e \u003cstrong\u003e133\u003c/strong\u003e, 54-66, doi:10.1093/toxsci/kft028 (2013).\u003c/li\u003e\n\u003cli\u003eBlack, M. B.\u003cem\u003e et al.\u003c/em\u003e Cross-species comparisons of transcriptomic alterations in human and rat primary hepatocytes exposed to 2,3,7,8-tetrachlorodibenzo-p-dioxin. \u003cem\u003eToxicol Sci\u003c/em\u003e \u003cstrong\u003e127\u003c/strong\u003e, 199-215, doi:10.1093/toxsci/kfs069 (2012).\u003c/li\u003e\n\u003cli\u003eFlaveny, C., Reen, R. K., Kusnadi, A. \u0026amp; Perdew, G. H. The mouse and human Ah receptor differ in recognition of LXXLL motifs. \u003cem\u003eArch Biochem Biophys\u003c/em\u003e \u003cstrong\u003e471\u003c/strong\u003e, 215-223, doi:10.1016/j.abb.2008.01.014 (2008).\u003c/li\u003e\n\u003cli\u003eRamadoss, P. \u0026amp; Perdew, G. H. Use of 2-azido-3-[125I]iodo-7,8-dibromodibenzo-p-dioxin as a probe to determine the relative ligand affinity of human versus mouse aryl hydrocarbon receptor in cultured cells. \u003cem\u003eMol Pharmacol\u003c/em\u003e \u003cstrong\u003e66\u003c/strong\u003e, 129-136, doi:10.1124/mol.66.1.129 (2004).\u003c/li\u003e\n\u003cli\u003eFlaveny, C. A., Murray, I. A., Chiaro, C. R. \u0026amp; Perdew, G. H. Ligand selectivity and gene regulation by the human aryl hydrocarbon receptor in transgenic mice. \u003cem\u003eMol Pharmacol\u003c/em\u003e \u003cstrong\u003e75\u003c/strong\u003e, 1412-1420, doi:10.1124/mol.109.054825 (2009).\u003c/li\u003e\n\u003cli\u003eBailey, T. L., Johnson, J., Grant, C. E. \u0026amp; Noble, W. S. The MEME Suite. \u003cem\u003eNucleic Acids Research\u003c/em\u003e \u003cstrong\u003e43\u003c/strong\u003e, W39-W49, doi:10.1093/nar/gkv416 (2015).\u003c/li\u003e\n\u003cli\u003eRacle, J., de Jonge, K., Baumgaertner, P., Speiser, D. E. \u0026amp; Gfeller, D. Simultaneous enumeration of cancer and immune cell types from bulk tumor gene expression data. \u003cem\u003eElife\u003c/em\u003e \u003cstrong\u003e6\u003c/strong\u003e, doi:10.7554/eLife.26476 (2017).\u003c/li\u003e\n\u003cli\u003eJiang, P.\u003cem\u003e et al.\u003c/em\u003e Signatures of T cell dysfunction and exclusion predict cancer immunotherapy response. \u003cem\u003eNature Medicine\u003c/em\u003e \u003cstrong\u003e24\u003c/strong\u003e, 1550-1558, doi:10.1038/s41591-018-0136-1 (2018).\u003c/li\u003e\n\u003cli\u003ePaludan, C.\u003cem\u003e et al.\u003c/em\u003e Endogenous MHC class II processing of a viral nuclear antigen after autophagy. \u003cem\u003eScience\u003c/em\u003e \u003cstrong\u003e307\u003c/strong\u003e, 593-596, doi:10.1126/science.1104904 (2005).\u003c/li\u003e\n\u003cli\u003eNuchtern, J. G., Biddison, W. E. \u0026amp; Klausner, R. D. Class II MHC molecules can use the endogenous pathway of antigen presentation. \u003cem\u003eNature\u003c/em\u003e \u003cstrong\u003e343\u003c/strong\u003e, 74-76, doi:10.1038/343074a0 (1990).\u003c/li\u003e\n\u003cli\u003eRiedel, A.\u003cem\u003e et al.\u003c/em\u003e Endogenous presentation of a nuclear antigen on MHC class II by autophagy in the absence of CRM1-mediated nuclear export. \u003cem\u003eEur J Immunol\u003c/em\u003e \u003cstrong\u003e38\u003c/strong\u003e, 2090-2095, doi:10.1002/eji.200737900 (2008).\u003c/li\u003e\n\u003cli\u003eLeung, C. S. K. Endogenous Antigen Presentation of MHC Class II Epitopes through Non-Autophagic Pathways. \u003cem\u003eFrontiers in Immunology\u003c/em\u003e \u003cstrong\u003e6\u003c/strong\u003e (2015).\u003c/li\u003e\n\u003cli\u003eCrotzer, V. L. \u0026amp; Blum, J. S. Autophagy and its role in MHC-mediated antigen presentation. \u003cem\u003eJ Immunol\u003c/em\u003e \u003cstrong\u003e182\u003c/strong\u003e, 3335-3341, doi:10.4049/jimmunol.0803458 (2009).\u003c/li\u003e\n\u003cli\u003evan Tuyn, J.\u003cem\u003e et al.\u003c/em\u003e Oncogene-Expressing Senescent Melanocytes Up-Regulate MHC Class II, a\u0026amp;#xa0;Candidate Melanoma Suppressor Function. \u003cem\u003eJournal of Investigative Dermatology\u003c/em\u003e \u003cstrong\u003e137\u003c/strong\u003e, 2197-2207, doi:10.1016/j.jid.2017.05.030 (2017).\u003c/li\u003e\n\u003cli\u003eChornoguz, O., Gapeev, A., O\u0026apos;Neill, M. C. \u0026amp; Ostrand-Rosenberg, S. Major histocompatibility complex class II+ invariant chain negative breast cancer cells present unique peptides that activate tumor-specific T cells from breast cancer patients. \u003cem\u003eMol Cell Proteomics\u003c/em\u003e \u003cstrong\u003e11\u003c/strong\u003e, 1457-1467, doi:10.1074/mcp.M112.019232 (2012).\u003c/li\u003e\n\u003cli\u003eAlspach, E.\u003cem\u003e et al.\u003c/em\u003e MHC-II neoantigens shape tumour immunity and response to immunotherapy. \u003cem\u003eNature\u003c/em\u003e \u003cstrong\u003e574\u003c/strong\u003e, 696-701, doi:10.1038/s41586-019-1671-8 (2019).\u003c/li\u003e\n\u003cli\u003eArmstrong, T. D., Clements, V. K., Martin, B. K., Ting, J. P. \u0026amp; Ostrand-Rosenberg, S. Major histocompatibility complex class II-transfected tumor cells present endogenous antigen and are potent inducers of tumor-specific immunity. \u003cem\u003eProc Natl Acad Sci U S A\u003c/em\u003e \u003cstrong\u003e94\u003c/strong\u003e, 6886-6891, doi:10.1073/pnas.94.13.6886 (1997).\u003c/li\u003e\n\u003cli\u003eArmstrong, T. D., Clements, V. K. \u0026amp; Ostrand-Rosenberg, S. Class II-transfected tumor cells directly present endogenous antigen to CD4+ T cells in vitro and are APCs for tumor-encoded antigens in vivo. \u003cem\u003eJ Immunother\u003c/em\u003e \u003cstrong\u003e21\u003c/strong\u003e, 218-224, doi:10.1097/00002371-199805000-00008 (1998).\u003c/li\u003e\n\u003cli\u003eArmstrong, T. D., Clements, V. K. \u0026amp; Ostrand-Rosenberg, S. MHC class II-transfected tumor cells directly present antigen to tumor-specific CD4+ T lymphocytes. \u003cem\u003eJ Immunol\u003c/em\u003e \u003cstrong\u003e160\u003c/strong\u003e, 661-666 (1998).\u003c/li\u003e\n\u003cli\u003eAbelin, J. G.\u003cem\u003e et al.\u003c/em\u003e Defining HLA-II Ligand Processing and Binding Rules with Mass Spectrometry Enhances Cancer Epitope Prediction. \u003cem\u003eImmunity\u003c/em\u003e \u003cstrong\u003e51\u003c/strong\u003e, 766-779.e717, doi:10.1016/j.immuni.2019.08.012 (2019).\u003c/li\u003e\n\u003cli\u003eHos, B. J.\u003cem\u003e et al.\u003c/em\u003e Cancer-specific T helper shared and neo-epitopes uncovered by expression of the MHC class II master regulator CIITA. \u003cem\u003eCell Rep\u003c/em\u003e \u003cstrong\u003e41\u003c/strong\u003e, 111485, doi:10.1016/j.celrep.2022.111485 (2022).\u003c/li\u003e\n\u003cli\u003eDean, J. W. \u0026amp; Zhou, L. Cell-intrinsic view of the aryl hydrocarbon receptor in tumor immunity. \u003cem\u003eTrends in Immunology\u003c/em\u003e \u003cstrong\u003e43\u003c/strong\u003e, 245-258, doi:https://doi.org/10.1016/j.it.2022.01.008 (2022).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-experimental-and-clinical-cancer-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jecc","sideBox":"Learn more about [Journal of Experimental \u0026 Clinical Cancer Research](http://jeccr.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/jecc/default.aspx","title":"Journal of Experimental \u0026 Clinical Cancer Research","twitterHandle":"@OncoBioMed","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"MHC-II, CIITA, AHR‒ARNT, tumor immunity, cancer immunotherapy","lastPublishedDoi":"10.21203/rs.3.rs-7796457/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7796457/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMHC-II molecules are traditionally restricted to professional antigen-presenting cells (pAPCs), but increasing evidence highlights their expression in cancer cells, where they are associated with enhanced immune infiltration and improved clinical outcomes. However, the mechanisms governing cancer cell-intrinsic MHC-II expression remain poorly understood. Here, through genome-wide CRISPR-Cas9 screening in human melanoma cells, we identify the aryl hydrocarbon receptor (AHR) and its dimerization partner (ARNT) as critical, ligand-responsive regulators of MHC-II expression. Our analyses reveal that AHR\u0026ndash;ARNT promotes transcription of the MHC-II transactivator \u003cem\u003eCIITA\u003c/em\u003e through direct binding to its promoter II (pII), independently of IFN-γ signaling. Clinically, an AHR\u0026ndash;ARNT loss-of-function signature correlates with reduced immune infiltration, poor response to immunotherapy, and inferior survival across cancer types. Together, our findings uncover a previously unrecognized, tumor-intrinsic regulatory axis of MHC-II expression and suggest that targeting the AHR\u0026ndash;ARNT pathway may enhance tumor immunogenicity and improve responses to immunotherapy.\u003c/p\u003e","manuscriptTitle":"AHR Activation Drives Cancer Cell-Intrinsic MHC-II expression in Human Melanoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-21 11:25:32","doi":"10.21203/rs.3.rs-7796457/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-17T15:17:57+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-17T14:56:43+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-11T18:45:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"270943970462373990832264542851084977492","date":"2025-10-28T19:41:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"15778826521844157527301283052418074517","date":"2025-10-27T17:03:02+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-08T10:31:36+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-08T03:54:08+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-08T03:53:57+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Experimental \u0026 Clinical Cancer Research","date":"2025-10-07T06:35:37+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-experimental-and-clinical-cancer-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jecc","sideBox":"Learn more about [Journal of Experimental \u0026 Clinical Cancer Research](http://jeccr.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/jecc/default.aspx","title":"Journal of Experimental \u0026 Clinical Cancer Research","twitterHandle":"@OncoBioMed","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8f0182c2-0a8d-4491-b8a0-fe84d53f995e","owner":[],"postedDate":"October 21st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-02-23T16:02:12+00:00","versionOfRecord":{"articleIdentity":"rs-7796457","link":"https://doi.org/10.1186/s13046-026-03673-y","journal":{"identity":"journal-of-experimental-and-clinical-cancer-research","isVorOnly":false,"title":"Journal of Experimental \u0026 Clinical Cancer Research"},"publishedOn":"2026-02-20 15:57:39","publishedOnDateReadable":"February 20th, 2026"},"versionCreatedAt":"2025-10-21 11:25:32","video":"","vorDoi":"10.1186/s13046-026-03673-y","vorDoiUrl":"https://doi.org/10.1186/s13046-026-03673-y","workflowStages":[]},"version":"v1","identity":"rs-7796457","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7796457","identity":"rs-7796457","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}