Published online Sep 26, 2026. doi: 10.4252/wjsc.124540
Revised: July 18, 2026
Accepted: September 18, 2026
Published online: September 26, 2026
Processing time: 96 Days and 17.1 Hours
Mediator complex subunit 12 (MED12) somatic mutations occur frequently in various tumors and are involved in tumor progression.
To evaluate the role and mechanism in lung cancer.
MED12-associated genes were subjected to enrichment analysis. MED12-mutant NCI-H322 cells and MED12-non-mutant PC9 cells were used to evaluate the mutation’s effects on stemness, employing sphere formation, chromatin immunoprecipitation quantitative polymerase chain reaction, and flow cytometry. The association between MED12 and the tumor immune microenvironment was analyzed by integrating bulk transcriptomic and single-cell transcriptomic data. Chemotaxis and cytotoxic functions of CD8+ T cells were assessed using a co-culture model of tumor cells and CD8+ T cells.
Stem cell maintenance and Notch pathway regulation were identified as the main enriched categories for MED12-associated genes by bioinformatics analysis. Com
This study reveals the key role of the MED12 mutation in lung cancer progression, providing a new theoretical basis for lung cancer immunotherapy.
Core Tip: Mediator complex subunit 12 mutation in lung cancer cells activates Notch signaling, which concurrently promotes cancer stemness - characterized by enhanced sphere formation and increased ALDH1+ cells - and induces immune evasion via upregulation of surface programmed death ligand-1 on tumor cells. This leads to impaired CD8+ T cell chemotaxis, reduced release of effector molecules (interferon-γ, tumor necrosis factor-α, perforin, granzyme-B), and increased programmed cell death 1 expression. Notch inhibitor DAPT reverses these pro-tumor effects.
- Citation: Yang Y, Zhong JL, Liu JY. MED12 mutation activates Notch signaling to enhance cancer stemness and suppress CD8+ T cell cytotoxicity in lung cancer. World J Stem Cells 2026; 18(9): 124540
- URL: https://www.wjgnet.com/1948-0210/full/v18/i9/124540.htm
- DOI: https://dx.doi.org/10.4252/wjsc.124540
Lung cancer originates from the bronchial mucosa or glands and is responsible for about 18.0% of cancer-related deaths worldwide[1,2]. The vast majority of lung cancer cases are non-small cell lung cancer (NSCLC), including lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC)[3]. Cancer stem cells (CSCs) are a subset of cells present in most tumors and possess self-renewal capacity[4]. Numerous studies have shown that CSCs play a key role in tumor development, recurrence, and metastasis due to their inherent self-renewal and tumor-initiating properties[5]. In lung cancer, accumulating evidence suggests that the disease is primarily driven by CSCs, which are more aggressive (prone to metastasis) and contribute to the formation of a tumor immune evasion microenvironment[6,7]. Therefore, elucidating the molecular mechanisms underlying the maintenance of tumor stemness in lung cancer is of great clinical significance for the development of drugs targeting CSCs and for achieving effective treatment of lung cancer.
The majority of cancer immunotherapies harness cytotoxic T lymphocytes (CTLs), the key effector T-cell subset that directly mediates tumor cytolysis[8]. Existing studies have shown that a CD8+ T cell activation is a central mechanism of anti-tumor immunity, with CTLs eliminating cancer cells via the Fas/FasL pathway, granzyme (e.g., granzyme B/perforin) secretion, and cytokine [e.g., interferon-γ (IFN-γ)/tumor necrosis factor-α (TNF-α)] release[9]. However, in most cases, tumors achieve immune evasion through various mechanisms, including secretion of transforming growth factor-β and expression of programmed death ligand-1 (PD-L1), thereby inducing functional exhaustion of CD8+ T cells[10]. CD8+ T cell exhaustion not only weakens their tumor-clearance ability but also leads to an immunosuppressive state in the tumor immune microenvironment[11,12]. Therefore, restoring an effective CD8+ T cell response is key to activating tumor immune surveillance and inhibiting cancer progression.
Single-gene mutations are ubiquitous across diverse tumor types, such as lung cancer, with specific mutations reported to significantly influence immune response prediction or clinical prognosis in NSCLC. For example, the KRAS G12D mutation, a common mutation, has been reported to inhibit immune responses and reduce the sensitivity of NSCLC to immune checkpoint inhibitors (ICIs)[13]. In recent years, studies have found that mutations in mediator complex subunit 12 (MED12) occur in various tumor types, including lung cancer[14], uterine leiomyomas[15], etc. MED12 mutations have been identified as biomarkers predictive of response and prognosis to immune checkpoint blockade, and they also drive the exhaustion of CD8+ T cells[16]. In addition, in uterine leiomyomas, MED12 mutations stimulate RANKL expression and stem cell proliferation, thereby promoting tumor progression[15]. The underlying molecular mechanisms linking MED12 mutations to lung cancer progression are not yet fully defined and require further investigation.
The present study sought to elucidate the potential functional impact of MED12 mutation on lung CSC maintenance and immune evasion, as well as the associated molecular pathways governing these processes in the cell line models examined. Our findings suggest a potential role for MED12 mutation in lung cancer cell stemness and immune modulation, and highlight the MED12-Notch axis as a candidate for further investigation in the context of lung cancer biology and therapy. However, given the non-isogenic nature of the cell lines used, these conclusions are preliminary and await confirmation in isogenic systems and in vivo models.
The genes were chosen after using the UALCAN (http://ualcan.path.uab.edu) online resource to find genes linked to MED12 expression in LUAD and LUSC. Enrichment analysis, including Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, was performed using Metascape (https://metascape.org).
The MED12-non-mutant cell line PC9 (Cell Bank of the Chinese Academy of Sciences, China) and the MED12-mutant cell line NCI-H322 (Shanghai Guandao Bioengineering, China) were used in this study. All cells were cultivated in a humidified incubator using RPMI 1640 medium (Gibco, NY, United States) supplemented with 10% fetal bovine serum (Corning, NY, United States) and 1% penicillin/streptomycin. Cells were treated with the Notch pathway inhibitor γ-secretase inhibitor (DAPT; 10 μmol/L; Sigma-Aldrich, MO, United States) for 72 hours.
Cells were collected by gentle centrifugation and trypsinized cells (500 cells/well) were seeded into low-adherence culture plates and cultured as previously reported[17]. On day 14, the formed spheres were photographed under an inverted Zeiss Axio Observer 7 microscope (Zeiss, Germany), and the number of spheres (diameter > 50 μm) was counted in five random fields per well.
Flow cytometry was used to assess apoptosis rate, the expression of the stemness marker ALDH1 and programmed cell death 1 (PD-1), and PD-L1. The apoptosis rate of lung cancer cells was analyzed using the Annexin V-PI Apoptosis Detection Kit (BD Pharmingen, CA, United States). Apoptosis rate was performed using a FACScan (FCM, Thermo Fisher Scientific, MA, United States). Surface staining of CD8+ T, PC9/NCI-H322 cells was performed with fluorescence-conjugated antibodies, including ALDH1 (1 μg/mL, Thermo Fisher Scientific, MA, United States), PD-1 (1 μg/mL, Thermo Fisher Scientific, MA, United States) and PD-L1 (1 μg/mL, Thermo Fisher Scientific, MA, United States) for 15 minutes. Data were acquired on a FACScan flow cytometer (Thermo Fisher Scientific, MA, United States).
Protein extraction from cells was performed as described in reference[18]. Proteins were deposited onto polyvinylidene fluoride membranes (Bio-Rad, CA, United States) after being separated by 7.5% sodium-dodecyl sulfate gel electrophoresis. After blocking with non-fat milk for 1 hour, the membranes were incubated with various primary antibodies, followed by incubation with secondary antibodies (Cell Signaling Technology, Danvers, MA, United States). Immunoreactivity was detected using an ECL kit (Beyotime, Shanghai, China).
PC9 and NCI-H322 cells were crosslinked with 1% formaldehyde for 10 minutes at room temperature, and the reaction was quenched with glycine. Cells were lysed and chromatin was sheared. An aliquot of the sonicated chromatin was saved as input control. The remaining samples were incubated overnight at 4 °C with an anti-MED12 antibody (abcam, United Kingdom) or normal rabbit immunoglobulin G (IgG) (abcam, United Kingdom) as a negative control (Millipore, Germany). Antibody-chromatin complexes were captured using Protein A/G magnetic beads (Millipore, Germany). After extensive washing, immunoprecipitated DNA was eluted and crosslinking was reversed by overnight incubation at
Transcriptomic datasets for LUAD were accessed through the UCSC Xena browser (https://xenabrowser.net/). To evaluate the enrichment of signature gene sets in distinct cell populations, ssGSEA was implemented using the GSVA v1.52.3 package[19]. The parameters of the gsva function were set as follows: Mx.diff = TRUE, method = “ssgsea”, kcdf = “Gaussian”. The ssGSEA scores were used to quantify the relative infiltration levels of cell populations in the tumor microenvironment based on transcriptomic data. Spearman correlation analysis was performed to analyze the correlation between MED12 expression and the infiltration levels of various immune cells.
The GEO public repository (https://www.ncbi.nlm.nih.gov/geo) provided the single-cell RNA-seq dataset GSE149655, with samples from two lung cancer patients and two normal lung tissue patients. The percentage of mitochondrial, ribosomal, and red blood cell genes was calculated using the Percentage FeatureSet function of the R package Seurat (https://satijalab.org/seurat/). The filtering thresholds were set as nFeature_RNA > 300, nFeature_RNA < 7000, and percent.mito < 10. Subsequently, the merged single-cell sequencing data were normalized. The Seurat FindVariable Features tool was used to identify the top 2000 highly variable genes, and the ScaleData function was used to scale each gene. Dimensionality reduction was then performed on the top 2000 highly variable genes using the Run PCA function. The “FindClusters” program was used to cluster cells, and non-linear dimensionality reduction was performed using the “RunUMAP” function. Cell clusters were annotated based on the CellMarker 2.0 database and canonical cell marker genes. Subsequently, macrophages and monocytes were extracted for further analysis. The analytical approach was similar to the clustering analysis described above for the single-cell sequencing data.
Gene set enrichment analysis was conducted employing version 4.1.0 software, using the MED12 expression matrix as input. A median-based cutoff of samples was divided into groups with high and low expression of MED12. The enriched pathways for each phenotype were ranked using the P value and normalized enrichment score.
Human CD8+ T cells (Immocell, Xiamen, Fujian Province, China) were cultured in complete RPMI 1640 medium supplemented with eBioscience™ Cell Stimulation Cocktail (Thermo Fisher Scientific, MA, United States) for 48 hours to achieve activation.
Chemotaxis of CD8+ T lymphocytes was measured in 24-well plates employing polycarbonate filters of 5-μm pore size (Corning, NY, United States)[20]. A total of 600 μL of lung cancer cell supernatant was added to the lower chamber of the Transwell system. CD8+ T lymphocytes (purity > 90%) were counted. Subsequently, we seeded 5 × 105 CD8+ T cells into the upper chamber and allowed them to incubate for 2 hours. Flow cytometric analysis with a 60-second finite acquisition was used to count cells in the lower chamber.
Two culture conditions were used for activated human CD8+ T cells: Co-culture with lung cancer cells at a 3:1 effector-to-target ratio for 36 hours; culture in lung cancer cell-conditioned medium (CM) for 48 hours. Subsequently, CD8+ T cells were harvested to detect the secretion of cytotoxic cytokines, or tumor cells were collected to measure lactate dehydrogenase (LDH) release using an LDH Cytotoxicity Assay Kit (Beyotime, Shanghai, China).
In order to assess CD8 T cell function, the quantities of IFN-γ, TNF-α, granzyme B, and perforin produced by CD8 T cells were evaluated using IFN-γ, TNF-α, granzyme B, and perforin ELISA kits (eBioscience, CA, United States) in accordance with the manufacturers’ instructions.
Data were analyzed using GraphPad Prism 10.0 software. All experiments were performed with three biological replicates, and the data are presented as mean ± SD. Differences between two groups were analyzed using the χ2 test or Student’s t-test. Intergroup differences were assessed by one-way ANOVA with Dunnett’s post-hoc adjustment, adopting a significance threshold of P < 0.05.
To investigate the potential role of MED12 in lung cancer development, a total of 250 genes that had the greatest relationship with MED12 expression were initially screened in LUAD and LUSC from The Cancer Genome Atlas (TCGA) data, ranked by correlation strength. Intersection of the two datasets yielded 82 genes commonly upregulated in both lung cancer subtypes. Functional enrichment analysis was then performed on these 82 genes (Figure 1A). GO enrichment results showed that these MED12-associated genes were mainly enriched in molecular functions such as “bile acid binding” and “carboxylic acid binding”; cellular components such as “mitotic spindle” and “midbody”; and biological processes including “mitotic nuclear division”, “regulation of stem cell population maintenance”, and “positive regulation of stem cell population maintenance” (Figure 1B-D). The linked genes were mainly implicated in signaling pathways like “cell cycle”, “Notch signaling pathway”, and “DNA replication” (Figure 1E).
NCI-H322 cells carrying a MED12 mutation were selected as the mutant group (MUT), and PC9 cells without MED12 mutation were used as the non-mutant group (non-MUT). Relative to the non-MUT group, the MUT group displayed markedly decreased apoptosis rate (Figure 2A). A significantly greater number of spheres was formed by the MUT group than by the non-MUT group, as determined by the sphere formation assay (Figure 2B). In addition, flow cytometry was used to detect the proportion of ALDH1-positive cells, a stem cell marker, and MED12 mutation increased the percentage of ALDH1+ cells according to the findings (Figure 2C). In summary, these outcomes implicate that MED12 mutation promotes lung cancer cell proliferation and is involved in the maintenance of CSCs.
KEGG pathway enrichment analysis suggested that MED12-associated genes were significantly enriched in the Notch signaling pathway. Previous studies have reported that MED12 mutations may activate this pathway[21]. To investigate whether MED12 is physically associated with Notch target gene chromatin in lung cancer cells, we performed chromatin immunoprecipitation (ChIP) followed by qPCR targeting the HES1 promoter region using an anti-MED12 antibody. As shown in Figure 3A, MED12 occupancy at the HES1 promoter was significantly enriched in NCI-H322 cells compared to PC9 cells. In parallel, western blot results showed that the protein levels of NOTCH1, N1ICD, HES1, and HEY1 were significantly elevated in NCI-H322 cells relative to PC9 cells (Figure 3B). These findings suggest that the MED12 protein physically associates with Notch target chromatin in this lung cancer cell model, with enhanced occupancy observed in the MED12-mutant NCI-H322 cells. To explore whether the observed CSC properties by MED12 mutation depends on the Notch pathway, the Notch pathway inhibitor DAPT was used. The data indicate that DAPT treatment partially reversed the increased cell viability and reduced apoptosis observed in cells with the MED12 mutation (Figure 3C). Sphere formation assay demonstrated that the enhanced sphereforming ability induced by MED12 mutation was partially reversed by DAPT treatment (Figure 3D). In addition, the increased proportion of ALDH1-positive cells caused by MED12 mutation was also partially reversed by DAPT treatment (Figure 3E). These findings indicate that Notch signaling contributes to the maintenance of stemness-associated characteristics in this MED12-mutant cell line model. However, because these experiments were performed in non-isogenic cell lines, we cannot definitively conclude that these effects are uniquely driven by the MED12 mutation; formal causal attribution awaits genetic perturbation studies in isogenic systems.
Transcriptome-derived immune cell infiltration profiles were generated using computational algorithms including ssGSEA. The findings demonstrated that the MED12-high and MED12-low expression groups had significantly different amounts of various immune cell infiltration (Figure 4A). Pan-cancer correlation analysis indicated that in LUAD and LUSC, higher MED12 expression was linked to lower abundance levels of T cells, CD8+ T cells, and macrophages (Figure 4B). Spearman correlation analysis further confirmed that in lung cancer, a marked negative correlation was observed between MED12 expression levels and the abundance of multiple immune cell types, including T cells, CD8+ T cells, macrophages, dendritic cells, and mast cells (Figure 4C-G), suggesting that high MED12 expression may inhibit the infiltration of these immune cells and thereby weaken the activation of the tumor immune response.
We downloaded and analyzed single-cell sequencing data from the GSE149655 dataset (comprising two normal and two lung cancer tissues) from GEO to investigate the cell-type specificity of MED12 expression. After quality control and removal of mitochondrial and ribosomal genes, a total of 5238 single-cell data points were obtained (normal group: C1 = 2873, C2 = 701; tumor group: T1 = 1111, T2 = 553) (Supplementary Figure 1A). Dimensionality reduction and clustering of all cells identified 12 cell clusters (Supplementary Figure 1B and C). Through UMAP clustering and annotation using canonical cell markers, six main cell subpopulations were identified: Adipocytes, endothelial cells, epithelial cells, fibroblasts, macrophages, and monocytes (Figure 5A). Among these, MED12 expression was primarily concentrated in the macrophages and monocyte subpopulations (Figure 5B). To further resolve the heterogeneity of these two subpopulations, sub-clustering analysis was performed on macrophages and monocytes, yielding 11 cell clusters after dimensionality reduction and clustering (Figure 5C). The distribution of MED12-high and MED12-low expression is shown in Figure 5D. Cell clusters with high and low MED12 expression were extracted for differential expression gene analysis (Figure 5E), and functional enrichment analysis revealed that the differentially expressed genes were mainly enriched in immune-related pathways, including “T cell activation”, “regulation of T cell activation”, and other immune regulatory processes (Figure 5F and G). Because no distinct T-cell populations were identified in GSE149655 after quality-control filtering and cell annotation, the present analysis was restricted to macrophage and monocyte populations. Therefore, the enrichment of T-cell-related pathways should be interpreted as reflecting potential immune regulatory interactions.
Taken together, integration of TCGA transcriptomic data and GSE149655 single-cell sequencing data suggests that MED12 expression is associated with immune-related transcriptional programs in lung cancer. However, these analyses were performed based on MED12 expression levels rather than MED12 mutation status; therefore, they provide supportive evidence for a potential role of MED12 in the tumor immune microenvironment but should not be interpreted as direct evidence for the immunological effects of MED12 mutations.
To assess whether MED12 mutation is associated with CD8+ T cell functional alterations, lung cancer cells with (NCI-H322) or without (PC9) MED12 mutation were co-cultured with CD8+ T cells. T cell migration assays revealed that significantly fewer CD8+ T cells migrated toward NCI-H322 cells compared to PC9 cells (Figure 6A). Furthermore, after co-culture with NCI-H322 cells, we observed a significant decrease in the supernatant levels of IFN-γ, TNF-α, perforin, and granzyme B from CD8+ T cells (Figure 6B), while the positive rate of the exhaustion marker PD-1 on CD8+ T cells was markedly increased (Figure 6C). Additionally, after co-culture with CD8+ T cells, the level of LDH in NCI-H322 cells was significantly decreased compared to the PC9 co-culture group (Figure 6D), suggesting that the killing ability of CD8+ T cells against MED12-mutant cancer cells is attenuated. Collectively, these results demonstrate an association between the presence of MED12 mutation and reduced CD8+ T cell chemotaxis and cytotoxicity in this cell line model. However, as these experiments were performed in non-isogenic cell lines, definitive causal attribution of these effects to MED12 mutation awaits isogenic validation.
To explore whether the observed reduction in CD8+ T cell function is associated with Notch pathway activity, the Notch pathway inhibitor DAPT was employed. Compared with the PC-9 co-culture system, the chemotaxis of CD8+ T cells were significantly decreased after DAPT treatment in the MED12-mutant lung cancer cells co-culture system (Figure 7A). In addition, the co-culture of MED12-mutated lung cancer cells in the DAPT-treated group showed that the level of functional cytokines secreted by CD8+ T cells was significantly decreased (Figure 7B), while the percentage of PD-1 positive cancer cells was significantly increased (Figure 7C). Moreover, surface PD-L1 on MED12-mutant tumor cells was significantly upregulated and partially reversed by DAPT (Figure 7D). Further assays revealed the LDH level of PC-9 co-culture with DAPT group was significantly higher than that of MUT with DAPT group (Figure 7E). Taken together, these findings suggest that Notch pathway activity contributes to the functional suppression of CD8+ T cells in this MED12-mutant cell line model, although the formal demonstration that this suppression is directly caused by MED12 mutation awaits verification in isogenic systems.
Lung cancer is the leading cause of cancer-related death worldwide[22]. Accumulating evidence indicates significant molecular and clinical heterogeneity within subgroups of lung cancer defined by oncogenic drivers (i.e., intra-driver heterogeneity), and genomic alterations caused by mutations represent an important basis for this heterogeneity[23]. Therefore, in-depth investigation of the mechanisms underlying lung cancer-related gene mutations not only helps reveal the molecular basis of lung cancer progression but also provides a theoretical foundation for guiding precision treatment. In the cell line models examined, this study suggests that MED12 mutation is associated with enhanced lung CSC properties and reduced CD8+ T cell chemotaxis and cytotoxicity. Mechanistically, our findings indicate that Notch pathway activation may mediate, at least in part, these phenotypic changes in the MED12-mutant context (Figure 8, Supplementary Table 1). While definitive causal attribution awaits validation in isogenic systems, this study offers preliminary mechanistic insights and highlights MED12 and the Notch pathway as potential candidates for further exploration in the context of lung cancer immunotherapy.
As part of the transcription pre-initiation complex, MED12 associates with CDK8/19 or cyclin C to launch the transcription process[24,25]. It has been reported that MED12 cooperates with BRD4 to sustain cancer cell proliferation[26]. Moreover, MED12 is frequently mutated in various tumors. The G44D mutation of MED12 significantly upregulated the expression of TDO2 and its metabolite kynurenine, thereby promoting excessive proliferation of uterine fibroid cells[27]. In uterine leiomyomas, the c.131G>A mutation in MED12 led to a single amino acid substitution (glycine to aspartic acid), causing instability of the MED12 protein and upregulating the expression of extracellular matrix components, including matrix metalloproteinases-2 (MMP-2), MMP-9, collagen type 4 alpha 2, and alpha-smooth muscle actin, thereby promoting tumor progression[28]. Mutant MED12 drives excessive cell proliferation under androgen-deficient conditions by upregulating GLI3 target genes[29]. Consistent with these previous reports, the present study found that lung cancer cells carrying a MED12 mutation exhibited significantly higher proliferation rates and significantly lower apoptosis rates than non-mutant cells, suggesting the oncogenic potential of MED12 mutation. Bioinformatics analysis indicated that MED12-associated genes are closely related to the maintenance of CSCs. Notably, CSCs are a subset of self-renewing cells present in most hematological and solid tumors, involved in multiple key processes including tumor initiation, dissemination, drug resistance, relapse, and post-treatment metastasis[30]. The present study found that MED12 mutation maintained the stemness phenotype. This observation corroborates prior evidence that MED12 mutation is known to drive stem cell expansion in uterine leiomyomas[15]. In summary, MED12 mutation exerts an oncogenic role in lung cancer.
A close bidirectional interaction exists between CSCs and the tumor microenvironment, mediated by direct cell contact and ligand-receptor interactions, which fuels tumor growth via crosstalk with non-neoplastic cells[31]. Recent studies have shown that in 21 solid malignancies, high stemness features were associated with poor immunogenic responses, suggesting cross-regulation between these two pro-tumor pathways[32]. Given the highly stem-like phenotype observed in MED12-mutant lung cancer cells in this study, we sought to explore whether MED12 - as a gene - may be broadly associated with immune microenvironment features. To this end, we performed exploratory transcriptomic analyses using TCGA bulk RNA-seq and GEO single-cell sequencing data, correlating MED12 expression levels (high vs low) with immune cell infiltration and functional status. These analyses revealed that MED12 expression levels are inversely associated with CD8+ T cell infiltration and functional markers. However, it is important to emphasize that these transcriptomic data are based on MED12 mRNA expression levels rather than mutation status, and therefore reflect correlative associations rather than mechanistic evidence linking MED12 mutation to immune modulation. Nevertheless, the correlative transcriptomic observations from our study, while consistent with an immunomodulatory role for MED12, cannot be used to directly support mechanistic conclusions about mutation-driven effects. Previous research has demonstrated that MED12 mutation inhibited CD8+ T cell cytotoxicity through the STAT1-TAP2 signaling axis and mediates resistance to immunotherapy in lung cancer[16]. This study further confirmed that MED12 mutation mediates the reduction of CD8+ T cell chemotaxis and cytotoxic function toward tumor cells.
Subsequently, we investigated the molecular mechanism mediated by MED12 mutation. Based on enrichment analysis results, MED12-associated genes were closely related to the regulation of the Notch signaling pathway. In the cell line models examined, we observed elevated protein levels of NOTCH1, NICD, HES1, and HEY1 in MED12-mutant NCI-H322 cells compared to PC9 cells, which is consistent with previous reports showing that under MED12 mutation conditions, the level of the NOTCH1 intracellular domain (NICD, the active form of NOTCH1) was elevated[21]. Furthermore, ChIP-PCR analysis revealed enhanced MED12 occupancy at the HES1 promoter in NCI-H322 cells, providing molecular evidence that MED12 physically associates with Notch target chromatin in this lung cancer cell model. These findings suggest a potential link between the MED12-mutant context and Notch pathway activity. However, as these experiments were performed in non-isogenic cell lines, we cannot definitively conclude that the observed Notch activation is uniquely driven by the MED12 mutation; formal causal attribution awaits isogenic vali
Notably, our data further suggest that Notch pathway activity in the MED12-mutant cell model is associated with immunosuppressive features. We found that NCI-H322 cells exhibited significantly higher cell-surface PD-L1 expression compared to PC9 cells, and this upregulation was reversed by Notch inhibition with DAPT, indicating that Notch signaling contributes to PD-L1 upregulation in this context. Previous studies have shown that inhibition of the Notch signaling pathway not only enhanced the killing capacity of CD8+ T cells but also promoted the production of pro-inflammatory cytokines (including IFN-γ, TNF-α, IL-1β, IL-6, and IL-8) in CD8+ T cells of colorectal cancer patients[36]. In addition, the Notch signaling pathway possessed potential immunosuppressive properties, possibly through induction of PD-1 expression[36]. Our co-culture experiments demonstrated that CD8+ T cells co-cultured with NCI-H322 cells exhibited reduced chemotaxis, decreased effector cytokine production, and upregulated PD-1, while DAPT treatment partially reversed these phenotypes. These observations are consistent with a model in which Notch-mediated PD-L1 upregulation on tumor cells contributes to CD8+ T cell exhaustion. In summary, this study integrate MED12 mutation-associated Notch pathway activity, maintenance of CSC properties, and CD8+ T cell immunosuppression into a preliminary mechanistic framework, suggesting a dual role for the MED12-Notch axis in both tumor-intrinsic stemness and tumor-extrinsic immune modulation. However, given the non-isogenic nature of the cell lines used, as well as the unresolved contributions of soluble factors and exosomal transfer mechanisms, this model should be interpreted as a working hypothesis that warrants further validation in isogenic systems and in vivo models.
This study has several limitations. First, the in vitro experiments were primarily based on two lung cancer cell lines, NCI-H322 (MED12-mutant) and PC9 (MED12-non-mutant), which may introduce cell line-specific bias. Consequently, the observed phenotypic differences, including enhanced stemness, Notch pathway activation, and CD8+ T cell suppression - cannot be definitively attributed solely to the MED12 mutation. Our findings in these cell lines should therefore be interpreted as correlative and hypothesis-generating, rather than as definitive causal proof. Future studies should validate the conclusions of this study in more lung cancer cell lines carrying different MED12 mutation sites and in primary cells, ideally using isogenic systems (e.g., MED12 knockdown/rescue or introduction of MED12 mutations into wild-type parental lines) to establish formal causality. Second, this study lacks in vivo experimental evidence. Although the in vitro co-culture experiments revealed the inhibitory effect of MED12 mutation on CD8+ T cell function, this has not yet been validated in animal models. Future studies should establish immunocompetent mouse models of lung cancer xenografts combined with MED12 mutation intervention strategies to evaluate its role in regulating the tumor immune microenvironment in vivo. Third, the detailed molecular mechanism of MED12 mutation involved in Notch signal transduction is still not fully understood, and it needs to be studied in the homologous system in the future.
Nevertheless, our mechanistic findings provide a translational rationale that warrants clinical investigation. Clinically, our findings suggest that MED12 mutation may serve as a predictive biomarker for anti-PD-1/PD-L1 immunotherapy in lung cancer, as MED12-mutant tumors exhibit elevated PD-L1 expression and CD8+ T cell exhaustion. Moreover, the Notch-PD-L1 axis provides a mechanistic rationale for combining Notch inhibitors with ICIs to counteract MED12-driven immune suppression. These observations warrant further clinical evaluation of MED12 mutation as both a patient selection biomarker and a therapeutic target.
In summary, this pioneering study elucidates that MED12 mutation concurrently preserves CSC properties and inhibits CD8+ T cell chemotaxis and cytotoxic function via Notch signaling activation, thereby facilitating tumor immune evasion. This work uncovers the key role of the MED12 mutation-Notch pathway axis in lung cancer progression and immune microenvironment remodeling, providing a new theoretical basis and potential intervention targets for lung cancer immunotherapy strategies targeting MED12 and related signaling pathways.
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