Microenvironment Cell Populations Research Articles

Microenvironment cell populations are associated with clinical outcomes in high-grade serous ovarian cancer

Microenvironment cell populations are associated with clinical outcomes in high-grade serous ovarian cancer

Identification of hub genes through integrated single-cell and microarray transcriptome analysis in osteoarthritic meniscus

BackgroundOsteoarthritis (OA) is marked by the progressive degradation of joint cartilage and subchondral bone. The precise molecular mechanisms driving meniscus deterioration in OA, especially at the single-cell level, remain poorly understood.MethodWe analyzed two datasets from the GEO database, GSE220243 and GSE98918, focusing on meniscus tissue sequencing data from OA and non-OA patients. The standard Seurat procedure was employed to process single-cell data and identify differentially expressed genes (DEGs). Immune cell infiltration was assessed using the Microenvironment Cell Populations (MCP) counter and CIBERSORT algorithms. For the microarray data, DEGs were identified with the limma package, and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed using ClusterProfiler. The overlapping DEGs from both datasets were imported into Cytoscape to generate protein-protein interaction (PPI) networks and identify hub genes. Transcription factor (TF) and miRNA interaction networks were analyzed using NetworkAnalyst, and gene-related predictive drugs were enriched through the DSigDB platform.ResultAfter quality control, 34,763 cells from the OA patients and 34,145 cells from the healthy controls were analyzed. UMAP identified and SingleR annotated 14 cell clusters. The 10 largest cell clusters were selected for further analysis. The OA group exhibited a notable increase in macrophages and a reduction in cytotoxic lymphocytes and endothelial cells in the meniscus. In GSE98918, 220 DEGs were identified, and the MCODE plug-in in Cytoscape pinpointed a key module containing 12 candidate genes. The MCC methodfiltered the top 20 DEGs in each GSE220243 cluster. Overlapping DEGs from GSE220243 and GSE98918 identified COL1A1, COL3A1, COL5A2, COL6A3, LOX, and VEGFA as significantly decreased in OA, with COL3A1, COL5A2, LOX, and VEGFA upregulated in meniscal chondrocytes. The interaction network highlighted 3 key miRNAs and 13 shared TFs. Ten gene-related predictive drug molecules were identified.ConclusionThis research highlights crucial genes in the OA meniscus and uncovers their differing regulatory patterns between chondrocytes and non-chondrocytes. These findings enhance our understanding of the molecular mechanisms driving OA pathogenesis and aid in identifying potential drug targets.

Dynamic reciprocal interactions between activated T cells and tumor associated macrophages drive macrophage reprogramming and proinflammatory T cell migration within prostate tumor models

Tumor-associated macrophages (TAMs) have been implicated as a tumor microenvironment (TME) cell population, which may be playing a vital role in the inhibition of effective T cell responses in the prostate TME. In this manuscript, we leverage a novel microscale cell culture platform, known as Stacks, to investigate mono-, co-, and tri-culture TME models comprised of prostate tumor cell lines, primary macrophages, and autologous T cells from patients with prostate cancer. Through multiplexed analysis of these multi-cellular prostate tumor models, we capture a dynamic interaction between primary TAMs and activated T cells that resulted in reciprocal proinflammatory activation of both cell populations upon interaction. These findings suggest that activated T cells are capable of reprogramming immunosuppressive TAMs in the context of prostate tumor models and that TAM reprogramming may play a key supportive role in restoring proinflammatory T cell tumor responses in the prostate TME.

Identification of the Neointimal Hyperplasia-Related LncRNA-mRNA-Immune Cell Regulatory Network in a Rat Carotid Artery Balloon Injury Model.

Excessive neointimal hyperplasia (NIH) of coronary vessels in patients is the main cause of restenosis (RS) after percutaneous coronary intervention (PCI). This study aimed to identify the regulatory genes related to NIH in a rat carotid artery balloon injury model.We established a rat model and performed RNA sequencing to identify differentially expressed long non-coding RNAs (DElncRNAs) and differentially expressed message RNAs (DEmRNAs). Immune cells were analyzed using a murine Microenvironment Cell Population counter. The Pearson correlation between DEmRNAs, DElncRNAs, and immune cells was analyzed, followed by function enrichment analysis. Core DEmRNA was identified using Cytoscape. Next, a core lncRNAs-mRNAs-immune cell regulatory network was constructed. NIH-related gene sets from the Gene Expression Omnibus and GeneCards databases were used for validation.A total of 2,165 DEmRNAs and 705 DElncRNAs were identified in rat carotid artery tissue. Four key immune cells were screened out, including mast cells, vessels, endothelial cells, and fibroblasts. Based on the Pearson correlation between DEmRNAs, DElncRNAs and 4 key immune cells, 246 DEmRNAs and 93 DElncRNAs were obtained. DEmRNAs that interact with lncRNAs were mainly involved in the cell cycle, MAPK signaling pathway, and PI3K-Akt signaling pathway. A core lncRNA-mRNA-immune cell regulatory network was constructed, including 9 mRNAs, 4 lncRNAs, and fibroblasts. External datasets validation confirmed the significant correlation of both these mRNAs and lncRNAs with NIH.In this study, an lncRNA-mRNA-immune cell regulatory network related to NIH was constructed, which provided clues for exploring the potential mechanism of RS in cardiovascular diseases.

Neutrophil estimation and prognosis analysis based on existing lung squamous cell carcinoma datasets: the development and validation of a prognosis prediction model.

Notwithstanding the rapid developments in precision medicine in recent years, lung cancer still has a low survival rate, especially lung squamous cell cancer (LUSC). The tumor microenvironment (TME) plays an important role in the progression of lung cancer, in which high neutrophil levels are correlated with poor prognosis, potentially due to their interactions with tumor cells via pro-inflammatory cytokines and chemokines. However, the precise mechanisms of how neutrophils influence lung cancer remain unclear. This study aims to explore these mechanisms and develop a prognosis predictive model in LUSC, addressing the knowledge gap in neutrophil-related cancer pathogenesis. LUSC datasets from the Xena Hub and Gene Expression Omnibus (GEO) databases were used, comprising 473 tumor samples and 195 tumor samples, respectively. Neutrophil contents in these samples were estimated using CIBERSORT, xCell, and microenvironment cell populations (MCP) counter tools. Differentially expressed genes (DEGs) were identified using DEseq2, and a weighted gene co-expression network analysis (WGCNA) was performed to identify neutrophil-related genes. A least absolute shrinkage and selection operator (LASSO) Cox regression model was constructed for prognosis prediction, and the model's accuracy was validated using Kaplan-Meier survival curves and time-dependent receiver operating characteristic (ROC) curves. Additionally, genomic changes, immune correlations, drug sensitivity, and immunotherapy response were analyzed to further validate the model's predictive power. Neutrophil content was significantly higher in adjacent normal tissue compared to LUSC tissue (P<0.001). High neutrophil content was associated with worse overall survival (OS) (P=0.02), disease-free survival (DFS) (P=0.02), and progression-free survival (PFS) (P=0.03) using different software estimates. Nine gene modules were identified, with blue and yellow modules showing strong correlations with neutrophil prognosis (P<0.001). Eight genes were selected for the prognostic model, which accurately predicted 1-, 3-, and 5-year survival in both the training set [area under the curve (AUC) value =0.60, 0.63, 0.66, respectively] and validation set (AUC value =0.58, 0.58, 0.59, respectively), with significant prognosis differences between high- and low-risk groups (P<0.001). The model's independent prognostic factors included risk group, pathologic M stage, and tumor stage (P<0.05). A further molecular mechanism analysis revealed differences between risk groups were revealed in immune checkpoint and human leukocyte antigen (HLA) gene expression, hallmark pathways, drug sensitivity, and immunotherapy responses. This study established a risk-score model that effectively predicts the prognosis of LUSC patients and sheds light on the molecular mechanisms involved. The findings enhance the understanding of neutrophil-tumor interactions, offering potential targets for personalized treatments. However, further experimental validation and clinical studies are required to confirm these findings and address study limitations, including reliance on public databases and focus on a specific lung cancer subtype.

A novel model based on lipid metabolism-related genes associated with immune microenvironment predicts metastasis of breast cancer

BackgroundBreast cancer (BC) is the most prevalent malignant tumor among women worldwide and a significant cause of cancer-related deaths in females. Recent studies have shown that lipid metabolism-related genes (LMRGs) exhibit prognostic potential in various types of tumors, including BC. Our study aimed to establish a novel model to predict the metastasis of BC.MethodsClinical information and corresponding RNA data of patients with BC were downloaded from The Cancer Genome Atlas and Gene Expression Omnibus databases. Consensus clustering was performed to identify novel molecular subgroups. Estimation of Stromal and Immune Cells in Malignant Tumor Tissues using Expression, microenvironment cell populations counter, microenvironment cell populations counter, and single-sample gene set enrichment analyses were employed to determine the tumor immune microenvironment and immune status of the identified subgroups. Functional analyses, including Gene Ontology and gene set enrichment analyses, were conducted to elucidate the underlying mechanisms. A prognostic risk model was constructed using the Least Absolute Shrinkage and Selection Operator algorithm and multivariate Cox regression analysis.ResultsThis study identified differential gene expression between patients with BC exhibiting metastasis and those without metastasis using public databases. Using the obtained data, we established predictive models based on six LMRGs. Furthermore, consensus clustering and prognostic score grouping analysis revealed that differentially expressed LMRGs influence tumor prognosis by regulating tumor immunity. To facilitate clinical application, we developed a nomogram integrating the risk model and clinical characteristics to accurately predict the prognosis of patients with BC.ConclusionWe developed and validated a novel signature associated with LMRGs for predicting disease-free survival in patients with BC. The expression of LMRGs correlates with the immune microenvironment of patients with BC, providing new insights and improved strategies for the diagnosis and treatment of BC.

The prognostic value of immune escape-related genes in lung adenocarcinoma.

Lung cancer is one of the most common cancers in humans, and lung adenocarcinoma (LUAD) has become the most common histological type of lung cancer. Immune escape promotes progression of LUAD from the early to metastatic late stages and is one of the main obstacles to improving clinical outcomes for immunotherapy targeting immune detection points. Our study aims to explore the immune escape related genes that are abnormally expressed in lung adenocarcinoma, providing assistance in predicting the prognosis of lung adenocarcinoma and targeted. RNA data and related clinical details of patients with LUAD were obtained from The Cancer Genome Atlas (TCGA) database. Through weighted gene coexpression network analysis (WGCNA), 3112 key genes were screened and intersected with 182 immune escape genes obtained from a previous study to identify the immune escape-related genes (IERGs). The role of IERGs in LUAD was systematically explored through gene ontology (GO) and Kyoto Encyclopedia of Genes and Genome (KEGG) analyses, which were used to enrich the relevant pathways of IERGs. The least absolute shrinkage and selection operator (LASSO) algorithm and multivariate Cox regression analysis were used to identify the key prognostic genes, and a prognostic risk model was constructed. Estimation of Stromal and Immune Cells in Malignant Tumor Tissues Using Expression Data (ESTIMATE) and microenvironment cell populations (MCP) counter methods (which can accurately assess the amount of eight immune cell populations and two stromal cell groups) were used to analyze the tumor immune status of the high and low risk subgroups. The protein expression level of the differentially expressed genes in lung cancer samples was determined by using the Human Protein Atlas (HPA) database. A nomogram was constructed, and the prognostic risk model was verified via the Gene Expression Omnibus (GEO) datasets GSE72094 and GSE30219. Twenty differentially expressed IERGs were obtained. GO analysis of these 20 IERGs revealed that they were mainly associated with the regulation of immune system processes, immune responses, and interferon-γ enrichment in mediating signaling pathways and apoptotic signaling pathways; meanwhile, KEGG analysis revealed that IERGs were associated with necroptosis, antigen processing and presentation, programmed cell death ligand 1 (PD-L1) expression and programmed cell death 1 (PD-1) pathway in tumors, cytokine-cytokine receptor interactions, T helper cell 1 (Th1) and Th2 differentiation, and tumor necrosis factor signaling pathways. Using LASSO and Cox regression analysis, we constructed a four-gene model that could predict the prognosis of patients with LUAD, and the model was validated with a validation cohort. The immunohistochemical results of the HPA database showed that AHSA1 and CEP55 had low expression in normal lung tissue but high expression in lung cancer tissue. We constructed an IERG-based model for predicting the prognosis of LUAD. Among the genes identified, CEP55 and AHSA1 may be potential prognostic and therapeutic targets, and reducing their expression may represent a novel approach in the treatment of LUAD.

Identification of potential novel N6-methyladenosine effector-related lncRNA biomarkers for serous ovarian carcinoma: a machine learning-based exploration in the framework of 3P medicine.

Serous ovarian carcinoma (SOC) is considered the most lethal gynecological malignancy. The current lack of reliable prognostic biomarkers for SOC reduces the efficacy of predictive, preventive, and personalized medicine (PPPM/3PM) in patients with SOC, leading to unsatisfactory therapeutic outcomes. N6-methyladenosine (m6A) modification-associated long noncoding RNAs (lncRNAs) are effective predictors of SOC. In this study, an effective risk prediction model for SOC was constructed based on m6A modification-associated lncRNAs. Transcriptomic data and clinical information of patients with SOC were downloaded from The Cancer Genome Atlas. Candidate lncRNAs were identified using univariate and multivariate and least absolute shrinkage and selection operator-penalized Cox regression analyses. The molecular mechanisms of m6A effector-related lncRNAs were explored via Gene Ontology, pathway analysis, gene set enrichment analysis, and gene set variation analysis (GSVA). The extent of immune cell infiltration was assessed using various algorithms, including CIBERSORT, Microenvironment Cell Populations counter, xCell, European Prospective Investigation into Cancer and Nutrition, and GSVA. The calcPhenotype algorithm was used to predict responses to the drugs commonly used in ovarian carcinoma therapy. In vitro experiments, such as migration and invasion Transwell assays, wound healing assays, and dot blot assays, were conducted to elucidate the functional roles of candidate lncRNAs. Six m6A effector-related lncRNAs that were markedly associated with prognosis were used to establish an m6A effector-related lncRNA risk model (m6A-LRM) for SOC. Immune microenvironment analysis suggested that the high-risk group exhibited a proinflammatory state and displayed increased sensitivity to immunotherapy. A nomogram was constructed with the m6A effector-related lncRNAs to assess the prognostic value of the model. Sixteen drugs potentially targeting m6A effector-related lncRNAs were identified. Furthermore, we developed an online web application for clinicians and researchers (https://leley.shinyapps.io/OC_m6A_lnc/). Overexpression of the lncRNA RP11-508M8.1 promoted SOC cell migration and invasion. METTL3 is an upstream regulator of RP11-508M8.1. The preliminary regulatory axis METTL3/m6A/RP11-508M8.1/hsa-miR-1270/ARSD underlying SOC was identified via a combination of in vitro and bioinformatic analyses. In this study, we propose an innovative prognostic risk model and provide novel insights into the mechanism underlying the role of m6A-related lncRNAs in SOC. Incorporating the m6A-LRM into PPPM may help identify high-risk patients and personalize treatment as early as possible.

A single-cell multiomics analysis of IRF4 in mediating treatment resistance in ibrutinib-treated chronic lymphocytic leukemia (CLL).

7051 Background: The Bruton tyrosine kinase (BTK) inhibitor ibrutinib (ibr) has revolutionized the treatment of CLL, showing efficacy in the majority of patients. Nevertheless, resistance occurs in a subset of patients, often with dismal clinical outcomes. Ibr resistance occurs through several mechanisms, including mutations affecting BTK or PLCG2, or upregulation of alternative survival pathways. We investigated at the single-cell level the molecular mechanisms that underlie ibr resistance in CLL cells and their interactions with non-malignant tumor microenvironment (TME) cell populations. Methods: We retrospectively identified 7 CLL patients with ibr resistance (as second line therapy). Samples were obtained before ibr treatment and at progression. Chromium Single Cell Multiome ATAC + Gene Expression (GEX) (10x Genomics) was applied to a mixture of CD19+CD5+ and CD19- cells to jointly analyze DNA accessibility and gene expression in the same single nuclei of CLL B and TME cells. An average of 4,800 cells (range 2,600-15,000) per sample was retained after QC filtering. Principal component analysis (PCA) was performed on GEX counts. ATAC peaks were called with MACS2, and latent semantic indexing was used for dimensionality reduction. We then computed a weighted nearest neighbour graph, identified clusters using the SLM algorithm and performed cell type assignment with established marker genes. Results: We identified 28 distinct cell clusters, representing CLL B-cells or cell types that constitute the TME, including CD4+ and CD8+ T cells, monocytes, dendritic and NK cells. CLL B-cell clusters were patient and timepoint-specific, while TME clusters were occupied by cells from multiple patients. Five patients showed multiple distinct CLL B-cell clusters pre-treatment or at progression, reflecting cellular heterogeneity within the tumor compartment. Interestingly, some clusters that were dominated by post-treatment CLL B-cells also included cells from pre-treatment samples, suggesting pre-existence of the clone giving rise to the relapse. For each patient, we performed pairwise differential expression between CLL B-cell clusters. Gene ontology analysis revealed increased activation of IRF4 and MHC-related pathways in CLL B-cells at progression compared to pre-treatment in 6 and 4 out of 7 patients, respectively. The elevated IRF4 pathway activity was underpinned by increased chromatin accessibility in IRF4 signature genes. Conclusions: Differential blockade of B-cell receptor signaling by ibr has been reported to downregulate activity of IRF4, a key transcription factor in B-cell activation. Our results suggest that CLL B-cells that are resistant to ibrutinib have re-activated IRF4 activity beyond pre-treatment levels, implying a potential role for IRF4 in ibrutinib resistance.

Abstract 2477: Selective IL-21 and IFN-alpha immunocytokines engineered using the AlphaSeq platform

Abstract Although cytokine therapies have demonstrated curative effects in some cancer patients, clinical use remains limited due to undesired toxicity profiles accompanying systemic administration. Next generation cytokine approaches include conditional signaling focused on sites of interest, such as the tumor microenvironment or specific immune cell populations. Here, we share a novel approach for generating detuned cytokine therapeutic candidates using the AlphaSeq platform, which involves the re-engineering of yeast agglutination and mating to quantitatively measure protein-protein interactions at a library-on-library scale. Engineering interferon-alpha (IFNα) and interleukin-21 (IL-21), we show how AlphaSeq measures cytokine-receptor interactions and identifies cytokine variants with a broad range of affinities. A saturated mutational library was created for IFNα and IL-21and subsequently screened against a second library consisting of human IFNAR2 or human IL-21R, species orthologs and off-target receptors. AlphaSeq enabled identification of hundreds of detuned IFNα and IL-21 variants against both human and mouse receptors in parallel assays. Cytokine variants with lower affinity than parental IFNα or IL-21 were recombinantly expressed as Fc fusion proteins to orthogonally measure affinity with biolayer interferometry and characterize potency with an in vitro human PBMC phosflow assay, which showed strong correlation with AlphaSeq affinity measurements. Finally, detuned cytokine candidates were fused to anti-CD8 and other localizing antibodies to demonstrate cell population-specific signaling. Candidate molecules showed 1000-fold or greater potency in the targeted cell population than non-targeted populations. Our results show the AlphaSeq platform can accurately quantitate thousands of cytokine variant affinities simultaneously against multiple relevant receptors, enabling the selection of candidate immunocytokine antibody fusion proteins with targeted cell biased signaling. AlphaSeq’s rapid, comprehensive affinity determination is being used to develop clinically relevant IFNα and IL-21 therapeutic immunocytokines with accompanying preclinical data. Citation Format: Charles Lin, Colleen Shikany, Leah Homad, Jessica Fint, Ruchi Bansal, Davis Goodnight, Jeff Adamo, Danielle Van Citters, Ryan Swanson, Randolph Lopez. Selective IL-21 and IFN-alpha immunocytokines engineered using the AlphaSeq platform [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2477.

Abstract 1441: Establishing a novel clinically relevant disease model of glioblastoma

Abstract Introduction: GBM is the most common primary malignancy of the CNS. Historically pre-clinical models have failed to predict response in humans, despite promising preclinical data. Moreover, current models seldom incorporate surgical resection and/or standard of care chemotherapy treatment. These models also commonly employ young animals exclusively, whose immune contexture differs from older patients. We therefore sought to establish an improved, orthotopic, preclinical GBM model, which better recapitulates patient response to standard-of-care and targeted treatments. Methods: NFpp10a-Luc2 GBM cells were orthotopically implanted into C57BL/6-mice (aged[&amp;gt;18months] and young[6-8weeks]) and weekly bioluminescence imaging performed to monitor tumor growth. Overall survival (OS) and tumor growth were assessed in response to (1) Surgical Resection, (2) Temozolomide (TMZ) (3) anti-PD1 (4) neoadjuvant anti-PD1 and (5) Regorafenib (REGO) therapy. Tissue collected post-mortem underwent bulk RNA sequencing followed by analysis via microenvironment cell population counter (MCP) and gene set enrichment (GSEA) to determine changes in the GBM-TME following treatment. Results: We demonstrated OS advantage in aged mice undergoing surgical resection (Resection:33.5 days vs Non-Resection: 18 days; p=0.0166) and observed age to be a significant prognostic factor (Young:62 days vs Aged: 22 days; p=0.0002). Subsequently, we observed that TMZ and anti-PD1 monotherapies had no impact on NFpp10-Luc2 growth (p=0.9001, p=0.7933) or survival (p=0.3035, p=0.6328). Neoadjuvant anti-PD1 treated mice demonstrated no significant survival advantage compared to IgG control (33 days vs 35 days; p=0.9429). Lastly, REGO treatment demonstrated a trend towards improved OS vs Vehicle (p=0.096). MCP analysis revealed both neoadjuvant anti-PD1 and REGO treatment induced influx of CD8+ T cells, B cells and monocytes into the TME, with neoadjuvant anti-PD1 associated with an upregulation of CXCR3 (p=0.0045). Conclusions: We have, for the first time, established and characterized response of the NFpp10a-C57BL/6 model to surgical resection, TMZ, anti-PD1 and REGO therapy in both young and aged mice. We have shown the model is markedly insensitive to intervention with chemotherapy and immune checkpoint therapy, mirroring what is seen clinically in patients. The model may therefore be employed in future pre-clinical studies to guide clinical trials in the setting of mesenchymal GBM. Citation Format: Kate Connor, Kieron White, James Clerkin, Kieron Sweeney, Liam Shiels, Thomas van Brussel, Ingrid Arijs, Diether Lambrechts, Gautam Shankar, Frederik de Smet, Stephen G. Maher, Laure Marignol, Patrick Dicker, Jochen Prehn, David O'Brien, Annette T. Byrne. Establishing a novel clinically relevant disease model of glioblastoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1441.

Influence of TP53 Comutation on the Tumor Immune Microenvironment and Clinical Outcomes With Immune Checkpoint Inhibitors in STK11-Mutant Non-Small-Cell Lung Cancer.

Non-small-cell lung cancer (NSCLC) with STK11mut has inferior outcomes to immune checkpoint inhibitors (ICIs). Using multiomics, we evaluated whether a subtype of STK11mut NSCLC with a uniquely inflamed tumor immune microenvironment (TIME) harboring TP53 comutations could have favorable outcomes to ICIs. NSCLC tumors (N = 16,896) were analyzed by next-generation sequencing (DNA-Seq/592 genes). A subset (n = 5,034) underwent gene expression profiling (RNA-Seq/whole transcriptome). Exome-level neoantigen load for STK11mut NSCLC was obtained from published pan-immune analysis. Tumor immune cell content was obtained from transcriptome profiles using the microenvironment cell population (MCP) counter. ICI data from POPLAR/OAK (n = 34) and the study by Rizvi et al (n = 49) were used to model progression-free survival (PFS), and a separate ICI-treated cohort (n = 53) from Dana-Farber Cancer Institute (DFCI) was used to assess time to treatment failure (TTF) and tumor RECIST response for STK11mutTP53mut versus STK11mutTP53wt NSCLC. Overall, 12.6% of NSCLC tumors had a STK11mut with the proportions of tumor mutational burden (TMB)-high (≥10 mut/Mb), PD-L1 ≥50%, and microsatellite instability-high being 38.3%, 11.8%, and 0.72%, respectively. Unsupervised hierarchical clustering of STK11mut (n = 463) for stimulator of interferon-gamma (STING) pathway genes identified a STING-high cluster, which was significantly enriched in TP53mut NSCLC (P < .01). Compared with STK11mutTP53wt, tumors with STK11mutTP53mut had higher CD8+T cells and natural killer cells (P < .01), higher TMB (P < .001) and neoantigen load (P < .001), and increased expression of MYC and HIF-1A (P < .01), along with higher expression (P < .01) of glycolysis/glutamine metabolism genes. Meta-analysis of data from OAK/POPLAR and the study by Rizvi et al showed a trend toward improved PFS in patients with STK11mutTP53mut. In the DFCI cohort, compared with the STK11mut TP53wt cohort, the STK11mutTP53mut tumors had higher objective response rates (42.9% v 16.7%; P = .04) and also had longer TTF (14.5 v 4.5 months, P adj = .054) with ICI. STK11mut NSCLC with TP53 comutation is a distinct subgroup with an immunologically active TIME and metabolic reprogramming. These properties should be exploited to guide patient selection for novel ICI-based combination approaches.

SNAI2 as a Prognostic Biomarker Based on Cancer-Associated Fibroblasts in Patients With Lung Adenocarcinoma.

Lung adenocarcinoma (LUAD) is a common type of malignant tumor with therapeutic challenges. Cancer-associated fibroblasts (CAFs) promote LUAD growth and metastasis, regulate the tumor immune response, and influence tumor treatment responses and drug resistance. However, the molecular mechanisms through which CAFs control LUAD progression are largely unknown. In this study, we aimed to determine the correlations between CAF-related genes and overall survival (OS) in patients with LUAD. We acquired the gene expression data and clinical information of 522 patients with LUAD patients from The Cancer Genome Atlas (TCGA) and 442 patients with LUAD from the Gene Expression Omnibus (GEO) databases. CAF infiltration levels were assessed using the Microenvironment Cell Population (MCP) counter, the Estimating the Proportions of Immune and Cancer cells (EPIC) algorithm, and Tumor Immune Dysfunction and Exclusion (TIDE) scores. A CAF-related gene network was constructed using the Weighted gene co-expression network analysis (WGCNA). Based on the CAF-related genes, univariate Cox regression and Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression analyses were performed to identify prognostic genes. Gene expression levels within the prognostic model were validated using the Cancer Cell Line Encyclopedia (CCLE) databases and Western blotting. Our results demonstrated that high CAF scores were associated with lower survival rates in patients with LUAD. Gene modules that were highly correlated with high CAF scores were closely associated with tissue characteristics and extracellular matrix structures in LUAD. In addition, correlations between CAF scores and responses to immunotherapy and chemotherapy were observed. Finally, we found that SNAI2 expression was higher in lung cancer tissues than in normal tissues. Deepening our understanding of the influence of CAFs on tumor progression and treatment response at the molecular level can aid the development of more effective therapeutic strategies. This study provides important insights into the functional mechanisms of action of CAFs in LUAD and highlights their clinical implications.

Identification of hub genes based on integrated analysis of single-cell and microarray transcriptome in patients with pulmonary arterial hypertension

BackgroundPulmonary arterial hypertension (PAH) is a devastating chronic cardiopulmonary disease without an effective therapeutic approach. The underlying molecular mechanism of PAH remains largely unexplored at single-cell resolution.MethodsSingle-cell RNA sequencing (scRNA-seq) data from the Gene Expression Omnibus (GEO) database (GSE210248) was included and analyzed comprehensively. Additionally, microarray transcriptome data including 15 lung tissue from PAH patients and 11 normal samples (GSE113439) was also obtained. Seurat R package was applied to process scRNA-seq data. Uniform manifold approximation and projection (UMAP) was utilized for dimensionality reduction and cluster identification, and the SingleR package was performed for cell annotation. FindAllMarkers analysis and ClusterProfiler package were applied to identify differentially expressed genes (DEGs) for each cluster in GSE210248 and GSE113439, respectively. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genome (KEGG) were used for functional enrichment analysis of DEGs. Microenvironment Cell Populations counter (MCP counter) was applied to evaluate the immune cell infiltration. STRING was used to construct a protein-protein interaction (PPI) network of DEGs, followed by hub genes selection through Cytoscape software and Veen Diagram.ResultsNineteen thousand five hundred seventy-six cells from 3 donors and 21,896 cells from 3 PAH patients remained for subsequent analysis after filtration. A total of 42 cell clusters were identified through UMAP and annotated by the SingleR package. 10 cell clusters with the top 10 cell amounts were selected for consequent analysis. Compared with the control group, the proportion of adipocytes and fibroblasts was significantly reduced, while CD8+ T cells and macrophages were notably increased in the PAH group. MCP counter revealed decreased distribution of CD8+ T cells, cytotoxic lymphocytes, and NK cells, as well as increased infiltration of monocytic lineage in PAH lung samples. Among 997 DEGs in GSE113439, module 1 with 68 critical genes was screened out through the MCODE plug-in in Cytoscape software. The top 20 DEGs in each cluster of GSE210248 were filtered out by the Cytohubba plug-in using the MCC method. Eventually, WDR43 and GNL2 were found significantly increased in PAH and identified as the hub genes after overlapping these DEGs from GSE210248 and GSE113439.ConclusionWDR43 and GNL2 might provide novel insight into revealing the new molecular mechanisms and potential therapeutic targets for PAH.

The Function and Mechanism Research of NLRP6 in Bone Marrow Microenvironment in Myeloproliferative Neoplasms

Since the important role of inflammation in myeloproliferative neoplasms (MPN) progression, inflammasome is an important regulator of inflammation involved in the development of tumor. Here, we aimed to investigate the function of NLRP6 in bone marrow (BM) microenvironment in MPN in the hope of getting better treatment for MPN. We first analyzed that NLRP6 was highly expressed in BM mononuclear cells from individuals newly diagnosed with ET, PV, PMF patients (with or without JAK2 V617Fmutation). Similar results were obtained in BM cells and BM stromal cells of MPL W515L transduced C57BL/6 mice (C57BL/6 MPN). The results were validated that NLRP6 of the BM microenvironment was involved in the development of MPN. We thus hypothesized that the absence of NLRP6 in BM microenvironment affects the development of MPN. NLRP6 -/- mice and C57BL/6 mice MPN models were established by the same method. The experiments demonstrated that overall survival of NLRP6 -/- MPN mice was significantly prolonged compared to C57BL/6 MPN mice. Loss of NLRP6 in BM microenvironment interfered with hematopoiesis of BM, but it had no effect on the extramedullary hematopoiesis (EMH) in spleen. Pathological analysis showed that the destruction of spleen structure in NLRP6 -/- MPN mice and the infiltration of atypical megakaryocytes were less than C57BL/6 MPN mice. The tumor load in spleen was significantly reduced and the structure of spleen was intact, the hematopoietic function of the BM returned to normal and the tumor load in the BM was significantly reduced at 5 weeks in NLRP6 -/- MPN mice after sequential transplantation. It suggested that knockout of NLRP6 in the BM microenvironment significantly attenuated the tumorigenicity of malignant HSCs, delayed or even inhibited the development of MPN disease. The expression of inflammatory cytokines, abnormal megakaryocytes and the phenomenon of aggregation and reticular fibers in BM microenvironment were significantly reduced in NLRP6 -/- MPN group compared with the control group. After sequential transplantation, the destruction of BM structure was alleviated, megakaryocytes aggregation and heteromorphic megakaryocytes in BM were reduced, and reticular fibers were significantly diminished. Furthermore, the cytokines promoting disease progression in BM microenvironment were significantly reduced in NLRP6 -/- MPN group, suggesting that knockout of NLRP6 can improve the BM microenvironment and alleviate the progression of myelofibrosis. Finally, to gain better insight into the role of NLRP6 inflammasome in MPN pathogenesis, we sought to dissect the changes with scRNA-seq and pathway identification based on available data and bioinformatic pathway analysis in NLRP6 -/- mice. The results of scRNA-seq analysis showed that C18 population of malignant HSCs of recipients in primary transplantation was added, C16, C13 and C9 cell populations disappeared after deletion of NLRP6 in BM microenvironment, while C5, C6, C7, C10 and C12 cell populations increased significantly. Regulon of various subpopulations of malignant HSCs changed significantly, mainly involving cell development and differentiation, immune response, inflammatory BM microenvironment, vascular endothelial homeostasis and tumor related genes. Our results indicate that knockout of NLRP6 in the BM microenvironment may affect the progression of MPN by altering some malignant HSCs subpopulations and regulons, participating in immune, inflammatory, tumor metabolism and other signaling pathways to regulate the functional characteristics of tumor stem cells of MPN.Our data here provide new evidence for NLRP6 as potential target in the treatment of MPN.

Cellular Deconvolution Reveals Unique Findings in Several Cell Type Fractions Within the Basal Cell Carcinoma Tumor Microenvironment

Introduction: Despite therapeutic advancements, locally advanced and metastatic basal cell carcinomas continue to carry poor prognoses and high recurrence rates. Current treatment options remain suboptimal due to limited efficacy and associated adverse events. The objectives of this study are to 1) characterize the basal cell carcinoma immune cell microenvironment and 2) identify novel therapeutic targets.&#x0D; Methods: Transcriptome data representing 25 basal cell carcinoma and 25 control tissue samples were obtained from the Gene Expression Omnibus. Cell type fraction estimates were derived by least-squares deconvolution. Population differences were determined by Mann-Whitney U test.&#x0D; Results: Most significantly, two deconvolution algorithms similarly observed greater B cell infiltration in tumor samples compared to normal tissue (P&lt;0.0001).&#x0D; Conclusion: Importantly, the results of this study provide new insight into the basal cell carcinoma tumor microenvironment and nominate testable immune cell populations for future therapeutic discovery. Study limitations include sample size and applicable background prediction levels of bulk deconvolution tools.

EPCO-29. SEXUALLY DIMORPHIC EFFECT OF H-FERRITIN GENETIC MANIPULATION ON SURVIVAL AND IMMUNE CELL INFILTRATION IN A MOUSE MODEL OF GLIOBLASTOMA

Abstract Iron plays a crucial role in promoting tumor growth. Recent research has shown that H-ferritin (FTH1), traditionally recognized as an iron storage protein, can transport iron to GBM cancer stem cells, reducing their invasion activity. Moreover, binding of extracellular FTH1 to human GBM tissues, and brain iron delivery in general, has been found to have a sex bias. This raises questions addressed in this study about whether H-ferritin levels extrinsic to the tumor can affect tumor cell pathways and if this impact is sex-specific. Systemic Fth1+/- mice were orthotopically implanted with mouse GBM cells (GL261). Littermate Fth1 +/+ mice were controls. Survival was evaluated and the tumors were subjected to next-generation sequencing protocols. We analyzed resulting data utilizing murine Microenvironment Cell Population (mMCP) method. mMCP estimates abundance of tissue infiltrating immune and stromal populations based on cell-specific gene signatures. Female Fth1+/- mice had significantly poorer survival than control females. Fth1 genetic status had no effect on survival in males. mMCP analysis revealed a significant reduction in T cells and CD8+ T cell infiltration tumors of Fth1+/- females as compared to Fth1+/+. Mast and fibroblast cell infiltration was increased in females and males with Fth1+/- background, respectively, compared to Fth1+/+ mice. Genetic manipulation of Fth1 which leads to reduced systemic levels of FTH1 protein had a sexually dimorphic impact on survival. Fth1+/- significantly worsened survival in females but not in male GBMs. Furthermore, genetic manipulation of Fth1 significantly affected tumor infiltration of T-cells, CD8+ T cells, fibroblasts, and mast cells in a sexually dimorphic manner. These results demonstrate a role for FTH1 and presumably iron status in establishing the tumor cellular landscape that ultimately impacts survival and further reveals a sex bias that may inform the population studies showing a sex effect on the prevalence of brain tumors.

EXTH-64. ESTABLISHING A CLINICALLY RELEVANT MOUSE MODEL OF MESENCHYMAL GLIOBLASTOMA FOR STUDYING TME TARGETING TREATMENTS

Abstract Historically, pre-clinical GBM models have largely failed to predict response in the clinical setting. Here, we establish a more clinically faithful model using the syngeneic NFpp10a-Luc2 mesenchymal-GBM cell line to study response to resection and temozolomide (TMZ), adjuvant/neo-adjuvant anti-PD1 and Regorafenib (REGO) therapy. Cells were orthotopically implanted in C57BL/6-mice. Response to surgical and therapeutic interventions was assessed using bioluminescence imaging (BLI). Murine Microenvironment cell-population (mMCP)-counter, GSEA and multiple iteractive labelling by antibody neodeposition (MILAN, high-dimensional single-cell multiplex analysis) were employed to characterise treatment-related TME effects. We observed survival advantage in aged mice undergoing resection (resection:33.5 days vs non-resection:18 days; p = 0.0166). TMZ/anti-PD1 had no impact on tumour growth (TMZ: p = 0.9001, anti-PD1: p = 0.7933) or survival (TMZ:p = 0.3035, anti-PD1-:p = 0.6328). Neo-adjuvant anti-PD1 also conferred no survival advantage in young mice (33 vs 35 days; p = 0.9429). REGO/REGO+TMZ treatment conferred no survival benefit in young mice (REGO:p = 0.0735 and REGO+TMZ:p = 0.3945). mMCP-counter (which estimates the abundance of TME-cell populations from gene-expression data) showed resection upregulated B-cells and mast-cells, whereas TMZ caused a decreased abundance of vessels. Anti-PD1 treatment caused an enrichment of B-cells, mast-cells, and CXCR3 expression (p = 0.0045). REGO/TMZ+REGO treatment upregulated cytotoxic-lymphocyte populations. MILAN analysis showed resection increased cytotoxic-T-cells (20.3% vs pre-resection:7%). In contrast, TMZ/REGO monotherapy increased tumour-cells and decreased cytotoxic-T-cells. Anti-PD1 decreased macrophage abundance (39.8% vs control:14.5%). Likewise, TMZ+REGO decreased macrophages (11.3% vs control:35.3%) but upregulated B-cells and vessels. Overall, we have characterised response of the NFpp10a mouse model to resection, TMZ, anti-PD1 and REGO. We have shown that the model is insensitive to chemotherapy and TME-targeting therapies, mirroring patient-response patterns. Nevertheless, we observe transcriptomic and proteomic changes following TME-targeting treatments. Further analyses of these TME-associated resistance properties may help guide novel combinatorial treatment regimens. Overall, the NFpp10a model of mesenchymal GBM may be employed in future pre-clinical studies to accurately guide future clinical trials.

Senescence gene expression in clear cell renal cell carcinoma: Role of tumor immune microenvironment and senescence-associated survival prediction.

Clear cell renal cell carcinoma (ccRCC) constitutes the most prevalent histopathologic subtype of renal cell carcinoma. The interplay between aging and cancer is complicated, and we provide a relatively new set of senescence genes that has not yet been used in the study of clear cell renal cell carcinoma. Our objective is to investigate the involvement of senescence in the development and diagnosis of ccRCC. RNA-seq and clinical data for ccRCC was obtained from the cancer genome atlas and gene expression omnibus databases. Consensus clustering analysis was performed to identify novel molecular subgroups. Tumor immune status was assessed using estimating stromal and immune cells in malignancy using expression data, microenvironment cell populations, and single-sample gene set enrichment analysis analyses. Functional analysis, including gene ontology, gene set variation analysis, and gene set enrichment analysis, was conducted to explore potential mechanisms. A prognostic risk model was constructed using the LASSO algorithm and multivariate Cox regression analysis. Decision trees and nomograms were developed for survival prediction. SenMayo classified ccRCC patients into 2 molecular subtypes with significantly different survival rates, and significant differences in their immune status, characterized by poor prognosis with relatively high immune status. Besides, the differentially expressed genes between the 2 subgroups were mainly enriched in immune-related pathways. The burden of aging tissues and cells may lead to immune dysregulation and drug resistance, which could contribute to poor prognosis in ccRCC patients. Risk models, decision trees, and nomogram for ccRCC survival prediction have great potential applications. In conclusion, our study establishes a clear association between aging in ccRCC and the immune microenvironment, demonstrating the predictive potential of senescence genes for ccRCC prognosis.

Multi-omics approaches establishing histone modification based prognostic model in glioma patients and further verification of the carcinogenesis mechanism.

Glioblastoma (GBM) emerges as the most common malignant brain tumor. Histone modifications, as an epigenetic regulatory mechanism of gene expression, are closely associated with malignant tumors. Gene set related to histone modification was extracted from the MSigDB database, and scored by the function of AddModuleScore. Pearson correlation analysis was utilized using the "rcorr" function of "Hmisc" R package. Genes were screened out using the LASSO Cox analysis. TCGA-GBM and CGGA_array_301 cohorts were employed for constructing model and validation. We calculated immune infiltration scores using microenvironment cell populations counter (MCPcounter), single-sample gene set enrichment analysis (ssGSEA), and xCell algorithms. U87-MG and CHG-5 cell lines were utilized to evaluate expression level of TMEM176A by western blot (WB). Transwell, EDU, colony formation analysis (CFA), and CKK-8 assays were conducted to investigate cell proliferation and migration rate. The malignant cells in GBM patients exhibited notable activation in the TGF-β and hypoxia pathway. Histone modifications were associated with adhesion and neuron development in GBM. We identified a model with five significant genes, namely NBEAL1, AEBP1, TMEM176A, FASTK, and CD81, with prognostic efficacy. Additionally, we observed increased infiltration of T cells and CD8+ T cells in the high-risk (HR) group. 5-Fluorouracil_1073 and Taselisib_1561 were predicted as potential treatment options for GBM patients, while ABT737_1910 and Wnt_C59-1622 exhibited superior response in GBM patients of the HR group. A spike in the TP53 mutation rate was observed in the HR group. TMEM176A played a role in regulating cell proliferation and migration in vitro. We presented a novel prognostic model for patients with GBM, based on histone modification-related genes. In addition, we identified the crucial role of the TMEM176A in the regulation of GBM carcinogenic phenotypes for the first time.