Immune Subtypes Research Articles

Potential crosstalk between SPP1 + TAMs and CD8 + exhausted T cells promotes an immunosuppressive environment in gastric metastatic cancer

BackgroundImmunotherapy brings new hope to patients with advanced gastric cancer. However, liver metastases can reduce the efficacy of immunotherapy in patients. Tumor-associated macrophages (TAMs) may be the cause of this reduction in efficacy. SPP1 + TAMs are considered to have immunosuppressive properties. We aimed to investigate the involvement of SPP1 + TAMs in the metastasis of gastric cancer.MethodsThe single-cell transcriptome was combined with batched BULK datasets for analysis. Animal models were used to verify the analysis results.ResultsWe reveal the interaction of SPP1 + TAMs with CD8 + exhausted T cells in metastatic cancer. Among these interactions, GDF15-TGFBR2 may play a key immunosuppressive role. We constructed an LR score to quantify interactions based on ligands and receptors. The LR score is highly correlated with various immune features and clinical molecular subtypes. The LR score may also guide the prediction of the efficacy of immunotherapy and prognosis.ConclusionsThe crosstalk between SPP1 + TAMs and CD8 + exhausted T cells plays a key immunosuppressive role in the gastric metastatic cancer microenvironment.Graphical

Proteomic characterization identifies clinically relevant subgroups of soft tissue sarcoma

Soft tissue sarcoma is a broad family of mesenchymal malignancies exhibiting remarkable histological diversity. We portray the proteomic landscape of 272 soft tissue sarcomas representing 12 major subtypes. Hierarchical classification finds the similarity of proteomic features between angiosarcoma and epithelial sarcoma, and elevated expression of SHC1 in AS and ES is correlated with poor prognosis. Moreover, proteomic clustering classifies patients of soft tissue sarcoma into 3 proteomic clusters with diverse driven pathways and clinical outcomes. In the proteomic cluster featured with the high cell proliferation rate, APEX1 and NPM1 are found to promote cell proliferation and drive the progression of cancer cells. The classification based on immune signatures defines three immune subtypes with distinctive tumor microenvironments. Further analysis illustrates the potential association between immune evasion markers (PD-L1 and CD80) and tumor metastasis in soft tissue sarcoma. Overall, this analysis uncovers sarcoma-type-specific changes in proteins, providing insights about relationships of soft tissue sarcoma.

Transcriptomic Profiling for Prognostic Biomarkers in Early-Stage Squamous Cell Lung Cancer (SqCLC).

Squamous cell lung carcinoma (SqCLC) is associated with high mortality and limited treatment options. Identification of therapeutic targets and prognostic biomarkers is still lacking. This research aims to analyze the transcriptomic profile of SqCLC samples and identify the key genes associated with tumorigenesis, overall survival (OS), and a profile of the tumor-infiltrating immune cells. Differential gene expression analysis, pathway enrichment analysis, and Gene Ontology analysis on RNA-seq data obtained from FFPE tumor samples (N = 23) and healthy tissues (N = 3) were performed (experimental cohort). Validation of the results was conducted on publicly available gene expression data using TCGA LUSC (N = 225) and GTEx healthy donors' cohorts (N = 288). We identified 1133 upregulated and 644 downregulated genes, common for both cohorts. The most prominent upregulated genes were involved in cell cycle and proliferation regulation pathways (MAGEA9B, MAGED4, KRT, MMT11/13), while downregulated genes predominately belonged to immune-related pathways (DEFA1B, DEFA1, DEFA3). Results of the survival analysis, conducted on the validation cohort and commonly deregulated genes, indicated that overexpression of HOXC4 (p < 0.001), LLGL1 (p = 0.0015), and SLC4A3 (p = 0.0034) is associated with worse OS in early-stage SqCLC patients. In contrast, overexpression of GSTZ1 (p = 0.0029) and LILRA5 (p = 0.0086) was protective, i.e., associated with better OS. By applying a single-sample gene-set enrichment analysis (ssGSEA), we identified four distinct immune subtypes. Immune cell distribution suggests that the memory T cells (central and effector) and follicular helper T cells could serve as important stratification parameters.

A novel risk score system based on immune subtypes for identifying optimal mRNA vaccination population in hepatocellular carcinoma.

Although mRNA vaccines have shown certain clinical benefits in multiple malignancies, their therapeutic efficacies against hepatocellular carcinoma (HCC) remains uncertain. This study focused on establishing a novel risk score system based on immune subtypes so as to identify optimal HCC mRNA vaccination population. GEPIA, cBioPortal and TIMER databases were utilized to identify candidate genes for mRNA vaccination in HCC. Subsequently, immune subtypes were constructed based on the candidate genes. According to the differential expressed genes among various immune subtypes, a risk score system was established using machine learning algorithm. Besides, multi-color immunofluorescence of tumor tissues from 72 HCC patients were applied to validate the feasibility and efficiency of the risk score system. Twelve overexpressed and mutated genes associated with poor survival and APCs infiltration were identified as potential candidate targets for mRNA vaccination. Three immune subtypes (e.g. IS1, IS2 and IS3) with distinct clinicopathological and molecular profiles were constructed according to the 12 candidate genes. Based on the immune subtype, a risk score system was developed, and according to the risk score from low to high, HCC patients were classified into four subgroups on average (e.g. RS1, RS2, RS3 and RS4). RS4 mainly overlapped with IS3, RS1 with IS2, and RS2+RS3 with IS1. ROC analysis also suggested the significant capacity of the risk score to distinguish between the three immune subtypes. Higher risk score exhibited robustly predictive ability for worse survival, which was further independently proved by multi-color immunofluorescence of HCC samples. Notably, RS4 tumors exhibited an increased immunosuppressive phenotype, higher expression of the twelve potential candidate targets and increased genome altered fraction, and therefore might benefit more from vaccination. This novel risk score system based on immune subtypes enabled the identification of RS4 tumor that, due to its highly immunosuppressive microenvironment, may benefit from HCC mRNA vaccination.

Location matters: spatial dynamics of tumor-infiltrating T cell subsets is prognostic in colon cancer.

Colon cancer is a heterogeneous disease and consists of various molecular subtypes. Despite advances in high-throughput expression profiling, limitations remain in predicting clinical outcome and assigning specific treatment to individual cases. Tumor-immune interactions play a critical role, with tumors that activate the immune system having better outcome for the patient. The localization of T cells within tumor epithelium, to enable direct contact, is essential for antitumor function, but bulk DNA/RNA sequencing data lacks spatial distribution information. In this study, we provide spatial T cell tumor distribution and connect these data with previously determined genomic data in the AC-ICAM colon cancer patient cohort. Colon cancer patients (n=90) with transcriptome data available were selected. We used a custom multiplex immunofluorescence assay on colon tumor tissue sections for quantifying T cell subsets spatial distribution in the tumor microenvironment, in terms of cell number, location, mutual distance, and distance to tumor cells. Statistical analyses included the previously determined Immunologic Constant of Rejection (ICR) transcriptome correlation and patient survival, revealing potential prognostic value in T cell spatial distribution. T cell phenotypes were characterized and CD3+CD8-FoxP3- T cells were found to be the predominant tumor-infiltrating subtype while CD3+FoxP3+ T cells and CD3+CD8+ T cells showed similar densities. Spatial distribution analysis elucidated that proliferative T cells, characterized by Ki67 expression, and Granzyme B-expressing T cells were predominantly located within the tumor epithelium. We demonstrated an increase in immune cell density and a decrease in the distance of CD3+CD8+ T cells to the nearest tumor cell, in the immune active, ICR High, immune subtypes. Higher densities of stromal CD3+FoxP3+ T cells showed enhanced survival outcomes, and patients exhibited superior clinical benefits when greater spatial distances were observed between CD3+CD8-FoxP3- or CD3+CD8+ T cells and CD3+FoxP3+ T cells. Our study's in-depth analysis of the spatial distribution and densities of major T cell subtypes within the tumor microenvironment has provided valuable information that paves the way for further research into the intricate relationships between immune cells and colon cancer development.

Abstract B016: Recapitulation of gene expression-based immune subtypes of breast cancer using immunohistochemical staining

Abstract Using gene expression profiling data, we previously identified three immune subtypes (low-TIL, high-TIL, high-ISG) of breast cancer (BC) among Chinese BC patients in Hong Kong (HK) (Zhu et al., Breast Cancer Res, 2017). To investigate if standard in situ immunohistochemical (IHC) staining could capture the BC immune landscape, we analyzed archival tissue sections from 148 HKBC patients who were previously characterized based on gene expression. Dual IHC staining for 8 immune markers (CD3, CD8, CD20, FOXP3, CD68, CD163, PDL1, and IDO1) and PanCK was performed. Digitized whole slide images for each marker were analyzed using high-accuracy machine learning algorithms trained to quantify the percentage of positively stained regions. Strong correlations were found between IHC immune markers and gene expression-based immune and intrinsic subtypes. Compared to patients with lower level of tumor-infiltrating lymphocytes (low-TIL) classified based on gene expression data, those in the high-TIL subtype (characterized by high T cell and immune checkpoint gene expression) showed higher proportions of CD3, CD8, CD20, FOXP3, CD68, and PDL1 IHC positively stained cells. Similarly, the high-ISG subtype (with increased interferon-stimulated gene expression and TP53 somatic mutations) had higher proportions of CD3, CD8, CD20, FOXP3, and CD68 stained cells than the low-TIL subtype and in particular exhibited high CD163 staining, indicative of a proinflammatory tumor microenvironment. CD8, CD20, CD163, PDL1, and IDO1 expression varied significantly by PAM50 subtypes, with luminal (A and B) subtypes showing lower staining than non-luminal (HER2 and Basal) subtypes. A distinctive feature, however, of luminal B tumors was the higher infiltration by CD163-expressing cells when compared with the luminal A subtype. Except for FOXP3, all examined IHC markers showed significantly higher expression in patients with somatic TP53 mutations (CD3: P= 0.003, CD8: P= 0.005, CD20: P= 0.003, CD68: P= 0.002, CD163: P= 0.002, PDL1: P= 0.003, and IDO1: P= 0.013). On the other hand, expression of these markers did not significantly vary by PIK3CA mutation or germline APOBEC3A/B deletion polymorphism, which is more prevalent among East Asians than other populations and was previously linked to BC risk and immune environment. Of the BC risk factors assessed, only body mass index (BMI) demonstrated associations with immune cell densities, with higher BMI associated with lower expression of CD3, CD8, and CD20. This study sheds further light on the in situ immune landscape of gene expression-based BC subtypes, suggesting that standard IHC assessment of immune markers could refine BC subtype classification for clinical and epidemiological use. Citation Format: Mustapha Abubakar, Hela Koka, Priscilla Lee, Scott Lawrence, Karun Mutreja, Difei Wang, Bin Zhu, Shelly Tse, Xiaohong R Yang. Recapitulation of gene expression-based immune subtypes of breast cancer using immunohistochemical staining [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Breast Cancer Research; 2023 Oct 19-22; San Diego, California. Philadelphia (PA): AACR; Cancer Res 2024;84(3 Suppl_1):Abstract nr B016.

Prediction Model for Immunotherapy Efficacy in Hepatocellular Carcinoma Based on Alternative Splicing Sequencing Data.

Background: Integrating immune checkpoint inhibitors with multi-target tyrosine kinase inhibitors presents an innovative and hopeful strategy in liver cancer treatment. Nonetheless, a degree of resistance to this treatment is noticeable in certain patients. Alternative splicing (AS) represents a common biological process that controls the variety of life functions via isoforms. Purpose: Investigating how gene AS affects the effectiveness of combined immunotherapy in treating hepatocellular carcinoma (HCC). Methods: Our retrospective examination focused on AS's effect on immune therapy effectiveness, utilizing accessible tissue sequencing and clinical records for HCC. For corroborating our results, we gathered samples of drug-resistant HCC tissue, nearby tissues, HCC tissue with high drug responsiveness, and healthy liver tissue from clinical studies. Results: The study revealed a link between the frequency of AS occurrences, the expression levels of programmed cell death 1 ligand 1, and the resistance to tumor medications. Our study detailed the AS occurrences in HCC, leading to the creation of a risk-assessment function and a predictive model using AS data. The results of our study revealed that the risk score effectively distinguished between various immune subtypes and the effectiveness of immune therapy. Additional examination of the chosen AS occurrences uncovered their effects on both the immune microenvironment and cellular immunity. Our investigation also delved into the regulatory framework of AS, uncovering the role of stringently controlled splicing factors in the emergence of tumors and the modulation of the body's immune response. Conclusions: Increased AS in HCC diminishes the efficacy of immunotherapy; conversely, more AS in peritumoral tissue elevates the likelihood of tumor immune evasion.

Defining Melanoma Immune Biomarkers-Desert, Excluded, and Inflamed Subtypes-Using a Gene Expression Classifier Reflecting Intratumoral Immune Response and Stromal Patterns.

The spatial distribution of tumor infiltrating lymphocytes (TILs) defines several histologically and clinically distinct immune subtypes-desert (no TILs), excluded (TILs in stroma), and inflamed (TILs in tumor parenchyma). To date, robust classification of immune subtypes still requires deeper experimental evidence across various cancer types. Here, we aimed to investigate, define, and validate the immune subtypes in melanoma by coupling transcriptional and histological assessments of the lymphocyte distribution in tumor parenchyma and stroma. We used the transcriptomic data from The Cancer Genome Atlas melanoma dataset to screen for the desert, excluded, and inflamed immune subtypes. We defined subtype-specific genes and used them to construct a subtype assignment algorithm. We validated the two-step algorithm in the qPCR data of real-world melanoma tumors with histologically defined immune subtypes. The accuracy of a classifier encompassing expression data of seven genes (immune response-related: CD2, CD53, IRF1, and CD8B; and stroma-related: COL5A2, TNFAIP6, and INHBA) in a validation cohort reached 79%. Our findings suggest that melanoma tumors can be classified into transcriptionally and histologically distinct desert, excluded, and inflamed subtypes. Gene expression-based algorithms can assist physicians and pathologists as biomarkers in the rapid assessment of a tumor immune microenvironment while serving as a tool for clinical decision making.

Bulk and single-cell transcriptome reveal the immuno-prognostic subtypes and tumour microenvironment heterogeneity in HCC.

Accumulating evidences suggest tumour microenvironment (TME) profoundly influence clinical outcome in hepatocellular carcinoma (HCC). Existing immune subtypes are susceptible to batch effects, and integrative analysis of bulk and single-cell transcriptome is helpful to recognize immune subtypes and TME in HCC. Based on the relative expression ordering (REO) of 1259 immune-related genes, an immuno-prognostic signature was developed and validated in 907 HCC samples from five bulk transcriptomic cohorts, including 72 in-house samples. The machine learning models based on subtype-specific gene pairs with stable REOs were constructed to jointly predict immuno-prognostic subtypes in single-cell RNA-seq data and validated in another single-cell data. Then, cancer characteristics, immune landscape, underlying mechanism and therapeutic benefits between subtypes were analysed. An immune-related signature with 29 gene pairs stratified HCC samples individually into two risk subgroups (C1 and C2), which was an independent prognostic factor for overall survival. The machine learning models verified the immune subtypes from five bulk cohorts to two single-cell transcriptomic data. Integrative analysis revealed that C1 had poorer outcomes, higher CNV burden and malignant scores, higher sensitivity to sorafenib, and exhibited an immunosuppressive phenotype with more regulators, e.g., myeloid-derived suppressor cells (MDSCs), Mø_SPP1, while C2 was characterized with better outcomes, higher metabolism, more benefit from immunotherapy, and displayed active immune with more effectors, e.g., tumour infiltrating lymphocyte and dendritic cell. Moreover, both two single-cell data revealed the crosstalk of SPP1-related L-R pairs between cancer and immune cells, especially SPP1-CD44, might lead to immunosuppression in C1. The REO-based immuno-prognostic subtypes were conducive to individualized prognosis prediction and treatment options for HCC. This study paved the way for understanding TME heterogeneity between immuno-prognostic subtypes of HCC.

Systematic analysis of the role of LDHs subtype in pan-cancer demonstrates the importance of LDHD in the prognosis of hepatocellular carcinoma patients

BackgroundLactate dehydrogenase (LDHs) is an enzyme involved in anaerobic glycolysis, including LDHA, LDHB, LDHC and LDHD. Given the regulatory role in the biological progression of certain tumors, we analyzed the role of LDHs in pan-cancers.MethodsCox regression, Kaplan–Meier curves, Receiver Operating Characteristic (ROC) curves, and correlation of clinical indicators in tumor patients were used to assess the prognostic significance of LDHs in pan-cancer. The TCGA, HPA, TIMER, UALCAN, TISIDB, and Cellminer databases were used to investigate the correlation between the expression of LDHs and immune subtypes, immune checkpoint genes, methylation levels, tumor mutational load, microsatellite instability, tumor-infiltrating immune cells and drug sensitivity. The cBioPortal database was also used to identify genomic abnormalities of LDHs in pan-cancer. A comprehensive assessment of the biological functions of LDHs was performed using GSEA. In vitro, HepG2 and Huh7 cells were transfected with LDHD siRNA and GFP-LDHD, the proliferation capacity of cells was examined using CCK-8, EdU, and colony formation assays; the migration and invasion of cells was detected by wound healing and transwell assays; western blotting was used to detect the levels of MMP-2, MMP-9, E-cadherin, N-cadherin and Akt phosphorylation.ResultsLDHs were differentially expressed in a variety of human tumor tissues. LDHs subtypes can act as pro-oncogenes or anti-oncogenes in different types of cancer and have an impact on the prognosis of patients with tumors by influencing their clinicopathological characteristics. LDHs were differentially expressed in tumor immune subtypes and molecular subtypes. In addition, LDHs expression correlated with immune checkpoint genes, tumor mutational load, and microsatellite instability. LDHD was identified to play an important role in the prognosis of HCC patients, according to a comprehensive analysis of LDHs in pan-cancer. In HepG2 and Huh7 cells, knockdown of LDHD promoted cell proliferation, migration, and invasion, promoted the protein expression levels of MMP-2, MMP-9, N-cadherin, and Akt phosphorylation, but inhibited the protein expression level of E-cadherin. In addition, LDHD overexpression showed the opposite changes.ConclusionLDHs subtypes can be used as potential prognostic markers for certain cancers. Prognostic and immunotherapeutic analysis indicated that LDHD plays an important role in the prognosis of HCC patients. In vitro experiments revealed that LDHD can affect HCC proliferation, migration, and invasion by regulating MMPs expression and EMT via Akt signaling pathway, which provides a new perspective on the anti-cancer molecular mechanism of LDHD in HCC.

Identification of novel molecular subtypes to improve the classification framework of nasopharyngeal carcinoma

BackgroundNasopharyngeal carcinoma (NPC) treatment is largely based on a ‘one-drug-fits-all’ strategy in patients with similar pathological characteristics. However, given its biological heterogeneity, patients at the same clinical stage or similar therapies exhibit significant clinical differences. Thus, novel molecular subgroups based on these characteristics may better therapeutic outcomes.MethodsHerein, 192 treatment-naïve NPC samples with corresponding clinicopathological information were obtained from Fujian Cancer Hospital between January 2015 and January 2018. The gene expression profiles of the samples were obtained by RNA sequencing. Molecular subtypes were identified by consensus clustering. External NPC cohorts were used as the validation sets.ResultsPatients with NPC were classified into immune, metabolic, and proliferative molecular subtypes with distinct clinical features. Additionally, this classification was repeatable and predictable as validated by the external NPC cohorts. Metabolomics has shown that arachidonic acid metabolites were associated with NPC malignancy. We also identified several key genes in each subtype using a weighted correlation network analysis. Furthermore, a prognostic risk model based on these key genes was developed and was significantly associated with disease-free survival (hazard ratio, 1.11; 95% CI, 1.07–1.16; P < 0.0001), which was further validated by an external NPC cohort (hazard ratio, 7.71; 95% CI, 1.39–42.73; P < 0.0001). Moreover, the 1-, 3-, and 5-year areas under the curve were 0.84 (95% CI, 0.74–0.94), 0.81 (95% CI, 0.73–0.89), and 0.82 (95% CI, 0.73–0.90), respectively, demonstrating a high predictive value.ConclusionsOverall, we defined a novel classification of nasopharyngeal carcinoma (immune, metabolism, and proliferation subtypes). Among these subtypes, metabolism and proliferation subtypes were associated with advanced stage and poor prognosis of NPC patients, whereas the immune subtype was linked to early stage and favorable prognosis.

Comprehensive assessment of immune context and immunotherapy response via noninvasive imaging in gastric cancer.

BACKGROUNDThe tumor immune microenvironment can provide prognostic and therapeutic information. We aimed to develop noninvasive imaging biomarkers from computed tomography (CT) for comprehensive evaluation of immune context and investigate their associations with prognosis and immunotherapy response in gastric cancer (GC).METHODSThis study involved 2,600 patients with GC from 9 independent cohorts. We developed and validated 2 CT imaging biomarkers (lymphoid radiomics score [LRS] and myeloid radiomics score [MRS]) for evaluating the IHC-derived lymphoid and myeloid immune context respectively, and integrated them into a combined imaging biomarker [LRS/MRS: low(-) or high(+)] with 4 radiomics immune subtypes: 1 (-/-), 2 (+/-), 3 (-/+), and 4 (+/+). We further evaluated the imaging biomarkers' predictive values on prognosis and immunotherapy response.RESULTSThe developed imaging biomarkers (LRS and MRS) had a high accuracy in predicting lymphoid (AUC range: 0.765-0.773) and myeloid (AUC range: 0.736-0.750) immune context. Further, similar to the IHC-derived immune context, 2 imaging biomarkers (HR range: 0.240-0.761 for LRS; 1.301-4.012 for MRS) and the combined biomarker were independent predictors for disease-free and overall survival in the training and all validation cohorts (all P < 0.05). Additionally, patients with high LRS or low MRS may benefit more from immunotherapy (P < 0.001). Further, a highly heterogeneous outcome on objective response ​rate was observed in 4 imaging subtypes: 1 (-/-) with 27.3%, 2 (+/-) with 53.3%, 3 (-/+) with 10.2%, and 4 (+/+) with 30.0% (P < 0.0001).CONCLUSIONThe noninvasive imaging biomarkers could accurately evaluate the immune context and provide information regarding prognosis and immunotherapy for GC.

Potential prognosis and immunotherapy predictor TFAP2A in pan-cancer.

TFAP2A is critical in regulating the expression of various genes, affecting various biological processes and driving tumorigenesis and tumor development. However, the significance of TFAP2A in carcinogenesis processes remains obscure. In our study, we explored multiple databases including TCGA, GTEx, HPA, cBioPortal, TCIA, and other well-established databases for further analysis to expound TFAP2A expression, genetic alternations, and their relationship with the prognosis and cellular signaling network alternations. GO term and KEGG pathway enrichment analysis as well as GSEA were conducted to examine the common functions of TFAP2A. RT-qPCR, Western Blot and Dual Luciferase Reporter assay were employed to perform experimental validation. TFAP2A mRNA expression level was upregulated and its genetic alternations were frequently present in most cancer types. The enrichment analysis results prompted us to investigate the changes in the tumor immune microenvironment further. We discovered that the expression of TFAP2A was significantly associated with the expression of immune checkpoint genes, immune subtypes, ESTIMATE scores, tumor-infiltrating immune cells, and the possible role of TFAP2A in predicting immunotherapy efficacy. In addition, high TFAP2A expression significantly correlated with several ICP genes, and promoted the expression of PD-L1 on mRNA and protein levels through regulating its expression at the transcriptional level. TFAP2A protein level was upregulated in fresh colon tumor tissue samples compared to that in the adjacent normal tissues, which essentially positively correlated with the expression of PD-L1. Our study suggests that targeting TFAP2A may provide a novel and effective strategy for cancer treatment.

Identification of ferroptosis-associated tumor antigens as the potential targets to prevent head and neck squamous cell carcinoma

Head and neck squamous cell carcinoma (HNSC) represents nearly 90% of all head and neck tumors. The current treatment modality for HNSC patients primarily involves surgical intervention and radiotherapy, but its therapeutic efficacy remains limited. The mRNA vaccine based on tumor antigens seems promising for cancer treatment. Ferroptosis, a novel form of cell death, is linked to tumor progression and cancer immunotherapy. Nevertheless, the effectiveness of ferroptosis-associated tumor antigens in treating HNSC remains uncertain. In this study, we identified three ferroptosis-associated tumor antigens, namely caveolin1 (CAV1), ferritin heavy chain (FTH1), and solute carrier 3A2 (SLC3A2), as being overexpressed and mutated based on data obtained from The Cancer Genome Atlas and Gene Expression Omnibus databases. These antigens were strongly associated with poor prognosis and infiltration of antigen-presenting cells in HNSC. We further identified two ferroptosis subtypes (FS1 and FS2) with distinct molecular, cellular, and clinical properties to identify antigen-sensitive individuals. Our findings indicate that FS1 exhibits an immune “hot” phenotype, whereas FS2 displays an immune “cold” phenotype. Additionally, differential expression of immunogenic cell death modulators and immune checkpoints was observed between these two immune subtypes. Further exploration of the HNSC's immune landscape revealed significant heterogeneity among individual patients. Our findings suggest that CAV1, FTH1, and SLC3A2 are potential targets to prevent HNSC in FS2 patients. Overall, our research reveals the potential of ferroptosis-associated mRNA vaccines for HNSC and identifies an effective patient population for vaccine treatment.

Interrogation of endothelial and mural cells in brain metastasis reveals key immune-regulatory mechanisms

Brain metastasis (BrM) is a common malignancy, predominantly originating from lung, melanoma, and breast cancers. The vasculature is a key component of the BrM tumor microenvironment with critical roles in regulating metastatic seeding and progression. However, the heterogeneity of the major BrM vascular components, namely endothelial and mural cells, is still poorly understood. We perform single-cell and bulk RNA-sequencing of sorted vascular cell types and detect multiple subtypes enriched specifically in BrM compared to non-tumor brain, including previously unrecognized immune regulatory subtypes. We integrate the human data with mouse models, creating a platform to interrogate vascular targets for the treatment of BrM. We find that the CD276 immune checkpoint molecule is significantly upregulated in the BrM vasculature, and anti-CD276 blocking antibodies prolonged survival in preclinical trials. This study provides important insights into the complex interactions between the vasculature, immune cells, and cancer cells, with translational relevance for designing therapeutic interventions.

Abstract A028: Multiomic modelling of pancreatic IPMN stroma reveals distinct tertiary lymphoid structure distribution: Mapping the transcriptomic landscape via regional bulk, single-cell and subcellular approaches

Abstract Background Intraductal Papillary Mucinous Neoplasms (IPMN) remain the largest subtype of cystic pancreatic cancer precursors. Cancer risk has been observed to vary between histological grades and histological subtypes. Spatial characterisation has revealed variation in immune cell distribution disease subtypes. Methods A cohort of 14 surgically resected IPMN tumors across a range of histological grades (Low-grade LG, high-grade HG and invasive IPMN cancers IPMN-PDAC) and subtypes (gastric, intestinal, pancreaticobiliary) underwent multimodal spatial interrogation. After expert histopathological annotation; tissue sections underwent regional whole transcriptome analysis with TempO-seq and 10x Visium. Extensive region analysis with NanoString GeoMx employing segmentation using staining for epithelium (PanCK) fibroblast stroma (aSMA) was performed. A tissue microarray was then constructed, and representative cores underwent single-cell and subcellular spatial transcriptomic analysis with NanoString CosMx. Raw count data and digital images were exported and analysed using Seurat, Giotto, SPATA2 and custom R pipelines. Region deconvolution, Harmony integration, Leiden Clustering, Gene Set Enrichment Analysis, Trajectory Mapping, and Moran’s I Analysis were performed. Results TempO-seq revealed up-regulation of canonical oncogene expression in all IPMN-PDAC cases as compared to LG and HG IPMN. There were no statistically significant differences in expression comparing LG and HG IPMN lesions. Visium differential gene expression and clustering models identified cancer epithelium, stroma and lymphatic components that correlated with histopathological annotations. Immune cell rich spots were identified via gene ontology of top 50 marker genes. The spatial trajectory of B-cell expression signatures in IPMN-PDAC showed greater concentration and less variability than HG tumors, Moran’s I -0.34 vs 0.12 (P = 0.021). B-cells in HG IPMN were showed greater concentration and less variability than LG IPMN, Moran’s I -0.15 vs 0.02 (P=0.05). Much of this concentration was in identified lymphoid aggregates or tertiary lymphoid structures. The spatial trajectory of T-cell expression. The spatial trajectory of macrophage expression showed greater concentration and less variability than in LG and HG tumors. GeoMx analysis of stromal regions identified up regulation of B cell signatures in IPMN PDAC and HG IPMN when compared to LG IPMN. Markers associated with TLS formation were found to be significantly upregulated including CXCL13, CXCR5 and LAMP3. Pancreaticobiliary subtypes were found to have the least concentrated B-cell distribution across subtypes. Conclusions Immune cell composition was found to vary across histological grades and subtypes of IPMN. A paradoxical relationship was observed between T-cell infiltrates and B-cell and macrophage populations. The greater proportion of tertiary lymphoid structures, and high levels of TLS specific gene expression, may indicate a propensity for b-cell activation in IPMN tumors which progress to malignancy. Citation Format: Andrew J. Cameron, Assya Legrini, Colin S. Wood, Craig Nourse, Yoana Doncheva, Claire Kennedy Dietrich, Colin Nixon, Jennifer Hay, Fraser Duthie, Pawel Herzyk, Jennifer Morton, Nigel B. Jamieson. Multiomic modelling of pancreatic IPMN stroma reveals distinct tertiary lymphoid structure distribution: Mapping the transcriptomic landscape via regional bulk, single-cell and subcellular approaches [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Pancreatic Cancer; 2023 Sep 27-30; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(2 Suppl):Abstract nr A028.

XRCC1: a potential prognostic and immunological biomarker in LGG based on systematic pan-cancer analysis.

X-ray repair cross-complementation group 1 (XRCC1) is a pivotal contributor to base excision repair, and its dysregulation has been implicated in the oncogenicity of various human malignancies. However, a comprehensive pan-cancer analysis investigating the prognostic value, immunological functions, and epigenetic associations of XRCC1 remains lacking. To address this knowledge gap, we conducted a systematic investigation employing bioinformatics techniques across 33 cancer types. Our analysis encompassed XRCC1 expression levels, prognostic and diagnostic implications, epigenetic profiles, immune and molecular subtypes, Tumor Mutation Burden (TMB), Microsatellite Instability (MSI), immune checkpoints, and immune infiltration, leveraging data from TCGA, GTEx, CELL, Human Protein Atlas, Ualcan, and cBioPortal databases. Notably, XRCC1 displayed both positive and negative correlations with prognosis across different tumors. Epigenetic analysis revealed associations between XRCC1 expression and DNA methylation patterns in 10 cancer types, as well as enhanced phosphorylation. Furthermore, XRCC1 expression demonstrated associations with TMB and MSI in the majority of tumors. Interestingly, XRCC1 gene expression exhibited a negative correlation with immune cell infiltration levels, except for a positive correlation with M1 and M2 macrophages and monocytes in most cancers. Additionally, we observed significant correlations between XRCC1 and immune checkpoint gene expression levels. Lastly, our findings implicated XRCC1 in DNA replication and repair processes, shedding light on the precise mechanisms underlying its oncogenic effects. Overall, our study highlights the potential of XRCC1 as a prognostic and immunological pan-cancer biomarker, thereby offering a novel target for tumor immunotherapy.

Predictive Panel for Immunotherapy in Low-Grade Glioma

BackgroundThe main treatment of lower-grade glioma (LGG) is still surgical resection followed by radiotherapy and/or chemotherapy, which has certain limitations, including side effects and drug resistance. Immunotherapy is a promising treatment for LGG, but it is generally hindered by the tumor microenvironment with the limited expression of tumor antigens. MethodsWe integrated RNA sequencing data sets and clinical information and conducted consistent cluster analysis to explore the most suitable patients for immune checkpoint therapy. Gene set enrichment analysis, UMAP analysis, mutation correlation analysis, TIMER analysis and TIDE analysis were used to identify the immune characteristics of three immune subtypes and the feasibility of five antigens as immune checkpoint markers. ResultsWe analyzed the isolation and mutation of homologous recombination repair genes（HRR） of the three immune subtypes, and the HRR genes of the three subtypes were obviously segregated. Among them, IS2 subtype has a large number of HRR gene mutations, which increases the immunogenicity of tumors, which is consistent with the results of tumor mutation load analysis of three immune subtypes. Then we evaluated the immune cell infiltration of immune subtypes and found that IS2 and IS3 subtypes were rich in immune cells. It is worth noting that there are many Treg cells and NK cells in IS1 subtype. In addition, when analyzing the immune checkpoint gene expression of the three subtypes, we found that they were up-regulated most in IS2 subtypes compared with other subtypes. Then when we further confirmed the role of immune-related genes in LGG, through TIDE analysis and TISIDB analysis, we obtained five markers that can predict the efficacy of ICB in patients with LGG. In addition, we confirmed that they were associated with poor prognosis through survival analysis. ConclusionsWe obtained three reliable immune subtypes, and patients with the IS2 subtype are suitable for immunotherapy, in which NAMPT, SLC11A1, TNC, VIM, and SPP1 are predictive panel markers for ICB in the LGG group. Our findings provide a rationale for immunotherapy selection and prediction of patient prognosis in LGG patients.

Pan-cancer analysis of SERPINE family genes as biomarkers of cancer prognosis and response to therapy.

Background: Serine protease inhibitor E (SERPINE) family genes participate in the tumor growth, cancer cell survival and metastasis. However, the SERPINE family members role in the prognosis and their clinical therapeutic potentials in various human cancer types have not been elaborately explored. Methods: We preliminarily analyzed expression levels and prognostic values of SERPINE family genes, and investigated the correlation between SERPINEs expression and tumor microenvironment (TME), Stemness score, clinical characteristic, immune infiltration, tumor mutational burden (TMB), immune subtype, and drug sensitivity in pan-cancer, which based on updated public databases and integrated some bioinformatics analysis methods. In addition, we conducted the enrichment analysis of SERPINEs from DAVID and KOBAS databases. Results: SERPINE1, SERPINE2, and SERPINE3 expression were upregulated in nine cancers, twelve cancers, and six cancers, respectively. The expression of SERPINE family genes was associated with the prognosis in several cancers from The Cancer Genome Atlas (TCGA). Furthermore, SERPINE family genes expression also had a significant relation to stromal and immune scores, and RNA stemness score and DNA stemness score in pan-cancer. SERPINE1 and SERPINE2 expression significantly increased in tumor advanced stage in colon adenocarcinoma (COAD). Results showed that SERPINE1 and SERPINE2 expression were negatively related with B cells and Monocytes, respectively. SERPINE2 expression had a significantly positive relation with B cells and Macrophages. In terms of TMB, SERPINE1, SERPINE2, and SERPINE3 were found to associated with TMB in seven cancers, fourteen cancers, and four cancers, respectively. Moreover, all SERPINE gene family members were significantly correlated with immune subtypes. SERPINE1 expression had a significantly positive or negative correlation with drug sensitivity. Conclusion: The study indicated the great potential of SERPINE family genes as biomarkers for prognosis and provided valuable strategies for further investigation of SERPINE family genes as potential targets in cancer.

Novel ferroptosis gene biomarkers and immune infiltration profiles in diabetic kidney disease via bioinformatics.

Diabetic kidney disease (DKD) is the primary cause of end-stage renal disease, exhibiting high disability and mortality rates. Ferroptosis is vital for the progression of DKD, but the exact mechanism remains unclear. This study aimed to explore the potential mechanism of ferroptosis-related genes in DKD and their relationship with the immune and to identify new diagnostic biomarkers to help treat and diagnose DKD. GSE30122 and GSE47185 were obtained from the Gene Expression Omnibus database and were integrated into a merged dataset, followed by functional enrichment analysis. Then potential differentially expressed genes were screened. Ferroptosis-related differentially-expressed genes were identified, followed by gene ontology analysis. Protein-protein interaction networks were constructed and hub genes were screened. The immune cell-infiltrating state in the dataset was assessed using appropriate algorithms. Immune signature subtypes were constructed using the consensus clustering analysis. Hub gene expression was validated using qRT-PCR and immunohistochemistry. A total of Eleven screened ferroptosis-related differentially expressed genes were screened. Six potentially diagnostically favorable ferroptosis-related hub genes were identified. Significantly increased expression of γδT cells, resting mast cells, and macrophages infiltration was observed in the DKD group. Additionally, two distinct immune signature subgroups were identified. Ferroptosis-related hub genes were significantly correlated with differentially infiltrated immune cells. Six hub genes were significantly upregulated in HK-2 cells following high glucose treatment and in human kidney tissues of patients with DKD. Six ferroptosis-related hub genes were identified as potential biomarkers of diabetic kidney disease, but further validation is needed.