Distinct Immune Microenvironments Research Articles

Advancing bladder cancer management: development of a prognostic model and personalized therapy

BackgroundBladder cancer (BLCA) was recognized as a significant public health challenge due to its high incidence and mortality rates. The influence of molecular subtypes on treatment outcomes was well-acknowledged, necessitating further exploration of their characterization and application. This study was aimed at enhancing the understanding of BLCA by mapping its molecular heterogeneity and developing a robust prognostic model using single-cell and bulk RNA sequencing data. Additionally, immunological characteristics and personalized treatment strategies were investigated through the risk score.MethodsSingle-cell RNA sequencing (scRNA-seq) data from GSE135337 and bulk RNA-seq data from several sources, including GSE13507, GSE31684, GSE32894, GSE69795, and TCGA-BLCA, were utilized. Molecular subtypes, particularly the basal-squamous (Ba/Sq) subtype associated with poor prognosis, were identified. A prognostic model was constructed using LASSO and Cox regression analyses focused on genes linked with the Ba/Sq subtype. this model was validated across internal and external datasets to ensure predictive accuracy. High- and low-risk groups based on the risk score derived from TCGA-BLCA data were analyzed to examine their immune-related molecular profiles and treatment responses.ResultsSix molecular subtypes were identified, with the Ba/Sq subtype being consistently associated with poor prognosis. The prognostic model, based on basal-squamous subtype-related genes (BSSRGs), was shown to have strong predictive performance across diverse clinical settings with AUC values at 1, 3, and 5 years indicating robust predictability in training, testing, and entire datasets. Analysis of the different risk groups revealed distinct immune infiltration and microenvironments. Generally higher tumor mutation burden (TMB) scores and lower tumor immune dysfunction and exclusion (TIDE) scores were exhibited by the low-risk group, suggesting varied potentials for systemic drug response between the groups. Finally, significant differences in potential systemic drug response rates were also observed between risk groups.ConclusionsThe study introduced and validated a new prognostic model for BLCA based on BSSRGs, which was proven effective in prognosis prediction. The potential for personalized therapy, optimized by patient stratification and immune profiling, was highlighted by our risk score, aiming to improve treatment efficacy. This approach was promised to offer significant advancements in managing BLCA, tailoring treatments based on detailed molecular and immunological insights.

Spatial transcriptomics reveals influence of microenvironment on intrinsic fates in melanoma therapy resistance.

Resistance to cancer therapy is driven by both cell-intrinsic and microenvironmental factors. Previous work has revealed that multiple resistant cell fates emerge in melanoma following treatment with targeted therapy and that, in vitro, these resistant fates are determined by the transcriptional state of individual cells prior to exposure to treatment. What remains unclear is whether these resistant fates are shared across different genetic backgrounds and how, if at all, these resistant fates interact with the tumor microenvironment. Through spatial transcriptomics and single-cell RNA sequencing, we uncovered distinct resistance programs in melanoma cells shaped by both intrinsic cellular states and the tumor microenvironment. Consensus non-negative matrix factorization revealed shared intrinsic resistance programs across different cell lines, highlighting the presence of universal and unique resistance pathways. In patient samples, we demonstrated that these resistance programs coexist within individual tumors and associate with diverse immune signatures, suggesting that the tumor microenvironment and distribution of resistant fates are closely connected. Single-cell resolution spatial transcriptomics in xenograft models revealed both intrinsically determined and extrinsically influenced resistant fates. Overall, this work demonstrates that each therapy resistant fate coexists with a distinct immune microenvironment in tumors and that, in vivo, tissue features, such as regions of necrosis, can influence which resistant fate is adopted.

MODL-06. USING HYDROGEL MODELS TO RECAPITULATE THE TUMOUR-IMMUNE MICROENVIRONMENT OF AGGRESSIVE GROUP 4 MEDULLOBLASTOMA TUMOURS

Abstract BACKGROUND Medulloblastoma is the most common malignant childhood brain-tumour, predominantly affecting children between the ages of one and nine. It is divided into four molecular subgroups amongst which, Group 3 (G3) and Group 4 (G4) subgroups have the worst prognosis. Subgroups possess distinct extracellular-matrix (ECM) and tumour immune microenvironments (TIME) directly influencing therapy-response and metastasis. Current models of medulloblastoma fail to precisely recreate the complex, subgroup-specific TIME and do not mimic intricate immune-tumour heterotypic signalling facilitating immune-evasion. Here we use a HyStem-hydrogel which forms a hyaluronan-rich environment mimicking the natural brain architecture. This overcomes several limitations of current MB models, allowing customisation of subgroup matching of ECM components, adjustable ECM-stiffness and co-culturing of immune cells. METHODS HyStem-hydrogels were co-assembled with ECM components (with/without laminin) and G4 medulloblastoma cell line CHLA-01-MED. Hydrogels were imaged every three-days using an EVOS M7000 system and assessed for cell-viability with PrestoBlue every seven days. Co-culture laminin-supplemented HyStem hydrogels were established with M1-like or M2-like resting/activated macrophages and their growth tracked in the same manner. On day twenty-eight, hydrogels were fixed and stained for markers of cellular proliferation (Ki-67), ECM (laminin, vitronectin) and macrophage markers. Media from hydrogel co-cultures was collected and ELISAs permofmed to assess cytokine production. RESULTS Hydrogels with/without laminin both remained viable and stable for 28 days. Laminin-supplemented hydrogels displayed a mixed laminar-nodular phenotype which mirrored the heterogenous nature of G4 patient-tumours. Co-culture of CHLA-01-MED cells with activated-macrophages led to cytokine production and altered growth patterns. CONCLUSION We show that HyStem hydrogels recapitulate biologically relevant behaviour of aggressive G4-MB tumours in vitro and represent a promising model for G4-tumours. Future research will involve inclusion of additional brain relevant cells/ECM components and comparisons to a fully customisable peptide-amphiphile hydrogel.

Molecular analysis of primary and metastatic sites in patients with renal cell carcinoma.

BACKGROUNDMetastases are the hallmark of lethal cancer, though underlying mechanisms that drive metastatic spread to specific organs remain poorly understood. Renal cell carcinoma (RCC) is known to have distinct sites of metastases, with lung, bone, liver, and lymph nodes being more common than brain, gastrointestinal tract, and endocrine glands. Previous studies have shown varying clinical behavior and prognosis associated with the site of metastatic spread; however, little is known about the molecular underpinnings that contribute to the differential outcomes observed by the site of metastasis.METHODSWe analyzed primary renal tumors and tumors derived from metastatic sites to comprehensively characterize genomic and transcriptomic features of tumor cells as well as to evaluate the tumor microenvironment at both sites.RESULTSWe included a total of 657 tumor samples (340 from the primary site [kidney] and 317 from various sites of metastasis). We show distinct genomic alterations, transcriptomic signatures, and immune and stromal tumor microenvironments across metastatic sites in a large cohort of patients with RCC.CONCLUSIONWe demonstrate significant heterogeneity among primary tumors and metastatic sites and elucidate the complex interplay between tumor cells and the extrinsic tumor microenvironment that is vital for developing effective anticancer therapies.

Integrating Single-cell and Bulk RNA Sequencing Reveals Stemness Phenotype Associated with Clinical Outcomes and Potential Immune Evasion Mechanisms in Hepatocellular Carcinoma

Aims: Bulk and single-cell RNA sequencing data were analyzed to explore the association of stemness phenotype with dysfunctional anti-tumor immunity and its impact on clinical outcomes of primary and relapse HCC. Background: The stemness phenotype is gradually acquired during cancer progression; however, it remains unclear the effect of stemness phenotype on recurrence and clinical outcomes in hepatocellular carcinoma (HCC). Methods: The stemness index (mRNAsi) calculated by a one-class logistic regression algorithm in multiple HCC cohorts was defined as the stemness phenotype of the patient. Using single-cell profiling in primary or early-relapse HCC, cell stemness phenotypes were evaluated by developmental potential. Differential analysis of stemness phenotype, gene expression and interactions between primary and recurrent samples revealed the underlying immune evasion mechanisms. Results: A strong correlation was discovered between mRNAsi and clinical outcomes in patient with HCC. The high and low mRNAsi groups had distinct tumor immune microenvironments. Cellular stemness phenotype varied by cell type. Moreover, compared with primary tumors, early-relapse tumors had increased stemness of dendritic cells and tumor cells and reduced stemness of T cells and B cells. Moreover, in relapse tumors, CD8+ T cells displayed a low stemness state, with a high exhausted state, unlike the high stemness state observed in primary HCC. Conclusions: The comprehensive characterization of the HCC stemness phenotype provides insights into the clinical outcomes and immune escape mechanisms associated with recurrence.

Cellular senescence-Related genes define the immune microenvironment and molecular characteristics in severe asthma patients

Recent studies have shown that cellular senescence is involved in the pathogenesis of severe asthma (SA). The objective of this study was to investigate the role of cellular senescence-related genes (CSGs) in the pathogenesis of SA. Here, 54 differentially expressed CSGs were identified in SA patients compared to healthy control individuals. Among the 54 differentially expressed CSGs, 3 CSGs (ETS2, ETS1 and AURKA) were screened using the LASSO regression analysis and logistic regression analysis to establish the CSG-based prediction model to predict severe asthma. Moreover, we found that the protein expression levels of ETS2, ETS1 and AURKA were increased in the severe asthma mouse model. Then, two distinct senescence subtypes of SA with distinct immune microenvironments and molecular biological characteristics were identified. Cluster 1 was characterized by increased infiltration of immature dendritic cells, regulatory T cells, and other cells. Cluster 2 was characterized by increased infiltration levels of eosinophils, neutrophils, and other cells. The molecular biological characteristics of Cluster 1 included aerobic respiration and oxidative phosphorylation, whereas the molecular biological characteristics of Cluster 2 included activation of the immune response and immune receptor activity. Then, we established an Random Forest model to predict the senescence subtypes of SA to guide treatment. Finally, potential drugs were searched for each senescence subgroup of SA patients via the Connectivity Map database. A peroxisome proliferator-activated receptor agonist may be a potential therapeutic drug for patients in Cluster 1, whereas a tachykinin antagonist may be a potential therapeutic drug for patients in Cluster 2. In summary, CSGs are likely involved in the pathogenesis of SA, which may lead to new therapeutic options for SA patients.

Identification and validation of a glycosyltransferase gene signature as a novel prognostic model for lung adenocarcinoma

BackgroundThe role of glycosyltransferase (GT) genes in lung adenocarcinoma (LUAD) needs further elucidation. Thus, our study aims to identify the prognostic gene signature of LUAD and explore its molecular functions. MethodsWe initially extracted GT gene sets from the database, and obtained mRNA expression levels and clinical data from The Cancer Genome Atlas (TCGA) database. For constructing a prognostic model for GT genes, we utilized univariate, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analyses. Using the model, patients were categorized into high- and low-risk groups. Additionally, we evaluated differences in tumor immune infiltration between these groups and identified potential therapeutic drugs. Finally, we experimentally validated the expression levels of these crucial prognostic genes. ResultsWe developed a risk score comprising nine GT genes (C1GALT1, FUT1, GALNT2, PLOD2, POMK, PYGB, ST3GAL6, UGT2B11, UGT3A1). Patients were then categorized into low- and high-risk groups based on this score. The low-risk group showed superior overall survival (OS) compared to the high-risk group. There were significantly distinct tumor immune microenvironment statuses observed between the two groups. We identified potential therapeutic drugs, including the MEK inhibitor (PD-184352). Finally, we verified the expression of these nine GT genes through immunohistochemistry (IHC) staining and quantitative real-time PCR (qPCR). ConclusionWe identified a distinct LUAD GT gene signature, and these differentially expressed mRNAs could serve as valuable prognostic biomarkers and therapeutic targets. Furthermore, we experimentally validated their expression levels and identified potential therapeutic agents.

Molecular subtypes of lung adenocarcinoma present distinct immune tumor microenvironments.

Overcoming resistance to immune checkpoint inhibitors is an important issue in patients with non-small-cell lung cancer (NSCLC). Transcriptome analysis shows that adenocarcinoma can be divided into three molecular subtypes: terminal respiratory unit (TRU), proximal proliferative (PP), and proximal inflammatory (PI), and squamous cell carcinoma (LUSQ) into four. However, the immunological characteristics of these subtypes are not fully understood. In this study, we investigated the immune landscape of NSCLC tissues in molecular subtypes using a multi-omics dataset, including tumor-infiltrating leukocytes (TILs) analyzed using flow cytometry, RNA sequences, whole exome sequences, metabolomic analysis, and clinicopathologic findings. In the PI subtype, the number of TILs increased and the immune response in the tumor microenvironment (TME) was activated, as indicated by high levels of tertiary lymphoid structures, and high cytotoxic marker levels. Patient prognosis was worse in the PP subtype than in other adenocarcinoma subtypes. Glucose transporter 1 (GLUT1) expression levels were upregulated and lactate accumulated in the TME of the PP subtype. This could lead to the formation of an immunosuppressive TME, including the inactivation of antigen-presenting cells. The TRU subtype had low biological malignancy and "cold" tumor-immune phenotypes. Squamous cell carcinoma (LUSQ) did not show distinct immunological characteristics in its respective subtypes. Elucidation of the immune characteristics of molecular subtypes could lead to the development of personalized immune therapy for lung cancer. Immune checkpoint inhibitors could be an effective treatment for the PI subtype. Glycolysis is a potential target for converting an immunosuppressive TME into an antitumorigenic TME in the PP subtype.

Abstract 1536: Deciphering the crosstalk within the tumor microenvironment of NSCLC by a virtual microdissection approach

Abstract Introduction: Non-small cell lung cancer (NSCLC) is a highly heterogenous disease with the largest number of cancer-related mortality worldwide, one of the reasons for this is the complex and diverse tumor microenvironment (TME) comprising of numerous cell types. Several studies have already highlighted the importance of TME in dictating progression steps and response to therapies; however, a transcriptome-based molecular subtyping of patients in lung adenocarcinomas (LUADs) and lung squamous cell carcinomas (LUSCs) can further determine the distinct tumor immune microenvironment (TiME), which can eventually provide a systematic overview to improve the diagnosis and prognosis of patients. Material and method: To elucidate such nature of interactions between tumor cells and cells comprising the TME, we exploited the transcriptome of 300 early stages (Ib-IIIa) NSCLC recruited in the prospective observational clinical trial PROMOLE. With the help of a clustering approach, initially we performed a molecular-based virtual stratification/dissection on the NSCLC patients. Next, to elucidate the relative cell-type abundance, a deconvolution approach was applied to identify the possibility of tumor infiltrating immune cells within these subgroups. Immunohistochemistry (IHC) was then used to substantiate these predictions on tumor cells. Results and discussion: The resulting subgroups of LUADs and LUSCs are biologically well-characterized by mutational and gene expression profiles. Cell-type abundance approach identified samples which are enriched with tumor infiltrating immune cells like Neutrophils, Tcells, macrophages, etc. These findings were positively confirmed by IHC with multiple cell markers such as MPO, CD4, CD8, CD68, etc. Integrating these two results highlighted the proportion of TiME in the two different sub-populations along with shedding some light on the crosstalk happening between different cancer-/immune- cell lines. Conclusion: The in-silico predictions on bulk RNA data by virtual micro-dissection, distinguished the two distinct NSCLC subtypes, each associated with clinical and molecular features. Furthermore, the immune cells infiltration suggests a possible role of infiltrating tumor immune cells with the prognosis of patients. Our analysis successfully performed an intra-sample and inter-sample comparison, which can unveil new prognostic markers that can provide relevant information for cancer immunotherapy. Citation Format: Sushant Parab, Francesca Napoli, Davide Corà, Gabriella Doronzo, Valentina Communanza, Luisella Righi, Luca Primo, Valentina Monica, Lorenzo Manganaro, Bianco Selene, Paolo Bironzo, Giorgio Scagliotti, Federico Bussolino. Deciphering the crosstalk within the tumor microenvironment of NSCLC by a virtual microdissection approach [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 1536.

Integrated single-cell sequencing, spatial transcriptome sequencing and bulk RNA sequencing highlights the molecular characteristics of parthanatos in gastric cancer.

Parthanatos is a novel programmatic form of cell death based on DNA damage and PARP-1 dependency. Nevertheless, its specific role in the context of gastric cancer (GC) remains uncertain. In this study, we integrated multi-omics algorithms to investigate the molecular characteristics of parthanatos in GC. A series of bioinformatics algorithms were utilized to explore clinical heterogeneity of GC and further predict the clinical outcomes. Firstly, we conducted a comprehensive analysis of the omics features of parthanatos in various human tumors, including genomic mutations, transcriptome expression, and prognostic relevance. We successfully identified 7 cell types within the GC microenvironment: myeloid cell, epithelial cell, T cell, stromal cell, proliferative cell, B cell, and NK cell. When compared to adjacent non-tumor tissues, single-cell sequencing results from GC tissues revealed elevated scores for the parthanatos pathway across multiple cell types. Spatial transcriptomics, for the first time, unveiled the spatial distribution characteristics of parthanatos signaling. GC patients with different parthanatos signals often exhibited distinct immune microenvironment and metabolic reprogramming features, leading to different clinical outcomes. The integration of parthanatos signaling and clinical indicators enabled the creation of novel survival curves that accurately assess patients' survival times and statuses. In this study, the molecular characteristics of parthanatos' unicellular and spatial transcriptomics in GC were revealed for the first time. Our model based on parthanatos signals can be used to distinguish individual heterogeneity and predict clinical outcomes in patients with GC.

Characterization of the distinct immune microenvironments between hepatocellular carcinoma and intrahepatic cholangiocarcinoma

As two major types of primary liver cancers, the tumor immune microenvironment (TIME) of hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC) have been well studied separately. However, a systemic assessment of the similarities and differences between the TIME of HCC and ICC is still lacking. In this study, we pictured a landscape of combined TIME of HCC and ICC by sequencing and integrating 41 single-cell RNA-seq samples from four different tissue types of both malignancies. We found that T cells in HCC tumors generally exhibit higher levels of immunosuppression and exhaustion than those in ICC tumors. Myeloid cells in HCC and ICC tumors also exhibit distinct phenotypes and may serve as a key factor driving the differences between their TIMEs. Besides, we identified a cluster of EGR1+ macrophages specifically enriched in HCC tumors. Together, our study provides new insights into cellular composition, states and interactions in the TIMEs of HCC and ICC, which could pave the way for the development of future therapeutic targets for liver cancers.

Syngeneic murine models with distinct immune microenvironments represent subsets of human intrahepatic cholangiocarcinoma

Cholangiocarcinoma (CCA) is a poorly immunogenic malignancy associated with limited survival. Syngeneic immunocompetent mouse models of CCA are an essential tool to elucidate the tumor immune microenvironment (TIME), understand mechanisms of tumor immune evasion, and test novel immunotherapeutic strategies. The scope of this study was to develop and characterize immunocompetent CCA models with distinct genetic drivers, and correlate tumor genomics, immunobiology, and therapeutic response. A multifaceted approach including scRNA-seq, CITE-seq, whole exome and bulk RNA sequencing was employed. FDA-approved PD-1/PD-L1 antibodies were tested in humanized PD-1/PD-L1 mice (HuPD-H1). A genetic mouse model of intrahepatic CCA (iCCA) driven by intrabiliary transduction of Fbxw7ΔF/Akt that mimics human iCCA was generated. From the Fbxw7ΔF/Akt tumors, a murine cell line (FAC) and syngeneic model with genetic and phenotypic characteristics of human iCCA were developed. Established SB1 (YAPS127A/Akt) and KPPC (KrasG12Dp53L/L) models were compared to the FAC model. Although the models had transcriptomic similarities, they had substantial differences as well. Mutation patterns of FAC, SB1, and KPPC cells matched different mutational signatures in Western and Japanese CCA patient cohorts. KPPC tumors had a high tumor mutation burden. FAC tumors had a T cell-infiltrated TIME, while SB1 tumors had apreponderance of suppressive myeloid cells. FAC, SB1, and KPPC tumors matched different immune signatures in human iCCA cohorts. Moreover, FAC, SB1, and KPPC tumor-bearing HuPD-H1 mice displayed differential responses to nivolumab or durvalumab. Syngeneic iCCA models display a correlation between tumor genotype and TIME phenotype, with differential responses to FDA-approved immunotherapies. This study underscores the importance of leveraging multiple preclinical models to understand responses to immunotherapy in different genetic subsets of human CCA. Understanding the relationship between tumor genotype and the phenotype of the immune microenvironment is an unmet need in cholangiocarcinoma (CCA). Herein, we use syngeneic murine models of intrahepatic CCA with different genetic drivers to demonstrate a correlation between tumor genotype and immune microenvironment phenotype in murine models, which is associated with differential responses to FDA-approved immunotherapies. This information will help guide other preclinical studies. Additionally, it emphasizes that immune checkpoint inhibition in patients with CCA is not a "one-size-fits-all" approach. Our observations suggest that, as for targeted therapies, patients should be stratified and selected for treatment according to their tumor genetics.

Interaction between immuno-stem dual lineages in jaw bone formation and injury repair.

The jawbone, a unique structure in the human body, undergoes faster remodeling than other bones due to the presence of stem cells and its distinct immune microenvironment. Long-term exposure of jawbones to an oral environment rich in microbes results in a complex immune balance, as shown by the higher proportion of activated macrophage in the jaw. Stem cells derived from the jawbone have a higher propensity to differentiate into osteoblasts than those derived from other bones. The unique immune microenvironment of the jaw also promotes osteogenic differentiation of jaw stem cells. Here, we summarize the various types of stem cells and immune cells involved in jawbone reconstruction. We describe the mechanism relationship between immune cells and stem cells, including through the production of inflammatory bodies, secretion of cytokines, activation of signaling pathways, etc. In addition, we also comb out cellular interaction of immune cells and stem cells within the jaw under jaw development, homeostasis maintenance and pathological conditions. This review aims to eclucidate the uniqueness of jawbone in the context of stem cell within immune microenvironment, hopefully advancing clinical regeneration of the jawbone.

Insights into the heterogeneity of iNKT cells: tissue-resident and circulating subsets shaped by local microenvironmental cues.

Invariant natural killer T (iNKT) cells are a distinct subpopulation of innate-like T lymphocytes. They are characterized by semi-invariant T cell receptors (TCRs) that recognize both self and foreign lipid antigens presented by CD1d, a non-polymorphic MHC class I-like molecule. iNKT cells play a critical role in stimulating innate and adaptive immune responses, providing an effective defense against infections and cancers, while also contributing to chronic inflammation. The functions of iNKT cells are specific to their location, ranging from lymphoid to non-lymphoid tissues, such as the thymus, lung, liver, intestine, and adipose tissue. This review aims to provide insights into the heterogeneity of development and function in iNKT cells. First, we will review the expression of master transcription factors that define subsets of iNKT cells and their production of effector molecules such as cytokines and granzymes. In this article, we describe the gene expression profiles contributing to the kinetics, distribution, and cytotoxicity of iNKT cells across different tissue types. We also review the impact of cytokine production in distinct immune microenvironments on iNKT cell heterogeneity, highlighting a recently identified circulating iNKT cell subset. Additionally, we explore the potential of exploiting iNKT cell heterogeneity to create potent immunotherapies for human cancers in the future.

An analysis of the prognostic role of reactive oxygen species-associated genes in breast cancer.

This study aimed to type breast cancer in relation to reactive oxygen species (ROS), clinical indicators, single nucleotide variant (SNV) mutations, functional differences, immune infiltration, and predictive responses to immunotherapy or chemotherapy, and constructing a prognostic model. We used uniCox analysis, ConsensusClusterPlus, and the proportion of ambiguous clustering (PAC) to analyze The Cancer Genome Atlas (TCGA) data to determine optimal groupings and obtain differentially expressed ROS-related genes. Clinical indicators were then combined with the classification results and the Chi-square test was used to assess differences. We further examined SNV mutations, and functional differences using gene set enrichment analysis (GSEA) analysis, the Kyoto Encyclopedia of Genes and Genomes (KEGG) database, immune cell infiltration, and response to immunotherapy and chemotherapy. A prognostic model for breast cancer was constructed using these differentially expressed genes, immunotherapy or chemotherapy responses, and survival curves. RT-qPCR was used to detect the differences in the expression of LCE3D, CA1, PIRT and SMR3A in breast cancer cell lines and normal breast epithelial cell line. We identified two distinct tumor types with significant differences in ROS-related gene expression, clinical indicators, SNV mutations, functional pathways, and immune infiltration. The response to specific chemotherapy drugs and immunotherapy treatments also documented significant differences. The prognostic model constructed with 16 genes linked to survival could efficiently divide patients into high- and low-risk groups. The high-risk group showed a poorer prognosis, higher tumor purity, distinct immune microenvironment, and lower immunotherapy response. RT-qPCR results showed that LCE3D, CA1, PIRT and SMR3A are highly expressed in breast cancer. Our methodical examination presented an enhanced insight into the molecular and immunological heterogeneity of breast cancer. It can contribute to the understanding of prognosis and offer valuable insights for personalized treatment strategies. Further, the prognostic model can potentially serve as a powerful tool for risk stratification and therapeutic decision-making in clinical settings.

Hepatitis B virus-related hepatocellular carcinoma exhibits distinct intratumoral microbiota and immune microenvironment signatures.

Emerging evidence supports a high prevalence of cancer type-specific microbiota residing within tumor tissues. The intratumoral microbiome in hepatocellular carcinoma (HCC), especially in viral (hepatitis B virus [HBV]/hepatitis C virus [HCV]) HCC, has not been well characterized for their existence, composition, distribution, and biological functions. We report herein a finding of specific microbial signature in viral HCC as compared to non-HBV/non-HCV (NBNC) HCC. However, the significantly diverse tumor microbiome was only observed in HBV-related HCC, and Cutibacterium was identified as the representative taxa biomarker. Biological function of the unique tumor microbiota in modulating tumor microenvironment (TME) was characterized by using formalin-fixed paraffin-embedded (FFPE) tissue-based multiplex immunofluorescence histochemistry (mIFH) allowing simultaneous in situ detection of the liver cancer cells surrounded with high/low density of microbiota, and the infiltrating immune cells. In HBV_HCC, the intratumoral microbiota are positively associated with increased tumor-infiltrating CD8+ T lymphocytes, but not the CD56+ NK cells. Two subtypes of myeloid-derived suppressor cells (MDSCs): monocytic MDSCs and polymorphonuclear MDSCs, were also found to be positively correlated with the intratumoral microbiota in HBV_HCC, indicating an inhibitory role of these microbial species in antitumor immunity and the contribution to the liver TME in combination of chronic viral hepatitis during HCC development.

SAA1-dependent reprogramming of adipocytes by tumor cells is associated with triple negative breast cancer aggressiveness.

Triple negative breast cancers (TNBC) are characterized by a poor prognosis and a lack of targeted treatments. Their progression depends on tumor cell intrinsic factors, the tumor microenvironment and host characteristics. Although adipocytes, the primary stromal cells of the breast, have been determined to be plastic in physiology and cancer, the tumor-derived molecular mediators of tumor-adipocyte crosstalk have not been identified yet. In this study, we report that the crosstalk between TNBC cells and adipocytes in vitro beyond adipocyte dedifferentiation, induces a unique transcriptional profile that is characterized by inflammation and pathways that are related to interaction with the tumor microenvironment. Accordingly, increased cancer stem-like features and recruitment of pro-tumorigenic immune cells are induced by this crosstalk through CXCL5 and IL-8 production. We identified serum amyloid A1 (SAA1) as a regulator of the adipocyte reprogramming through CD36 and P2XR7 signaling. In human TNBC, SAA1 expression was associated with cancer-associated adipocyte infiltration, inflammation, stimulated lipolysis, stem-like properties, and a distinct tumor immune microenvironment. Our findings constitute evidence that the interaction between tumor cells and adipocytes through the release of SAA1 is relevant to the aggressiveness of TNBC.

P1192 Deciphering the colonic immune landscape of UC from cellular senescence by integrated bioinformatics analysis and machine learning

Abstract Background Nowadays, there has been extensive interest in cellular senescence in gastrointestinal diseases. However, the pathological mechanism of cellular senescence in UC and its involvement in colonic immune characteristics remain enigmatic. Therefore, we aimed to investigate the contribution of cellular senescence-related signatures to the colonic immune landscape in UC based on bioinformatics combined machine learning strategy. Methods Three gene sets of UC were selected from the Gene Expression Omnibus database, including two training sets and one testing set. We identified cellular senescence-associated differentially expressed genes (DEGs) in UC and performed functional enrichment analysis. Afterwards, signature genes were determined by machine learning algorithms including least absolute shrinkage and selection operator (LASSO) regression, support vector machine recursive feature elimination (SVM-RFE), and artificial neural network (ANN). Furthermore, distinct immune microenvironment abnormalities in UC were comprehensively explored, and the correlation between the signature genes and the colonic immune landscape of UC was also evaluated. Based on the signature genes, we clustered UC samples into distinct subtypes by consensus clustering analysis. Finally, we performed validation of the signature genes in dextran sulfate sodium salt (DSS)-induced colitis. Results 41 cellular senescence-associated DEGs in UC were identified by intersecting DEGs with the cellular senescence-associated genes from the CellAge database, which were associated with cellular senescence and immune pathways. Following that, seven signature genes were determined by the machine learning method of LASSO regression, SVM-RFE, and ANN, comprising CXCL1, MMP9, CYR61, BAG3, PEX19, RPS6KA6, and ETS2, which were also validated in the testing set. Furthermore, we obtained the most correlated pairs of UC signature genes-immunocytes and found these signature genes were highly corelated with neutrophils and activated CD4+ memory T cells. Additionally, the expression profiling of human leukocyte antigen genes and immune checkpoints were also analyzed. Subsequently, two molecular subtypes related to senescence that exhibit significantly different immune characteristics were identified. Finally, the mRNA levels of the signature genes were confirmed in DSS-induced colitis, which were congruent with results gained from the datasets. Conclusion In summary, the result has emphasized the essential function of cellular senescence-associated genes in controlling the immune microenvironment for UC. The exploration of the etiopathogenesis of UC through cellular senescence is anticipated to lead to novel advancements in disease diagnosis and potential treatments.

AllergoOncology: Biomarkers and refined classification for research in the allergy and glioma nexus-A joint EAACI-EANO position paper.

Epidemiological studies have explored the relationship between allergic diseases and cancer risk or prognosis in AllergoOncology. Some studies suggest an inverse association, but uncertainties remain, including in IgE-mediated diseases and glioma. Allergic disease stems from a Th2-biased immune response to allergens in predisposed atopic individuals. Allergic disorders vary in phenotype, genotype and endotype, affecting their pathophysiology. Beyond clinical manifestation and commonly used clinical markers, there is ongoing research to identify novel biomarkers for allergy diagnosis, monitoring, severity assessment and treatment. Gliomas, the most common and diverse brain tumours, have in parallel undergone changes in classification over time, with specific molecular biomarkers defining glioma subtypes. Gliomas exhibit a complex tumour-immune interphase and distinct immune microenvironment features. Immunotherapy and targeted therapy hold promise for primary brain tumour treatment, but require more specific and effective approaches. Animal studies indicate allergic airway inflammation may delay glioma progression. This collaborative European Academy of Allergy and Clinical Immunology (EAACI) and European Association of Neuro-Oncology (EANO) Position Paper summarizes recent advances and emerging biomarkers for refined allergy and adult-type diffuse glioma classification to inform future epidemiological and clinical studies. Future research is needed to enhance our understanding of immune-glioma interactions to ultimately improve patient prognosis and survival.

Development of an m6A-Related lncRNAs Signature Predicts Tumor Stemness and Prognosis for Low-Grade Glioma Patients.

Growing evidence has revealed that m6A modification of long noncoding RNAs (lncRNAs) dynamically controls tumor stemness and tumorigenesis-related processes. However, the prognostic significance of m6A-related lncRNAs and their associations with stemness in low-grade glioma (LGG) remain to be clarified. A multicenter transcriptome analysis of lncRNA expression in 1,247 LGG samples was performed in this study. The stemness landscape of LGG tumors was presented and associations with clinical features were revealed. The m6A-related lncRNAs were identified between stemness groups and were further prioritized via least absolute shrinkage and selection operator Cox regression analysis. A risk score model based on m6A-related lncRNAs was constructed and validated in external LGG datasets. Based on the expression of LINC02984, PFKP-DT, and CRNDE, a risk model and nomogram were constructed; they successfully predicted the survival of patients and were extended to external datasets. Significant correlations were observed between the risk score and tumor stemness. Moreover, patients in different risk groups exhibited distinct tumor immune microenvironments and immune signatures. We finally provided several potential compounds suitable for specific risk groups, which may aid in LGG treatment. This novel signature presents noteworthy value in the prediction of prognosis and stemness status for LGG patients and will foster future research on the development of clinical regimens.