Tumor Immune Status Research Articles

DNA methylation variations of DNA damage response correlate survival and local immune status in melanomas.

We aimed to explore the impact of DNA methylation alterations on the DNA damage response (DDR) in melanoma prognosis and immunity. MATERIAL &METHODS: Different melanoma cohorts with molecular and clinical data were included. Hierarchical clustering utilizing different combinations of DDR-relevant CpGs yielded distinct melanoma subtypes, which were characteristic of different prognoses, transcriptional function profiles of DDR, and immunity and immunotherapy responsesbut were associated with similar tumor mutation burdens. We then constructed and validated a clinically applicable 4-CpG risk-score signature for predicting survival and immunotherapy response. Our study describes the close interrelationship amongDNA methylation, DDR machinery, local tumor immune status, melanoma prognosis, and immunotherapy response.

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.

Intratumoral microbiota associates with systemic immune inflammation state in nasopharyngeal carcinoma

BackgroundThe nasopharynx serves as a crucial niche for the microbiome of the upper respiratory tract. However, the association between the intratumoral microbiota and host systemic inflammation and immune status in nasopharyngeal carcinoma (NPC) remain uncertain. MethodsWe performed 5R 16S rDNA sequencing on NPC tissue samples, followed by diversity analysis, LEfSe differential analysis, and KEGG functional prediction. The analyses were based on indices such as AISI, SIRI, PAR, PLR, and NAR. Correlation analyses between microbes and these indices were performed to identify microbes associated with inflammation and immune status. Additionally, regression analysis based on tumor TNM stage was performed to identify key microbes linked to tumor progression. The head and neck squamous cell carcinoma (HNSC) transcriptome and the paired HNSC microbiome data from TCGA were utilized to validate the analyses. ResultsThe Proteobacteria, Actinobacteria, Firmicutes, and Bacteroidetes were the most enriched phyla in NPC tissues. Microbes within these phyla demonstrated high sensitivity to changes in host systemic inflammation and immune status. Proteobacteria and Firmicutes showed significant differences between inflammation groups. Actinobacteria varied specifically with platelet-related inflammatory indices, and Bacteroidetes genera exhibited significant differences between NAR groups. Corynebacterium and Brevundimonas significantly impacted the T stage of tumors, with a high load of Corynebacterium within tumors associated with a better prognosis ConclusionOur analysis indicates that Proteobacteria play a crucial role in the inflammatory state of NPC, while Bacteroidetes are more sensitive to the tumor immune status.

Fe3O4@R837 Nanoplatform Enhances Chemical Dynamic Therapy and Immunotherapy: Integrated Transcriptomic Analysis Reveals Key Genes in Breast Cancer Prognosis.

Breast cancer represents a substantial contributor to mortality rates among women with cancer. Chemical dynamic therapy is a promising anticancer strategy that utilizes the Fenton reaction to transform naturally occurring hydrogen peroxide (H2O2) into hydroxyl radicals (•OH). Additionally, cancer immunotherapy using immune drugs, such as imiquimod (R837), has shown promise in activating T cells to kill tumor cells. In this study, we proposed a Fe3O4@R837 smart nanoplatform that can trigger the Fenton reaction and induce immune responses in breast cancer treatment. Furthermore, we performed transcriptome sequencing on breast cancer samples and used the R package (limma) to analyze differential expression profiles and select differentially expressed genes (DEGs). We obtained clinical information and RNA expression matrix data from The Cancer Genome Atlas database to perform survival analysis and identify prognostic-related genes (PRGs) and molecular subtypes with distinct prognoses. We used the TIMER 2.0 web and other methods to determine the tumor immune microenvironment and immune status of different prognostic subtypes. We identified DPGs by taking the intersection of DEGs and PRGs and performed functional analyses, including gene ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis, to elucidate potential mechanisms. Subsequently, we constructed a protein-protein interaction network using the STRING database to visualize the interactions between the DPGs. We screened hub genes from the DPGs using the Cytoscape plugin and identified six hub genes: CD3E, GZMK, CD27, SH2D1A, ZAP70, and TIGIT. Our results indicate that these six key genes regulate immune cell recruitment to increase T-cell cytotoxicity and kill tumors. Targeting these key genes can enhance immunotherapy and improve the breast cancer prognosis.

Mitochondrial DNA-boosted dendritic cell-based nanovaccination triggers antitumor immunity in lung and pancreatic cancers

Low migratory dendritic cell (DC) levels pose a challenge in cancer immune surveillance, yet their impact on tumor immune status and immunotherapy responses remains unclear. We present clinical evidence linking reduced migratory DC levels to immune-cold tumor status, resulting in poor patient outcomes. To address this, we develop an autologous DC-based nanovaccination strategy using patient-derived organoid or cancer cell lysate-pulsed cationic nanoparticles (cNPs) to load immunogenic DC-derived microvesicles (cNPcancer cell@MVDC). This approach transforms immune-cold tumors, increases migratory DCs, activates Tcells and natural killer cells, reduces tumor growth, and enhances survival in orthotopic pancreatic and lung cancer models, surpassing conventional methods. Invivo imaging reveals superior cNPcancer cell@MVDC accumulation in tumors and lymph nodes, promoting immune cell infiltration. Mechanistically, cNPs enrich mitochondrial DNA, enhancing cGAS-STING-mediated DC activation and migration. Our strategy shifts cold tumors to a hot state, enhancing antitumor immunity for potential personalized cancer treatments.

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.

Utility of neutrophil-to-lymphocyte ratio as an indicator of tumor immune status in non-small cell lung cancer.

Neutrophil-to-lymphocyte ratio (NLR) has been reported as a prognostic biomarker in non-small cell lung cancer (NSCLC); however, the underlying biological rationale remains unclear. The present study aimed to explore the potential utility of NLR as a surrogate biomarker for immune response to cancer and to elucidate the underlying mechanism. This retrospective study included the medical records of 120 patients with NSCLC who underwent surgery at the study institution in 2012. NLR in peripheral blood was determined from blood test within 30days before surgery. Tumor immune status was evaluated using immunohistochemical staining to identify CD3+, CD8+ and FOXP3+ tumor-infiltrating lymphocytes (TILs), and the relationship of NLR, with clinicopathologic characteristics including 5-year overall survival (OS), and the tumor immune status was investigated. The median values of NLR and TIL count were used as cutoff points. The 5-year OS was significantly better in patients with low NLR (<2.2) than in those with high NLR (≥2.2) (70.1% vs. 56.8%, P=0.042) and in patients with high CD3+ TIL count (≥242) than in those with low CD3+ TIL count (<242) (70% vs. 56.8%, P=0.019). Additionally, the CD3+ TIL count was negatively correlated with preoperative NLR (P=0.005). NLR might potentially reflect the immune status of tumor microenvironment, explaining its impact on prognosis of patients with NSCLC.

The identification of a N6-methyladenosin-modifed immune pattern to predict immunotherapy response and survival in urothelial carcinoma.

Dysregulation of the immune system and N6-methyladenosine (m6A) contribute to immune therapy resistance and cancer progression in urothelial carcinoma (UC). This study aims to identify immune-related molecules, that are m6A-modified, and that are associated with tumor progression, poor prognosis, and immunotherapy response. We identified prognostic immune genes (PIGs) using Cox analysis and random survival forest variable hunting algorithm (RSF-VH) on immune genes retrieved from the Immunology Database and Analysis Portal database (ImmPort). The RM2Target database and MeRIP-seq analysis, combined with a hypergeometric test, assessed m6A methylation in these PIGs. We analyzed the correlation between the immune pattern and prognosis, as well as their association with clinical factors in multiple datasets. Moreover, we explored the interplay between immune patterns, tumor immune cell infiltration, and m6A regulators. 28 PIGs were identified, of which the 10 most significant were termed methylated prognostic immune genes (MPIGs). These MPIGs were used to create an immune pattern score. Kaplan-Meier and Cox analyses indicated this pattern as an independent risk factor for UC. We observed significant associations between the immune pattern, tumor progression, and immune cell infiltration. Differential expression analysis showed correlations with m6A regulators expression. This immune pattern proved effective in predicting immunotherapy response in UC in real-world settings. The study identified a m6A-modified immune pattern in UC, offering prognostic and therapeutic response predictions. This emphasizes that immune genes may influence tumor immune status and progression through m6A modifications.

Radiomics signature for dynamic changes of tumor-infiltrating CD8+ T cells and macrophages in cervical cancer during chemoradiotherapy

BackgroundOur previous study suggests that tumor CD8+ T cells and macrophages (defined as CD68+ cells) infiltration underwent dynamic and heterogeneous changes during concurrent chemoradiotherapy (CCRT) in cervical cancer patients, which correlated with their short-term tumor response. This study aims to develop a CT image-based radiomics signature for such dynamic changes.MethodsThirty cervical squamous cell carcinoma patients, who were treated with CCRT followed by brachytherapy, were included in this study. Pre-therapeutic CT images were acquired. And tumor biopsies with immunohistochemistry at primary sites were performed at baseline (0 fraction (F)) and immediately after 10F. Radiomics features were extracted from the region of interest (ROI) of CT images using Matlab. The LASSO regression model with ten-fold cross-validation was utilized to select features and construct an immunomarker classifier and a radiomics signature. Their performance was evaluated by the area under the curve (AUC).ResultsThe changes of tumor-infiltrating CD8+T cells and macrophages after 10F radiotherapy as compared to those at baseline were used to generate the immunomarker classifier (AUC= 0.842, 95% CI:0.680–1.000). Additionally, a radiomics signature was developed using 4 key radiomics features to predict the immunomarker classifier (AUC=0.875, 95% CI:0.753-0.997). The patients stratified based on this signature exhibited significant differences in treatment response (p = 0.004).ConclusionThe radiomics signature could be used as a potential predictor for the CCRT-induced dynamic alterations of CD8+ T cells and macrophages, which may provide a less invasive approach to appraise tumor immune status during CCRT in cervical cancer compared to tissue biopsy.

PD-L1 expression in pancreaticobiliary adenosquamous carcinoma: a single-institution case series.

The programmed cell death protein 1 (PD-1)/programmed cell death ligand 1 (PD-L1) pathway is a potent negative regulator of T-cell-mediated immune response that is upregulated in many neoplasms. Pancreaticobiliary adenosquamous carcinoma (PB-ASC) is an aggressive cancer that carries a poorer prognosis compared with pure pancreaticobiliary adenocarcinoma (PB-AC). To date, there is little published information regarding PD-L1 expression in PB-ASC. The aim of the study was to examine the relationship between PD-L1 expression and tumor-infiltrating lymphocytes in PB-ASC and PB-AC. We evaluated 15 PB-ASCs (10 pancreatic, 5 gallbladder) and 34 control PB-ACs (22 pancreatic ductal, and 12 gallbladder) for tumor expression of PD-L1 using anti-PD-L1 (E1L3N) antibody. All tumors were classified into three immune phenotypes: immune inflamed (II), immune excluded (IE), and immune desert (ID) according to the distribution of tumor-infiltrating lymphocytes in tumor tissues. The frequency of PD-L1 expression was significantly higher in PB-ASC (10/15; 66.7%) than in PB-AC (3/34; 8.8%). In PB-ASC, PD-L1 expression occurred exclusively in the squamous component in six cases, exclusively in the glandular component in one case, and in both the squamous and the glandular components in three cases. PD-L1 expression in PB-ASC was irrespective of the tumor immune status, whereas its expression in PB-AC was observed only in tumors with the II or IE phenotype. The ID phenotype was relatively rare (4/15; 26.7%) in PB-ASC compared with PB-AC (22/34; 65%; P=0.02). PB-ASCs are notably enriched in inflammatory response and showed significantly higher PD-L1 expression than PB-AC (P<0.001), suggesting a potential therapeutic role for immune checkpoint inhibitors in managing patients with PB-ASC.

Assessment and prognostic significance of a serum cytokine panel in diffuse large B‑cell lymphoma.

The objective of the present study was to assess the levels of circulating cytokines in patients with diffuse large B-cell lymphoma (DLBCL), and to examine the associations between the cytokine levels, clinicopathological manifestations and patient prognosis. The study enrolled 49 patients with DLBCL, 11 patients with chronic lymphocytic leukemia/small lymphocytic lymphoma and 67 healthy controls from Zhejiang Provincial People's Hospital (Hangzhou, China) between January 2017 and January 2020. The serum levels of interleukin (IL)-2, IL-4, IL-6, IL-10, IL-17, tumor necrosis factor (TNF)-α and interferon (IFN)-γ were measured using flow cytometry. The IL-6, IL-10 and IFN-γ levels were significantly raised in patients with DLBCL compared with those in the healthy controls (P<0.05). The levels of IL-10 were significantly higher in patients with raised levels of circulating lactate dehydrogenase (P<0.05), while increases in both IL-6 and IL-10 were associated with raised C-reactive protein (CRP) levels, with IL-6 levels positively associated with those of serum CRP (P<0.01; r=0.66). Additionally, International Prognostic Index (IPI) risk stratification of patients with DLBCL was strongly associated with circulating IL-6 and IL-10 levels. Raised IL-6, IL-10 and TNF-α levels were linked with worse short-term treatment efficacies (P<0.05). Moreover, the accuracy of the model predicting short-term treatment response in patients with DLBCL, obtained using the support vector machine algorithm, was 81.63%. It was also found that raised serum IL-6 and IL-10 levels, together with reduced levels of IL-17, were associated with survival of <1 year in patients with DLBCL (P<0.05), although no significant link was found between cytokine levels and long-term overall survival. In conclusion, the serum levels of IL-6, IL-10, IL-17, TNF-α and IFN-γ can potentially serve as biological indicators of DLBCL tumor immune status, and combined application with the IPI score can be a robust prognostic indicator in patients with DLBCL.

Abstract 7667: Neutrophil to lymphocyte ratio (NLR) as an indicator of tumor immune status in non-small cell lung cancer

Abstract Background: Neutrophil to lymphocyte ratio (NLR) has been reported as a prognostic biomarker in non-small cell lung cancer (NSCLC), while its biological rationale has yet to be clarified. In cancer patients, inflammation and immune status plays crucial roles in tumor growth and the prognosis. While several studies have explored the relationship between NLR and inflammation, there are few reports on the relationship between NLR and immune status in the tumor microenvironment of cancer patient so far. The purpose of this study is to explore the potential utility of NLR as a surrogate biomarker for assessing the immune response to tumors. Methods: The medical records of NSCLC patients who underwent surgery at our institution in 2012 were retrospectively reviewed. The patients’ clinical information including age, sex, performance status, smoking history, histology, clinical stage (7th), and NLR, was obtained. NLR was calculated based on blood tests conducted within a month before surgery. Tumor immune status was evaluated by Immunohistochemical staining for CD3, CD8, and FOXP3 in their surgical specimens. The relationship between NLR, clinicopathology, and tumor immune status was examined. The survival curves were estimated using the Kaplan-Meier method and compared using the log-rank test. Blood cell counts and ratios between different groups were compared using nonpaired t test or Mann-Whitney U test, with a significance threshold set at p = 0.05. Results: A total of 120 patients were included. The 5-year overall survival rate (OS) in all 120 patients was 63.4%. The 5-year OS of the patients with lower NLR was significantly better than those with high NLR (low &amp;lt; 2.2 vs high ≥ 2.2; 70.1% vs 56.8%, p = 0.042). The 5-year OS of the patients with high density of CD3+ TILs was significantly better than that in patients with low density of CD3+ TILs (high ≥ 242 vs low &amp;lt; 242; 70% vs 56.8%, p = 0.019). The 5-year OS of the patients with high density of CD8+ TILs was significantly better than that in patients with low density of CD8+ TILs (high ≥ 112 vs low &amp;lt; 112; 72.4% vs 54.1%, p = 0.011). Conversely, the 5-year OS of the patients with low density of FOXP3+ TILs was significantly better than that in patients with high density of FOXP3+ TILs (high ≥ 7.5 vs low &amp;lt; 7.5; 46.2.% vs 79.6%, p = 0.0006). The number of CD3+ TILs had negative correlation with NLR (p = 0.005), while the number of CD8+ TILs and FOXP3+ TILs didn’t exhibit an association with NLR. Conclusion: Our results suggest that NLR might potentially reflect immune status in the tumor microenvironment, explaining its impact on the prognosis of NSCLC patients. While further research is needed, these findings may offer valuable insights for guiding treatment options. Citation Format: Ryo Yoshichika, Kazuma Iwata, Ken Suzawa, Yin Min Thu, Daisuke Mizuno, Tomoaki Higashihara, Naohiro Hayashi, Atsushi Matsuoka, Mao Yoshikawa, Fumiaki Mukohara, Kazuhiko Shien, Hiromasa Yamamoto, Shinichi Toyooka. Neutrophil to lymphocyte ratio (NLR) as an indicator of tumor immune status in non-small cell lung cancer [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 7667.

Abstract 914: Combination analysis of tumor-associated collagen frameworks and tumor immune phenotype of lung carcinomas using virtual staining

Abstract An emerging predictive parameter of immunotherapy response is the patient’s tumor immune status, generally classified as “inflamed”, "immune excluded” and “desert”. Historically, classification was performed semi-quantitatively with somewhat subjective parameters as evidenced by the recent Delphi Workshop consensus. There are a variety of approaches to classifying immune status, most based upon analysis of H&amp;E images (counting tumor infiltrating lymphocytes, TILs), or counting subpopulations of immune cells using immunologic staining. However, use of multiplexed or multiple stains has recently been explored to better quantify the counts and types of immune cells in individual tissue compartments. While the immune microenvironment represents an important parameter in predicting patient response, other regions such as the extracellular matrix (ECM) may yield complementary information, as collagen-rich ECMs may present a barrier to drug diffusion and the orientation of fibers may direct the migration of malignant cells. Here we present a proof of concept virtual staining enhanced image analysis pipeline, which converts autofluorescence signals from a single tissue section into virtual H&amp;E and Masson’s Trichrome along with virtual detection of pan cytokeratin (AE1/AE3/PCK26) and CD45 (LCA) positive cells. We apply image analysis to the panel of virtual stains to study the spatial and case-by-case heterogeneity of tumor collagen frameworks and immune phenotype. Using a standard slide scanner (Axio Scan Z1, Zeiss), multiple autofluorescence images were captured from unstained sections (4-6um thick) of lung tissue. The virtual staining was performed by four deep neural networks trained in a supervised learning fashion. Select chemical stains were performed on previously scanned tissues and reviewed by pathologists side-by-side with virtual stains to ensure consistency and quality control. Four perfectly registered WSI virtual stain images were generated from each tissue section. The multi-stain results were rendered from a variety of lung cancers, including tissue microarray slides. Image analysis was performed using HALO (Indica Labs) and custom python scripts. We identified unique areas of tumor and adjacent stroma with heterogeneous immune phenotype and collagen characteristics, suggesting that an interplay might exist that could be utilized to improve patient selection or prognosis when deploying advanced AI tools. Future work includes applying this virtual stain technique retrospectively to cases with known immunotherapy treatment responses to determine the prognostic significance of the combined immune/ECM phenotype. Citation Format: Serge Alexanian, Yair Rivenson, Ning Xuan, Brian Cone, Zihang Fang, Sean Meyering, Luis A. Carvalho, David Palacios, Raymond Kozikowski. Combination analysis of tumor-associated collagen frameworks and tumor immune phenotype of lung carcinomas using virtual staining [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 914.

Abstract 6546: Spatial immunology landscapes that correlate with survival in triple negative breast cancer

Abstract Triple negative breast cancer (TNBC) is an aggressive subtype of breast cancer characterized by negative immunohistochemistry staining for estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2). Treatment options for TNBCs are limited due to lack of tumor-specific targeted therapies, resulting in poor prognosis and patient survival. This is exacerbated by lack of prognostic markers of response. While current studies suggest tumor immune interactions are important, current immune activity quantifications, such as scores quantifying abundance of tumor-infiltrating lymphocytes (TILs), are not sufficient to define tumor immune status. We used imaging mass cytometry (IMC) to measure 33 markers on primary tumors of 100 patients undergone chemotherapy with long-term follow-up to explore the tumor immune microenvironment of TNBCs. Concordant with previous studies, a higher ratio of T-cells was associated with response to chemotherapy and longer overall survival (adjusted p-value&amp;lt;0.05). Moreover, using Kmeans clustering we identified 13 distinct cellular neighborhoods, reflective of the heterogenous nature of TNBCs. Among others, we identified a neighborhood, characterized by low CD14+ expression, low cytokeratin, high vimentin expression and high CD45RO, that contributes highest impact on correlation with overall survival (adjusted p-value&amp;lt;0.01). These results suggest an important role for memory T cells in the stroma. Thus, resolving spatial organization of the tissue is important to understanding patient response to therapy in TNBCs. Citation Format: Ali Foroughi pour, Jan Martinek, Te-Chia Wu, Santhosh Sivajothi, Zichao Liu, Jie Zhou, David Rimm, Paul Robson, Katherine Bates, Karolina Palucka, Jeffrey H. Chuang. Spatial immunology landscapes that correlate with survival in triple negative breast cancer [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 6546.

Identification of a novel inflammatory-related gene signature to evaluate the prognosis of gastric cancer patients.

Gastric cancer (GC) is a highly aggressive malignancy with a heterogeneous nature, which makes prognosis prediction and treatment determination difficult. Inflammation is now recognized as one of the hallmarks of cancer and plays an important role in the aetiology and continued growth of tumours. Inflammation also affects the prognosis of GC patients. Recent reports suggest that a number of inflammatory-related biomarkers are useful for predicting tumour prognosis. However, the importance of inflammatory-related biomarkers in predicting the prognosis of GC patients is still unclear. To investigate inflammatory-related biomarkers in predicting the prognosis of GC patients. In this study, the mRNA expression profiles and corresponding clinical information of GC patients were obtained from the Gene Expression Omnibus (GEO) database (GSE66229). An inflammatory-related gene prognostic signature model was constructed using the least absolute shrinkage and selection operator Cox regression model based on the GEO database. GC patients from the GSE26253 cohort were used for validation. Univariate and multivariate Cox analyses were used to determine the independent prognostic factors, and a prognostic nomogram was established. The calibration curve and the area under the curve based on receiver operating characteristic analysis were utilized to evaluate the predictive value of the nomogram. The decision curve analysis results were plotted to quantify and assess the clinical value of the nomogram. Gene set enrichment analysis was performed to explore the potential regulatory pathways involved. The relationship between tumour immune infiltration status and risk score was analysed via Tumour Immune Estimation Resource and CIBERSORT. Finally, we analysed the association between risk score and patient sensitivity to commonly used chemotherapy and targeted therapy agents. A prognostic model consisting of three inflammatory-related genes (MRPS17, GUF1, and PDK4) was constructed. Independent prognostic analysis revealed that the risk score was a separate prognostic factor in GC patients. According to the risk score, GC patients were stratified into high- and low-risk groups, and patients in the high-risk group had significantly worse prognoses according to age, sex, TNM stage and Lauren type. Consensus clustering identified three subtypes of inflammation that could predict GC prognosis more accurately than traditional grading and staging. Finally, the study revealed that patients in the low-risk group were more sensitive to certain drugs than were those in the high-risk group, indicating a link between inflammation-related genes and drug sensitivity. In conclusion, we established a novel three-gene prognostic signature that may be useful for predicting the prognosis and personalizing treatment decisions of GC patients.

A comprehensive multi-omics analysis identifies a robust scoring system for cancer-associated fibroblasts and intervention targets in colorectal cancer

BackgroundCancer-associated fibroblasts (CAF) play a critical role in promoting tumor growth, metastasis, and immune evasion. While numerous studies have investigated CAF, there remains a paucity of research on their clinical application in colorectal cancer (CRC).MethodsIn this study, we collected differentially expressed genes between CAF and normal fibroblasts (NF) from previous CRC studies, and utilized machine learning analysis to differentiate two distinct subtypes of CAF in CRC. To enable practical application, a CAF-related genes (CAFGs) scoring system was developed based on multivariate Cox regression. We then conducted functional enrichment analysis, Kaplan–Meier plot, consensus molecular subtypes (CMS) classification, and Tumor Immune Dysfunction and Exclusion (TIDE) algorithm to investigate the relationship between the CAFGs scoring system and various biological mechanisms, prognostic value, tumor microenvironment, and response to immune checkpoint blockade (ICB) therapy. Moreover, single-cell transcriptomics and proteomics analyses have been employed to validate the significance of scoring system-related molecules in the identity and function of CAF.ResultsWe unveiled significant distinctions in tumor immune status and prognosis not only between the CAF clusters, but also across high and low CAFGs groups. Specifically, patients in CAF cluster 2 or with high CAFGs scores exhibited higher CAF markers and were enriched for CAF-related biological pathways such as epithelial–mesenchymal transition (EMT) and angiogenesis. In addition, CAFGs score was identified as a risk index and correlated with poor overall survival (OS), progression-free survival (PFS), disease-free survival (DFS), and recurrence-free survival (RFS). High CAFGs scores were observed in patients with advanced stages, CMS4, as well as lymphatic invasion. Furthermore, elevated CAFG scores in patients signified a suppressive tumor microenvironment characterized by the upregulation of programmed death-ligand 1 (PD-L1), T-cell dysfunction, exclusion, and TIDE score. And high CAFGs scores can differentiate patients with lower response rates and poor prognosis under ICB therapy. Notably, single-cell transcriptomics and proteomics analyses identified several molecules related to CAF identity and function, such as FSTL1, IGFBP7, and FBN1.ConclusionWe constructed a robust CAFGs score system with clinical significance using multiple CRC cohorts. In addition, we identified several molecules related to CAF identity and function that could be potential intervention targets for CRC patients.

Discovering ferroptosis-associated tumor antigens and ferroptosis subtypes in pancreatic adenocarcinoma to facilitate mRNA vaccine development

Pancreatic adenocarcinoma (PAAD) is an aggressive, heterogeneous malignancy. We studied the potential of ferroptosis-related tumor vaccines for PAAD treatment. Ferroptosis-related genes, gene expression profiles, and clinical information were extracted from the FerrDB, UCSC Xena, and International Cancer Genome Consortium databases. Differential expression levels and prognostic indices were calculated, genetic alterations and correlations with immune-infiltrating cells were explored, and consensus clustering analysis was performed to identify ferroptosis subtypes and gene modules. Immune enrichment scores were calculated using gene set enrichment analysis, and gene modules were screened using weighted gene co-expression network analysis. The ferroptosis subtype distribution was visualized using graph learning-based dimensionality reduction analysis of the Monocle package with a Gaussian distribution. We identified four ferroptosis-related tumor antigens, AGPS, KDM5A, NRAS, and OSBPL9, which were associated with pancreatic cancer prognosis and antigen-presenting cell infiltration. We determined three minor ferroptosis subtypes, with different clinical prognosis and tumor immune status. Of the subtypes, FS3 may be more suitable for mRNA therapy. We constructed a PAAD ferroptosis landscape to identify the ferroptosis status of patients and predict their prognosis. Finally, we found that the eigengene of the green module was an independent prognostic factor, with a significantly better prognosis in the high-score group than in the low-score group. In conclusion, we identified four ferroptosis-related genes as targets for mRNA vaccines and three ferroptosis subtypes, providing a theoretical basis for the anti-PAAD mRNA vaccine and defining suitable patients for vaccination.

Expression of overall survival-EMT-immune cell infiltration genes predict the prognosis of glioma

This study investigates the crucial role of immune- and epithelial-mesenchymal transition (EMT)-associated genes and non-coding RNAs in glioma development and diagnosis, given the challenging 5-year survival rates associated with this prevalent CNS malignant tumor. Clinical and RNA data from glioma patients were meticulously gathered from CGGA databases, and EMT-related genes were sourced from dbEMT2.0, while immune-related genes were obtained from MSigDB. Employing consensus clustering, novel molecular subgroups were identified. Subsequent analyses, including ESTIMATE, TIMER, and MCP counter, provided insights into the tumor microenvironment (TIME) and immune status. Functional studies, embracing GO, KEGG, GSVA, and GSEA analyses, unraveled the underlying mechanisms governing these molecular subgroups. Utilizing the LASSO algorithm and multivariate Cox regression, a prognostic risk model was crafted. The study unveiled two distinct molecular subgroups with significantly disparate survival outcomes. A more favorable prognosis was linked to low immune scores, high tumor purity, and an abundance of immune infiltrating cells with differential expression of non-coding RNAs, including miRNAs. Functional analyses illuminated enrichment of immune- and EMT-associated pathways in differentially expressed genes and non-coding RNAs between these subgroups. GSVA and GSEA analyses hinted at abnormal EMT status potentially contributing to glioma-associated immune disorders. The risk model, centered on OS-EMT-ICI genes, exhibited promise in accurately predicting survival in glioma. Additionally, a nomogram integrating the risk model with clinical characteristics demonstrated notable accuracy in prognostic predictions for glioma patients. In conclusion, OS-EMT-ICI gene and non-coding RNA expression emerges as a valuable indicator intricately linked to immune microenvironment dysregulation, offering a robust tool for precise prognosis prediction in glioma patients within the OBMRC framework.

Ultrasound Imaging of Tumor Vascular CD93 with MMRN2 Modified Microbubbles for Immune Microenvironment Prediction.

Vascular microenvironment is found to be closely related to immunotherapy efficacy. Identification and ultrasound imaging of the unique vascular characteristics, able to predict immune microenvironment, is important for immunotherapy decision-making. Herein, it is proved that high CD93 expression in the tumor vessels is closely related to the poor immune response of prostate cancer. For ultrasound molecular imaging of CD93, CD93-targeted microbubbles (MBs) consist a gaseous core and the MMRN2 (Multimerin-2) containing cell membrane (CM) /lipid hybrid membrane is then synthesized. In vitro and in vivo assays demonstrate that these MBs can recognize CD93 efficiently and then accumulate within tumor regions highly expressing CD93. Contrast-enhanced ultrasound (CEUS) imaging with CD93-targeted MBs demonstrates that targeted ultrasound intensity is negatively related to inflammatory tumor immune microenvironment (TIME) and cytotoxic T cell infiltration. Together, endothelial expression of CD93 in tumor is a unique predictor of immunosuppressive microenvironment and CD93-targeted MBs have a great potential to evaluate tumor immune status.

Predicting prognosis and immune status in sarcomas by identifying necroptosis-related lncRNAs.

Sarcomas are a type of highly heterogeneous malignant tumors originating from mesenchymal tissues. Necroptosis is intricately connected to the oncogenesis and progression of tumors. The main goal of this research is to assess the prognostic value of necroptosis-related lncRNAs (NRlncRNAs) in sarcomas and to develop a risk model based on NRlncRNAs to evaluate prognostic and immune status of the sarcomas. We screened NRlncRNAs using the gene co-expression network, developed a prognostic risk model of sarcomas, and then verified the model. Following that, various bioinformatics analysis algorithms were employed to analyze the distinct characteristics of patients of the risk model. Furthermore, the function and regulatory mechanism of NRlncRNA SNHG6 in sarcomas were investigated through osteosarcoma cell experiments, such as qRT-PCR, Western blot, CCK-8, clone formation, and transwell assay. We successfully developed a NRlncRNAs-related prognostic risk model and screened 5 prognosis-related NRlncRNAs, with SNGH6 being the most significant for prognosis of patients. According to results, the significant differences exist in prognosis, clinical characteristics, and tumor immune status among patients of the risk model. The experiments of osteosarcoma cells demonstrated that NRlncRNA SNHG6 knockdown significantly attenuated the cells' proliferation, migration, and invasion. qRT-PCR and WB results showed that SNHG6 regulated AXL and AKT signaling. We have developed an innovative investigation on NRlncRNAs, which can serve as a reference for diagnosis, therapy, and prognosis of sarcomas. Additionally, we demonstrated that NRlncRNA SNHG6 regulated AXL and AKT signaling in osteosarcoma cells and the proliferation, migration, and invasion of tumor cells.