Characterization Of Tumor Microenvironment Research Articles

Abstract PO5-24-11: An integrated approach for comprehensive molecular and tumor microenvironment characterization of invasive lobular carcinoma

Abstract Introduction: Although recent studies have shown that invasive lobular carcinoma (ILC) is associated with worse long-term outcomes compared to invasive breast cancer of no special type (NST), they are treated in a similar manner. Therefore, classification approaches to help understand the molecular characteristics associated with the different BC subtypes are needed to personalize therapy. Here, we describe an integrated analytical approach to comprehensively characterize ILC samples. Methods: We collected genomic and transcriptomic data for 16,087 ILC and NST samples, with clinical and pathological annotation, from 59 datasets and used an internally developed algorithm based on clustering performed after gene expression analysis to classify them into 5 intrinsic subtypes: Basal, Luminal A (LumA), Luminal B (LumB), HER2 high, and HER2 low. We performed gene set enrichment analyses and utilized the DESeq2 algorithm to perform differential expression analysis of RNA-seq data. We performed molecular grade subtyping using methods described by Antysheva et al. (Cancer Res., 2022). Samples that were listed as ILC or had an inactivated CDH1 gene by mutation, deletion or low expression (< −2.5 MAD after median scaling) were defined as having an ILC histomolecular subtype. Using methods described by Bagaev et. al. (Cancer Cell, 2021), 29 functional gene expression signatures were selected, and unsupervised dense Louvain clustering was performed to identify TME subtypes. The Mann-Whitney U test was used for statistical analysis. The tertiary lymphoid structures (TLS) gene signature was evaluated using methods described by Sautès-Fridman et al. (Nat. Rev. Cancer, 2019). Results: In the ILC samples (n=1,887), we identified LumA (46%), LumB (40%), Basal (8%,), HER2 high (3%), and HER2 low (3%) subtypes. In contrast, the NST samples were classified as LumB (41%), LumA (25%), Basal (19%), HER2 high (9%), and HER2 low (5%). Molecular grade subtyping revealed that 67% of ILC samples were low grade and 33% were high grade, compared to NST where 55% were high grade and 45% were low grade. Notably, among high-grade ILC, LumB (64%) and Basal (23%) subtypes were predominant. Key differentially expressed genes included downregulated CDH1 and upregulated MMP23B, IGF1, and TFAP2B across LumA and LumB ILC samples. Further analysis revealed downregulation of TACSTD2 and CDH3 in the Basal subtype for ILC samples compared to NST samples. Transcriptomic-based TME subtyping revealed distinct TME distributions across the BC subtypes and molecular grades for ILC samples (Table 1). In high-grade ILC LumB samples, 38% had a Desert (D; predictive of poor immunotherapy response) and 39% had an Immune-Enriched (IE; predictive of favorable immunotherapy response) TME. In low-grade ILC LumB samples we found that while 51% had a D TME, only 14% had an IE TME. We found an overrepresentation of the TLS signature [Solid TLS] in ILC compared to NST samples, and higher matrix remodeling and CAF signatures in LumA compared to the LumB subtype among low-grade ILC samples. Samples with high CD274 expression levels had higher percentages of T cells and M1 macrophages, and LumA samples had significantly higher (p < 0.001) CD274 expression compared to the other BC subtypes. Conclusion: These results provide in-depth molecular and tumor microenvironment characterization of ILC. Further optimization of this analytical approach could lead to the development of more effective therapeutic strategies. Table 1. ILC subtypes classification Citation Format: Jason Mouabbi, Konstantin Chernyshov, Oleg Baranov, Vladimir Kushnarev, Polina Turova, Anna Butusova, Sofia Menshikova, Jessica Brown, Nikita Kotlov, Patrick Clayton, Krystle Nomie, Nathan Fowler, Debu Tripathy. An integrated approach for comprehensive molecular and tumor microenvironment characterization of invasive lobular carcinoma [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO5-24-11.

DNA methylation regulator-based molecular subtyping and tumor microenvironment characterization in hepatocellular carcinoma.

Although recent studies have reported the regulation of the immune response in hepatocellular carcinoma (HCC) through DNA methylation, the comprehensive impact methylation modifications on tumor microenvironment characteristics and immunotherapy efficacy has not been fully elucidated. In this research, we conducted a comprehensive assessment of the patterns of DNA methylation regulators and the profiles of the tumor microenvironment (TME) in HCC, focusing on 21 specific DNA methylation regulators. We subsequently developed a unique scoring system, a DNA methylation score (DMscore), to assess the individual DNA methylation modifications among the three distinct methylation patterns for differentially expressed genes (DEGs). Three distinct methylation modification patterns were identified with distinct TME infiltration characteristics. We demonstrated that the DMscore could predict patient subtype, TME infiltration, and patient prognosis. A low DMscore, characterized by an elevated tumor mutation burden (TMB), hepatitis B virus (HBV)/hepatitis C virus (HCV) infection, and immune activation, indicates an inflamed tumor microenvironment phenotype with a 5-year survival rate of 7.8%. Moreover, a low DMscore appeared to increase the efficacy of immunotherapy in the anti-CTLA-4/PD-1/PD-L1 cohort. In brief, this research has enhanced our understanding of the correlation between modifications in DNA methylation patterns and the profile of the tumor microenvironment in individuals diagnosed with HCC. The DMscore may serve as an alternative biomarker for survival and efficacy of immunotherapy in patients with HCC.

Abstract 7433: CGC-hosted comprehensive pipeline for spatial transcriptomics analysis

Abstract Spatial transcriptomics has been experiencing significant growth in the past years. Inspired by two widely adopted methodologies, particularly in situ hybridization and next-generation sequencing, with an emphasis on the single-cell RNA sequencing (scRNA-seq), this hybrid method facilitates whole transcriptome profiling while preserving spatial context at high resolutions, thus allowing the exploration of novel insights in cancer research. We introduce the highly configurable solution for sequencing-based technologies that allow comprehensive spatial analysis. The pipeline is available on NCI-funded Cancer Genomics Cloud (CGC) platform, powered by Seven Bridges. The CGC platform provides a collaborative cloud base computation infrastructure that collocates computation, over 1000 bioinformatics workflows, and 4+ PB data to researchers, making the analysis of the Cancer Research data Commons (CRDC) datasets accessible from any environment. This pipeline is developed using reliable, widely adopted packages and can process datasets generated by several leading technologies. It comprises a set of common steps including quality control, data preprocessing, dimensionality reduction, cluster identification, detection of spatially variable features, and integration with scRNA-Seq reference. The pipeline is highly configurable, allowing for various settings to be modified and optimized for result refinement. Additionally, it allows the selection of only specific components of the pipeline to be executed. The key steps of the pipeline are visually represented, allowing researchers to get a detailed insight into results. Here, we demonstrate a typical flow of spatial transcriptomics analysis on publicly available datasets using the developed pipeline. We illustrate the impact of pipeline settings on the analysis outcomes. Furthermore, we identify spatially variable genes whose expressions show a distinct localization within the tissue. Finally, we perform the data integration to predict the cell type composition within the determined spatial domains. Spatial transcriptomics analysis is a powerful method that has highly improved critical aspects of cancer research, such as the characterization of tumor microenvironment, the discovery of novel biomarkers and the clarification of drug resistance mechanisms. The ongoing evolution of this method is expected to play vital role in our deep understanding of complex spatial relationships within tissues. This CGC-hosted workflow is developed to contribute to the promising advancements. Citation Format: Miona Rankovic, Nevena Vukojicic, Nevena Ilic Raicevic, Vida Matovic, Divya Sain, Jack DiGiovanna, Brandi Davis-Dusenbery. CGC-hosted comprehensive pipeline for spatial transcriptomics analysis [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 7433.

Abstract 5075: Transcriptomic landscape of tumor microenvironment in gastric cancer in treatment with different types of cancer drugs

Abstract Gastric cancer (GC) in the fifth-leading types of cancer and the third-leading cause of death from cancer. Characterization of tumor microenvironment (TME) in GC is important not only to comprehensively understand tumor biology in GC but also to develop a novel cancer therapeutics targeting TME. In this study, we conducted multi-omics analyses of single-cell RNA sequencing (scRNA-seq), bulk RNA sequencing (bulk RNA-seq), and whole exome sequencing (WES) using biopsied tumor samples through endoscopy from 23 GC patients: 3 treatment-naïve patients, 7 patients in treatment with chemotherapy (Chemo), 5 patients in combination treatment with chemotherapy and immune checkpoint blockade (Chemo+CPI), 3 patients in combination treatment with chemotherapy and Her2-antibody targeted therapy (Chemo+Her2i), and 5 patients in third-line or later treatment. We initially classified the GC samples into 4 molecular subtypes (CIN, GS, MSI, and EBV) using datasets of WES, bulk RNA-seq and scRNA-seq, showing that our samples were mainly composed of CIN (n=7) and GS (n=16) types. In scRNA-seq, we revealed that drug treatments profoundly modulated not only proportions of TME components but also their molecular characteristics. For instance, Chemo+CPI treated patients showed higher proportion of CD8+ cytotoxic T cells with activation and/or exhaustion markers. Combination treatments with Chemo+Her2i, in addition, resulted in higher Trail and Estrogen signaling pathways in epithelial cells of TME components. These findings suggest that interventions with certain cancer therapeutic drugs could substantially convert transcriptome landscape and reconstitute GC TME components. High-resolution of GC TME analyses should provide a clue for developing a next generation of cancer therapeutic drugs. Citation Format: Hiroki Yamashita, Jason Guoqiang Zhang, Chiaki Mashima, Ryo Kamata, Tomoko Yamamori, Izuma Nakayama, Akihito Kawazoe, Kohei Shitara, Tomonori Yano, Qunli Xu, Akihiro Ohashi, Takeshi Saito. Transcriptomic landscape of tumor microenvironment in gastric cancer in treatment with different types of cancer drugs [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 5075.

Abstract A078: In vivo tumor micro-environment characterization: preliminary results of an ovarian cancer patients-derived co-colture organoids platform

Abstract Introduction: Most patients with ovarian cancer (OC) are diagnosed at advanced stage. Current standard of care is based on surgery and platinum-based chemotherapy (CHT) in association with target therapies according to molecular characteristics (BRCA status and Homologous Recombination Repair (HRR) status). However, 15-20% progressed to first line CHT and mechanisms of resistance remain far from being completely known. In this scenario the role of tumoral microenvironment (TME) represents an open question. Methods: We designed a study to establish OC patients-derived co-colture organoids (ID PICTURE, IRB approval 4531). Both peripheral Blood Mononuclear Cells (PBMC) and isolated immune infiltrating cells from tumor tissue were planned to be used. The first endpoint of the study was the feasibility of this approach including (i) establishment OC patients-derived organoids (ii) PBMC isolation from whole blood and characterization (iii) immune infiltrate isolation from tumor tissue and characterization. For the latter, the evaluation of the immune phenotype was carried out by cytofluorimetry analyses, through target cells labelling with monoclonal antibodies conjugates with fluorescent molecules (CD45, CD19, CD3, CD4, CD8, 7AAD). Correlation between tumor immune infiltrate and mutational and clinical findings was also accomplished. Results: From January 2022 to May 2023, 36 patients with EOC, 92% with high grade serous OC and 95% with an advanced stage (from stage III according to FIGO, all high tumor load according to the Fagotti’s score) were enrolled. 23 patients (64%) were BRCA wild type while HRR status was available only for 17 patients of whom 28% were deficient. Considering the model feasibility, 5 organoids were obtained, and 8 and 20 million immune cells were isolated from tissue and peripheral blood, respectively. The immune infiltrate analysis was performed on 21 samples obtained from primary debulking surgery and 15 from diagnostic laparoscopies. 75% were treated with carboplatin paclitaxel (weekly or q21); among patients undergoing neoadjuvant treatment, 3 had a complete response (CRS3). The infiltration of B and T lymphocytes was described in all patients: 73% were positive for CD3, 20% for CD45, 7% for CD19. The lymphocytes T characterization was available for 7 patients with a median value of CD4+ 51.3, CD8+ 44.4, CD8+CD4+ 2,6 and CD8-CD4- 1.6. A trend for statistically significant difference was observed for CD45+ in stage IV vs stage I-III (p=0.021) and in CD19+ B cells in BRCA mutant vs BRCA WT (p=0.026). Finally, CD3+ T cells showed a significantly higher concentration in the 4 patients experiencing progression of disease at 6 months follow-up (p=0.014). Conclusion: These preliminary results suggest that patients-derived organoids co-cultured with tumor immune infiltrate could be challenging because of small numbers of overall cell content. Nevertheless, a correlation between specific components of B and T cells and molecular features and cancer progression deserves further investigation. Citation Format: Floriana Camarda, Clara Di Mario, Camilla Nero, Barbara Tolusso, Stefano Alivernini, Francesca Sillano, Sonia Mastrogiovanni, Tina Pasciuto, Elisa Gremese, Eleonora Cesari, Alessandra Ciucci, Claudio Sette, Giovanni Scambia. In vivo tumor micro-environment characterization: preliminary results of an ovarian cancer patients-derived co-colture organoids platform [abstract]. In: Proceedings of the AACR Special Conference on Ovarian Cancer; 2023 Oct 5-7; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(5 Suppl_2):Abstract nr A078.

Comprehensive analysis of disulfidptosis-related genes: a prognosis model construction and tumor microenvironment characterization in clear cell renal cell carcinoma.

Disulfidptosis, a form of cell death induced by abnormal intracellular accumulation of disulfides, is a newly recognized variety of cell death. Clear cell renal cell carcinoma (ccRCC) is a usual urological tumor that poses serious health risks. There are few studies of disulfidptosis-related genes (DRGs) in ccRCC so far. The expression, transcriptional variants, and prognostic role of DRGs were assessed. Based on DRGs, consensus unsupervised clustering analysis was performed to stratify ccRCC patients into various subtypes and constructed a DRG risk scoring model. Patients were stratified into high or low-risk groups by this model. We focused on assessing the discrepancy in prognosis, TME, chemotherapeutic susceptibility, and landscape of immune between the two risk groups. Finally, we validated the expression and explored the biological function of the risk scoring gene FLRT3 through in vitro experiments. The different subtypes had significantly different gene expression, immune, and prognostic landscapes. In the two risk groups, the high-risk group had higher TME scores, more significant immune cell infiltration, and a higher probability of benefiting from immunotherapy, but had a worse prognosis. There were also remarkable differences in chemotherapeutic susceptibility between the two risk groups. In ccRCC cells, the expression of FLRT3 was shown to be lower and its overexpression caused a decrease in cell proliferation and metastatic capacity. Starting from disulfidptosis, we established a new risk scoring model which can provide new ideas for doctors to forecast patient survival and determine clinical treatment plans.

Radiation Synergizes with IL2/IL15 Stimulation to Enhance Innate Immune Activation and Antitumor Immunity.

Ionizing radiation is known to possess immune modulatory properties. However, how radiotherapy (RT) may complement with different types of immunotherapies to boost antitumor responses is unclear. In mice implanted with EO771 syngeneic tumors, NL-201 a stable, highly potent CD25-independent agonist to IL2 and IL15 receptors with enhanced affinity for IL2Rβγ was given with or without RT. Flow analysis and Western blot analysis was performed to determine the mechanisms involved. STING (-/-) and CD11c+ knockout mice were implanted with EO771 tumors to confirm the essential signaling and cell types required to mediate the effects seen. Combination of RT and NL-201 to enhance systemic immunotherapy with an anti-PD-1 checkpoint inhibitor was utilized to determine tumor growth inhibition and survival, along characterization of tumor microenvironment as compared with all other treatment groups. Here, we showed that RT, synergizing with NL-201 produced enhanced antitumor immune responses in murine breast cancer models. When given together, RT and NL-201 enhanced activation of the cytosolic DNA sensor cyclic GMP-AMP synthase-stimulator of IFN genes (cGAS-STING) pathway, resulting in increased type I IFN production in dendritic cells (DC), and consequently greater tumor infiltration and more efficient priming of antigen-specific T cells. The immune stimulatory mechanisms triggered by NL-201 and RT resulted in superior tumor growth inhibition and survival benefit in both localized and metastatic cancers. Our results support further preclinical and clinical investigation of this novel synergism regimen in locally advanced and metastatic settings.

Link between m6A modification and infiltration characterization of tumor microenvironment in lung adenocarcinoma.

N6-methyladenosine (m6A) RNA methylation plays a pivotal role in immune responses and the onset and advancement of cancer. Nonetheless, the precise impact of m6A modification in lung adenocarcinoma (LUAD) and its associated tumor microenvironment (TME) remains to be fully elucidated. Here, we distinguished distinct m6A modification patterns within two separate LUAD cohorts using a set of 21 m6A regulators. The TME characteristics associated with these two patterns align with the immune-inflamed and immune-excluded phenotypes, respectively. We identified 2064 m6A-related genes, which were used as a basis to divide all LUAD samples into three distinct m6A gene clusters. We applied a scoring system to evaluate the m6A gene signature of the m6A modification pattern in individual patients. To authenticate the categorization significance of m6A modification patterns, we established a correlation between m6A score and TME infiltration profiling, tumor somatic mutations, and responses to immunotherapy. A high level of m6A modification may be associated with the aggressiveness and poor prognosis of LUAD. Further studies should investigate the mechanism of action of m6A regulators and m6A-related genes to improve the diagnosis and treatment of patients with LUAD.

Characterization of tumor microenvironment and tumor immunology based on the double-stranded RNA-binding protein related genes in cervical cancer

BackgroundCervical cancer is one of the most common gynecological cancers threatening women’s health worldwide. Double-stranded RNA-binding proteins (dsRBPs) regulate innate immunity and are therefore believed to be involved in virus-related malignancies, however, their role in cervical cancer is not well known.MethodsWe performed RNA-seq of tumor samples from cervical cancer patients in local cohort and also assessed the RNA-seq and clinical data derived from public datasets. By using single sample Gene Set Enrichment Analysis (ssGSEA) and univariate Cox analysis, patients were stratified into distinct dsRBP clusters. Stepwise Cox and CoxBoost were performed to construct a risk model based on optimal dsRBPs clusters-related differentially expressed genes (DEGs), and GSE44001 and CGCI-HTMCP-CC were employed as two external validation cohorts. Single cell RNA sequencing data from GSE168652 and Scissor algorithm were applied to evaluated the signature-related cell population.ResultsThe expression of dsRBP features was found to be associated with HPV infection and carcinogenesis in CESC. However, only Adenosine deaminases acting on RNA (ADAR) and Dicer, Drosha, and Argonautes (DDR) exhibited significant correlations with the overall survival (OS) of CESC patients. Based on these findings, CESC patients were divided into three dsRBP clusters. Cluster 3 showed superior OS but lower levels of ADAR and DDR. Additionally, Cluster 3 demonstrated enhanced innate immunity, with significantly higher activity in cancer immunity cycles, immune scores, and levels of tumor-infiltrating immune cells, particularly CD8+ T cells. Furthermore, a risk model based on nine dsRBP cluster-related DEGs was established. The accuracy of survival prediction for 1 to 5 years was consistently above 0.78, and this model’s robust predictive capacity was confirmed by two external validation sets. The low-risk group exhibited significantly higher levels of immune checkpoints, such as PDCD1 and CTLA4, as well as a higher abundance of CD8+ T cells. Analysis of single-cell sequencing data revealed a significant association between the dsRBP signature and glycolysis. Importantly, low-risk patients showed improved OS and a higher response rate to immunotherapy, along with enduring clinical benefits from concurrent chemoradiotherapy.ConclusionsdsRBP played a crucial role in the regulation of prognosis and tumor immunology in cervical cancer, and its prognostic signature provides a strategy for risk stratification and immunotherapy evaluation.

Characterization of tumor microenvironment and sensitive chemotherapy drugs based on cuproptosis-related signatures in renal cell carcinoma.

Cuproptosis is a novel type of copper-induced cell death and is considered as a new therapeutic target for many cancers. Distant metastases occur in about 40% of patients with advanced renal cell carcinoma (RCC), with a poor 5-year prognosis of about 10%. Through a series of comprehensive analyses, four differentially expressed cuproptosis-related lncRNAs (DECRLs) were identified as candidate biomarkers for RCC. The risk model constructed by using these four DECRLs can better predict the prognosis of patients with RCC, which is determined by the receiver operating characteristic (Time dependent area under curve value at 1-year, 3-year, 5-year, and 10-year were 0.82, 0.80, 0.76, and 0.73 respectively). There were significant differences in immune status between high-risk and low-risk RCC patients. The differentially expressed gene enrichment terms between high- and low-risk patients was also dominated by immune-related terms. The risk score was also correlated with immunotherapy as measured by the tumor immune dysfunction and exclusion (TIDE) score. In addition, we also found that the sensitivity of many chemotherapy drugs varies widely between high- and low-risk patients. The sensitivity of the three chemotherapy drugs (AZD4547, Vincristine, and WEHI-539) varied among high- and low-risk patients, and was significantly negatively correlated with risk values, suggesting that they could be used as clinical treatment drugs for RCC. Our study not only obtained four potential biomarkers, but also provided guidance for immunotherapy and chemotherapy treatment of RCC, as well as new research strategies for the screening of other cancer biomarkers and sensitive drugs.

Deciphering the implications of mitophagy-related signatures in clinical outcomes and microenvironment heterogeneity of clear cell renal cell carcinoma.

The role of mitophagy in various cancer-associated biological processes is well recognized. Nonetheless, the comprehensive implications of mitophagy in clear cell renal cell carcinoma (ccRCC) necessitate further exploration. Based on the transcriptomic data encompassing 25 mitophagy-related genes (MRGs), we identified the distinct mitophage patterns in 763 ccRCC samples. Subsequently, a mitophage-related predictive signature with machine learning algorithms was constructed, designated as RiskScore, to quantify the individual mitophagy status in ccRCC patients. Employing multispectral immunofluorescence (mIF) and immunohistochemistry (IHC) staining, we detected the effect of PTEN-induced putative kinase 1 (PINK1) in the prognosis and immune microenvironment of ccRCC. Our analysis initially encompassed a comprehensive assessment of the expression profiling, genomic variations, and interactions among the 25 MRGs in ccRCC. Subsequently, the consensus clustering algorithm was applied to stratify ccRCC patients into three clusters with distinct prognostic outcomes, tumor microenvironment (TME) characteristics, and underlying biological pathways. We screened eight pivotal genes (CLIC4, PTPRB, SLC16A12, ENPP5, FLRT3, HRH2, PDK4, and SCD5) to construct a mitophagy-related predictive signature, which showed excellent prognostic value for ccRCC patients. Moreover, patient subgroups divided by the RiskScore showed contrasting expression levels of immune checkpoints (ICPs), abundance of immune cells, and immunotherapy response. Additionally, a nomogram was established with robust predictive power integrating the RiskScore and clinical features. Notably, we observed that PINK1 expression markedly correlated with favorable treatment response and advanced maturation stages of tertiary lymphoid structures, which potentially shed light on enhancing anti-tumor immunity of ccRCC. Collectively, this study initially developed a signature associated with mitophagy, which demonstrated an excellent ability to predict the clinical prognosis, TME characterization, and responsiveness to targeted therapy and immunotherapy for ccRCC patients. Of particular note is the pivotal role of PINK1 in mediating the treatment response and immune microenvironment for ccRCC patients.

Characterization of tumor microenvironment in glioblastoma multiforme identifies ITGB2 as a key immune and stromal related regulator in glial cell types

Glioblastoma multiforme (GBM) is the most aggressive form of brain tumor characterized by inter and intra-tumor heterogeneity and complex tumor microenvironment. To uncover the molecular targets in this milieu, we systematically identified immune and stromal interactions at the glial cell type level that leverages on RNA-sequencing data of GBM patients from The Cancer Genome Atlas. The perturbed genes between the high vs low immune and stromal scored patients were subjected to weighted gene co-expression network analysis to identify the glial cell type specific networks in immune and stromal infiltrated patients. The intramodular connectivity analysis identified the highly connected genes in each module. Combining it with univariable and multivariable prognostic analysis revealed common vital gene ITGB2, between the immune and stromal infiltrated patients enriched in microglia and newly formed oligodendrocytes. We found following unique hub genes in immune infiltrated patients; COL6A3 (microglia), ITGAM (oligodendrocyte precursor cells), TNFSF9 (microglia), and in stromal infiltrated patients, SERPINE1 (microglia) and THBS1 (newly formed oligodendrocytes, oligodendrocyte precursor cells). To validate these hub genes, we used external GBM patient single cell RNA-sequencing dataset and this identified ITGB2 to be significantly enriched in microglia, newly formed oligodendrocytes, T-cells, macrophages and adipocyte cell types in both immune and stromal datasets. The tumor infiltration analysis of ITGB2 showed that it is correlated with myeloid dendritic cells, macrophages, monocytes, neutrophils, B-cells, fibroblasts and adipocytes. Overall, the systematic screening of tumor microenvironment components at glial cell types uncovered ITGB2 as a potential target in primary GBM.

Tumor microenvironment characterization in colorectal cancer to identify prognostic and immunotherapy genes signature

BackgroundThe tumor microenvironment (TME) plays a crucial role in tumorigenesis, progression, and therapeutic response in many cancers. This study aimed to comprehensively investigate the role of TME in colorectal cancer (CRC) by generating a TMEscore based on gene expression.MethodsThe TME patterns of CRC datasets were investigated, and the TMEscores were calculated. An unsupervised clustering method was used to divide samples into clusters. The associations between TMEscores and clinical features, prognosis, immune score, gene mutations, and immune checkpoint inhibitors were analyzed. A TME signature was constructed using the TMEscore-related genes. The results were validated using external and clinical cohorts.ResultsThe TME pattern landscape was for CRC was examined using 960 samples, and then the TMEscore pattern of CRC datasets was evaluated. Two TMEscore clusters were identified, and the high TMEscore cluster was associated with early-stage CRC and better prognosis in patients with CRC when compared with the low TMEscore clusters. The high TMEscore cluster indicated elevated tumor cell scores and tumor gene mutation burden, and decreased tumor purity, when compared with the low TMEscore cluster. Patients with high TMEscore were more likely to respond to immune checkpoint therapy than those with low TMEscore. A TME signature was constructed using the TMEscore-related genes superimposing the results of two machine learning methods (LASSO and XGBoost algorithms), and a TMEscore-related four-gene signature was established, which had a high predictive value for discriminating patients from different TMEscore clusters. The prognostic value of the TMEscore was validated in two independent cohorts, and the expression of TME signature genes was verified in four external cohorts and clinical samples.ConclusionOur study provides a comprehensive description of TME characteristics in CRC and demonstrates that the TMEscore is a reliable prognostic biomarker and predictive indicator for patients with CRC undergoing immunotherapy.

Proteomic Profiling and Tumor Microenvironment Characterization Reveal Molecular and Immunological Hallmarks of Left-Sided and Right-Sided Colon Cancer Tumorigenesis.

Left-sided and right-sided colon cancer (LSCC and RSCC) display different biological and clinical characteristics. However, the differences in their tumorigenesis and tumor microenvironment remain unclear. In this study, we profiled the proteomic landscapes of LSCC and RSCC with data-independent acquisition mass spectrometry (DIA-MS) using fresh tumor and adjacent normal tissues from 24 patients. A total of 7403 proteingroups were primarily identified with DIA-MS. After quality control, 7212 proteingroups were used for further analysis. Through comparing the difference in proteomic profiles between LSCC and RSCC samples, 2556 commonly and 1982 region-type-specific regulated proteingroups were characterized. During the development of LSCC and RSCC, metabolic, growth, cell division, cell adhesion, and migration pathways were found to be significantly dysregulated (P < 0.05), which was further confirmed by transcriptome data from TCGA. Compared to RSCC, most parts of the immune-related signatures, immune cell infiltration scores, and overall immune scores of LSCC were higher. The systematic elucidation of proteomic and transcriptomic profiles in this work improves our understanding of tumorigenesis and immune microenvironment characteristics of LSCC and RSCC.

Characterizing cancer metabolism from bulk and single-cell RNA-seq data using METAFlux

Cells often alter metabolic strategies under nutrient-deprived conditions to support their survival and growth. Characterizing metabolic reprogramming in the tumor microenvironment (TME) is of emerging importance in cancer research and patient care. However, recent technologies only measure a subset of metabolites and cannot provide in situ measurements. Computational methods such as flux balance analysis (FBA) have been developed to estimate metabolic flux from bulk RNA-seq data and can potentially be extended to single-cell RNA-seq (scRNA-seq) data. However, it is unclear how reliable current methods are, particularly in TME characterization. Here, we present a computational framework METAFlux (METAbolic Flux balance analysis) to infer metabolic fluxes from bulk or single-cell transcriptomic data. Large-scale experiments using cell-lines, the cancer genome atlas (TCGA), and scRNA-seq data obtained from diverse cancer and immunotherapeutic contexts, including CAR-NK cell therapy, have validated METAFlux’s capability to characterize metabolic heterogeneity and metabolic interaction amongst cell types.

Expression Profile of Microenvironmental Factors in the Interface Zone of Colorectal Cancer: Histological-Stromal Biomarkers and Cancer Cell-Cancer-Associated Fibroblast-Related Proteins Combined for the Assessment of Tumor Progression

Introduction: The characterization of tumor microenvironment (TME) related factors and their impact on tumor progression have attracted much interest. We investigated cancer cells and cancer-associated fibroblasts (CAFs) to evaluate biomarkers that are associated with neoplastic progression, observing them in different interface zones of colorectal cancer. Methods: On 357 CRC tissue microarrays, using immunohistochemistry, we examined the associations of podoplanin and α-SMA expressed in cancer cells and CAFs and evaluated them in different areas: tumor core, invasive front, tumor budding, tumor-stroma ratio (TSR) scoring, and desmoplastic stroma. Results: CAFs expressing α-SMA were found in more than 90% of the cases. Podoplanin+ was detected in cancer cells and CAFs, with positivities of 38.6% and 70%, respectively. Higher α-SMA+ CAFs and podoplanin+ cancer cells were observed predominantly at the TSR score area: 94.3% and 64.3% of cases, respectively. The status of podoplanin in CAFs+ was higher in the desmoplastic area (71.6%). Stroma-high tumors showed increased expression of α-SMA and podoplanin in comparison with stroma-low tumors. The status of podoplanin in cancer cells was observed in association with lymphatic invasion and distant metastasis. Conclusion: The substance of the CRC was composed predominantly of the surrounding stroma-α-SMA+ CAFs. Podoplanin expressed in the prognosticator zones was associated with unfavorable pathological features. The combination of histologic and protein-related biomarkers can result in a tool for the stratification of patients with CRC.

Comprehensive characterization of tumor microenvironment in colorectal cancer via molecular analysis.

Colorectal cancer (CRC) remains a challenging and deadly disease with high tumor microenvironment (TME) heterogeneity. Using an integrative multi-omics analysis and artificial intelligence-enabled spatial analysis of whole-slide images, we performed a comprehensive characterization of TME in colorectal cancer (CCCRC). CRC samples were classified into four CCCRC subtypes with distinct TME features, namely, C1 as the proliferative subtype with low immunogenicity; C2 as the immunosuppressed subtype with the terminally exhausted immune characteristics; C3 as the immune-excluded subtype with the distinct upregulation of stromal components and a lack of T cell infiltration in the tumor core; and C4 as the immunomodulatory subtype with the remarkable upregulation of anti-tumor immune components. The four CCCRC subtypes had distinct histopathologic and molecular characteristics, therapeutic efficacy, and prognosis. We found that the C1 subtype may be suitable for chemotherapy and cetuximab, the C2 subtype may benefit from a combination of chemotherapy and bevacizumab, the C3 subtype has increased sensitivity to the WNT pathway inhibitor WIKI4, and the C4 subtype is a potential candidate for immune checkpoint blockade treatment. Importantly, we established a simple gene classifier for accurate identification of each CCCRC subtype. Collectively our integrative analysis ultimately established a holistic framework to thoroughly dissect the TME of CRC, and the CCCRC classification system with high biological interpretability may contribute to biomarker discovery and future clinical trial design.

Disulfidptosis-related classification patterns and tumor microenvironment characterization in skin cutaneous melanoma.

To identify distinct disulfidptosis-molecular subtypes and develop a novel prognostic signature. We integrated into this study multiple SKCM transcriptomic datasets from the Cancer Genome Atlas database and Gene Expression Omnibus dataset. The consensus clustering algorithm was applied to categorize SKCM patients into different DRG subtypes. Three distinct DRG subtypes were identified, which were correlated to different clinical outcomes and signaling pathways. Then, a disulfidptosis-relaed signature and nomogram were constructed, which could accurately predict the individual OS of patients with SKCM. The high-risk group was less sensitive to immunotherapy than the low-risk group. The signature can assist healthcare professionals in making more accurate and individualized treatment choices for patients with SKCM.

Multi-omics analysis of pyroptosis regulation patterns and characterization of tumor microenvironment in patients with hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is a primary malignant tumor of the liver, and pyroptosis has been identified as a novel cellular program that plays a role in numerous diseases including cancer. However, the functional role of pyroptosis in HCC remains unclear. The purpose of this study is to explore the relationship between the two found hub genes and provide targets for clinical treatment. The Cancer Genome Atlas (TCGA) database was used to collect the gene data and clinically-related information of patients with HCC. After the differentially expressed genes (DEGs) were identified, they were intersected with the genes related to pyroptosis, and a risk prediction model was established to predict the overall survival (OS). Subsequently, drug sensitivity analysis, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Set Enrichment Analysis (GSEA), and Gene Set Variation Analysis (GSVA) was used to analyze the biological characteristics of the DEGs. Different immune cell infiltration and related pathways were analyzed, and hub genes were identified by protein-protein interaction (PPI). Finally, the expression of hub genes was verified by real-time quantitative PCR (qRT-PCR) and immunohistochemistry. We conducted a comprehensive bioinformatics analysis to investigate the molecular mechanisms of pyroptosis in hepatocellular carcinoma (HCC). A total of 8,958 differentially expressed genes were identified, and 37 differentially expressed genes were associated with pyroptosis through intersection. Moreover, we developed an OS model with excellent predictive ability and discovered the differences in biological function, drug sensitivity, and immune microenvironment between high-risk and low-risk groups. Through enrichment analysis, we found that the differentially expressed genes are related to various biological processes. Then, 10 hub genes were identified from protein-protein interaction networks. Finally, midkine (MDK) was screened from the 10 hub genes and further verified by PCR and immunohistochemistry, which revealed its high expression in HCC. We have developed a reliable and consistent predictive model based on the identification of potential hub genes, which can be used to accurately forecast the prognosis of patients, thus providing direction for further clinical research and treatment.

A Deep Learning Approach for Histology-Based Nucleus Segmentation and Tumor Microenvironment Characterization

Microscopic examination of pathology slides is essential to disease diagnosis and biomedical research. However, traditional manual examination of tissue slides is laborious and subjective. Tumor whole-slide image (WSI) scanning is becoming part of routine clinical procedures and produces massive data that capture tumor histologic details at high resolution. Furthermore, the rapid development of deep learning algorithms has significantly increased the efficiency and accuracy of pathology image analysis. In light of this progress, digital pathology is fast becoming a powerful tool to assist pathologists. Studying tumor tissue and its surrounding microenvironment provides critical insight into tumor initiation, progression, metastasis, and potential therapeutic targets. Nucleus segmentation and classification are critical to pathology image analysis, especially in characterizing and quantifying the tumor microenvironment (TME). Computational algorithms have been developed for nucleus segmentation and TME quantification within image patches. However, existing algorithms are computationally intensive and time consuming for WSI analysis. This study presents Histology-based Detection using Yolo (HD-Yolo), a new method that significantly accelerates nucleus segmentation and TME quantification. We demonstrate that HD-Yolo outperforms existing WSI analysis methods in nucleus detection, classification accuracy, and computation time. We validated the advantages of the system on 3 different tissue types: lung cancer, liver cancer, and breast cancer. For breast cancer, nucleus features by HD-Yolo were more prognostically significant than both the estrogen receptor status by immunohistochemistry and the progesterone receptor status by immunohistochemistry. The WSI analysis pipeline and a real-time nucleus segmentation viewer are available at https://github.com/impromptuRong/hd_wsi.