Mesenchymal Subtype Research Articles

N6-Methyladenosine Regulator-Mediated Immue Patterns and Tumor Microenvironment Infiltration Characterization in Glioblastoma.

BackgroundEpigenetic modifications, according to emerging evidence, perform a critical role for cellular immune response and tumorigenesis. Nonetheless, the role of N6-methyladenosine modification in shaping of the glioblastoma tumor microenvironment is unknown.MethodsN6-methyladenosine(m6A) methylation patterns in GBM patients were evaluated via multiple omics analysis of 15 m6A regulators and systematically correlated with tumor immune features. For quantification of N6-methyladenosine methylation patterns of individual patients, GM-score was developed and correlated with clinical and immunological characteristics.ResultsGlioblastoma has two different m6A methylation patterns that are strongly associated with TME characteristics, tumor subtype, immunotherapy response, and patient prognosis. High-GM-score is associated with an immune tolerance phenotype dominated by the IDH1 wild molecular subtype and the Mesenchymal tissue subtype, as well as a high infiltration of immune cells and stromal cells and a poor prognosis. Furthermore, despite higher immune checkpoint expression, individuals with a high-GM-score have a poorer response to anti-CTLA4 immunotherapy regimens due to T-cells dysfunctional. Low-GM-score individuals had an immunodeficient phenotype dominated by IDH mutant molecular subtypes and Proneural tissue subtypes, with less immune cell infiltration and a better prognosis. Furthermore, patients with low-GM-scores had higher microsatellite instability (MSI) and t-cell exclusion scores, as well as a better response to anti-CTLA4 immunotherapy regimens.ConclusionThis study demonstrated that m6A modification patterns play an important role in the shaping of TME complexity and diversity. The GM-score could identify m6A modification patterns in individual patients, resulting in a more personalization and efficacious anti-tumor immunotherapy strategy.

Epigenetic Activation of lncRNA MIR155HG Mediated by Promoter Hypomethylation and SP1 is Correlated with Immune Infiltration in Glioma.

PurposeThe lncRNA MIR155 host gene (MIR155HG) plays a role in the progression of several malignant cancers. However, the specific mechanisms of MIR155HG in glioma progression have not been clearly established. The purpose of this study was to investigate the function of MIR155HG in glioma at the transcriptome level and relationship with immune infiltration.Patients and MethodsTotally, 697 RNA-seq and 594 DNA methylation data were retrieved from The Cancer Genome Atlas (TCGA) dataset while 325 RNA-seq data were retrieved from the Chinese Glioma Genome Atlas (CGGA) dataset. The DNA methylation levels of MIR155HG CpG islands were assessed through bisulfite amplicon sequencing (BSAS). The regulatory mechanism of SP1 on MIR155HG was examined by chromatin immunoprecipitation (ChIP) and luciferase reporter assays. R language was used as the main tool for statistical analysis and graphical work.ResultsMIR155HG was predominantly expressed in the isocitrate dehydrogenase (IDH) wild-type as well as mesenchymal subtype gliomas. Promoter methylation levels of MIR155HG in glioblastoma (GBM) were remarkably decreased compared with those in lower-grade glioma (LGG). In addition, there were negative correlations between promoter methylation levels and MIR155HG expressions but positive correlations with patients’ overall survival. In vitro studies further revealed that MIR155HG expression was regulated by DNA promoter methylation and transcription factor (SP1) binding to the promoter. Moreover, there was a close association between MIR155HG expression and immune as well as stromal cell infiltrations, inflammatory activities, and immune checkpoints. Clinically, univariate and multivariate Cox analyses revealed that MIR155HG is an independent prognostic marker for glioma patients.ConclusionOur results established that MIR155HG is a potential biomarker for prognosis and an immunotherapeutic target in glioma.

Slow-Cycling Cells in Glioblastoma: A Specific Population in the Cellular Mosaic of Cancer Stem Cells.

Simple SummaryA major challenge in successfully managing glioblastoma is that we do not understand the types and dynamic behaviors of the cells that constitute these tumors. Combining bioinformatics and functional studies, we describe the presence of multiple independent lineages of cancer stem cells driving the heterogeneic nature of glioblastoma. Our results help us decode and map the transcriptional and functional diversity of glioblastoma cells. By revealing potential mechanisms underlying tumor resilience, the root of resistance to treatment, our study may inform novel strategies to develop precision and effective therapies to treat brain cancer. Glioblastoma (GBM) exhibits populations of cells that drive tumorigenesis, treatment resistance, and disease progression. Cells with such properties have been described to express specific surface and intracellular markers or exhibit specific functional states, including being slow-cycling or quiescent with the ability to generate proliferative progenies. In GBM, each of these cellular fractions was shown to harbor cardinal features of cancer stem cells (CSCs). In this study, we focus on the comparison of these cells and present evidence of great phenotypic and functional heterogeneity in brain cancer cell populations with stemness properties, especially between slow-cycling cells (SCCs) and cells phenotypically defined based on the expression of markers commonly used to enrich for CSCs. Here, we present an integrative analysis of the heterogeneity present in GBM cancer stem cell populations using a combination of approaches including flow cytometry, bulk RNA sequencing, and single cell transcriptomics completed with functional assays. We demonstrated that SCCs exhibit a diverse range of expression levels of canonical CSC markers. Importantly, the property of being slow-cycling and the expression of these markers were not mutually inclusive. We interrogated a single-cell RNA sequencing dataset and defined a group of cells as SCCs based on the highest score of a specific metabolic signature. Multiple CSC groups were determined based on the highest expression level of CD133, SOX2, PTPRZ1, ITGB8, or CD44. Each group, composed of 22 cells, showed limited cellular overlap, with SCCs representing a unique population with none of the 22 cells being included in the other groups. We also found transcriptomic distinctions between populations, which correlated with clinicopathological features of GBM. Patients with strong SCC signature score were associated with shorter survival and clustered within the mesenchymal molecular subtype. Cellular diversity amongst these populations was also demonstrated functionally, as illustrated by the heterogenous response to the chemotherapeutic agent temozolomide. In conclusion, our study supports the cancer stem cell mosaicism model, with slow-cycling cells representing critical elements harboring key features of disseminating cells.

Abstract PD3-04: Multi-omics characterization of triple-negative breast cancer identifies therapeutic vulnerabilities and epigenetic immune suppression in the mesenchymal subtype

Abstract Triple-negative breast cancer (TNBC) is a collection of biologically diverse cancers characterized by distinct transcriptional patterns, biology and immune composition. TNBCs display transcriptional diversity with at least four tumor-intrinsic subtypes that include two basal-like (BL1, BL2), a mesenchymal (M) and a luminal androgen receptor (LAR) subtype. Using integrative analyses of transcriptomic, epigenetic, proteomic and phospho-proteomic patterns we have identified subtype-specific vulnerabilities, which advanced our understanding of the cellular origins of TNBC subtypes. In mesenchymal subtype tumors we observed high mutation load and genomic instability, absence of immune cells, low PD-L1 expression, loss of global DNA methylation and transcriptional repression of antigen presentation genes through the polycomb repressor complex 2 (PRC2). Using cell line models, we demonstrate that MHC-I expression is epigenetically silenced by the PRC2 in mesenchymal TNBC cell lines and inhibition of EZH2 restores expression. Pharmacological inhibition of EZH2 enhances chemotherapy efficacy in syngeneic murine tumor models providing a rationale for using PRC2 inhibitors in PD-L1 negative mesenchymal tumors. Citation Format: Brian D Lehmann, Antonio Colaprico, Tiago C Silva, Jianjiao Chen, Hanbing An, Yuguang Ban, Lily Wang, Jamaal L James, Justin Balko, Paula I Gonzalez-Ericsson, Melinda E Sanders, Bing Zhang, Jennifer A Pietenpol, Xi S Chen. Multi-omics characterization of triple-negative breast cancer identifies therapeutic vulnerabilities and epigenetic immune suppression in the mesenchymal subtype [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr PD3-04.

Subtyping of high grade serous ovarian carcinoma: histopathological and immunohistochemical approach

BackgroundHigh-grade serous ovarian carcinoma (HGSOC) is classified into four molecular subtypes; mesenchymal, proliferative, immunoreactive, and differentiated, with suggested different prognosis. Addressing the presence of histopathological and immunohistochemical differences in HGSOC that parallel the molecular subtypes can help in tailoring the management protocol to improve therapeutic response and patient outcome.MethodsThis retrospective study was conducted on 85 specimens for cases of HGSOC. Cases were classified according to histopathological findings into mesenchymal, proliferative, immunoreactive, and differentiated subtypes. Cases were immunostained with ER, PR, Ki67, CD8, E-cadherin, and vimentin.ResultsBy applying histopathological data, cases were subdivided into 4 groups; mesenchymal type represented by 25 cases, proliferative type which included 14 cases, the immunoreactive type included 14 cases, and differentiated type represented by 32 cases; 13 of them had SET features and 19 had papillary architectural features. A significant correlation was found between Ki67 and proliferative subtype, as well as between CD8 and immunoreactive subtype. ER showed significantly higher expression in proliferative subtype in the group treated by primary debulking. CD8 showed a significant correlation with solid endometroid transitional (SET) pattern in the group that underwent interval debulking. In terms of prognosis, the shortest median progression-free survival (PFS) was for mesenchymal subtype, while the longest median PFS was for differentiated subtype with SET architectural pattern with statistically significant correlation. No correlation was found between any of the studied parameters and overall survival.ConclusionHistopathological features and immunohistochemistry can help to stratify HGSOC into prognostic distinct groups.

CD44-Mediated Poor Prognosis in Glioma Is Associated With M2-Polarization of Tumor-Associated Macrophages and Immunosuppression.

BackgroundGlioma is the most common primary brain tumor with a poor prognosis. Key genes that are negatively related to prognosis may provide the therapy targets to cure glioma. To clarify the role of CD44 in glioma, we explored its function at bulk-transcriptome, spatial and single-cell transcriptome levels.MethodsIn total, expression profiles with survival data of whole-grade glioma from The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA), RNA-seq data with anatomic information of glioblastoma (GBM) from the Ivy Glioblastoma Atlas Project, RNA-sequencing (RNA-seq) data from recurrent GBM receiving adjuvant anti-PD-1 immunotherapy accessed through GSE121810, and single-cell RNA-seq data of GBM under accession GSE103224 were enrolled in this study. CD44-specific findings were further analyzed by R language.ResultsCD44 is positively correlated with WHO grade of malignancy and is negatively related to prognosis in glioma. Meanwhile, CD44 predominantly expresses in GBM mesenchymal subtype, and gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses reveal that CD44 positively coexpressed genes are closely related to glioma immunity. Moreover, CD44+ cells mainly distribute in perinecrotic region with high expression of immune factors. At single-cell resolution, only malignant tumor cells, tumor-associated macrophages (TAMs), and T cells express CD44 in GBM. CD44+ malignant tumor cells are in mesenchymal-1-like (MES1-like) cellular state, and CD44+ TAMs are in M2 phenotype. CD44+ T cells have high expression of both PD-1 and PD-L1. CD44 and its directly interacted inhibitory immunomodulators are upregulated in patients with nonresponder recurrent GBM treated with PD-1 blockade therapy.ConclusionOur work demonstrates that CD44, a new M2 TAM biomarker, is involved in immune suppressor and promote glioma progression in glioma microenvironment. These results expand our understanding of CD44-specific clinical and immune features in glioma.

Alpha-crystallin B chains enhance cell migration in basal-like 2 triple-negative breast cancer cells.

Triple-negative breast cancer (TNBC) can be divided into six subtypes. Among these subtypes, the basal-like 2 (BL2) subtype shows the lowest five-year survival rate and highest risk of metastasis. Alpha-crystallin B chains (αB-crystallin), a small heat shock protein that is known to be involved in breast cancer metastasis, is highly expressed in the basal-like subtype but not in the other non-basal subtypes. Thus, we hypothesized that αB-crystallin may be an important factor involved in the worse prognosis of the BL2 subtype compared with those of the other TNBC subtypes. Here, we examined the role of αB-crystallin in cell motility in two TNBC cell lines: HCC1806 (BL2 subtype) and, as control, MDA-MB-436 (mesenchymal stem-like subtype). HCC1806 showed greater cell migration capacity and a higher expression level of the gene encoding αB-crystallin (CRYAB) than did MDA-MB-436. Short interfering RNA-mediated silencing of CRYAB expression significantly reduced the cell migration capacity of HCC1806 cells, whereas it had no effect in MDA-MB-436 cells, indicating that αB-crystallin is essential for the migration of HCC1806 cells. Thus, high αB-crystallin expression may be a contributing factor to the poor prognosis of BL2 TNBC.

Selective multi-kinase inhibition sensitizes mesenchymal pancreatic cancer to immune checkpoint blockade by remodeling the tumor microenvironment.

SummaryKRAS-mutant pancreatic ductal adenocarcinoma (PDAC) is highly immunosuppressive and resistant to targeted and immunotherapies. Among the different PDAC subtypes, basal-like mesenchymal PDAC, which is driven by allelic imbalance, increased gene-dosage and subsequent high expression levels of oncogenic KRAS, shows the most aggressive phenotype and strongest therapy resistance. Here, we perform a systematic high-throughput combinatorial drug screen and identify a synergistic interaction between the MEK inhibitor trametinib and the multi-kinase inhibitor nintedanib, which targets KRAS-directed oncogenic signaling in mesenchymal PDAC. This combinatorial treatment induces cell cycle arrest and cell death, and initiates a context-dependent remodeling of the immunosuppressive cancer cell secretome. Using a combination of single cell RNA sequencing, CRISPR screens and immunophenotyping, we show that this combination therapy promotes intra-tumor infiltration of cytotoxic and effector T cells, which sensitizes mesenchymal PDAC to PD-L1 immune checkpoint inhibition. Overall, our results open new avenues to target this aggressive and therapy-refractory mesenchymal PDAC subtype.

The Role of RAB GTPases and Its Potential in Predicting Immunotherapy Response and Prognosis in Colorectal Cancer.

Background: Colorectal cancer (CRC) is the third most common cancer worldwide, in which aberrant activation of the RAS signaling pathway appears frequently. RAB proteins (RABs) are the largest Ras small GTPases superfamily that regulates intracellular membrane trafficking pathways. The dysregulation of RABs have been found in various diseases including cancers. Compared with other members of Ras families, the roles of RABs in colorectal cancer are less well understood. Methods: We analyzed the differential expression and clinicopathological association of RABs in CRC using RNA sequencing and genotyping datasets from TCGA samples. Moreover, the biological function of RAB17 and RAB34 were investigated in CRC cell lines and patient samples. Results: Of the 62 RABs we analyzed in CRC, seven (RAB10, RAB11A, RAB15, RAB17, RAB19, RAB20, and RAB25) were significantly upregulated, while six (RAB6B, RAB9B, RAB12, RAB23, RAB31, and RAB34) were significantly downregulated in tumor tissues as compared to normal. We found that the upregulated-RABs, which were highly expressed in metabolic activated CRC subtype (CMS3), are associated with cell cycle related pathways enrichment and positively correlated with the mismatch repair (MMR) genes in CRC, implying their role in regulating cell metabolism and tumor growth. While, high expression of the downregulated-RABs were significantly associated with poor prognostic CRC mesenchymal subtypes (CMS4), immune checkpoint genes, and tumor infiltrating immune cells, indicating their role in predicting prognosis and immunotherapy efficacy. Interestingly, though RAB34 mRNA is downregulated in CRC, its high expression is significantly associated with poor prognosis. In vitro experiments showed that RAB17 overexpression can promote cell proliferation via cell cycle regulation. While, RAB34 overexpression can promote cell migration and invasion and is associated with PD-L1/PD-L2 expression increase in CRC cells. Conclusions: Our study showed that RABs may play important roles in regulating cell cycle and immune-related pathways, therefore might be potential biomarkers in predicting prognosis and immunotherapy response in CRC.

Stemness Refines the Classification of Colorectal Cancer With Stratified Prognosis, Multi-Omics Landscape, Potential Mechanisms, and Treatment Options.

BackgroundStemness refers to the capacities of self-renewal and repopulation, which contributes to the progression, relapse, and drug resistance of colorectal cancer (CRC). Mounting evidence has established the links between cancer stemness and intratumoral heterogeneity across cancer. Currently, the intertumoral heterogeneity of cancer stemness remains elusive in CRC.MethodsThis study enrolled four CRC datasets, two immunotherapy datasets, and a clinical in-house cohort. Non-negative matrix factorization (NMF) was performed to decipher the heterogeneity of cancer stemness. Multiple machine learning algorithms were applied to develop a nine-gene stemness cluster predictor. The clinical outcomes, multi-omics landscape, potential mechanisms, and immune features of the stemness clusters were further explored.ResultsBased on 26 published stemness signatures derived by alternative approaches, we decipher two heterogeneous clusters, low stemness cluster 1 (C1) and high stemness cluster 2 (C2). C2 possessed a higher proportion of advanced tumors and displayed worse overall survival and relapse-free survival compared with C1. The MSI-H and CMS1 tumors tended to enrich in C1, and the mesenchymal subtype CMS4 was the prevalent subtype of C2. Subsequently, we developed a nine-gene stemness cluster predictor, which robustly validated and reproduced our stemness clusters in three independent datasets and an in-house cohort. C1 also displayed a generally superior mutational burden, and C2 possessed a higher burden of copy number deletion. Further investigations suggested that C1 enriched numerous proliferation-related biological processes and abundant immune infiltration, while C2 was significantly associated with mesenchyme development and differentiation. Given results derived from three algorithms and two immunotherapeutic cohorts, we observed C1 could benefit more from immunotherapy. For patients with C2, we constructed a ridge regression model and further identified nine latent therapeutic agents, which might improve their clinical outcomes.ConclusionsThis study proposed two stemness clusters with stratified prognosis, multi-omics landscape, potential mechanisms, and treatment options. Current work not only provided new insights into the heterogeneity of cancer stemness, but also shed light on optimizing decision-making in immunotherapy and chemotherapy.

The Non-N6-Methyladenosine Epitranscriptome Patterns and Characteristics of Tumor Microenvironment Infiltration and Mesenchymal Transition in Glioblastoma.

BackgroundAn increasing number of RNA modification types other than N6-methyladenosine (m6A) modification have been detected. Nonetheless, the probable functions of RNA modifications beyond m6A in the tumor microenvironment (TME), mesenchymal (MES) transition, immunotherapy, and drug sensitivity remain unclear.MethodsWe analyzed the characteristics of 32 non-m6A RNA modification regulators in 539 glioblastoma (GBM) patients and the TME cell infiltration and MES transition patterns. Using principal component analysis, a non-m6A epitranscriptome regulator score (RM score) model was established. We estimated the association between RM score and clinical characteristics, TME status, GBM subtypes, and drug and immunotherapy response.ResultsThree definite non-m6A RNA modification patterns associated with diverse biological pathways and clinical characteristics were identified. The high RM score group was characterized by a poor prognosis, enhanced immune infiltration, and MES subtype. Further analysis indicated that the high RM score group had a lower tumor mutation burden as well as a weaker response to immunotherapy. The higher RM score group may benefit more from drugs targeting the EGFR and WNT signaling pathways.ConclusionOur study exposed the potential relationship of non-m6A RNA modification regulators with clinical features, TME status, and GBM subtype and clarified its therapeutic value.

Deep Learning-Based Classification of Epithelial-Mesenchymal Transition for Predicting Response to Therapy in Clear Cell Renal Cell Carcinoma.

Epithelial–mesenchymal transition (EMT) profoundly impacts prognosis and immunotherapy of clear cell renal cell carcinoma (ccRCC). However, not every patient is tested for EMT status because this requires additional genetic studies. In this study, we developed an EMT gene signature to classify the H&E-stained slides from The Cancer Genome Atlas (TCGA) into epithelial and mesenchymal subtypes, then we trained a deep convolutional neural network to classify ccRCC which according to our EMT subtypes accurately and automatically and to further predict genomic data and prognosis. The clinical significance and multiomics analysis of the EMT signature was investigated. Patient cohorts from TCGA (n = 252) and whole slide images were used for training, testing, and validation using an algorithm to predict the EMT subtype. Our approach can robustly distinguish features predictive of the EMT subtype in H&E slides. Visualization techniques also detected EMT-associated histopathological features. Moreover, EMT subtypes were characterized by distinctive genomes, metabolic states, and immune components. Deep learning convolutional neural networks could be an extremely useful tool for predicting the EMT molecular classification of ccRCC tissue. The underlying multiomics information can be crucial in applying the appropriate and tailored targeted therapy to the patient.

NOX2-Induced High Glycolytic Activity Contributes to the Gain of COL5A1-Mediated Mesenchymal Phenotype in GBM.

Simple SummaryGlioblastoma multiforme (GBM) is the most aggressive type of glioma and exhibits extensive heterogeneity and poor prognosis with a high recurrence rate. Among the genetic alterations in GBM with different phenotypic states, a mesenchymal subtype has been associated with a worse outcome in patients with GBM. The mechanisms for the gain of the mesenchymal subtype in GBM remain unclear. Our aim was to investigate whether NOX2-induced high glycolytic activity could contribute to the gain of the mesenchymal phenotype in GBM. We revealed that NOX2-induced high glycolytic activity can induce the gain of the COL5A1-mediated mesenchymal phenotype in GBM. Our findings will provide the molecular mechanism by which NOX2 contributes to the gain of mesenchymal phenotype in GBM.The alteration of the cellular metabolism is a hallmark of glioma. The high glycolytic phenotype is a critical factor in the pathogenesis of high-grade glioma, including glioblastoma multiforme (GBM). GBM has been stratified into three subtypes as the proneural, mesenchymal, and classical subtypes. High glycolytic activity was found in mesenchymal GBM relative to proneural GBM. NADPH oxidase 2 (NOX2) has been linked to cellular metabolism and epithelial-mesenchymal transition (EMT) in tumors. The role of NOX2 in the regulation of the high glycolytic phenotype and the gain of the mesenchymal subtype in glioma remain unclear. Here, our results show that the levels of NOX2 were elevated in patients with GBM. NOX2 induces hexokinase 2 (HK2)-dependent high glycolytic activity in U87MG glioma cells. High levels of NOX2 are correlated with high levels of HK2 and glucose uptake in patients with GBM relative to benign glioma. Moreover, NOX2 increases the expression of mesenchymal-subtype-related genes, including COL5A1 and FN1 in U87MG glioma cells. High levels of NOX2 are correlated with high levels of COL5A1 and the accumulation of extracellular matrix (ECM) in patients with GBM relative to benign glioma. Furthermore, high levels of HK2 are correlated with high levels of COL5A1 in patients with GBM relative to benign glioma. Our results suggest that NOX2-induced high glycolytic activity contributes to the gain of the COL5A1-mediated mesenchymal phenotype in GBM.

Metabolic Alterations and WNT Signaling Impact Immune Response in HGSOC.

Our study used transcriptomic and metabolomic strategies to determine the molecular profiles of HGSOC patient samples derived from primary tumor and ascites cells. These data identified clinically relevant heterogeneity among and within patients and highlighted global and patient-specific cellular responses to neoadjuvant chemotherapy (NACT). Tissue from 61 treatment-naïve patients with HGSOC were collected. In addition, 11 benign, 32 ascites, and 18 post-NACT samples (matched to the individual patient's pre-NACT sample) were collected. RNA sequencing (RNA-seq) was performed on all samples collected. Two-dimensional spatial proteomic data was collected for two pairs of pre- and post-NACT. Untargeted metabolomics data using GCxGC-MS was generated for 30 treatment-naive tissues. Consensus clustering, analysis of differential expression, pathway enrichment, and survival analyses were performed. Treatment-naïve HGSOC tissues had distinct transcriptomic and metabolomic profiles. The mesenchymal subtype harbored a metabolomic profile distinct from the other subtypes. Compared with primary tumor tissue, ascites showed significant changes in immune response and signaling pathways. NACT caused significant alterations in gene expression and WNT activity, and this corresponded to altered immune response. Overall, WNT signaling levels were inversely correlated with immune cell infiltration in HGSOC tissues and WNT signaling post-NACT was inversely correlated with progression-free survival. Our study concluded that HGSOC is a heterogenous disease at baseline and growing molecular differences can be observed between primary tumor and ascites cells or within tumors in response to treatment. Our data reveal potential exploratory biomarkers relevant for treatment selection and predicting patient outcomes that warrant further research.

Analyses of molecular subtypes and their association to mechanisms of radioresistance in patients with HPV-negative HNSCC treated by postoperative radiochemotherapy

PurposeTo assess the relation of the previously reported classification of molecular subtypes to the outcome of patients with HNSCC treated with postoperative radio(chemo)therapy (PORT-C), and to assess the association of these subtypes with gene expressions reflecting known mechanisms of radioresistance. Material and methodsGene expression analyses were performed using the GeneChip Human Transcriptome Array 2.0 on a multicentre retrospective patient cohort (N = 128) of the German Cancer Consortium Radiation Oncology Group (DKTK-ROG) with locally advanced HNSCC treated with PORT-C. Tumours were assigned to four molecular subtypes, and correlation analyses between subtypes and clinical risk factors were performed. In addition, the classifications of eight genes or gene signatures related to mechanisms of radioresistance, which have previously shown an association with outcome of patients with HNSCC, were compared between the molecular subtypes. The endpoints loco-regional control (LRC) and overall survival (OS) were evaluated by log-rank tests and Cox regression. ResultsTumours were classified into the four subtypes basal (19.5%), mesenchymal (18.8%), atypical (15.6%) and classical (14.1%). The remaining tumours could not be classified (32.0%). Tumours of the mesenchymal subtype showed a lower LRC compared to the other subtypes (p = 0.012). These tumours were associated with increased epithelial-mesenchymal transition (EMT) and overexpression of a gene signature enriched in DNA repair genes. The majority of the eight considered gene classifiers were significantly associated to LRC or OS in the whole cohort. ConclusionMolecular subtypes, previously identified on HNSCC patients treated with primary radio(chemo)therapy or surgery, were related to LRC for patients treated with PORT-C, where mesenchymal tumours presented with worse prognosis. After prospective validation, subtype-based patient stratification, potentially in combination with other molecular classifiers, may be considered in future interventional studies in the context of personalised radiotherapy and may guide the development of combined treatment approaches.

Comprehensive omics analyses profile genesets related with tumor heterogeneity of multifocal glioblastomas and reveal LIF/CCL2 as biomarkers for mesenchymal subtype.

Rationale: Around 10%-20% patients with glioblastoma (GBM) are diagnosed with more than one tumor lesions or multifocal GBM (mGBM). However, the understanding on genetic, DNA methylomic, and transcriptomic characteristics of mGBM is still limited.Methods: In this study, we collected nine tumor foci from three mGBM patients followed by whole genome sequencing, whole genome bisulfite sequencing, RNA sequencing, and immunohistochemistry. The data were further examined using public GBM databases and GBM cell line.Results: Analysis on genetic data confirmed common features of GBM, including gain of chr.7 and loss of chr.10, loss of critical tumor suppressors, high frequency of PDGFA and EGFR amplification. Through profiling DNA methylome of individual tumor foci, we found that promoter methylation status of genes involved in detection of chemical stimulus, immune response, and Hippo/YAP1 pathway was significantly changed in mGBM. Although both CNV and promoter methylation alteration were involved in heterogeneity of different tumor foci from same patients, more CNV events than promoter hypomethylation events were shared by different tumor foci, implying CNV were relatively earlier than promoter methylation alteration during evolution of different tumor foci from same mGBM. Moreover, different tumor foci from same mGBM assumed different molecular subtypes and mesenchymal subtype was prevalent in mGBM, which might explain the worse prognosis of mGBM than single GBM. Interestingly, we noticed that LIF and CCL2 was tightly correlated with mesenchymal subtype tumor focus in mGBM and predicted poor survival of GBM patients. Treatment with LIF and CCL2 produced mesenchymal-like transcriptome in GBM cells.Conclusions: Together, our work herein comprehensively profiled multi-omics features of mGBM and emphasized that components of extracellular microenvironment, such as LIF and CCL2, contributed to the evolution and prognosis of tumor foci in mGBM patients.

Aberrant Protein Glycosylation in Brain Cancers, with Emphasis on Glioblastoma.

Aberrant glycosylation has been associated with several processes of tumorigenesis from cell signaling, migration and invasion, to immune regulation and metastasis formation. The biosynthesis of glycoconjugates is regulated through concerted and finely tuned enzymatic reactions. This includes the levels and activity of glycosyltransferases and glycosidases, nucleotide sugar metabolism, substrate availability, epigenetic condition, and cellular functional state. Glioblastoma (GBM) is the most aggressive brain tumor, frequently occurring in adults with overall survival not surpassing 17months after diagnosis. GBM has been classified by the World Health Organization (WHO) as a grade 4 astrocytoma and stratified into G-CIMP, proneural, classical, and mesenchymal subtypes. Several biomolecular features associated with GBM aggressiveness have been elucidated; however, more studies are needed to elucidate the role of glycosylation in GBM pathology, looking at their potential as cancer targets. Here, we focus on the alteration of genes involved in protein N- and O-linked glycosylation in GBM. Specifically, the mRNA levels of glycogenes were analyzed using astrocytoma-TCGA-RNAseq datasets from public repositories. A total of 68 genes were differentially regulated in the most aggressive, mesenchymal subtype of GBM compared to the proneural and classical subtypes, and the expression of these genes was compared to normal brain tissues. Among them, we focused on 38 genes coding for proteins that belong to: 1) asparagine glycosylation (ALG); 2) glycosyltransferases (B3T, B4T); 3) fucosyltransferase (FUT); 4) acetylgalactosaminyltransferases (GALNT); 5) hexosaminidase (HEX); 6) mannosidase (MAN); 7) acetylglucosaminyltransferase (MGAT); 8) sialidase or neuraminidase (NEU); 9) solute carrier 35 family (SLC); and 10) sialyltransferase (ST). The differential expression of some genes was already reported in several solid tumors; however, several of them were found to be dysregulated in GBM for the first time. These data represent an important starting point to perform further orthogonal and functional validations to pinpoint the role of these glycogenes in GBM as diagnostic and therapeutic targets.

Association of hyperglycemia and molecular subclass on survival in IDH-wildtype glioblastoma.

Hyperglycemia has been associated with worse survival in glioblastoma. Attempts to lower glucose yielded mixed responses which could be due to molecularly distinct GBM subclasses. Clinical, laboratory, and molecular data on 89 IDH-wt GBMs profiled by clinical next-generation sequencing and treated with Stupp protocol were reviewed. IDH-wt GBMs were sub-classified into RTK I (Proneural), RTK II (Classical) and Mesenchymal subtypes using whole-genome DNA methylation. Average glucose was calculated by time-weighting glucose measurements between diagnosis and last follow-up. Patients were stratified into three groups using average glucose: tertile one (<100 mg/dL), tertile two (100-115 mg/dL), and tertile three (>115 mg/dL). Comparison across glucose tertiles revealed no differences in performance status (KPS), dexamethasone dose, MGMT methylation, or methylation subclass. Overall survival (OS) was not affected by methylation subclass (P = .9) but decreased with higher glucose (P = .015). Higher glucose tertiles were associated with poorer OS among RTK I (P = .08) and mesenchymal tumors (P = .05), but not RTK II (P = .99). After controlling for age, KPS, dexamethasone, and MGMT status, glucose remained significantly associated with OS (aHR = 5.2, P = .02). Methylation clustering did not identify unique signatures associated with high or low glucose levels. Metabolomic analysis of 23 tumors showed minimal variation across metabolites without differences between molecular subclasses. Higher average glucose values were associated with poorer OS in RTKI and Mesenchymal IDH-wt GBM, but not RTKII. There were no discernible epigenetic or metabolomic differences between tumors in different glucose environments, suggesting a potential survival benefit to lowering systemic glucose in selected molecular subtypes.

Prognostic Value of Tumor-microenvironment-associated Genes in Ovarian Cancer

Abstract Background: Ovarian cancer (OV) is the fifth leading cause of cancer death among women. Growing evidence supports a key role of the tumor microenvironment in the growth, progression, and metastasis of OV. However, the prognostic effects of gene expression signatures associated with the OV microenvironment have not been well established. This study was aimed at applying the Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) algorithm to identify tumor-microenvironment-associated genes that predict outcomes in patients with OV. Methods: The gene expression profiles of OV samples were downloaded from The Cancer Genome Atlas database. The immune and stromal scores of 469 OV samples on the basis of the ESTIMATE algorithm were available. To better understand the effects of gene expression signatures associated with the OV microenvironment on prognosis, we categorized these samples into groups with high and low ESTIMATE scores. A different OV cohort from the Gene Expression Omnibus (GEO) database and immunohistochemistry from The Human Protein Atlas database were used for external validation. Results: The molecular subtypes of patients with OV correlated with the stromal scores, and the mesenchymal subtype had the highest stromal scores. Patients with higher stromal scores had lower 5-year overall survival; 449 differentially expressed genes in the stromal score group were identified, 26 of which were significantly associated with poor prognosis in patients with OV (p &lt; 0.05). In another OV cohort from the Gene Expression Omnibus database, six genes were further validated to be significantly associated with poor prognosis. Immunohistochemistry data from The Human Protein Atlas database confirmed the overexpression of CX3CR1, GFPT2, NBL1, TFPI2, and ZFP36 in OV tissues compared with normal tissues. Conclusion: Our findings suggest that CX3CR1, GFPT2, NBL1, TFPI2, and ZFP36 may be promising biomarkers for OV prognosis, with clinical implications for therapeutic strategies.

Immune landscape-based machine-learning-assisted subclassification, prognosis, and immunotherapy prediction for glioblastoma.

As a malignant brain tumor, glioblastoma (GBM) is characterized by intratumor heterogeneity, a worse prognosis, and highly invasive, lethal, and refractory natures. Immunotherapy has been becoming a promising strategy to treat diverse cancers. It has been known that there are highly heterogeneous immunosuppressive microenvironments among different GBM molecular subtypes that mainly include classical (CL), mesenchymal (MES), and proneural (PN), respectively. Therefore, an in-depth understanding of immune landscapes among them is essential for identifying novel immune markers of GBM. In the present study, based on collecting the largest number of 109 immune signatures, we aim to achieve a precise diagnosis, prognosis, and immunotherapy prediction for GBM by performing a comprehensive immunogenomic analysis. Firstly, machine-learning (ML) methods were proposed to evaluate the diagnostic values of these immune signatures, and the optimal classifier was constructed for accurate recognition of three GBM subtypes with robust and promising performance. The prognostic values of these signatures were then confirmed, and a risk score was established to divide all GBM patients into high-, medium-, and low-risk groups with a high predictive accuracy for overall survival (OS). Therefore, complete differential analysis across GBM subtypes was performed in terms of the immune characteristics along with clinicopathological and molecular features, which indicates that MES shows much higher immune heterogeneity compared to CL and PN but has significantly better immunotherapy responses, although MES patients may have an immunosuppressive microenvironment and be more proinflammatory and invasive. Finally, the MES subtype is proved to be more sensitive to 17-AAG, docetaxel, and erlotinib using drug sensitivity analysis and three compounds of AS-703026, PD-0325901, and MEK1-2-inhibitor might be potential therapeutic agents. Overall, the findings of this research could help enhance our understanding of the tumor immune microenvironment and provide new insights for improving the prognosis and immunotherapy of GBM patients.