Epithelial-mesenchymal Transition Subtypes Research Articles

Molecular subtypes based on immunologic and epithelial-mesenchymal transition gene sets reveal tumor immune microenvironment characteristics and implications for immunotherapy of patients with glioma

The tumor immune microenvironment (TIME) significantly influences cancer progression and treatment. This study sought to uncover novel TIME-related glioma biomarkers to advance antitumor immunotherapies by integrating data from sequencing of bulk RNA as well as scRNA. Immunologic and epithelial-mesenchymal transition (EMT) characteristics were used to classify glioma patients into two immune subtypes (ISs) and two EMT subtypes (ESs). Patients in IS1 and ES1, characterized by high immune infiltration and low stemness scores, exhibited poor clinical outcomes and limited responsiveness to immunotherapy. A new risk signature was developed using 16 genes and validated in independent glioma cohorts. Among these, HAVCR2, IL18, LAGLS9, and PTPN6 emerged as hub genes, with IL18 identified as a potential independent indicator. The upregulation of IL18 in high-grade gliomas and U-251 MG cells aligned with bioinformatics analysis. These insights deepen the understanding of TIME-related mechanisms in glioma and highlight potential therapeutic targets, offering a theoretical foundation for effective antitumor immunotherapies in glioma.

Synergism of Fusobacterium periodonticum and N-nitrosamines promote the formation of EMT subtypes in ESCC by modulating Wnt3a palmitoylation.

N-Nitrosamine disinfection by-products (NAs-DBPs) have been well proven for its role in esophageal carcinogenesis. However, the role of intratumoral microorganisms in esophageal squamous cell carcinoma (ESCC) has not yet been well explored in the context of exposure to NAs-DBPs. Here, the multi-omics integration reveals F. periodonticum (Fp) as "facilitators" is highly enriched in cancer tissues and promotes the epithelial mesenchymal transition (EMT)-like subtype formation of ESCC. We demonstrate that Fp potently drives de novo synthesis of fatty acids, migration, invasion and EMT phenotype through its unique FadAL adhesin. However, N-nitrosomethylbenzylamine upregulates the transcription level of FadAL. Mechanistically, co-immunoprecipitation coupled to mass spectrometry shows that FadAL interacts with FLOT1. Furthermore, FLOT1 activates PI3K-AKT/FASN signaling pathway, leading to triglyceride and palmitic acid (PA) accumulation. Innovatively, the results from the acyl-biotin exchange demonstrate that FadAL-mediated PA accumulation enhances Wnt3A palmitoylation on a conserved cysteine residue, Cys-77, and promotes Wnt3A membrane localization and the translocation of β-catenin into the nucleus, further activating Wnt3A/β-catenin axis and inducing EMT phenotype. We therefore propose a "microbiota-cancer cell subpopulation" interaction model in the highly heterogeneous tumor microenvironment. This study unveils a mechanism by which Fp can drive ESCC and identifies FadAL as a potential diagnostic and therapeutic target for ESCC.

DNA methylation profiling deciphers three EMT subtypes with distinct prognoses and therapeutic vulnerabilities in breast cancer.

Background: Epithelial-mesenchymal transition (EMT), deemed a pivotal hallmark of tumours, is intricately regulated by DNA methylation and encompasses multiple states along tumour progression. The potential mechanisms that drive the intrinsic heterogeneity of breast cancer (BC) via EMT transformation have not been identified, presenting a significant obstacle in clinical diagnosis and treatment. Methods: A total of 7,602 patients have been included in this study. We leveraged integrated multiomics data (epigenomic, genomic, and transcriptomic data) to delineate the comprehensive landscape of EMT in BC. Subsequently, a subtyping classifier was developed through a machine learning framework proposed by us. Results: We classified the BC samples into three methylation-driven EMT subtypes with distinct features, namely, C1 (the mammary duct development subtype with TP53 activation), C2 (the immune infiltration subtype with high TP53 mutation), and C3 (the ERBB2 amplification subtype with an unfavorable prognosis). Specifically, patients with the C1 subtype might respond to endocrine therapy or the p53-MDM2 antagonist nutlin-3. Patients with the C2 subtype might benefit from combined therapeutic regimens involving radiotherapy, PARP inhibitors, and immune checkpoint blockade therapy. Patients with the C3 subtype might benefit from anti-HER2 agents such as lapatinib. Notably, to increase the clinical applicability of the EMT subtypes, we devised a 96-gene panel-based classifier via a machine learning framework. Conclusions: Our study identified three methylation-driven EMT subtypes with distinct prognoses and biological traits to capture heterogeneity in BC and provided a rationale for the use of this classification as a powerful tool for developing new strategies for clinical trials.

Prognostic Significance of Excision Repair Cross-Complementation Group 1 on Circulating Tumor Cells for Nasopharyngeal Carcinoma.

Liquid biopsy, including the detection of circulating tumor cells (CTCs), has emerged as a promising tool for cancer diagnosis and monitoring. However, the prognostic value of CTCs in nasopharyngeal carcinoma (NPC) remains unclear due to the lack of phenotypic characterization. The expression of Excision Repair Cross-Complementation Group 1 (ERCC1) and CTCs epithelial-mesenchymal transition (EMT) have been associated with treatment efficacy. In this study, we aimed to evaluate the prognostic significance of ERCC1 expression on CTCs and their EMT subtypes before treatment in NPC. We retrospectively analyzed 108 newly diagnosed locally advanced NPC patients who underwent CanPatrol™ CTC testing between November 2018 and November 2021. CTCs were counted and classified into epithelial, epithelial-mesenchymal hybrid, and mesenchymal subtypes. ERCC1 expression was divided into negative and positive groups. Clinical features and survival outcomes were analyzed. The positive rate of CTCs was 92.6% (100/108), with an ERCC1 positivity rate of 74% (74/100). Further analysis of the subtypes showed that positive ERCC1 on mesenchymal CTCs was associated with a later N stage (P = .01). Positive ERCC1 expression was associated with poor overall survival (OS; P = .039) and disease-free survival (DFS; P = .035). Further analysis of subtypes showed that the positive ERCC1 on mesenchymal-type CTCs was associated with poor OS (P = .012) and metastasis-free survival (MFS; P = .001). Our findings suggest that ERCC1 expression on CTCs may serve as a new prognostic marker for NPC patients. Evaluating CTCs subtypes may become an auxiliary tool for personalized and precise treatment.

Immune perturbation network identifies an EMT subtype with chromosomal instability and tumor immune-desert microenvironment

Most gastric cancer (GC) subtypes are identified through transcriptional profiling overlooking dynamic changes and interactions in gene expression. Based on the background network of global immune genes, we constructed sample-specific edge-perturbation matrices and identified four molecular network subtypes of GC (MNG). MNG-1 displayed the best prognosis and vigorous cell cycle activity. MNG-2 was enriched by immune-hot phenotype with the potential for immunotherapy response. MNG-3 and MNG-4 were identified with epithelial-mesenchymal transition (EMT) peculiarity and worse prognosis, termed EMT subtypes. MNG-3 was characterized by low mutational burden and stromal cells and considered areplica of previous subtypes associated with poor prognosis. Notably, MNG-4 was considered a previously undefined subtype with a dismal prognosis, characterized by chromosomal instability and immune-desert microenvironment. This subtype tended to metastasize and was resistant to respond to immunotherapy. Pharmacogenomics analysis showed three therapeutic agents (NVP-BEZ235, LY2606368, and rutin)were potential interventions for MNG-4.

EMT-Related Gene Signature Predicts the Prognosis in Uveal Melanoma Patients.

Background Uveal melanoma (UVM) is the most common primary intraocular malignancy in adults. Epithelial-mesenchymal transition (EMT) is an essential regulator of the UVM's immune microenvironment. However, the precise role of EMT in UVM remains to be explored and the development of a related treatment strategy is urgently needed. Methods Multiomics data and clinical information for TCGA-UVM were used to identify the EMT subtypes and analyze their regulatory role in the immune microenvironment in UVM. A machine-learning method based on the identified subtypes was utilized to construct the EMT feature-based prognostic model. External validation cohorts GSE84976 and GSE22138 were employed to validate the model's robustness. Immunotherapy cohort IMvigor210 was used to explore the model's potential to predict immunotherapy responsiveness. Results Two EMT subtypes were identified in UVM. The role of EMT in shaping the immune microenvironment and regulating cancer-immunity circle of UVM was analyzed. A robust prognostic model was presented and validated to predict patient prognosis. The model also predicted patient's immune features and immunotherapy responsiveness. Conclusion The EMT-mediated immune features in UVM were illustrated, providing a reliable model to facilitate precise UVM treatment. This research may assist in decision-making during clinical UVM therapy.

Abstract 5726: Splicing-based classifier for gastric cancer identifies epithelial-mesenchymal transition subtypes associated with survival

Abstract Alternatively spliced RNA isoforms are a hallmark of tumors, but their nature, prevalence, and clinical implications in gastric cancer are unknown. We systematically profiled the splicing landscape of 83 gastric tumors and matched normal mucosa. We identified and experimentally validated eight splicing events that can classify all gastric cancers into three subtypes: Epithelial-splicing, Mesenchymal-splicing, and Hybrid-splicing. These subtypes were associated with distinct molecular signatures and epithelial-mesenchymal transition markers. Subtype-specific splicing events were enriched in motifs for splicing factors RBM24 and ESRP1, which were upregulated in Mesenchymal-splicing and Epithelial-splicing tumors, respectively. A simple classifier based only on RNA levels of RBM24 and ESRP1, and which is thus readily implementable in the clinic, is sufficient to distinguish gastric cancer subtypes and predict patient survival in multiple independent patient cohorts Citation Format: Yukyung Jun, Yun-Suhk Suh, SungHee Park, Jieun Lee, Jong-Il Kim, Sanghyuk Lee, Wan-Ping Lee, Olga Anczuków, Han-Kwang Yang, Charles Lee. Splicing-based classifier for gastric cancer identifies epithelial-mesenchymal transition subtypes associated with survival [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5726.

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.

Targeting Subtype-Specific Metabolic Preferences in Nucleotide Biosynthesis Inhibits Tumor Growth in a Breast Cancer Model.

Investigating metabolic rewiring in cancer can lead to the discovery of new treatment strategies for breast cancer subtypes that currently lack targeted therapies. In this study, we used MMTV-Myc-driven tumors to model breast cancer heterogeneity, investigating the metabolic differences between two histologic subtypes, the epithelial-mesenchymal transition (EMT) and the papillary subtypes. A combination of genomic and metabolomic techniques identified differences in nucleotide metabolism between EMT and papillary subtypes. EMT tumors preferentially used the nucleotide salvage pathway, whereas papillary tumors preferred de novo nucleotide biosynthesis. CRISPR/Cas9 gene editing and mass spectrometry-based methods revealed that targeting the preferred pathway in each subtype resulted in greater metabolic impact than targeting the nonpreferred pathway. Knocking out the preferred nucleotide pathway in each subtype has a deleterious effect on in vivo tumor growth, whereas knocking out the nonpreferred pathway has a lesser effect or may even result in increased tumor growth. Collectively, these data suggest that significant differences in metabolic pathway utilization distinguish EMT and papillary subtypes of breast cancer and identify said pathways as a means to enhance subtype-specific diagnoses and treatment strategies. SIGNIFICANCE: These findings uncover differences in nucleotide salvage and de novo biosynthesis using a histologically heterogeneous breast cancer model, highlighting metabolic vulnerabilities in these pathways as promising targets for breast cancer subtypes.

Metabolomic profiling of mouse mammary tumor-derived cell lines reveals targeted therapy options for cancer subtypes.

Breast cancer is a heterogeneous disease with several subtypes that currently do not have targeted therapeutic options. Metabolomics has the potential to uncover novel targeted treatment strategies by identifying metabolic pathways required for cancer cells to survive and proliferate. The metabolic profiles of two histologically distinct breast cancer subtypes from a MMTV-Myc mouse model, epithelial-mesenchymal-transition (EMT) and papillary, were investigated using mass spectrometry-based metabolomics methods. Based on metabolic profiles, drugs most likely to be effective against each subtype were selected and tested. We found that the EMT and papillary subtypes display different metabolic preferences. Compared to the papillary subtype, the EMT subtype exhibited increased glutathione and TCA cycle metabolism, while the papillary subtype exhibited increased nucleotide biosynthesis compared to the EMT subtype. Targeting these distinct metabolic pathways effectively inhibited cancer cell proliferation in a subtype-specific manner. Our results demonstrate the feasibility of metabolic profiling to develop novel personalized therapy strategies for different subtypes of breast cancer. Schematic overview of the experimental design for drug selection based on breast cancer subtype-specific metabolism. The epithelial mesenchymal transition (EMT) and papillary tumors are histologically distinct mouse mammary tumor subtypes from the MMTV-Myc mouse model. Cell lines derived from tumors can be used to determine metabolic pathways that can be used to select drug candidates for each subtype.

Abstract 3712: Proliferative and invasive colorectal tumors in pet dogs provide unique insights into human colorectal cancer

Abstract Spontaneous tumors in pet dogs represent a valuable but undercharacterized cancer model. To better use this resource, we performed an initial global comparison between proliferative and invasive colorectal tumors from 20 canine cases and evaluated their molecular homology to human colorectal cancer (CRC). We sequenced 15 canine intestinal samples for WGS and 26 for RNA-seq. We investigated alterations in the genome and transcriptome, using state-of-the-art analysis tools. We aslso investigated their microbiome, by mapping WGS and RNA-seq read pairs that could not be placed onto the canine genome to two microbial genome databases: The Human Microbiome Project database and all bacterial genomic sequences. We performed the same analysis with TCGA (The Cancer Genome Atlas) colon cancer data. Based on our analysis, proliferative canine tumors harbor overactivated WNT/β-catenin pathways and recurrent CTNNB1 (β-catenin) mutations S45F/P, D32Y and G34E. Invasive canine tumors harbor prominent fibroblast proliferation and overactivated stroma. Both groups have recurrent TP53 mutations. We observed three invasion patterns in canine tumors: collective, crypt-like and epithelial-mesenchymal transition (EMT). We detected enriched Helicobacter bilis and Alistipes finegoldii in proliferative and crypt-like tumors, but depleted mucosa-microbes in the EMT tumor. Additionally, guided by our canine findings, we classified 79% of 478 human colon cancers from TCGA into four subtypes: primarily proliferative, or with collective, crypt-like or EMT invasion features. Their molecular characteristics match those of canine tumors. We showed that consensus molecular subtype 4 (mesenchymal) of human CRC should be further divided into EMT and crypt-like subtypes, which differ in TGF-β activation and mucosa-microbe content. Our canine tumors share the same pathogenic pathway as human CRCs. Dog-human integration identifies three CRC invasion patterns and improves CRC subtyping. Citation Format: Jin Wang, Tianfang Wang, Yanfang Sun, Yuan Feng, William C. Kisseberth, Carolyn J. Henry, Irene Mok, Susan E. Lana, Kevin Dobbin, Nicole Northrup, Elizabeth W. Howerth, Shaying Zhao, Houjian Cai. Proliferative and invasive colorectal tumors in pet dogs provide unique insights into human colorectal cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 3712.

Proliferative and Invasive Colorectal Tumors in Pet Dogs Provide Unique Insights into Human Colorectal Cancer.

Spontaneous tumors in pet dogs represent a valuable but undercharacterized cancer model. To better use this resource, we performed an initial global comparison between proliferative and invasive colorectal tumors from 20 canine cases, and evaluated their molecular homology to human colorectal cancer (CRC). First, proliferative canine tumors harbor overactivated WNT/β-catenin pathways and recurrent CTNNB1 (β-catenin) mutations S45F/P, D32Y and G34E. Invasive canine tumors harbor prominent fibroblast proliferation and overactivated stroma. Both groups have recurrent TP53 mutations. We observed three invasion patterns in canine tumors: collective, crypt-like and epithelial–mesenchymal transition (EMT). We detected enriched Helicobacter bilis and Alistipes finegoldii in proliferative and crypt-like tumors, but depleted mucosa-microbes in the EMT tumor. Second, guided by our canine findings, we classified 79% of 478 human colon cancers from The Cancer Genome Atlas into four subtypes: primarily proliferative, or with collective, crypt-like or EMT invasion features. Their molecular characteristics match those of canine tumors. We showed that consensus molecular subtype 4 (mesenchymal) of human CRC should be further divided into EMT and crypt-like subtypes, which differ in TGF-β activation and mucosa-microbe content. Our canine tumors share the same pathogenic pathway as human CRCs. Dog-human integration identifies three CRC invasion patterns and improves CRC subtyping.

Loss of discoidin domain receptor 1 (DDR1) via CpG methylation during EMT in epithelial ovarian cancer

Epithelial ovarian cancer (EOC) can be stratified according to the stages of epithelial-mesenchymal transition (EMT). Here, we aim to identify tyrosine kinases (TKs) in EOC that correlate with the EMT subtypes. By gene expression microarray, we analyzed the expression levels of tyrosine kinases in EOC cell lines in correlation with EMT. Among the candidate TKs identified, DDR1 was expressed mainly in EOC cells with an Epithelial phenotype. Its expression was validated by qPCR, ELISA and western blotting. Using Infinium HumanMethylation27K BeadChip array and pyrosequencing, we further analyzed the CpG methylation levels at the DDR1 promoter in EOC cells and found that the CpG methylation levels of DDR1 promoter correlated negatively with the expression of DDR1 along the EMT spectrum. Therefore, EMT stratification could be a potential biomarker to predict patient response to DDR1-targeting drugs.

MicroRNAs as Critical Regulators Involved in Regulating Epithelial- Mesenchymal Transition

The epithelial-mesenchymal transition (EMT) is a fundamental biological process that is involved in normal embryogenesis, would healing, and tissue repair, as well as numerous pathologies, including organ fibrosis, malignant transformation, and cancer progression. Both transcriptional and post-transcriptional regulatory mechanisms contribute to a complex and tightly controlled regulatory network during the EMT process, and a growing body of evidence now demonstrates that microRNAs (miRNAs) are crucial regulators of this network. miRNAs are a class of small non-coding RNAs that regulate gene expression through translational repression or mRNA degradation. A set of miRNAs have been discovered that have the potential to target multiple components of the signaling pathways and downstream effectors of the EMT. Our understanding of the roles that miRNAs play during the EMT process suggests that these miRNAs may eventually serve as novel biomarkers and therapeutic targets for various EMT-based pathological conditions. This review summarizes the current knowledge concerning how miRNAs mechanistically regulate the EMT and discusses the specific roles that miRNAs play in three EMT subtypes. We hope that a more comprehensive understanding of the functions of miRNAs in the EMT process will lead to the rapid development of novel diagnostic techniques and molecular-based strategies for controlling EMT.