A three marker signature to distinguish Small cell from large cell neuroendocrine carcinoma: Study at transcriptomic and protein level
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ABSTRACT:
ORGANISM(S): Homo sapiens
SUBMITTER: Muhammad Bari
PROVIDER: E-GEOD-44447 | biostudies-arrayexpress |
REPOSITORIES: biostudies-arrayexpress
Similar Datasets
Project description:To identify potential markers for distinction of Small cell (SCLC) from large cell neuroendocrine (LCNEC) carcinoma, we have conducted the gene expression profiling on precisely isolated cancer cells and bronchial epithelial cells from these tumours and normal background lung. After statistical analysis and finding highly differentially expressed genes between these tumour groups, we performed qRT-PCR to delineate markers to test at protein level using immunohistochemistry. Markers showing statistical significance at qRT-PCR level were taken forward to stain tissue microarray sections of SCLC and LCNEC to further delineate markers. This resulted in identification of BAI3, CDX2, VIL1 and CD99 as potential candidates. These markers were then taken forward to stain whole sections of tumour sample available in house (University Hospitals Coventry and Warwickshire) which resulted in elimination of CD99. Thus, BAI3, CDX2 and VIL1 were taken further forward to stain tumour samples acquired externally in UK.
2013-11-01 | GSE44447 | GEO
Project description:There is a clear need to expand the toolkit of adequate mouse models and cell lines available for preclinical studies of high-grade neuroendocrine lung carcinoma (Small Cell Lung Carcinoma, SCLC and Large Cell Neuroendocrine Carcinoma, LCNEC), two highly aggressive tumor types with dismal prognosis and few therapeutic options. Actually, paucity is extreme in the case of LCNEC. Given the lack of murine cell lines and transplant models of LCNEC the need is imperative/unmet. In this study, we have generated and examined new models of LCNEC and SCLC transplantable cell lines derived from our previously developed primary mouse LCNEC and SCLC tumors. RNA-seq analysis demonstrated that our cell lines and syngeneic tumors maintained the transcriptome program from the original transgenic primary tumor, and display strong similarities to human SCLC or LCNEC. Importantly, the SCLC transplanted cell lines show the ability of metastasize, mimicking this characteristic of the human condition. In summary, we have generated mouse cell line tools that allow further basic and translational research, as well as preclinical testing of new treatment strategies for SCLC and LCNEC, as they retain important features of their human counterparts and address the lack of LCNEC disease models.
2023-10-29 | GSE242422 | GEO
Project description:Two prognostically significant subtypes of high-grade lung neuroendocrine tumors independent of small-cell and large-cell neuroendocrine carcinomas identified by gene expression profiles. BACKGROUND: Classification of high-grade neuroendocrine tumors (HGNT) of the lung currently recognises large-cell neuroendocrine carcinoma (LCNEC) and small-cell lung carcinoma (SCLC) as distinct groups. However, a similarity in histology for these two carcinomas and uncertain clinical course have led to suggestions that a single HGNT classification would be more appropriate. Gene expression profiling, which can reproduce histopathological classification, and often defines new subclasses with prognostic significance, can be used to resolve HGNT classification. METHODS: We used cDNA microarrays with 40386 elements to analyze the gene expression profiles of 38 surgically resected samples of lung neuroendocrine tumors and 11 SCLC cell lines. Samples of large-cell carcinoma, adenocarcinoma, and normal lung were also included to give a total of 105 samples analyzed. The data were subjected to filtering to yield informative genes before unsupervised hierarchical clustering that identified relatedness of tumor samples. FINDINGS: Distinct groups for carcinoids, large-cell carcinoma, adenocarcinoma, and normal lung were readily identified. However, we were unable to distinguish LCNEC from SCLC by gene expression profiling. Three independent rounds of unsupervised hierarchical clustering consistently divided SCLC samples into two main groups with LCNEC samples largely integrated with these groups. Furthermore, patients in one of the groups identified by clustering had a significantly better clinical outcome than the other (83% vs 12% survived for 5 years; p=0.0094. None of the highly proliferative SCLC cell lines subsequently analyzed clustered with this good-prognosis group. INTERPRETATION: Our findings show that HGNT of the lung can be classified into two groups independent of SCLC and LCNEC. To this end, we have identified many genes, some of which encode well-characterized markers of cancer that distinguish the HGNT groups. These results have implications for the diagnosis, classification, and treatment of lung neuroendocrine tumors, and provide important insights into their underlying biology. Keywords: other
2004-02-10 | E-GEOD-1037 | biostudies-arrayexpress
Project description:Purpose: Large cell neuroendocrine carcinoma (LCNEC) is a high-grade neuroendocrine malignancy that, like the more common small cell lung cancer (SCLC), is associated with an absence of druggable oncogenic driver mutations, a clinically aggressive disease course, and dismal prognosis. In contrast to SCLC, however, there is little evidence to guide optimal treatment strategies which are often adapted from SCLC and non-small cell lung cancer (NSCLC) approaches. While there have been some efforts to describe the molecular landscape of LCNEC, to date there are few links between distinct biologic phenotypes of LCNEC and therapeutic vulnerabilities. Experimental design: To better define the biology of LCNEC, we analyzed cell line and patient genomic data and performed immunohistochemistry and single-cell (sc)RNAseq of core needle biopsies from LCNEC patients and preclinical models. Results: Here, we demonstrate that the presence or absence of YAP1 distinguishes two subsets of LCNEC. The YAP1-high subset is mesenchymal and inflamed and characterized, alongside TP53 mutations, by co-occurring alterations in CDKN2A/B and SMARCA4. Therapeutically, the YAP1-high subset demonstrates vulnerability to MEK and AXL targeting strategies, including a novel preclinical AXL CAR-T cell, as well as predicted vulnerability to SMARCA2 degraders and CDK4/6 inhibitors. Meanwhile, the YAP1-low subset is epithelial and immune-cold and more commonly features TP53 and RB1 co-mutations, similar to those observed in pure SCLC. Notably, the YAP1-low subset is also characterized by expression of SCLC subtype-defining transcription factors - especially ASCL1 and NEUROD1 - and, as expected given its transcriptional similarities to SCLC, exhibits putative vulnerabilities reminiscent of SCLC, including Delta-like ligand 3 (DLL3) and CD56 targeting, as with novel preclinical DLL3 and CD56 CAR T-cells, and DNA damage repair (DDR) inhibition. Conclusion: YAP1 defines distinct subsets of LCNEC with unique biology. These findings highlight the potential for YAP1 to guide personalized treatment strategies for LCNEC.
2024-08-05 | GSE269942 | GEO
Project description:Background Neuroendcrine carcinoma (NEC) of lung consists of small-cell lung carcinoma (SCLC) and large cell neuroendocrine carcinoma (LCNEC). Although long-term outcomes of SCLC patients are usually poor, a few patients achieve long-term survivals. Prognostic factors in SCLC have not been fully elucidated. microRNAs (miRNAs) are known to negatively regulate gene expression and to be relevant to tumorigenesis, tumor classification and prognosis. Methods 63 samples (46 neuroendocrine carcinoma including 35 SCLC and 11 LCNEC, 4 adenocarcinoma, 5 squamous cell carcinoma and 8 normal lung) were obtained through surgical resection. miRNA expression in each sample was comprehensively investigated using miRNA microarray. Results Unsupervised hierarchial clustering classified the all NEC into two subgroups, group 1 and 2. Group 1 was felt into an independent branch that consisted of only NEC, whereas group 2 was included in a branch that contained both NEC and non-NEC. Compared with SCLC of group 1 (SCLC 1), SCLC of group 2 (SCLC 2) was a distinct subgroup with surprisingly good prognosis (0% v 79% survived for 5 years; p=0.007). Serum proGRP level was significantly lower in SCLC 2 than in SCLC 1 despite of similar tumor stages between the two groups. Among 12 cases treated by presurgical chemotherapy, PR:NC ratio was 6:3 in SCLC 1 and 2:1 in SCLC 2, suggesting no apparent difference of chemosensitivity. Histologically, distinction between both groups was difficult. Cox regression analysis demonstrated that three miRNA signature was correlated with survival of SCLC patients. These prognostic miRNAs were differentially expressed between SCLC 1 and 2, which were validated by quantitative real time RT-PCR. Bioinformatics analysis predicted that these miRNAs targeted not only genes involving with tumorigenesis but also genes associated with neuroendocrine function. Conclusion miRNA expression profiling identified a distinct subgroup of SCLC with favorable prognosis. This subgroup might have less neuroendocrine character than usual SCLC. All samples were tissue samples obtained by surgical resection. 35 samples of small-cell lung cancer were investigated. 11 samples of larege cell neuroendocrine cancer, 4 samples of adenocarcinoma, 5 samples of squamous cell carcinoma and 8 samples of normal lung were also included as reference.
2013-01-31 | E-GEOD-19945 | biostudies-arrayexpress
Project description:Two prognostically significant subtypes of high-grade lung neuroendocrine tumors independent of small-cell and large-cell neuroendocrine carcinomas identified by gene expression profiles. BACKGROUND: Classification of high-grade neuroendocrine tumors (HGNT) of the lung currently recognises large-cell neuroendocrine carcinoma (LCNEC) and small-cell lung carcinoma (SCLC) as distinct groups. However, a similarity in histology for these two carcinomas and uncertain clinical course have led to suggestions that a single HGNT classification would be more appropriate. Gene expression profiling, which can reproduce histopathological classification, and often defines new subclasses with prognostic significance, can be used to resolve HGNT classification. METHODS: We used cDNA microarrays with 40386 elements to analyze the gene expression profiles of 38 surgically resected samples of lung neuroendocrine tumors and 11 SCLC cell lines. Samples of large-cell carcinoma, adenocarcinoma, and normal lung were also included to give a total of 105 samples analyzed. The data were subjected to filtering to yield informative genes before unsupervised hierarchical clustering that identified relatedness of tumor samples. FINDINGS: Distinct groups for carcinoids, large-cell carcinoma, adenocarcinoma, and normal lung were readily identified. However, we were unable to distinguish LCNEC from SCLC by gene expression profiling. Three independent rounds of unsupervised hierarchical clustering consistently divided SCLC samples into two main groups with LCNEC samples largely integrated with these groups. Furthermore, patients in one of the groups identified by clustering had a significantly better clinical outcome than the other (83% vs 12% survived for 5 years; p=0.0094. None of the highly proliferative SCLC cell lines subsequently analyzed clustered with this good-prognosis group. INTERPRETATION: Our findings show that HGNT of the lung can be classified into two groups independent of SCLC and LCNEC. To this end, we have identified many genes, some of which encode well-characterized markers of cancer that distinguish the HGNT groups. These results have implications for the diagnosis, classification, and treatment of lung neuroendocrine tumors, and provide important insights into their underlying biology. Keywords: other
2004-02-11 | GSE1037 | GEO
Project description:Small cell lung cancer (SCLC) frequently harbors mutually exclusive genomic amplification of MYC family members. Therefore, it has been long suggested that they are functionally equivalent; however, more recently, their expression has been associated with specific neuroendocrine markers and distinct histopathology. Integrated transcriptomic and epigenomic analyses showed that c-Myc and L-Myc impart neuronal and non-neuroendocrine associated transcriptional programs, respectively, both associated with distinct SCLC lineage. Genetic replacement of c-Myc with L-Myc in c-Myc-SCLC induced a neuronal state but was insufficient to induce ASCL1 lineage. In contrast, c-Myc induced transition from ASCL1-SCLC to NeuroD1-SCLC characterized by distinct LCNEC-like histopathology. Collectively, we characterize an undescribed role of historically defined general oncogenes, c-Myc and L-Myc, for regulating lineage plasticity across molecular subtypes as well as histological subclasses.
2020-12-31 | GSE156328 | GEO
Project description:Small cell lung cancer (SCLC) frequently harbors mutually exclusive genomic amplification of MYC family members. Therefore, it has been long suggested that they are functionally equivalent; however, more recently, their expression has been associated with specific neuroendocrine markers and distinct histopathology. Integrated transcriptomic and epigenomic analyses showed that c-Myc and L-Myc impart neuronal and non-neuroendocrine associated transcriptional programs, respectively, both associated with distinct SCLC lineage. Genetic replacement of c-Myc with L-Myc in c-Myc-SCLC induced a neuronal state but was insufficient to induce ASCL1 lineage. In contrast, c-Myc induced transition from ASCL1-SCLC to NeuroD1-SCLC characterized by distinct LCNEC-like histopathology. Collectively, we characterize an undescribed role of historically defined general oncogenes, c-Myc and L-Myc, for regulating lineage plasticity across molecular subtypes as well as histological subclasses.
2020-12-31 | GSE156333 | GEO
Project description:Background and Aims: Colorectal adenomas (CRAs) are precursor lesions that can progress to adenocarcinomas. Current clinical guidelines categorize patients into risk groups based on adenoma characteristics observed during index colonoscopy, but this may lead to overtreatment. Our aim was to establish a molecular feature-based risk allocation framework towards improved patient stratification. Methods: Deep Visual Proteomics (DVP) is a novel approach that combines image-based artificial intelligence with automated microdissection and ultra-high sensitive mass spectrometry. Here we used DVP on formalin-fixed, paraffin-embedded (FFPE) CRA tissues from nine patients. Immunohistological staining for Caudal-type homeobox 2 (CDX2), a gene implicated in colorectal cancer, enabled the characterization of cellular heterogeneity within distinct tissue regions and across patients. Results: DVP seamlessly integrated with current pathology workflows and equipment, identifying deleted in malignant brain tumors 1 (DMBT1), myristoylated alanine rich protein kinase C (MARCKS), and cluster of differentiation 99 (CD99) correlated with disease recurrence history, making them potential markers of risk stratification. The spatial and cell type specific capabilities of DVP uncovered a metabolic switch towards anaerobic glycolysis in areas of high dysplasia, which was specific for the cells with high CDX2 expression. Conclusion: The application of spatially resolved proteomics to CRA revealed three new potential markers for early-stage tumor development, and provided novel insights into metabolic reprogramming. Our findings underscore the potential of this technology to refine early-stage detection and contribute to personalized patient management strategies.
2024-08-04 | PXD046999 | Pride
Project description:Small cell lung carcinoma (SCLC) and large cell neuroendocrine carcinoma (LCNEC) are high-grade pulmonary neuroendocrine tumors. The neural basic helix-loop-helix (bHLH) transcription factors ASCL1 and NEUROD1 have been shown to play crucial roles in promoting the malignant behavior and survival of human SCLC cell lines. In this study, we find ASCL1 and NEUROD1 identify distinct neuroendocrine tumors, bind distinct genomic loci, and regulate mostly distinct genes. ASCL1 and NEUROD1 are often bound in super-enhancers that are associated with highly expressed genes in their respective SCLC cell lines suggesting different cell lineage of origin for these tumors. ASCL1 targets oncogenic genes such as MYCL1, RET, and NFIB, while NEUROD1 targets the oncogenic gene MYC. Although ASCL1 and NEUROD1 regulate different genes, many of these gene targets commonly contribute to neuroendocrine and cell migration function. ASCL1 in particular also regulates genes in the NOTCH pathway and genes important in cell-cycle dynamics. Finally, we demonstrate ASCL1 but not NEUROD1 is required for SCLC and LCNEC tumor formation in current in vivo genetic mouse models of pulmonary neuroendocrine tumors RNA-seq analysis performed on two ASCL1high and two NEUROD1high human SCLC cell lines to identify gene expression patterns in these cells. Also, we performed RNA-seq in mouse neuroendocrine lung tumors obtained from Trp53;Rb1;Rbl2 triple knockout model mice treated with Adeno-CMVCRE intratracheally.
2016-07-21 | E-GEOD-69393 | biostudies-arrayexpress