ScRNA-seq of Small Intestine Neuroendocrine Tumors
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE292163 | GEO | 2025/03/18
REPOSITORIES: GEO
Similar Datasets
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:Small cell lung cancer (SCLC) is a neuroendocrine tumor treated clinically as a single disease with poor outcomes. Distinct SCLC molecular subtypes have been defined based on expression of lineage-related transcription factors: ASCL1, NEUROD1, POU2F3 or YAP1, but their origins remain unknown. Here, we develop an in vitro model of MYC-driven SCLC tumor cell progression and perform a time-series analysis of single-cell transcriptome profiling to reveal that MYC drives the dynamic evolution of SCLC subtypes. Analyses of these single-cell RNA seq data reveal that MYC promotes a temporal shift from an Ascl1-to-Neurod1-to-Yap1+ state from a neuroendocrine cell of origin. They also support our findings that MYC activates Notch signaling to dedifferentiate tumor cells to non-neuroendocrine fates. Additional single-cell RNA sequencing of 4 bulk Rb1/Trp53/MycT58A (RPM) tumors reveal individual tumors to consist of cells at nearly every stage of RPM tumor evolution modeled in vitro. Together, these single-cell RNA sequencing data place 3 of 4 SCLC subtypes on a defined trajectory and suggest that genetics, cell of origin, and tumor cell plasticity determine SCLC subtype.
2020-05-28 | GSE149179 | GEO
Project description:Small Cell Lung Cancer (SCLC) is the most aggressive type of lung cancer with early metastatic dissemination and invariable development of resistant disease for which no effective treatment is available to date. Mouse models of SCLC based on inactivation of Rb1 and Trp53 developed earlier showed frequent amplifications of two transcription factor genes: Nfib and Mycl. Overexpression of Nfib but not Mycl in SCLC mouse results in an enhanced and altered metastatic profile, and appears to be associated with genomic instability. NFIB promotes tumor heterogeneity with the concomitant expansive growth of poorly differentiated, highly proliferative, and invasive tumor cell populations. Consistent with the mouse data, NFIB expression in high-grade human neuroendocrine carcinomas correlates with advanced stage III/IV disease warranting its further assessment as a potentially valuable progression marker in a clinical setting. Genomic DNA from mouse small cell lung tumor samples was analyzed by mate pair sequencing and low coverage sequencing. And RNA from Nfib overexpressing mouse small cell lung cancer cell lines was further analyzed for high quality RNA profiles using Illumina Hiseq2500. This series contains only RNA-seq data.
2016-07-29 | E-GEOD-82005 | biostudies-arrayexpress
Project description:The World Health Organization has subclassified adenocarcinoma based upon predominant cell morphology and growth pattern such as bronchioloalveolar carcinoma (BAC), adenocarcinoma with mixed subtypes (AC-mixed), and homogenously invasive tumors with a variety of histological patterns Pure invasive adenocarcinomas are often devoid of bronchioloalveolar morphology. The clinical importance of lung adenocarcinoma invasion is supported by several recent studies indicating that the risk of death in non-mucinous BAC is significantly lower than that of pure invasive tumors and in tumors with greater than 0.6 cm of fibrosis or linear invasion (J Thorac Oncol 6:244-285) To identify human tumor cell signatures associated with lung adenocarcinoma subtype and invasion, we performed microarray gene expression profiling of microdissected tumor cells noninvasive AC and AC-Mixed invasive tumors. 17 cases of noninvasive AC tumors and 23 cases of AC-mixed subtype invasive lung adenocarcinomas resected from 2002 to 2006 were examined (Columbia Lung Adenocarcinoma dataset)
2011-09-14 | E-GEOD-27716 | biostudies-arrayexpress
Project description:The World Health Organization has subclassified adenocarcinoma based upon predominant cell morphology and growth pattern such as bronchioloalveolar carcinoma (BAC), adenocarcinoma with mixed subtypes (AC-mixed), and homogenously invasive tumors with a variety of histological patterns Pure invasive adenocarcinomas are often devoid of bronchioloalveolar morphology. The clinical importance of lung adenocarcinoma invasion is supported by several recent studies indicating that the risk of death in non-mucinous BAC is significantly lower than that of pure invasive tumors and in tumors with greater than 0.6 cm of fibrosis or linear invasion (J Thorac Oncol 6:244-285) To identify human tumor cell signatures associated with lung adenocarcinoma subtype and invasion, we performed microarray gene expression profiling of microdissected tumor cells noninvasive AC and AC-Mixed invasive tumors.
2011-09-15 | GSE27716 | 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
2004-02-11 | GSE1037 | GEO
Project description:Background: Neoadjuvant chemotherapy (NAC) is the standard treatment for muscle-invasive bladder cancer (MIBC), yet 40% of patients progress, emphasizing the need for biomarkers predictive for response or chemoresistance. Gene expression-based subtypes may serve as biomarkers, though which subtypes will respond, notably when it comes to the basal subtype, remains contentious. Patients and methods: This post hoc study analyzed 300 NAC-treated patients enrolled in the GETUG/AFU VESPER trial, with transurethral diagnostic formalin-fixed paraffin-embedded which underwent pathological review before being sequenced. ‘Mixed’ subtype was defined for tumors displaying at least two different Consensus molecular subtypes in separate regions. We evaluated the association between molecular subtypes and outcome after NAC. Tumors with remaining tissue at cystectomy (n ¼ 83) were compared with pre-treatment tumors. Results: Cases were classified basal/squamous (Ba/Sq) (n ¼ 84), luminal unstable (n = 57), stroma-rich (n = 53), mixed (n = 48), luminal papillary (n ¼ 39), luminal non-specific (n = 18), and neuroendocrine-like (n ¼ 1), with 30/48 mixed cases including a Ba/Sq component. Compared with other molecular subtypes in a multivariate Cox model, Ba/Sq (pure or mixed) patients had an increased hazard ratio (HR) of progression-free survival [HR 2.0, 95% confidence interval (CI) 1.36-3.0]. Mixed tumors were associated with decreased metabolic activity that could account for chemoresistance. Ba/Sq and mixed non-responders mostly maintained their subtype at cystectomy and have fewer myeloid dendritic cells after NAC. Tumors classified luminal papillary at transurethral resection of the urinary bladder tumor exhibited an increase in T CD4þ and macrophage signatures after NAC. Other subtypes did not show significant immune changes after NAC. Our study design relied on detailed pathological review, which precluded evaluating the mixed subtype in published datasets. Furthermore, the sample size for post-NAC analyses constrained the statistical power of these findings. Conclusions: Our findings underscore the importance of recognizing intra-tumor heterogeneity in MIBC and its role in chemoresistance associated with Ba/Sq subtype, and provide valuable insights that could help future treatment development and improve patient outcomes.
2024-10-19 | GSE279390 | GEO
Project description:Small-cell lung cancer (SCLC) is the most fatal form of lung cancer. Intra-tumoral heterogeneity, marked by neuroendocrine (NE) and non-neuroendocrine (non-NE) cell states, defines SCLC, but the drivers of SCLC plasticity are poorly understood. To map the landscape of SCLC tumor microenvironment (TME), we apply spatially resolved transcriptomics and quantitative mass spectrometry-based proteomics to metastatic SCLC tumors obtained via rapid autopsy. The phenotype and overall composition of non-malignant cells in the tumor microenvironment (TME) exhibits substantial variability, closely mirroring the tumor phenotype, suggesting TME-driven reprogramming of NE cell states. We identify cancer-associated fibroblasts (CAF) as a crucial element of SCLC TME heterogeneity, contributing to immune exclusion, and predicting an exceptionally poor prognosis. Together, our work provides the first comprehensive map of SCLC tumor and TME ecosystems, emphasizing their pivotal role in SCLCs adaptable nature, opening possibilities for re-programming the intercellular communications that shape SCLC tumor states.
2024-05-15 | PXD052033 | Pride
Project description:Intratumoral heterogeneity underlies cancer treatment resistance, but approaches to neutralize it remain elusive. Here, we recast heterogeneity in a systems perspective that considers cancer cell functional tasks inherited from cells of origin. We apply Archetype Analysis to bulk transcriptomics data from small cell lung cancer (SCLC), which forms tumors composed of neuroendocrine (NE) and non-neuroendocrine (non-NE) transcriptional subtypes. SCLC subtypes fit well in a 5-dimensional polytope whose vertices optimize tasks reminiscent of pulmonary NE cells, the SCLC normal counterpart, and include injury repair, slithering, and chemosensation. SCLC cells near a vertex are specialists for a task, while more distant cells are generalists, bearing gene signatures of multiple archetypes. Evolutionary theory and dynamical systems modeling suggest a division of labor strategy for adaptation to treatment, based on task trade-offs amongst specialists and generalists. Cell Transport Potential, a metric derived from single-cell RNA velocity, uncovers plasticity trends from specialists to generalists, and NE to non-NE subtypes. Transcription factor network simulations indicate that MYC overexpression increases plasticity by de-stabilizing NE subtypes. Framing heterogeneity in archetype space provides insights into transformative cancer treatments aimed at tumor cell plasticity.
2022-07-05 | GSE193960 | GEO
Project description:Intratumoral heterogeneity underlies cancer treatment resistance, but approaches to neutralize it remain elusive. Here, we recast heterogeneity in a systems perspective that considers cancer cell functional tasks inherited from cells of origin. We apply Archetype Analysis to bulk transcriptomics data from small cell lung cancer (SCLC), which forms tumors composed of neuroendocrine (NE) and non-neuroendocrine (non-NE) transcriptional subtypes. SCLC subtypes fit well in a 5-dimensional polytope whose vertices optimize tasks reminiscent of pulmonary NE cells, the SCLC normal counterpart, and include injury repair, slithering, and chemosensation. SCLC cells near a vertex are specialists for a task, while more distant cells are generalists, bearing gene signatures of multiple archetypes. Evolutionary theory and dynamical systems modeling suggest a division of labor strategy for adaptation to treatment, based on task trade-offs amongst specialists and generalists. Cell Transport Potential, a metric derived from single-cell RNA velocity, uncovers plasticity trends from specialists to generalists, and NE to non-NE subtypes. Transcription factor network simulations indicate that MYC overexpression increases plasticity by de-stabilizing NE subtypes. Framing heterogeneity in archetype space provides insights into transformative cancer treatments aimed at tumor cell plasticity.
2022-07-05 | GSE193959 | GEO