Single-Cell Transcriptome Profiling Reveals Intratumoral Heterogeneity and Molecular Features in Ductal Carcinoma In Situ of the Breast
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE196208 | GEO | 2022/08/02
REPOSITORIES: GEO
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Project description:Purpose: To gain insight into genomic and trancriptomic subtypes of ductal carcinomas in situ of the breast (DCIS). Results: Thirty-two DCIS exhibited a luminal phenotype; 21were ERBB2 positive, and 4 were ERBB2/estrogen receptor (ER) negativewith1harboring a bona fide basal-like phenotype.Based on a CGHanalysis, genomic types were identified in this series of DCISwith the 1q gain/16q loss combination observed in 3 luminal DCIS, themixed amplifier pattern including all ERBB2,12 luminal and 1ERBB2/ER- DCIS, and the complex copy number alteration profile encompassing 14 luminal and 1ERBB2/ER- DCIS. Eight cases (8 of 57; 14%) presented aTP53 mutation, all being amplifiers. Unsupervised analysis of gene expression profiles of 26 of the 57 DCIS showed that luminal and ERBB2-amplified, ER-negative cases clustered separately.We further investigated the effect of high and low copy number changes on gene expression. Strikingly, amplicons but also low copy number changes especially on 1q, 8q, and 16q in DCIS regulated the expression of a subset of genes in a very similar way to that recently described in invasive ductal carcinomas. Conclusions: These combined approaches show that the molecular heterogeneity of breast ductal carcinomas exists already in in situ lesions and further indicate that DCIS and invasive ductal carcinomas share genomic alterations with a similar effect on gene expression profile.
2016-12-22 | GSE92697 | GEO
Project description:Background: Ductal carcinoma in situ (DCIS) is the most common type of in situ premalignant breast cancers. What drives DCIS to invasive breast cancer is unclear. Basal-like invasive breast cancers are aggressive. We have previously shown that N-Ras is highly expressed selectively in basal-like subtypes of invasive breast cancers and can promote their growth and progression. In this study, we investigated whether NRAS expression at the DCIS stage can control transition from luminal DCIS to basal-like invasive breast cancers. Methods: Wilcoxon rank-sum test was performed to assess expression of NRAS in DCIS compared to invasive breast tumors in patients. NRAS levels were also determined by fluorescence in situ hybridization in patient tumor microarrays (TMAs) with concurrent normal, DCIS, and invasive breast cancer, and association of NRAS mRNA levels with DCIS and invasive breast cancer was assessed by paired Wilcoxon signed-rank test. Pearson’s correlation was calculated between NRAS mRNA levels and basal biomarkers in the TMAs, as well as in patient datasets. RNA-seq data was generated in cell lines and unsupervised hierarchical clustering was performed after combining with RNA-seq data from a previously published patient cohort. Results: Invasive breast cancers showed higher NRAS mRNA levels compared to DCIS samples. These NRAShigh lesions were also enriched with basal-like features, such as basal gene expression signatures, lower ER, and higher p53 protein and Ki67 levels. We have shown previously that N-Ras drives aggressive features in DCIS-like and basal-like SUM102PT cells. Here, we found that NRAS-silencing induced a shift to a luminal gene expression pattern. Conversely, NRAS overexpression in the luminal DCIS SUM225 cells induced a basal-like gene expression pattern, as well as an epithelial-to-mesenchymal transition signature. Furthermore, these cells formed disorganized mammospheres containing cell masses with an apparent reduction in adhesion. Conclusions: These data suggest that elevated NRAS levels in DCIS is not only a marker but can also control the emergence of basal-like features leading to more aggressive tumor activity, thus supporting the therapeutic hypothesis that targeting N-Ras and/or downstream pathways may block disease progression for a subset of DCIS patients with high NRAS.
2022-10-18 | GSE215407 | GEO
Project description:To obtain insight into the molecular basis of ductal carcinoma in situ (DCIS) and its progression by infiltrating surrounding tissue, we have performed cellular-based gene expression analysis of pure DCIS and DCIS with co-existing invasive ductal carcinoma (IDC) and compared the histological and molecular aspects between these morphologically identical lesions seeking to find key genes involved in DCIS progression. For that, 30 samples were evaluated, 4 non-neoplastic (N), 5 pure DCIS, 11 DCIS with co-existing IDC (DCIS-IDC) and 10 IDC. All samples were laser capture microdissected and RNAs were amplified using T7-based methodology. Microarray technology was performed using a customized cDNA platform containing 4,608 human genes. To classify the 4 sample groups according to molecular similarity we performed comparisons of their general expression pattern using ANOVA test (pFDR<0,01) followed by Tukey´s test (fold>l2l). Among the 4 sample groups, as expected, non-neoplastic cells reported the most distinct gene expression pattern. However, among the 3 groups of neoplastic cells, in contrast to morphological aspects, pure DCIS had the most distinct expression profile when compared to the other lesions: DCIS-IDC and IDC. Additionally, by comparison among pure DCIS, DCIS-IDC and N, we identified 147 genes potentially involved in DCIS progression. Unsupervised hierarchical cluster based on expression profile of this gene-set could discriminate DCIS-IDC from 60% of pure DCIS samples. Keywords: disease state analysis Fresh-frozen human breast samples were retrieved from the Tumor Tissue Biobank of the Medical and Research Center - Hospital A. C. Camargo, São Paulo. This research was approved by Ethic Committee of the Medical and Research Center - Hospital A. C. Camargo under number 587/04 and a written informed consent signed by all participants. All patients had presented at least 5 years of follow-up. Thirty samples were evaluated, 4 non-neoplastic breast samples (MN), 5 pure DCIS (stage 0; DCIS-0), 11 DCIS with co-existing IDC (DCIS) and 10 IDC. The non-neoplastic samples were obtained from perilesional mammary specimens from patients submitted to resection of benign lesions. All cells types were laser capture microdissected using PixCell II LCM system (Arcturus Engineering, Mountain View, CA). The total RNA was extracted by using the PicoPureä RNA Isolation kit (Arcturus Engineering # KT0204). A two-round linear amplification procedure based on T7-driven amplification was carried out. Amplified RNA was then used in a transcriptase reverse reaction into cDNA in the presence of Cy3- or Cy5-labeled dCTP. HB4a normal luminal epithelial mammary cell line (O’Hare et al 1991) was amplified following the same protocol and used as reference for microarray hybridizations. Dye swap was performed for each sample analyzed and used as replicate samples. For the raw_data (lowess_data), see Web Link below.
2008-04-04 | E-GEOD-11042 | biostudies-arrayexpress
Project description:To understand how Wnt signaling affects AM phenotype in vitro, we treated AMs pooled from 5 WT mice with or without Wnt3a for overnight and performed single-cell RNA sequencing analysis in the cells. Dimensionality reduction analysis of combined control or Wnt3a-treated AMs revealed eight clusters based on their gene expression pattern, and control or Wnt3a-treated AMs were almost mutually exclusive as visualized in UMAP plot.
2021-05-05 | GSE164792 | GEO
Project description:We performed single-cell RNA sequencing on esophageal PDOs from 4 non-EoE control patients to investigate the molecular heterogeneity of human esophageal organoids. Seurat’s unsupervised dimensionality reduction and clustering along with uniform manifold approximation and projection visualization showed 4 distinct cell clusters in esophageal PDOs cultures with or without IL13
2023-03-22 | GSE227593 | GEO
Project description:To understand how a new insulin sensitizer MSDC-0602K affects lung phenotype in vivo, lungs from WT mice treated with DMSO or a new insulin sensitizer MSDC-0602K at day 4 post influenza virus infection were utilized for single-cell RNA sequencing analysis. Dimensionality reduction analysis of combined DMSO or MSDC-0602K-treated lungs revealed 25 clusters based on their gene expression pattern, and cells from those two groups showed similarity as visualized in UMAP plot. Interestingly, MSDC-0602K treatment diminished infiltration of proinflammatory cell types including monocytes or neutrophils while increased percentage of alveolar macrophages or Type I epithelial cells compared to DMSO treatment. Moreover, a number of proinflammatory gene expression was decreased in alveolar macrophages in lungs from MSDC-0602K treated mice.
2023-02-23 | GSE181798 | GEO
Project description:To obtain insight into the molecular basis of ductal carcinoma in situ (DCIS) and its progression by infiltrating surrounding tissue, we have performed cellular-based gene expression analysis of pure DCIS and DCIS with co-existing invasive ductal carcinoma (IDC) and compared the histological and molecular aspects between these morphologically identical lesions seeking to find key genes involved in DCIS progression. For that, 30 samples were evaluated, 4 non-neoplastic (N), 5 pure DCIS, 11 DCIS with co-existing IDC (DCIS-IDC) and 10 IDC. All samples were laser capture microdissected and RNAs were amplified using T7-based methodology. Microarray technology was performed using a customized cDNA platform containing 4,608 human genes. To classify the 4 sample groups according to molecular similarity we performed comparisons of their general expression pattern using ANOVA test (pFDR<0,01) followed by Tukey´s test (fold>l2l). Among the 4 sample groups, as expected, non-neoplastic cells reported the most distinct gene expression pattern. However, among the 3 groups of neoplastic cells, in contrast to morphological aspects, pure DCIS had the most distinct expression profile when compared to the other lesions: DCIS-IDC and IDC. Additionally, by comparison among pure DCIS, DCIS-IDC and N, we identified 147 genes potentially involved in DCIS progression. Unsupervised hierarchical cluster based on expression profile of this gene-set could discriminate DCIS-IDC from 60% of pure DCIS samples. Keywords: disease state analysis
2008-04-05 | GSE11042 | GEO
Project description:Purpose HER2 gene amplification or protein overexpression (HER2+) defines a clinically challenging subgroup of breast cancer with variable prognosis and response to therapy. We aimed to investigate the heterogeneous biological appearance and clinical behavior of HER2+ tumors using molecular profiling. Materials and Methods Hierarchical clustering of gene expression data from 58 HER2-amplified tumors of various stage, histological grade and estrogen receptor (ER) status was used to construct a HER2-derived prognostic predictor that was further evaluated in several large independent breast cancer data sets. Results Unsupervised analysis identified three subtypes of HER2+ tumors with mixed stage, histological grade and ER-status. One subtype had a significantly worse clinical outcome. A prognostic predictor was created based on differentially expressed genes between the subtype with worse outcome and the other subtypes. The predictor was able to define patient groups with better and worse outcome in HER2+ breast cancer across multiple independent breast cancer data sets and identify a sizable HER2+ group with long disease-free survival and low mortality. Significant correlation to prognosis was also observed in basal-like, ER−, lymph node positive or high-grade tumors, irrespective of HER2-status. The predictor included genes associated to immune response, tumor invasion and metastasis. Conclusion The HER2-derived prognostic predictor provides further insight into the heterogeneous biology of HER2+ tumors and may become useful for improved selection of patients that need additional treatment with new drugs targeting the HER2 pathway. Array comparative genomic hybridization (aCGH) identified 58 breast tumors with amplification of HER2 from a larger cohort of approx 500 tumors breast. Global gene expression profiles were obtained using 70-mer oligonucleotide microarrays. Unsupervised hierarchical clustering of the 58 tumors, using Pearson correlation and complete linkage, identified three main clusters. One cluster showed significantly poorer clinical outcome. Significance of microarray (SAM) analysis was performed to identify 158 genes separating the poor outcome cluster compared to the other two clusters. Gene expression centroids, based on the 158 genes, were created for each cluster for validation in independent breast cancer data sets.
2010-03-16 | E-GEOD-18328 | biostudies-arrayexpress
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2021-05-31 | GSE175817 | GEO