Single cell RNA sequencing of primary breast cancer.
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE75688 | GEO | 2016/12/09
SECONDARY ACCESSION(S): PRJNA305054
REPOSITORIES: GEO
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Project description:Lymph node involvement is a major prognostic variable in breast cancer. Whether the molecular mechanisms that drive breast cancer cells to colonize lymph nodes are shared with their capacity to form distant metastases is yet to be established. In a transcriptomic survey aimed at identifying molecular factors associated with lymph node involvement of ductal breast cancer, we found that luminal differentiation, assessed by the expression of estrogen receptor (ER) and/or progesterone receptor (PR) and GATA3, was only infrequently lost in node-positive primary tumors and in matched lymph node metastases. The transcription factor GATA3 critically determines luminal lineage specification of mammary epithelium and is widely considered a tumor and metastasis suppressor in breast cancer. Strong expression of GATA3 and ER in a majority of primary node-positive ductal breast cancer was corroborated by quantitative RT-PCR and immunohistochemistry in the initial sample set, and by immunohistochemistry in an additional set from 167 patients diagnosed of node-negative and positive primary infiltrating ductal breast cancer, including 102 samples from loco-regional lymph node metastases matched to their primary tumors, as well as 37 distant metastases. These observations suggest that loss of luminal differentiation is not a major factor driving the ability of breast cancer cells to colonize regional lymph nodes. The transcriptomic study comprises 16 samples from Lymph node metastasis from infiltrating ductal breast carcinoma, 18 samples from Primary node-positive infiltrating ductal,7 samples from Primary node-negative infiltrating ductal and 3 samples from Unaffected lymph node were included. Their RNA was isolated and prepared for hybridization to human Affymetrix GeneChip arrays.
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Project description:Predictors built from gene expression data accurately predict ER, PR, and HER2 status, and divide tumor grade into high-grade and low-grade clusters; intermediate-grade tumors are not a unique group. In contrast, gene expression data cannot be used to predict tumor size or lymphatic-vascular invasion. Experiment Overall Design: Microarray data from the tumors of 129 patients were analyzed for the ability to predict biomarkers (ER, PR, HER2), histologic features (grade and lymphatic-vascular invasion), and stage-related information (tumor size and lymph node metastasis). Multiple statistical predictors were used and the prediction accuracy determined by error rates of prediction and by dimensional scaling and visualization of the states under study. Models to predict lymph node metastasis were built by combinations of molecular, histologic and anatomic features.
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Project description:Our findings indicate that the integration of expression signatures and clinicopathological factors can better determine the individual risk of recurrence for newly diagnosed patients with lymph-node negative ER-positive breast cancer. Models incorporating other variables yet to be discovered will be needed to obtain robust prognostic models for ER-negative and HER2-positive breast cancer patients. A large data set was created by combining five different publicly available microarray datasets of node-negative breast cancer patients treated with local therapy only. The microarray gene expression data was combined using the batch effect adjustment by the Distance Weighted Discrimination method.
2010-10-25 | E-GEOD-15393 | biostudies-arrayexpress
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.
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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
Project description:Hormones and growth factors accelerate cell proliferation of breast cancer cells, and these molecules are well investigated targets for drug development and application. The mechanisms of cell proliferation of breast cancers lacking estrogen receptor (ER) and HER2 have not been fully understood. The purpose of the present study is to find genes that are differentially expressed in breast cancers and that might significantly contribute to cell proliferation in these cancers. Forty tumor samples, consisting of ten each of immunohistochemically ER(+)/HER2(-), ER(+)/HER2(+), ER(-)/HER2(+), and ER(-)/HER2(-) cancer were analyzed using oligonucleotide microarrays. Both genes and tumor samples were subjected to hierarchical clustering. ER(+)/HER2(-) breast cancers and ER(-)/HER2(-) cancers tended to form a tumor cluster, but HER2 positive breast cancers were split into different tumor clusters. Significant differential expression between IHC-ER(-)/HER2(-) and other tumors was defined as having an expression level at least 2-fold higher or 2-fold lower, and analyzed by multi-step two-way ANOVA. Genes overexpressed differently in IHC-ER(-)/HER2(-) breast cancers compared to other all three types were 8 genes (FABP7, GABRP, GAL, CXCL13, CDC42EP4, C2F, FOXM1, CSDA), and underexpressed genes were nine including ITGB5, KIAA0310, MAGED2, PRSS11, SORL1, TGFB3, KRT18, CPE, BCAS1. No gene was directly related to cell proliferation such as cyclins, cyclin-dependent kinase, p53, p16, and the pRb and p21 families. We had a particular focus on a transcriptional factor E2F-5 from a list of genes overexpressed in ER negative breast cancers compared to ER positive breast cancers, and further examined. Gene amplification of E2F-5 was detected in 5/57 (8.8%) in breast cancers by FISH. No point mutation was found at the binding domain with DNA or dimerization partner of E2F-5. Immunohistochemically E2F-5 positive cancers were more frequent in ER(-)/HER2(-) cancer (14/27, 51.9%) than in other types of cancer (5/30, 16.7%) (p=0.05). E2F-5 positive cancers had higher Ki-67 labeling index (59.5%) than E2F-5 negative cancers (36.3%). E2F-5 positive cancers showed higher histological grade including metaplastic carcinoma, and worse clinical outcome with shorter disease free survival in node negative patients. In conclusion, we demonstrated that there is a population of breast cancer with overexpression of a cell cycle related transcriptional factor E2F-5. E2F-5 positive breast cancers were frequent in ER(-)/HER2(-) group with high Ki-67 labeling index, high histological grade and worse clinical outcome. Keywords: immunohistochemical phenotype
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