Homo sapiens
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PROVIDER: PRJNA281183 | ENA |
REPOSITORIES: ENA
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Project description:Exome sequencing of invasive breast cancers with heterogeneous HER2 gene amplification
| PRJNA263956 | ENA
Project description:We separately analyzed the HER2-negative and HER2-positive components of 12 HER2 heterogeneous breast cancers using gene copy number profiling and massively parallel sequencing, and identified potential driver genetic alterations restricted to the HER2-negative cells in each case. Our results indicate that even driver genetic alterations, such as HER2, can be heterogeneously distributed in a cancer, and that the HER2-negative components are likely driven by genetic alterations not present in the HER2-positive components, including BRF2 and DSN1 amplification.
2015-04-16 | GSE67908 | GEO
Project description:Background: Breast cancer is a heterogeneous neoplasm. Distinct subtypes of breast cancer have been defined, suggesting the existence of molecular differences contributing to their clinical outcomes. However, the molecular differences between HER2 positive and negative breast cancer tumors remain unclear. Objective: The aim of this study was to identify a gene expression profile for breast tumors based on HER2 status. Material and methods: The HER2 status was determined by immunohistochemistry (IHC) and fluorescence in situ hybridization (FISH) in 54 breast tumor samples. Using Affymetrix microarray data from these breast tumors, we established the expression profiling of breast cancer based on HER2 IHC and FISH results. To validate microarray experiment data, real-time quantitative reverse transcription-PCR was performed. Results: We found significant differences between the HER2-positive and HER2-negative breast tumor samples, which included overexpression of HER2, as well as other genes located on 17q12, and genes functionally related to migration. Conclusion: Our study shows the potential of integrated genomics profiling to shed light on the molecular knowledge of HER2-positive breast tumors. The tumor samples under study correspond to 54 primary breast carcinomas. They included 15 cases with a HER2 IHC3+ score with HER2 gene amplification, 13 cases with IHC2+ score with amplification and 13 without HER2 gene amplification, and 13 cases IHC0/1+ score without HER2 gene amplification. 12 samples of breast normal tissues from breast cancer patients were also included as a reference. Neither overexpression nor amplification of HER2 was observed.
2011-12-22 | E-GEOD-29431 | biostudies-arrayexpress
Project description:Intra-tumor Genetic Heterogeneity in Rectal Canger
| PRJNA296805 | ENA
Project description:Breast cancer accounts for roughly 30% of all cancers in women worldwide, has a 15% death rate, and incidence rates are increasing at a rate of about 0.5% per year. Breast cancer comprises a heterogeneous group of tumor subtypes, whether defined by the histopathology of the primary tumor, the expression pattern of hormone receptors (estrogen and/or progesterone receptors; ER/PR) and epidermal growth factor receptor 2 (HER2), genetic alterations of transcriptomic traits. These patient-to-patient differences (as known as �쁦ntertumoral heterogeneity��, largely affect patient prognosis and treatment options. Alongside intertumoral heterogeneity, many studies reported that breast cancers heterogeneous consisting of many different cells or subclones of which different gene expression profiles within a patient�셲 primary tumor and individual metastases. These differences within the tumor are referred to as intratumor heterogeneity, which is caused by a combination of extrinsic factors from the tumor microenvironment and intrinsic parameters including genetic, epigenetic and transcriptomic traits, ability of proliferation, migration and invasion, cell plasticity, and the extent of stemness. These heterogeneities endow tumors with multiple capabilities and biological characteristics, making them more prone to metastasis, recurrence, and drug resistance. To overcome these facing challenges, understanding the proteome mechanisms behind transcriptome profiling from the aspect of treatment can help to improve resistance to cancer therapy. Recent proteomics technologies based on mass spectrometry enable an unbiased investigation of drug-induced changes in protein abundance and post-translational modifications. Several studies on resistance to chemotherapy have recently published data on mass spectrometry-based chemotherapeutic proteome profiling, which has the potential to discover molecular subtypes and related pathway features that may have been missed in prior transcriptome analyses. Nevertheless, few proteomics studies to date explore three types of drug-specific resistance of breast cancer signatures. In this study, we employed tandem mass tag (TMT) based proteomics technology to process the acquired mass spectrometry data to test the hypothesis that the chemotherapy in breast cancer cells may have distinct protein profiles that may result in their drug properties and new clinical implications. By unraveling the protein signatures across tamoxifen, doxorubicin, and paclitaxel and their relationship between drug-resistant cell lines and normal breast cancer cells, our study advances the understanding of drug-specific resistance and provides potential diagnostic and prognostic markers, as well as testable targets of therapy specific to breast cancer resistant cells.
2022-04-04 | PXD030881 | Pride
Project description:Abstract Motivation Breast cancer is a heterogeneous disease with distinct subtypes. Even within these subtypes differences at the molecular level are present which are reflected in variable responses to chemotherapy. We set out to identify genes associated with chemotherapy resistance by analyzing a set of HER2-positive breast cancers. Methods We collected, gene expression profiled and analyzed 60 HER2-positive breast tumor biopsies, obtained from patients scheduled to undergo neoadjuvant therapy. In addition to conventional supervised approaches for the detection of reporters of resistance, we report on a novel approach specifically tailored to the detection of small groups of resistant samples that show aberrant gene expression patterns. Results We propose a novel analytical approach that takes heterogeneity in response into account. We show that this approach is more powerful than classical approaches for detecting small subgroups of samples showing aberrant expression in a controlled setting. We applied this approach to our 60 breast cancer samples prior to neoadjuvant chemotherapy, and generated candidate response reporter lists for each subtype. Discussion Using a novel analytical approach we report on the mRNA gene expression analysis of a cohort of breast cancers prior to neoadjuvant chemotherapy. An important characteristic of this approach is that it takes heterogeneity in neoadjuvant treatment response into account. Such approaches are needed to identify biomarkers for predicting treatment response. We collected, gene expression profiled and analyzed 60 breast tumor biopsies, obtained from patients scheduled to undergo neoadjuvant therapy.
2013-10-22 | E-GEOD-41656 | biostudies-arrayexpress
Project description:HER2 gene amplification and protein overexpression (HER2+) define a clinically challenging subgroup of breast cancer with variable prognosis and response to therapy. Although gene expression profiling has identified an ERBB2 molecular subtype of breast cancer, it is clear that HER2+ tumors reside in all molecular subtypes and represent a genomically and biologically heterogeneous group. Genome-wide DNA copy number profiling, using BAC array comparative genomic hybridization (aCGH) were performed on 200 tumors with mixed clinical characteristics and amplification of HER2. Genomic Identification of Significant Targets in Cancer (GISTIC) was used to identify significant copy number aberrations (CNAs) in HER2+ tumors. This analysis sheds further light on the genomically complex and heterogeneous nature of HER2+ tumors in relation to other subgroups of breast cancer.
2010-04-14 | GSE21259 | GEO
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
2009-03-14 | GSE6367 | GEO
Project description:HER2 gene amplification and protein overexpression (HER2+) define a clinically challenging subgroup of breast cancer with variable prognosis and response to therapy. Although gene expression profiling has identified an ERBB2 molecular subtype of breast cancer, it is clear that HER2+ tumors reside in all molecular subtypes and represent a genomically and biologically heterogeneous group. Genome-wide DNA copy number profiling, using BAC array comparative genomic hybridization (aCGH) were performed on 200 tumors with mixed clinical characteristics and amplification of HER2. Genomic Identification of Significant Targets in Cancer (GISTIC) was used to identify significant copy number aberrations (CNAs) in HER2+ tumors. This analysis sheds further light on the genomically complex and heterogeneous nature of HER2+ tumors in relation to other subgroups of breast cancer. Genomic profiling of 200 breast tumors using tiling BAC aCGH (32K, 33K and 38K). A number of cases were hybridized as replicates or dye-swaps.
2010-04-14 | E-GEOD-21259 | 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. 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