The gene expression signature of genomic instability in breast cancer is an independent predictor of clinical outcome
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE11901 | GEO | 2009/12/01
SECONDARY ACCESSION(S): PRJNA105737
REPOSITORIES: GEO
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Project description:Recently, expression profiling of breast carcinomas has revealed gene signatures that predict clinical outcome, and discerned prognostically relevant breast cancer subtypes. Measurement of the degree of genomic instability provides a very similar stratification of prognostic groups. We therefore hypothesized that these features are linked. We used gene expression profiling of 48 breast cancer specimens that profoundly differed in their degree of genomic instability and identified a set of 12 genes that defines the two groups. The biological and prognostic significance of this gene set was established through survival prediction in published datasets from patients with breast cancer. Of note, the gene expression signatures that define specific prognostic subtypes in other breast cancer datasets predicted genomic instability in our samples. This remarkable congruence suggests a biological dependency of poor-prognosis gene signatures, breast cancer subtypes, genomic instability, and clinical outcome. Keywords: disease state analysis 44 samples
2009-11-30 | E-GEOD-11901 | biostudies-arrayexpress
Project description:Breast cancer is an extremely heterogeneous disease. This heterogeneity can be observed at multiple levels, including gene expression, chromosomal aberrations, and disease pathology. A clear understanding of these differences is important since they impact upon treatment efficacy and clinical outcome. Many studies have shown that the tumor microenvironment also plays a critical role in cancer initiation and progression. Although genomic technologies have been used to gain a better understanding of the impact of gene expression heterogeneity on breast cancer outcome by identifying gene expression signatures associated with clinical outcome, histopathological breast cancer subtypes, and a variety of cancer--related pathways and processes, relatively little is known about the influence of of heterogeneity in the tumor microenvironment on these factors. We show that gene expression in the breast tumor microenvironment is highly heterogeneous, identifying at least six different classes of tumor stroma with distinct expression patterns and distinct biological processes. Two of these classes recapitulate the processes identified in the stroma--derived prognostic predictor, while the others are new classes of stroma associated with distinct clinical outcomes. One of these is associated with matrix remodeling and is strongly associated with the basal molecular subtype of breast cancer. The remainder are independent of the previously published molecular subtypes of breast cancer. Additionally, based on independent data from over 800 tumors, the combinations of stroma classes and breast cancer subtypes identify new subgroups of breast tumors that show better discrimination between good and poor outcome individuals than the molecular breast cancer subtypes or the stroma classes alone, suggesting a novel classification scheme for breast cancer . This further demonstrates an important role for the tumor microenvironment in defining breast cancer heterogeneity, with a consequent impact upon clinical outcome.
2011-01-15 | GSE12622 | GEO
Project description:Epithelial ovarian cancer is morphologically and clinically heterogeneous. Transcriptional profiling has revealed molecular subtypes (referred to as M-bM-^@M-^\C-signaturesM-bM-^@M-^]) that correlate to biological as well as clinical features. We aimed to determine gene expression differences between malignant, benign and borderline serous ovarian tumors, and to investigate similarities to the intrinsic molecular subtypes of breast cancer. Global gene expression profiling was performed using Illumina's HT12 Bead Arrays and applied to 59 fresh-frozen ovarian tumors. SAM analysis revealed enrichment of cell cycel processes among the malignant tumors, in line with malignant tumors being highly proliferative. The borderline tumors were split between the malignant and benign tumor clusters, indicating that borderline tumors have both malignant and benign features. Furthermore, nearest centroid classification was performed applying previously published gene profiles for the ovarian cancer C-signatures and the intrinsic breast cancer subtypes, respectively, and showed significant correlations between the malignant serous tumors and the highly aggressive C1, C2 and C4 ovarian cancer signatures, and the basal-like breast cancer subtype. The benign and borderline serous tumors together were significantly correlated to the normal-like breast cancer subtype and the ovarian cancer C3 signature. The borderline tumors, on the other hand, correlated significantly to the Luminal A breast cancer subtype. These findings remained when analyzed in a large, independent dataset. The data in this study link the transcriptional profiles of serous ovarian cancer to the intrinsic molecular subtypes of breast cancer, in line with the shared clinical and molecular features between high-grade serous ovarian cancer and basal-like breast cancer, including an aggressive phenotype, frequent TP53 mutations and a high degree of genomic instability, and suggest that biomarkers and targeted therapies may overlap between these subsets of ovarian and breast cancers. Finally, the link between benign and borderline ovarian cancer and luminal breast cancer may indicate endocrine responsiveness in a subset of ovarian cancers. Total RNA obtained from serous ovarian adenocarcinomas, adenomas and borderline tumors. Gene expression profiling using Illumina's HT12 v4 bead arrays. Application of ovarian cancer molecular subtypes and intrinsic breast cancer subtypes using nearest centroid classification. KRAS and BRAF mutation analyses in the malignant and borderline tumors.
2014-08-15 | E-GEOD-57477 | biostudies-arrayexpress
Project description:Epithelial ovarian cancer is morphologically and clinically heterogeneous. Transcriptional profiling has revealed molecular subtypes (referred to as “C-signatures”) that correlate to biological as well as clinical features. We aimed to determine gene expression differences between malignant, benign and borderline serous ovarian tumors, and to investigate similarities to the intrinsic molecular subtypes of breast cancer. Global gene expression profiling was performed using Illumina's HT12 Bead Arrays and applied to 59 fresh-frozen ovarian tumors. SAM analysis revealed enrichment of cell cycel processes among the malignant tumors, in line with malignant tumors being highly proliferative. The borderline tumors were split between the malignant and benign tumor clusters, indicating that borderline tumors have both malignant and benign features. Furthermore, nearest centroid classification was performed applying previously published gene profiles for the ovarian cancer C-signatures and the intrinsic breast cancer subtypes, respectively, and showed significant correlations between the malignant serous tumors and the highly aggressive C1, C2 and C4 ovarian cancer signatures, and the basal-like breast cancer subtype. The benign and borderline serous tumors together were significantly correlated to the normal-like breast cancer subtype and the ovarian cancer C3 signature. The borderline tumors, on the other hand, correlated significantly to the Luminal A breast cancer subtype. These findings remained when analyzed in a large, independent dataset. The data in this study link the transcriptional profiles of serous ovarian cancer to the intrinsic molecular subtypes of breast cancer, in line with the shared clinical and molecular features between high-grade serous ovarian cancer and basal-like breast cancer, including an aggressive phenotype, frequent TP53 mutations and a high degree of genomic instability, and suggest that biomarkers and targeted therapies may overlap between these subsets of ovarian and breast cancers. Finally, the link between benign and borderline ovarian cancer and luminal breast cancer may indicate endocrine responsiveness in a subset of ovarian cancers.
2014-08-15 | GSE57477 | GEO
Project description:Purpose: Multiple studies from last decades have shown that the microenvironment of carcinomas plays an important role in the initiation, progression and metastasis of cancer. Our group has previously identified novel cancer stroma gene expression signatures associated with outcome differences in breast cancer by gene expression profiling of two tumors of fibroblasts as surrogates for physiologic stromal expression patterns. The aim of this study is to find additional new types of tumor stroma gene expression patterns. Results: 53 tumors were sequenced by 3SEQ with an average of 29 million reads per sample. Both the elastofibroma (EF) and fibroma of tendon sheath (FOTS) gene signatures demonstrated robust outcome results for survival in the four breast cancer datasets. The EF signature positive breast cancers (20-33% of the cohort) demonstrated significantly better outcome for survival. In contrast, the FOTS signature positive breast cancers (11-35% of the cohort) had a worse outcome. The combined stromal signatures of EF, FOTS, and our previously identified DTF, and CSF1 signatures characterize, in part, the stromal expression profile for the tumor microenvironment for between 74%-90% of all breast cancers. Conclusions: We defined and validated two new stromal signatures in breast cancer (EF and FOTS), which are significantly associated with prognosis. Gene expression profiling by 3SEQ was performed on 8 additional types of fibrous tumors, to identify different fibrous tumor specific gene expression signatures. We then determined the significance of the fibrous tumor gene signatures in four publically available breast cancer datasets (GSE1456, GSE4922, GSE3494, NKI Dataset).
2013-11-21 | E-GEOD-42948 | 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
Project description:Breast cancer is a leading cause of cancer-death among women, where the clinicopathological features of tumors are used to prognosticate and guide therapy. DNA copy number alterations (CNAs), which occur frequently in breast cancer and define key pathogenetic events, are also potentially useful prognostic or predictive factors. Here, we report a genome-wide array-based comparative genomic hybridization (array CGH) survey of CNAs in 89 breast tumors from a patient cohort with locally advanced disease. Statistical analysis links distinct cytoband loci harboring CNAs to specific clinicopathological parameters, including tumor grade, estrogen receptor status, presence of TP53 mutation, and overall survival. Notably, distinct spectra of CNAs also underlie the different subtypes of breast cancer recently defined by expression-profiling, implying these subtypes develop along distinct genetic pathways. In addition, higher numbers of gains/losses are associated with the "basal-like" tumor subtype, while high-level DNA amplification is more frequent in "luminal-B" subtype tumors, suggesting also that distinct mechanisms of genomic instability might underlie their pathogenesis. The identified CNAs may provide a basis for improved patient prognostication, as well as a starting point to define important genes to further our understanding of the pathobiology of breast cancer.
2006-12-01 | E-SMDB-3878 | biostudies-arrayexpress
Project description:Breast cancer is a leading cause of cancer-death among women, where the clinicopathological features of tumors are used to prognosticate and guide therapy. DNA copy number alterations (CNAs), which occur frequently in breast cancer and define key pathogenetic events, are also potentially useful prognostic or predictive factors. Here, we report a genome-wide array-based comparative genomic hybridization (array CGH) survey of CNAs in 89 breast tumors from a patient cohort with locally advanced disease. Statistical analysis links distinct cytoband loci harboring CNAs to specific clinicopathological parameters, including tumor grade, estrogen receptor status, presence of TP53 mutation, and overall survival. Notably, distinct spectra of CNAs also underlie the different subtypes of breast cancer recently defined by expression-profiling, implying these subtypes develop along distinct genetic pathways. In addition, higher numbers of gains/losses are associated with the "basal-like" tumor subtype, while high-level DNA amplification is more frequent in "luminal-B" subtype tumors, suggesting also that distinct mechanisms of genomic instability might underlie their pathogenesis. The identified CNAs may provide a basis for improved patient prognostication, as well as a starting point to define important genes to further our understanding of the pathobiology of breast cancer.
2006-11-20 | E-SMDB-3877 | biostudies-arrayexpress
Project description:Breast cancer is a heterogeneous disease. This heterogeneity can be observed at multiple levels, including gene expression, chromosomal aberrations, and disease pathology. A clear understanding of these differences is important since they impact upon treatment efficacy and clinical outcome. Many studies have shown that the tumor microenvironment also plays a critical role in cancer initiation and progression. Although genomic technologies have been used to gain a better understanding of the impact of gene expression heterogeneity on breast cancer outcome by identifying gene expression signatures associated with clinical outcome, histopathological breast cancer subtypes, and a variety of cancer-related pathways and processes, relatively little is known about the spectrum of heterogeneity in the tumor microenvironment.
2017-09-05 | GSE83591 | GEO
Project description:[original title] Combined gene expression and genomic profiling define two intrinsic molecular subtypes of urothelial carcinoma and gene signatures for molecular grading and outcome. In the present investigation we sought to refine the classification of urothelial carcinoma by combining information on gene expression, genomic, and gene mutation levels. For these purposes we performed gene expression analysis of 144 carcinomas, and whole genome array-CGH analysis and mutation analyses of FGFR3, PIK3CA, KRAS, HRAS, NRAS, TP53, CDKN2A, and TSC1, in 103 of these cases. Hierarchical cluster analysis identified two intrinsic molecular subtypes, MS1 and MS2, which were validated and defined by the same set of genes in three independent bladder cancer data sets. The two subtypes differed with respect to gene expression and mutation profiles, as well as with the level of genomic instability. The data shows that genomic instability was the most distinguishing genomic feature of MS2 tumors, and that this trait was not dependent on TP53/MDM2 alterations. By combining molecular and pathological data it was possible to distinguish two molecular subtypes of Ta and T1 tumors, respectively. In addition, we define gene signatures validated in two independent data sets that classify urothelial carcinoma into low (G1/G2) and high grade (G3) tumors as well as non-muscle and muscle-invasive tumors with high precisions and sensitivities, suggesting molecular grading as a relevant complement to standard pathological grading. We also present a gene expression signature with independent prognostic impact on metastasis and disease specific survival. We conclude that the combination of molecular and histopathological classification systems may provide a strong improvement for bladder cancer classification and produce new insights into the development of this tumor type.
2010-06-14 | GSE19915 | GEO