Transcription profiling of human tumor-associated stroma derived from primary clinical breast cancer samples
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ABSTRACT:
ORGANISM(S): Homo sapiens
SUBMITTER: Finak Greg
PROVIDER: S-ECPF-GEOD-9014 | biostudies-other |
REPOSITORIES: biostudies-other
Similar Datasets
Project description:Tumor-associated breast stroma was laser-capture microdissected from IDC breast cancer cases. The goal of the study was to characterize the heterogeneity of breast tumor-assocaited stroma and identify gene expression signatures predictive of clinical outcome. Keywords: disease state analysis
2008-04-11 | GSE9014 | GEO
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:Tumor-associated breast stroma was laser-capture microdissected from IDC breast cancer cases. The goal of the study was to characterize the heterogeneity of breast tumor-assocaited stroma and identify gene expression signatures predictive of clinical outcome. Experiment Overall Design: Common reference design, 53 samples, with dye-swap replicates. Some samples replicated three or four times, a total of 111 arrays.
2008-06-16 | E-GEOD-9014 | biostudies-arrayexpress
Project description:Background Previous studies demonstrated breast cancer tumor tissue samples could be classified into different subtypes based upon DNA microarray profiles. The most recent study presented evidence for the existence of five different subtypes: normal breast-like, basal, luminal A, luminal B, and ERBB2+. Results Based upon the analysis of 599 microarrays (five separate cDNA microarray datasets) using a novel approach, we present evidence in support of the most consistently identifiable subtypes of breast cancer tumor tissue microarrays being: ESR1+/ERBB2-, ESR1-/ERBB2-, and ERBB2+ (collectively called the ESR1/ERBB2 subtypes). We validate all three subtypes statistically and show the subtype to which a sample belongs is a significant predictor of overall survival and distant-metastasis free probability. Conclusion As a consequence of the statistical validation procedure we have a set of centroids which can be applied to any microarray (indexed by UniGene Cluster ID) to classify it to one of the ESR1/ERBB2 subtypes. Moreover, the method used to define the ESR1/ERBB2 subtypes is not specific to the disease. The method can be used to identify subtypes in any disease for which there are at least two independent microarray datasets of disease samples.
| S-ECPF-SMDB-3829 | biostudies-other
Project description:Background Previous studies demonstrated breast cancer tumor tissue samples could be classified into different subtypes based upon DNA microarray profiles. The most recent study presented evidence for the existence of five different subtypes: normal breast-like, basal, luminal A, luminal B, and ERBB2+. Results Based upon the analysis of 599 microarrays (five separate cDNA microarray datasets) using a novel approach, we present evidence in support of the most consistently identifiable subtypes of breast cancer tumor tissue microarrays being: ESR1+/ERBB2-, ESR1-/ERBB2-, and ERBB2+ (collectively called the ESR1/ERBB2 subtypes). We validate all three subtypes statistically and show the subtype to which a sample belongs is a significant predictor of overall survival and distant-metastasis free probability. Conclusion As a consequence of the statistical validation procedure we have a set of centroids which can be applied to any microarray (indexed by UniGene Cluster ID) to classify it to one of the ESR1/ERBB2 subtypes. Moreover, the method used to define the ESR1/ERBB2 subtypes is not specific to the disease. The method can be used to identify subtypes in any disease for which there are at least two independent microarray datasets of disease samples.
| S-ECPF-SMDB-3828 | biostudies-other
Project description:Brain metastasis is one of the most feared complications of cancer and the most common intracranial malignancy in adults. Its underlying mechanisms remain unknown. From breast cancer patients with metastatic disease we isolated cell populations that aggressively colonize the brain. Transcriptomic analysis of these cells yielded overlapping gene sets whose expression is selectively associated with brain metastasis. The expression of seventeen of these genes in primary breast tumors is associated with brain relapse in breast cancer patients. Some of these genes are also associated with metastasis to lung but not to liver, bone or lymph nodes, providing a molecular basis for the long-observed link between brain and lung metastasis. Among the functionally validated brain metastasis genes, the cyclooxigenase COX-2, the EGFR ligand HB-EGF, and the brain-specific 2-6 sialyltransferase ST6GALNAC5 mediate cancer cell passage through the blood-brain barrier. Other brain metastasis genes encode inflammatory factors and brain-specific proteolytic regulators, suggesting a multifaceted program for breast cancer colonization of the brain. Experiment Overall Design: 204 primary tumors from breast cancer patients with known site of relapse were studied, focussing on brain relapse versus other relapse. Identified genes were validated in this cohort.
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Project description:mRNA expression data were collected from patients with brain tumor to improve diagnostic of gliomas on molecular level.
| S-ECPF-GEOD-4290 | biostudies-other
Project description:Histological grading of breast cancer defines morphological subtypes informative of metastatic potential, although not without considerable inter-observer disagreement and clinical heterogeneity particularly among the moderately differentiated grade II (G2) tumors. We posited that a gene expression signature capable of discerning tumors of grade I (G1) and grade III (G3) histology might provide a more objective measure of grade with prognostic benefit for patients with moderately differentiated disease. To this end, we studied the expression profiles of 347 primary invasive breast tumors analyzed on Affymetrix microarrays. Using class prediction algorithms, we identified 264 robust grade-associated markers, six of which could accurately classify G1 and G3 tumors, and separate G2 tumors into two highly discriminant classes (termed G2a and G2b genetic grades) with patient survival outcomes highly similar to those with G1 and G3 histology, respectively. Statistical analysis of conventional clinical variables further distinguished G2a and G2b subtypes from each other, but also from histologic G1 and G3 tumors. In multivariate analyses, genetic grade was consistently found to be an independent prognostic indicator of disease recurrence comparable to that of lymph node status and tumor size. When incorporated into the Nottingham Prognostic Index, genetic grade enhanced detection of patients with less harmful tumors, likely to benefit little from adjuvant therapy. Our findings show that a genetic grade signature can improve prognosis and therapeutic planning for breast cancer patients, and support the view that low and high grade disease, as defined genetically, reflect independent pathobiological entities rather than a continuum of cancer progression. Three separate breast cancer cohorts were analyzed: 1) Uppsala (n=249), 2) Stockholm (n=58), 3) Singapore (n=40). The Uppsala and Singapore data can be accessed here. The Stockholm cohort data can be accessed at GEO Series GSE1456. Experiment Overall Design: All tumor specimens were assessed on U133 A and B arrays.
| S-ECPF-GEOD-4922 | biostudies-other
Project description:We measured baseline gene expression profiles for a set of breast tumors. Note: this experiment was modified in June 2008 when the CEL files associated with some hybridizations were changed.
| S-ECPF-TABM-158 | biostudies-other
Project description:Tumor epithelium and surrounding stromal cells were isolated using laser capture microdissection of human breast cancer to examine differences in gene expression based on tissue types from inflammatory and non-inflammatory breast cancer Experiment Overall Design: We applied LCM to obtain samples enriched in tumor epithelium and stroma from 15 IBC and 35 non-IBC cases to study the relative contribution of each component to the IBC phenotype and to patient survival.
| S-ECPF-GEOD-5847 | biostudies-other