Transcription profiling of human breast cancer samples - relapse free survival
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
SUBMITTER: Wang Y
PROVIDER: S-ECPF-GEOD-2034 | biostudies-other | 2006 Nov
REPOSITORIES: biostudies-other
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Project description:This series represents 180 lymph-node negative relapse free patients and 106 lymph-node negate patients that developed a distant metastasis. Please see attached patient clinical parameters sheet for more information. Keywords: other
2005-02-23 | GSE2034 | GEO
Project description:This series represents 180 lymph-node negative relapse free patients and 106 lymph-node negate patients that developed a distant metastasis. Please see attached patient clinical parameters sheet for more information (http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?view=data&acc=GSE2034&id=40089&db=GeoDb_blob26). Keywords: other
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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.
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Project description:Validation of lung metastasis signature (LMS) and its association with risk of developing lung metastasis and with primary tumor size. Keywords: Disease state analysis
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Project description:Enlargement of normal terminal duct lobular units (TDLUs) by hyperplastic columnar epithelial cells is one of the most common abnormalities of growth in the adult female human breast. These hyperplastic enlarged lobular units (HELUs) are important clinically as the earliest histologically identifiable potential precursor of breast cancer. The causes of the hyperplasia are unknown but may include estrogen-simulated growth mediated by estrogen receptor alpha, which is highly elevated in HELUs and may be fundamental to their development. This study used DNA microarray technology and RNA from microdissected pure epithelial cells to learn more about changes in gene expression and molecular pathways associated with the development of HELUs from TDLUs Experiment Overall Design: Samples for the microarray studies were derived from 8 formalin-fixed paraffin-embedded tissue (FFPET) biopsies containing paired normal TDLUs and well-developed HELUs from non-cancerous adult female human breasts obtained within the past 3 years. Populations of nearly pure (>95%) epithelial cells were isolated from the TDLUs and HELUs in each biopsy (approximately 25,000 cells/sample) by laser capture microdissection (LCM)
| S-ECPF-GEOD-7377 | biostudies-other
Project description:Cancer evolves dynamically, as clonal expansions supersede or overlap one another, driven by shifting selective pressures, mutational processes and disrupted cancer genes. These processes mark the genome, such that a cancer's life history is encrypted in the timing, ploidy, clonality and patterns of somatic mutation. We developed bioinformatic algorithms to decipher this narrative, and applied them to 21 breast cancer genomes. We find that mutational processes evolve across the lifespan of a breast tumor, with cancer-specific signatures of point mutations and chromosomal instability often emerging late but contributing extensive genetic variation. Subclonal diversification is prominent, providing insight into the dynamics of clonal expansion in breast cancer. Most point mutations are found in just a fraction of tumor cells, together with frequent variegation in chromosomal copy number. Every tumor studied here has a dominant subclonal lineage, representing more than 50% of tumor cells. Minimal expansion of these subclones occurs until many hundreds to thousands of mutations have accumulated, implying the existence of long-lived, quiescent lineages of cells that are capable of substantial proliferation upon acquisition of enabling genomic changes. Expansion of the dominant subclone to an appreciable mass may therefore represent the final rate-limiting step in a breast cancer's development, triggering diagnosis.
| S-ECPF-MTAB-1088 | 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-3830 | 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-3831 | biostudies-other
Project description:Background:; One of the main fields of lung cancer research is identifying patients who are at high risk of post-resection recurrence. Individual recurrence risk evaluation by accurate but simple and reproducible method is needed for the clinical practice. Results:; The log-rank test and further selection by our criteria of assayability generated 87 genes from microarray data with significant level 5%. Of these, by PTQ-PCR, the expression of most significant 18 genes was obtained. Using these gene expression information and clinical parameters, by stepwise variable selection method, the recurrence prediction model, which composed of 6 genes (CALB1, MMP7, SLC1A7, GSTA1, CCL19, IFI44) and pStage and cell differentiation, were developed. Validation into the two independent cohorts showed good results of the proposed model (p=0.0314, 0.0305, respectively). The predicted median recurrence-free survival times for each patient were reflected real ones well. Conclusions:; Our method of individualized recurrence risk prediction is accurate, technically simple and reproducible to be used in clinical practice. Therefore, it would be useful in customizing the lung cancer management strategies. Experiment Overall Design: Methods: Experiment Overall Design: At first, we selected the statistically significant genes from the analysis of time-to-recurrence and censoring information from 138 whole-genome wide microarray data. Then, we further reduced the number of genes which could be reliably reproducible by RTQ-PCR. With these assayable genes and clinical parameters, construction of recurrence prediction model by Cox proportional hazard regression was done. After validation into two independent cohorts (n=59 and n=56), the model was transformed into recurrence prediction for the each patient.
| S-ECPF-GEOD-8894 | biostudies-other
Project description: Not available
| S-DIXA-D-1187 | biostudies-other