Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

Transcription profiling of human breast cancer samples to generate an expression signature for p53 status

ABSTRACT: The biological tumor samples (ie, breast tumor specimens) consisted of freshly frozen breast tumors from a population-based cohort of 315 women representing 65% of all breast cancers resected in Uppsala County, Sweden, from January 1, 1987 to December 31, 1989. Estrogen receptor status was determined by biochemical assay as part of the routine clinical procedure. An experienced pathologist determined the Elston-Ellis grades of the tumors, classifying the tumors into low, medium and high-grade tumors. The clinico-pathological characteristics accompanying each tumor include p53 status, ER status, tumor grade, lymph node status and patient age. Experiment Overall Design: All tumor specimens were assessed on U133 A and B arrays.

ORGANISM(S): Homo sapiens

SUBMITTER: Lance David Miller

PROVIDER: E-GEOD-3494 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Publications

An expression signature for p53 status in human breast cancer predicts mutation status, transcriptional effects, and patient survival.

Miller Lance D LD Smeds Johanna J George Joshy J Vega Vinsensius B VB Vergara Liza L Ploner Alexander A Pawitan Yudi Y Hall Per P Klaar Sigrid S Liu Edison T ET Bergh Jonas J

Proceedings of the National Academy of Sciences of the United States of America 20050902 38

Perturbations of the p53 pathway are associated with more aggressive and therapeutically refractory tumors. However, molecular assessment of p53 status, by using sequence analysis and immunohistochemistry, are incomplete assessors of p53 functional effects. We posited that the transcriptional fingerprint is a more definitive downstream indicator of p53 function. Herein, we analyzed transcript profiles of 251 p53-sequenced primary breast tumors and identified a clinically embedded 32-gene express ...[more]

PMID: 16141321

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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.

Project description:We have analyzed, using DNA microarrays, putative differences in gene-expression level between hereditary BRCA1 mutation-linked and sporadic breast cancer. Our results show that a previously reported marked difference between BRCA1-mutation linked and sporadic breast cancer was probably due to uneven stratification of samples with different ER status and basal-like versus luminal-like subtype. We observed that apparent difference between BRCA1-linked and other types of breast cancer found in univariate analysis was diminished when data were corrected for ER status and molecular subtype in multivariate analyses. In fact, the difference in gene expression pattern of BRCA1-mutated and sporadic cancer is very discrete. These conclusions were supported by the results of Q-PCR validation. We also found that BRCA1 gene inactivation due to promoter hypermethylation had similar effect on general gene expression profile as mutation-induced protein truncation. This suggests that in the molecular studies of hereditary breast cancer, BRCA1 gene methylation should be recognized and considered together with gene mutation. We analyzed 35 breast cancer specimens. Surgical samples obtained during mastectomy were flash-frozen in liquid nitrogen and stored at -80°C. Only samples from patients without neoadjuvant chemotherapy were used in this study as chemotherapy may seriously affect gene expression profile. All tissue samples were collected at the Pomeranian Medical University in Szczecin. Seventeen tumor samples were collected from patients with hereditary breast cancer: 12 were derived from tumors affecting women with hereditary BRCA1 mutation, the only one from a woman with BRCA2 mutation, while another eight cases had familial history of breast/ovarian cancer, but were negative for the BRCA1/2 mutations (so called BRCAx cases). Proportion of BRCA1 and BRCA2 mutated tumors was typical for the Polish population. Ten samples were derived from patients with apparently sporadic disease (no familial history of cancer) while 4 patients had a history of familial cancer aggregation (FCA) but without prevalence of breast/ovarian cancers. Thus, these samples were merged with sporadic samples in most of the analyses. All BRCA1 mutation-linked tumors in our study were negative for estrogen receptor (by immunohistochemistry, standard procedures for ER, PGR and HER2 staining were applied), while the only BRCA2-mutated tumor was ER-positive. There were 26 ductal and 5 medullary carcinomas within the study group, which is consistent with the distribution of histopathological types in BRCA1 mutation carriers. Patients were diagnosed at stage T1-2, N0-1 and M0. Caution: this submission contains the data from 6 microarrays done on the normal/pathologically unchanged breast tissue from breast cancer patiets. The data from normal tissues was not analyzed in the paper BRCA1-related gene signature in breast cancer is strongly influenced by ER status and molecular type by Lisowska et al., 2011, Front Biosci (Elite Ed). 2011 Jan 1;3:125-36

Project description:Background: Histologic grade in breast cancer provides clinically important prognostic information. However, 30%-60% of tumors are classified as histologic grade 2. This grade is associated with an intermediate risk of recurrence and is thus not informative for clinical decision making. We examined whether histologic grade was associated with gene expression profi les of breast cancers and whether such profi les could be used to improve histologic grading. Methods: We analyzed microarray data from 189 invasive breast carcinomas and from three published gene expression datasets from breast carcinomas. We identified differentially expressed genes in a training set of 64 estrogen receptor (ER)-positive tumor samples by comparing expression profiles between histologic grade 3 tumors and histologic grade 1 tumors and used the expression of these genes to define the gene expression grade index. Data from 597 independent tumors were used to evaluate the association between relapse-free survival and the gene expression grade index in a Kaplan-Meier analysis. All statistical tests were two-sided. Results: We identified 97 genes in our training set that were associated with histologic grade; most of these genes were involved in cell cycle regulation and proliferation. In validation datasets, the gene expression grade index was strongly associated with histologic grade 1 and 3 status; however, among histologic grade 2 tumors, the index spanned the values for histologic grade 1-3 tumors. Among patients with histologic grade 2 tumors, a high gene expression grade index was associated with a higher risk of recurrence than a low gene expression grade index (hazard ratio = 3.61, 95% confidence interval = 2.25 to 5.78; P<.001, log-rank test). Conclusions: Gene expression grade index appeared to reclassify patients with histologic grade 2 tumors into two groups with high versus low risks of recurrence. This approach may improve the accuracy of tumor grading and thus its prognostic value. NB: The patients coming from Uppsala Hospital have been also used in other studies as in GSE3494. You can find the common set of patients in removing the abbreviation "UPP_" from the sample names and compare the results with the "INDEX (ID)" from the GSE3494 series. Experiment Overall Design: 64 microarray experiments from primary breast tumors used as training set to identify genes differentially expressed in grade 1 and 3. Experiment Overall Design: 129 microarray experiments from primary breast tumors of untreated patients used as validation set to validate the list of genes and its correlation with survival. Experiment Overall Design: No replicate, no reference sample. **NOTE** There are some inconsistencies between the sample annotation provided by GEO for this experiment in the GSE2990_family.soft.gz file and the supplementary data file GSE2990_suppl_info.txt. ***

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.

Dataset Information

Transcription profiling of human breast cancer samples to generate an expression signature for p53 status

Publications

An expression signature for p53 status in human breast cancer predicts mutation status, transcriptional effects, and patient survival.

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