Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

Gene expression signatures in breast cancer distinguish phenotype charact., histological subtypes, and tumor invasivness

ABSTRACT: Background. The development of reliable gene expression profiling technology is having an increasing impact on our understanding of breast cancer biology. Methods. In this study, microarray analysis was performed in order to establish gene signatures for different breast cancer phenotypes, determine differentially expressed gene sequences at different stages of the disease, and identify sequences with biological significance for tumor progression. Samples were taken from patients before their treatment. After microarray analysis, the expression level of 153 selected genes was studied by qPCR. Results. A number of gene sequences were differentially expressed in tumor versus control samples and were also associated with different breast cancer phenotypes, ER status, tumor histology, and grade of tumor differentiation. In N0 tumors were found a set of genes related to tumor differentiation grade. Conclusion. A number of differentially expressed gene sequences were found at different stages of the breast cancer disease. Key Words: Breast cancer, gene expression signature, tumor invasiveness, microarrays, qPCR In total, 58 samples were studied, 31 tumors and 27 controls. Some of the samples are paired

ORGANISM(S): Homo sapiens

SUBMITTER: Mª Esther Farez-Vidal

PROVIDER: E-GEOD-10810 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

ACCESS DATA

Publications

Gene expression signatures in breast cancer distinguish phenotype characteristics, histologic subtypes, and tumor invasiveness.

Pedraza Vincente V Gomez-Capilla Jose A JA Escaramis Georgia G Gomez Carolina C Torné Pablo P Rivera Jose M JM Gil Angel A Araque Patricia P Olea Nicolas N Estivill Xavier X Fárez-Vidal M Esther ME

Cancer 20100101 2

<h4>Background</h4>The development of reliable gene expression profiling technology is having an increasing impact on the understanding of breast cancer biology.<h4>Methods</h4>In this study, microarray analysis was performed to establish gene signatures for different breast cancer phenotypes, to determine differentially expressed gene sequences at different stages of the disease, and to identify sequences with biologic significance for tumor progression. Samples were taken from patients before ...[more]

PMID: 20029976

Similar Datasets

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:Personalized biological insights into heterogeneous tumors, such as breast cancer could improve clinical management. While genomic analysis has contributed significantly towards dissecting breast cancer heterogeneity, limitations in clinical application are partly rooted in the inter-tumor variability arising from a largely uncharacterized interactive exchange between diverse cell types in the tumor microenvironment. Here we first identified a common response signature to stromal coculture across breast cancer of varying clinicopathologic phenotypes. Proximity to fibroblasts resulted in gene transcript alterations of >2-fold for 107 probe sets, collectively designated as Fibroblast Triggered Gene Expression in Tumor (FTExT). Prominent features of tumor cell response included transcript repression related to biofunctions encompassing inflammatory signaling, cell movement, cell death, and cell growth and proliferation. In an evaluation of intertumor heterogeneity, the FTExT classifier stratified moderate and high histopathologic grade breast cancer according to clinical outcome (dataset 1, n=401, p=0.031; dataset 2, n=200, p=0.013), delineating a novel phenotype of stromal crosstalk underlying the prognostic potential of tumor grade. Extending correlative data through functional analysis of stromal-epithelial cocultures of both malignant and nonmalignant derivation, significant differences in cell cycle regulation, rate of proliferation, resistance to therapy-induced apoptosis, and growth arrest were observed in FTExT-based subgroups. Instead of a stromal impact that is uniformly cancer promoting, our data demonstrate striking variability in tumor cell response that directly contributes to contrasting functional aggressiveness of malignant breast tissue. Our findings uniquely reveal dynamically interacting paracrine components underlying the molecular and functional heterogeneity of breast cancer, thus presenting novel opportunities for tumor targeting. 10 tumor samples cocultured with fibroblast were profiled in their gene expression with microarrays, and compared with 7 tumor samples cultured without fibroblast. Immortalized tumor cell lines of varying histologic grade were developed and maintained in a growth median as described in Dairkee, et al., Oncogene, 2007. In the coculture set up, epithelial cells were seeded in 6-well plates, and fibroblasts in 0.4 μm inserts with hanging geometry (BD Biosciences, Franklin Lakes, NJ) at a 3:1 ratio in a common pool of growth medium for 3-day harvests. Controls were comprised of each epithelial sample maintained in the absence of fibroblast-seeded inserts under the same culture conditions.

Project description:Current prognostic factors are insufficient for precise risk-discrimination in breast cancer patients with low grade breast tumors, which, in disagreement with theoretical prognosis, occasionally form early lymph node metastasis. To identify markers for this group of patients, we employed iTRAQ-2DLC-MS/MS proteomics to 24 lymph node positive and 24 lymph node negative grade 1 luminal A primary breast tumors. Another group of 48 high-grade tumors (luminal B, triple negative, Her-2 subtypes) was also analyzed to investigate marker specificity for grade 1 luminal A tumors. From the total of 4405 proteins identified (FDR<5%), the top 65 differentially expressed together with 30 previously identified and control markers were analyzed also at transcript level. Increased levels of carboxypeptidase B1 (CPB1), PDZ and LIM domain protein 2 (PDLIM2) and ring finger protein 25 (RNF25) were associated specifically with lymph node positive grade 1 tumors, whereas stathmin 1 (STMN1) and thymosin beta 10 (TMSB10) associated with aggressive tumor phenotype also in high grade tumors at both protein and transcript level. For CPB1, these differences were also observed by immunohistochemical analysis on tissue microarrays. Upregulation of putative biomarkers in lymph node positive (vs. negative) luminal A tumors was validated by gene expression analysis of an independent published dataset (N=343) for CPB1 (p=0.00155), PDLIM2 (p=0.02027) and RELA (p=0.00015). Moreover, statistically significant connections with patient survival were identified in another public dataset (N=1678). Our findings indicate unique pro-metastatic mechanisms in grade 1 tumors that can include up-regulation of CPB1, activation of NF-κB pathway and changes in cell survival and cytoskeleton. These putative biomarkers have potential to identify the specific minor sub-population of breast cancer patients with low grade tumors who are at higher than expected risk of recurrence and who would benefit from more intensive follow-up and may require more personalized therapy.

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.

Dataset Information

Gene expression signatures in breast cancer distinguish phenotype charact., histological subtypes, and tumor invasivness

Publications

Gene expression signatures in breast cancer distinguish phenotype characteristics, histologic subtypes, and tumor invasiveness.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets