Dataset Information

Expression Profiles of Breast Tumors from Singapore and Europe

ABSTRACT: To facilitate a better understanding of the molecular heterogeneity of breast cancer and to uncover gene-survival associations that segregate with distinct molecular subtypes, we recently compiled a meta-analytical database of over 2,000 human breast tumor expression profiles variously annotated for clinical and pathological variables and derived from various institutions worldwide. This database was utilized for the breast tumor study cited below: Nagalla, et. al., Genome Biology, 2013. While the majority of the tumor expression profiles included in this database derived from publicly accessible microarray repositories, a number of the samples had not been previously published. Here, we describe the origin of these samples and provide their associated clinical and pathological annotations with the hope that other investigators will be able to utilize these data in their own research. Breast tumor samples derived from the Institut Jules Bordet (IJB; Belgium), John Radcliffe Hospital (JRH; Oxford) and the National University Hospital (NUH, Singapore) were profiled on the Affymetrix U133A platform. The number of tumors profiled (n) and the corresponding dates of tumor diagnosis are as follows. IJB: n=41, 1994-2001; JRH: n=8, 1990-1993; and NUH: n=100, 2000-2002.

ORGANISM(S): Homo sapiens

SUBMITTER: Chou Jeff

PROVIDER: S-ECPF-GEOD-45255 | biostudies-other |

REPOSITORIES: biostudies-other

ACCESS DATA

Similar Datasets

Project description:PurposeTo present a method for identifying intrinsic imaging phenotypes in breast cancer tumors and to investigate their association with prognostic gene expression profiles.Materials and methodsThe authors retrospectively analyzed dynamic contrast material-enhanced (DCE) magnetic resonance (MR) images of the breast in 56 women (mean age, 55.6 years; age range, 37-74 years) diagnosed with estrogen receptor-positive breast cancer between 2005 and 2010. The study was approved by the institutional review board and compliant with HIPAA. The requirement to obtain informed consent was waived. Primary tumors were assayed with a validated gene expression assay that provides a score for the likelihood of recurrence. A multiparametric imaging phenotype vector was extracted for each tumor by using quantitative morphologic, kinetic, and spatial heterogeneity features. Multivariate linear regression was performed to test associations between DCE MR imaging features and recurrence likelihood. To identify intrinsic imaging phenotypes, hierarchical clustering was performed on the extracted feature vectors. Multivariate logistic regression was used to classify tumors at high versus low or medium risk of recurrence. To determine the additional value of intrinsic phenotypes, the phenotype category was tested as an additional variable. Receiver operating characteristic analysis and the area under the receiver operating characteristic curve (Az) were used to assess classification performance.ResultsThere was a moderate correlation (r = 0.71, R(2) = 0.50, P < .001) between DCE MR imaging features and the recurrence score. DCE MR imaging features were predictive of recurrence risk as determined by the surrogate assay, with an Az of 0.77 (P < .01). Four dominant imaging phenotypes were detected, with two including only low- and medium-risk tumors. When the phenotype category was used as an additional variable, the Az increased to 0.82 (P < .01).ConclusionIntrinsic imaging phenotypes exist for breast cancer tumors and correlate with recurrence likelihood as determined with gene expression profiling. These imaging biomarkers could ultimately help guide treatment decisions.

Project description:Array CGH analysis of breast tumors has contributed to the identification of different genomic profiles in these tumors. Loss of DNA repair by BRCA1 functional deficiency in breast cancer has been proposed as a relevant contribution to breast cancer progression for tumors with no germline mutation. Identifying the genomic alterations taking place in BRCA1 not expressing tumors will lead us to a better understanding of the cellular functions affected in this heterogeneous disease. Moreover, specific genomic alterations may contribute to the identification of potential therapeutic targets and offer a more personalized treatment to breast cancer patients.Forty seven tumors from hereditary breast cancer cases, previously analyzed for BRCA1 expression, and screened for germline BRCA1 and 2 mutations, were analyzed by Array based Comparative Genomic Hybridization (aCGH) using Agilent 4x44K arrays. Overall survival was established for tumors in different clusters using Log-rank (Mantel-Cox) Test. Gene lists obtained from aCGH analysis were analyzed for Gene Ontology enrichment using GOrilla and DAVID tools.Genomic profiling of the tumors showed specific alterations associated to BRCA1 or 2 mutation status, and BRCA1 expression in the tumors, affecting relevant cellular processes. Similar cellular functions were found affected in BRCA1 not expressing and BRCA1 or 2 mutated tumors. Hierarchical clustering classified hereditary breast tumors in four major, groups according to the type and amount of genomic alterations, showing one group with a significantly poor overall survival (p = 0.0221). Within this cluster, deletion of PLEKHO1, GDF11, DARC, DAG1 and CD63 may be associated to the worse outcome of the patients.These results support the fact that BRCA1 lack of expression in tumors should be used as a marker for BRCAness and to select these patients for synthetic lethality approaches such as treatment with PARP inhibitors. In addition, the identification of specific alterations in breast tumors associated with poor survival, immune response or with a BRCAness phenotype will allow the use of a more personalized treatment in these patients.

Dataset Information

Expression Profiles of Breast Tumors from Singapore and Europe

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets