Dataset Information

Predicting the clinical status of human breast cancer by using gene expression profiles.

ABSTRACT: Prognostic and predictive factors are indispensable tools in the treatment of patients with neoplastic disease. For the most part, such factors rely on a few specific cell surface, histological, or gross pathologic features. Gene expression assays have the potential to supplement what were previously a few distinct features with many thousands of features. We have developed Bayesian regression models that provide predictive capability based on gene expression data derived from DNA microarray analysis of a series of primary breast cancer samples. These patterns have the capacity to discriminate breast tumors on the basis of estrogen receptor status and also on the categorized lymph node status. Importantly, we assess the utility and validity of such models in predicting the status of tumors in crossvalidation determinations. The practical value of such approaches relies on the ability not only to assess relative probabilities of clinical outcomes for future samples but also to provide an honest assessment of the uncertainties associated with such predictive classifications on the basis of the selection of gene subsets for each validation analysis. This latter point is of critical importance in the ability to apply these methodologies to clinical assessment of tumor phenotype.

SUBMITTER: West M

PROVIDER: S-EPMC58752 | biostudies-literature | 2001 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Predicting the clinical status of human breast cancer by using gene expression profiles.

West M M Blanchette C C Dressman H H Huang E E Ishida S S Spang R R Zuzan H H Olson J A JA Marks J R JR Nevins J R JR

Proceedings of the National Academy of Sciences of the United States of America 20010918 20

Prognostic and predictive factors are indispensable tools in the treatment of patients with neoplastic disease. For the most part, such factors rely on a few specific cell surface, histological, or gross pathologic features. Gene expression assays have the potential to supplement what were previously a few distinct features with many thousands of features. We have developed Bayesian regression models that provide predictive capability based on gene expression data derived from DNA microarray ana ...[more]

PMID: 11562467

Similar Datasets

Project description:The occurrence of large publically available repositories of human breast tumor gene expression profiles provides an important resource to discover new breast cancer biomarkers and therapeutic targets. For example, knowledge of the expression of the estrogen and progesterone hormone receptors (ER and PR), and that of the ERBB2 in breast tumor samples enables choice of therapies for the breast cancer patients that express these proteins. Identifying new biomarkers and therapeutic agents affecting the activity of signaling pathways regulated by the hormone receptors or ERBB2 might be accelerated by knowledge of their expression levels in large gene expression profiling data sets. Unfortunately, the status of these receptors is not invariably reported in public databases of breast tumor gene expression profiles. Attempts have been made to employ a single probe set to identify ER, PR and ERBB2 status, but the specificity or sensitivity of their prediction is low. We enquired whether estimation of ER, PR and ERBB2 status of profiled tumor samples could be improved by using multiple probe sets representing these three genes and others with related expression.We used 8 independent datasets of human breast tumor samples to define gene expression signatures comprising 24, 51 and 14 genes predictive of ER, PR and ERBB2 status respectively. These signatures, as demonstrated by sensitivity and specificity measures, reliably identified hormone receptor and ERBB2 expression in breast tumors that had been previously determined using protein and DNA based assays. Our findings demonstrate that gene signatures can be identified which reliably predict the expression status of the estrogen and progesterone hormone receptors and that of ERBB2 in publically available gene expression profiles of breast tumor samples. Using these signatures to query transcript profiles of breast tumor specimens may enable discovery of new biomarkers and therapeutic targets for particular subtypes of breast cancer.

Project description:IntroductionGene expression profiling of breast carcinomas has increased our understanding of the heterogeneous biology of this disease and promises to impact clinical care. The aim of this study was to evaluate the prognostic value of gene expression-based classification along with established prognostic markers and mutation status of the TP53 gene (tumour protein p53) in a group of breast cancer patients with long-term (12 to 16 years) follow-up.MethodsThe clinical and histopathological parameters of 200 breast cancer patients were studied for their effects on clinical outcome using univariate/multivariate Cox regression. The prognostic impact of mutations in the TP53 gene, identified using temporal temperature gradient gel electrophoresis and sequencing, was also evaluated. Eighty of the samples were analyzed for gene expression using 42 K cDNA microarrays and the patients were assigned to five previously defined molecular expression groups. The strength of the gene expression based classification versus standard markers was evaluated by adding this variable to the Cox regression model used to analyze all samples.ResultsBoth univariate and multivariate analysis showed that TP53 mutation status, tumor size and lymph node status were the strongest predictors of breast cancer survival for the whole group of patients. Analyses of the patients with gene expression data showed that TP53 mutation status, gene expression based classification, tumor size and lymph node status were significant predictors of survival. Breast cancer cases in the 'basal-like' and 'ERBB2+' gene expression subgroups had a very high mortality the first two years, while the 'highly proliferating luminal' cases developed the disease more slowly, showing highest mortality after 5 to 8 years. The TP53 mutation status showed strong association with the 'basal-like' and 'ERBB2+' subgroups, and tumors with mutation had a characteristic gene expression pattern.ConclusionTP53 mutation status and gene-expression based groups are important survival markers of breast cancer, and these molecular markers may provide prognostic information that complements clinical variables. The study adds experience and knowledge to an ongoing characterization and classification of the disease.

Project description:Background: Gene expression profiling of breast carcinomas has increased our understanding of the heterogeneous biology of this disease and promises to impact clinical care. The aim of this study was to evaluate the prognostic value of gene expression-based classification along with established prognostic markers and mutation status of the TP53 gene, in a group of breast cancer patients with long-term (12-16 years) follow-up. Methods: The clinical and histopathological parameters of 200 breast cancer patients were studied for their effects on clinical outcome using univariate/multivariate Cox regression. The prognostic impact of mutations in the TP53 gene, identified using TTGE and sequencing, was also evaluated. Eighty of the samples were analyzed for gene expression using 42K cDNA microarrays and the patients were assigned to five previously defined molecular expression groups. The strength of the gene expression based classification versus standard markers was evaluated by adding this variable to the Cox regression model used to analyze all samples. Results: Both univariate and multivariate analysis showed that TP53 mutation status, tumor size and lymph node status were the strongest predictors of breast cancer survival for the whole group of patients. Analyses of the patients with gene expression data showed that TP53 mutation status, gene expression based classification, tumor size and lymph node status were significant predictors of survival. The TP53 mutation status showed strong association with the ?basal-like? and ?ERBB2+? gene expression subgroups, and tumors with mutation had a characteristic gene expression pattern. Conclusions: TP53 mutation status and gene-expression based groups are important survival markers of breast cancer, and these molecular markers may provide prognostic information that complements clinical variables. The study adds experience and knowledge to an ongoing characterization and classification of the disease. Experiment set consisting of 80 primary breast carcinomas collected at Ulleval University Hospital (ULL-samples), Oslo, Norway from 1990-94, and one normal sample from breast reduction surgery.

Project description:Breast cancer is a heterogeneous disease for which prognosis and treatment strategies are largely governed by the receptor status (estrogen, progesterone and Her2-neu) of the tumor cells. Gene expression profiling of whole breast tumors further stratifies breast cancer into several molecular subtypes which also co-segregate with the receptor status of the tumor cells. We postulated that cancer associated fibroblasts (CAFs) within the tumor stroma may exhibit subtype specific gene expression profiles and thus contribute to the biology of the disease in a subtype specific manner. Several studies have reported gene expression profile differences between CAFs and normal breast fibroblasts but in none of these studies were the results stratified based on tumor subtypes. To address whether gene expression in breast cancer associated fibroblasts varies between breast cancer subtypes, we compared the gene expression profiles of early passage primary CAFs isolated from twenty human breast cancer samples representing three main subtypes; seven ER+, seven triple negative (TNBC) and six Her2+. We observed significant expression differences between CAFs derived from Her2+ breast cancer and CAFs from TNBC and ER+ cancers, particularly in pathways associated with cytoskeleton and integrin signaling. In the case of Her2+ breast cancer, the signaling pathways found to be selectively up regulated in CAFs may contribute to the more invasive properties and unfavorable prognosis of Her2+ breast cancer. These data demonstrate that in addition to the distinct molecular profiles that characterize the neoplastic cells, CAF gene expression is also differentially regulated in distinct subtypes of breast cancer. We isolated CAFs from twenty primary breast cancer samples representing three main subtypes (ER+ (n=7), TNBC (n=7), Her2+ (n=6)) and performed gene expression profile analyses on RNA isolated from these early passage CAFs. Those samples were done in two batches with 4 samples repeated in both batches. One TNBC sample was found to be an outlier and not used in the analysis.

Dataset Information

Predicting the clinical status of human breast cancer by using gene expression profiles.

Publications

Predicting the clinical status of human breast cancer by using gene expression profiles.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets