Dataset Information

The fibromatosis signature defines a robust stromal response in breast carcinoma.

ABSTRACT: Breast cancer is a heterogeneous disease, and the influence of stromal gene and protein expression patterns on the biological and clinical heterogeneity of the disease is poorly understood. We previously demonstrated that evaluation of the gene expression patterns of two soft-tissue tumors (desmoid-type fibromatosis (DTF) and solitary fibrous tumor) could be used to identify distinct stromal reaction patterns in breast carcinoma. In the current study, we examined four additional data sets obtained from four different institutions and containing gene expression data from a total of 561 breast cancer patients. We identified a core set of 66 DTF-associated genes that were consistently coordinately expressed in a subset of 25-35% of breast cancers. Breast carcinomas defined by high levels of coordinated expression of DTF core genes tend to be lower grade, express estrogen receptor, and show significantly longer survival across the four data sets. Using multiple tissue microarrays of archival breast cancer specimens obtained from a total of 745 patients, we demonstrated that a subset of breast cancers show coordinate expression of DTF core proteins by stromal cells in the tumor microenvironment. We evaluated the protein expression of a single DTF core protein (SPARC) on a tissue microarray with clinical outcome data and demonstrated that breast cancers with strong stromal protein expression of SPARC show a trend for increased survival. Our data demonstrate that the DTF core gene set is a robust descriptor of a distinct stromal response that is associated with improved clinical outcome in breast cancer patients.

SUBMITTER: Beck AH

PROVIDER: S-EPMC4847137 | biostudies-literature | 2008 Jun

REPOSITORIES: biostudies-literature

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The fibromatosis signature defines a robust stromal response in breast carcinoma.

Beck Andrew H AH Espinosa Inigo I Gilks C Blake CB van de Rijn Matt M West Robert B RB

Laboratory investigation; a journal of technical methods and pathology 20080414 6

Breast cancer is a heterogeneous disease, and the influence of stromal gene and protein expression patterns on the biological and clinical heterogeneity of the disease is poorly understood. We previously demonstrated that evaluation of the gene expression patterns of two soft-tissue tumors (desmoid-type fibromatosis (DTF) and solitary fibrous tumor) could be used to identify distinct stromal reaction patterns in breast carcinoma. In the current study, we examined four additional data sets obtain ...[more]

PMID: 18414401

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Project description:Systemic chemotherapy in the adjuvant setting can cure breast cancer in some patients that would otherwise recur with incurable, metastatic disease. However, since only a fraction of patients would have recurrence after surgery alone, the challenge is to stratify high-risk patients (who stand to benefit from systemic chemotherapy) from low-risk patients (who can safely be spared treatment related toxicities and costs).We focus here on risk stratification in node-negative, ER-positive, HER2-negative breast cancer. We use a large database of publicly available microarray datasets to build a random forests classifier and develop a robust multi-gene mRNA transcription-based predictor of relapse free survival at 10 years, which we call the Random Forests Relapse Score (RFRS). Performance was assessed by internal cross-validation, multiple independent data sets, and comparison to existing algorithms using receiver-operating characteristic and Kaplan-Meier survival analysis. Internal redundancy of features was determined using k-means clustering to define optimal signatures with smaller numbers of primary genes, each with multiple alternates.Internal OOB cross-validation for the initial (full-gene-set) model on training data reported an ROC AUC of 0.704, which was comparable to or better than those reported previously or obtained by applying existing methods to our dataset. Three risk groups with probability cutoffs for low, intermediate, and high-risk were defined. Survival analysis determined a highly significant difference in relapse rate between these risk groups. Validation of the models against independent test datasets showed highly similar results. Smaller 17-gene and 8-gene optimized models were also developed with minimal reduction in performance. Furthermore, the signature was shown to be almost equally effective on both hormone-treated and untreated patients.RFRS allows flexibility in both the number and identity of genes utilized from thousands to as few as 17 or eight genes, each with multiple alternatives. The RFRS reports a probability score strongly correlated with risk of relapse. This score could therefore be used to assign systemic chemotherapy specifically to those high-risk patients most likely to benefit from further treatment.

Dataset Information

The fibromatosis signature defines a robust stromal response in breast carcinoma.

Publications

The fibromatosis signature defines a robust stromal response in breast carcinoma.

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