Dataset Information

Gene signature model for breast cancer risk prediction for women with sclerosing adenosis.

ABSTRACT: Benign breast disease (BBD) is diagnosed in 1-2 million women/year in the US, and while these patients are known to be at substantially increased risk for subsequent development of breast cancer, existing models for risk assessment perform poorly at the individual level. Here, we describe a DNA-microarray-based transcriptional model for breast cancer risk prediction for patients with sclerosing adenosis (SA), which represent ¼ of all BBD patients. A training set was developed from 86 patients diagnosed with SA, of which 27 subsequently developed cancer within 10 years (cases) and 59 remained cancer-free at 10 years (controls). An diagonal linear discriminate analysis-prediction model for prediction of cancer within 10 years (SA TTC10) was generated from transcriptional profiles of FFPE biopsy-derived RNA. This model was tested on a separate validation case-control set composed of 65 SA patients. The SA TTC10 gene signature model, composed of 35 gene features, achieved a clear and significant separation between case and control with receiver operating characteristic area under the curve of 0.913 in the training set and 0.836 in the validation set. Our results provide the first demonstration that benign breast tissue contains transcriptional alterations that indicate risk of breast cancer development, demonstrating that essential precursor biomarkers of malignancy are present many years prior to cancer development. Furthermore, the SA TTC10 gene signature model, which can be assessed on FFPE biopsies, constitutes a novel prognostic biomarker for patients with SA.

SUBMITTER: Degnim AC

PROVIDER: S-EPMC4519591 | biostudies-literature | 2015 Aug

REPOSITORIES: biostudies-literature

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Gene signature model for breast cancer risk prediction for women with sclerosing adenosis.

Degnim Amy C AC Nassar Aziza A Stallings-Mann Melody M Keith Anderson S S Oberg Ann L AL Vierkant Robert A RA Frank Ryan D RD Wang Chen C Winham Stacey J SJ Frost Marlene H MH Hartmann Lynn C LC Visscher Daniel W DW Radisky Derek C DC

Breast cancer research and treatment 20150723 3

Benign breast disease (BBD) is diagnosed in 1-2 million women/year in the US, and while these patients are known to be at substantially increased risk for subsequent development of breast cancer, existing models for risk assessment perform poorly at the individual level. Here, we describe a DNA-microarray-based transcriptional model for breast cancer risk prediction for patients with sclerosing adenosis (SA), which represent ¼ of all BBD patients. A training set was developed from 86 patients di ...[more]

PMID: 26202055

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Project description:The identification as well as the molecular characterization of breast precancerous lesions in terms of increased risk of progression and/or recurrence is becoming a critical issue today as improved non-surgical procedures are detecting cancer at an earlier stage. The strategy we have been pursuing to identify early apocrine breast lesions is based on the postulate that invasive apocrine carcinomas evolve from epithelial cells in terminal duct lobular units (TDLUs) in a stepwise manner that involves apocrine metaplasia of normal breast epithelia, hyperplasia, atypia, and apocrine carcinoma in situ. First, we identify specific protein biomarkers for benign apocrine metaplasia and thereafter we search for biomarkers that are highly overexpressed by pure invasive apocrine carcinomas. Here we present studies in which we have used antibodies against components of a benign apocrine signature that includes 15-prostaglandin dehydrogenase (15-PGDH), a protein that is expressed by all benign apocrine lesions, and markers that are highly overexpressed by pure invasive apocrine carcinomas such as MRP14 (S100A9), psoriasin (S100A7), and p53 to identify precancerous lesions in sclerosing adenosis (SA) with apocrine metaplasia. The latter is a benign proliferative lesion of the breast that exhibits an increase in the size of the TDLUs and characterized by retained two-cell lining, and myoepithelial (ME) and stromal hyperplasia. SA with apocrine metaplasia, i.e. apocrine adenosis (AA), presents with a higher degree of atypical apocrine hyperplasia, and these lesions are believed to be precursors of apocrine carcinoma, in situ and invasive. Analysis of 24 selected SA samples with apocrine metaplasia revealed non-obligate putative apocrine precancerous lesions that displayed some, or in same cases all the three markers associated with pure invasive apocrine carcinomas. These studies also revealed p53 positive, non-apocrine putative precancerous lesions as well as novel phenotypes for ME and some luminal cells characterized by the expression of cytokeratin 15.

Project description:Clinical guidelines for breast cancer treatment differ in their selection of patients at a high risk of recurrence who are eligible to receive adjuvant systemic treatment (AST). The 70-gene signature is a molecular tool to better guide AST decisions. The aim of this study was to evaluate whether adding the 70-gene signature to clinical risk prediction algorithms can optimize outcome prediction and consequently treatment decisions in early stage, node-negative breast cancer patients. A 70-gene signature was available for 427 patients participating in the RASTER study (cT1-3N0M0). Median follow-up was 61.6 months. Based on 5-year distant-recurrence free interval (DRFI) probabilities survival areas under the curve (AUC) were calculated and compared for risk estimations based on the six clinical risk prediction algorithms: Adjuvant! Online (AOL), Nottingham Prognostic Index (NPI), St. Gallen (2003), the Dutch National guidelines (CBO 2004 and NABON 2012), and PREDICT plus. Also, survival AUC were calculated after adding the 70-gene signature to these clinical risk estimations. Systemically untreated patients with a high clinical risk estimation but a low risk 70-gene signature had an excellent 5-year DRFI varying between 97.1 and 100 %, depending on the clinical risk prediction algorithms used in the comparison. The best risk estimation was obtained in this cohort by adding the 70-gene signature to CBO 2012 (AUC: 0.644) and PREDICT (AUC: 0.662). Clinical risk estimations by all clinical algorithms improved by adding the 70-gene signature. Patients with a low risk 70-gene signature have an excellent survival, independent of their clinical risk estimation. Adding the 70-gene signature to clinical risk prediction algorithms improves risk estimations and therefore might improve the identification of early stage node-negative breast cancer patients for whom AST has limited value. In this cohort, the PREDICT plus tool in combination with the 70-gene signature provided the best risk prediction.

Dataset Information

Gene signature model for breast cancer risk prediction for women with sclerosing adenosis.

Publications

Gene signature model for breast cancer risk prediction for women with sclerosing adenosis.

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