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

Amy C Degnim, Aziza Nassar, Melody Stallings-Mann, S. Keith Anderson, Ann L Oberg, Robert A. Vierkant, Ryan D. Frank, Chen Wang, Stacey J Winham, Marlene H. Frost, Lynn C. Hartmann, Daniel W Visscher, Derek C Radisky

Research output: Contribution to journalArticle

7 Citations (Scopus)

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.

Original languageEnglish (US)
Pages (from-to)687-694
Number of pages8
JournalBreast Cancer Research and Treatment
Volume152
Issue number3
DOIs
StatePublished - Aug 31 2015

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Breast Neoplasms
Genes
Breast Diseases
Neoplasms
Biomarkers
Biopsy
Oligonucleotide Array Sequence Analysis
ROC Curve
Area Under Curve
Breast
RNA

Keywords

  • Benign breast disease
  • Breast cancer
  • Formalin-fixed paraffin-embedded
  • Risk prediction
  • Transcriptional model
  • Transcriptional profiling

ASJC Scopus subject areas

  • Oncology
  • Cancer Research

Cite this

Gene signature model for breast cancer risk prediction for women with sclerosing adenosis. / Degnim, Amy C; Nassar, Aziza; Stallings-Mann, Melody; Keith Anderson, S.; Oberg, Ann L; Vierkant, Robert A.; Frank, Ryan D.; Wang, Chen; Winham, Stacey J; Frost, Marlene H.; Hartmann, Lynn C.; Visscher, Daniel W; Radisky, Derek C.

In: Breast Cancer Research and Treatment, Vol. 152, No. 3, 31.08.2015, p. 687-694.

Research output: Contribution to journalArticle

Degnim, Amy C ; Nassar, Aziza ; Stallings-Mann, Melody ; Keith Anderson, S. ; Oberg, Ann L ; Vierkant, Robert A. ; Frank, Ryan D. ; Wang, Chen ; Winham, Stacey J ; Frost, Marlene H. ; Hartmann, Lynn C. ; Visscher, Daniel W ; Radisky, Derek C. / Gene signature model for breast cancer risk prediction for women with sclerosing adenosis. In: Breast Cancer Research and Treatment. 2015 ; Vol. 152, No. 3. pp. 687-694.
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AU - Vierkant, Robert A.

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AU - Wang, Chen

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AB - 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.

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