70-gene signature for breast cancer risk validated

2006 ◽  
Keyword(s):  
Breast Cancer ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Gene Signature

Multi-institutional comparison of breast cancer risk stratification by 70-gene signature and 21-gene assay.

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. 522-522 ◽  
Author(s):  
David J. Dabbs ◽  
Charles E. Cox ◽  
Steven Shivers ◽  
Nathaniel Bouganim ◽  
Jamil Asselah ◽  
...  
Keyword(s):  
Breast Cancer ◽  
High Risk ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Risk Stratification ◽  
Cleveland Clinic ◽  
Gene Signature ◽  
Risk Category ◽  
Gene Assay ◽  
Mcgill University

522 Background: Breast cancer risk stratification with the 70-gene signature (70-GS) provides a binary low risk (LR) or high risk (HR) result; by contrast the 21-gene assay (21-GA) provides LR, intermediate (IR), and HR results. Results from these two assays were compared for 769 patients from 5 institutions. Methods: The study included patients from McGill University (n = 86), UPMC (n = 437), USF (n = 135), Morton Plant North Bay Hospital (n = 79), and Cleveland Clinic (n = 32, all 21-GA IR). Results: With the 70-GS, 487 (63%) patients had a LR and 282 (37%) patients had a HR result. Excluding 32 cases selected for 21-GA IR results (n = 737), the 21-GA gave 369 (50%), 250 (34%), and 118 (16%) patients with LR, IR, and HR scores, respectively. Using the TAILORx cutoff, there were 134 (18%), 432 (59%), and 171 (23%) patients with LR, IR, and HR scores, respectively. There were 329 (45%) and 486 (66%) patients who were not classified in the same risk category by both assays using the clinical and TAILORx cutoffs for IR, respectively. Conclusions: In a large multi-institutional study the 70-GS and 21-GA results were discordant in 45-66% of patients, and the proportion of patients with a 21-GA score in the IR range varied from 34-59%. The 70-GS provided clinically actionable results for all patients. [Table: see text]

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

2015 ◽  
Vol 33 (28_suppl) ◽  
pp. 18-18
Author(s):  
Derek C Radisky ◽  
Amy C. Degnim ◽  
Aziza Nassar ◽  
Melody Stallings-Mann ◽  
S. Keith Anderson ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Risk Prediction ◽  
Subsequent Development ◽  
Gene Signature ◽  
Cancer Development ◽  
Training Set ◽  
Increased Risk ◽  
Sclerosing Adenosis

18 Background: 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), a lesion found in ¼ of all BBD patients that is characterized by epithelial proliferation, disordered acinar architecture, and stromal fibrosis. Methods: A training set was developed from 86 patients diagnosed with sclerosing adenosis (SA), of which 27 subsequently developed cancer within 10 years (cases) and 59 remained cancer-free at 10 years (controls). A diagonal linear discriminate analysis (DLDA)-prediction model for prediction of cancer within 10 years (SA TTC10) was generated from transcriptional profiles of formalin fixed, paraffin-embedded (FFPE) biopsy-derived RNA. This model was tested on a separate validation case-control set composed of 65 SA patients. Results: 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. Conclusions: Our results provide the first demonstration that benign breast tissue contains transcriptional alterations that indicate risk of breast cancer development, and 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.

scholarly journals Development of Malignancy-Risk Gene Signature Assay for Predicting Breast Cancer Risk

2020 ◽  
Vol 245 ◽  
pp. 153-162
Author(s):  
James Sun ◽  
Dung-Tsa Chen ◽  
Jiannong Li ◽  
Weihong Sun ◽  
Sean J. Yoder ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Gene Signature ◽  
Malignancy Risk ◽  
Risk Gene

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

2015 ◽  
Vol 152 (3) ◽  
pp. 687-694 ◽  
Author(s):  
Amy C. Degnim ◽  
Aziza Nassar ◽  
Melody Stallings-Mann ◽  
S. Keith Anderson ◽  
Ann L. Oberg ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Risk Prediction ◽  
Gene Signature ◽  
Sclerosing Adenosis

Prophylactic Mastectomy - Evaluation and Treatment of High Risk Patients

Swiss Surgery ◽  
2002 ◽  
Vol 8 (2) ◽  
pp. 45-52 ◽  
Author(s):  
Remmel ◽  
Harder
Keyword(s):  
Breast Cancer ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Risk Reduction ◽  
Prophylactic Mastectomy ◽  
Interdisciplinary Approach ◽  
Reconstructive Procedures ◽  
Medline Search ◽  
Breast Cancer Risk Reduction ◽  
Cancer Risk Reduction

Prophylactic mastectomy is an aggressive strategy for breast cancer risk reduction. The indications and efficiency of this procedures are not yet clearly defined. Randomized, prospective studies, comparing different surgical procedures with other modalities of breast cancer risk reduction are lacking. The report evaluates the existing controversy, based on Medline search in the following sequence: risk factors, possibilities of risk reduction, effectiveness of risk reduction, technical considerations and recommendations. Patient selection is difficult and needs an interdisciplinary approach. The women have to be well informed about all treatment alternatives and various reconstructive procedures. An appropriate risk reduction strategy should be selected individually for each patient. Up to now, there exist only recommendations from different institutions but no definitive guidelines.

Sign in / Sign up

Export Citation Format

Share Document