scholarly journals Population-based study of the effect of gene expression profiling on adjuvant chemotherapy use in breast cancer patients under the age of 65 years

Cancer ◽  
2015 ◽  
Vol 121 (22) ◽  
pp. 4062-4070 ◽  
Author(s):  
Arnold L. Potosky ◽  
Suzanne C. O'Neill ◽  
Claudine Isaacs ◽  
Huei-Ting Tsai ◽  
Calvin Chao ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Adjuvant Chemotherapy ◽  
Gene Expression Profiling ◽  
Cancer Patients ◽  
Expression Profiling ◽  
Population Based ◽  
Breast Cancer Patients ◽  
Population Based Study

Breast cancer patients stratification by microarray-based gene expression profiling from FFPET samples

2009 ◽  
Vol 27 (15_suppl) ◽  
pp. e22041-e22041
Author(s):  
C. Lih ◽  
Y. Li ◽  
L. Trinh ◽  
S. Chien ◽  
X. Wu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Gene Expression Profiling ◽  
Cancer Patients ◽  
Expression Profiling ◽  
Gene Expression Signature ◽  
Biological Data ◽  
Clinical Samples ◽  
Breast Cancer Patients ◽  
Disease Stratification

e22041 Background: Microarrays have been used to monitor global genes expression and have aided the identification of novel biomarkers for patients stratification and drug response prediction . To date there has been limited application of microarray- based gene expression analysis to formalin fixed paraffin embedded tissues (FFPET). FFPE tissues are the most commonly available clinical samples with documented clinical information for retrospective clinical analysis. However, FFPET RNA has proven to be an obstacle for microarray analysis because of low yield and compromised RNA integrity. Methods: Using a novel RNA amplification method, Single Primer Isothermal Amplification (SPIA, NuGEN Inc, San Carlos, CA), we amplified FFPET RNA, hybridized amplified, and labeled cDNA onto Affymetrix HG U133plus2 GeneChips. Results: We found that SPIA amplification successfully overcomes the problems of poor quality of FFPET RNA, and produced informative biological data. Comparing the gene expression data from 5 different types of FFPET archival cancer samples (breast, lung, ovarian, colon, and melanoma), we demonstrated that gene expression signatures clearly distinguish the tissue of origin. Further, from an analysis of 91 FFPET samples comprised of ER+, HER2+, triple negative breast cancer patients, and normal breast tissue, we have identified a 103 gene signature that distinguishes the intrinsic sub-types of breast cancer. Finally, the accuracy of gene expression measured by microarray was verified by real time PCR quantitation of the ERBB2 gene, resulting in a significant correlation (R = 0.88). Conclusions: We have demonstrated the feasibility of global gene expression profiling using RNA extracted from FFPET and have shown that a gene expression signature can stratify patient samples into different subtypes of disease. This study paves the way to identify novel molecular biomarkers for disease stratification and therapy response from archival FFPET samples, leading to the goals of personalized medicine. No significant financial relationships to disclose.

Molecular classification of breast cancer patients by gene expression profiling

2001 ◽  
Vol 195 (3) ◽  
pp. 312-320 ◽  
Author(s):  
Andr� Ahr ◽  
Uwe Holtrich ◽  
Christine Solbach ◽  
Anton Scharl ◽  
Klaus Strebhardt ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Gene Expression Profiling ◽  
Cancer Patients ◽  
Expression Profiling ◽  
Molecular Classification ◽  
Breast Cancer Patients

scholarly journals Prediction and prognosis: impact of gene expression profiling in personalized treatment of breast cancer patients

2010 ◽  
Vol 1 (3) ◽  
pp. 421-437 ◽  
Author(s):  
Michael R. Mallmann ◽  
Andrea Staratschek-Jox ◽  
Christian Rudlowski ◽  
Michael Braun ◽  
Andrea Gaarz ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Gene Expression Profiling ◽  
Cancer Patients ◽  
Expression Profiling ◽  
Personalized Treatment ◽  
Breast Cancer Patients

Identification of high risk breast-cancer patients by gene expression profiling

The Lancet ◽  
2002 ◽  
Vol 359 (9301) ◽  
pp. 131-132 ◽  
Author(s):  
André Ahr ◽  
Thomas Karn ◽  
Christine Solbach ◽  
Tanja Seiter ◽  
Klaus Strebhardt ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
High Risk ◽  
Gene Expression Profiling ◽  
Cancer Patients ◽  
Expression Profiling ◽  
Breast Cancer Patients ◽  
High Risk Breast Cancer ◽  
High Risk Breast ◽  
Risk Breast Cancer
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