scholarly journals Gene Expression Profiling to Predict Chemotherapy Response in Primary Breast Cancer

Breast Care ◽  
2006 ◽  
Vol 1 (6) ◽  
pp. 362-367
Author(s):  
Andreas Schneeweiss ◽  
Peter Lichter ◽  
Christof Sohn ◽  
Meinhard Hahn
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Primary Breast Cancer ◽  
Chemotherapy Response

scholarly journals Gene expression profiling of lymph node positive-negative metastasis of primary breast cancer in Saudi Arabian patients

BMC Genomics ◽  
2014 ◽  
Vol 15 (Suppl 2) ◽  
pp. P55 ◽  
Author(s):  
Adnan Merdad ◽  
Sajjad Karim ◽  
Hans-Juergen Schulten ◽  
Ashraf Dallol ◽  
Abdelbaset S Buhmeida ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Lymph Node ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Primary Breast Cancer ◽  
Saudi Arabian ◽  
Node Positive ◽  
Lymph Node Positive

Prospective analysis of mRNA expression signatures as predictive tests in primary breast cancer

2007 ◽  
Vol 25 (18_suppl) ◽  
pp. 2531-2531
Author(s):  
J. Hannemann ◽  
H. Halfwerk ◽  
A. Velds ◽  
C. Loo ◽  
E. J. Rutgers ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Complete Remission ◽  
Gene Expression Profiling ◽  
Preoperative Chemotherapy ◽  
Expression Profiling ◽  
Primary Breast Cancer ◽  
Molecular Subtypes ◽  
Expression Profiles ◽  
Gene Expression Profiles

2531 Background: Preoperative chemotherapy is increasingly employed to treat primary breast cancer, allowing an ‘in vivo chemosensitivity test’. Markers which predict a pathological complete response are urgently needed to refine this strategy. This study was conducted to evaluate the use of gene expression profiling to predict response to neoadjuvant anthracycline- or taxane-based chemotherapy. Methods: Patients with operable or locally advanced HER2-negative breast cancer received preoperative chemotherapy: either dose- dense doxorubicin and cyclophosphamide (ddAC) or capecitabine and docetaxel (CD). Core needle biopsies were taken before treatment and gene expression profiling was performed using 35k oligo microarrays. Results: Gene expression profiles were obtained from pretreatment biopsies of 63 tumors. 27% of the patients achieved a (near) pathologic complete remission (pCR), 40% of the patients had a partial remission and 33% of the patients did not respond to chemotherapy. Based on the gene expression profiles, tumors were assigned to the previously identified “molecular subtypes” luminal, basal-like or ERBB2-like (Sorlie et al., PNAS 98: 10869, 2001). 13 out of 25 patients with a basal-like tumor (52%) achieved a complete remission, whereas for the luminal tumors a pCR was only obtained in 2 out of 29 patients. Using four published gene expression classifiers of response to chemotherapy, a reasonable separation between responders and non-responders could be observed for two of these. We also performed exploratory supervised classification analyses on our dataset to identify a novel classifier. This resulted in a classifier for response to therapy irrespective of the chemotherapy regimen used and a second classifier specifically associated with response to ddAC chemotherapy. We will perform validation of these classifiers in samples from patients that are currently being enrolled in the study. Conclusions: Basal-like tumors have a better response to neoadjuvant chemotherapy as compared to other tumor types. The identification of robust gene expression signatures for better response prediction may require larger patient groups and should probably be established separately for each of the molecular subtypes of breast cancer. No significant financial relationships to disclose.

Gene expression profiling of primary breast cancer

2005 ◽  
Vol 7 (1) ◽  
pp. 38-44 ◽  
Author(s):  
Roman Rouzier ◽  
Peter Wagner ◽  
Paolo Morandi ◽  
Lajos Pusztai
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Primary Breast Cancer

scholarly journals Mammary Fat of Breast Cancer: Gene Expression Profiling and Functional Characterization

PLoS ONE ◽  
2014 ◽  
Vol 9 (10) ◽  
pp. e109742 ◽  
Author(s):  
Fengliang Wang ◽  
Sheng Gao ◽  
Fei Chen ◽  
Ziyi Fu ◽  
Hong Yin ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Functional Characterization ◽  
Cancer Gene ◽  
Breast Cancer Gene

scholarly journals Gene expression profiling and breast cancer care: What are the potential benefits and policy implications?

2005 ◽  
Vol 7 (6) ◽  
pp. 380-389 ◽  
Author(s):  
Nina Oestreicher ◽  
Scott D Ramsey ◽  
Hannah M Linden ◽  
Jeannine S McCune ◽  
Laura J van't Veer ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Gene Expression Profiling ◽  
Cancer Care ◽  
Expression Profiling ◽  
Policy Implications ◽  
Breast Cancer Care ◽  
Potential Benefits
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