scholarly journals Optimizing the Use of Gene Expression Profiling in Early-Stage Breast Cancer

2016 ◽  
Vol 34 (36) ◽  
pp. 4390-4397 ◽  
Author(s):  
Hyun-seok Kim ◽  
Christopher B. Umbricht ◽  
Peter B. Illei ◽  
Ashley Cimino-Mathews ◽  
Soonweng Cho ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Decision Making ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Early Stage ◽  
Recurrence Score ◽  
Clinical Samples ◽  
Risk Category ◽  
Clinical Risk

Purpose Gene expression profiling assays are frequently used to guide adjuvant chemotherapy decisions in hormone receptor–positive, lymph node–negative breast cancer. We hypothesized that the clinical value of these new tools would be more fully realized when appropriately integrated with high-quality clinicopathologic data. Hence, we developed a model that uses routine pathologic parameters to estimate Oncotype DX recurrence score (ODX RS) and independently tested its ability to predict ODX RS in clinical samples. Patients and Methods We retrospectively reviewed ordered ODX RS and pathology reports from five institutions (n = 1,113) between 2006 and 2013. We used locally performed histopathologic markers (estrogen receptor, progesterone receptor, Ki-67, human epidermal growth factor receptor 2, and Elston grade) to develop models that predict RS-based risk categories. Ordering patterns at one site were evaluated under an integrated decision-making model incorporating clinical treatment guidelines, immunohistochemistry markers, and ODX. Final locked models were independently tested (n = 472). Results Distribution of RS was similar across sites and to reported clinical practice experience and stable over time. Histopathologic markers alone determined risk category with > 95% confidence in > 55% (616 of 1,113) of cases. Application of the integrated decision model to one site indicated that the frequency of testing would not have changed overall, although ordering patterns would have changed substantially with less testing of estimated clinical risk–high or clinical risk–low cases and more testing of clinical risk–intermediate cases. In the validation set, the model correctly predicted risk category in 52.5% (248 of 472). Conclusion The proposed model accurately predicts high- and low-risk RS categories (> 25 or ≤ 25) in a majority of cases. Integrating histopathologic and molecular information into the decision-making process allows refocusing the use of new molecular tools to cases with uncertain risk.

Access to personalized medicine: Factors influencing the use and value of gene expression profiling in treatment decision making.

2013 ◽  
Vol 31 (31_suppl) ◽  
pp. 10-10
Author(s):  
Yvonne Bombard ◽  
Linda Rozmovits ◽  
Maureen E. Trudeau ◽  
Natasha B. Leighl ◽  
Ken Deal ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Decision Making ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Clinical Utility ◽  
Perceived Value ◽  
Treatment Decision ◽  
Treatment Decision Making ◽  
Comparison Results

10 Background: Genomic information is increasingly used to personalize health care. One example is gene-expression profiling (GEP) tests that estimate recurrence risk to inform chemotherapy decisions in breast cancer treatment. Recently, GEP tests were publicly funded in Ontario. We assessed the clinical utility of GEP tests, exploring the factors facilitating their use and value in treatment decision-making. Methods: As part of a mixed-methods clinical utility study, we conducted interviews with oncologists (n=14), and focus groups and interviews with breast cancer patients (n=28) who underwent GEP, recruited through oncology clinics in Ontario. Data were analyzed using content analysis and constant comparison. Results: Various factors governing access to GEP have given rise to challenges for patients and oncologists. Oncologists are positioned as gatekeepers of GEP, providing access in medically appropriate cases. However, varying perceptions of appropriateness led to perceived inequities in access and negative impacts on the doctor-patient relationship. Media attention facilitated patient awareness of GEP but complicated gatekeeping. Additional administration burden and long waits for results led to increased patient anxiety and delayed treatment. Collectively, these factors inadvertently heightened GEP’s perceived value for patients relative to other prognostic indicators because of barriers to access. Conclusions: This study delineates the factors facilitating and restricting access to GEP, and highlights the roles of the media and organization of services in GEP’s perceived value and utilization. Results identify a need for administrative changes and practice guidelines to support streamlined and standardized utilization of the test.

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.

scholarly journals Underutilization of gene expression profiling for early-stage breast cancer in California

2016 ◽  
Vol 27 (6) ◽  
pp. 721-727 ◽  
Author(s):  
Rosemary D. Cress ◽  
Yingjia S. Chen ◽  
Cyllene R. Morris ◽  
Helen Chew ◽  
Kenneth W. Kizer
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Early Stage ◽  
Early Stage Breast Cancer
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