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 ◽  
...  

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.

2020 ◽  
Vol 27 (17) ◽  
pp. 2826-2839 ◽  
Author(s):  
Roberta Caputo ◽  
Daniela Cianniello ◽  
Antonio Giordano ◽  
Michela Piezzo ◽  
Maria Riemma ◽  
...  

The addition of adjuvant chemotherapy to hormonal therapy is often considered questionable in patients with estrogen receptor-positive early breast cancer. Low risk of disease relapse after endocrine treatment alone and/or a low sensitivity to chemotherapy are reasons behind not all patients benefit from chemotherapy. Most of the patients could be exposed to unnecessary treatment- related adverse events and health care costs when treatment decision-making is based only on classical clinical histological features. Gene expression profile has been developed to refine physician’s decision-making process and to tailor personalized treatment to patients. In particular, these tests are designed to spare patients the side effects of unnecessary treatment, and ensure that adjuvant chemotherapy is correctly recommended to patients with early breast cancer. In this review, we will discuss the main diagnostic tests and their potential clinical applications (Oncotype DX, MammaPrint, PAM50/Prosigna, EndoPredict, MapQuant Dx, IHC4, and Theros-Breast Cancer Gene Expression Ratio Assay).


2016 ◽  
Vol 34 (36) ◽  
pp. 4390-4397 ◽  
Author(s):  
Hyun-seok Kim ◽  
Christopher B. Umbricht ◽  
Peter B. Illei ◽  
Ashley Cimino-Mathews ◽  
Soonweng Cho ◽  
...  

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.


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