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Updated: Mar 9, 2026
Optimization of a Multiplex RNA-based Expression Assay Using Breast Cancer Archival Material
Published on: August 1, 2018
Optimizing the Use of Gene Expression Profiling in Early-Stage Breast Cancer
Hyun-Seok Kim1, Christopher B Umbricht1, Peter B Illei1
1Hyun-seok Kim, Christopher B. Umbricht, Peter B. Illei, Ashley Cimino-Mathews, Soonweng Cho, Nivedita Chowdhury, Maria Cristina Figueroa-Magalhaes, Catherine Pesce, Stacie C. Jeter, Deborah K. Armstrong, Roisin M. Connolly, John H. Fetting, Ben Ho Park, Vered Stearns, Antonio C. Wolff, and Leslie Cope, The Johns Hopkins University School of Medicine; Kala Visvanathan, The Johns Hopkins University Bloomberg School of Public Health, Baltimore; Charles Mylander, Martin Rosman, and Lorraine Tafra, Anne Arundel Medical Center, Annapolis, MD; Bradley M. Turner, David G. Hicks, University of Rochester Medical Center, Rochester, NY; Robert S. Miller, American Society of Clinical Oncology, Alexandria, VA; and Tyler A. Jensen and Dylan V. Miller, Intermountain Healthcare, Salt Lake City, UT.
Abstract:
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.
