Optimizing the Use of Gene Expression Profiling in Early-Stage Breast Cancer
Menée à partir de données portant sur 1 113 patientes atteintes d'un cancer du sein de stade précoce, puis validée sur 472 patientes complémentaires, cette étude évalue la performance d'un modèle mathématique, intégrant 5 variables histopathologiques utilisées en routine (expression des récepteurs hormonaux, de l'antigène Ki-67, du récepteur EGFR-2 et grade d'Elston-Ellis), pour estimer le score de récidive du test moléculaire Oncotype DX et ne réserver l'utilisation de ce dernier qu'en cas de résultats ambigus
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.
Journal of Clinical Oncology , résumé, 2016