Abstract P4-09-02: A robust signature of long-term clinical outcome in breast cancer
Authors: Boudreau A , Elias SG , Yau C , Wolf DM , van't Veer LJ 
Published: August 2014

Abstract
Background: Multigene prognostic signatures derived from high-dimensional mRNA expression data have been proposed to forecast patient outcome and predict chemotherapy benefit more accurately than standard clinical parameters. However, many prognostic signatures fail to predict late recurrences occurring 10 or more years following initial diagnosis. Furthermore, breast cancer subtypes canonically associated with favorable biology, particularly estrogen-receptor positive disease, are characterized by a higher frequency of late recurrences. Patients identified as being at a higher risk of late recurrence might benefit from more prolonged systemic (hormonal) therapy; as such, the goal of this research has been to develop a prognostic signature that can faithfully stratify patient risk up to and beyond 10 years of follow-up.
Methods: A novel multiplexed Cox modeling approach was applied to microarray data obtained from an untreated, node-negative patient cohort with long-term follow-up (n = 141) to train the signature. The long-term prognostic signature was subsequently validated in an additional group of patients (n = 154) that were mostly node-positive (94%) and administered adjuvant chemotherapy (71%). The performance of the signature was compared to existing clinicopathological parameters and genomic signatures using Cox proportional hazards analysis. Logistic regression modeling was employed to evaluate the added benefit to discriminatory accuracy obtained by incorporating the long-term prognostic signature alongside current biomarkers to predict 10-year overall survival.
Results: The long-term signature was able to stratify patient risk with unprecedented accuracy compared to standard clinicopathological and genomic features in both the training and the validation cohorts; none of the patients predicted to have good biology (n = 47 and 38) died within 10 years in either cohort, whereas only 34% and 44% of patients predicted to have poor biology (n = 47 and 46) survived 10 years in the training and validation cohorts, respectively. Within the validation series, the signature was able to identify patients at risk of metastasis with the highest hazard ratio in comparison to other prognostic signatures (univariate hazard ratio of 14.0 [95% CI 3.2–58; p = 0.00083], adjusted hazard ratio of 5.2 [95% CI 1.2–22; p = 0.0257] when corrected for standard clinicopathological markers). Adding the long-term prognostic signature to existing prognostic biomarkers led to significantly improved classification of patients into appropriate 10-year overall survival risk categories (Net Reclassification Improvement of 25.1% in validation series at &gt;5% risk threshold, p = 0.0123).
Conclusions: We were able to identify a 200-gene long-term signature able to stratify patient risk with superior accuracy over a relatively long follow-up period. We are currently using this algorithm to develop prognostic signatures for other cancer types, and are using similar multiplexed algorithms to develop gene signatures able to predict response to neoadjuvant therapy.
Citation Information: Cancer Res 2012;72(24 Suppl):Abstract nr P4-09-02.