[HTML][HTML] Concordance among gene expression-based predictors for ER-positive breast cancer treated with adjuvant tamoxifen

A Prat, JS Parker, C Fan, MCU Cheang, LD Miller… - Annals of …, 2012 - Elsevier
A Prat, JS Parker, C Fan, MCU Cheang, LD Miller, J Bergh, SKL Chia, PS Bernard…
Annals of Oncology, 2012Elsevier
ABSTRACT Background ER-positive (ER+) breast cancer includes all of the intrinsic
molecular subtypes, although the luminal A and B subtypes predominate. In this study, we
evaluated the ability of six clinically relevant genomic signatures to predict relapse in
patients with ER+ tumors treated with adjuvant tamoxifen only. Methods Four microarray
datasets were combined and research-based versions of PAM50 intrinsic subtyping and risk
of relapse (PAM50-ROR) score, 21-gene recurrence score (OncotypeDX), Mammaprint …
Background
ER-positive (ER+ ) breast cancer includes all of the intrinsic molecular subtypes, although the luminal A and B subtypes predominate. In this study, we evaluated the ability of six clinically relevant genomic signatures to predict relapse in patients with ER+ tumors treated with adjuvant tamoxifen only.
Methods
Four microarray datasets were combined and research-based versions of PAM50 intrinsic subtyping and risk of relapse (PAM50-ROR) score, 21-gene recurrence score (OncotypeDX), Mammaprint, Rotterdam 76 gene, index of sensitivity to endocrine therapy (SET) and an estrogen-induced gene set were evaluated. Distant relapse-free survival (DRFS) was estimated by Kaplan–Meier and log-rank tests, and multivariable analyses were done using Cox regression analysis. Harrell's C-index was also used to estimate performance.
Results
All signatures were prognostic in patients with ER+ node-negative tumors, whereas most were prognostic in ER+ node-positive disease. Among the signatures evaluated, PAM50-ROR, OncotypeDX, Mammaprint and SET were consistently found to be independent predictors of relapse. A combination of all signatures significantly increased the performance prediction. Importantly, low-risk tumors (>90% DRFS at 8.5 years) were identified by the majority of signatures only within node-negative disease, and these tumors were mostly luminal A (78%–100%).
Conclusions
Most established genomic signatures were successful in outcome predictions in ER+ breast cancer and provided statistically independent information. From a clinical perspective, multiple signatures combined together most accurately predicted outcome, but a common finding was that each signature identified a subset of luminal A patients with node-negative disease who might be considered suitable candidates for adjuvant endocrine therapy alone.
Elsevier