A Novel Breast Cancer Index for Prediction of Distant Recurrence in HR Early-Stage Breast Cancer with One to Three Positive Nodes.

The study objective was to characterize the prognostic performance of a novel Breast Cancer Index model (BCIN), an integration of BCI gene expression, tumor size, and grade, specifically developed for assessment of distant recurrence (DR) risk in HR breast cancer patients with one to three positive lymph nodes (pN1). Analysis was conducted in a well-annotated retrospective series of pN1 patients ( = 402) treated with adjuvant endocrine therapy with or without chemotherapy using a prespecified model. The primary endpoint was time-to-DR. Results were determined blinded to clinical outcome. Kaplan-Meier estimates of overall (0-15 years) and late (≥5 years) DR, HRs, and 95% confidence interval (CIs) were estimated. Likelihood ratio statistics assessed relative contributions of prognostic information. BCIN classified 81 patients (20%) as low risk with a 15-year DR rate of 1.3% (95% CI, 0.0%-3.7%) versus 321 patients as high risk with a DR rate of 29.0% (95% CI, 23.2%-34.4%). In patients DR-free for ≥5 years ( = 349), the late DR rate was 1.3% (95% CI, 0.0%-3.7%) and 16.1% (95% CI, 10.6%-21.3%) in low- and high-risk groups, respectively. BCI gene expression alone was significantly prognostic (ΔLR-χ = 20.12; < 0.0001). Addition of tumor size (ΔLR-χ = 13.29, = 0.0003) and grade (ΔLR-χ = 12.72; = 0.0004) significantly improved prognostic performance. BCI added significant prognostic information to tumor size (ΔLR-χ = 17.55; < 0.0001); addition to tumor grade was incremental (ΔLR-χ = 2.38; = 0.1) with considerable overlap between prognostic values (ΔLR-χ = 17.74). The integrated BCIN identified 20% of pN1 patients with limited risk of recurrence over 15 years, in whom extended endocrine treatment may be spared. Ongoing studies will characterize combined clinical-genomic risk assessment in node-positive patients. .