Novel prognostic immunohistochemical biomarker panel for estrogen receptor-positive breast cancer

Patients with breast cancer experience progression and respond to treatment in diverse ways, but prognostic and predictive tools for the oncologist are limited. We have used gene expression data to guide the production of hundreds of novel antibody reagents to discover novel diagnostic tools for stratifying carcinoma patients.

One hundred forty novel and 23 commercial antisera, selected on their ability to differentially stain tumor samples, were used to stain paraffin blocks from a retrospective breast cancer cohort. Cox proportional hazards and regression tree analysis identified minimal panels of reagents able to predict risk of recurrence. We tested the prognostic association of these prospectively defined algorithms in two independent cohorts.

In both validation cohorts, the Kaplan-Meier estimates of recurrence confirmed that both the Cox model using five reagents (p53, NDRG1, CEACAM5, SLC7A5, and HTF9C) and the regression tree model using six reagents (p53, PR, Ki67, NAT1, SLC7A5, and HTF9C) distinguished estrogen receptor (ER)-positive patients with poor outcomes. The Cox model was superior and distinguished patients with poor outcomes from patients with good or moderate outcomes with a hazard ratio of 2.21 (P = .0008) in validation cohort 1 and 1.88 (P = .004) in cohort 2. In multivariable analysis, the calculated risk of recurrence was independent of stage, grade, and lymph node status. A model proposed for ER-negative patients failed validation in the independent cohorts.

A panel of five antibodies can significantly improve on traditional prognosticators in predicting outcome for ER-positive breast cancer patients.