Can clinically relevant prognostic subsets of breast cancer patients with four or more involved axillary lymph nodes be identified through immunohistochemical biomarkers? A tissue microarray feasibility study.

INTRODUCTION

Primary breast cancer involving four or more axillary lymph nodes carries a poor prognosis. We hypothesized that use of an immunohistochemical biomarker scoring system could allow for identification of variable risk subgroups.

METHODS

Patients with four or more positive axillary nodes were identified from a clinically annotated tissue microarray of formalin-fixed paraffin-embedded primary breast cancers and randomized into a 'test set' and a 'validation set'. A prospectively defined prognostic scoring model was developed in the test set and was further assessed in the validation set combining expression for eight biomarkers by immunohistochemistry, including estrogen receptor, human epidermal growth factor receptors 1 and 2, carbonic anhydrase IX, cytokeratin 5/6, progesterone receptor, p53 and Ki-67. Survival outcomes were analyzed by the Kaplan-Meier method, log rank tests and Cox proportional-hazards models.

RESULTS

A total of 313 eligible patients were identified in the test set for whom 10-year relapse-free survival was 38.3% (SEM 2.9%), with complete immunohistochemical data available for 227. Tumor size, percentage of positive axillary nodes and expression status for the progesterone receptor, Ki-67 and carbonic anhydrase IX demonstrated independent prognostic significance with respect to relapse-free survival. Our combined biomarker scoring system defined three subgroups in the test set with mean 10-year relapse-free survivals of 75.4% (SEM 7.0%), 35.3% (SEM 4.1%) and 19.3% (SEM 7.0%). In the validation set, differences in relapse-free survival for these subgroups remained statistically significant but less marked.

CONCLUSION

Biomarkers assessed here carry independent prognostic value for breast cancer with four or more positive axillary nodes and identified clinically relevant prognostic subgroups. This approach requires refinement and validation of methodology.