Is the TNM Staging System for Breast Cancer Still Relevant in the Era of Biomarkers and Emerging Personalized Medicine for Breast Cancer - An Institution's 10-year Experience

摘要

We have previously demonstrated that TNM status and age were significant predictors of overall survival (OS) in our study population of Caucasian patients with invasive breast carcinoma (2000-2004 study period). However, estrogen receptor (ER), progesterone receptor (PR), and epidermal growth factor receptor 2 (HER2) biomarker expression was not predictive of OS when using the five-group ER/PR/HER2 subtype classification system recommended by St. Gallen International Consensus Panel in 2011. The current study reassessed the relevance of tumor biomarkers (ER/PR/HER2) in our study population using a recently proposed biologic TNM (bTNM) classification system in which the inclusion of triple negative ER/PR/HER2 phenotype (TNP) could improve the prognostic accuracy of TNM for staging, prognosis and treatment of breast cancer patients. Seven hundred eighty-two Caucasian women diagnosed with invasive ductal carcinoma from 1998 to 2008 were grouped according to their TNM stage and TNP versus non-TNP ER/PR/HER2 phenotype. OS was measured comparing these categories using Kaplan Meier curves and Cox regression analysis. TNM stage (Stage II=HR 1.41, 95% CI 1.01-1.97; Stage III=HR 3.96, 95% CI 2.68-5.88; Stage IV=HR 27.25, 95% CI 16.84-44.08), and age (HR 1.05, 95% CI 1.04-1.06) were significant predictors of OS. TNP significantly worsened prognosis/survival only in higher TNM stages (Stage III=HR 3.08, 95% CI 1.88-5.04, Stage IV=HR 24.36, 95% CI 13.81-42.99), but not in lower stages (I and II). Our data support the traditional TNM staging as a continued relevant predictive tool for breast cancer outcomes and show that biomarkers primarily improve the accuracy of TNM staging in advanced stages of breast cancer. We suspect that type of ER/PR/HER2 classification system(s) (St. Gallen, TNP, etc.), characteristics of populations studied (Caucasians, minorities, etc.), and the time period chosen for a study are major factors that determine impact of biomarkers on the prognostic accuracy of TNM. We propose systematic analyses of these factors before biomarkers are fully incorporated into the TNM staging system (bTNM).