A Comprehensive Model for Predicting Recurrence and Survival in Cases of Chinese Postoperative Invasive Breast Cancer

Document Type : Research Articles

Authors

1 Department of Chemotherapy, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China

2 Department of Internal Medicine Oncology, Hainan General Hospital, Haikou, Hainan, China

3 Surgery Department, Hainan General Hospital, Haikou, Hainan, China

Abstract

 
We investigated relationships between clinical pathologic data, molecular biomarkers and prognosis of invasive breast cancer based on a Chinese population. Immunohistochemistry (IHC) was used to assess the status of ER, PR, HER-2 and Ki-67, with fluorescence in situ hybridization (FISH) performed to further confirm HER-2 positivity with an equivocal result (IHC 2+). Subsequently, Kaplan-Meier univariate and multivariate COX regression analyses of ER, PR, HER-2, Ki-67, clinical features, therapeutic status and follow-up data were performed according to the establishment principle of the Nottingham prognostic index (NPI). From this study, age, tumor size, lymph node status, ER, HER-2, Ki-67 status were found to be associated with prognosis. Eventually, a prognostic model of (PI= (1.5×age) - size + (0.1×lymph node status) - (0.5×ER) + (2×HER-2) - (0.2×Ki-67)) was established with 288 randomly selected patients and verified with another 100 cases with invasive breast cancer. Pearson correlation analysis demonstrated a significant positive correlation index of 0.376 (P=0.012<0.05) between the prognostic index (PI) and actual prognosis. Remarkably, the consistency with the model predicted recurrence was 93% in the validation set. Therefore, it appears feasible to predict the prognosis of individuals with invasive breast cancer and to determine optimal therapeutic strategy with this model.

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Asian Pacific Journal of Cancer Prevention
Volume 18, Issue 3
March 2017
Pages 727-733

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History

  • Receive Date: 22 January 2017
  • Revise Date: 14 March 2017
  • Accept Date: 11 April 2017

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