Prognostic Significance of Metastatic Lymph Nodes Ratio (MLNR) Combined with Protein-Tyrosine Phosphatase H1 (PTPH1) Expression in Operable Breast Invasive Ductal Carcinoma.

PURPOSE

The metastatic lymph node ratio (MLNR) is one of the most important factors in prognostic analysis of breast cancer. The objective of this study was to determine if MLNR combined with protein-tyrosine phosphatase H1 (PTPH1) pathological expression can be used to predict the prognosis of patients with breast invasive ductal carcinoma (IDC) better than one factor only.

PATIENTS AND METHODS

A total of 136 patients with invasive ductal carcinoma (IDC) of breast who underwent modified radical mastectomy and were treated with chemotherapy after operation at Qilu Hospital of Shandong University from December 2008 to October 2011 were included. PTPH1 expression was evaluated by immunohistochemistry in surgical specimens retrospectively collected from patients with histologically proven invasive ductal breast cancer. Kaplan-Meier survival analysis and Cox regression analysis were performed to assess the prognostic significance of PTPH1 expression. A prognostic factor for disease-free survival (DFS) was identified by univariate and multivariate analyses. ROC analysis was used to evaluate the performance of single factors and combined feature.

RESULTS

One hundred and thirty-six patients were included in the analysis. By cut-point survival analysis, MLNR cut-off was designed as 0.2. On multivariate analysis, a MLNR>0.2 was associated with a worse DFS (HR=2.581, 95% CI=1.303-5.113, =0.007). PTPH1 overexpression is correlated with a better DFS (HR=0.391, 95% CI=0.162-0.945, =0.037). In addition, MLNR and PTPH1 combined feature had better performance in predicting clinical outcomes after surgery long before recurrence had occurred (Area under the curve=0.795 [95% CI=0.694-0.896], <0.001).

CONCLUSION

These findings indicate that both PTPH1 and MLNR are accurate independent prognostic parameters in patients with IDC of the breast. Better information on IDC prognosis could be obtained from the combined feature.