Introduction

Breast cancer remains the most frequently diagnosed cancer and the leading cause of cancer death in females worldwide, despite the improvement of early screening and adjuvant treatment. It is a heterogeneous disease highly variable with respect to its biology, etiology, and treatment options. Molecular pathological characterization and gene expression profiling are useful for identifying breast cancer subtypes with different clinical characteristics and developing therapeutic options[1]. DNA microarray analysis has classified tumors based on gene expression patterns and each distinct pattern correlates with prognostic markers of overall survival (OS) and disease-free survival (DFS). There are five different subtypes: luminal A and B, HER2-enriched, basal-like, and normal breast-like tumors[2]. Triple-negative breast cancer (TNBC), an aggressive variant of breast cancer characterized by lack of expression of the estrogen receptor (ER) , progesterone receptor (PR) and the human epidermal growth factor receptor 2 (HER-2) accounts for 15-26% of breast cancer[3,4].

A majority of TNBC is invasive ductal carcinoma of no special type, and the remaining is medullary carcinoma, invasive lobular, metaplastic carcinoma, etc. The triple negativity can occur in many histological subtypes of breast cancer, with possible implications on their pathogenesis, progression and prognosis[5,6]. On the other hand, most triple-negative tumors have pathobiological features in common with basal-like breast cancers. Basal-like breast tumors are preferentially low in ER and HER2 expression, and are significantly associated with several basal cytokeratin (CK) markers, including CK5/6, CK14, CK17, and the epidermal growth factor receptor (EGFR) [7]. A common misconception is that all basal-like breast cancers are TNBC; however, only 77% of basal-like breast cancers are triple-negative, with 71%–91% of TNBC being basal-like[8].

The breast cancer patients with higher histologic grade, larger size, high ERK protein expression, low E-cadherin expression and Ki-67 staining may have a tendency toward local and visceral metastases[3,9–11]. However, they are less predictive power for TNBC patients, despite that a large numbers of clinical and pathological factors have been studied to determine their value in predicting prognosis in patients who were diagnosed with TNBC. In addition, the Nottingham Prognostic Index (NPI) calculated by using tumor size, grade and lymph node score, is currently used for all types of breast cancer[12]. However, the value of the NPI was tested in fewer cohort of TNBC subgroup[13,14]. Thus, the prognostic model that can better predict the outcome of TNBC patients is clinically needed.

In this study, we accessed several clinical and pathologic characteristics of 185 TNBC patients in order to identify additional prognostic markers that can identify tumors with more aggressive behavior and develop a prognostic model comprised of the significant biomarkers. We then validated the model in additional 319 patients in the same institution and demonstrated that the predictive model is successfully to discriminate the high-risk group of TNBC patients. Our proposed model provides the potential value that could be used to tailor treatment and surveillance strategies.

Methods

Results

Prognostic model

Because the lymph node status, Cathepsin-D and Ki-67 index were factors strongly associated with outcome of TNBC patients, we built up a prognostic model based on the summation of points scored by the features of these three factors in tumor tissues. For example, positive for ≥4 lymph nodes, positive for Cathepsin-D expression or Ki-67 index ≥20% would be individually recorded 1 point to the prognostic score. According to the prognostic score, patients were assigned to eight categories (Table 3). Based on the prognostic score, patients in the low-risk group (0 and 1 point) and the high-risk group (2 and 3 points) had significant different outcome (HR=4.03, P<0.001 for DFS; HR=4.87, P<0.001 for OS) (Figure 1A and Figure 1B). The 5-year DFS rates in low-risk group and high-risk group were 85.9% and 50.0%, respectively (P<0.001), while the 5-year OS rates were 93.9% and 73.3%, respectively (P<0.001). The multivariate analysis showed that the combination of ≥4 lymph nodes Cathepsin-D positivity and high Ki-67 index was a more significant prognostic indicator than either factor alone.

	Number of patients	Status of predictors
category		≥4+ nodes	Cathepsin-D	Ki-67≥20%	Risk points	Risk group	HR for DFS	HR for OS
1	14	-	-	-	0	Low risk	1.0	1.0
2	6	+	-	-	1	Low risk
3	57	-	+	-	1	Low risk
4	23	-	-	+	1	Low risk
5	15	+	+	-	2	High risk	4.029	4.866
6	2	+	-	+	2	High risk	(2.379-6.824)	(2.311-10.247)
7	50	-	+	+	2	High risk
8	18	+	+	+	3	High risk

Table 3. Prognostic model comprised of risk categories based on the status of predictors.

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Figure 1. Kaplan-Meier curves for DFS (A) and OS (B) of TNBC patients in the training set according to the prognostic model.

Kaplan-Meier curves for DFS (C) and OS (D) in the validation set.

https://doi.org/10.1371/journal.pone.0083081.g001

Discussion

In this study, we evaluated the relationships between the clinical outcomes, clnicopathological characterics, and expression levels of prognostic biomarkers in patients with confirmed triple-negative breast cancer. Our analysis demonstrated that the combination of 3 prognostic factors, including positive for ≥4 lymph nodes , high Cathepsin-D expression and high Ki-67 index, would provide a strong prognostic power to differentiate the patients with worse outcome. Based on the score system consisted of local or distant metastasis in multiple lymph nodes, elevated Cathepsin-D expression and high Ki-67 proliferation index , we built up a mathematic prognostic model to distinguish the TNBC patients with the low survival rate (high-risk group) and the high survival rate (low-risk group). The proposed model demonstrates that the high-risk group has worse outcomethan the low-risk group in both training and validation sets. Our model would help select the subgroup of patients with high prevalence of local recurrence or distant metastasis. The cost-benefit ration and the selection of the most efficient design can be determined on the basis of the population selected using the model.

Despite the fact that Cathepsin-D plays a critical role in a non-specific protein degradation in a markedly acidic environment of lysosomes, overexpression of Cathepsin-D in breast tumors is associated with the increased metastatic potential and the poor survival rate, in part by facilitaing cancer cell proliferation, fibroblast outgrowth, angiogenesis and metastasis. A recent study found that overexpression of Cathepsin-D in neuroblastoma cells attenuates doxorubicin-induced apoptosis through the activation of both the Akt-dependent pro-survival and the Bcl-2-dependent anti-apoptotic pathways[16], suggesting that elevated Cathepsin-D expression in tumors may protect cancer cells from chemotherapy. Indeed, a large-scale study of 2,810 lymph node-negative breast cancer patients revealed that patients with a high or moderate expression level of Cathepsin-D in a primary tumor have a poor prognosis , independent of histologic grade, hormone receptor status, or tumor size[17]. In addition, Cathepsin-D expression level is not associated with number of lymph nodes positive for metastasis, the histological grade and the dimension of the tumor[18]. Consistent with these observations, the multivariate analysis in our study showed that Cathepsin-D expression remains to be an independent prognostic factor for DFS of the TNBC patients. However, its prognostic values for OS decreases as the lymph node status is the only factor associated with the overall survival rate of patients. This data supports the finding in previous studies that Cathepsin-D expression is not correlated with survival in either lymph node-negative or -positive patients[19,20].

Ki-67 is a non-histone nuclear protein that is closely linked to proliferation cells. High Ki-67 expression is associated with a higher histologic grade, larger tumor size, the presence of axillary lymph nodal metastasis, and worse outcome[21–23]. In neoadjuvant chemotherapy (NAC), higher Ki-67 expression is associated with better pathologic complete response (pCR); however, it is a strong prognostic factor for worse survival of patients who fail to achieve pCR after NAC[24–26], suggesting that Ki-67 can be used for classification of patients with different responses and prognosis. Ki-67 is one of the markers for chemosensitivity in breast carcinomas, but the correlation between Ki-67 expression and chemosensitivity in the triple-negative phenotype is not clear[10], probably due to heterogeneous characteristics of TNBC. Furthermore, standardized staining methods and image analysis for making a consistent cut-off value for Ki-67 may be required to achieve a comparative measurement[10,27]. Our study indicates that a cut-off level of 20% for Ki-67 index is adequate to demonstrate a correlation with recurrence.

The size of the primary tumor and the number of positive lymph nodes have an inverse relationship with prognosis and survival in breast cancer. The sixth edition of staging in breast cancer has included the nodal status as the most important prognostic factor. In large retrospective analysis, TNBC patients with N0/1 have better DFS than those with N2/3 (68% vs. 43%, P<0.001) [28]. On contrary, the prognostic value of other biomarkers, such as p53, is ambiguous. The previous studies found that p53 status was a specific prognostic factor in TNBC patients treated with an adjuvant anthracycline-based regimen or in the subgroup younger than 50 years, although the sample size was small[29,30]. Some studies showed that p53 status was not a specific prognostic factor in TNBC patients treated by adjuvant chemotherapy[10]. Our analysis data showed that high p53 expression had a moderate association with a good outcome (P=0.102). It is conceivable that p53 mutation may provide a more important value in predicting patient prognosis than p53 expression level alone[31].

Currently, the factors associated with the poor clinical outcomes in TNBC patients have been studied using gene expression profiles and more advanced genomic techniques. However, these approaches are not widely available. The application of therapeutic options and the assessment of the accompanied prognosis are dependent on the common tumor-related characteristics in patients. Based on the nodal status, Cathepsin-D expression and Ki-67 index in a group of patients as a training set, we built a faithful model that had considerable clinical value in predicting recurrence in another group of TNBC patients. This prognostic model can help clinicians to provide TNBC patients with appropriate adjuvant treatments and surveillance visits.