Diagnostic and Prognostic Nomograms for Lung Metastasis in Triple-Negative Breast Cancer

Background The lungs are one of the common sites of metastasis of triple-negative breast cancer (TNBC). Patients with lung metastases (LM) have a shorter duration of survival. This study is aimed at determining the prognostic factors of patients with TNBC with LM and constructing two nomograms to assess the risk of LM and the prognosis of patients with TNBC with LM. Methods Clinicopathological and follow-up data of patients with TNBC between 2010 and 2015 were retrieved from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate Cox regression analyses were used to screen for independent predictors of LM in patients with TNBC and identify the independent prognostic factors of patients with TNBC with LM. The two nomograms were appraised using calibration curves, receiver operating characteristic (ROC) curves, and decision curve analysis (DCA). Results A total of 27,048 patients with TNBC were included in this study. Age, tumour size, T stage, and N stage were identified as independent risk factors for LM in patients with TNBC. Histological type, marital status, prior surgery, chemotherapy, bone metastases, brain metastases, and LM were confirmed as independent prognostic factors for patients with TNBC with LM. The area under the ROC curve (AUC) of the diagnostic nomogram was 0.838 (95% confidence interval 0.817-0.860) in the training cohort and 0.894 (95% confidence interval 0.875-0.917) in the verification cohort. The AUC values of the 6-, 12-, and 18-month prognostic nomograms in the training cohort were 0.809 (95% confidence interval 0.771-0.868), 0.779 (95% confidence interval 0.737-0.834), and 0.735 (95% confidence interval 0.699-0.811), respectively, and the corresponding AUC values in the validation cohort were 0.735(95% confidence interval 0.642-0.820), 0.672 (95% confidence interval 0.575-0.758), and 0.705 (95% confidence interval 0.598-0.782), respectively. According to the calibration curves and data analysis, both nomograms exhibited good performance. Conclusion We successfully constructed and verified two valuable nomograms for predicting the incidence of LM and prognosis of patients TNBC with LM.


Introduction
Breast cancer is the most common malignant tumour in women, and breast cancer-specific deaths accounted for approximately 15% of cancer-related deaths in women in 2018 [1]. Triple-negative breast cancer (TNBC) accounts for approximately 10-20% of all breast cancer cases [2,3]. Chemotherapy is the mainstay of treatment for TNBC because of the lack of expression of oestrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) in patients [4]. Patients with TNBC have a worse prognosis than those with other types of breast cancer and have a mortality rate of 40% within the first 5 years of diagnosis [5].
Moreover, approximately 50% of patients with TNBC develop distant metastasis [6]. The mortality rate of patients with distant metastases is higher than that of patients with carcinoma in situ [7]. The lungs are one of the most common sites of distant metastasis, accounting for 40% of the cases of metastasis. The median survival time of patients with metastatic TNBC is 1-1.5 years [8]. Therefore, determining a new method to predict the risk of lung metastasis (LM) and the prognosis of patients with TNBC is extremely important. ENY2, KCNK9, TNFRSF11B, KXNMB2, race, and marital status have been identified as risk factors and prognostic variables of LM [9,10]. To the best of our knowledge, no in-depth studies performed thus far have used predictive models to determine the incidence and prognosis of TNBC with LM; therefore, risk factors cannot be combined to effectively assess individual outcomes, and implementation of precision medicine is thus hampered.
A nomogram is a convenient tool that can accurately predict individual outcomes and exhibit good accuracy in assessing the prognosis of various cancers [11]. In this study, we aimed to construct two nomograms to predict the risk of LM in patients with TNBC and the prognosis of these patients based on the data from the Surveillance, Epidemiology, and End Results (SEER) database.

Study Population Selection.
The SEER * Stat software (version 8.3.6) was used to download patient data from the SEER database. Patients diagnosed with TNBC from 2010  3 Computational and Mathematical Methods in Medicine cohort was used to develop a nomogram, which was externally verified in the verification cohort.

Data Collection.
We used demographic variables, including age, race, insurance status, marital status, tumour characteristics, tumour size, grade, histological type, T stage, and N stage, to identify the risk factors of TNBC with LM. Additionally, we used data pertaining to metastasis to the bone, brain, and liver and treatment modalities, including surgery, chemotherapy, and radiotherapy, to determine the prognostic factors associated with TNBC with LM.

Statistical Analysis.
All statistical analyses were performed using SPSS 25.0 and R software (version 3.6.1). A P value < 0.05 (bilateral) was considered statistically significant. Univariate logistic analysis and multivariate binary logistic regression analysis were performed to determine the independent risk factors of TNBC with LM. Univariate and multivariate Cox regression analyses were performed to identify the independent prognostic factors.
The receiver operating characteristic (ROC) curve and time-dependent ROC curve of the predicted nomogram were generated. The area under the ROC curve (AUC) signified the distinctiveness of the nomogram and was further compared with the AUC of all independent prognostic factors. In addition, calibration curves were established to compare the consistency between the actual results and those predicted by the line graph. The range of threshold probability and the size of benefits were determined using decision curve analysis (DCA). In addition, patients were divided into the high-and low-risk groups based on the median risk score. Kaplan-Meier (KM) curves were generated, and the logarithmic rank test was performed.

Baseline Characteristics of Patients.
The baseline characteristics of 27,048 patients with TNBC are shown in Table 1. The tumour size of most patients with TNBC was <5 cm, and of all patients, 19,402 (71.7%), were Caucasian. Most patients (80.3%) had stage III disease.

Risk Factors of TNBC with LM.
To determine the LMrelated variables of TNBC, we used single-factor logistic analysis to screen for risk factors and found that the age, tumour size, histological type, T stage, N stage, insurance status, and marital status of patients with TNBC were related to LM (Table 2). Furthermore, multivariate logistic analysis showed that age, tumour size, T stage, and N stage were independent predictors of LM in patients with TNBC (Table 2).

Construction and Validation of a Diagnostic Nomogram
for TNBC with LM. The diagnostic nomogram of LM for patients with TNBC was constructed by including the corresponding independent risk factors ( Figure 1). The AUC values of the training and verification cohort were 0.838 (95% confidence interval 0.817-0.860) and 0.894 (95% confidence interval 0.875-0.917), respectively (Figures 2(a) and 2(d)). Additionally, we generated ROC curves for each independent predictor ( Figure 3) and found that the AUC of the nomogram was higher than that of all individual

Prognostic Factors of TNBC with LM.
To determine the prognostic factors, we examined the data from 480 patients with TNBC with LM (Table 3). Of these patients, 324 (67.5%) were Caucasian, 120 (25.0%) were Black, and 36 (7.5%) belonged to other races. Most patients received radiotherapy. Univariate and multivariate Cox proportional hazards regression were used to identify histological type, prior surgery, chemotherapy, bone metastasis, brain metastasis, liver metastasis, and marital status as independent prognostic factors of TNBC with LM (Table 4).

Comparison of Discrimination between Prognostic
Nomogram and Independent Prognostic Factors. To assess the advantages of the prognostic nomogram, we generated ROC curves inclusive of independent prognostic factors and found that the AUC value of part prognostic factor was >0.650, which signified that part individual factors can   7 Computational and Mathematical Methods in Medicine be used as reliable prognostic factors. However, the AUCs of all prognostic factors were lower than those of the prognostic nomogram (Figure 7). Overall, we confirmed that the function of a rosette combining different information from individual patients is superior to the predictive power of evaluating individual risk factors.

Role of Prognostic Nomograms in Risk Stratification of
Patients with TNBC with LM. The overall prognostic score of all patients TNBC with LM was calculated based on the nomogram. The KM curve showed that patients in the low-risk group survived longer than those in the high-risk group (Figures 5(b) and 5(d)). The threshold values identified in the training cohort were used for the validation cohort. The prognosis in both risk groups was significantly different (P < 0:0001). Overall, our system of risk stratification worked very well.

Discussion
TNBC is an aggressive tumour, which is prone to distant metastasis [12]. The lungs are a common site for distant metastasis. In this study, we constructed a diagnostic and a prognostic nomogram to predict LM in patients with TNBC. The risk of LM can be easily identified using these nomograms. The prognostic nomogram was used to assess the prognosis of TNBC patients with LM and provide guidance for further clinical management. The two nomograms accurately assessed the risk of LM and predicted survival and may help clinicians in decision-making and disease monitoring.
Although the prognosis of patients with TNBC with LM is extremely poor, early detection of LM is essential for patients to receive appropriate treatment [13]. Therefore, identifying risk factors of TNBC with LM is very important to guide clinical treatment. Several biomarkers and prognostic factors have been identified, including linc-ZNF469-3, miR-629-3p, age, T stage, and N stage [14][15][16][17]. However, to the best of our knowledge, no risk-prediction nomograms have been constructed to date; therefore, individual risk of LM cannot be quantified. Our results showed that age, tumour size, T stage, and N stage are independent predictors of TNBC with LM, which is consistent with the results of previous studies.
In addition, our results showed that patients with invasive ductal carcinoma (IDC), who were married, with no    Computational and Mathematical Methods in Medicine brain metastases, no liver metastases, and no bone metastases had a better prognosis after undergoing surgery and chemotherapy. Based on seven independent prognostic factors, a prognostic nomogram was constructed, which can be used as an effective tool to identify high-risk patients. IDC is the most common histological type of patients with TNBC [18]. Zhao et al. [19] and Li et al. [20] reported that the survival rate of patients with IDC is higher than that of patients with other histological types. These findings are consistent with those reported in our study. Patients who were married had a better prognosis. Previous studies have shown that the risk of cancer metastasis and cancer-related deaths is lower in married patients than in unmarried patients [21]. This relationship may be attributed to the important role of marital status in regulating the functions of the endocrine and immune systems [22]. Moreover, TNBC has a high recurrence rate [12]. We found that patients with distant metastases had a lower survival rate, which was consistent with the results of a study by Wang et al. [23]. In addition, different sites of metastasis affect the survival of patients with TNBC. Studies have reported that the prognosis of patients with visceral metastasis is worse than that of patients with bone metastasis [24]. Typically, the treatment of patients with advanced disease should focus on improving the survival rate of these patients. Chemotherapy and surgery are both favourable prognostic factors for patients with TNBC with LM. In addition, previous studies have shown that chemotherapy and surgery can significantly improve the prognosis of patients with LM [25]. Currently, chemotherapy remains the standard treatment for patients with TNBC [26]. The NCCN guidelines recommend a combination treatment plan based on taxanes, anthracycline, cyclophosphamide, cisplatin, and fluorouracil [6]. Previous studies have shown that despite metastasis to distant organs, patients can benefit from surgery [27,28]. Our prognostic nomogram showed that surgery and chemotherapy were beneficial for the survival of patients with TNBC with LM. Therefore, identification of independent prognostic factors may help to identify high-risk patients and establish a specific monitoring plan. However, our study has several limitations. The SEER database contains data regarding the first diagnosis of the disease; therefore, LM that occurs in advanced stages cannot be recorded. Furthermore, this is a retrospective study with large sample size; therefore, selection bias is inevitable. The prognostic nomogram was constructed and verified at a single institution, which may affect its clinical applicability to a certain extent. Therefore, further calibration of the nomogram is required in future studies.

Conclusion
Age, tumour size, T stage, and N stage are the risk factors of TNBC with LM. Histological type, marital status, brain metastasis, liver metastasis, bone metastasis, surgery, and chemotherapy are independent prognostic factors. The results of this study are very useful for determining individualised treatment and ensuring appropriate management of patients with TNBC with LM.

Data Availability
The dataset from SEER database generated and/or analyzed during the current study are available in the SEER dataset repository (https://seer.cancer.gov/).

Ethical Approval
The research did not involve animal experiments and human specimens and no ethical-related issues.

Conflicts of Interest
The authors declare that they have no competing interests.

Authors' Contributions
YD conceived and directed the project. JW designed the study and analyzed the data. JW, HZ, and LY wrote the manuscript. JL, HZ, CZ, QR, YC, and YX reviewed the data.