In the current study, a predictive model named TNMpBC-NeoBCSS was built based on clinicopathologic features, in a cohort from multi-centers data in China and the SEER database, for evaluating the 3- years and 5-years BCSS for patients with TNMpBC who had received NeoAT. The model of TNMpBC-NeoBCSS was established by combining the information of age at diagnosis, T stage, N stage and response to NeoAT. So far as we know, our model of TNMpBC-NeoBCSS is the first established to provide the predictive basis for the 3- years and 5-years BCSS for patients with TNMpBC who have received NeoAT and help the decision-making for clinicians and TNMpBC patients with NeoAT.
It was well documented that MpBC patients usually presented with larger tumor size and higher proportion of positive lymph nodes compared with those with invasive ductal carcinoma.[11, 12, 34] These patients were likely to have a dismal survival outcome with an average survival even less than 1 year.[27, 35] And subjects with MpBC tended to have worse clinical outcome and higher risk to develop distant metastasis in comparison with ones with TNBC.[11] Therefore, the effects of NeoAT in downgrading stage and preventing potential local metastasis[3, 5] were supposed to exert its predominance. However, due to the lower chemosensitivity in preoperative chemotherapy, and the chemotherapy- refractory characteristics, there weren’t sufficient evidences to demonstrated the benefits of NeoAT for patients with triple negative MpBC (TNMpBC) .[7, 17–22] In the current study, the total pathologic complete response (pCR) rate was 29.0%, which was higher than that of 10%-23% reported previouly,[9, 22] and lower that of 33%-61% in TNBC.[36–38] Meanwhile, among the 6 eligible NeoAT cases in our multi-centers data, as high as 33% (2/6) approached pCR. In addition, median age of patients in our data (46 years old) significantly younger than the reported recordation (50 years old)[12]. The main reason probably lied in the differences in the criteria of exclusion and inclusion. However, the precise prognosis value of response to NeoAT for TNMpBC patients remained unknowable.
As a powerful prognostic indicator for prediction of long-term clinical benefit in TNBC, the achievement of pCR after NeoAT meant improved survival. [36–38] In the current study, patients in the pCR subgroup had the most favorable BCSS than those in the other group. Such a result suggested that the administration of NeoAT had positive effect on improving BCSS for certain types of patients with MpBC, and reinforced that patients who attained pCR had improved survival. Therefore, it was necessary to forecast the breast cancer-specific survivorship benefit for the TNMpBC patients after NeoAT to help do the treatment decision-making in clinical works, based on the factors affecting BCSS.
Through PSM analysis, univariate and multivariate Cox regression analysis, we observed patients in PR and NR subgroups had nearly 10 times, 20 times higher risk in death from breast cancer, when compared to those acquired pCR. In addition, patients in T4 tumor had also stunning breast cancer death risk (HR = 30.17 & HR = 19.40, Table 2 & Table 3), which indicated the remarkable survival effect caused by tumor stage, while the lymph nodes status also influenced the breast cancer death significantly. On the basis of multivariate Cox regression analysis, we noticed that age at diagnosis, T stage, N stage and response to NeoAT manifested significantly statistical association with BCSS. Such results were in accordance with the previous works.[24, 28, 39, 40] Additionally, one research that published recently indicated that patients in no-response group exposed higher risk in death from breast cancer.[9]
Based on selected variables identified by the LASSO regression and reported previously, the predictive model named TNMpBC-NeoBCSS model was constructed to provide more accurate evidence on BCSS estimation and therapy evaluation in patients with TNMpBC. The C-index of 0.82 in training cohort indicated the good performance of the nomogram in predicting the BCSS outcome for MpBC patients after NeoAT. The point to each factor could be attained by drawing a downward vertical line on the nomogram, and the aggregate point would be easily calculated, then the survival probabilities would be acquired. Subsequently, an online nomogram tool was designed to the clinical work conveniently. The points of all cases were extracted by specific R package further, anticipating to calculate more accurately. Based on the provided information for a 45 years old woman with PR status at T3N1 stage, the total score on the TNMpBC-NeoBCSS model would be nearly 139 scores. Meanwhile the patient was classified in low risk population with a good prognosis: the 3- and 5-year survival probabilities for this patient were 83% and 76%, could be observed clearly.
To validate the ability of the predictive model to discriminate between patients at different risk levels, a risk score system was also established based on the scores in TNMpBC-NeoBCSS model. And then patients were divided into low and high risk subgroups based on the cutoff score of 150.0 from the TNMpBC-NeoBCSS model through X- tile software. It is shocking that patients in the low risk group had as high as 37.5% pCR rate, and improved BCSS, and patients in the high risk group had a pCR rate of 0%, which indicated the low chemosensitivity and the corresponding BCSS. Notably, elder age, poorer differentiation level, higher proportion of advanced tumor stage and involved lymph node were observed for patients in high risk subgroup. Survival analysis concurrently revealed that these people had the poorest prognosis. Such results further confirmed the great significance of prediction effect in TNMpBC-NeoBCSS model and provide the important recommendations for appropriate population in the application of NeoAT among the MpBC.
The reliability of the current study lied on its sufficient sample size, which enabled us to comprehensively analyzed and constructed the model to predict the prognosis of MpBC patients with NeoAT. The robust AUC value represented the confidential prediction efficacy, enhancing the application of the model to forecast BCSS for related population. This study also had several shortcomings. First, as a retrospective research, it couldn’t avoid the existence of the selection bias. Patients who underwent AT following NeoAT and those who had uncertain pCR status were excluded, which may influence the value of pCR rate. Second, some important clinicopathological variables weren’t included in the current study, such as family history, patient anxiety, concrete regimens of NeoAT and AT, frailty, or comorbid conditions. Thirdly, on account of the inclusion criteria enrolling the TNMpBC patients only, the TNMpBC-NeoBCSS model wasn’t appropriate to the patients with positive hormone receptor or positive HER2 receptor (human epidermal growth factor receptor-2). Lastly, the result need to be verified further in a larger multi-center clinical cohort. Despite these limitations, the conclusions and the predictive model had well application value.