Triple-negative apocrine carcinoma as a rare cause of a breast lump in a Syrian female: a case report and review of the literature

Emerging targeted therapeutic strategies for the treatment of triple-negative breast cancer

Triple-negative breast cancer (TNBC), a subtype of breast cancer that lacks expression of oestrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER-2), has clinical features including a high degree of invasiveness, an elevated risk of metastasis, tendency to relapse, and poor prognosis. It constitutes around 10-15% of all breast cancer, and having heredity of BRCA1 mutated breast cancer could be a reason for the occurrence of TNBC in women. Overexpression of cellular and molecular targets, i.e. CD44 receptor, EGFR receptor, Folate receptor, Transferrin receptor, VEGF receptor, and Androgen receptor, have emerged as promising targets for treating TNBC. Signalling pathways such as Notch signalling and PI3K/AKT/mTOR also play a significant role in carrying out and managing crucial pro-survival and pro-growth cellular processes that can be utilised for targeted therapy against triple-negative breast cancer. This review sheds light on various targeting strategies, including cellular and molecular targets, signalling pathways, poly (ADP-ribose) polymerase inhibitors, antibody-drug conjugates, and immune checkpoint inhibitors PARP, immunotherapy, ADCs have all found a place in the current TNBC therapeutic paradigm. The role of photothermal therapy (PTT) and photodynamic therapy (PDT) has also been explored briefly.

Nomogram for predicting overall survival in patients with triple-negative apocrine breast cancer: Surveillance, epidemiology, and end results-based analysis

PURPOSE

Triple-negative apocrine carcinoma (TNAC) is a sort of triple-negative breast cancer (TNBC) that is rare and prognosis of these patients is unclear. The present study constructed an effective nomogram to assist in predicting TNAC patients overall survival (OS).

METHODS

A total of 373 TNAC patients from the surveillance, epidemiology, and end results (SEER) got extracted from 2010 to 2016 and were divided into training (n = 261) and external validation (n = 112) groups (split ratio, 7:3) randomly. A Cox regression model was utilized to creating a nomogram according to the risk factors affecting prognosis. The predictive capability of the nomogram was estimated with receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA).

RESULTS

Multivariate Cox regression analysis revealed age, surgery, chemotherapy, stage, and first malignant primary as independent predictors of OS. A prediction model was constructed and virtualized using the nomogram. The time-dependent area under the curve (AUC) showed satisfactory discrimination of the nomogram. Good consistency was shown on the calibration curves in OS between actual observations and the nomogram prediction. What's more, DCA showed that the nomogram had incredible clinical utility. Through separating the patients into groups of low and high risk group that connects with the risk system that shows a huge difference between the low-risk and high risk OS (P < 0.001).

CONCLUSION

To predict the OS in TNAC patients, the nomogram utilizing the risk stratification system that is corresponding. These tools may help to evaluate patient prognosis and guide treatment decisions.