Prognosis of Patients With Triple-negative Breast Cancer: A Population-based Study From SEER Database

Triple-negative breast cancer survival prediction: population-based research using the SEER database and an external validation cohort

Introduction

Triple-negative breast cancer (TNBC) is linked to a poorer outlook, heightened aggressiveness relative to other breast cancer variants, and limited treatment choices. The absence of conventional treatment methods makes TNBC patients susceptible to metastasis. The objective of this research was to assess the clinical and pathological traits of TNBC patients, predict the influence of risk elements on their outlook, and create a prediction model to assist doctors in treating TNBC patients and enhancing their prognosis.

Methods

We included 23,394 individuals with complete baseline clinical data and survival information who were diagnosed with primary TNBC between 2010 and 2015 based on the SEER database. External validation utilised a group from The Affiliated Lihuili Hospital of Ningbo University. Independent risk factors linked to TNBC prognosis were identified through univariate, multivariate, and least absolute shrinkage and selection operator regression methods. These characteristics were chosen as parameters to develop 3- and 5-year overall survival (OS) and breast cancer-specific survival (BCSS) nomogram models. Model accuracy was assessed using calibration curves, consistency indices (C-indices), receiver operating characteristic curves (ROCs), and decision curve analyses (DCAs). Finally, TNBC patients were divided into groups of high, medium, and low risk, employing the nomogram model for conducting a Kaplan-Meier survival analysis.

Results

In the training cohort, variables such as age at diagnosis, marital status, grade, T stage, N stage, M stage, surgery, radiation, and chemotherapy were linked to OS and BCSS. For the nomogram, the C-indices stood at 0.762, 0.747, and 0.764 in forecasting OS across the training, internal validation, and external validation groups, respectively. Additionally, the C-index values for the training, internal validation, and external validation groups in BCSS prediction stood at 0.793, 0.755, and 0.811, in that order. The findings revealed that the calibration of our nomogram model was successful, and the time-variant ROC curves highlighted its effectiveness in clinical settings. Ultimately, the clinical DCA showcased the prospective clinical advantages of the suggested model. Furthermore, the online version was simple to use, and nomogram classification may enhance the differentiation of TNBC prognosis and distinguish risk groups more accurately.

Conclusion

These nomograms are precise tools for assessing risk in patients with TNBC and forecasting survival. They can help doctors identify prognostic markers and create more effective treatment plans for patients with TNBC, providing more accurate assessments of their 3- and 5-year OS and BCSS.

Assessing of case-cohort design: a case study for breast cancer patients in Xinjiang, China

Objective

To assess the effectiveness and clinical value of case-cohort design and determine prognostic factors of breast cancer patients in Xinjiang on the basis of case-cohort design.

Methods

The survival data with different sample characteristics were simulated by using Cox proportional risk models. To evaluate the effectiveness for the case-cohort, entire cohort, and simple random sampling design by comparing the mean, coefficient of variation, etc., of covariate parameters. Furthermore, the prognostic factors of breast cancer patients in Xinjiang were determined based on case-cohort sampling designs. The models were comprehensively evaluated by likelihood ratio test, the area under the receiver operating characteristic curve (AUC), and Akaike Information Criterion (AIC).

Results

In a simulations study, the case-cohort design shows better stability and improves the estimation efficiency when the censored rate is high. In the breast cancer data, molecular subtypes, T-stage, N-stage, M-stage, types of surgery, and postoperative chemotherapy were identified as the prognostic factors of patients in Xinjiang. These models based on the different sampling designs both passed the likelihood ratio test (p<0.05). Moreover, the model constructed under the case-cohort design had better fitting effect (AIC=3,999.96) and better discrimination (AUC=0.807).

Conclusion

Simulations study confirmed the effectiveness of case-cohort design and further determined the prognostic factors of breast cancer patients in Xinjiang based on this design, which presented the practicality of case-cohort design in actual data.

Postmarketing Safety of Sacituzumab Govitecan: A Pharmacovigilance Study Based on the FDA Adverse Event Reporting System

Sacituzumab govitecan is widely used for the treatment of breast cancer and urothelial carcinoma, but available information regarding adverse events (AEs) is limited. We aim to explore the AE induced by sacituzumab govitecan by mining the FDA Adverse Event Reporting System (FAERS) database. The association between sacituzumab govitecan and AEs was evaluated using the information component. A multivariate logistic regression analysis was conducted for all identified signals to explore the risk factors associated with AEs leading to hospitalization. In total, 1,884 reports related to sacituzumab govitecan were retrieved, and 114 AE signals involving 20 systems were identified. The median time for onset of AEs was ~ 6-7 days after initiating treatment with sacituzumab govitecan, with over 80% of AEs occurring within 30 days. Subgroup analysis revealed that 14 signals were reported in men and 110 in women. There were 58 signals reported in patients under 65 following the use of sacituzumab govitecan, 59 signals in patients over 65, and 31 signals were present in both groups. Multivariable analysis showed that being male and the occurrence of colitis, pneumonitis, febrile neutropenia, pyrexia, sepsis, dehydration, and diarrhea were risk factors leading to hospitalization with an area under the curve (AUC) of 0.89. Additionally, sensitivity analysis revealed that this study had good robustness. This is the first retrospective analysis based on FAERS to review the safety of sacituzumab govitecan. The results highlight the need to closely monitor adverse reactions such as neutropenia, diarrhea, colitis, and sepsis when using sacituzumab govitecan.