Predicting pathological complete response after neoadjuvant chemotherapy: A nomogram combining clinical features and ultrasound semantics in patients with invasive breast cancer.

Background: Early identification of response to neoadjuvant chemotherapy (NAC) is instrumental in predicting patients prognosis. However, since a fixed criterion with high accuracy cannot be generalized to molecular subtypes, our study first aimed to redefine grades of clinical response to NAC in invasive breast cancer patients (IBC). And then developed a prognostic model based on clinical features and ultrasound semantics. Methods: A total of 480 IBC patients were enrolled who underwent anthracycline and taxane-based NAC between 2018 and 2020. The decrease rate of the largest diameter was calculated by ultrasound after NAC and their cut-off points were determined among subtypes. Thereafter, a nomogram was constructed based on clinicopathological and ultrasound-related data, and validated using the calibration curve, receiver operating characteristic (ROC) curve, decision curve analysis (DCA), and clinical impact curve (CIC). Results: The optimal cut-off points for predicting pCR were 53.23%, 51.56%, 41.89%, and 53.52% in luminal B-like (HER2 negative), luminal B-like (HER2 positive), HER2 positive, and triple-negative, respectively. In addition, time interval, tumor size, molecular subtypes, largest diameter decrease rate, and change of blood perfusion were significantly associated with pCR (all p < 0.05). The prediction model based on the above variables has great predictive power and clinical value. Conclusion: Taken together, our data demonstrated that calculated cut-off points of tumor reduction rates could be reliable in predicting pathological response to NAC and developed nomogram predicting prognosis would help tailor systematic regimens with high precision.

ZNF384-ZEB1 feedback loop regulates breast cancer metastasis.

BACKGROUND: Breast cancer has become the most frequently diagnosed cancer worldwide. Increasing evidence indicated that zinc finger proteins (ZNFs), the largest family of transcription factors, contribute to cancer development and progression. Although ZNF384 is overexpressed in several types of human cancer, the role of ZNF384 in breast cancer remains unknown. Therefore, our research focused on ZNF384 regulation of the malignant phenotype of breast cancer and the underlying molecular mechanisms. METHODS: CCK-8 and colony formation assays were used to evaluate cell proliferation. Transwell and scratch assays were used to evaluate the cell migration and invasion. Chromatin immunoprecipitation (ChIP)-qPCR and luciferase reporter assays were used to confirm the target relationship between ZNF384 and zinc finger E-box binding homeobox 1 (ZEB1). Xenografts were used to monitor the targets in vivo effects. RESULTS: We noted that ZNF384 was significantly overexpressed in breast cancer and highlighted the oncogenic mechanism of ZNF384. ZNF384 transactivated ZEB1 expression and induced an epithelial and mesenchymal-like phenotype, resulting in breast cancer metastasis. Furthermore, ZNF384 may be a target of miR-485-5p, and ZEB1 can up-regulate ZNF384 expression by repressing miR-485-5p expression. Together, we unveiled a feedback loop of ZNF384-ZEB1 in breast cancer metastasis. CONCLUSIONS: The findings suggest that ZNF384 can serve as a prognostic factor and a therapeutic target for breast cancer patients.

Nomograms predicting the overall and cancer-specific survival of patients with buccal mucosa cancer.

OBJECTIVES: To construct predictive models for predicting overall survival (OS) and cancer-specific survival (CSS) of patients with buccal mucosa cancer (BMC). STUDY DESIGN: Data of 936 patients with BMC were extracted from the Surveillance, Epidemiology, and End Results (SEER) database from 2004 to 2015. Nomograms were constructed based on multivariate Cox regression analyses, and validated using calibration plots, time-dependent receiver operating characteristic curves, and decision curve analyses. RESULTS: Age at diagnosis, marital status, grade, histopathology, SEER stage, tumor size, and surgery were associated with OS, whereas age at diagnosis, grade, histopathology, SEER stage, tumor size, and surgery were associated with CSS (all P < .05). The concordance indexes for OS and CSS were 0.79 and 0.80 in the training cohort, respectively, and those in the validation cohort were 0.78 and 0.80. Time-dependent receiver operating characteristic curves showed great predictability in nomograms. Decision curve analyses demonstrated good clinical value for OS (4%-88%) and CSS (3%-77%) nomograms. Patients were stratified into 3 risk groups, with the worst prognosis in the high-risk subgroup (P < .001). CONCLUSIONS: We developed and validated 2 nomograms predicting OS and CSS and established the corresponding risk classification systems in patients with BMC. These models assisted in precise administration of individual therapeutic regimens.

Association of marital status with cardiovascular outcome in patients with breast cancer.

Background: The influences of marital status on cardiovascular death risk in patients with breast cancer remained unclear. This study aimed to evaluate the associations of different marital status with cardiovascular death risk in patients with breast cancer. Methods: A total of 182,666 female breast cancer patients were enrolled in this study from the Surveillance, Epidemiology, and End Results (SEER) database from 2010 to 2014, and was divided into two groups: married (N=107,043) and unmarried (N=75,623). A 1:1 propensity score matching (PSM) was applied to reduce inter-group bias between the two groups. Competing-risks model was used to assess the associations between different marital status and cardiovascular death risk in patients with breast cancer. Results: After PSM, marital status was an independent predictor for cardiovascular death in patients with breast cancer. Unmarried condition was associated with increased cardiovascular death risk than married condition among breast cancer patients [unadjusted model: hazard ratio (HR) =2.012, 95% confidence interval (CI): 1.835-2.208, P<0.001; Model 1: HR =1.958, 95% CI: 1.785-2.148, P<0.001; Model 2: HR =1.954, 95% CI: 1.781-2.144, P<0.001; Model 3: HR =1.920, 95% CI: 1.748-2.107, P<0.001]. With the exception of separated condition (adjusted HR =0.886, 95% CI: 0.474-1.658, P=0.705), further unmarried subgroups analysis showed that the other three unmarried status were associated with increased cardiovascular death risk as follows: single (adjusted HR =1.623, 95% CI: 1.421-1.853, P<0.001), divorced (adjusted HR =1.394, 95% CI: 1.209-1.608, P<0.001), and widowed (adjusted HR =2.460, 95% CI: 2.227-2.717, P<0.001). In particularly, widowed condition showed the highest cardiovascular death risk in all 4 unmarried subgroups. Conclusions: Unmarried condition (e.g., single, divorced and widowed) was associated with elevated cardiovascular death risk compared with their married counterparts in patients with breast cancer, suggesting that more attention and humanistic care should be paid to unmarried breast cancer patients (especially the widowed patients) in the management of female breast cancer patients.

Cardiovascular and Other Competing Causes of Death in Male Breast Cancer Patients: A Population-Based Epidemiologic Study.

PURPOSE: Male breast cancer (MBC) is a rare disease that tends to occur in elderly men. Little is known about the causes of death in MBC because of the small sample size of most studies. This study aimed to investigate the causes of death in MBC patients. PATIENTS AND METHODS: MBC patient data were obtained from the Surveillance, Epidemiology, and End Results database (1975-2016). Time trends of MBC mortality in the US population were analyzed using Joinpoint software. We calculated the proportion of each cause of death in the overall cohort and in different patient subgroups. Competing risk models were used to calculate cumulative mortality at different follow-up times. The risk of cardiovascular death (CVD) in MBC patients was compared to that of the age-matched general population by calculating standardized mortality ratio (SMR). RESULTS: In total, 6426 patients were included in the analysis. MBC mortality rate increased between 2004 and 2019 (annual percentage change=1.16, 95% confidence interval [CI]: 0.50, 1.80). There were 1757 patients (27.3%) who died of non-breast cancer causes. CVD was the leading cause of death in patients who were elderly or had localized disease. MBC patients had a 6.58-fold higher risk of CVD than the general population (SMR=6.58, 95% CI: 6.14, 7.05). CONCLUSION: Non-breast cancer death accounts for the majority of deaths in MBC patients who are elderly or have localized cancer. Compared to the general population, MBC patients have an increased risk of CVD. These results highlight the importance of monitoring cardiovascular comorbidities in MBC patients.

Increased Risk of Cardiovascular Death in Breast Cancer Patients Without Chemotherapy or (and) Radiotherapy: A Large Population-Based Study.

BACKGROUND: Cardiovascular death (CVD) in breast cancer patients without chemotherapy (CT) or (and) radiotherapy (RT) has not been studied yet. This study evaluates the correlation between breast cancer and CVD risk independent of chemotherapy or (and) radiotherapy. METHODS: Data of female breast cancer patients without receiving CT or RT were retrieved from the Surveillance, Epidemiology, and End Result (SEER) database (2004-2015). Data were divided into two cohorts: tumor resection cohort and no resection cohort. The CVD risk in patients was expressed as standardized mortality ratios (SMRs). A 1:1 propensity score matching (PSM) was applied to balance inter-group bias, and competing risk regressions were utilized to evaluate the impact of tumor resection on CVD. RESULTS: The CVD risk was significantly higher (SMR = 2.196, 95% CI: 2.148-2.245, P<0.001) in breast cancer patients who did not receive CT or RT compared to the general population. Breast cancer patients without tumor resection showed higher CVD risk than patients who underwent tumour resection (tumor resection SMR = 2.031, 95% CI: 1.983-2.079, P<0.001; no resection SMR = 5.425, 95% CI: 5.087-5.781, P<0.001). After PSM, the CVD risk among patients without tumor resection indicated an increase of 1.165-fold compared to patients with tumor resection (HR=1.165, 95% CI: 1.039-1.306, P=0.009). CONCLUSIONS: Female breast cancer patients are at higher risk of CVD despite unexposure to cardio-toxic CT or RT. However, female breast cancer patients subjected to tumor resection have decreased CVD risk. These results indicated that monitoring female breast cancer patients not receiving RT or CT might serve as a preventative measure against CVD.

Nomograms and risk classification systems predicting overall and cancer-specific survival in primary malignant cardiac tumor.

BACKGROUND: Primary malignant cardiac tumors (PMCTs) are fatal, but up to now, there is still a lack of survival prediction model for prognosis evaluation. We developed nomograms to predict overall survival (OS) and cancer-specific survival (CSS) for PMCTs by the Surveillance, Epidemiology, and End Result (SEER) database. METHODS: A total of 506 PMCTs participants were identified in the SEER database from 1973 to 2014 and were randomly assigned into the training cohort (N = 354) and the validation cohort (N = 152). The prognostic factors for PMCTs were identified by Kaplan-Meier and multivariate Cox analysis and further incorporated to build OS and CSS nomograms. The nomograms were internally and externally validated via concordance indexes (C-index) and calibration curves. RESULTS: The independent prognostic factors for OS and CSS in PMCTs were associated with age at diagnosis, histopathology, tumor stage, cancer-directed surgery, and chemotherapy (all P < .05). In the internal validation, the C-index values were 0.71 (95% confidence interval [CI]: 0.68-0.75) for OS nomogram, and 0.70 (95% CI: 0.67-0.74) for CSS nomogram. In the external validation, the C-index values were 0.71 (95% CI: 0.66-0.77) for OS nomogram, and 0.71 (95% CI: 0.65-0.77) for CSS nomogram. The calibration curves of internal and external validation showed consistency between the nomograms and the actual observation. The risk stratification of PMCTs was significant distinction (P < .05). CONCLUSION: We developed and validated credible nomograms to predict OS and CSS in PMCTs. These nomograms can be offered to clinicians to more precisely estimate the survival and identify risk stratification of PMCTs.