Efficacy and safety of first-line treatment for metastatic triple-negative breast cancer:A network meta-analysis

Background:Metastatic triple-negative breast cancer(mTNBC)is an aggressive histological subtype with poor prognosis.Several first-line treatments are currently available for mTNBC.This study conducted a network meta-analysis to compare these first-line regimens and to determine the regimen with the best efficacy.Methods:A systematic search of PubMed,EMBASE,the Cochrane Central Register of Controlled Bases,and mi-nutes of major conferences was performed.Progression-free survival(PFS),overall survival(OS),and objective response rate(ORR)were analyzed via network meta-analysis using the R software(R Core Team,Vienna,Austria).The efficacy of the treatment regimens was compared using hazard ratios and 95%confidence intervals.Results:A total of 29 randomized controlled trials involving 4607 patients were analyzed.The ranking was based on the surface under the cumulative ranking curve.Network meta-analysis results showed that cisplatin combined with nab-paclitaxel or paclitaxel was superior to docetaxel plus capecitabine in terms of PFS and ORR.For programmed death-ligand 1(PD-L1)and breast cancer susceptibility gene(BRCA)mutation-positive tumors,atezolizumab/pembrolizumab combined with nab-paclitaxel and talazoparib was superior to docetaxel plus capecitabine.No significant difference was observed among the treatments in Os.Neutropenia,diarrhea,and fatigue were common serious adverse events.Conclusion:Cisplatin combined with nab-paclitaxel or paclitaxel is the preferred first-line treatment for mTNBC.For PD-L1 and BRCA mutation-positive tumors,atezolizumab/pembrolizumab combined with nab-paclitaxel and talazoparib is an effective treatment option,Neutropenia,diarrhea,and fatigue are frequently occurring serious adverseevents.

Association between high body mass index and prognosis of patients with early-stage breast cancer: A systematic review and meta-analysis

Background:A high body mass index(BMI)can indicate overweight or obesity and is a crucial risk factor for breast cancer survivors.However,the association between high BMI and prognosis in early-stage breast cancer(EBC)remains unclear.We aimed to assess the effects of high BMI on the prognosis of patients with EBC.Methods:The PubMed,Embase,and Cochrane Library databases and proceedings of major oncological conferences related to the effects of BMI on the prognosis of breast cancer were searched up to November 2021.Fixedand random-effects models were used for meta-analyses.Pooled hazard ratios(HRs)and 95%confidence intervals(CIs)for disease-free survival(DFS)and overall survival(OS)were extracted from the included literature.Results:Twenty retrospective cohort studies with 33,836 patients with EBC were included.Overweight patients had worse DFS(HR:1.16,95%CI:1.05-1.27,P=0.002)and OS(HR:1.20;95%CI:1.09-1.33,P<0.001).Obesity also had adverse effects on DFS(HR:1.17,95%CI:1.07-1.29,P=0.001)and OS(HR:1.30,95%CI:1.17-1.45,P<0.001).Likewise,patients with high BMI had worse DFS(HR:1.16,95%CI:1.08-1.26,P<0.001)and OS(HR:1.25,95%CI:1.14-1.39,P<0.001).In subgroup analyses,overweight had adverse effects on DFS(HR:1.11,95%CI:1.04-1.18,P=0.001)and OS(HR:1.18,95%CI:1.11-1.26,P<0.001)in multivariate analyses,whereas the relationship that overweight had negative effects on DFS(HR:1.21,95%CI:0.99-1.48,P=0.058)and OS(HR:1.39,95%CI:0.92-2.10,P=0.123)was not statistically significant in univariate analysis.By contrast,obesity had adverse effects on DFS(HR:1.21,95%CI:1.06-1.38,P=0.004 and HR:1.14,95%CI:1.08-1.22,P<0.001)and OS(HR:1.33,95%CI:1.15-1.54,P<0.001 and HR:1.23,95%CI:1.15-1.31,P<0.001)in univariate and multivariate analyses,respectively.Conclusions:Compared with normal weight,increased body weight(overweight,obesity,and high BMI)led to worse DFS and OS in patients with EBC.Once validated,these results should be considered in the development of prevention programs.

Survival nomogram for patients with de novo metastatic breast cancer based on the SEER database and an external validation cohort

Background On average,5-10%of patients are diagnosed with metastatic breast cancer(MBC)at the initial diagnosis.This study aimed to develop a nomogram to predict the overall survival(OS)in these patients.Methods The nomogram was based on a retrospective study of 9435 patients with de novo MBC from the Surveillance,Epidemiology,and End Results(SEER)database.The predictive accuracy and discriminative ability of the nomogram were determined using the concordance index(C-index),area under the time-dependent receiver operating characteristic curve(AUC),and calibration curve.Decision curve analysis(DCA)was employed to evaluate the benefits and advantages of our new predicting model over the 8th edition of the American Joint Committee on Cancer(AJCC)Tumor Node Metastasis(TNM)staging system.The results were validated in a retrospective study of 103 patients with de novo MBC from January 2013 to June 2022 at an institution in northwest China.Results Multivariate analysis of the primary cohort revealed that independent factors for survival were age at diagnosis,pathological type,histological grade,T stage,N stage,molecular subtype,bone metastasis,brain metastasis,liver metastasis,lung metastasis,surgery,chemotherapy,and radiotherapy.The nomogram achieved a C-index of 0.688(95%confidence interval[CI],0.682-0.694)in the training cohort and 0.875(95%CI,0.816-0.934)in the validation cohort.The AUC of the nomograms indicated good specificity and sensitivity in the training and validation cohorts,respectively.Calibration curves showed favorable consistency between the predicted and actual survival probabilities.Additionally,the DCA curve produced higher net gains than by the AJCC-TNM staging system.Finally,risk stratification can accurately identify groups of patients with de novo MBC at different risk levels.Conclusions The nomogram showed favorable predictive and discriminative abilities for OS in patients with de novo MBC.Other populations from different countries or prospective studies are needed to further validate the nomogram.