Dataset Information

The prediction models for postoperative overall survival and disease-free survival in patients with breast cancer.

ABSTRACT: The goal of this study is to establish a method for predicting overall survival (OS) and disease-free survival (DFS) in breast cancer patients after surgical operation. The gene expression profiles of cancer tissues from the patients, who underwent complete surgical resection of breast cancer and were subsequently monitored for postoperative survival, were analyzed using cDNA microarrays. We detected seven and three probes/genes associated with the postoperative OS and DFS, respectively, from our discovery cohort data. By incorporating these genes associated with the postoperative survival into MammaPrint genes, often used to predict prognosis of patients with early-stage breast cancer, we constructed postoperative OS and DFS prediction models from the discovery cohort data using a Cox proportional hazard model. The predictive ability of the models was evaluated in another independent cohort using Kaplan-Meier (KM) curves and the area under the receiver operating characteristic curve (AUC). The KM curves showed a statistically significant difference between the predicted high- and low-risk groups in both OS (log-rank trend test P = 0.0033) and DFS (log-rank trend test P = 0.00030). The models also achieved high AUC scores of 0.71 in OS and of 0.60 in DFS. Furthermore, our models had improved KM curves when compared to the models using MammaPrint genes (OS: P = 0.0058, DFS: P = 0.00054). Similar results were observed when our model was tested in publicly available datasets. These observations indicate that there is still room for improvement in the current methods of predicting postoperative OS and DFS in breast cancer.

SUBMITTER: Shigemizu D

PROVIDER: S-EPMC5504310 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature

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The prediction models for postoperative overall survival and disease-free survival in patients with breast cancer.

Shigemizu Daichi D Iwase Takuji T Yoshimoto Masataka M Suzuki Yasuyo Y Miya Fuyuki F Boroevich Keith A KA Katagiri Toyomasa T Zembutsu Hitoshi H Tsunoda Tatsuhiko T

Cancer medicine 20170524 7

The goal of this study is to establish a method for predicting overall survival (OS) and disease-free survival (DFS) in breast cancer patients after surgical operation. The gene expression profiles of cancer tissues from the patients, who underwent complete surgical resection of breast cancer and were subsequently monitored for postoperative survival, were analyzed using cDNA microarrays. We detected seven and three probes/genes associated with the postoperative OS and DFS, respectively, from ou ...[more]

PMID: 28544536

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Project description:BACKGROUND:Breast cancer is the most common malignancy in female patients worldwide. Because of its heterogeneity in terms of prognosis and therapeutic response, biomarkers with the potential to predict survival or assist in making treatment decisions in breast cancer patients are essential for an individualised therapy. Epigenetic alterations in the genome of the cancer cells, such as changes in DNA methylation pattern, could be a novel marker with an important role in the initiation and progression of breast cancer. METHOD:DNA methylation and RNA-seq datasets from The Cancer Genome Atlas (TCGA) were analysed using the Least Absolute Shrinkage and Selection Operator (LASSO) Cox model. Applying gene ontology (GO) and single sample gene set enrichment analysis (ssGSEA) an epigenetic signature associated with the survival of breast cancer patients was constructed that yields the best discrimination between tumour and normal breast tissue. A predictive nomogram was built for the optimal strategy to distinguish between high- and low-risk cases. RESULTS:The combination of mRNA-expression and of DNA methylation datasets yielded a 13-gene epigenetic signature that identified subset of breast cancer patients with low overall survival. This high-risk group of tumor cases was marked by upregulation of known cancer-related pathways (e.g. mTOR signalling). Subgroup analysis indicated that this epigenetic signature could distinguish high and low-risk patients also in different molecular or histological tumour subtypes (by Her2-, EGFR- or ER expression or different tumour grades). Using Gene Expression Omnibus (GEO) the 13-gene signature was confirmed in four external breast cancer cohorts. CONCLUSION:An epigenetic signature was discovered that effectively stratifies breast cancer patients into low and high-risk groups. Since its efficiency appears independent of other known classifiers (such as staging, histology, metastasis status, receptor status), it has a high potential to further improve likely individualised therapy in breast cancer.

Project description:ObjectiveThis study aims to investigate the potential prognostic significance of programmed death ligand-1 (PD-L1) protein expression in tumor cells of breast cancer patients received neoadjuvant chemotherapy (NACT).MethodsUsing semiquantitative immunohistochemistry, the PD-L1 protein expression in breast cancer tissues was analyzed. The correlations between PD-L1 protein expression and clinicopathologic characteristics were analyzed using Chi-square test or Fisher's exact test. The survival curve was stemmed from Kaplan-Meier assay, and the log-rank test was used to compare survival distributions against individual index levels. Univariate and multivariate Cox proportional hazards regression models were accessed to analyze the associations between PD-L1 protein expression and survival outcomes. A predictive nomogram model was constructed in accordance with the results of multivariate Cox model. Calibration analyses and decision curve analyses (DCA) were performed for the calibration of the nomogram model, and subsequently adopted to assess the accuracy and benefits of the nomogram model.ResultsA total of 104 breast cancer patients received NACT were enrolled into this study. According to semiquantitative scoring for IHC, patients were divided into: low PD-L1 group (61 cases) and high PD-L1 group (43 cases). Patients with high PD-L1 protein expression were associated with longer disease free survival (DFS) (mean: 48.21 months vs. 31.16 months; P=0.011) and overall survival (OS) (mean: 83.18 months vs. 63.31 months; P=0.019) than those with low PD-L1 protein expression. Univariate and multivariate analyses indicated that PD-L1, duration of neoadjuvant therapy, E-Cadherin, targeted therapy were the independent prognostic factors for patients' DFS and OS. Nomogram based on these independent prognostic factors was used to evaluate the DFS and OS time. The calibration plots shown PD-L1 based nomogram predictions were basically consistent with actual observations for assessments of 1-, 3-, and 5-year DFS and OS time. The DCA curves indicated the PD-L1 based nomogram had better predictive clinical applications regarding prognostic assessments of 3- and 5-year DFS and OS, respectively.ConclusionHigh PD-L1 protein expression was associated with significantly better prognoses and longer DFS and OS in breast cancer patients. Furthermore, PD-L1 protein expression was found to be a significant prognostic factor for patients who received NACT.

Project description:BackgroundThe treatment for locally advanced breast cancer (LABC) is a severe clinical problem. The postoperative radiotherapy is a conventional treatment method for patients with LABC, whereas the effect of preoperative radiotherapy on outcome of LABC remains controversial. This study aimed to examine and compare the overall survival (OS) in patients with LABC who underwent preoperative radiotherapy or postoperative radiotherapy.MethodsThis retrospective cohort study included 41,618 patients with LABC from the National Cancer Database (NCDB) between 2010 and 2014. We collected patients' demographic, clinicopathologic, treatment and survival information. Propensity score was used to match patients underwent pre-operative radiotherapy with those who underwent post-operative radiotherapy. Cox proportional hazard regression model was performed to access the association between variables and OS. Log-rank test was conducted to evaluate the difference in OS between groups.ResultsThe estimated median follow-up of all included participants was 69.6 months (IQR: 42.84-60.22); 70.1 months (IQR: 46.85-79.97) for postoperative radiotherapy, 68.5 (IQR: 41.13-78.23) for preoperative radiotherapy, and 67.5 (IQR: 25.92-70.99) for no radiotherapy. The 5-year survival rate was 80.01% (79.56-80.47) for LABC patients who received postoperative radiotherapy, 64.08% (57.55-71.34) for preoperative radiotherapy, and 59.67% (58.60-60.77) for no radiotherapy. Compared with no radiation, patients receiving postoperative radiotherapy had a 38% lower risk of mortality (HR=0.62, 95%CI: 0.60-0.65, p<0.001), whereas those who received preoperative radiotherapy had no significant survival benefit (HR=0.88, 95%CI: 0.70-1.11, p=0.282). Propensity score matched analysis indicated that patients treated with preoperative radiotherapy had similar outcomes as those treated with postoperative radiotherapy (AHR=1.23, 95%CI: 0.88-1.72, p=0.218). Further analysis showed that in C0 (HR=1.45, 95%CI: 1.01-2.07, p=0.044) and G1-2 (AHR=1.74, 95%CI: 1.59-5.96, p=0.001) subgroup, patients receiving preoperative radiotherapy showed a worse OS than those who received postoperative radiotherapy.ConclusionsPatients with LABC underwent postoperative radiotherapy had improved overall survival, whereas no significant survival benefit was observed in patients receiving preoperative radiotherapy. Preoperative radiotherapy did not present a better survival than postoperative radiotherapy for LABC patients.

Project description:IntroductionBreast cancer is the most common malignant tumor in women worldwide. However, advanced multidisciplinary therapy cannot rescue the mortality of high-risk breast cancer metastasis. Ferroptosis is a newly discovered form of regulating cell death that related to cancer treatment, especially in eradicating aggressive malignancies that are resistant to traditional therapies. However, the prognostic value of ferroptosis-related gene in breast cancer remains unknown.Materials and methodsIn this study, a total of 1,057 breast cancer RNA expression data with clinical and follow-up information were downloaded from the TCGA cohort, multivariate Cox regression was used to construct the 11-gene prognostic ferroptosis-related gene signature. The breast cancer patients from the GEO cohort were used for validation. The expression levels of core prognostic genes were also verified in erastin-treated breast cancer cell lines by real-time polymerase chain action (PCR).Results and discussionOur results showed that 78% ferroptosis-related genes were differentially expressed between breast cancer tumor tissue and adjacent non-tumorous tissues, including 29 of them which were significantly correlated with OS in the univariate Cox regression analysis. Patients were divided into high-risk group and low-risk group by the 11-gene signature. Patients with high-risk scores had a higher probability of death earlier than the low-risk group both in the TCGA construction cohort and in the GEO validation cohort (all P < 0.001). Meanwhile, the risk score was proved to be an independent predictor for OS in both univariate and multivariate Cox regression analyses (HR > 1, P < 0.01). The predictive efficacy of the prognostic signature for OS was further verified by the time-dependent ROC curves. Moreover, we also enriched many immune-related biological processes in later functional analysis; the immune status showed a statistical difference between the two risk groups. In addition, the differences in expression levels of 11 core prognostic genes were examined in ferroptosis inducer-treated breast cancer cell lines.ConclusionIn conclusion, a novel ferroptosis-related gene model can be used for prognostic prediction in breast cancer. New ferroptosis-related genes may be used for breast cancer targeting therapy in the future.

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The prediction models for postoperative overall survival and disease-free survival in patients with breast cancer.

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The prediction models for postoperative overall survival and disease-free survival in patients with breast cancer.

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