Establishment and Validation of Prognostic Nomograms Based on Serum Copper Level for Patients With Early-Stage Triple-Negative Breast Cancer

Background: Altered copper levels have been observed in several cancers, but studies on the relationship between serum copper and early-stage triple-negative breast cancer (TNBC) remain scare. We sought to establish a predictive model incorporating serum copper levels for individualized survival predictions.Methods: We retrospectively analyzed clinicopathological information and baseline peripheric blood samples of patients diagnosed with early-stage TNBC between September 2005 and October 2016 at Sun Yat-sen University Cancer Center. The optimal cut-off point of serum copper level was determined using maximally selected log-rank statistics. Kaplan-Meier curves were used to estimate survival probabilities. Independent prognostic indicators associated with survival were identified using multivariate Cox regression analysis, and subsequently, prognostic nomograms were established to predict individualized disease-free survival (DFS) and overall survival (OS). The nomograms were validated in a separate cohort of 86 patients from the original randomized clinical trial SYSUCC-001 (SYSUCC-001 cohort).Results: 350 patients were eligible in this study, including 264 in the training cohort and 86 in the SYSUCC-001 cohort. An optimal cut-off value of 21.3 μmol/L of serum copper was determined to maximally divide patients into low- and high-copper groups. After a median follow-up of 87.1 months, patients with high copper levels had significantly worse DFS (p = 0.002) and OS (p < 0.001) than those with low copper levels in the training cohort. Multivariate Cox regression analysis revealed that serum copper level was an independent factor for DFS and OS. Further, prognostic models based on serum copper were established for individualized predictions. These models showed excellent discrimination [C-index for DFS: 0.689, 95% confidence interval (CI): 0.621–0.757; C-index for OS: 0.728, 95% CI: 0.654–0.802] and predictive calibration, and were validated in the SYSUCC-001 cohort.Conclusion: Serum copper level is a potential predictive biomarker for patients with early-stage TNBC. Predictive nomograms based on serum copper might be served as a practical tool for individualized prognostication.

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Analysis of CK5/6 and EGFR and Its Effect on Prognosis of Triple Negative Breast Cancer

Frontiers in Oncology ◽

10.3389/fonc.2020.575317 ◽

2021 ◽

Vol 10 ◽

Author(s):

Zhen Wang ◽

Lei Liu ◽

Ying Li ◽

Zi’an Song ◽

Yi Jing ◽

...

Keyword(s):

Breast Cancer ◽

Regression Analysis ◽

Neoadjuvant Chemotherapy ◽

Triple Negative Breast Cancer ◽

Triple Negative ◽

Cox Regression ◽

Multivariate Logistic Regression Analysis ◽

Pathological Examination ◽

Cox Regression Analysis ◽

Tumor Stages

BackgroundTriple-negative breast cancer (TNBC) is considered to be higher grade, more aggressive and have a poorer prognosis than other types of breast cancer. Discover biomarkers in TNBC for risk stratification and treatments that improve prognosis are in dire need.MethodsClinical data of 195 patients with triple negative breast cancer confirmed by pathological examination and received neoadjuvant chemotherapy (NAC) were collected. The expression levels of EGFR and CK5/6 were measured before and after NAC, and the relationship between EGFR and CK5/6 expression and its effect on prognosis of chemotherapy was analyzed.ResultsThe overall response rate (ORR) was 86.2% and the pathological complete remission rate (pCR) was 29.2%. Univariate and multivariate logistic regression analysis showed that cT (clinical Tumor stages) stage was an independent factor affecting chemotherapy outcome. Multivariate Cox regression analysis showed pCR, chemotherapy effect, ypT, ypN, histological grades, and post- NAC expression of CK5/6 significantly affected prognosis. The prognosis of CK5/6-positive patients after NAC was worse than that of CK5/6-negative patients (p=0.036). Changes in CK5/6 and EGFR expression did not significantly affect the effect of chemotherapy, but changes from positive to negative expression of these two markers are associated with a tendency to improve prognosis.ConclusionFor late-stage triple negative breast cancer patients receiving NAC, patients who achieved pCR had a better prognosis than those with non- pCR. Patients with the change in expression of EGFR and CK5/6 from positive to negative after neoadjuvant chemotherapy predicted a better prognosis than the change from negative to positive group.

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An 8-lncRNA signature predicts the survival of triple-negative breast cancer patients without germline BRCA1/2 mutation

Medical Research ◽

10.6913/mrhk.202109_3(3).0003 ◽

2021 ◽

Vol 3 (3) ◽

pp. 15-32

Author(s):

Minling LIU ◽

Wei DAI ◽

Mengyuan ZHU ◽

Xueying LI ◽

Min WEI ◽

...

Keyword(s):

Breast Cancer ◽

Overall Survival ◽

Triple Negative Breast Cancer ◽

Triple Negative ◽

Cox Regression ◽

The Cancer Genome Atlas ◽

Risk Scores ◽

Training Cohort ◽

Cox Regression Analysis ◽

Platinum Based Chemotherapy

Purpose: TNBC with germline BRCA1/2 mutation (gBRCAm) have higher sensitivity to DNA damaging agents including platinum-based chemotherapy and PARP inhibitors. But the treatment of TNBC without gBRCAm remains challenging. This study aimed to develop a long non-coding RNA (lncRNA) signature of TNBC patients without gBRCAm to improve risk stratification and optimize individualized treatment. Methods: 98 TNBC patients without gBRCAm were acquired from The Cancer Genome Atlas database. The univariable Cox regression analysis and LASSO Cox regression model were applied to establish an lncRNA signature in the training cohort. Then Kaplan–Meier survival curve and time-dependent ROC curve were used to validate the prognostic ability of the signature. The qPCR assay was performed to confirm the expressions and clinicopathological correlations of two potential lncRNAs HAGLROS and TONSL-AS1 in 30 paired clinical triple-negative breast cancer samples without gBRCAm. Results: We developed an 8-lncRNA signature in the training cohort including HAGLROS, AL139002.1, AL391244.2, AP000696.1, AL391056.1, AL513304.1, TONSL-AS1 and AL031008.1. Patients with higher risk scores showed significantly worse overall survival compared to those with lower risk scores (P=0.00018 and P =0.0068 respectively). 30 paired specimens of TNBC without gBRCAm in our center showed that two potential lncRNAs HAGLROS and TONSL-AS1 were found frequently overexpressed, and significantly associated with tumor grade and invasion. Conclusion: We constructed a novel 8-lncRNA signature which significantly associated with the overall survival of TNBC patients without gBRCAm. Among those 8 lncRNAs, HAGLROS and TONSL-AS1 may be potential therapeutic targets which function needed further exploration.

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Construction and Validation of a Prognostic Risk Model for Triple Negative Breast Cancer Based on Autophagy-Related Genes

10.21203/rs.3.rs-1141994/v1 ◽

2021 ◽

Author(s):

Cheng Yan ◽

Qingling Liu ◽

Ruoling Jia

Keyword(s):

Breast Cancer ◽

Overall Survival ◽

Regression Analysis ◽

Triple Negative Breast Cancer ◽

Triple Negative ◽

Risk Model ◽

Cox Regression ◽

Risk Group ◽

Lasso Regression ◽

Cox Regression Analysis

Abstract Background: Autophagy plays an important role in triple negative breast cancer (TNBC). However, the prognostic value of autophagy-related genes (ARGs) in TNBC remains unknown. In this study, we established a survival model to evaluate the prognosis of TNBC patients using ARGs signature.Methods: A total of 222 autophagy-related genes were downloaded from The Human Autophagy Database. The RNA-sequencing data and corresponding clinical data of TNBC were obtained from the TCGA database. Differential gene expression of ARGs (DE-ARGs) between normal samples and TNBC samples was determined by the EdgeR software package. Then, univariate Cox, Lasso, and multivariate Cox regression analyses were performed. According to the Lasso regression results based on univariate Cox, we identified a prognostic signature for overall-survival (OS), which was further validated by using GEO cohort. We also found an independent prognostic marker that can predict the clinicopathological features of TNBC. Furthermore, a nomogram was drawn to predict the survival probability of TNBC patients, which could help in clinical decision for TNBC treatment. Finally, we validated the requirement of a ARG in our model for TNBC cell survival and metastasis.Results: There are 43 differentially expressed ARGs (DE-ARGs) were identified between normal and tumor samples. A risk model for OS using CDKN1A, CTSD, CTSL, EIF4EBP1, TMEM74 and VAMP3 by Lasso regression analysis was established based on univariate Cox regression analysis. Overall survival of TNBC patients was significantly shorter in the high-risk group than in the low-risk group for both the training and validation cohorts. Using the Kaplan-Meier curves and ROC curves, we demonstrated the accuracy of the prognostic model. Multivariate Cox regression analysis was used to verify risk score as independent predictor. Then a nomogram was proposed to predict 1-, 3-, and 5-year survival for TNBC patients. The calibration curves showed great accuracy of the model for survival prediction. Finally, we found that depletion of EIF4EBP1, one of ARGs in our model, significantly reduced cell proliferation and metastasis of TNBC cells. Conclusion: An autophagy-related prognosis model in TNBCs was constructed using ARGs signature containing CDKN1A, CTSD, CTSL, EIF4EBP1, TMEM74 and VAMP3. It could serve as an independent prognostic biomarker in TNBC.

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SCUBE3 Serves as an Independent Poor Prognostic Factor in Breast Cancer

10.21203/rs.3.rs-129257/v1 ◽

2020 ◽

Author(s):

Qin Huo ◽

Xi He ◽

Zhenwei Li ◽

Fan Yang ◽

Shengnan He ◽

...

Keyword(s):

Breast Cancer ◽

Regression Analysis ◽

Prognostic Factor ◽

Poor Prognosis ◽

Triple Negative ◽

Cox Regression ◽

Functional Enrichment ◽

High Expression ◽

Cox Regression Analysis ◽

Expression Levels

Abstract Background: Accumulating evidences indicate that the signal peptide-CUB-EGF-like domain-containing protein 3 (SCUBE3) plays a key role in the development and progression of many human cancers. However, the underlying mechanism and prognosis value of SCUBE3 in breast cancer are still unclear. Methods: The clinical data of 137 patients with breast cancer who underwent surgical resection in Taizhou Hospital of Zhejiang Province were retrospectively analyzed. We first conducted a comprehensive study on the expression pattern of SCUBE3 using the Tumor Immune Estimation Resource (TIMER) and UALCAN databases. In addition, the expression of SCUBE3 in breast tumor tissues was confirmed by immunohistochemistry. The protein-protein interaction analysis and functional enrichment analysis of SCUBE3 were analyzed using the STRING and Enrichr databases. Moreover, tissue microarray (TMA) was used to analyze the relationship between SCUBE3 expression levels and clinical-pathological parameters, such as histological type, grade, the status of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor (HER2). We further supplemented and identified the above results using the UALCAN and bc-GenExMiner v4.4 databases from TCGA data. The correlation between the expression of SCUBE3 and survival was calculated by univariate Cox regression analysis to investigate whether SCUBE3 expression may be an independent prognostic factor of breast cancer. Results: We found that the expression level of SCUBE3 was significantly upregulated in breast cancer tissue compared with adjacent normal tissues. SCUBE3 expression was significantly correlated with ER (p = 0.018), PR (p = 0.015), HER2 (p = 0.039), and triple-negative status (p = 0.049) of patients with breast cancer. We further verified that the expression levels of SCUBE3 mRNA was significantly correlated with ER (p = 0.0007), PR (p = 0.0295), and triple-negative (p = 0.0024), respectively. There was no significant expression difference in the expression of SCUBE3 mRNA in age, HER2 receptor status, and nodal status (p > 0.05). In addition, the high expression of SCUBE3 was associated with relatively poor prognosis of ER- (p = 0.012), PR- (p = 0.029), HER2+ (p = 0.007). The univariate Cox regression analysis showed that the hazard ratio (HR) was 2.80 (95% CI: 1.20-6.51, p = 0.0168) in individuals with high SCUBE3 expression, and HR was increased by 1.86 (95% CI: 1.06-3.25, p = 0.0300) for per 1-point increase of SCUBE3 expression.Conclusions: These findings demonstrate that the high expression of SCUBE3 indicates poor prognosis in breast cancer. SCUBE3 expression may serve as a potential diagnostic indicator of breast cancer.

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De-escalation of five years adjuvant endocrine therapy duration in patients with ER-low positive (immunohistochemical 1% to 10%) early-stage breast cancer: A propensity-matched analysis from the prospectively maintained database.

Journal of Clinical Oncology ◽

10.1200/jco.2021.39.15_suppl.538 ◽

2021 ◽

Vol 39 (15_suppl) ◽

pp. 538-538

Author(s):

Keda Yu ◽

Yuwen Cai ◽

Zhiming Shao

Keyword(s):

Breast Cancer ◽

Regression Analysis ◽

Propensity Score ◽

Endocrine Therapy ◽

Cox Regression ◽

Early Stage ◽

Adjuvant Endocrine Therapy ◽

Short Term ◽

Cox Regression Analysis ◽

Positive Breast Cancer

538 Background: There are limited data on endocrine therapy benefits for patients with estrogen receptor (ER)-low positive breast cancer (staining 1% to 10% of tumor nuclei by immunohistology). We aimed to compare the effect of short-term 2-3 years versus standard 5 years of adjuvant endocrine therapy on survival outcomes in patients with ER-low positive early breast cancer. Methods: We used data from the prospectively maintained Breast Surgery Database of Fudan University Shanghai Cancer Center for this propensity-matched analysis. Women with ER-low positive, operable, and unilateral early-stage invasive ductal breast cancer were enrolled in this study. Patients with advanced disease, having received neoadjuvant chemotherapy or ovarian function suppression, or with unknown duration or longer than 5 years of adjuvant endocrine therapy were excluded. Enrolled patients were divided into three groups: received no endocrine therapy; received 2-3 years of endocrine therapy; and received approximately 5 years of endocrine therapy. The primary endpoint was disease-free survival (DFS). Multivariate Cox regression analysis and propensity score matching were performed to minimize bias. Hazard ratios (HR) with 95% CIs were calculated. Results: From 2012 to 2017, 634 patients with ER-low positive breast cancer in the database met the inclusion criteria. At a median follow-up of 60 months (interquartile range, 46-74), the 5-year DFS of the whole cohort was 84.9%, with 77.6% for patients who received no endocrine therapy (N = 89), 83.7% for patients who received 2-3 years endocrine treatment (N = 185), and 87.5% for patients who received 5 years endocrine therapy (N = 360). When compared with those receiving no endocrine therapy, patients receiving 5 years treatment was associated with a significantly improved DFS (HR, 0.55; 95% CI 0.32-0.95; P = 0.03); however, there was no significant difference in DFS between patients receiving 2-3 years and 5 years endocrine therapy (HR, 0.79; 95% CI, 0.48-1.28; P = 0.33). In the multivariate Cox regression analysis of the propensity score-matched samples of 360 patients, the DFS was not significantly better for patients who received 5 years of endocrine therapy than 2-3 years treatment (HR, 0.74; 95% CI 0.41-1.34; P = 0.32). An exploratory analysis of re-biopsy of the recurrence lesions indicated more than half of relapsed disease displayed ER-negative, and less than 5% lesions were proved to be ER ≥10% positive. Conclusions: Our data did not support the necessity of 5 years duration of endocrine therapy for patients with ER-low positive breast cancer. Short-term 2-3 years duration might be an alternative option. Further translational research on identifying endocrine-sensitive cases within ER-low positive patients is needed.

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Identification and development of an independent immune-related genes prognostic model for breast cancer

BMC Cancer ◽

10.1186/s12885-021-08041-x ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Lin Chen ◽

Yuxiang Dong ◽

Yitong Pan ◽

Yuhan Zhang ◽

Ping Liu ◽

...

Keyword(s):

Breast Cancer ◽

Regression Analysis ◽

Cox Regression ◽

Gene Set Enrichment Analysis ◽

Risk Scores ◽

Validation Dataset ◽

Immune Gene ◽

Cox Regression Analysis ◽

Immune Genes ◽

Immune Related Genes

Abstract Background Breast cancer is one of the main malignant tumors that threaten the lives of women, which has received more and more clinical attention worldwide. There are increasing evidences showing that the immune micro-environment of breast cancer (BC) seriously affects the clinical outcome. This study aims to explore the role of tumor immune genes in the prognosis of BC patients and construct an immune-related genes prognostic index. Methods The list of 2498 immune genes was obtained from ImmPort database. In addition, gene expression data and clinical characteristics data of BC patients were also obtained from the TCGA database. The prognostic correlation of the differential genes was analyzed through Survival package. Cox regression analysis was performed to analyze the prognostic effect of immune genes. According to the regression coefficients of prognostic immune genes in regression analysis, an immune risk scores model was established. Gene set enrichment analysis (GSEA) was performed to probe the biological correlation of immune gene scores. P < 0.05 was considered to be statistically significant. Results In total, 556 immune genes were differentially expressed between normal tissues and BC tissues (p < 0. 05). According to the univariate cox regression analysis, a total of 66 immune genes were statistically significant for survival risk, of which 30 were associated with overall survival (P < 0.05). Finally, a 15 immune genes risk scores model was established. All patients were divided into high- and low-groups. KM survival analysis revealed that high immune risk scores represented worse survival (p < 0.001). ROC curve indicated that the immune genes risk scores model had a good reliability in predicting prognosis (5-year OS, AUC = 0.752). The established risk model showed splendid AUC value in the validation dataset (3-year over survival (OS) AUC = 0.685, 5-year OS AUC = 0.717, P = 0.00048). Moreover, the immune risk signature was proved to be an independent prognostic factor for BC patients. Finally, it was found that 15 immune genes and risk scores had significant clinical correlations, and were involved in a variety of carcinogenic pathways. Conclusion In conclusion, our study provides a new perspective for the expression of immune genes in BC. The constructed model has potential value for the prognostic prediction of BC patients and may provide some references for the clinical precision immunotherapy of patients.

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The Investigation of Associations between TP53 rs1042522, BBC3 rs2032809, CCND1 rs9344, EGFR rs2227983 Polymorphisms and Breast Cancer Phenotype and Prognosis

Diagnostics ◽

10.3390/diagnostics11081419 ◽

2021 ◽

Vol 11 (8) ◽

pp. 1419

Author(s):

Justina Bekampytė ◽

Agnė Bartnykaitė ◽

Aistė Savukaitytė ◽

Rasa Ugenskienė ◽

Erika Korobeinikova ◽

...

Keyword(s):

Breast Cancer ◽

Regression Analysis ◽

Cox Regression ◽

Cancer Prognosis ◽

Nucleotide Polymorphisms ◽

Single Nucleotide ◽

Cox Regression Analysis ◽

Pcr Rflp ◽

Oncological Diseases ◽

Cancer Phenotype

Breast cancer is one of the most common oncological diseases among women worldwide. Cell cycle and apoptosis—related genes TP53, BBC3, CCND1 and EGFR play an important role in the pathogenesis of breast cancer. However, the roles of single nucleotide polymorphisms (SNPs) in these genes have not been fully defined. Therefore, this study aimed to analyze the association between TP53 rs1042522, BBC3 rs2032809, CCND1 rs9344 and EGFR rs2227983 polymorphisms and breast cancer phenotype and prognosis. For the purpose of the analysis, 171 Lithuanian women were enrolled. Genomic DNA was extracted from peripheral blood; PCR-RFLP was used for SNPs analysis. The results showed that BBC3 rs2032809 was associated with age at the time of diagnosis, disease progression, metastasis and death. CCND1 rs9344 was associated with tumor size, however an association resulted in loss of significance after Bonferroni correction. In survival analysis, significant associations were observed between BBC3 rs2032809 and OS, PFS and MFS. EGFR rs2227983 also showed some associations with OS and PFS (univariate Cox regression analysis). However, the results were in loss of significance (multivariate Cox regression analysis). In conclusion, BBC3 rs2032809 polymorphism was associated with breast cancer phenotype and prognosis. Therefore, it could be applied as potential markers for breast cancer prognosis.

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Factors Predictive of Distant Metastases in Patients With Breast Cancer Who Have a Pathologic Complete Response After Neoadjuvant Chemotherapy

Journal of Clinical Oncology ◽

10.1200/jco.2005.11.124 ◽

2005 ◽

Vol 23 (28) ◽

pp. 7098-7104 ◽

Cited By ~ 78

Author(s):

Ana M. Gonzalez-Angulo ◽

Sean E. McGuire ◽

Thomas A. Buchholz ◽

Susan L. Tucker ◽

Henry M. Kuerer ◽

...

Keyword(s):

Breast Cancer ◽

Regression Analysis ◽

Neoadjuvant Chemotherapy ◽

Distant Metastasis ◽

Cox Regression ◽

Clinical Stage ◽

Pathologic Complete Response ◽

Distant Metastases ◽

Complete Response ◽

Cox Regression Analysis

Purpose To identify clinicopathological factors predictive of distant metastasis in patients who had a pathologic complete response (pCR) after neoadjuvant chemotherapy (NC). Methods Retrospective review of 226 patients at our institution identified as having a pCR was performed. Clinical stage at diagnosis was I (2%), II (36%), IIIA (27%), IIIB (23%), and IIIC (12%). Eleven percent of all patients were inflammatory breast cancers (IBC). Ninety-five percent received anthracycline-based chemotherapy; 42% also received taxane-based therapy. The relationship of distant metastasis with clinicopathologic factors was evaluated, and Cox regression analysis was performed to identify independent predictors of development of distant metastasis. Results Median follow-up was 63 months. There were 31 distant metastases. Ten-year distant metastasis-free rate was 82%. Multivariate Cox regression analysis using combined stage revealed that clinical stages IIIB, IIIC, and IBC (hazard ratio [HR], 4.24; 95% CI, 1.96 to 9.18; P < .0001), identification of ≤ 10 lymph nodes (HR, 2.94; 95% CI, 1.40 to 6.15; P = .004), and premenopausal status (HR, 3.08; 95% CI, 1.25 to 7.59; P = .015) predicted for distant metastasis. Freedom from distant metastasis at 10 years was 97% for no factors, 88% for one factor, 77% for two factors, and 31% for three factors (P < .0001). Conclusion A small percentage of breast cancer patients with pCR experience recurrence. We identified factors that independently predicted for distant metastasis development. Our data suggest that premenopausal patients with advanced local disease and suboptimal axillary node evaluation may be candidates for clinical trials to determine whether more aggressive or investigational adjuvant therapy will be of benefit.

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Immune-related gene based prognostic signature for the risk stratification analysis of breast cancer

Current Bioinformatics ◽

10.2174/1574893616666211005110732 ◽

2021 ◽

Vol 16 ◽

Author(s):

Dongqing Su ◽

Qianzi Lu ◽

Yi Pan ◽

Yao Yu ◽

Shiyuan Wang ◽

...

Keyword(s):

Breast Cancer ◽

Regression Analysis ◽

Cancer Patients ◽

Risk Score ◽

Cox Regression ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Breast Cancer Patients ◽

Cox Regression Analysis ◽

Score Model

Background: Breast cancer has plagued women for many years and caused many deaths around the world. Method: In this study, based on the weighted correlation network analysis, univariate Cox regression analysis and least absolute shrinkage and selection operator, 12 immune-related genes were selected to construct the risk score for breast cancer patients. The multivariable Cox regression analysis, gene set enrichment analysis and nomogram were also conducted in this study. Results: Good results were obtained in the survival analysis, enrichment analysis, multivariable Cox regression analysis and immune-related feature analysis. When the risk score model was applied in 22 breast cancer cohorts, the univariate Cox regression analysis demonstrated that the risk score model was significantly associated with overall survival in most of the breast cancer cohorts. Conclusion: Based on these results, we could conclude that the proposed risk score model may be a promising method, and may improve the treatment stratification of breast cancer patients in the future work.

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