Meta-analysis of 59 gene expression based biomarker candidates predicting survival after tamoxifen treatment in breast cancer.

2013 ◽  
Vol 31 (15_suppl) ◽  
pp. e11564-e11564
Author(s):  
Balazs Gyorffy ◽  
Mate Kormos ◽  
Andras Lanczky ◽  
Zsuzsanna Mihaly
Keyword(s):  
Breast Cancer ◽  
Overall Survival ◽  
Meta Analysis ◽  
Statistical Significance ◽  
Tamoxifen Treatment ◽  
Endocrine Treatment ◽  
Relapse Free Survival ◽  
Free Survival ◽  
Kaplan Meier ◽  
Microarray Datasets

e11564 Background: To date, three molecular markers (ER, PR and CYP2D6) have been used in clinical setting to predict the benefit of the anti-estrogen tamoxifen therapy. Our aim was to validate new biomarker candidates predicting response to tamoxifen treatment by evaluating these in a meta-analysis of available microarray datasets with known treatment and follow-up. Methods: Biomarker candidates were identified in Pubmed 2007-2012 and in the 2010-2012 ASCO and SABCS abstracts. Breast cancer microarray datasets were downloaded from GEO and EGA. Of the biomarker candidates, only those identified or already validated in a clinical cohort were included. In the transcriptomic datasets, only patients with tamoxifen treatment for relapse-free survival and endocrine treatment for overall survival were eligible. The raw microarray data was re-processed and integrated into two databases. Relapse free survival (RFS) up to 5 years was used as endpoint in a ROC analysis in the GEO datasets. In the EGA dataset, Kaplan-Meier analysis was performed for overall survival (OS). Statistical significance was set at p<0.01. Results: The transcriptomic datasets included 667 GEO-based and 1208 EGA-based patient samples. All together 59 biomarker candidates were identified. Of these, the best performing genes were PGR (AUC=0.64, p=2.3E-07), MAPT (AUC=0.62, p=7.8E-05), SLC7A5 (AUC=0.62, p=9.2E-05) and TP53 (AUC=0.60, p=1.2E-03). Further genes significantly correlated to relapse-free survival include BTG2, HOXB7, DRG1, CXCL10, BCL2, TPM4, IGF1R and SMC3. Correlation to overall survival was significant for PGR (HR=0.67, p=1.7E-04), MAPT (HR=0.7, p=7.2E-04) and SLC7A5 (HR=1.6, p=1.6E-05). None of the remaining genes including ESR1 reached statistical significance for relapse-free survival. Conclusions: We validated two genes (MAPT and SLC7A5) as being capable to select those patients most likely benefit from tamoxifen treatment.

scholarly journals The Alterations and Potential Roles of MCMs in Breast Cancer

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Xinyu Liu ◽  
Ying Liu ◽  
Qiangshan Wang ◽  
Siqi Song ◽  
Lingjun Feng ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Network ◽  
Relapse Free Survival ◽  
Minichromosome Maintenance ◽  
Free Survival ◽  
Oncomine Database ◽  
Kaplan Meier ◽  
Eukaryotic Dna ◽  
Coexpressed Genes ◽  
Kaplan Meier Plotter

The minichromosome maintenance (MCM) protein family plays a key role in eukaryotic DNA replication and has been confirmed to be associated with the occurrence and progression of many tumors. However, the expression levels, functions, and prognostic values of MCMs in breast cancer (BC) have not been clearly and systematically explained. In this article, we studied the transcriptional levels of MCMs in BC based on the Oncomine database. Kaplan-Meier plotter was used to analyze prognostic value of MCMs in human BC patients. Furthermore, we constructed a MCM coexpression gene network and performed functional annotation analysis through DAVID to reveal the functions of MCMs and coexpressed genes. The data showed that the expression of MCM2–8 and MCM10 but not MCM1 and MCM9 was upregulated in BC. Kaplan-Meier plotter analysis revealed that high transcriptional levels of MCM2, MCM4–7, and MCM10 were significantly related to low relapse-free survival (RFS) in BC patients. In contrast, high levels of MCM1 and MCM9 predicted high RFS for BC patients. This study suggests that MCM2, MCM4–7, and MCM10 possess great potential to be valuable prognostic biomarkers for BC and that MCM1 and MCM9 may serve as potential treatment targets for BC patients.

scholarly journals Immunohistochemical analysis of CD155 expression in triple-negative breast cancer patients

PLoS ONE ◽  
2021 ◽  
Vol 16 (6) ◽  
pp. e0253176
Author(s):  
Katsuhiro Yoshikawa ◽  
Mitsuaki Ishida ◽  
Hirotsugu Yanai ◽  
Koji Tsuta ◽  
Mitsugu Sekimoto ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Overall Survival ◽  
Tumor Cells ◽  
Triple Negative Breast Cancer ◽  
Triple Negative ◽  
Expression Profiles ◽  
Histological Grade ◽  
Relapse Free Survival ◽  
Free Survival ◽  
Antitumor Immunotherapy

Introduction CD155 is an immune checkpoint protein. Its overexpression is an indicator of poor prognosis in some types of cancer. However, the significance of CD155 expression in patients with triple-negative breast cancer, and the relationship between CD155 and programmed death-ligand 1 (PD-L1) expression, have not yet been analyzed in detail. Methods Using immunohistochemical staining and tissue microarrays, we analyzed the expression profiles of CD155 and PD-L1 in 61 patients with triple-negative breast cancer. Relapse-free survival and overall survival rates were compared according to CD155 expression. The correlation between CD155 expression and clinicopathological factors, including PD-L1 expression (using SP142 and 73–10 assays), was also examined. Results CD155 expression was noted in 25 patients (41.0%) in this cohort. CD155 expression did not correlate with pathological stage, histological grade, Ki-67 labeling index, or stromal tumor-infiltrating lymphocytes. Only PD-L1 expression in tumor cells by SP142 assay significantly correlated with CD155 expression (p = 0.035); however, PD-L1 expression in tumor cells by 73–10 assay did not show a correlation (p = 0.115). Using the 73–10 assay, 59% of patients showed CD155 and/or PD-L1 expression in tumor cells. Moreover, using the SP142 assay, 63.3% of patients showed CD155 and/or PD-L1 expression in immune cells. CD155 expression did not correlate with either relapse-free survival or overall survival (p = 0.485 and 0.843, respectively). Conclusions CD155 may be a novel target for antitumor immunotherapy. The results of this study indicate that CD155 may expand the pool of candidates with triple-negative breast cancer who could benefit from antitumor immunotherapy.

Pepsinogen C is a new prognostic marker in primary breast cancer.

1995 ◽  
Vol 13 (1) ◽  
pp. 54-61 ◽  
Author(s):  
F Vizoso ◽  
L M Sánchez ◽  
I Díez-Itza ◽  
A M Merino ◽  
C López-Otín
Keyword(s):  
Breast Cancer ◽  
Overall Survival ◽  
Multivariate Analysis ◽  
Prognostic Value ◽  
Survival Duration ◽  
Relapse Free Survival ◽  
Free Survival ◽  
Node Negative ◽  
Pepsinogen C ◽  
Node Positive

PURPOSE Here we evaluate in breast cancer patients the prognostic value of pepsinogen C, a proteolytic enzyme involved in the digestion of proteins in the stomach that is also synthesized by a significant percentage of breast carcinomas. PATIENTS AND METHODS Pepsinogen C expression was examined by immunoperoxidase staining in a series of 243 breast cancer tissue sections, and results obtained were quantified using the HSCORE system, which considers both the intensity and the percentage of cells staining at each intensity. Evaluation of the prognostic value of pepsinogen C was performed retrospectively in corresponding patients by multivariate analysis that took into account conventional prognostic factors. The mean follow-up period was 48.5 months. RESULTS A total of 113 carcinomas (46.5%) stained positively for this proteinase, but there were clear differences among them with regard to the intensity and percentage of stained cells. Pepsinogen C values were significantly higher in well differentiated (grade I, 89.1) and moderately differentiated (grade II, 88.5) tumors than in poorly differentiated (grade III, 27.7) tumors (P < .001). Similarly, significant differences in pepsinogen C content were found between estrogen receptor (ER)-positive tumors and ER-negative tumors (85.9 v 41.2, respectively; P < .05). Moreover, results indicated that low pepsinogen C content predicted shorter relapse-free survival duration and overall survival duration (P < .0001). Separate Cox multivariate analysis for relapse-free survival and overall survival in subgroups of patients as defined by node status showed that pepsinogen C expression was the strongest factor to predict both relapse-free survival and overall survival in node-positive patients (P < .0001 for both) and node-negative patients (P < .005 and P < .01, respectively). CONCLUSION Pepsinogen C is a new prognostic factor for early recurrence and death in both node-positive and node-negative breast cancer. In addition, and in contrast to most studies that concern the prognostic significance of proteolytic enzymes in cancer, pepsinogen C production by breast cancer cells is associated with lesions of favorable evolution.

Chromosome 17 Abnormalities Are Associated with Worse Overall and Relapse Free Survival in Patients with Acute Myelogenous Leukemia and Poor-Risk Cytogenetics.

Blood ◽  
2008 ◽  
Vol 112 (11) ◽  
pp. 1501-1501
Author(s):  
Gautam Borthakur ◽  
Constantine Tam ◽  
Hagop Kantarjian ◽  
E. Lin ◽  
Jorge Cortes ◽  
...  
Keyword(s):  
Overall Survival ◽  
Acute Myelogenous Leukemia ◽  
Myelogenous Leukemia ◽  
Chromosome 17 ◽  
Relapse Free Survival ◽  
Free Survival ◽  
Cytogenetic Abnormalities ◽  
Kaplan Meier ◽  
Variate Analysis ◽  
Acute Myelogenous

Abstract Purpose: Chromosome 17 abnormalities define a group of patients with acute myelogenous leukemia (AML) (Nahi, H. et al. Leukemia and Lymphoma2008;49:508) with poor outcomes. We analyzed the additional impact of chromosome 17 abnormalities (−17, −17p, −17q, der17) among patients with AML and cytogenetic abnormalities traditionally considered to be of adverse prognosis. Patients and Methods: 1086 patients with AML [excluding inv 16, t (8;21), t (15;17), Diploid/-y abnormality] were included in this analysis. Based on cytogenetic abnormalities patients were grouped into: −5,−7,−5and −7, complex. The following parameters were included in uni and multi-variate analysis: age, performance status, WBC, hemoglobin, platelets, marrow blast percentage, bilirubin, creatinine, albumin, LDH, chromosome 17 abnormality (yes/no). Results: Four hundred and fourteen (45%) patients achieved complete remission (CR) or CR with incomplete platelet recovery (CRp) and 267 (64.5%) patients relapsed. Two hundred seventy (24.9%) patients had abnormalities of chromosome 17. Abnormalities of chromosome 17 were associated with lower CR or CRp rate (p=0.02) and higher possibility of having cytogenetic abnormality of −5 or −7 (p&lt;0.0001). Multivariate analysis showed that patients with abnormalities of chromosome 17 had worse overall survival (OS) compared to patients without (p= 0.003)(Fig.1). Multi-variate analysis within cytogenetic subgroups showed that chromosome 17 abnormalities were associated with worse OS in patients with chromosome 5 abnormality(p=.02) (data not shown) and in those with complex cytogenetics (p=.04)(Fig.2) and not in patients with chromosome 7 (p=.17)or combined 5 and 7 abnormalities (p=.33). Similar analysis restricted to patients achieving CR/CRp after induction therapy showed that impact of chromosome 17 abnormalities on relapse free survival (RFS) mirrored their impact on OS. Conclusion: chromosome 17 abnormalities are associated with worse OS and RFS in patients with AML and adverse cytogenetics and have additional negative impact on the outcomes in certain well-known adverse cytogenetic subgroups. Figure 1: Kaplan-Meier estimates of overall survival by status of chromosome 17 Figure 1:. Kaplan-Meier estimates of overall survival by status of chromosome 17 Figure 2: Kaplan-Meier estimates of overall survival by status of chromosome 17 in subgroup of patient, complex Figure 2:. Kaplan-Meier estimates of overall survival by status of chromosome 17 in subgroup of patient, complex

scholarly journals Differential expression of the DREAM MuvB core complex component Lin37 in cancers of the breast.

2020 ◽  
Author(s):  
Shahan Mamoor
Keyword(s):  
Breast Cancer ◽  
Differential Expression ◽  
Normal Breast ◽  
Core Complex ◽  
Relapse Free Survival ◽  
Gene Encoding ◽  
Significant Differential Expression ◽  
Primary Tumors ◽  
Free Survival ◽  
Microarray Datasets

Breast cancer affects women at relatively high frequency (1). We mined published microarray datasets (2, 3) to determine in an unbiased fashion and at the systems level genes most differentially expressed in the primary tumors of patients with breast cancer. We report here significant differential expression of the gene encoding the MuvB core complex and DREAM component Lin37 when comparing primary tumors of the breast to the tissue of origin, the normal breast. Expression of Lin37 in primary tumors of the breast was significantly higher than in normal breast tissue, and patients with low expression of Lin37 in primary tumors had greater relapse-free survival. Lin37 may be of relevance to initiation, maintenance or progression of cancers of the female breast.

scholarly journals Efficacy and safety of Nab-paclitaxel in breast cancer: a meta-analysis

2019 ◽  
Author(s):  
Upendra Yadav ◽  
Pradeep Kumar ◽  
Vandana Rai
Keyword(s):  
Breast Cancer ◽  
Overall Survival ◽  
Side Effects ◽  
Cancer Treatment ◽  
Meta Analysis ◽  
Progression Free Survival ◽  
Breast Cancer Treatment ◽  
Efficacy And Safety ◽  
Free Survival ◽  
Key Terms

AbstractWorldwide breast cancer is the leading cause of cancer related death in women. Paclitaxel is an effective drug used for the treatment of breast cancer but it has many side effects. Nab-paclitaxel (nanoparticle albumin-bound paclitaxel) is an FDA approved drug for the treatment of breast cancer. Currently many clinical trials are conducted to deliver nab-paclitaxel into the tumor cells. But the efficacy and safety of this nab-paclitaxel over conventional paclitaxel still remains questionable. So, we performed a meta-analysis to evaluate the efficacy and safety of nab-paclitaxel in breast cancer treatment.Electronic databases were searched for the suitable studies using key terms “nab-paclitaxel”, “paclitaxel”, and “clinical trial” with the combination of “breast cancer” up to August 11, 2019. Risk ratio (RR) and odds ratio (OR) with corresponding 95% confidence intervals (CIs) were calculated. All statistical analyses were performed by the Open Meta-Analyst program. A total of eight studies which fulfilled our criteria were included in this study. For efficacy we retrieved data of 12 months progression free survival, 24 months progression free survival, and overall survival (up to 3 years) and for the safety we took data of nausea, anemia, leukopenia, neutropenia, fatigue, diarrhea and pain.We did not found any difference in efficacy of nab-paclitaxel over paclitaxel (12 months progression free survival-RRFE= 0.86, 95%CI= 0.77-0.97, p= 0.02, I2= 25.07%; 24 months progression free survival-RRFE= 0.86, 95% CI= 0.64-1.16, p= 0.34, I2= 0%; and 3 years survival-RRFE= 1.20, 95%CI= 0.92-1.56, p= 0.16, I2= 37.55%). The meta-analysis of studies used nab-paclitaxel showed reduced adverse effect of anemia (ORFE= 1.66, 95% CI= 1.26-2.19; p= <0.001; I2= 0%) and leukopenia (ORFE= 1.37; 95%CI= 1.06-1.75; p= 0.01; I2= 48.63%). However, in case of other adverse effects no significant association was found with nab-paclitaxel (nausea-ORFE=1.15, 95%CI= 0.94-1.41, p= 0.15, I2= 50.12%; neutropenia-ORRE= 0.75, 95%CI= 0.30-1.87, p= 0.54, I2= 94.45%; fatigue-ORRE= 1.11, 95%CI= 0.77-1.62, p= 0.55, I2= 56.02; diarrhea-ORFE= 1.11, 95%CI= 0.77-1.62, p= 0.55; I2= 34.26; pain-ORRE= 1.15, 95%CI= 0.78-1.69, p= 0.45, I2= 52.96%).In conclusion the use of nab-paclitaxel has reduces the side effects of anemia and leukopenia in breast cancer treatment in comparison to paclitaxel but nab-paclitaxel has no effect on the overall survival of the patients.

scholarly journals Neural Network Analysis of Follow-Up Data in Primary Breast Cancer

2000 ◽  
Vol 15 (1) ◽  
pp. 116-122 ◽  
Author(s):  
N. Harbeck ◽  
R. Kates ◽  
K Ulm ◽  
H. Graeff ◽  
M. Schmitt
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Risk Group ◽  
Neural Network Analysis ◽  
Relapse Free Survival ◽  
Free Survival ◽  
Kaplan Meier ◽  
The Neural Network ◽  
Pai 1

This paper reports on the performance of a recently developed neural network environment incorporating likelihood-based optimization and complexity reduction techniques in the analysis of breast cancer follow-up data with the goal of building up a clinical decision support system. The inputs to the neural network include classical factors such as grading, age, tumor size, estrogen and progesterone receptor measurements, as well as tumor biological markers such as PAI-1 and uPA. The network learns the structural relationship between these factors and the follow-up data. Examples of neural models for relapse-free survival are presented, which are based on data from 784 breast cancer patients who received their primary therapy at the Department of Obstetrics and Gynecology, Technische Universität München, Germany. The performance of the neural analysis as quantified by various indicators (likelihood, Kaplan-Meier curves, log-rank tests) was very high. For example, dividing the patients into two equally sized groups based on the neural score (i.e., cutoff = median score) leads to an estimated difference in relapse-free survival of 40% or better (80% vs. 40%) after 10 years in Kaplan-Meier analysis. Evidence for factor interactions as well as for time-varying impacts is presented. The neural network weights included in the models are significant at the 5% level. The use of neural network analysis and scoring in combination with strong tumor biological factors such as uPA and PAI-1 appears to result in a very effective risk group discrimination. Considerable additional comparison of data from different patient series will be required to establish the generalization capability more firmly. Nonetheless, the improvement of risk group discrimination represents an important step toward the use of neural networks for decision support in a clinical framework and in making the most of biological markers.

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