Biological and clinical parameters in breast cancer: A multifactorial analysis

1987 ◽  
Vol 28 ◽  
pp. 20
Author(s):  
T. Coialbu ◽  
R. Tatarek ◽  
S. Bonassi ◽  
G. Nicolò ◽  
S. Toma
Keyword(s):  
Breast Cancer ◽  
Clinical Parameters ◽  
Multifactorial Analysis

Impact of adjuvant radiotherapy on biological and clinical parameters in right-sided breast cancer

2021 ◽  
Author(s):  
D.C. Lauffer ◽  
P. Miglierini ◽  
P.A. Kuhn ◽  
S.U. Thalmann ◽  
N. Gutierres-Demierre ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Adjuvant Radiotherapy ◽  
Clinical Parameters

Race, insurance status and treating facility type associated with completeness of medical record in patients with breast cancer.

2021 ◽  
Vol 39 (28_suppl) ◽  
pp. 143-143
Author(s):  
Marita Yaghi ◽  
Nadeem Bilani ◽  
Iktej Jabbal ◽  
Leah Elson ◽  
Maroun Bou Zerdan ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Missing Data ◽  
Medical Record ◽  
Real World ◽  
Private Insurance ◽  
Insurance Status ◽  
Clinical Parameters ◽  
Facility Type ◽  
Missing Variables ◽  
White Patients

143 Background: The National Cancer Database (NCDB) is a large registry that collates real-world medical record data from millions of patients in the United States. A previous published study using the NCDB found that gaps in the medical record were associated with worse overall survival outcomes. We investigated cases of breast cancer in this registry to understand which factors were predictive of records with missing data. Methods: We screened for missing data in 54 clinical parameters documented by the NCDB pertaining to the diagnosis, workup, management and survival of patients with breast cancer diagnosed between 2004 and 2017. We performed univariate statistics to describe gaps in the dataset, followed by multivariate logistic regression modeling to identify factors associated lack of completeness of the medical record – defined as the presence of > 3 missing variables. Results: A total of n = 2,981,732 patients were included in this analysis. The median number of missing variables per record was 3 (5.6% of clinical parameters surveyed). 52.1% of records had ≤ 3 variables missing, while 47.9% had > 3 variables missing. Predictors of a record with missing data in > 3 variables were: age, race, insurance status and facility type . Regarding race, we found that records of Asian patients were less likely to have missing data as compared to records of White patients (OR 0.75, 95% CI: 0.74-0.76, p < 0.001). Conversely, there was no difference in completeness of the medical record between Black and White patients (OR 0.99, 95% CI: 0.99-1.01, p = 0.890). Patients with private insurance (OR 0.77, 95% CI 0.76-0.79, p < 0.001), or Medicaid (OR 0.65, 95% CI 0.64-0.67, p < 0.001) or Medicare (OR 0.66, 95% CI 0.64-0.67, p < 0.001) were also less likely to have missing data compared to uninsured patients, with patients on private insurance being the least likely to have incomplete records. Finally, patient records from academic programs (OR 0.91, 95% CI 0.90-0.92, p < 0.001) were less likely to contain > 3 missing variables compared to records from patients treated at community cancer programs. Conclusions: Despite high fidelity of NCDB data, social determinants of health including insurance status and treating facility type, were associated with differences in the completeness of the medical record. Improvements in documentation and data quality are necessary to optimize use of real-world data in cancer registries. Further research is needed to determine how these differences could be independently associated with inferior outcomes.

LEPTOMENINGEAL CARCINOMATOSIS IN PATIENTS WITH BREAST CANCER: PATHOLOGICAL, CLINICAL PARAMETERS AND OUTCOME. A SINGLE INSTITUTION’S EXPERIENCE

The Breast ◽  
2019 ◽  
Vol 48 ◽  
pp. S75-S76
Author(s):  
Nektarios Alevizopoulos ◽  
Konstantinos Folinas ◽  
Theodoros Tegos ◽  
Areti Dimitriadou ◽  
Michail Pavlakis ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Leptomeningeal Carcinomatosis ◽  
Clinical Parameters

17β-hydroxysteroid dehydrogenase in human breast cancer: Analysis of kinetic and clinical parameters

Steroids ◽  
1988 ◽  
Vol 51 (3-4) ◽  
pp. 299-316 ◽  
Author(s):  
Donald Leszczynski ◽  
Steven J. Santner ◽  
Peter D. Feil ◽  
Richard J. Santen
Keyword(s):  
Breast Cancer ◽  
Human Breast Cancer ◽  
Hydroxysteroid Dehydrogenase ◽  
Clinical Parameters ◽  
Human Breast

scholarly journals Comprehensive Analysis of Metabolic Genes in Breast Cancer Based on Multi-Omics Data

2021 ◽  
Vol 27 ◽  
Author(s):  
Yu Hua ◽  
Lihong Gao ◽  
Xiaobo Li
Keyword(s):  
Breast Cancer ◽  
Dna Methylation ◽  
Arachidonic Acid ◽  
Copy Number Variation ◽  
Immune Cells ◽  
Copy Number ◽  
Clinical Parameters ◽  
Omics Data ◽  
Metabolic Genes ◽  
Number Variation

Background: Reprogramming of cell metabolism is one of the most important hallmarks of breast cancer. This study aimed to comprehensively analyze metabolic genes in the initiation, progression, and prognosis of breast cancer.Materials and Methods: Data from The Cancer Genome Atlas (TCGA) in breast cancer were downloaded including RNA-seq, copy number variation, mutation, and DNA methylation. A gene co-expression network was constructed by the weighted correlation network analysis (WGCNA) package in R. Association of metabolic genes with tumor-related immune cells and clinical parameters were also investigated.Results: We summarized 3,620 metabolic genes and observed mutations in 2,964 genes, of which the most frequently mutated were PIK3CA (51%), TNN (26%), and KMT2C (15%). Four genes (AKT1, ERBB2, KMT2C, and USP34) were associated with survival of breast cancer. Significant association was detected in the tumor mutation burden (TMB) of metabolic genes with T stage (p = 0.045) and N stage (p = 0.004). Copy number variations were significantly associated with recurrence and prognosis of breast cancer. The co-expression network for differentially expressed metabolic genes by WGCNA suggested that the modules were associated with glycerophospholipid, arachidonic acid, carbon, glycolysis/gluconeogenesis, and pyrimidine/purine metabolism. Glycerophospholipid metabolism correlated with most of the immune cells, while arachidonic acid metabolism demonstrated a significant correlation with endothelial cells. Methylation and miRNA jointly regulated 14 metabolic genes while mutation and methylation jointly regulated PIK3R1.Conclusion: Based on multi-omics data of somatic mutation, copy number variation, mRNA expression, miRNA expression, and DNA methylation, we identified a series of differentially expressed metabolic genes. Metabolic genes are associated with tumor-related immune cells and clinical parameters, which might be therapy targets in future clinical application.

scholarly journals Relationship between thymidine kinase 1 before radiotherapy and prognosis in breast cancer patients with diabetes

2020 ◽  
Vol 40 (4) ◽  
Author(s):  
Zhiwu Wang ◽  
Wei Zhang ◽  
Bingjie Huo ◽  
Liang Dong ◽  
Jing Zhang
Keyword(s):  
Breast Cancer ◽  
Thymidine Kinase ◽  
Cancer Patients ◽  
Poor Prognosis ◽  
Univariate Analysis ◽  
Clinical Parameters ◽  
Breast Cancer Patients ◽  
Thymidine Kinase 1 ◽  
Node Number ◽  
Significant Difference

Abstract In a retrospective study design, we explored the relationship between serum thymidine kinase 1 (TK1) concentration before radiotherapy and clinical parameters and evaluated the prognostic value of serum TK1 concentration before radiotherapy in breast cancer patients with type 2 diabetes mellitus. The present study finally consisted of 428 breast cancer patients with a mean age of 53.0 years. Compared with low TK1 group, the high TK1 group tended to have larger tumor size (P=0.011) and had more lymph node number (P=0.021). Significant differences were also observed in clinical stages I, II and III (P=0.000). There was no significant difference between TK1 and other clinical parameters. For disease-free survival (DFS), the univariate analysis indicated that the high TK1 increased the risk of poor prognosis (HR = 2.38, 95% CI: 1.64–4.23, P=0.000). The Kaplan–Meier curve indicated the high TK1 group was poorer than that in the low TK1 group (P=0.002). For the overall survival (OS), similar results were found that the high TK1 was related to poor OS (HR = 1.89, 95% CI: 1.34–3.67, P=0.000). The multivariate Cox regression indicated that the TK1 was still associated with DFS (HR = 1.83, 95% CI: 1.22–3.17, P=0.001) and OS (HR = 1.63, 95% CI: 1.19–2.08, P=0.006). The high pretreatment serum TK1 levels in breast cancer patients were associated with poor OS and DFS. TK1 could be a potential predictive factor in differential diagnosis of poor prognosis from all patients.

Comparison of MammaPrint and TargetPrint with clinical parameters in patients with breast cancer: Findings from a prospective U.S. cohort.

2011 ◽  
Vol 29 (15_suppl) ◽  
pp. e11094-e11094
Author(s):  
K. B. Deck ◽  
R. Sinha ◽  
D. Kerlin ◽  
J. Barone ◽  
E. Rivera ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Clinical Parameters

Immunohistochemical determination of PD-L1 expression with three different antibody clones and determination of SOX10 expression in triple negative breast cancer.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e15237-e15237
Author(s):  
Margit Maria Guhl ◽  
R.M. Bohle ◽  
Martin Ertz ◽  
Mariz Kasoha ◽  
Mohammed Eid Hammadeh ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Tumor Cell ◽  
Immune Cells ◽  
Triple Negative ◽  
Immune Cell ◽  
Clinical Parameters ◽  
Cytoplasmic Staining ◽  
Cell Staining ◽  
Sox10 Expression

e15237 Background: In triple negative breast cancer (TBNC), checkpoint inhibitors directed against PD-L1/PD-1 show an improvement of therapy. For the immunohistochemical diagnosis of the predictive biomarker PD-L1, there are various antibodies and kits available. We examined the expression of PD-L1 in TNBC with different antibody clones to compare these results with regard to the staining of tumor cell membranes, staining of immune cells and cytoplasmic staining in order to find out whether the different clones or methods can be exchanged. It was also checked whether PD-L1 or SOX10 expression correlates with the existing clinical parameters. Methods: Breast cancer tissue of 60 patients with TNBC were examined for the expression of PD-L1 and SOX10 by immunohistochemistry. The detection kit used, was the ultraView universal alkaline phosphatase red detection kit for the antibodies anti-human PD-L1 clone 22C3 from Dako, the clone 28-8 from abcam and the SOX10 antibody clone SP267 from Cell Marque. The anti-PD-L1 antibody clone SP142 was detected with OptiView DAB. The cut-offs for the expression of PD-L1 at the tumor cell membrane were < 1%, > 1 to < 50% and > 50%. In the evaluation of SP142 the staining of the immune cells was evaluated with the score: percentage of PD-L1 positive immune cells to the tumor cells that were present. For SOX10, the nuclear staining was evaluated with the score: < 1, > 1 to 50, > 50 to > 100 and 100. The relationship between PD-L1 expression (TC and IC) and SOX10 expression was evaluated with the clinical parameters of the patients from the time of diagnosis until the end of data collection. Results: The antibodies clone 22C3 and clone 28-8 lead to the same results (22,0 % PD-L1 (TC) positive). Clone SP142 showed significantly (p < 0,001) more PD-L1 positive cases(40,7%). For the additional evaluation of the cytoplasmic staining with clone 22C3 and clone 28-8 it could be shown that the PD-L1 Expression is equivalent (40,7%) to the immune cell staining with clone SP142. With exception of Ki67 we were unable to demonstrate any correlation between PD-L1 (membrane and cytoplasmic), SOX10 and other clinical parameters in TBNC. Conclusions: The antibodies clone 22C3 and 28-8 can be used equivalently for PD-L1 determination in TBNC. Clone SP142 showed different results. The cytoplasmatic staining with 22C3 and 28-8 could gain importance in the future because the results were equivalent to the immune cell staining with clone SP142. There is no correlation between PD-L1 and SOX10 in TNBC.

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