scholarly journals Association of Perioperative Cell-free DNA (cfDNA) Concentrations with Risk of Recurrence in Patients with Breast Cancer

2021 ◽  
Author(s):  
Fara Hassan ◽  
Jiang Huai Wang ◽  
Carolyn Cullinane ◽  
Michael Ita ◽  
Mark Corrigan ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Patients ◽  
Cox Regression ◽  
Perioperative Period ◽  
Breast Cancer Patients ◽  
Cell Free Dna ◽  
Cox Regression Analysis ◽  
Free Dna ◽  
Post Operative Period ◽  
Potential Biomarker

Abstract Background: Circulating cell-free DNA (cfDNA) is a potential biomarker of disease status in cancer patients and provides valuable diagnostic and prognostic information in breast cancer. In this study, we sought to quantify the cfDNA concentrations in the perioperative period and to investigate its prognostic relevance in breast cancer patients.Methods: Sixty-three (n=63) breast cancer patients undergoing curative surgery were screened for inclusion. Blood samples were collected: pre-operatively (Pre-op), post-operatively (POD) within weeks 1-2, weeks 3-4 and weeks 5-12 following surgery. cfDNA was extracted and quantified using nanodrop spectrophotometer. All patients were followed up for 5 years.Results: The median age was 52(26-84) years. During the perioperative period, patients with high cfDNA concentrations(cutoff:480ng/ml) had inferior recurrence free survival (RFS) than those with lower cfDNA concentrations (pre-operative period: median RFS: 30(3-60) months versus 60(6-60) months (p<0.0001), post-operative period: median RFS: 24(3-60) months versus 60(6-60) months (p<0.0001). Multivariate Cox regression analysis showed that post op cfDNA concentration (p=0.017), subtypes (p=0.011) and tumour size (p=0.006) were negative prognostic factor for RFS in the pre-operative period and post-operative period.Conclusion: Our study demonstrated the prognostic ability of perioperative cfDNA concentrations in breast cancer patients. Further, prospective studies are warranted to validate its clinical utility in breast cancer.

scholarly journals Analysis of ESR1 and PIK3CA mutations in plasma cell-free DNA from ER-positive breast cancer patients

Oncotarget ◽  
2017 ◽  
Vol 8 (32) ◽  
pp. 52142-52155 ◽  
Author(s):  
Takashi Takeshita ◽  
Yutaka Yamamoto ◽  
Mutsuko Yamamoto-Ibusuki ◽  
Mai Tomiguchi ◽  
Aiko Sueta ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Patients ◽  
Plasma Cell ◽  
Breast Cancer Patients ◽  
Cell Free Dna ◽  
Pik3ca Mutations ◽  
Free Dna ◽  
Er Positive ◽  
Positive Breast Cancer

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

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.

scholarly journals Treatment outcomes and its associated factors among breast cancer patients at Kitui Referral Hospital

2022 ◽  
Vol 10 ◽  
pp. 205031212110678
Author(s):  
Mwendwa Dickson Wambua ◽  
Amsalu Degu ◽  
Gobezie T Tegegne
Keyword(s):  
Breast Cancer ◽  
Regression Analysis ◽  
Confidence Interval ◽  
Treatment Outcomes ◽  
Cancer Patients ◽  
Medical Records ◽  
Cox Regression ◽  
Breast Cancer Patients ◽  
Cox Regression Analysis ◽  
Study Setting

Objectives: Despite breast cancer treatment outcomes being relatively poor or heterogeneous among breast cancer patients, there was a paucity of data in the African settings, especially in Kenya. Hence, this study aimed to determine treatment outcomes among breast cancer patients at Kitui Referral Hospital. Methods: A hospital-based retrospective cohort study design was conducted among adult patients with breast cancer. All eligible breast cancer patients undergoing treatment from January 2015 to June 2020 in the study setting were included. Hence, a total of 116 breast cancer patients’ medical records were involved in the study. Patients’ medical records were retrospectively reviewed using a predesigned data abstraction tool. The data were entered, cleaned, and analyzed using SPSS (Statistical Package for Social Sciences) version 26 software. Descriptive analysis—such as percentage, frequency, mean, and figures—was used to present the data. Kaplan–Meier survival analysis was used to estimate the mean survival estimate across different variables. A Cox regression analysis was employed to determine factors associated with mortality. Results: The study showed that the overall survival and mortality rate was 62.9% (73) and 37.1% (43), respectively. The regression analysis showed that patients who had an advanced stage of disease had a 3.82 times risk of dying (crude hazard ratio= 3.82, 95% confidence interval = 1.5–9.8) than an early stage of the disease. Besides, patients with distant metastasis had 4.4 times more hazards of dying than (crude hazard ratio = 4.4, 95% confidence interval = 2.1–9.4) their counterparts. Conclusion: The treatment outcome of breast cancer patients was poor, and its overall mortality among breast cancer patients was higher in the study setting. In the multivariate Cox regression analysis, the tumor size was the only statistically significant predictor of mortality among breast cancer patients. Stakeholders at each stage should, therefore, prepare a relevant strategy to improve treatment outcomes.

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