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Background: The B Prognostic Score (BPS) is a clinical decision-making tool in metastatic breast cancer (MBC) that provides risk classification based on routine parameters. This study validates the BPS in an independent series of MBC for the whole study group and for each intrinsic subtype.
Patients And Methods: We analyzed 641 metastasized patients, treated in 17 German certified breast cancer centers between 2001 and 2009. They were classified into low, intermediate, and high-risk groups according to BPS. Overall survival (OS) curves for the various BPS groups were compared with Kaplan-Meier method.
Results: According to the BPS formula, 42.3% of patients were classified as low risk, 25.4% as intermediate risk and 32.3% as high risk. Intermediate- and high-risk patients had a statistically significant decreased OS compared with BPS low-risk patients: (intermediate-risk: hazard ratio, 1.36; 95% confidence interval, 1.04-1.77; P = .023; high-risk: hazard ratio, 2.62; 95% confidence interval, 2.06-3.32; P < .001). The 5-year survival rates of low-, intermediate-, and high-risk patients were 41.3%, 26.9%, and 10.2%, respectively. The distribution of BPS risk groups varied significantly within the intrinsic subtypes. For each intrinsic subtype, BPS gives an additional risk classification.
Conclusions: This study demonstrates the reproducibility of the BPS based on routinely assessable parameters and confirms its prognostic value in an independent entire cohort of MBC as well as in the separate intrinsic subtypes. It therefore can help in counseling and individualizing the therapeutic regimens of those patients.
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http://dx.doi.org/10.1016/j.clbc.2019.04.015 | DOI Listing |
JCO Glob Oncol
May 2025
Department of Biochemistry, All India Institute of Medical Sciences, New Delhi, India.
Purpose: Breast cancer remains a significant public health challenge globally, as well as in India, where it is the most frequently diagnosed cancer in females. Significant disparities in incidence, mortality, and access to health care across India's sociodemographically diverse population highlight the need for increased awareness, policy reform, and research.
Design: This review consolidates data from national cancer registries, global cancer databases, and institutional findings from a tertiary care center to examine the epidemiology, clinical challenges, and management gaps specific to India.
J Med Screen
September 2025
The Cancer Registry of Norway, Department of Screening programs, Norwegian Institute of Public Health, Oslo, Norway.
ObjectiveTo study the implications of implementing artificial intelligence (AI) as a decision support tool in the Norwegian breast cancer screening program concerning cost-effectiveness and time savings for radiologists.MethodsIn a decision tree model using recent data from AI vendors and the Cancer Registry of Norway, and assuming equal effectiveness of radiologists plus AI compared to standard practice, we simulated costs, effects and radiologist person-years over the next 20 years under different scenarios: 1) Assuming a €1 additional running cost of AI instead of the €3 assumed in the base case, 2) varying the AI-score thresholds for single vs. double readings, 3) varying the consensus and recall rates, and 4) reductions in the interval cancer rate compared to standard practice.
View Article and Find Full Text PDFJ Natl Cancer Inst
September 2025
Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, Maryland, United States.
Background: Among childhood cancer survivors, germline rare variants in autosomal dominant cancer susceptibility genes (AD CSGs) could increase subsequent neoplasm (SNs) risks, but risks for rarer SNs and by age at onset are not well understood.
Methods: We pooled the Childhood Cancer Survivor Study and St Jude Lifetime Cohort (median follow-up = 29.7 years, range 7.
PLoS One
September 2025
Institute of Computational Science and Technology, Guangzhou University, Guangzhou, China.
MicroRNAs (miRNAs) are critical regulators of gene expression in cancer biology, yet their spatial dynamics within tumor microenvironments (TMEs) remain underexplored due to technical limitations in current spatial transcriptomics (ST) technologies. To address this gap, we present STmiR, a novel XGBoost-based framework for spatially resolved miRNA activity prediction. STmiR integrates bulk RNA-seq data (TCGA and CCLE) with spatial transcriptomics profiles to model nonlinear miRNA-mRNA interactions, achieving high predictive accuracy (Spearman's ρ > 0.
View Article and Find Full Text PDFPLoS One
September 2025
Department of Hepatobiliary Surgery, The First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China.
Objective: This study employs integrated network toxicology and molecular docking to investigate the molecular basis underlying 4-nonylphenol (4-NP)-mediated enhancement of breast cancer susceptibility.
Methods: We integrated data from multiple databases, including ChEMBL, STITCH, Swiss Target Prediction, GeneCards, OMIM and TTD. Core compound-disease-associated target genes were identified through Protein-Protein Interaction (PPI) network analysis.