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Purpose: Tumor microenvironment (TME) immune markers have been correlated with both response to neoadjuvant therapy and prognosis in patients with breast cancer. Here, immune-cell activity of breast cancer tumors was inferred by expression-based analysis to determine if it is prognostic and/or predictive of response to neoadjuvant paclitaxel-based therapy in the GeparSepto (G7) trial (NCT01583426).
Experimental Design: Pre-study biopsies from 279 patients with HER2-negative breast cancer in the G7 trial underwent RNA-seq-based profiling of 104 immune-cell-specific genes to assess inferred Immune Cell Activity (iICA) of 23 immune-cell types. Hierarchical clustering was used to classify tumors as iICA "hot," "warm," or "cold" by comparison of iICA in the G7 cohort relative to that of 1,467 samples from a tumor database established by Nantomics LLC. Correlations between iICA cluster, pathology-assessed tumor-infiltrating lymphocytes (TIL), and hormone receptor (HR) status for pathologic complete response (pCR), disease-free survival (DFS), and overall survival (OS) were determined.
Results: iICA cluster correlated with TIL levels. The highest pCR rates were observed in hot cluster tumors, and those with relatively higher TILs. Greater inferred activity of several T-cell types was significantly associated with pCR and survival. DFS and OS were prolonged in patients with hot or warm cluster tumors, the latter particularly for HR negative tumors, even if TILs were relatively low.
Conclusions: Overall, TIL level better predicted pCR, but iICA cluster better predicted survival. Differences in associations between TILs, cluster, pCR, and survival were observed for HR-positive tumors versus HR-negative tumors, suggesting expanded study of the implication of these findings is warranted.
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http://dx.doi.org/10.1158/1078-0432.CCR-22-2213 | 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.