ArticleNPJ breast cancer2025
Machine learning-based spatial characterization of tumor-immune microenvironment in the EORTC 10994/BIG 1-00 early breast cancer trial.
Article in NPJ breast cancer, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT00017095 (First Prospective Intergroup Translational Research Trial Assessing the Potential Predictive Value of p53 Using a Functional Assay in Yeast in Patients With Locally Advanced/Inflammatory or Large Operable Breast Cancer Prospectively Randomised to a Taxane Versus a Non Taxane Regimen), which is not on this map. Cited by 5 papers.
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First Prospective Intergroup Translational Research Trial Assessing the Potential Predictive Value of p53 Using a Functional Assay in Yeast in Patients With Locally Advanced/Inflammatory or Large Operable Breast Cancer Prospectively Randomised to a Taxane Versus a Non Taxane Regimen
Who cites it
5 citing papers in PubMed.
- Development of functional spatial mapping of the tumour immune microenvironment (FuncO:TiME) in advanced melanomas.BJC reports · 2026Article
- STAT3D as a new image-tailored Xenium-based spatial transcriptomics tool for accurately dissecting the tumor microenvironment.Nucleic acids research · 2026Article
- AI-powered and manual assessment of tumor-infiltrating lymphocytes in early HER2-positive breast cancer in NSABP B-41.NPJ breast cancer · 2026Article
- Multiscale tumor characterization in histopathology via self-distilled transformers and topology-aware visual encoding.Scientific reports · 2025Article
- Bioinformatics Strategies in Breast Cancer Research.Biomolecules · 2025Review
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17 authors.
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Abstract
Breast cancer (BC) represents a heterogeneous ecosystem and elucidation of tumor microenvironment components remains essential. Our study aimed to depict the composition and prognostic correlates of immune infiltrate in early BC, at a multiplex and spatial resolution. Pretreatment tumor biopsies from patients enrolled in the EORTC 10994/BIG 1-00 randomized phase III neoadjuvant trial (NCT00017095) were used; the CNN11 classifier for H&E-based digital TILs (dTILs) quantification and multiplex immunofluorescence were applied, coupled with machine learning (ML)-based spatial features. dTILs were higher in the triple-negative (TN) subtype, and associated with pathological complete response (pCR) in the whole cohort. Total CD4+ and intra-tumoral CD8+ T-cells expression was associated with pCR. Higher immune-tumor cell colocalization was observed in TN tumors of patients achieving pCR. Immune cell subsets were enriched in TP53-mutated tumors. Our results indicate the feasibility of ML-based algorithms for immune infiltrate characterization and the prognostic implications of its abundance and tumor-host interactions.
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