dc.contributor
Institut Català de la Salut
dc.contributor
[Zerdes I] Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden. Theme Cancer, Karolinska Comprehensive Cancer Center and University Hospital, Stockholm, Sweden. [Matikas A] Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden. Breast Center, Theme Cancer, Karolinska Comprehensive Cancer Center and University Hospital, Stockholm, Sweden. [Mezheyeuski A] Department of Immunology, Genetics, and Pathology, Uppsala University, Uppsala, Sweden. 5 Molecular Oncology Group, Vall d’Hebron Institute of Oncology (VHIO), Barcelona, Spain. [Manikis G] Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden. Computational BioMedicine Laboratory (CBML), Foundation for Research and Technology-Hellas (FORTH), Heraklion, Greece. [Acs B] Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden. Department of Clinical Pathology and Cancer Diagnostics, Karolinska University Hospital, Stockholm, Sweden. [Johansson H] Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden
dc.contributor
Vall d'Hebron Barcelona Hospital Campus
dc.contributor.author
Manikis, Georgios C.
dc.contributor.author
Acs, Balazs
dc.contributor.author
Johansson, Hemming
dc.contributor.author
Zerdes, Ioannis
dc.contributor.author
Matikas, Alexios
dc.contributor.author
Mezheyeuski, Artur
dc.date.accessioned
2025-10-25T05:38:20Z
dc.date.available
2025-10-25T05:38:20Z
dc.date.issued
2025-04-22T07:48:36Z
dc.date.issued
2025-04-22T07:48:36Z
dc.date.issued
2025-03-07
dc.identifier
Zerdes I, Matikas A, Mezheyeuski A, Manikis G, Acs B, Johansson H, et al. Machine learning-based spatial characterization of tumor-immune microenvironment in the EORTC 10994/BIG 1-00 early breast cancer trial. npj Breast Cancer. 2025 Mar 7;11:23.
dc.identifier
http://hdl.handle.net/11351/12970
dc.identifier
10.1038/s41523-025-00730-1
dc.identifier
001439371400001
dc.identifier.uri
http://hdl.handle.net/11351/12970
dc.description.abstract
Machine learning; Tumor-immune microenvironment; Breast cancer
dc.description.abstract
Aprendizaje automático; Microambiente inmunitario tumoral; Cáncer de mama
dc.description.abstract
Aprenentatge automàtic; Microambient immunitari tumoral; Càncer de mama
dc.description.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.
dc.description.abstract
Open access funding provided by Karolinska Institute.
dc.format
application/pdf
dc.publisher
Nature Portfolio
dc.relation
npj Breast Cancer;11
dc.relation
https://doi.org/10.1038/s41523-025-00730-1
dc.rights
Attribution 4.0 International
dc.rights
http://creativecommons.org/licenses/by/4.0/
dc.rights
info:eu-repo/semantics/openAccess
dc.subject
Mama - Càncer - Aspectes immunològics
dc.subject
Mama - Càncer - Tractament
dc.subject
Cèl·lules canceroses
dc.subject
Aprenentatge automàtic
dc.subject
DISEASES::Neoplasms::Neoplasms by Site::Breast Neoplasms
dc.subject
Other subheadings::Other subheadings::Other subheadings::/immunology
dc.subject
ANATOMY::Cells::Blood Cells::Leukocytes::Leukocytes, Mononuclear::Lymphocytes::Lymphocytes, Tumor-Infiltrating
dc.subject
PHENOMENA AND PROCESSES::Mathematical Concepts::Algorithms::Artificial Intelligence::Machine Learning
dc.subject
ANALYTICAL, DIAGNOSTIC AND THERAPEUTIC TECHNIQUES, AND EQUIPMENT::Therapeutics::Combined Modality Therapy::Neoadjuvant Therapy
dc.subject
ANALYTICAL, DIAGNOSTIC AND THERAPEUTIC TECHNIQUES, AND EQUIPMENT::Diagnosis::Prognosis
dc.subject
ENFERMEDADES::neoplasias::neoplasias por localización::neoplasias de la mama
dc.subject
Otros calificadores::Otros calificadores::Otros calificadores::/inmunología
dc.subject
ANATOMÍA::células::células sanguíneas::leucocitos::leucocitos mononucleares::linfocitos::linfocitos infiltrantes de tumor
dc.subject
FENÓMENOS Y PROCESOS::conceptos matemáticos::algoritmos::inteligencia artificial::aprendizaje automático
dc.subject
TÉCNICAS Y EQUIPOS ANALÍTICOS, DIAGNÓSTICOS Y TERAPÉUTICOS::terapéutica::tratamiento combinado::tratamiento neoadyuvante
dc.subject
TÉCNICAS Y EQUIPOS ANALÍTICOS, DIAGNÓSTICOS Y TERAPÉUTICOS::diagnóstico::pronóstico
dc.title
Machine learning-based spatial characterization of tumor-immune microenvironment in the EORTC 10994/BIG 1-00 early breast cancer trial
dc.type
info:eu-repo/semantics/article
dc.type
info:eu-repo/semantics/publishedVersion
