Machine learning-based spatial characterization of tumor-immune microenvironment in the EORTC 10994/BIG 1-00 early breast cancer trial
| Title: | Machine learning-based spatial characterization of tumor-immune microenvironment in the EORTC 10994/BIG 1-00 early breast cancer trial |
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| Authors: | Zerdes, Ioannis; Matikas, Alexios; Mezheyeuski, Artur; Manikis, Georgios; Acs, Balazs; Johansson, Hemming; Boyaci, Ceren; Boman, Caroline; Poncet, Coralie; Ignatiadis, Michail; Bai, Yalai; Rimm, David L.; Cameron, David; Bonnefoi, Herve; Bergh, Jonas; Macgrogan, Gaetan; Foukakis, Theodoros |
| Publisher Information: | Uppsala universitet, Cancerprecisionsmedicin; Karolinska Inst, Dept Oncol Pathol, Stockholm, Sweden.;Karolinska Comprehens Canc Ctr, Theme Canc, Stockholm, Sweden.;Univ Hosp, Stockholm, Sweden.; Karolinska Inst, Dept Oncol Pathol, Stockholm, Sweden.;Univ Hosp, Stockholm, Sweden.;Karolinska Comprehens Canc Ctr, Breast Ctr, Theme Canc, Stockholm, Sweden.; Vall dHebron Inst Oncol, Mol Oncol Grp, Barcelona, Spain.; Karolinska Inst, Dept Oncol Pathol, Stockholm, Sweden.;Fdn Res & Technol Hellas FORTH, Computat Biomed Lab CBML, Iraklion, Greece.; Karolinska Inst, Dept Oncol Pathol, Stockholm, Sweden.;Karolinska Univ Hosp, Dept Clin Pathol & Canc Diagnost, Stockholm, Sweden.; Karolinska Inst, Dept Oncol Pathol, Stockholm, Sweden.; European Org Res & Treatment Canc Headquarters, Brussels, Belgium.; Inst Jules Bordet, Dept Med Oncol, Brussels, Belgium.;Univ Libre Bruxelles ULB, Brussels, Belgium.;Inst Jules Bordet, Acad Trials Promoting Team ATPT, Brussels, Belgium.; Yale Sch Med, Dept Pathol, New Haven, CT USA.;Yale Sch Med, Yale Canc Ctr, New Haven, CT USA.; Univ Edinburgh, Inst Genet & Canc, Canc Ctr, Edinburgh, Scotland.; Univ Bordeaux, Inst Bergonie Unicanc, Dept Med Oncol, INSERM,U1218, Bordeaux, France.; Inst Bergonie Unicanc, Dept Biopathol, INSERM, U1312, Bordeaux, France. |
| Publication Year: | 2025 |
| Collection: | Uppsala University: Publications (DiVA) |
| Subject Terms: | Cancer and Oncology; Cancer och onkologi |
| Description: | 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. |
| Document Type: | article in journal/newspaper |
| File Description: | application/pdf |
| Language: | English |
| Relation: | npj Breast Cancer, 2025, 11:1; PMID 40055382; ISI:001439371400001 |
| DOI: | 10.1038/s41523-025-00730-1 |
| Availability: | http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-553358; https://doi.org/10.1038/s41523-025-00730-1 |
| Rights: | info:eu-repo/semantics/openAccess |
| Accession Number: | edsbas.BB8C1880 |
| Database: | BASE |