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Machine learning-based spatial characterization of tumor-immune microenvironment in the EORTC 10994/BIG 1-00 early breast cancer trial

Authors: Ioannis Zerdes; Alexios Matikas; Artur Mezheyeuski; Georgios Manikis; Balazs Acs; Hemming Johansson; Ceren Boyaci; +10 Authors

Machine learning-based spatial characterization of tumor-immune microenvironment in the EORTC 10994/BIG 1-00 early breast cancer trial

Abstract

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.

Keywords

Cancer och onkologi, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, ANATOMY::Cells::Blood Cells::Leukocytes::Leukocytes, Mononuclear::Lymphocytes::Lymphocytes, Tumor-Infiltrating, ANATOMÍA::células::células sanguíneas::leucocitos::leucocitos mononucleares::linfocitos::linfocitos infiltrantes de tumor, Limfòcits, Article, ANALYTICAL, DIAGNOSTIC AND THERAPEUTIC TECHNIQUES, AND EQUIPMENT::Therapeutics::Combined Modality Therapy::Neoadjuvant Therapy, Otros calificadores::Otros calificadores::Otros calificadores::/inmunología, Cancer and Oncology, Mama - Càncer - Tractament, Aprenentatge automàtic, TÉCNICAS Y EQUIPOS ANALÍTICOS, DIAGNÓSTICOS Y TERAPÉUTICOS::terapéutica::tratamiento combinado::tratamiento neoadyuvante, PHENOMENA AND PROCESSES::Mathematical Concepts::Algorithms::Artificial Intelligence::Machine Learning, Cèl·lules canceroses, ANALYTICAL, DIAGNOSTIC AND THERAPEUTIC TECHNIQUES, AND EQUIPMENT::Diagnosis::Prognosis, FENÓMENOS Y PROCESOS::conceptos matemáticos::algoritmos::inteligencia artificial::aprendizaje automático, Mama - Càncer - Aspectes immunològics, TÉCNICAS Y EQUIPOS ANALÍTICOS, DIAGNÓSTICOS Y TERAPÉUTICOS::diagnóstico::pronóstico, Other subheadings::Other subheadings::Other subheadings::/immunology, ENFERMEDADES::neoplasias::neoplasias por localización::neoplasias de la mama, RC254-282, DISEASES::Neoplasms::Neoplasms by Site::Breast Neoplasms

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
3
Top 10%
Average
Average
Green
gold
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Cancer Research