[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126267-en":3,"doc-seo-126267-105":31,"detail-sidebar-cat-0-en-105":93},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126267,2336475104362,"Eden","https://ap-avatar.wpscdn.com/avatar/22000c4c46a41b752dd?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786595829695023868",8,"Research & Report","Machine learning-based spatial characterization of tumor-immune microenvironment in the EORTC 10994/BIG 1-00 early breast cancer trial","Breast cancer represents a heterogeneous ecosystem, and defining tumor microenvironment components is essential to interpret tumor-host interactions. This study depicts the composition and prognostic correlates of immune infiltrate in early breast cancer at multiplex spatial resolution. Pretreatment tumor biopsies from the EORTC 10994/BIG 1-00 randomized phase III neoadjuvant trial were analyzed using a CNN-based quantification of H&E digital TILs, multiplex immunofluorescence, and machine-learning spatial features. Immune features linked to pCR include higher CD4+ and intra-tumoral CD8+ expression, greater immune-tumor colocalization in triple-negative tumors, and enrichment in TP53-mutated tumors, supporting the feasibility of ML for prognostic immune landscape characterization.","npj | breast cancer Article  \nPublished in partnership with the Breast Cancer Research Foundation  \n[https://doi.org/10.1038/s41523-025-00730-1](https://doi.org/10.1038/s41523-025-00730-1)  \nMachine learning-based spatial characterization of tumor-immune microenvironment in the EORTC 10994/ BIG 1-00 early breast cancer trial  \n Check for updates  \nIoannis Zerdes  1,2,15 , Alexios Matikas  1,3,15, Artur Mezheyeuski4,5, Georgios Manikis  1,6, Balazs Acs 1,7, Hemming Johansson1, Ceren Boyaci 1,7, Caroline Boman1,3, Coralie Poncet8, Michail Ignatiadis 9, Yalai Bai 10,11, David L. Rimm 10,11, David Cameron12, Hervé Bonnefoi13, Jonas Bergh 1,3, Gaetan MacGrogan 14 & Theodoros Foukakis 1,3  \nBreast 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 CNN11classifier 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 inTN 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.  \nBreast cancer (BC) represents a clinically and biologically heterogeneous disease ecosystem. Further characterization of the tumor microenvironment (TME), its components and molecular drivers could elucidate the complexity of tumor-host interactions and provide rationale for biomarker development. One of the principal TME components, tumor-infiltrating lymphocytes (TILs), are both prognostic for outcomes and predictive for response to chemotherapy1–5.  \nOur understanding of the immune cell composition and spatial interactions within the TME is evolving. Towards this end, artificialintelligence (AI) and machine-learning (ML) methods—which have revolutionized digital pathology and diagnostics—could represent valuable tools for studying the TME6,7. Indeed, recent studies have reported on the performance, prognostic implications, advantages, and challenges of digitalassisted scoring of TILs and multiplex immunofluorescence methods for  \n1Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden. 2Theme Cancer, Karolinska Comprehensive Cancer Center and University Hospital, Stockholm, Sweden. 3Breast Center, Theme Cancer, Karolinska Comprehensive Cancer Center and University Hospital, Stockholm, Sweden. 4Department of Immunology, Genetics, and Pathology, Uppsala University, Uppsala, Sweden. 5Molecular Oncology Group, Vall d’Hebron Institute of Oncology, Barcelona, Spain. 6Computational BioMedicine Laboratory (CBML), Foundation for Research and Technology-Hellas (FORTH), Heraklion, Greece. 7Department of Clinical Pathology and Cancer Diagnostics, Karolinska University Hospital, Stockholm, Sweden. 8European Organisation for Research and Treatment of Cancer Headquarters, Brussels, Belgium. 9Department of Medical Oncology, Institut Jules Bordet and L’Université Libre de Bruxelles (U. L. B), Brussels; Academic Trials Promoting Team (ATPT), Institut Jules Bordet, Brussels, Belgium. 10Department of Pathology, Yale School of Medicine, New Haven, CT, USA. 11Yale Cancer Center, Yale School of Medicine, New Haven, CT, USA. 12Edinburgh University Cancer Centre, Institute of Genetics and Cancer, University of Edinburgh,  \nEdinburgh, UK. 13Department o","cbCaip97hXSMBe17","https://ap.wps.com/l/cbCaip97hXSMBe17","pdf",4643631,5,1,12,"English","en",105,"# Introduction\n# Study aims and rationale\n# Results\n## Patient characteristics\n## Digital image analysis of TILs and outcome correlation","[{\"question\":\"What clinical trial setting and samples were used for this machine-learning spatial analysis?\",\"answer\":\"Pretreatment tumor biopsies from patients enrolled in the EORTC 10994/BIG 1-00 randomized phase III neoadjuvant trial were used, with final analysis based on available digital TIL and multiplex immunofluorescence data.\"},{\"question\":\"Which immune measures were associated with pathological complete response (pCR)?\",\"answer\":\"Higher triple-negative immune infiltrate related to pCR, and total CD4+ together with intra-tumoral CD8+ T-cell expression correlated with pCR. Higher immune-tumor colocalization was also observed in triple-negative pCR-achieving tumors.\"},{\"question\":\"How did TP53 mutational status relate to immune infiltrate patterns?\",\"answer\":\"Immune cell subsets were enriched in TP53-mutated tumors, indicating a link between tumor genetics and immune landscape organization.\"}]","Machine learning-based spatial characterization of tumor-immune microenvironment in the EORTC 10994/BIG 1-00 early breast cancer trial | PDF",1785904142,30,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":88,"head_meta":90,"extra_data":92,"updated_unix":29},"machine-learning-based-spatial-characterization-of-tumor-immune-microenvironment-in-the-eortc-10994big-1-00-early-breast-cancer-trial","",{"@graph":37,"@context":87},[38,55,70],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/document/research-report/",3,{"item":53,"name":13,"@type":44,"position":54},"https://docshare.wps.com/document/machine-learning-based-spatial-characterization-of-tumor-immune-microenvironment-in-the-eortc-10994big-1-00-early-breast-cancer-trial/126267/",4,{"url":53,"name":13,"@type":56,"author":57,"headline":13,"publisher":59,"fileFormat":62,"inLanguage":24,"description":14,"dateModified":63,"datePublished":64,"encodingFormat":62,"isAccessibleForFree":65,"interactionStatistic":66},"DigitalDocument",{"name":9,"@type":58},"Person",{"url":42,"name":60,"@type":61},"DocShare","Organization","application/pdf","2026-08-24","2026-08-05",true,{"@type":67,"interactionType":68,"userInteractionCount":20},"InteractionCounter",{"@type":69},"ViewAction",{"@type":71,"mainEntity":72},"FAQPage",[73,79,83],{"name":74,"@type":75,"acceptedAnswer":76},"What clinical trial setting and samples were used for this machine-learning spatial analysis?","Question",{"text":77,"@type":78},"Pretreatment tumor biopsies from patients enrolled in the EORTC 10994/BIG 1-00 randomized phase III neoadjuvant trial were used, with final analysis based on available digital TIL and multiplex immunofluorescence data.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"Which immune measures were associated with pathological complete response (pCR)?",{"text":82,"@type":78},"Higher triple-negative immune infiltrate related to pCR, and total CD4+ together with intra-tumoral CD8+ T-cell expression correlated with pCR. Higher immune-tumor colocalization was also observed in triple-negative pCR-achieving tumors.",{"name":84,"@type":75,"acceptedAnswer":85},"How did TP53 mutational status relate to immune infiltrate patterns?",{"text":86,"@type":78},"Immune cell subsets were enriched in TP53-mutated tumors, indicating a link between tumor genetics and immune landscape organization.","https://schema.org",{"og:url":53,"og:type":89,"og:title":13,"og:site_name":60,"og:description":14},"article",{"robots":91,"canonical":53},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":94},[95,99,103,107,111,116,121,123,128,131,135],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":96,"show_sort_weight":97,"slug":98},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":100,"show_sort_weight":101,"slug":102},"Literature",80,"literature",{"id":54,"doc_module":4,"doc_module_name":47,"category_name":104,"show_sort_weight":105,"slug":106},"Exam",70,"exam",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":30,"slug":122},"research-report",{"id":124,"doc_module":4,"doc_module_name":47,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":47,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":47,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":47,"category_name":137,"show_sort_weight":20,"slug":138},19,"General","general"]