Spatial transcriptomics reveals tumor microenvironment-driven subtypes of invasive lobular carcinoma

• 成果类型: Article
• 署名作者: Serra, Matteo; Rediti, Mattia; Collet, Laetitia; Lifrange, Frederic; Venet, David; Occelli, Nicola; Papagiannis, Andreas; Vincent, Delphine; Rouas, Ghizlane; Larsimont, Denis; Vikkula, Miikka; Duhoux, Francois P.; Rothe, Francoise; Sotiriou, Christos
• 署名单位: Institut Jules Bordet; Universite Libre de Bruxelles; IFOM - FIRC Institute of Molecular Oncology; University of Liege; Institut Jules Bordet; Universite Catholique Louvain; Universite Libre de Bruxelles; Cliniques Universitaires Saint-Luc; Universite Catholique Louvain; Universite Catholique Louvain; Cliniques Universitaires Saint-Luc
• 刊物名称: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• ISSN/ISSBN: 0027-8424; 1091-6490
• DOI: 10.1073/pnas.2517567123
• 发表日期: 2026-02-10
• 页码: e2517567123
• 关键词: spatial transcriptomics; invasive lobular carcinoma; tumor microenvironment; molecular subtypes; breast-cancer; gene-expression; recurrence; predictor; therapy; atlas; grade

摘要

Invasive lobular carcinoma (ILC) is the second most common histological subtype of breast cancer and displays distinct clinical and biological behavior compared to breast cancer of no special type. However, current molecular classifications largely overlook its complex spatial organization and tumor microenvironment (TME). Here, we performed spatial transcriptomics on 43 hormone receptor-positive, HER2-negative (HR+/HER2-) ILC tumors with detailed morphological annotation and long-term clinical follow-up. By integrating spatial gene expression with histology and single-cell deconvolution, we characterized the composition and architecture of the TME and revealed high inter-and intratumor heterogeneity. Spatial clustering uncovered cell populations and pathways linked to clinical outcome. We then developed a multimodal classification of ILC by integrating gene expression, morphology, and spatial metrics, identifying four distinct subtypes: normal/stroma-enriched (NSE), proliferative (P), androgen receptor-enriched (ARE), and metabolic/immune-enriched (MIE). These subtypes, collectively termed ILC4TME, reflect the interplay between tumor and microenvironmental features. Gene signatures derived from the spatial data enabled subtype assignment in external bulk RNA-seq and microarray datasets (SCAN-B, METABRIC), revealing reproducible biology and significant associations with survival. In multivariable models, ILC4TME retained prognostic value beyond established gene signatures and clinicopathological variables. Notably, the P subtype was linked to poor prognosis, even in patients treated with endocrine therapy alone, while the NSE subtype was associated with favorable outcomes. Our findings uncover spatial and cellular heterogeneity in ILC that is not captured by existing classification approaches, offering a refined framework for risk stratification and therapeutic targeting based on tumor microenvironment architecture.