转录组
生物
乳腺癌
肿瘤微环境
癌症研究
浸润性小叶癌
计算生物学
微阵列
基因
癌症
基因表达谱
病理
基因表达
基因签名
前列腺癌
微阵列分析技术
癌
生物信息学
候选基因
内分泌系统
基质
乳腺肿瘤
危险分层
作者
M. Serra,Mattia Rediti,L. Collet,F. Lifrange,David Venet,Nicola Occelli,Andreas Papagiannis,Delphine Vincent,G. Rouas,D. Larsimont,Miikka Vikkula,Francois P. Duhoux,Françoise Rothé,Christos Sotiriou
标识
DOI:10.1073/pnas.2517567123
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.
科研通智能强力驱动
Strongly Powered by AbleSci AI