RESUMEN
Single-cell analyses have revealed extensive heterogeneity between and within human tumours1-4, but complex single-cell phenotypes and their spatial context are not at present reflected in the histological stratification that is the foundation of many clinical decisions. Here we use imaging mass cytometry5 to simultaneously quantify 35 biomarkers, resulting in 720 high-dimensional pathology images of tumour tissue from 352 patients with breast cancer, with long-term survival data available for 281 patients. Spatially resolved, single-cell analysis identified the phenotypes of tumour and stromal single cells, their organization and their heterogeneity, and enabled the cellular architecture of breast cancer tissue to be characterized on the basis of cellular composition and tissue organization. Our analysis reveals multicellular features of the tumour microenvironment and novel subgroups of breast cancer that are associated with distinct clinical outcomes. Thus, spatially resolved, single-cell analysis can characterize intratumour phenotypic heterogeneity in a disease-relevant manner, with the potential to inform patient-specific diagnosis.
Asunto(s)
Neoplasias de la Mama/patología , Imagen Molecular , Análisis de la Célula Individual , Biomarcadores de Tumor/análisis , Neoplasias de la Mama/clasificación , Neoplasias de la Mama/diagnóstico , Humanos , Estimación de Kaplan-Meier , Fenotipo , Modelos de Riesgos Proporcionales , Tasa de Supervivencia , Microambiente TumoralRESUMEN
Single-cell, spatially resolved omics analysis of tissues is poised to transform biomedical research and clinical practice. We have developed an open-source, computational histology topography cytometry analysis toolbox (histoCAT) to enable interactive, quantitative, and comprehensive exploration of individual cell phenotypes, cell-cell interactions, microenvironments, and morphological structures within intact tissues. We highlight the unique abilities of histoCAT through analysis of highly multiplexed mass cytometry images of human breast cancer tissues.
Asunto(s)
Comunicación Celular/fisiología , Citometría de Flujo/métodos , Imagen Molecular/métodos , Proteoma/metabolismo , Programas Informáticos , Análisis de Matrices Tisulares/métodos , Algoritmos , Interpretación de Imagen Asistida por Computador/métodos , Interfaz Usuario-ComputadorRESUMEN
Genomic alterations shape cell phenotypes and the structure of tumor ecosystems in poorly defined ways. To investigate these relationships, we used imaging mass cytometry to quantify the expression of 37 proteins with subcellular spatial resolution in 483 tumors from the METABRIC cohort. Single-cell analysis revealed cell phenotypes spanning epithelial, stromal and immune types. Distinct combinations of cell phenotypes and cell-cell interactions were associated with genomic subtypes of breast cancer. Epithelial luminal cell phenotypes separated into those predominantly impacted by mutations and those affected by copy number aberrations. Several features of tumor ecosystems, including cellular neighborhoods, were linked to prognosis, illustrating their clinical relevance. In summary, systematic analysis of single-cell phenotypic and spatial correlates of genomic alterations in cancer revealed how genomes shape both the composition and architecture of breast tumor ecosystems and will enable greater understanding of the phenotypic impact of genomic alterations.