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Single-cell and spatially resolved analysis uncovers cell heterogeneity of breast cancer.

ABSTRACT: The heterogeneity and the complex cellular architecture have a crucial effect on breast cancer progression and response to treatment. However, deciphering the neoplastic subtypes and their spatial organization is still challenging. Here, we combine single-nucleus RNA sequencing (snRNA-seq) with a microarray-based spatial transcriptomics (ST) to identify cell populations and their spatial distribution in breast cancer tissues. Malignant cells are clustered into distinct subpopulations. These cell clusters not only have diverse features, origins and functions, but also emerge to the crosstalk within subtypes. Furthermore, we find that these subclusters are mapped in distinct tissue regions, where discrepant enrichment of stromal cell types are observed. We also inferred the abundance of these tumorous subpopulations by deconvolution of large breast cancer RNA-seq cohorts, revealing differential association with patient survival and therapeutic response. Our study provides a novel insight for the cellular architecture of breast cancer and potential therapeutic strategies.

SUBMITTER: Liu SQ

PROVIDER: S-EPMC8895670 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature

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Single-cell and spatially resolved analysis uncovers cell heterogeneity of breast cancer.

Liu Si-Qing SQ Gao Zhi-Jie ZJ Wu Juan J Zheng Hong-Mei HM Li Bei B Sun Si S Meng Xiang-Yu XY Wu Qi Q

Journal of hematology & oncology 20220303 1

The heterogeneity and the complex cellular architecture have a crucial effect on breast cancer progression and response to treatment. However, deciphering the neoplastic subtypes and their spatial organization is still challenging. Here, we combine single-nucleus RNA sequencing (snRNA-seq) with a microarray-based spatial transcriptomics (ST) to identify cell populations and their spatial distribution in breast cancer tissues. Malignant cells are clustered into distinct subpopulations. These cell ...[more]

PMID: 35241110

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Project description:BackgroundCurrent bulk transcriptomic classification systems for bladder cancer do not consider the level of intratumor subtype heterogeneity.ObjectiveTo investigate the extent and possible clinical impact of intratumor subtype heterogeneity across early and more advanced stages of bladder cancer.Design setting and participantsWe performed single-nucleus RNA sequencing (RNA-seq) of 48 bladder tumors and additional spatial transcriptomics for four of these tumors. Total bulk RNA-seq and spatial proteomics data were available from the same tumors for comparison, along with detailed clinical follow-up of the patients.Outcome measurements and statistical analysisThe primary outcome was progression-free survival for non-muscle-invasive bladder cancer. Cox regression analysis, log-rank tests, Wilcoxon rank-sum tests, Spearman correlation, and Pearson correlation were used for statistical analysis.Results and limitationsWe found that the tumors exhibited varying levels of intratumor subtype heterogeneity and that the level of subtype heterogeneity can be estimated from both single-nucleus and bulk RNA-seq data, with high concordance between the two. We found that a higher class 2a weight estimated from bulk RNA-seq data is associated with worse outcome for patients with molecular high-risk class 2a tumors. The sparsity of the data generated using the DroNc-seq sequencing protocol is a limitation.ConclusionsOur results indicate that discrete subtype assignments from bulk RNA-seq data may lack biological granularity and that continuous class scores may improve clinical risk stratification of patients with bladder cancer.Patient summaryWe found that several molecular subtypes can exist within a single bladder tumor and that continuous subtype scores can be used to identify a subgroup of patients with poor outcomes. Use of these subtype scores may improve risk stratification for patients with bladder cancer, which can help in making decisions on treatment.

Dataset Information

Single-cell and spatially resolved analysis uncovers cell heterogeneity of breast cancer.

Publications

Single-cell and spatially resolved analysis uncovers cell heterogeneity of breast cancer.

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