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A single-cell and spatially resolved atlas of human breast cancers.

ABSTRACT: Breast cancers are complex cellular ecosystems where heterotypic interactions play central roles in disease progression and response to therapy. However, our knowledge of their cellular composition and organization is limited. Here we present a single-cell and spatially resolved transcriptomics analysis of human breast cancers. We developed a single-cell method of intrinsic subtype classification (SCSubtype) to reveal recurrent neoplastic cell heterogeneity. Immunophenotyping using cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) provides high-resolution immune profiles, including new PD-L1/PD-L2⁺ macrophage populations associated with clinical outcome. Mesenchymal cells displayed diverse functions and cell-surface protein expression through differentiation within three major lineages. Stromal-immune niches were spatially organized in tumors, offering insights into antitumor immune regulation. Using single-cell signatures, we deconvoluted large breast cancer cohorts to stratify them into nine clusters, termed 'ecotypes', with unique cellular compositions and clinical outcomes. This study provides a comprehensive transcriptional atlas of the cellular architecture of breast cancer.

SUBMITTER: Wu SZ

PROVIDER: S-EPMC9044823 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature

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A single-cell and spatially resolved atlas of human breast cancers.

Wu Sunny Z SZ Al-Eryani Ghamdan G Roden Daniel Lee DL Junankar Simon S Harvey Kate K Andersson Alma A Thennavan Aatish A Wang Chenfei C Torpy James R JR Bartonicek Nenad N Wang Taopeng T Larsson Ludvig L Kaczorowski Dominik D Weisenfeld Neil I NI Uytingco Cedric R CR Chew Jennifer G JG Bent Zachary W ZW Chan Chia-Ling CL Gnanasambandapillai Vikkitharan V Dutertre Charles-Antoine CA Gluch Laurence L Hui Mun N MN Beith Jane J Parker Andrew A Robbins Elizabeth E Segara Davendra D Cooper Caroline C Mak Cindy C Chan Belinda B Warrier Sanjay S Ginhoux Florent F Millar Ewan E Powell Joseph E JE Williams Stephen R SR Liu X Shirley XS O'Toole Sandra S Lim Elgene E Lundeberg Joakim J Perou Charles M CM Swarbrick Alexander A

Nature genetics 20210906 9

Breast cancers are complex cellular ecosystems where heterotypic interactions play central roles in disease progression and response to therapy. However, our knowledge of their cellular composition and organization is limited. Here we present a single-cell and spatially resolved transcriptomics analysis of human breast cancers. We developed a single-cell method of intrinsic subtype classification (SCSubtype) to reveal recurrent neoplastic cell heterogeneity. Immunophenotyping using cellular inde ...[more]

PMID: 34493872

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Project description:BackgroundBreast cancer's complex transcriptional landscape requires an improved understanding of cellular diversity to identify effective treatments. The study of genetic variations among breast cancer subtypes at single-cell resolution has potential to deepen our insights into cancer progression.MethodsIn this study, we amalgamate single-cell RNA sequencing data from patient tumours and matched lymph metastasis, reduction mammoplasties, breast cancer patient-derived xenografts (PDXs), PDX-derived organoids (PDXOs), and cell lines resulting in a diverse dataset of 117 samples with 506 719 total cells. These samples encompass hormone receptor positive (HR+), human epidermal growth factor receptor 2 positive (HER2+), and triple-negative breast cancer (TNBC) subtypes, including isogenic model pairs. Herein, we delineated similarities and distinctions across models and patient samples and explore therapeutic drug efficacy based on subtype proportions.ResultsPDX models more closely resemble patient samples in terms of tumour heterogeneity and cell cycle characteristics when compared with TNBC cell lines. Acquired drug resistance was associated with an increase in basal-like cell proportions within TNBC PDX tumours as defined with SCSubtype and TNBCtype cell typing predictors. All patient samples contained a mixture of subtypes; compared to primary tumours HR+ lymph node metastases had lower proportions of HER2-Enriched cells. PDXOs exhibited differences in metabolic-related transcripts compared to PDX tumours. Correlative analyses of cytotoxic drugs on PDX cells identified therapeutic efficacy was based on subtype proportion.ConclusionsWe present a substantial multimodel dataset, a dynamic approach to cell-wise sample annotation, and a comprehensive interrogation of models within systems of human breast cancer. This analysis and reference will facilitate informed decision-making in preclinical research and therapeutic development through its elucidation of model limitations, subtype-specific insights and novel targetable pathways.Key pointsPatient-derived xenografts models more closely resemble patient samples in tumour heterogeneity and cell cycle characteristics when compared with cell lines. 3D organoid models exhibit differences in metabolic profiles compared to their in vivo counterparts. A valuable multimodel reference dataset that can be useful in elucidating model differences and novel targetable pathways.

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A single-cell and spatially resolved atlas of human breast cancers.

Publications

A single-cell and spatially resolved atlas of human breast cancers.

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