Comprehensive analysis of immune evasion in breast cancer by single-cell RNA-seq

Yin, Jie; Z, Li; C, Yan; Fang, Enhao; T, Wang; Zhou, Hanlin; Luo, W.; Zhou, Qing; Zhang, Jinfen; Hu, Jian; Jin, Haoxuan; Wang, L; Zhao, Xin; Li, Jian; X, Qi; Zhou, Weiping; Huang, Chen; He, Chufeng; Yang, Huanming; Kristiansen, Karsten; Hou, Yayi; Zhu, Shuifang; Zhou, Dongxian; Dean, Michael; K, Wu; Hu, Haiyan; G, Li

doi:10.1101/368605

Cited by 6 publications

(5 citation statements)

References 43 publications

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“…As a first assessment, the 10cRNA-seq data were visualized by uniform manifold approximation and projection (UMAP) (37). Consistent with past descriptions of carcinoma heterogeneity by scRNA-seq (20,38,39), we found that 10cRNA-seq data clustered tightly by patient ( Fig. 2A).…”

Section: Crna-seq Transcriptomes Retain the Inter-tumor And Intra-tsupporting

confidence: 82%

“…scRNA-seq of dissociated tumors has identified (partial-)EMT states in some carcinomas (41,42) but not others (55). EMT-like transcriptional profiles also arise normally in the cell type of origin for serous ovarian cancer (56).…”

Section: Rhegs Are Enriched For Emt Signatures and Correlate With Canmentioning

confidence: 99%

“…scRNA-seq of dissociated tumors has identified (partial-)EMT states in some carcinomas (38,39) but not others (48). For breast cancer, changes along the EMT spectrum are mostly described in hormone-negative cell lines, but more-recent work reports EMT-like activation patterns in 65-85% of primary luminal breast cancers (49).…”

Section: Rhegs Are Enriched For Emt Signatures and Cofluctuate With Cmentioning

confidence: 99%

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Pan-cancer Drivers are Recurrent Transcriptional Regulatory Heterogeneities in Early-stage Luminal Breast Cancer

Singh

Sutcliffe

Repich

et al. 2020

Preprint

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The authors declare no potential conflicts of interest. Running title: Stochastic profiling of luminal breast cancer by 10cRNA-seqThe heterogeneous composition of solid tumors is known to impact disease progression and response to therapy. Malignant cells coexist in different regulatory states that can be accessed transcriptomically by single-cell RNA sequencing, but these methods have many caveats related to sensitivity, noise, and sample handling. We revised a statistical fluctuation analysis called stochastic profiling to combine with 10-cell RNA sequencing, which was designed for laser-capture microdissection (LCM) and extended here for immuno-LCM. When applied to a cohort of late-onset, early-stage luminal breast cancers, the integrated approach identified thousands of candidate regulatory heterogeneities. Intersecting the candidates from different tumors yielded a relatively stable set of 710 recurrent heterogeneously expressed genes (RHEGs) that were significantly variable in >50% of patients. RHEGs were not confounded by dissociation artifacts, cell cycle oscillations, or driving mutations for breast cancer. Rather, we detected RHEG enrichments for epithelial-to-mesenchymal transition genes and, unexpectedly, the latest pan-cancer assembly of driver genes across cancer types other than breast.Heterogeneous transcriptional regulation conceivably provides a faster, reversible mechanism for malignant cells to sample the effects of potential oncogenes or tumor suppressors on cancer hallmarks. Statement of significanceProfiling intratumor heterogeneity of luminal breast carcinoma cells identifies a recurrent set of genes suggesting sporadic activation of pathways known to drive other types of cancer.

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Section: Crna-seq Transcriptomes Retain the Inter-tumor And Intra-tsupporting

confidence: 82%

Section: Rhegs Are Enriched For Emt Signatures and Correlate With Canmentioning

confidence: 99%

Section: Rhegs Are Enriched For Emt Signatures and Cofluctuate With Cmentioning

confidence: 99%

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Pan-cancer Drivers are Recurrent Transcriptional Regulatory Heterogeneities in Early-stage Luminal Breast Cancer

Singh

Sutcliffe

Repich

et al. 2020

Preprint

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“…The previous analytical methods are categorized within this approach [23, 24, 26–34, 36, 40, 43–46, 48, 49, 51, 52, 55].…”

Section: Resultsmentioning

confidence: 99%

scTensor detects many-to-many cell–cell interactions from single cell RNA-sequencing data

Tsuyuzaki

Ishii

Nikaido

2022

Preprint

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Complex biological systems are described as a multitude of cell-cell interactions (CCIs). Recent single-cell RNA-sequencing studies focus on CCIs based on ligand-receptor (L-R) gene co-expression but the analytical methods are not appropriate to detect many-to-many CCIs. In this work, we propose scTensor, a novel method for extracting representative triadic relationships (or hypergraphs), which include ligand-expression, receptor-expression, and related L-R pairs. Through extensive studies with simulated and empirical datasets, we have shown that scTensor can detect some hypergraphs that cannot be detected using conventional CCI detection methods, especially when they include many-to-many relationships. scTensor is implemented as a freely available R/Bioconductor package.

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“…Recently, several studies investigated the tumor microenvironment of breast cancer. 23 , 24 The tumor microenvironment could be involved in paracrine interactions with malignant cells, leading to evading the immune surveillance and tumor progression. In this study, we focused on the malignant epithelial cells for network construction and revealed the essential roles of the critical genes.…”

Section: Discussionmentioning

confidence: 99%

Single-cell RNA-seq dissects the intratumoral heterogeneity of triple-negative breast cancer based on gene regulatory networks

Zhou

Huang

Liu

et al. 2021

Molecular Therapy - Nucleic Acids

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Triple-negative breast cancer (TNBC) is a subtype of breast cancer with high intratumoral heterogeneity. Recent studies revealed that TNBC patients might comprise cells with distinct molecular subtypes. In addition, gene regulatory networks (GRNs) constructed based on single-cell RNA sequencing (scRNA-seq) data have demonstrated the significance for decoding the key regulators. We performed a comprehensive analysis of the GRNs for the intrinsic subtypes of TNBC patients using scRNA-seq. The copy number variations (CNVs) were inferred from scRNA-seq data and identified 545 malignant cells. The subtypes of the malignant cells were assigned based on the PAM50 model. The cell-cell communication analysis revealed that the macrophage plays a dominant role in the tumor microenvironment. Next, the GRN for each subtype was constructed through integrating gene co-expression and enrichment of transcription-binding motifs. Then, we identified the critical genes based on the centrality metrics of genes. Importantly, the critical gene ETV 6 was ubiquitously upregulated in all subtypes, but it exerted diverse roles in each subtype through regulating different target genes. In conclusion, the construction of GRNs based on scRNA-seq data could help us to dissect the intratumoral heterogeneity and identify the critical genes of TNBC.

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Comprehensive analysis of immune evasion in breast cancer by single-cell RNA-seq

Cited by 6 publications

References 43 publications

Pan-cancer Drivers are Recurrent Transcriptional Regulatory Heterogeneities in Early-stage Luminal Breast Cancer

Pan-cancer Drivers are Recurrent Transcriptional Regulatory Heterogeneities in Early-stage Luminal Breast Cancer

scTensor detects many-to-many cell–cell interactions from single cell RNA-sequencing data

Single-cell RNA-seq dissects the intratumoral heterogeneity of triple-negative breast cancer based on gene regulatory networks

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