Single-cell RNA sequencing reveals cell heterogeneity and transcriptome profile of breast cancer lymph node metastasis

Xu, Kun; Wang, Runtian; Xie, Hui; Hu, Longfei; Wang, Cong; Xu, Jiali; Zhu, Chengjun; Liu, Yiqiu; Gao, Fangyan; Li, Xintong; Wang, Cenzhu; Huang, Jinyi; Zhou, Wenbin; Zhou, Guohua; Shu, Yongqian; Guan, Xiaoxiang

doi:10.1038/s41389-021-00355-6

Cited by 91 publications

(95 citation statements)

References 55 publications

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“…In metastatic pancreas carcinoma samples, a distinction between primary tumor and metastatic tumor cells was not possible using single cell RNA-sequencing ( Lin et al, 2020; Pan et al, 2021 ). This could also be seen in breast cancer single cell RNA-sequencing comparing lymph node metastasis with primary tumors ( Xu et al, 2021 ), which is totally in line with our findings regarding the MBC project dataset, not forming visual clusters in any considered approach. The presented results support the linear progression model to some extent, at least for the transcriptomic differences between metastasis and primary tumor, indicating the need for further research to combine genomic alterations with transcriptomic features to clarify the (clonal) evolution of metastasis.…”

Section: Discussionsupporting

confidence: 88%

Visual Clustering of Transcriptomic Data from Primary and Metastatic Tumors – Dependencies and Novel Pitfalls

Marquardt

Kollmannsberger

Krebs

et al. 2021

Preprint

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1.AbstractPersonalized Oncology is a rapidly evolving area and offers cancer patients therapy options more specific than ever. Yet, there is still a lack of understanding regarding transcriptomic similarities or differences of metastases and corresponding primary sites. Approaching this question, we used two different unsupervised dimension reduction methods – t-SNE and UMAP – on three different metastases datasets – prostate cancer, neuroendocrine prostate cancer, and skin cutaneous melanoma – including 682 different samples, with three different underlying data transformations – unprocessed FPKM values, log10 transformed FPKM values, and log10+1 transformed FPKM values – to visualize potential underlying clusters. The approaches resulted in formation of different clusters that were independent of respective resection sites. Additionally, data transformation critically affected cluster formation in most cases. Of note, our study revealed no tight link between the metastasis resection site and specific transcriptomic features. Instead, our analysis demonstrates the dependency of cluster formation on the underlying data transformation and the dimension reduction method applied. These observations propose data transformation as another key element in the interpretation of visual clustering approaches apart from well-known determinants such as initialization and parameters. Furthermore, the results show the need for further evaluation of underlying data alterations based on the biological question and subsequently used methods and applications.

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Section: Discussionsupporting

confidence: 88%

Visual Clustering of Transcriptomic Data from Primary and Metastatic Tumors – Dependencies and Novel Pitfalls

Marquardt

Kollmannsberger

Krebs

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…In metastatic pancreas carcinoma samples, a distinction between primary tumor and metastatic tumor cells was not possible using single cell RNA-sequencing (Lin et al, 2020;Pan et al, 2021). This could also be seen in breast cancer single cell RNA-sequencing comparing lymph node metastasis with primary tumors (Xu et al, 2021), which is totally in line with our ndings regarding the MBC project dataset, not forming visual clusters in any considered approach. The presented results support the linear progression model to some extent, at least for the transcriptomic differences between metastasis and primary tumor, indicating the need for further research to combine genomic alterations with transcriptomic features to clarify the (clonal) evolution of metastasis.…”

Section: Primary Tumors and Metastases Of The Same Entity Share Commo...supporting

confidence: 82%

Visual Clustering of Transcriptomic Data from Primary and Metastatic Tumors: Dependencies and Novel Pitfalls

Marquardt¹,

Kollmannsberger

Krebs

et al. 2022

Preprint

View full text Add to dashboard Cite

Background: Personalized Oncology is a rapidly evolving area and offers cancer patients therapy options more specific than ever. Yet, there is still a lack of understanding regarding transcriptomic similarities or differences of metastases and corresponding primary sites. Methods: Approaching this question, we used two different unsupervised dimension reduction methods – t-SNE and UMAP – on three different metastases datasets – prostate cancer, neuroendocrine prostate cancer, and skin cutaneous melanoma – including 682 different samples, with three different underlying data transformations – unprocessed FPKM values, log10 transformed FPKM values, and log10+1 transformed FPKM values – to visualize potential underlying clusters. Results: The approaches resulted in formation of different clusters that were independent of respective resection sites. Additionally, data transformation critically affected cluster formation in most cases. Of note, our study revealed no tight link between the metastasis resection site and specific transcriptomic features. Instead, our analysis demonstrates the dependency of cluster formation on the underlying data transformation and the dimension reduction method applied. Conclusion: These observations propose data transformation as another key element in the interpretation of visual clustering approaches apart from well-known determinants such as initialization and parameters. Furthermore, the results show the need for further evaluation of underlying data alterations based on the biological question and subsequently used methods and applications.

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“…To confirm our findings in clinical samples, we reanalyzed the integrated 3 public scRNA-seq datasets [8, 31, 32] and data from the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) [33]. First, we analyzed the integrated scRNA-seq dataset of 19 TNBC samples (Figure 5A).…”

Section: Resultsmentioning

confidence: 86%

“…and GSE180286 [32] were also downloaded from the GEO database. The scRNA-seq datasets were analyzed with Seurat vignettes, and the integration of datasets was performed by Harmony [56].…”

Section: Analysis Of the Public Scrna-seq Datasetmentioning

confidence: 99%

Identification of two cancer stem cell-like populations in triple-negative breast cancer xenografts

Nakayama

Matsunaga

Arikawa

et al. 2021

Preprint

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Gene expression analysis at the single-cell scale by next generation sequencing has revealed the existence of clonal dissemination in cancer metastasis. The current spatial analysis technologies elucidate the heterogeneity of cell-cell interactions in situ; however, further analysis is needed to elucidate the nature of tumor heterogeneity. To reveal the expressional heterogeneity and cell-cell interactions in primary tumors and metastases, we performed transcriptomic analysis of microtissues dissected from a triple-negative breast cancer (TNBC) cell line MDA-MB-231 xenograft model by our automated tissue microdissection punching technology. This multiple-microtissue transcriptome analysis revealed that there were existed three cell-type clusters in the primary tumor and axillary lymph node metastasis, two of which were cancer stem cell-like clusters (CD44/MYC-high, HMGA1-high). The CD44/MYC-high cluster showed aggressive proliferation with MYC expression. The HMGA1-high cluster exhibited HIF1A activation and upregulation of ribosomal processes. Furthermore, we developed a cell-cell Interaction (CCI) analysis to investigate the ligand-receptor interactions (cancer cell to stroma and stroma to cancer cell) in each spot. The CCI analysis revealed the interaction dynamics generated by the combination of cancer cells and stromal cells in primary tumors and metastases. Two cancer stem cell-like populations were also detected by the scRNA-seq analysis of TNBC patients. In addition, the gene signature of the HMGA1-high cancer stem cell-like cluster has the potential to serve as a novel biomarker for diagnosis. The mixture of these multiple cancer stem cell-like populations may cause differential anticancer drug resistance, increasing the difficulty of curing this cancer.

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Single-cell RNA sequencing reveals cell heterogeneity and transcriptome profile of breast cancer lymph node metastasis

Cited by 91 publications

References 55 publications

Visual Clustering of Transcriptomic Data from Primary and Metastatic Tumors – Dependencies and Novel Pitfalls

Visual Clustering of Transcriptomic Data from Primary and Metastatic Tumors – Dependencies and Novel Pitfalls

Visual Clustering of Transcriptomic Data from Primary and Metastatic Tumors: Dependencies and Novel Pitfalls

Identification of two cancer stem cell-like populations in triple-negative breast cancer xenografts

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