TY - JOUR
T1 - The Breast Cancer Single-Cell Atlas
T2 - Defining cellular heterogeneity within model cell lines and primary tumors to inform disease subtype, stemness, and treatment options
AU - Dave, Arpit
AU - Charytonowicz, Daniel
AU - Francoeur, Nancy J.
AU - Beaumont, Michael
AU - Beaumont, Kristin
AU - Schmidt, Hank
AU - Zeleke, Tizita
AU - Silva, Jose
AU - Sebra, Robert
N1 - Publisher Copyright:
© 2022, The Author(s).
PY - 2023
Y1 - 2023
N2 - Purpose: Breast Cancer (BC) is the most diagnosed cancer in women; however, through significant research, relative survival rates have significantly improved. Despite progress, there remains a gap in our understanding of BC subtypes and personalized treatments. This manuscript characterized cellular heterogeneity in BC cell lines through scRNAseq to resolve variability in subtyping, disease modeling potential, and therapeutic targeting predictions. Methods: We generated a Breast Cancer Single-Cell Cell Line Atlas (BSCLA) to help inform future BC research. We sequenced over 36,195 cells composed of 13 cell lines spanning the spectrum of clinical BC subtypes and leveraged publicly available data comprising 39,214 cells from 26 primary tumors. Results: Unsupervised clustering identified 49 subpopulations within the cell line dataset. We resolve ambiguity in subtype annotation comparing expression of Estrogen Receptor, Progesterone Receptor, and Human Epidermal Growth Factor Receptor 2 genes. Gene correlations with disease subtype highlighted S100A7 and MUCL1 overexpression in HER2 + cells as possible cell motility and localization drivers. We also present genes driving populational drifts to generate novel gene vectors characterizing each subpopulation. A global Cancer Stem Cell (CSC) scoring vector was used to identify stemness potential for subpopulations and model multi-potency. Finally, we overlay the BSCLA dataset with FDA-approved targets to identify to predict the efficacy of subpopulation-specific therapies. Conclusion: The BSCLA defines the heterogeneity within BC cell lines, enhancing our overall understanding of BC cellular diversity to guide future BC research, including model cell line selection, unintended sample source effects, stemness factors between cell lines, and cell type-specific treatment response.
AB - Purpose: Breast Cancer (BC) is the most diagnosed cancer in women; however, through significant research, relative survival rates have significantly improved. Despite progress, there remains a gap in our understanding of BC subtypes and personalized treatments. This manuscript characterized cellular heterogeneity in BC cell lines through scRNAseq to resolve variability in subtyping, disease modeling potential, and therapeutic targeting predictions. Methods: We generated a Breast Cancer Single-Cell Cell Line Atlas (BSCLA) to help inform future BC research. We sequenced over 36,195 cells composed of 13 cell lines spanning the spectrum of clinical BC subtypes and leveraged publicly available data comprising 39,214 cells from 26 primary tumors. Results: Unsupervised clustering identified 49 subpopulations within the cell line dataset. We resolve ambiguity in subtype annotation comparing expression of Estrogen Receptor, Progesterone Receptor, and Human Epidermal Growth Factor Receptor 2 genes. Gene correlations with disease subtype highlighted S100A7 and MUCL1 overexpression in HER2 + cells as possible cell motility and localization drivers. We also present genes driving populational drifts to generate novel gene vectors characterizing each subpopulation. A global Cancer Stem Cell (CSC) scoring vector was used to identify stemness potential for subpopulations and model multi-potency. Finally, we overlay the BSCLA dataset with FDA-approved targets to identify to predict the efficacy of subpopulation-specific therapies. Conclusion: The BSCLA defines the heterogeneity within BC cell lines, enhancing our overall understanding of BC cellular diversity to guide future BC research, including model cell line selection, unintended sample source effects, stemness factors between cell lines, and cell type-specific treatment response.
KW - Breast Cancer
KW - Cell Lines
KW - Disease Subtyping
KW - Stemness Scoring
KW - Therapeutic Prediction
KW - scRNAseq
UR - http://www.scopus.com/inward/record.url?scp=85145549643&partnerID=8YFLogxK
U2 - 10.1007/s13402-022-00765-7
DO - 10.1007/s13402-022-00765-7
M3 - Article
C2 - 36598637
AN - SCOPUS:85145549643
SN - 2211-3428
JO - Cellular oncology (Dordrecht)
JF - Cellular oncology (Dordrecht)
ER -