Landscapes of cellular phenotypic diversity in breast cancer xenografts and their impact on drug response.
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ICR Authors
Authors
Georgopoulou, D
Callari, M
Rueda, OM
Shea, A
Martin, A
Giovannetti, A
Qosaj, F
Dariush, A
Chin, S-F
Carnevalli, LS
Provenzano, E
Greenwood, W
Lerda, G
Esmaeilishirazifard, E
O'Reilly, M
Serra, V
Bressan, D
IMAXT Consortium,
Mills, GB
Ali, HR
Cosulich, SS
Hannon, GJ
Bruna, A
Caldas, C
Callari, M
Rueda, OM
Shea, A
Martin, A
Giovannetti, A
Qosaj, F
Dariush, A
Chin, S-F
Carnevalli, LS
Provenzano, E
Greenwood, W
Lerda, G
Esmaeilishirazifard, E
O'Reilly, M
Serra, V
Bressan, D
IMAXT Consortium,
Mills, GB
Ali, HR
Cosulich, SS
Hannon, GJ
Bruna, A
Caldas, C
Document Type
Journal Article
Date
2021-03-31
Date Accepted
2021-02-26
Abstract
The heterogeneity of breast cancer plays a major role in drug response and resistance and has been extensively characterized at the genomic level. Here, a single-cell breast cancer mass cytometry (BCMC) panel is optimized to identify cell phenotypes and their oncogenic signalling states in a biobank of patient-derived tumour xenograft (PDTX) models representing the diversity of human breast cancer. The BCMC panel identifies 13 cellular phenotypes (11 human and 2 murine), associated with both breast cancer subtypes and specific genomic features. Pre-treatment cellular phenotypic composition is a determinant of response to anticancer therapies. Single-cell profiling also reveals drug-induced cellular phenotypic dynamics, unravelling previously unnoticed intra-tumour response diversity. The comprehensive view of the landscapes of cellular phenotypic heterogeneity in PDTXs uncovered by the BCMC panel, which is mirrored in primary human tumours, has profound implications for understanding and predicting therapy response and resistance.
Citation
Nature communications, 2021, 12 (1), pp. 1998 - ?
Source Title
Publisher
NATURE PORTFOLIO
ISSN
2041-1723
eISSN
2041-1723
Collections
Research Team
Preclinical Modelling of Paediatric Cancer Evolution
Preclinical Modelling of Paediatric Cancer Evolution
Preclinical Modelling of Paediatric Cancer Evolution