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Landscapes of cellular phenotypic diversity in breast cancer xenografts and their impact on drug response.

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.

SUBMITTER: Georgopoulou D

PROVIDER: S-EPMC8012607 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature

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Landscapes of cellular phenotypic diversity in breast cancer xenografts and their impact on drug response.

Georgopoulou Dimitra D Callari Maurizio M Rueda Oscar M OM Shea Abigail A Martin Alistair A Giovannetti Agnese A Qosaj Fatime F Dariush Ali A Chin Suet-Feung SF Carnevalli Larissa S LS Provenzano Elena E Greenwood Wendy W Lerda Giulia G Esmaeilishirazifard Elham E O'Reilly Martin M Serra Violeta V Bressan Dario D Mills Gordon B GB Ali H Raza HR Cosulich Sabina S SS Hannon Gregory J GJ Bruna Alejandra A Caldas Carlos C

Nature communications 20210331 1

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 bre ...[more]

PMID: 33790302

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Project description:Background-Triple-negative breast cancer is a prevalent breast cancer subtype with the lowest 5-year survival. Several factors contribute to its treatment response, but the inherent molecular and cellular tumor heterogeneity are increasingly acknowledged as crucial determinants.Methods-Spatial transcriptomic profiling was performed on FFPE tissues from a retrospective, treatment-naive group of women with differential prognoses (17 with >15 years survival- good prognosis (GPx) and 15 with <3 years survival-poor prognosis (PPx)) using GeoMX® Digital Spatial Profiler. Regions of interest were segmented on pan-cytokeratin and analyzed for tumor and stromal components, probed using GeoMx human whole transcriptome atlas (WTA) panel. Data quality control, normalization, and differential analysis was performed in R using GeomxTools and linear mixed models. Additional analyses including cell-type deconvolution, spatial entropy, functional enrichment, TF-target / ligand-receptor analysis and convolution neural networks were employed to identify significant gene signatures contributing to differential prognosis.Results-Here we report on the spatial and molecular heterogeneity underlying differential prognosis. We observe that the state of the epithelia and its microenvironment (TME) are transcriptionally distinct between the two groups. Invasive epithelia in GPx show a significant increase in immune transcripts with the TME exhibiting increased immune cell presence (via IF), while in PPx they are more metabolically and translationally active, with the TME being more mesenchymal/fibrotic. Specifically, pre-cancerous epithelia in PPx display a prescience of aggressiveness as evidenced by increased EMT-signaling. We identify distinct epithelial gene signatures for PPx and GPx, that can, with high accuracy, classify samples at the time of diagnosis and likely inform therapy.Conclusions-To the best of our knowledge, this is the first study to leverage spatial transcriptomics for an in-depth delineation of the cellular and molecular underpinnings of differential prognosis in TNBC. Our study highlights the potential of spatial transcriptomics to not only uncover the molecular drivers of differential prognosis in TNBC but also to pave the way for precision diagnostics and tailored therapeutic strategies, transforming the clinical landscape for this aggressive breast cancer subtype.

Dataset Information

Landscapes of cellular phenotypic diversity in breast cancer xenografts and their impact on drug response.

Publications

Landscapes of cellular phenotypic diversity in breast cancer xenografts and their impact on drug response.

Similar Datasets

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