Single-cell analysis of breast cancer metastasis reveals epithelial-mesenchymal plasticity signatures associated with poor outcomes
Juliane Winkler, … , Spyros Darmanis, Zena Werb
Juliane Winkler, … , Spyros Darmanis, Zena Werb
Published September 3, 2024
Citation Information: J Clin Invest. 2024;134(17):e164227. https://doi.org/10.1172/JCI164227.
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Research Article Oncology

Single-cell analysis of breast cancer metastasis reveals epithelial-mesenchymal plasticity signatures associated with poor outcomes

Abstract

Metastasis is the leading cause of cancer-related deaths. It is unclear how intratumor heterogeneity (ITH) contributes to metastasis and how metastatic cells adapt to distant tissue environments. The study of these adaptations is challenged by the limited access to patient material and a lack of experimental models that appropriately recapitulate ITH. To investigate metastatic cell adaptations and the contribution of ITH to metastasis, we analyzed single-cell transcriptomes of matched primary tumors and metastases from patient-derived xenograft models of breast cancer. We found profound transcriptional differences between the primary tumor and metastatic cells. Primary tumors upregulated several metabolic genes, whereas motility pathway genes were upregulated in micrometastases, and stress response signaling was upregulated during progression. Additionally, we identified primary tumor gene signatures that were associated with increased metastatic potential and correlated with patient outcomes. Immune-regulatory control pathways were enriched in poorly metastatic primary tumors, whereas genes involved in epithelial-mesenchymal transition were upregulated in highly metastatic tumors. We found that ITH was dominated by epithelial-mesenchymal plasticity (EMP), which presented as a dynamic continuum with intermediate EMP cell states characterized by specific genes such as CRYAB and S100A2. Elevated expression of an intermediate EMP signature correlated with worse patient outcomes. Our findings identified inhibition of the intermediate EMP cell state as a potential therapeutic target to block metastasis.

Authors

Juliane Winkler, Weilun Tan, Catherine M.M. Diadhiou, Christopher S. McGinnis, Aamna Abbasi, Saad Hasnain, Sophia Durney, Elena Atamaniuc, Daphne Superville, Leena Awni, Joyce V. Lee, Johanna H. Hinrichs, Patrick S. Wagner, Namrata Singh, Marco Y. Hein, Michael Borja, Angela M. Detweiler, Su-Yang Liu, Ankitha Nanjaraj, Vaishnavi Sitarama, Hope S. Rugo, Norma Neff, Zev J. Gartner, Angela Oliveira Pisco, Andrei Goga, Spyros Darmanis, Zena Werb

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Figure 4

EMP is a key feature of tumor heterogeneity.

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EMP is a key feature of tumor heterogeneity. (A) Scatter plot showing th...
(A) Scatter plot showing the correlation of the mean EMP signature gene expression of the primary tumor and metastatic cells colored by tumor. Linear regression with 95% CIs and Pearson’s correlation coefficient are shown. (B) Violin plot showing the EMP signature per tumor ordered by metastatic potential using the Smart-Seq2 data set. (C) Bubble plot showing the correlation of the EMP signature with PCs 1–5 using the Smart-Seq2 data set. (D) UMAP projections of single-cell transcriptomes for individual tumors are color coded by the magnitude of EMP signature gene expression. (E) Cells in the Smart-Seq2 data set ranked by the EMP signature exhibited 3 cell states: epithelial-like (blue), intermediate EMP (purple), and mesenchymal-like cells (red). (F) Bar chart showing the proportion of EMP cell states in each tumor ranked by the increasing proportion of mesenchymal-like cells. Grayscale boxes indicate the metastatic potential. Other annotations indicate ER status and BC subtype. The Smart-Seq2 data set is shown. (G) Violin plots show the expression of EMT-associated TFs in cells expressing these TFs, grouped by EMP cell state (Epi, epithelial-like; Inter, intermediate EMP, Mes, mesenchymal-like cells). Bar charts show the fraction of TF-expressing cells colored in gray. The Smart-Seq2 data set is shown.