Phenotypic Heterogeneity Driven by Plasticity of the Intermediate EMT State Governs Disease Progression and Metastasis in Breast Cancer

Title:

Phenotypic Heterogeneity Driven by Plasticity of the Intermediate EMT State Governs Disease Progression and Metastasis in Breast Cancer

Link:

https://www.science.org/doi/full/10.1126/sciadv.abj8002

Abstract:

The epithelial-to-mesenchymal transition (EMT) is frequently co-opted by cancer cells to enhance migratory and invasive cell traits. It is a key contributor to heterogeneity, chemoresistance, and metastasis in many carcinoma types, where the intermediate EMT state plays a critical tumor-initiating role. We isolate multiple distinct single-cell clones from the SUM149PT human breast cell line spanning the EMT spectrum having diverse migratory, tumor-initiating, and metastatic qualities, including three unique intermediates. Using a multiomics approach, we identify CBFβ as a key regulator of metastatic ability in the intermediate state. To quantify epithelial-mesenchymal heterogeneity within tumors, we develop an advanced multiplexed immunostaining approach using SUM149-derived orthotopic tumors and find that the EMT state and epithelial-mesenchymal heterogeneity are predictive of overall survival in a cohort of stage III breast cancer. Our model reveals previously unidentified insights into the complex EMT spectrum and its regulatory networks, as well as the contributions of epithelial-mesenchymal plasticity (EMP) in tumor heterogeneity in breast cancer.

Citation:

Meredith S. Brown, Behnaz Abdollahi, Owen M. Wilkins, Hanxu Lu, Priyanka Chakraborty, Nevena B. Ognjenovic, Kristen E. Muller, Mohit Kumar Jolly, Brock C. Christensen, Saeed Hassanpour, Diwakar R. Pattabiraman, “Phenotypic Heterogeneity Driven by Plasticity of the Intermediate EMT State Governs Disease Progression and Metastasis in Breast Cancer”, Science Advances, 8:31, 2022.

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Quantifying Epithelial-Mesenchymal Heterogeneity and EMT Scoring in Tumor Samples via Tyramide Signal Amplification (TSA)

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Predicting Oncogene Mutations of Lung Cancer Using Deep Learning and Histopathologic Features on Whole-Slide Images