A Model of Dormant-Emergent Metastatic Breast Cancer Progression Enabling Exploration of Biomarker Signatures

休眠-突发性转移性乳腺癌进展模型,可用于探索生物标志物特征

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Abstract

Breast cancer mortality predominantly results from dormant micrometastases that emerge as fatal outgrowths years after initial diagnosis. In order to gain insights concerning factors associated with emergence of liver metastases, we recreated spontaneous dormancy in an all-human ex vivo hepatic microphysiological system (MPS). Seeding this MPS with small numbers (<0.05% by cell count) of the aggressive MDA-MB-231 breast cancer cell line, two populations formed: actively proliferating ("growing"; EdU(+)), and spontaneously quiescent ("dormant"; EdU(-)). Following treatment with a clinically standard chemotherapeutic, the proliferating cells were eliminated and only quiescent cells remained; this residual dormant population could then be induced to a proliferative state ("emergent"; EdU(+)) by physiologically-relevant inflammatory stimuli, lipopolysaccharide (LPS) and epidermal growth factor (EGF). Multiplexed proteomic analysis of the MPS effluent enabled elucidation of key factors and processes that correlated with the various tumor cell states, and candidate biomarkers for actively proliferating (either primary or secondary emergence) versus dormant metastatic cells in liver tissue. Dormancy was found to be associated with signaling reflective of cellular quiescence even more strongly than the original tumor-free liver tissue, whereas proliferative nodules presented inflammatory signatures. Given the minimal tumor burden, these markers likely represent changes in the tumor microenvironment rather than in the tumor cells. A computational decision tree algorithm applied to these signatures indicated the potential of this MPS for clinical discernment of each metastatic stage from blood protein analysis.

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