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Single-Cell Imaging of Metastatic Potential of Cancer Cells.

ABSTRACT: Molecular imaging of metastatic "potential" is an unvanquished challenge. To engineer biosensors that can detect and measure the metastatic "potential" of single living cancer cells, we carried out a comprehensive analysis of the pan-cancer phosphoproteome to search for actin remodelers required for cell migration, which are enriched in cancers but excluded in normal cells. Only one phosphoprotein emerged, tyr-phosphorylated CCDC88A (GIV/Girdin), a bona fide metastasis-related protein across a variety of solid tumors. We designed multi-modular biosensors that are partly derived from GIV, and because GIV integrates prometastatic signaling by multiple oncogenic receptors, we named them "'integrators of metastatic potential (IMP)." IMPs captured the heterogeneity of metastatic potential within primary lung and breast tumors at steady state, detected those few cells that have acquired the highest metastatic potential, and tracked their enrichment during metastasis. These findings provide proof of concept that IMPs can measure the diversity and plasticity of metastatic potential of tumor cells in a sensitive and unbiased way.

SUBMITTER: Midde K

PROVIDER: S-EPMC6263091 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature

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Single-Cell Imaging of Metastatic Potential of Cancer Cells.

Midde Krishna K Sun Nina N Rohena Cristina C Joosen Linda L Dhillon Harsharan H Ghosh Pradipta P

iScience 20181115

Molecular imaging of metastatic "potential" is an unvanquished challenge. To engineer biosensors that can detect and measure the metastatic "potential" of single living cancer cells, we carried out a comprehensive analysis of the pan-cancer phosphoproteome to search for actin remodelers required for cell migration, which are enriched in cancers but excluded in normal cells. Only one phosphoprotein emerged, tyr-phosphorylated CCDC88A (GIV/Girdin), a bona fide metastasis-related protein across a v ...[more]

PMID: 30500482

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Project description:Despite major advances in understanding the molecular and genetic basis of cancer, metastasis remains the cause of >90% of cancer-related mortality. Understanding metastasis initiation and progression is critical to developing new therapeutic strategies to treat and prevent metastatic disease. Prevailing theories hypothesize that metastases are seeded by rare tumour cells with unique properties, which may function like stem cells in their ability to initiate and propagate metastatic tumours. However, the identity of metastasis-initiating cells in human breast cancer remains elusive, and whether metastases are hierarchically organized is unknown. Here we show at the single-cell level that early stage metastatic cells possess a distinct stem-like gene expression signature. To identify and isolate metastatic cells from patient-derived xenograft models of human breast cancer, we developed a highly sensitive fluorescence-activated cell sorting (FACS)-based assay, which allowed us to enumerate metastatic cells in mouse peripheral tissues. We compared gene signatures in metastatic cells from tissues with low versus high metastatic burden. Metastatic cells from low-burden tissues were distinct owing to their increased expression of stem cell, epithelial-to-mesenchymal transition, pro-survival, and dormancy-associated genes. By contrast, metastatic cells from high-burden tissues were similar to primary tumour cells, which were more heterogeneous and expressed higher levels of luminal differentiation genes. Transplantation of stem-like metastatic cells from low-burden tissues showed that they have considerable tumour-initiating capacity, and can differentiate to produce luminal-like cancer cells. Progression to high metastatic burden was associated with increased proliferation and MYC expression, which could be attenuated by treatment with cyclin-dependent kinase (CDK) inhibitors. These findings support a hierarchical model for metastasis, in which metastases are initiated by stem-like cells that proliferate and differentiate to produce advanced metastatic disease.

Dataset Information

Single-Cell Imaging of Metastatic Potential of Cancer Cells.

Publications

Single-Cell Imaging of Metastatic Potential of Cancer Cells.

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