<i>In Vivo</i>Monitoring of Rare Circulating Tumor Cell and Cluster Dissemination in a Multiple Myeloma Xenograft Model

Patil, Rahul; Tan, Xiaojie; Bartosik, Peter; Detappe, Alexandre; Runnels, Judith; Ghobrial, Irène M.; Lin, Cai; Niedre, Mark

doi:10.1101/516641

Cited by 5 publications

(8 citation statements)

References 34 publications

(63 reference statements)

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“…3E). The onset of this single cell-cluster transition recapitulates well the size distribution measured in a mouse model of myeloma (20) that peaks for a cluster size of n=4 cells (Fig. 3E).…”

Section: The Single Cell-cluster Migration Transition Recapitulates Csupporting

confidence: 80%

“…This simple physical model can quantitatively reproduce different cluster size distributions observed in the bloodstream of patients and/or in experimental mouse models. These experimental datasets exhibit a tremendous variability, ranging from cases where large clusters are highly improbable to cases of collective migration(4,14,(19)(20)(21)24), all of which can be captured by the model for varying parameter combinations.…”

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confidence: 99%

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A biophysical model of Epithelial-Mesenchymal Transition uncovers the frequency and size distribution of Circulating Tumor Cell clusters across cancer types

Bocci

2019

Preprint

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The gain of cellular motility via the epithelial-mesenchymal transition (EMT) is considered crucial in the metastatic cascade. Cells undergoing EMT to varying extents are launched into the bloodstream as single circulating tumor cells (CTCs) or multi-cellular clusters. The frequency and size distributions of these multicellular clusters has been recently measured, but the underlying mechanisms enabling these different modes of migration remain poorly understood. We present a biophysical model that couples the epithelialmesenchymal phenotypic transition and cell migration to explain these different modes of cancer cell migration. With this reduced physical model, we identify a transition from individual migration to clustered cell migration that is regulated by the rate of EMT and the degree of cooperativity between cells during migration. This single cell to clustered migration transition can robustly recapitulate cluster size distributions observed experimentally across several cancer types, thus suggesting the existence of common features in the mechanisms of cell migration during metastasis. Furthermore, we identify three main mechanisms that can facilitate the formation and dissemination of large clusters: first, mechanisms that prevent a complete EMT and instead increase the population of hybrid Epithelial/Mesenchymal (E/M) cells; second, multiple intermediate E/M states that give rise to heterogeneous clusters formed by cells with different epithelial-mesenchymal traits; and third, non-cell-autonomous induction of EMT via cell-tocell signaling that gives rise to spatial correlations among cells in a tissue. Overall, this biophysical model represents a first step toward bridging the gap between the molecular and biophysical understanding of EMT and various modes of cancer cell migration, and highlights that a complete EMT might not be required for metastasis. Statement of significanceThe Epithelial-Mesenchymal Transition (EMT) confers motility and invasive traits to cancer cells. These cells can then enter the circulatory system both as single cells or as multi-cellular clusters to initiate metastases.We develop a biophysical model to investigate how EMT at the single cell level can give rise to a solitary or clustered cell migration. This model quantitatively reproduces cluster size distributions reported in human circulation and mouse models, therefore suggesting similar mechanisms in cancer cell migration across different cancer types. Moreover, we show that a partial EMT to a hybrid epithelial/mesenchymal cell state is sufficient to explain both single cell and clustered migration, therefore questioning the necessity of a complete EMT for cancer metastasis.

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Section: The Single Cell-cluster Migration Transition Recapitulates Csupporting

confidence: 80%

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confidence: 99%

A biophysical model of Epithelial-Mesenchymal Transition uncovers the frequency and size distribution of Circulating Tumor Cell clusters across cancer types

Bocci

2019

Preprint

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“…This simple biophysical model can quantitatively reproduce different cluster size distributions observed in the bloodstream of patients and/or in experimental mouse models. These experimental datasets exhibit a tremendous variability, ranging from cases in which large clusters are highly improbable to cases of collective migration (3,15,(21)(22)(23)(24), all of which can be captured by the model for varying parameter combinations.…”

Section: Discussionmentioning

confidence: 99%

A Biophysical Model Uncovers the Size Distribution of Migrating Cell Clusters across Cancer Types

2019

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“… (A) best model fit on the (k,c) parameter space for various CTC cluster size distributions [34–40]. Each starred dot represents a CTC cluster size distribution that was fitted with the 5-state model; the x- and y-coordinates indicate the (k,c) parameter combination yielding the best fit defined in terms of minimal root square distance between experimental distribution and model prediction.…”

Section: Resultsmentioning

confidence: 99%

“…To investigate this prediction, we analyze several CTC cluster size distributions obtained experimentally through the lens of our model. In these datasets, which were obtained from different cancer types, single CTCs and CTC clusters were isolated with various techniques to obtain a frequency count or probability to observe CTC clusters with variable number of cells [34][35][36][37][38][39][40]. By fitting the model's CTC size distribution to the experimental distributions, we identify the parameter combinations (k, c) that can best fit corresponding experimental data.…”

Section: Analysis Of Ctc Cluster Size Distribution Reveals Emt Score mentioning

confidence: 99%

Investigating epithelial-mesenchymal heterogeneity of tumors and circulating tumor cells with transcriptomic analysis and biophysical modeling

Bocci

Mandal

Tejaswi

et al. 2020

Preprint

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Introduction: Cellular heterogeneity along the Epithelial-Mesenchymal Plasticity (EMP) spectrum is a paramount feature observed in tumors and circulating tumor cells (CTCs). High-throughput techniques now offer unprecedented details on this variability at a single- cell resolution. Yet, there is no current consensus about how EMP in tumors propagates to that in CTCs. To investigate a relationship between EMP associated heterogeneity of tumors and that of CTCs, we integrated transcriptomic analysis and biophysical modeling. Methods: We apply three EMT (Epithelial-Mesenchymal Transition) scoring metrics to multiple tumor samples and CTC datasets from several cancer types. Moreover, we develop a biophysical model that couples EMT associated phenotypic switching in a primary tumor with cell migration. Finally, we integrate EMT transcriptomic analysis and in silico modeling to evaluate the predictive power of several measurements of tumor aggressiveness, including tumor EMT score, CTC EMT score, fraction of CTC clusters found in circulation, and CTC cluster size distribution. Results: Analysis of high-throughput datasets reveals a pronounced heterogeneity without a well-defined relation between EMT traits in tumors and CTCs. Moreover, mathematical modeling predicts different phases where CTCs can be less, equally, or more mesenchymal than primary tumor depending on the dynamics of phenotypic transition and cell migration. Consistently, various datasets of CTC cluster size distribution from different cancer types are fitted onto different regimes of the model. By further constraining the model with experimental measurements of tumor EMT score, CTC EMT score, and fraction of CTC cluster in bloodstream, we show that none of these assays alone can provide sufficient information to predict the other variables. Conclusions: By integrating analysis of single cell gene expression and in silico modeling, we propose that the relationship between EMT progression in tumors and CTCs can be variable, and in general, predicting one from the other may not be as straightforward as tacitly assumed.

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In VivoMonitoring of Rare Circulating Tumor Cell and Cluster Dissemination in a Multiple Myeloma Xenograft Model

Cited by 5 publications

References 34 publications

A biophysical model of Epithelial-Mesenchymal Transition uncovers the frequency and size distribution of Circulating Tumor Cell clusters across cancer types

A biophysical model of Epithelial-Mesenchymal Transition uncovers the frequency and size distribution of Circulating Tumor Cell clusters across cancer types

A Biophysical Model Uncovers the Size Distribution of Migrating Cell Clusters across Cancer Types

Investigating epithelial-mesenchymal heterogeneity of tumors and circulating tumor cells with transcriptomic analysis and biophysical modeling

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