Evolution of cellular morpho-phenotypes in cancer metastasis

Wu, Pei Hsun; Phillip, Jude M.; Khatau, Shyam B.; Chen, Wei Chiang; Stirman, Jeffrey N.; Rosseel, Sophie; Tschudi, Katherine; Patten, Joshua Van; Wong, Michael K.; Gupta, Sonal; Baras, Alexander S.; Leek, Jeffrey T.; Maitra, Anirban; Wirtz, Denis

doi:10.1038/srep18437

Cited by 103 publications

(111 citation statements)

References 52 publications

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“…Previous studies have linked heterogeneity of cell shape to metastatic potential. For example, lower variation in morphology is predictive of cells derived from metastatic sites, but not associated with any particular somatic mutations (27). In addition, the dynamics of breast cancer cell shape heterogeneity can impact response to therapy.…”

Section: Discussionmentioning

confidence: 99%

Cell shape, and not 2D migration, predicts ECM-driven 3D cell invasion in breast cancer

Guarin

et al. 2020

Preprint

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Metastasis, the leading cause of death in cancer patients, requires the invasion of tumor cells through the stroma in response to migratory cues, such as those provided by the extracellular matrix (ECM). Recent advances in proteomics have led to the identification of hundreds of ECM proteins which are more abundant in tumors relative to healthy tissue. Our goal was to develop a pipeline to easily predict which of these ECM proteins is more likely to have an effect on cancer invasion and metastasis. We evaluated the effect of 4 ECM proteins upregulated in breast tumor tissue in multiple human breast cancer cell lines in 3 assays. We found there was no linear relationship between the 11 cell shape parameters we quantified when cells adhere to ECM proteins and 2D cell migration speed, persistence or 3D invasion. We then used classifiers and partial-least squares regression analysis to identify which metrics best predicted ECM-driven 2D migration and 3D invasion responses. ECM-driven 2D cell migration speed or persistence did not correlate with or predict 3D invasion in response to that same cue. However, cell adhesion, and in particular cell elongation and irregularity accurately predicted the magnitude of ECM-driven 2D migration and 3D invasion in all cell lines. Testing predictions revealed that our models are good at predicting the effect of novel ECM proteins within a given cell line, but that ECM responses are cell-line specific. Overall, our studies identify the cell morphological features that determine 3D invasion responses to individual ECM proteins. This platform will help provide insight into the functional role of ECM proteins abundant tumor tissue and help prioritize strategies for targeting tumor-ECM interactions to treat metastasis. to M.J.O.]; Tufts University [Start-up funds from the School of Engineering to M.J.O.] and funds from NSF REU to A.W.Conflict-of-interest: None. Insight BoxMetastasis, the dissemination of tumor cells, is driven by the interaction of invading tumor cells with their local environment, in particular with the ECM, which provides structure and support to our tissues. This study presents an integrated approach to predict the effect of individual ECM proteins on 3D invasion and metastasis based on simple adhesion assays which quantify cell shape. Machine learning classification and partial-least squares regression models reveal that ECM-driven 2D cell migration metrics are not predictive of 3D invasion, and that cell shape of cells adhered to ECM can predict that protein's effect on 3D invasion. These data provide a pipeline for predicting the effect of ECM proteins on breast cancer cell invasion and metastasis.

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Section: Discussionmentioning

confidence: 99%

Cell shape, and not 2D migration, predicts ECM-driven 3D cell invasion in breast cancer

Guarin

et al. 2020

Preprint

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“…High-content imaging of cancer cell lines in response to drug treatment is a standard assay applied in preclinical studies for identification of different mechanisms of drug action [9,10,11,12]. The main criteria in every morphophenotypic screen for early drug discovery is the selection of biomarkers and detection modalities (antibodies, chemical and enzymatic probes, reporter detection tags).…”

Section: Introductionmentioning

confidence: 99%

Multidimensional Profiling of Drug-Treated Cells by Imaging Mass Cytometry

Bouzekri

Esch

Ornatsky

2019

Preprint

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In pharmaceutical research, high-content screening is an integral part of lead candidate development. Drug response in vitro over 40 parameters including biomarkers, signaling molecules, cell morphological changes, proliferation indexes and toxicity in a single sample could significantly enhance discovery of new therapeutics. As a proof of concept, we present a workflow for multidimensional Imaging Mass Cytometry™ (IMC™) and data processing with open source computational tools. CellProfiler was used to identify single cells through establishing cellular boundaries, followed by histoCAT™ (histology topography cytometry analysis toolbox) for extracting single-cell quantitative information visualized as t-SNE plots and heatmaps. Human breast cancer-derived cell lines SKBR3, HCC1143 and MCF-7 were screened for expression of cellular markers to generate digital images with a resolution comparable to conventional fluorescence microscopy. Predicted pharmacodynamic effects were measured in MCF-7 cells dosed with three target-specific compounds: growth stimulatory EGF, microtubule depolymerization agent nocodazole and genotoxic chemotherapeutic drug etoposide. We show strong pairwise correlation between nuclear markers pHistone3 S28 , Ki-67 and p4E-BP1 T37/T46 in classified mitotic cells and anti-correlation with cell surface markers. Our study demonstrates that IMC data expands the number of measured parameters in single cells and brings higherdimension analysis to the field of cell-based screening in early lead compound discovery.

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“…A candidate for this proposal is the work of Yin et al [37] where a Support Vector Machine (SVM), a supervised machine learning model, was used to identify discrete shapes from 211 geometric and texture properties of each segmented Drosphilia Kc cell. Another example is the work of Wu et al [38] with their image analysis tool VAMPIRE. The tool reduces the morphological description for an outline into similar modes of shape using PCA before analysing their distribution in cancer metastasis.…”

Section: Discussionmentioning

confidence: 99%

CASTLE: Cell Adhesion with Supervised Training and Learning Environment

Gilbert

Krautter

Cooper

et al. 2020

Preprint

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Different types of microscopy are used to uncover signatures of cell adhesion and mechanics. Automating the identification and analysis often involve sacrificial routines of cell manipulation such as in vitro staining. Phase-contrast microscopy (PCM) is rarely used in automation due to the difficulties with poor quality images. However, it is the least intrusive method to provide insights into the dynamics of cells, where other types of microscopy are too destructive to monitor. In this study, we propose an efficient workflow to automate cell counting and morphology in PCM images. We introduce Cell Adhesion with Supervised Training and Learning Environment (CASTLE), available as a series of additional plugins to ImageJ. CASTLE combines effective techniques for phase-contrast image processing with statistical analysis and machine learning algorithms to interpret the results. The proposed workflow was validated by comparing the results to a manual count and manual segmentation of cells in images investigating different adherent cell types, including monocytes, neutrophils and platelets. In addition, the effect of different molecules on cell adhesion was characterised using CASTLE. For example, we demonstate that Galectin-9 leads to differences in adhesion of leukocytes. CASTLE also provides information using machine learning techniques, namely principal component analysis and k-means clustering, to distinguish morphology currently inaccessible with manual methods. All scripts and documentation is open-source and available at the corresponding GitLab project.

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Evolution of cellular morpho-phenotypes in cancer metastasis

Cited by 103 publications

References 52 publications

Cell shape, and not 2D migration, predicts ECM-driven 3D cell invasion in breast cancer

Cell shape, and not 2D migration, predicts ECM-driven 3D cell invasion in breast cancer

Multidimensional Profiling of Drug-Treated Cells by Imaging Mass Cytometry

CASTLE: Cell Adhesion with Supervised Training and Learning Environment

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