Enhanced Dendritic Actin Network Formation in Extended Lamellipodia Drives Proliferation in Growth-Challenged Rac1P29S Melanoma Cells

Mohan, Ashwathi S.; Dean, Kevin M.; Isogai, Tadamoto; Kasitinon, Stacy Y.; Murali, Vasanth Siruvallur; Roudot, Philippe; Groisman, Alex; Reed, Dana Kim; Welf, Erik S.; Han, Sangyoon J.; Noh, Jungsik; Danuser, Gaudenz

doi:10.1016/j.devcel.2019.04.007

Cited by 40 publications

(42 citation statements)

References 71 publications

(105 reference statements)

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“…S14). Recent work has suggested mechanistic links between enhanced branched actin formation in pseudopdial cell compartments and enhanced cell cycle progression (Mohan et al, 2019;Molinie et al, 2019), especially in micrometastases. Therefore, we lean towards an interpretation that connects the predicted metastatic efficiency under pseudopod formation to increased proliferation and survival.…”

Section: Interpretation Of Latent Features Discriminating High and Lomentioning

confidence: 99%

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Interpretable deep learning of label-free live cell images uncovers functional hallmarks of highly-metastatic melanoma

Zaritsky

Jamieson

Welf

et al. 2020

Preprint

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Deep convolutional neural networks have emerged as a powerful technique to identify hidden patterns in complex cell imaging data. However, these machine learning techniques are often criticized as uninterpretable "black-boxes" -lacking the ability to provide meaningful explanations for the cell properties that drive the machine's prediction. Here, we demonstrate that the latent features extracted from label-free live cell images by an adversarial auto-encoding deep convolutional neural network capture subtle details of cell appearance that allow classification of melanoma cell states, including the metastatic efficiency of seven patientderived xenograft models that reflect clinical outcome. Although trained exclusively on patientderived xenograft models, the same classifier also predicted the metastatic efficiency of immortalized melanoma cell lines suggesting that the latent features capture properties that are specifically associated with the metastatic potential of a melanoma cell regardless of its origin.We used the autoencoder to generate "in-silico" cell images that amplified the cellular features driving the classifier of metastatic efficiency. These images unveiled pseudopodial extensions and increased light scattering as functional hallmarks of metastatic cells. We validated this interpretation by analyzing experimental image time-lapse sequences in which melanoma cells spontaneously transitioned between states indicative of low and high metastatic efficiency.Together, this data is an example of how the application of Artificial Intelligence supports the identification of processes that are essential for the execution of complex integrated cell functions but are too subtle to be identified by a human expert. -Gurion University of the Negev, Israel (to AZ). We thank Sean Morrison for PDX-derived cell models. We thank Andrew R. Cohen for LEVER. Author ContributionAZ, ESW and GD conceived the study. ESW designed the experimental assay, optimized culture conditions for PDX cells, and trained AN and JC to perform the experiments. AN performed all live imaging and mouse experiments. JC performed additional experiments. UE generated the PDX samples. AZ and ARJ developed analytic tools, analyzed, and interpreted the data. AZ drafted the manuscript. All authors wrote and edited the manuscript and approved its content.GD mentored all authors.

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Section: Interpretation Of Latent Features Discriminating High and Lomentioning

confidence: 99%

“…Cells were then washed four times with 1X PBS and imaged at 20x magnification on a Nikon Eclipse Ti live cell microscope. Proliferation was quantified in these images using a point source detection algorithm described previously (Mohan et al, 2019).…”

Section: Measuring Extracellular Acidification Rate Oxygen Consumptimentioning

confidence: 99%

Interpretable deep learning of label-free live cell images uncovers functional hallmarks of highly-metastatic melanoma

Zaritsky

Jamieson

Welf

et al. 2020

Preprint

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“…Consistently, the ectopic expression of RAC1 P29S in melanoma cell lines increases resistance to BRAF and MEK inhibitor therapies (Van Allen et al, 2014;Watson et al, 2014;Araiza-Olivera et al, 2018). Indeed, using a mouse xenograft model, the authors observed that this mutation enhances melanoma growth and invasion and confers drug resistance against BRAF and MEK kinase inhibitors (Revach et al, 2016;Mohan et al, 2019) (Table 1). More recently, using transgenic mouse models, it was also demonstrated that Rac1 P29S can cooperate with BRAF V600E to promote melanoma tumorigenesis (Lionarons et al, 2019).…”

Section: Rho Family Members Involved In Melanoma Migration and Invasionmentioning

confidence: 71%

“…Remarkably, the increased mitotic rate observed is due to the formation of lamellipodia, which are dependent on continuous actin polymerization and activate proliferative signaling cascades by inhibiting the neurofibromatosis type 2 (NF2)/Merlin tumor suppressor (Mohan et al, 2019). Consequently, NF2/Merlin inhibition by phosphorylation of the Rac effector PAK promotes melanoma cell resistance to MAPK inhibitors (Mohan et al, 2019) (Table 1). Consistently, the ectopic expression of RAC1 P29S in melanoma cell lines increases resistance to BRAF and MEK inhibitor therapies (Van Allen et al, 2014;Watson et al, 2014;Araiza-Olivera et al, 2018).…”

Section: Rho Family Members Involved In Melanoma Migration and Invasionmentioning

confidence: 99%

“…From a clinical point of view, RAC1 P29S mutation is associated with increased melanoma thickness, high mitotic rate, ulceration, and the occurrence of the nodular melanoma subtype ( Mar et al, 2014 ) ( Table 1 ). Remarkably, the increased mitotic rate observed is due to the formation of lamellipodia, which are dependent on continuous actin polymerization and activate proliferative signaling cascades by inhibiting the neurofibromatosis type 2 (NF2)/Merlin tumor suppressor ( Mohan et al, 2019 ). Consequently, NF2/Merlin inhibition by phosphorylation of the Rac effector PAK promotes melanoma cell resistance to MAPK inhibitors ( Mohan et al, 2019 ) ( Table 1 ).…”

Section: Rho Family Members Involved In Melanoma Migration and Invasimentioning

confidence: 99%

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Subversion of Ras Small GTPases in Cutaneous Melanoma Aggressiveness

Brito¹,

Barral

Pojo³

2020

Front. Cell Dev. Biol.

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The rising incidence and mortality rate associated with the metastatic ability of cutaneous melanoma represent a major public health concern. Cutaneous melanoma is one of the most invasive human cancers, but the molecular mechanisms are poorly understood. Moreover, currently available therapies are not efficient in avoiding melanoma lethality. In this context, new biomarkers of prognosis, metastasis, and response to therapy are necessary to better predict the disease outcome. Additionally, the knowledge about the molecular alterations and dysregulated pathways involved in melanoma metastasis may provide new therapeutic targets. Members of the Ras superfamily of small GTPases regulate various essential cellular activities, from signaling to membrane traffic and cytoskeleton dynamics. Therefore, it is not surprising that they are differentially expressed, and their functions subverted in several types of cancer, including melanoma. Indeed, Ras small GTPases were found to regulate melanoma progression and invasion. Hence, a better understanding of the mechanisms regulated by Ras small GTPases that are involved in melanoma tumorigenesis and progression may provide new therapeutic strategies to block these processes. Here, we review the current knowledge on the role of Ras small GTPases in melanoma aggressiveness and the molecular mechanisms involved. Furthermore, we summarize the known involvement of these proteins in melanoma metastasis and how these players influence the response to therapy.

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The stressful tumour environment drives plasticity of cell migration programmes, contributing to metastasis

Nikolaou

Machesky

2020

The Journal of Pathology

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Tumours evolve to cope with environmental stresses or challenges such as nutrient starvation, depletion of survival factors, and unbalanced mechanical forces. The uncontrolled growth and aberrant deregulation of core cell homeostatic pathways induced by genetic mutations create an environment of stress. Here, we explore how the adaptations of tumours to the changing environment can drive changes in the motility machinery of cells, affecting migration, invasion, and metastasis. Tumour cells can invade individually or collectively, or they can be extruded out of the surrounding epithelium. These mechanisms are thought to be modifications of normal processes occurring during development or tissue repair. Therefore, tumours may activate these pathways in response to environmental stresses, enabling them to survive in hostile environments and spread to distant sites.

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Enhanced Dendritic Actin Network Formation in Extended Lamellipodia Drives Proliferation in Growth-Challenged Rac1P29S Melanoma Cells

Cited by 40 publications

References 71 publications

Interpretable deep learning of label-free live cell images uncovers functional hallmarks of highly-metastatic melanoma

Interpretable deep learning of label-free live cell images uncovers functional hallmarks of highly-metastatic melanoma

Subversion of Ras Small GTPases in Cutaneous Melanoma Aggressiveness

The stressful tumour environment drives plasticity of cell migration programmes, contributing to metastasis

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