MicroRNA-222 Regulates Melanoma Plasticity

Lionetti, Maria Chiara; Cola, Filippo; Chepizhko, Oleksandr; Fumagalli, Maria Rita; Font-Clos, Francesc; Ravasio, Roberto; Minucci, Saverio; Canzano, Paola; Camera, Marina; Tiana, Guido; Zapperi, Stefano; Porta, Caterina A. M. La

doi:10.3390/jcm9082573

Cited by 9 publications

(10 citation statements)

References 45 publications

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“…Our study characterizes phenotypes based on dynamics of the TF regulatory network. However, previous studies have also identified translational, epigenetic and metabolic features of distinct phenotypes, suggesting multiple interconnected levels of regulation of phenotypic plasticity in melanoma ( Bettum et al., 2015 ; Hugo et al., 2015 ; Falletta et al., 2017 ; Lionetti et al., 2020 ). Variations in chromatin accessibility, DNA and histone modifications can be mapped to transcriptional heterogeneity seen in melanoma ( Hugo et al., 2015 ; Verfaillie et al., 2015 ).…”

Section: Discussionmentioning

confidence: 99%

Systems-level network modeling deciphers the master regulators of phenotypic plasticity and heterogeneity in melanoma

Pillai

Jolly

2021

iScience

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Summary Phenotypic (i.e. non-genetic) heterogeneity in melanoma drives dedifferentiation, recalcitrance to targeted therapy and immunotherapy, and consequent tumor relapse and metastasis. Various markers or regulators associated with distinct phenotypes in melanoma have been identified, but, how does a network of interactions among these regulators give rise to multiple “attractor” states and phenotypic switching remains elusive. Here, we inferred a network of transcription factors (TFs) that act as master regulators for gene signatures of diverse cell-states in melanoma. Dynamical simulations of this network predicted how this network can settle into different “attractors” (TF expression patterns), suggesting that TF network dynamics drives the emergence of phenotypic heterogeneity. These simulations can recapitulate major phenotypes observed in melanoma and explain de-differentiation trajectory observed upon BRAF inhibition. Our systems-level modeling framework offers a platform to understand trajectories of phenotypic transitions in the landscape of a regulatory TF network and identify novel therapeutic strategies targeting melanoma plasticity.

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Section: Discussionmentioning

confidence: 99%

Systems-level network modeling deciphers the master regulators of phenotypic plasticity and heterogeneity in melanoma

Pillai

Jolly

2021

iScience

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“…Our study characterizes phenotypes based on dynamics of TF regulatory network. However, previous studies have also identified translational, epigenetic and metabolic features of distinct phenotypes, suggesting multiple interconnected levels of regulation of phenotypic plasticity in melanoma (Bettum et al, 2015; Falletta et al, 2017; Hugo et al, 2015; Lionetti et al, 2020). Variations in chromatin accessibility, DNA and histone modifications can be mapped to transcriptional heterogeneity seen in melanoma (Hugo et al, 2015; Verfaillie et al, 2015).…”

Section: Discussionmentioning

confidence: 99%

Systems-level network modeling deciphers the master regulators of phenotypic plasticity and heterogeneity in melanoma

Pillai

Jolly

2021

Preprint

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Phenotypic (i.e. non-genetic) heterogeneity in melanoma drives dedifferentiation, recalcitrance to targeted therapy and immunotherapy, and consequent tumor relapse and metastasis. Various markers or regulators associated with distinct phenotypes in melanoma have been identified, but, how does a network of interactions among these regulators give rise to multiple 'attractor' states and phenotypic switching remains elusive. Here, we inferred a network of transcription factors (TFs) that act as master regulators for gene signatures of diverse cell-states in melanoma. Dynamical simulations of this network predicted how this network can settle into different 'attractors' (TF expression patterns), suggesting that TF network dynamics drives the emergence of phenotypic heterogeneity. These simulations can recapitulate major phenotypes observed in melanoma and explain de-differentiation trajectory observed upon BRAF inhibition. Our systems-level modeling framework offers a platform to understand trajectories of phenotypic transitions in the landscape of a regulatory TF network and identify novel therapeutic strategies targeting melanoma plasticity.

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“…MiR-222 was studied by Lionetti et al [ 73 ] in human IGFR39 melanoma cells. In the study, the mathematical model of phenotypic switching cancer cells to cancer stem cells (CSC) was applied and experiments were conducted, which explained that switching into cancer stem cells is controlled by MicroRNA-222 in CSC.…”

Section: Examples Of Melanoma-related Micrornasmentioning

confidence: 99%

MicroRNA as a Diagnostic Tool, Therapeutic Target and Potential Biomarker in Cutaneous Malignant Melanoma Detection—Narrative Review

Poniewierska-Baran

Zadroga

Danilyan

et al. 2023

IJMS

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Melanoma is the most serious type of skin cancer, causing a large majority of deaths but accounting for only ~1% of all skin cancer cases. The worldwide incidence of malignant melanoma is increasing, causing a serious socio-economic problem. Melanoma is diagnosed mainly in young and middle-aged people, which distinguishes it from other solid tumors detected mainly in mature people. The early detection of cutaneous malignant melanoma (CMM) remains a priority and it is a key factor limiting mortality. Doctors and scientists around the world want to improve the quality of diagnosis and treatment, and are constantly looking for new, promising opportunities, including the use of microRNAs (miRNAs), to fight melanoma cancer. This article reviews miRNA as a potential biomarker and diagnostics tool as a therapeutic drugs in CMM treatment. We also present a review of the current clinical trials being carried out worldwide, in which miRNAs are a target for melanoma treatment.

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MicroRNA-222 Regulates Melanoma Plasticity

Cited by 9 publications

References 45 publications

Systems-level network modeling deciphers the master regulators of phenotypic plasticity and heterogeneity in melanoma

Systems-level network modeling deciphers the master regulators of phenotypic plasticity and heterogeneity in melanoma

Systems-level network modeling deciphers the master regulators of phenotypic plasticity and heterogeneity in melanoma

MicroRNA as a Diagnostic Tool, Therapeutic Target and Potential Biomarker in Cutaneous Malignant Melanoma Detection—Narrative Review

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