Drug-Tolerant Idling Melanoma Cells Exhibit Theory-Predicted Metabolic Low-Low Phenotype

Jia, Dongya; Paudel, B. Bishal; Hayford, Corey E.; Hardeman, Keisha N.; Levine, Herbert; Onuchic, José N.; Quaranta, Vito

doi:10.3389/fonc.2020.01426

Cited by 27 publications

(26 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Two regulatory networks corresponding to proliferative and invasive phenotypes have been identified, with SOX10 and MITF acting as key regulators of the proliferative network and TEAD and AP-1 as regulators of the invasive one. How epigenetic silencing enables reversible or irreversible transitions ( Jia et al., 2019 ) in melanoma, remains to be yet investigated. Besides diverse epigenetic signatures, the proliferative and invasive phenotypes have been characterized to have distinct metabolic profiles too.…”

Section: Discussionmentioning

confidence: 99%

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

Pillai

Jolly

2021

iScience

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionmentioning

confidence: 99%

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

Pillai

Jolly

2021

iScience

View full text Add to dashboard Cite

show abstract

“…We analyzed the gene expression profiles of the SKMEL5 subclones (reported by Jia et al . [42]) and observed that the expression of PC is upregulated in the SC10 subclone upon PLX4720 treatment (Fig. 6 B).…”

Section: Resultsmentioning

confidence: 93%

“…2 D in Jia et al . [42]). We further analyzed the gene expression profiles of melanoma tissue samples obtained from patients before and after treatment with a BRAF signaling inhibitor [43] (Gene Expression Omnibus [46] accession GSE75299).…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

A Mechanistic Modeling Framework Reveals the Key Principles Underlying Tumor Metabolism

Tripathi

Park²,

Pudakalakatti

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

While aerobic glycolysis, or the Warburg effect, has for a long time been considered a hallmark of tumor metabolism, recent studies have revealed a far more complex picture. Tumor cells exhibit widespread metabolic heterogeneity, utilizing glycolysis, oxidative phosphorylation, or both, and can switch between different metabolic phenotypes. A framework to analyze the observed metabolic heterogeneity and plasticity is, however, lacking. Using a mechanistic model that includes the key metabolic pathways active in tumor cells, we show that the inhibition of phosphofructokinase by excess ATP in the cytoplasm can drive a preference for aerobic glycolysis in fast-proliferating tumor cells. The differing rates of ATP utilization by tumor cells can therefore drive metabolic heterogeneity. Building upon this idea, we couple the metabolic phenotype of tumor cells to their migratory phenotype, and show that our model predictions are in agreement with previous experiments. We report that the reliance of proliferating cells on different anaplerotic pathways depends on the relative availability of glucose and glutamine, and can further drive metabolic heterogeneity. Finally, using treatment of melanoma cells with a BRAF inhibitor as an example, we show that our model can be used to predict the metabolic and gene expression changes in cancer cells in response to drug treatment. By making predictions that are far more generalizable and interpretable as compared to previous tumor metabolism modeling approaches, our framework identifies key principles that govern tumor cell metabolism, and the reported heterogeneity and plasticity. These principles could be key to targeting the metabolic vulnerabilities of cancer.SignificanceThis study presents an interpretable mathematical framework for analyzing the metabolic heterogeneity and plasticity exhibited by tumor cells.

show abstract

“…Figure 1 summarizes the known CSCs' metabolic phenotypes and how these phenotypes switch with metabolic stressors like nutrient deprivation and hypoxia. However, melanoma cells attain a drug-tolerant "idling state" after enduring MAPK inhibition (MAPKi) and this state has a metabolically Low/ Low (L/L) phenotype, where both AMPK/HIF-1 activity and OXPHOS/glycolysis are minimal (189). L/L phenotype does not favor tumorigenicity but supports cell division.…”

Section: Future Challengesmentioning

confidence: 99%

Cancer Stem Cells: Metabolic Characterization for Targeted Cancer Therapy

Kaur

Bhattacharyya

2021

Front. Oncol.

View full text Add to dashboard Cite

The subpopulation of cancer stem cells (CSCs) within tumor bulk are known for tumor recurrence and metastasis. CSCs show intrinsic resistance to conventional therapies and phenotypic plasticity within the tumor, which make these a difficult target for conventional therapies. CSCs have different metabolic phenotypes based on their needs as compared to the bulk cancer cells. CSCs show metabolic plasticity and constantly alter their metabolic state between glycolysis and oxidative metabolism (OXPHOS) to adapt to scarcity of nutrients and therapeutic stress. The metabolic characteristics of CSCs are distinct compared to non-CSCs and thus provide an opportunity to devise more effective strategies to target CSCs. Mechanism for metabolic switch in CSCs is still unravelled, however existing evidence suggests that tumor microenvironment affects the metabolic phenotype of cancer cells. Understanding CSCs metabolism may help in discovering new and effective clinical targets to prevent cancer relapse and metastasis. This review summarises the current knowledge of CSCs metabolism and highlights the potential targeted treatment strategies.

show abstract

Drug-Tolerant Idling Melanoma Cells Exhibit Theory-Predicted Metabolic Low-Low Phenotype

Cited by 27 publications

References 39 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

A Mechanistic Modeling Framework Reveals the Key Principles Underlying Tumor Metabolism

Cancer Stem Cells: Metabolic Characterization for Targeted Cancer Therapy

Contact Info

Product

Resources

About