CD73 controls Myosin II–driven invasion, metastasis, and immunosuppression in amoeboid pancreatic cancer cells

Samain, Remi; Maiques, Oscar; Monger, Joanne; Lam, Hoyin; Candido, Juliana; George, Samantha; Ferrari, Nicola; KohIhammer, Leonie; Lunetto, Sophia; Varela, Adrian; Orgaz, Jose L.; Vilardell, Felip; Olsina, Jorge Juan; Matias-Guiu, Xavier; Sarker, Debashis; Biddle, Adrian; Balkwill, Frances R.; Eyles, Jim; Wilkinson, Robert W.; Kocher, Hemant M.; Calvo, Fernando; Wells, Claire M.; Sanz-Moreno, Victoria

doi:10.1126/sciadv.adi0244

Cited by 6 publications

(2 citation statements)

References 77 publications

(122 reference statements)

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“…The down-regulation pattern of SIRT6 in pancreatic cancer identified by VaMP is consistent with the experimental study finding SIRT6 inactivation accelerates pancreatic ductal adenocarcinoma progression and metastasis via up-regulation of LIN28B , a negative regulator of the let-7 microRNA [34]. In the case of NT5E , among two mixed patterns, the up-regulation pattern in pancreatic cancer is aligned with the study describing NT5E gene was almost absent in normal pancreas but strongly up-regulated in pancreatic tumors [35].…”

Section: Resultssupporting

confidence: 86%

Learning the Relationship Between Variants, Metabolic Fluxes and Phenotypes

Kim,

Han,

Nam

et al. 2024

Preprint

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In the dynamic field of cancer research, the fusion of genetics and machine learning presents a groundbreaking opportunity to understand the intricate links between genomic variations and cancer phenotypes. Despite the wealth of genetic data, translating it into actionable insights remains a challenge, particularly in the context of complex cellular metabolism. VaMP (Variants,Metabolic Fluxes &Phenotypes), our novel approach, addresses this by integrating neural networks and metabolic models, offering a comprehensive framework for deciphering cancer biology. The developed method leverages a novel end-to-end neural network architecture, integrating genome-scale metabolic models (GSMs) to capture the intricate relationship between genetic variations and cellular phenotypes. VaMP’s encoder maps genetic variants to metabolic fluxes through a series of carefully designed steps utilizing neural network and GSM, while the decoder predicts phenotype probabilities using the differences between input and reference fluxes. The training data comprise mutation information and phenotypes, eliminating the need for explicit metabolic flux data preparation. Validation experiments on five cancer types demonstrate VaMP’s ability to identify significant genes and metabolic signatures. Further utilizing SCREENER and co-occurrence analyses, the assessment reveals VaMP’s capacity to anticipate established gene-disease relationships. The identified metabolic signatures are robustly substantiated by diverse literature grounded in experimental studies. Furthermore, an in-depth exploration of the outcomes from five VaMP models involved the correlation analysis for variants and fluxes to establish connections between significant genes and cancer-causing variants. Overall, VaMP can be used as a promising tool for unraveling the complex interplay between genetic alterations and cancer phenotypes, with implications for understanding disease mechanisms and identifying novel therapeutic targets.

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Section: Resultssupporting

confidence: 86%

Learning the Relationship Between Variants, Metabolic Fluxes and Phenotypes

Kim,

Han,

Nam

et al. 2024

Preprint

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“…Amoeboid-based migration, which can be either protease-dependent (Orgaz et al, 2014) or independent (Driscoll et al, 2022;Sahai and Marshall, 2003;Wolf et al, 2003), is characterized by high actomyosin contractility, with blebs being the prominent functional protrusions (Sahai and Marshall, 2003). Recent studies by Remi et al show ROCK-Myosin II activity in pancreatic ductal adenocarcinoma (PDAC) supports transcriptional program conferring amoeboid invasive abilities (Samain et al, 2023). A point not discussed but shown in the paper through gene set enrichment analysis is the elevated glycolytic state of amoeboid cells.…”

Section: Discussionmentioning

confidence: 94%

RhoA activation promotes glucose uptake to elevate proliferation in MAPK inhibitor resistant melanoma cells

Murali,

Rajendran,

Isogai

et al. 2024

Preprint

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Cutaneous melanomas harboring a B-RafV600Emutation are treated with immune check point inhibitors or kinase inhibitor combination therapies relying on MAPK inhibitors (MAPKi) Dabrafenib and Trametinib (Curti and Faries, 2021). However, cells become resistant to treatments over the timespan of a few months. Resistance to MAPKi has been associated with adoption of an aggressive amoeboid phenotype characterized by elevated RhoA signaling, enhanced contractility and thick cortical filamentous actin (F-actin) structures (Kim et al., 2016; Misek et al., 2020). Targeting active RhoA through Rho-kinase (ROCK) inhibitors, either alone or in combination with immunotherapies, reverts MAPKi-resistance (Misek et al., 2020; Orgaz et al., 2020). Yet, the mechanisms for this behavior remain largely unknown. Given our recent findings of cytoskeleton’s role in cancer cell proliferation (Mohan et al., 2019), survival (Weems et al., 2023), and metabolism (Park et al., 2020), we explored possibilities by which RhoA-driven changes in cytoskeleton structure may confer resistance. We confirmed elevated activation of RhoA in a panel of MAPKi-resistant melanoma cell lines, leading to a marked increase in the presence of contractile F-actin bundles. Moreover, these cells had increased glucose uptake and glycolysis, a phenotype disrupted by pharmacological perturbation of ROCK. However, glycolysis was unaffected by disruption of F-actin bundles, indicating that glycolytic stimulation in MAPKi-resistant melanoma is independent of F-actin organization. Instead, our findings highlight a mechanism in which elevated RhoA signaling activates ROCK, leading to the activation of insulin receptor substrate 1 (IRS1) and P85 of the PI3K pathway, which promotes cell surface expression of GLUT1 and elevated glucose uptake. Application of ROCK inhibitor GSK269962A results in reduced glucose uptake and glycolysis, thus impeding cell proliferation. Our study adds a mechanism to the proposed use of ROCK inhibitors for long-term treatments on MAPKi-resistant melanomas.

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Associations between HIFs and tumor immune checkpoints: mechanism and therapy

Liu,

Jiang,

Chen

et al. 2024

Discov Onc

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Hypoxia, which activates a variety of signaling pathways to enhance tumor cell growth and metabolism, is among the primary features of tumor cells. Hypoxia-inducible factors (HIFs) have a substantial impact on a variety of facets of tumor biology, such as epithelial-mesenchymal transition, metabolic reprogramming, angiogenesis, and improved radiation resistance. HIFs induce hypoxia-adaptive responses in tumor cells. Many academics have presented preclinical and clinical research targeting HIFs in tumor therapy, highlighting the potential applicability of targeted HIFs. In recent years, the discovery of numerous pharmacological drugs targeting the regulatory mechanisms of HIFs has garnered substantial attention. Additionally, HIF inhibitors have attained positive results when used in conjunction with traditional oncology radiation and/or chemotherapy, as well as with the very promising addition of tumor immunotherapy. Immune checkpoint inhibitors (CPIs), which are employed in a range of cancer treatments over the past decades, are essential in tumor immunotherapy. Nevertheless, the use of immunotherapy has been severely hampered by tumor resistance and treatment-related toxicity. According to research, HIF inhibitors paired with CPIs may be game changers for multiple malignancies, decreasing malignant cell plasticity and cancer therapy resistance, among other things, and opening up substantial new pathways for immunotherapy drug development. The structure, activation mechanisms, and pharmacological sites of action of the HIF family are briefly reviewed in this work. This review further explores the interactions between HIF inhibitors and other tumor immunotherapy components and covers the potential clinical use of HIF inhibitors in combination with CPIs.

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CD73 controls Myosin II–driven invasion, metastasis, and immunosuppression in amoeboid pancreatic cancer cells

Cited by 6 publications

References 77 publications

Learning the Relationship Between Variants, Metabolic Fluxes and Phenotypes

Learning the Relationship Between Variants, Metabolic Fluxes and Phenotypes

RhoA activation promotes glucose uptake to elevate proliferation in MAPK inhibitor resistant melanoma cells

Associations between HIFs and tumor immune checkpoints: mechanism and therapy

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