Novel direct AMPK activator suppresses non-small cell lung cancer through inhibition of lipid metabolism

Chen, Xi; Xie, Chun; Fan, Xing‐Xing; Jiang, Ze‐Bo; Xu, Jiahui; Yao, Xiaojun; Liu, Liang; Leung, Elaine Lai‐Han

doi:10.18632/oncotarget.21716

Cited by 26 publications

(25 citation statements)

References 77 publications

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“…The low-risk genes are associated with energy metabolism ( p value = 0.03), ferroptosis ( p value = 0.037), and AMPK signaling pathway ( p value = 0.046), all related to energy metabolism, particularly lipid metabolism. AMPK signaling pathway activation by an AMPK agonist was shown to suppresses non-small cell lung cancer through inhibition of lipid metabolism 22 . AMPK signaling and energy metabolism are also enriched in gene set enrichment analysis.…”

Section: Resultsmentioning

confidence: 99%

A meta-learning approach for genomic survival analysis

et al. 2020

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RNA sequencing has emerged as a promising approach in cancer prognosis as sequencing data becomes more easily and affordably accessible. However, it remains challenging to build good predictive models especially when the sample size is limited and the number of features is high, which is a common situation in biomedical settings. To address these limitations, we propose a meta-learning framework based on neural networks for survival analysis and evaluate it in a genomic cancer research setting. We demonstrate that, compared to regular transfer-learning, meta-learning is a significantly more effective paradigm to leverage high-dimensional data that is relevant but not directly related to the problem of interest. Specifically, meta-learning explicitly constructs a model, from abundant data of relevant tasks, to learn a new task with few samples effectively. For the application of predicting cancer survival outcome, we also show that the meta-learning framework with a few samples is able to achieve competitive performance with learning from scratch with a significantly larger number of samples. Finally, we demonstrate that the meta-learning model implicitly prioritizes genes based on their contribution to survival prediction and allows us to identify important pathways in cancer.

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Section: Resultsmentioning

confidence: 99%

A meta-learning approach for genomic survival analysis

et al. 2020

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“…Small molecules-induced ferroptosis has a strong inhibition of tumor growth and enhances the sensitivity of chemotherapeutic drugs, especially in drug resistance (Lu et al, 2018). AMPK plays a central role in the control of cell growth, proliferation and autophagy through the regulation of mTOR activity and lipid metabolism (Chen et al, 2017;Han et al, 2013). The link between cancer and metabolism is worth investigating in future studies.…”

Section: Discussionmentioning

confidence: 99%

“…The low-risk genes are associated with energy metabolism (p value 0.03), ferroptosis (p value 0.037) and AMPK signaling pathway (p value 0.046), all related to energy metabolism, particularly lipid metabolism. AMPK signaling pathway activation by an AMPK agonist was shown to suppresses non-small cell lung cancer through inhibition of lipid metabolism (Chen et al, 2017). AMPK signaling and energy metabolism are also enriched in gene set enrichment analysis ( Figure 5C).…”

Section: Meta-learning Achieves Competitive Predictive Performance Comentioning

confidence: 93%

“…This problem setting with limited exposure to a new task is also known as few-shot learning: learn to generalize well, given very few examples (called shots) of a new task (Li et al, 2016). Recent advances in meta-learning have shown that, compared to transfer-learning, it is a more effective approach to few-shot classification (Chen et al, 2017;Devos and Grossglauser, 2019), regression (Finn et al, 2017), and reinforcement learning (Duan et al, 2016;Finn et al, 2017). In this study, we propose a meta-learning framework based on neural networks for survival analysis applied in a cancer research setting.…”

Section: Introductionmentioning

confidence: 99%

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A meta-learning approach for genomic survival analysis

Qiu

Zheng

Devos

et al. 2020

Preprint

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RNA sequencing has emerged as a promising approach in cancer prognosis as sequencing data becomes more easily and affordably accessible. However, it remains challenging to build good predictive models especially when the sample size is limited and the number of features is high, which is a common situation in biomedical settings. To address these limitations, we propose a meta-learning framework based on neural networks for survival analysis and evaluate it in a genomic cancer research setting. We demonstrate that, compared to regular transferlearning, meta-learning is a significantly more effective paradigm to leverage high-dimensional data that is relevant but not directly related to the problem of interest. Specifically, meta-learning explicitly constructs a model, from abundant data of relevant tasks, to learn a new task with few samples effectively. For the application of predicting cancer survival outcome, we also show that the metalearning framework with a few samples is able to achieve competitive performance with learning from scratch with a significantly larger number of samples. Finally, we demonstrate that the meta-learning model implicitly prioritizes genes based on their contribution to survival prediction and allows us to identify important pathways in cancer.

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“…For example, Chen et al . [27] found that a novel direct-AMPK agonist, D561-0775, caused cell cycle arrest in Gefitinib-resistant non-small cell lung cancer cells at a concentration of 20μM. Law et al .…”

Section: Discussionmentioning

confidence: 99%

Novel chemotherapeutic agent, FND-4b, activates AMPK and inhibits colorectal cancer cell proliferation

et al. 2019

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Colorectal cancer (CRC) is the second leading cause of cancer deaths in the US with the majority of deaths due to metastatic disease. Current chemotherapeutic regimens involve highly toxic agents, which limits their utility; therefore, more effective and less toxic agents are required to see a reduction in CRC mortality. Novel fluorinated N,N’-diarylureas (FND) were developed and characterized by our group as potent activators of adenosine monophosphate-activated kinase (AMPK) that inhibit cell cycle progression. The purpose of this study was to determine the effect of a lead FND compound, FND-4b, either alone or combined with PI-103 (a dual PI3K/mTOR inhibitor) or SN-38 (active metabolite of irinotecan) on cell cycle arrest and apoptosis of CRC cell lines (both commercially-available and novel lines established from our patient population). Treatment with FND-4b for 24h resulted in a marked induction of phosphorylated AMPK expression and a concomitant reduction in markers of cell proliferation, such as cyclin D1, in all CRC cell lines. Apoptosis was also notably increased in CRC cells treated with FND-4b. Regardless of the genetic profile of the CRC cells, FND-4b treatment alone resulted in decreased cell proliferation. Moreover, the combination of FND-4b with PI-103 resulted in increased cell death in all cell lines, while the combination of FND-4b with SN-38 resulted in increased cell death in select cell lines. Our findings identify FND-4b, which activates AMPK at micromolar concentrations, as a novel and effective inhibitor of CRC growth either alone or in combination with PI-103 and SN-38.

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Novel direct AMPK activator suppresses non-small cell lung cancer through inhibition of lipid metabolism

Cited by 26 publications

References 77 publications

A meta-learning approach for genomic survival analysis

A meta-learning approach for genomic survival analysis

A meta-learning approach for genomic survival analysis

Novel chemotherapeutic agent, FND-4b, activates AMPK and inhibits colorectal cancer cell proliferation

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