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The autophagic tumor stroma model of cancer: Role of oxidative stress and ketone production in fueling tumor cell metabolism

Open access
Pavlides, Stephanos, Tsirigos, Aristotelis, Migneco, Gemma, Whitaker-Menezes, Diana, Chiavarina, Barbara, Flomenberg, Neal, Frank, Philippe G., Casimiro, Mathew C., Wang, Chenguang, Pestell, Richard G., Martinez-Outschoorn, Ubaldo E., Howell, Anthony, Sotgia, Federica, Lisanti, Michael P.·2010·Cell Cycle

Abstract

A loss of stromal Cav-1 in the tumor fibroblast compartment is associated with early tumor recurrence, lymph-node metastasis, and tamoxifen-resistance, resulting in poor clinical outcome in breast cancer patients. Here, we have used Cav-1 (-/-) null mice as a pre-clinical model for this "lethal tumor micro-environment." Metabolic profiling of Cav-1 (-/-) mammary fat pads revealed the upregulation of numerous metabolites (nearly 100), indicative of a major catabolic phenotype. Our results are consistent with the induction of oxidative stress, mitochondrial dysfunction, and autophagy/mitophagy. The two most prominent metabolites that emerged from this analysis were ADMA (asymmetric dimethyl arginine) and BHB (beta-hydroxybutyrate; a ketone body), which are markers of oxidative stress and mitochondrial dysfunction, respectively. Transcriptional profiling of Cav-1 (-/-) stromal cells and human tumor stroma from breast cancer patients directly supported an association with oxidative stress, mitochondrial dysfunction, and autophagy/mitophagy, as well as ADMA and ketone production. MircoRNA profiling of Cav-1 (-/-) stromal cells revealed the upregulation of two key cancer-related miR's, namely miR-31 and miR-34c. Consistent with our metabolic findings, these miR's are associated with oxidative stress (miR-34c) or activation of the hypoxic response/HIF1a (miR-31), which is sufficient to drive authophagy/mitophagy. Thus, via an unbiased comprehensive analysis of a lethal tumor micro-environment, we have identified a number of candidate biomarkers (ADMA, ketones, and miR-31/34c) that could be used to identify high-risk cancer patients at diagnosis, for treatment stratification and/or for evaluating therapeutic efficacy during anti-cancer therapy. We propose that the levels of these key biomarkers (ADMA, ketones/BHB, miR-31, and miR-34c) could be (1) assayed using serum or plasma from cancer patients, or (2) performed directly on excised tumor tissue. Importantly, induction of oxidative stress and autophagy/mitophagy in the tumor stromal compartment provides a means by which epithelial cancer cells can directly "feed off" of stromal-derived essential nutrients, chemical building blocks (amino acids, nucleotides), and energy-rich metabolites (glutamine, pyruvate, ketones/BHB), driving tumor progression and metastasis. Essentially, aggressive cancer cells are "eating" the cancer-associated fibroblasts via autophagy/mitophagy in the tumor micro-environment. Lastly, we discuss that this "Autophagic Tumor Stroma Model of Cancer Metabolism" provides a viable solution to the "Autophagy Paradox" in cancer etiology and chemo-therapy.

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Metadata

Authors
Pavlides, S., Tsirigos, A., Migneco, G., Whitaker-Menezes, D., Chiavarina, B., Flomenberg, N., Frank, P. G., Casimiro, M. C., Wang, C., Pestell, R. G., Martinez-Outschoorn, U. E., Howell, A., Sotgia, F., & Lisanti, M. P.
Venue
Cell Cycle
Published
2010-09
Volume
9
Issue
17
Pages
3485-3505
ISSN
  • 1538-4101 (ISSN-L)
  • 1551-4005
OpenAlex
W1991222141
Cited by
235
References
10
FWCI
8.13
Type
journal article
Language
en
Open access
hybrid

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Pavlides, S., Tsirigos, A., Migneco, G., Whitaker-Menezes, D., Chiavarina, B., Flomenberg, N., Frank, P. G., Casimiro, M. C., Wang, C., Pestell, R. G., Martinez-Outschoorn, U. E., Howell, A., Sotgia, F., & Lisanti, M. P. (2010). The autophagic tumor stroma model of cancer: Role of oxidative stress and ketone production in fueling tumor cell metabolism. Cell Cycle. https://doi.org/10.4161/cc.9.17.12721

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.