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Record W2399613518 · doi:10.1158/1557-3125.metca15-b02

Abstract B02: mTOR/S6K pathway-dependent metabolic reprogramming in cancer cells mediates resistance to glycolytic inhibitors

2016· article· en· W2399613518 on OpenAlexaff
Raju V. Pusapati, Min Gao, Anneleen Daemen, Georgia Hatzivassiliou, Jeffrey Settleman

Bibliographic record

VenueMolecular Cancer Research · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer, Hypoxia, and Metabolism
Canadian institutionsDiscovery Centre
Fundersnot available
KeywordsGlycolysisPentose phosphate pathwayBiologyCancer researchGlutamineMetabolic pathwayFlux (metallurgy)BiochemistryCell biologyChemistryMetabolism

Abstract

fetched live from OpenAlex

Abstract The targeting of “glycolytic addiction” in cancer has been an attractive proposition since the time the Warburg effect was reported in the 1940s. However this has not been successful in the clinic thus far, as the outcome of numerous clinical trials with glycolytic inhibitors has not been encouraging either due to minimal efficacy at lower tolerable doses or undue toxicity at higher effective doses. We hypothesized that the extensive cross-talk between the glycolytic pathway and other networks of cellular metabolism offers prospective avenues through which cancer cells can escape inhibition of any one node in the glycolytic pathway. By continuously exposing several glycolysis-addicted cancer cell lines to the glycolytic inhibitor 2-deoxyglucose (2-DG), we have generated derivative Glycolysis Independent lines (GIs) lines with substantially reduced glycolytic dependency. We also employed 2-DG innately resistant glycolysis independent lines to compare and contrast with the derivative GI lines. GIs, both acquired and innate, equally exhibit glycolysis independence to the knockdown of the glycolytic enzyme glucose-6-phosphate isomerase (PGI), which is blocked by 2-DG. GIs, but not their parental counterparts, exhibit elevated OX-PHOS rates to compensate for the reduced glycolytic rates. Steady state and targeted flux analyses revealed extensive metabolic reprogramming in GIs: 1. GIs increasingly utilize glutamine to feed the TCA cycle, OXPHOS and pyrimidine synthesis. 2. GIs effectively circumvent 2-DG-induced block downstream of glucose-6 phosphate (G6P) by shunting G6P through the pentose phosphate pathway back into the glycolysis, thereby generating acetyl CoA for the TCA cycle and for fatty acid biosynthesis. We determined that the S6 kinase axis of the mTOR pathway critically underlies the metabolic re-wiring of GIs, both acquired and innate. Pharmacologically targeting either S6K1 or OX-PHOS or glutaminase (the rate-limiting enzyme for glutamine breakdown in the mitochondria) not only re-sensitized GIs to 2-DG, but also pre-empted the acquisition of resistance to glycolytic inhibitors. Interestingly we were also able to re-sensitize GIs (both acquired and innate) to PGI knockdown by pharmacologically targeting either S6K1 or OXPHOS indicating the robustness of our models. Furthermore, the combination of either everolimus (clinically approved mTOR inhibitor) or phenformin (mitochondrial complex I inhibitor) with the inducible knockdown of PGI significantly reduced tumor volumes in both the acquired and innate GI line xenograft models whereas either of the drugs or PGI knockdown alone was ineffective in reducing tumor burden. Taken together, our findings suggest that cancer cells can acquire resistance to glycolytic inhibitors via mTOR/S6K pathway-mediated re-wiring of glycolytic and mitochondrial metabolic networks. Therefore, the combined targeting of glycolysis and mTOR/S6K1 or mitochondrial metabolism may be a viable therapeutic strategy to reduce tumor burden in patients across various indications. Citation Format: Raju Pusapati, Min Gao, Anneleen Daemen, Georgia Hatzivassiliou, Jeffrey Settleman. mTOR/S6K pathway-dependent metabolic reprogramming in cancer cells mediates resistance to glycolytic inhibitors. [abstract]. In: Proceedings of the AACR Special Conference: Metabolism and Cancer; Jun 7-10, 2015; Bellevue, WA. Philadelphia (PA): AACR; Mol Cancer Res 2016;14(1_Suppl):Abstract nr B02.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.321
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.028
GPT teacher head0.337
Teacher spread0.310 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2016
Admission routes1
Has abstractyes

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