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Record W4387771126 · doi:10.1186/s40170-023-00318-y

Aspirin reprogrammes colorectal cancer cell metabolism and sensitises to glutaminase inhibition

2023· article· en· W4387771126 on OpenAlex
Amy K. Holt, Arafath K. Najumudeen, T J Collard, Hao Li, Laura M. Millett, Ashley J. Hoskin, Danny Legge, E Mortensson, Dustin J. Flanagan, Nicholas Jones, Madhu Kollareddy, Penny Timms, Matthew D. Hitchings, J. Cronin, Owen J. Sansom, Ann C. Williams, Emma E. Vincent

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueCancer & Metabolism · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer, Hypoxia, and Metabolism
Canadian institutionsnot available
FundersMedical Research CouncilJames Tudor FoundationUniversity of BristolDiabetes UKEuropean Research CouncilCancer Research UKWorld Cancer Research FundMcGill UniversityBowel and Cancer Research
KeywordsGlutaminaseColorectal cancerAspirinCancerMetabolismMedicineCancer researchInternal medicineGlutamate receptor

Abstract

fetched live from OpenAlex

Abstract Background To support proliferation and survival within a challenging microenvironment, cancer cells must reprogramme their metabolism. As such, targeting cancer cell metabolism is a promising therapeutic avenue. However, identifying tractable nodes of metabolic vulnerability in cancer cells is challenging due to their metabolic plasticity. Identification of effective treatment combinations to counter this is an active area of research. Aspirin has a well-established role in cancer prevention, particularly in colorectal cancer (CRC), although the mechanisms are not fully understood. Methods We generated a model to investigate the impact of long-term (52 weeks) aspirin exposure on CRC cells, which has allowed us comprehensively characterise the metabolic impact of long-term aspirin exposure (2–4mM for 52 weeks) using proteomics, Seahorse Extracellular Flux Analysis and Stable Isotope Labelling (SIL). Using this information, we were able to identify nodes of metabolic vulnerability for further targeting, investigating the impact of combining aspirin with metabolic inhibitors in vitro and in vivo. Results We show that aspirin regulates several enzymes and transporters of central carbon metabolism and results in a reduction in glutaminolysis and a concomitant increase in glucose metabolism, demonstrating reprogramming of nutrient utilisation. We show that aspirin causes likely compensatory changes that render the cells sensitive to the glutaminase 1 (GLS1) inhibitor—CB-839. Of note given the clinical interest, treatment with CB-839 alone had little effect on CRC cell growth or survival. However, in combination with aspirin, CB-839 inhibited CRC cell proliferation and induced apoptosis in vitro and, importantly, reduced crypt proliferation in Apc fl/fl mice in vivo . Conclusions Together, these results show that aspirin leads to significant metabolic reprogramming in colorectal cancer cells and raises the possibility that aspirin could significantly increase the efficacy of metabolic cancer therapies in CRC.

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.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.544
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.012
GPT teacher head0.274
Teacher spread0.262 · 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