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Record W3083608894 · doi:10.1158/1538-7445.am2020-3731

Abstract 3731: Targeting the isocitrate dehydrogenase 1 (IDH1) metabolic enzyme in prostate cancer

2020· article· en· W3083608894 on OpenAlexaff
Kevin Gonthier, Cindy Weidmann, Lilianne Frégeau-Proulx, Étienne Audet‐Walsh

Bibliographic record

VenueCancer Research · 2020
Typearticle
Languageen
FieldMedicine
TopicProstate Cancer Treatment and Research
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsIsocitrate dehydrogenaseIDH1Cell growthPI3K/AKT/mTOR pathwayCancer researchProstate cancerBiologyAnabolismCancer cellEnzymeChemistryCancerBiochemistryCell biologySignal transductionMutantGenetics

Abstract

fetched live from OpenAlex

Abstract Prostate cancer cells (PCa) are dependent on the androgen receptor (AR) for their aberrant proliferation and survival. We have recently discovered that AR induces a reprogramming of PCa cell metabolism by controlling the cytoplasmic wild-type enzyme isocitrate dehydrogenase 1 (IDH1), which results in an enhanced proliferation of tumour cells. However, the specific metabolic functions of IDH1 in PCa or how to use the reliance of tumour cells on this enzyme as a therapeutic avenue, is elusive. In in vitro human PCa models, we showed that IDH1 protein levels and activity are increased in an AR-dependent manner. Using pharmacological and genetic tools, we showed that IDH1 is a major contributor to the replenishment of NADPH levels in PCa. This cofactor plays a key role in the synthesis of biomaterials required for cellular division, and our results indicate that IDH1 contribute to 30-40% of total cellular NADPH levels. In that context, blockade of IDH1 was shown to alter the mTOR signaling, a central regulator of cellular anabolism, which is linked to decreased cellular proliferation rates. FDA-approved pharmacological inhibitors of mutant IDH1 significantly inhibited IDH activity and proliferation in PCa cells, suggesting that such inhibitors could be used to treat PCa patients even in absence of IDH1 mutation. Globally, our results demonstrate that IDH1 is a key player in proliferative anabolic pathways in PCa. Importantly, they also support the hypothesis that inhibition of IDH1 using already-approved molecules represents one viable therapeutic solution. Citation Format: Kevin Gonthier, Cindy Weidmann, Lilianne Frégeau-Proulx, Étienne Audet-Walsh. Targeting the isocitrate dehydrogenase 1 (IDH1) metabolic enzyme in prostate cancer [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 3731.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.109
GPT teacher head0.423
Teacher spread0.314 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations1
Published2020
Admission routes1
Has abstractyes

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