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Record W4360856474 · doi:10.3390/metabo13040467

Therapy-Resistant Acute Myeloid Leukemia Stem Cells Are Resensitized to Venetoclax + Azacitidine by Targeting Fatty Acid Desaturases 1 and 2

2023· article· en· W4360856474 on OpenAlexaff
Rachel Culp‐Hill, Brett M. Stevens, Courtney L. Jones, Shanshan Pei, Monika Dzieciątkowska, Mohammad Minhajuddin, Craig T. Jordan, Angelo D’Alessandro

Bibliographic record

VenueMetabolites · 2023
Typearticle
Languageen
FieldMedicine
TopicAcute Myeloid Leukemia Research
Canadian institutionsPrincess Margaret Cancer CentreUniversity of Toronto
FundersNational Cancer Institute
KeywordsVenetoclaxMyeloid leukemiaAzacitidineCancer researchFatty acidLeukemiaStem cellBeta oxidationMedicinePharmacologyChemistryInternal medicineBiologyBiochemistryGeneChronic lymphocytic leukemiaCell biology

Abstract

fetched live from OpenAlex

Recent advances in targeting leukemic stem cells (LSCs) using venetoclax with azacitidine (ven + aza) has significantly improved outcomes for de novo acute myeloid leukemia (AML) patients. However, patients who relapse after traditional chemotherapy are often venetoclax-resistant and exhibit poor clinical outcomes. We previously described that fatty acid metabolism drives oxidative phosphorylation (OXPHOS) and acts as a mechanism of LSC survival in relapsed/refractory AML. Here, we report that chemotherapy-relapsed primary AML displays aberrant fatty acid and lipid metabolism, as well as increased fatty acid desaturation through the activity of fatty acid desaturases 1 and 2, and that fatty acid desaturases function as a mechanism of recycling NAD+ to drive relapsed LSC survival. When combined with ven + aza, the genetic and pharmacologic inhibition of fatty acid desaturation results in decreased primary AML viability in relapsed AML. This study includes the largest lipidomic profile of LSC-enriched primary AML patient cells to date and indicates that inhibition of fatty acid desaturation is a promising therapeutic target for relapsed AML.

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)0.001
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 score0.031
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
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 head0.024
GPT teacher head0.284
Teacher spread0.260 · 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

Citations23
Published2023
Admission routes1
Has abstractyes

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