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Record W4282927888 · doi:10.1158/1538-7445.am2022-5662

Abstract 5662: Targeting N-myristoylation for therapy of adult acute myeloid leukemia

2022· article· en· W4282927888 on OpenAlexaff
Jay M. Gamma, Aishwarya Iyer, Megan C. Yap, Zoulika Zak, Krista M. Vincent, Cassidy Ekstrom, Qiang Liu, Erwan Beauchamp, Lynne‐Marie Postovit, Jean Wang, John R. Mackey, Naveen Pemmaraju, Gautam Borthakur, Joseph Brandwein, Luc G. Berthiaume

Bibliographic record

VenueCancer Research · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRetinoids in leukemia and cellular processes
Canadian institutionsUniversity of TorontoQueen's UniversityUniversity Health NetworkUniversity of Alberta
Fundersnot available
KeywordsCancer researchMyristoylationCell cultureBiologyCancerTranscriptomeApoptosisCell growthCellInternal medicinePhosphorylationMedicineGene expressionCell biologyGeneBiochemistryGenetics

Abstract

fetched live from OpenAlex

Abstract Two N-myristoyltransferases (NMTs) NMT1 and NMT2 catalyze the reaction. NMT1 is ubiquitous expressed and is essential for cell survival while NMT2 is more variably expressed and non-essential suggesting that their substrate specificity and activity levels differ. Historically, inhibition of myristoylation was suggested as a therapeutic anti-cancer target since NMTs expression were shown to be increased in numerous types of cancers and myristoylation was shown to be essential for proper localization and activity of some important proto-oncogenes such as Src Family Kinases (SFKs). Recently, we showed NMT2 expression is lost in numerous haematological cancer cell lines (including AML) and that these haematological cancer cell lines are exquisitely sensitive to the pan-NMT inhibitor PCLX-001. PCLX-001 recently entered human clinical trials as once daily oral therapy for relapsed/refractory B-cell Non-Hodgkin Lymphoma and advanced solid malignances. Dysregulation and oncogenic activity of SFKs occurs frequently in AML, suggesting NMT inhibition could provide therapeutic benefit in this indication. Data analysis from the TCGA transcriptome database revealed that high NMT1 and low NMT2 were associated with reduced overall and event-free survival in adult AML. Moreover, high NMT1 - but not NMT2 - expression is associated with proliferative gene sets in AML cell lines. AML cell lines treated with PCLX-001 showed a significant reduction in total protein myristoylation, reduced levels of SFK proteins and SFK phosphorylation as well as significant increases in ER stress marker BIP protein and caspase 3 cleavage. PCLX-001 induced apoptosis in AML cell lines and patient blasts at concentrations that spared a large proportion of peripheral blood lymphocytes and monocytes from healthy individuals. PCLX-001 monotherapy had dose-dependent anticancer activity in an AML MV-4-11 cell line derived xenograft (CDX) and two AML patient derived xenografts (PDXs) and produced complete remissions in subcutaneous AML CDX. In tail-vein injected PDX models, PCLX-001 treatment resulted in up to 95% reduction of human CD45+ cells in peripheral blood and bone marrow. PCLX-001 preferentially targeted AML cells inducing apoptosis and reducing leukemic burden. These findings validate NMT inhibition as a novel therapeutic strategy for AML and warrant the evaluation of PCLX-001 in clinical trials for adult AML. Citation Format: Jay Gamma, Aishwarya Iyer, Megan Yap, Zoulika Zak, Krista Vincent, Cassidy Ekstrom, Qiang Liu, Erwan Beauchamp, Lynne Postovit, Jean Wang, John R. Mackey, Naveen Pemmaraju, Gautam Borthakur, Joseph Brandwein, Luc Berthiaume. Targeting N-myristoylation for therapy of adult acute myeloid leukemia [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5662.

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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.034
GPT teacher head0.362
Teacher spread0.328 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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