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Investigating the Potential Use of L-Asparaginase in Myeloid Leukemia,

2011· article· en· W2595496294 on OpenAlexaff
Sung Ah Jun, Lusia Sepiashvili, Thomas Kislinger, Mark D. Minden

Bibliographic record

VenueBlood · 2011
Typearticle
Languageen
FieldMedicine
TopicAcute Myeloid Leukemia Research
Canadian institutionsUniversity Health NetworkOntario Institute for Cancer ResearchUniversity of Toronto
Fundersnot available
KeywordsPropidium iodideAsparagine synthetaseMyeloid leukemiaCancer researchLeukemiaAsparaginaseGlutamineAsparagineK562 cellsMyeloidCell cultureBiologyProgrammed cell deathApoptosisChemistryBiochemistryImmunologyEnzymeAmino acid

Abstract

fetched live from OpenAlex

Abstract Abstract 3641 Introduction: L-asparaginase (LA) in combination with other drugs has been one of the standard components of acute lymphocytic leukemia (ALL) therapy for decades. Its antineoplastic effects are likely caused by its depletion of extracellular asparagine and glutamine creating a state of amino acid deficiency and subsequent inhibition of protein synthesis. Despite its efficacy in ALL, LA has been used only occasionally in the treatment of other leukemias and solid tumors. Previous in vitro studies have observed varied response to LA in acute myeloid leukemia (AML) across the French-American-British subtypes. We wanted to elucidate the possible resistance mechanisms of myeloid leukemic cells during LA treatment to increase the efficacy of LA for AML treatment. One of the candidate proteins identified in multiple studies was asparagine synthetase (ASNS), an intracellular enzyme catalyzing the reverse reaction of LA whose expression is up-regulated during nutrient stress. The aim of this study was to investigate the potential of repositioning LA for AML treatment by identifying key components of the cellular response to LA in myeloid leukemic cell lines and primary AML samples. Results: In all the cell lines treated with LA, we observed an inhibition of growth rate and colony formation. Furthermore, we detected apoptotic death by annexin V and propidium iodide staining in most cell lines except for K562. We also observed a leftward shift towards monosomes in polysome profiles of LA sensitive but not insensitive cells, indicating a role for global inhibition of protein synthesis in the effect of LA. To further understand the differences in the responses between resistant and sensitive cell lines at the protein level, we utilized MudPIT (multidimensional protein identification technology) that combines 2-dimensional liquid chromatography coupled to mass spectrometry to separate and identify proteins. Using DAVID, an online program that identifies statistically significant enriched biological themes in gene lists, we compared the MudPIT identified proteomes in LA treated and untreated HL-60 (LA sensitive) and K562 (LA resistant) cells. In HL60, up-regulated proteins in the treated sample were enriched for carbohydrate metabolism (aldolase A, lactate dehydrogenase, 6-phosphogluconolactonase). We also observed decreased expression of proteins involved in cell division (replication factor C, proliferating cell nuclear antigen, minichromosome maintenance complex component 3). The data from K562 is currently being analyzed. A reported predictor of sensitivity to LA is the level of ASNS. To see if this was involved in the resistance of K562 to LA we used shRNA to knockdown ASNS in these cells. While there was some increase in the sensitivity of the cells to LA, the degree of killing did not approach that of other cell lines. Finally, AML primary samples treated with LA were inhibited in their ability to form colonies compared to untreated controls. Interestingly there was no correlation between the level of ASNS and sensitivity of the primary cells. Taken together these studies suggest that other factors are important in mediating the response of cells to LA. Conclusions: Our study shows that LA is effective in killing some forms of AML by inhibiting growth, blocking protein synthesis and inducing apoptosis. Increased sensitivity to LA in ASNS knockdown cell lines indicate a role for ASNS in LA resistance but the absence of strong correlation between ASNS expression and LA resistance in primary samples suggest that ASNS is not solely responsible. The availability of sensitive and resistant myeloid cells provides us with the opportunity to identify mechanisms of resistance. The identification of differentially expressed proteins in the sensitive and resistant cells using MudPIT will help to identify targets that if blocked can synergize with LA and render a resistant cell sensitive. Disclosures: Off Label Use: L-asparaginase is a drug used to treat acute lymphocytic leukemia.

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 score0.001
Threshold uncertainty score0.003

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)0.0010.000

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 head0.053
GPT teacher head0.273
Teacher spread0.220 · 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

Citations3
Published2011
Admission routes1
Has abstractyes

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