MétaCan
Menu
Back to cohort
Record W4393087447 · doi:10.1158/1538-7445.am2024-272

Abstract 272: Novel role for eIF4E in homing of acute myeloid leukemia cells

2024· article· en· W4393087447 on OpenAlexaff
Leandro Marcelo Martinez, Eloisi Caldas Lopes, Mayumi Sugita, Jadwiga Gasiorek, Kata Alilovic, Katherine L. B. Borden, Mónica L. Guzmán

Bibliographic record

VenueCancer Research · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtein Degradation and Inhibitors
Canadian institutionsUniversité de MontréalInstitute for Research in Immunology and Cancer
Fundersnot available
KeywordsMyeloid leukemiaHoming (biology)Cancer researchMedicineLeukemiaComputational biologyImmunologyBiologyEcology

Abstract

fetched live from OpenAlex

Abstract Acute myeloid leukemia (AML) remains with dismal outcomes, mostly attributed to the inability of therapies to eliminate leukemia stem cells (LSCs) leading to relapse. LSCs home to the bone marrow (BM) niche where they maintain their quiescence state once in contact with mesenchymal stromal cells (MSCs). Therefore, understanding what controls AML-niche interactions could lead to therapeutic approaches that can prevent relapse. First, we implemented a novel 3D in vitro system to mimic human AML-niche interactions. This 3D structure consists of a spheroid formed with BM-MSCs (HS5 cell line), recreating a hypoxic microenvironment. When AML cells (MM6 cell line) are added to the spheroid in a 1:1 proportion, AML cells migrate to the spheroid. After 24 hours, 0.18% of the AML cells home/colonize the spheroid (n=8). Next, we assessed the cell cycle status of AML cells in co-culture with the spheroid and compare to those in liquid culture or placed on a MSC monolayer (2D). We found that cells upon contact with MSCs increased their quiescence, 18.63% G0 in 2D and significantly higher in 3D conditions (25.03%; p=0.0048). It is also important to point out that colonizing cells have a lower proliferate capacity than non-colonizing cells after 7 days of co-culture (p=0.0286). This quiescence feature suggests a stem-like behavior of cells homing to the spheroid. Eukaryotic translation initiation factor 4E (eIF4E) has been found altered in AML. While the capacity of eIF4E to increase cell migration and growth has been shown in solid cancers, the mechanism of eIF4E to accelerate AML progression remains unknown. Thus, we hypothesized that eIF4E may play a role in homing of AML cells to the niche. To this end, we used CRISPR to deplete eIF4E in MM6 cells and found that CRISPR 4E cells have significantly decreased homing capacity to the spheroid compared with control cells (p=0.0065). Importantly, we found a lower percentage of CRISPR 4E cells underwent quiescence after co-culture with MSCs (2D or 3D) (p<0.0001). We corroborated observations using xenograft mice. After 20 hours of intravenous injection, we observed a higher percentage of AML cells in the blood of the mice injected with CRISPR 4E cells compared with control cells (p=0.0286), suggesting that CRISPR 4E cells are not able to effectively exit from peripheral blood to home to their niche. Altogether, we stablished a novel in vitro 3D model that mimics stem cell features of the AML-BM niche. We demonstrated that the depletion of eIF4E in AML cells decreases their homing and prevents them from acquiring niche-induced quiescence. Citation Format: Leandro M. Martinez, Eloisi Caldas Lopes, Mayumi Sugita, Jadwiga Gasiorek, Kata Alilovic, Katherine L. Borden, Monica L. Guzman. Novel role for eIF4E in homing of acute myeloid leukemia cells [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 272.

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 score0.003
Threshold uncertainty score0.011

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)0.0030.001

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 head0.038
GPT teacher head0.374
Teacher spread0.336 · 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
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueCancer ResearchSame topicProtein Degradation and InhibitorsFrench-language works237,207