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Record W2091214711 · doi:10.1158/1538-7445.am2013-911

Abstract 911: Tyrosine kinase inhibitor Gefitinib selectivley induces apoptosis in Zap70+ chronic lymphocytic leukemia cells and inhibits B cell receptor signaling.

2013· article· en· W2091214711 on OpenAlexaff
Rebecca Dielschneider, Wenyan Xiao, Ju‐Yoon Yoon, Edward Noh, James B. Johnston, Spencer B. Gibson

Bibliographic record

VenueCancer Research · 2013
Typearticle
Languageen
FieldMedicine
TopicChronic Lymphocytic Leukemia Research
Canadian institutionsCancerCare ManitobaUniversity of Manitoba
Fundersnot available
KeywordsZAP70Chronic lymphocytic leukemiaGefitinibCancer researchTyrosine kinaseSykJurkat cellsTyrosine phosphorylationBruton's tyrosine kinaseT cellBiologyIL-2 receptorLeukemiaImmunologyCell biologySignal transductionReceptorEpidermal growth factor receptorBiochemistryImmune system

Abstract

fetched live from OpenAlex

Abstract Chronic lymphocytic leukemia (CLL), the most common adult leukemia in the western world, is characterized by an accumulation of B cells in the peripheral blood, lymph nodes, and bone marrow leading to immunosuppression. Patients can be stratified into two distinct groups based upon the expression of a T-cell receptor kinase, ZAP70. CLL patients with ≥20% cells staining for the ZAP70 have more aggressive disease and survive shorter times compared with patients with lower ZAP70 expression. Thus, tyrosine kinase inhibitors could be attractive therapeutic agents targeting ZAP70 over expressing CLL cells. In this study, we investigated tyrosine kinase inhibitor gefitinib that has been shown to inhibit tyrosine kinase Syk (a ZAP70 family member) and is not a myelo- or immuno-suppressive drug as other chemotherapeutic agents are in treating CLL. We found using MTT viability assay that the tyrosine kinase inhibitor gefitinib is effective in primary CLL cells with a significant preference for Zap70+ CLL; gefitinib had a median IC50 of 4.5 μM in Zap70+ patient cells and >15.0 μM in Zap70- patient cells. Flow cytometry analysis showed a decrease of viable Zap70+ CLL cells 24 hours post gefitinib treatment. Although gefitinib decreased the viability of the Zap70+ Jurkat T leukemia cell line, it failed to affect T cells from CLL patients. Western blot analysis showed gefitinib inhibited tyrosine phosphorylation of ZAP70 and Syk in CLL cells. In addition, both basal and B cell receptor-stimulated tyrosine phosphorylation was reduced after gefinitib treatment. Compared with other known inhibitors dasatinib and ibrutinib, gefitinib has the same downstream inhibition of Erk and Akt phosphorylation in Zap70+ cells. The significance of Zap70 expression was further determined by treating the Raji B lymphoma cell line transduced with vector control or vector encoding Zap70; indeed Zap70 expression sensitized the B cell line Raji to gefitinib treatment whereas other tyrosine kinase inhibitors were less effective. Therefore, gefitinib inhibits the Zap70 signaling pathway in CLL cells and holds potential as a targeted therapy for aggressive CLL. Citation Format: Rebecca Dielschneider, Wenyan Xiao, Ju Yoon Yoon, Edward Noh, James B. Johnston, Spencer B. Gibson. Tyrosine kinase inhibitor Gefitinib selectivley induces apoptosis in Zap70+ chronic lymphocytic leukemia cells and inhibits B cell receptor signaling. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 911. doi:10.1158/1538-7445.AM2013-911

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 head0.039
GPT teacher head0.335
Teacher spread0.296 · 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
Published2013
Admission routes1
Has abstractyes

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