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Record W2532994364 · doi:10.1182/blood.v110.11.737.737

Clinical Relevance of a Pharmacogenetic Approach Using Multiple Candidate Gene Polymorphisms To Predict Response and Resistance to Imatinib Mesylate Therapy in Chronic Myeloid Leukemia.

2007· article· en· W2532994364 on OpenAlexaff
Dong Hwan Kim, Lakshmi Sriharsha, Wei Xu, Suzanne Kamel‐Reid, Xiangdong Liu, Katherine Siminovitch, Hans A. Messner, Jeffrey H. Lipton

Bibliographic record

VenueBlood · 2007
Typearticle
Languageen
FieldMedicine
TopicChronic Myeloid Leukemia Treatments
Canadian institutionsUniversity of TorontoUniversity Health NetworkPrincess Margaret Cancer Centre
Fundersnot available
KeywordsImatinib mesylateSingle-nucleotide polymorphismMyeloid leukemiaPharmacogeneticsOncologyMedicinePopulationInternal medicineImatinibBiologyGenotypeImmunologyGeneticsGene

Abstract

fetched live from OpenAlex

Abstract Background: Imatinib resistance (IR) is a well known cause of treatment failure in chronic myeloid leukemia (CML) patients being treated with imatinib mesylate (IM). Several cellular and genetic mechanisms of IR have been proposed including amplification and overexpression of the BCR-ABL gene, the presence of specific point mutations and MDR1 gene overexpression. Methods: We investigated the impact of 16 single nucleotide polymorphisms (SNPs) in 5 genes potentially associated with pharmacogenetics of IM (ABCB1, multidrug resistance 1; ABCG2, breast-cancer resistance protein; CYP3A5, cytochrome P450 3A5; HOCT1, human organic cation transporter 1; AGP1, alpha-1-acid glycoprotein-1, plasma protein binding to IM). The major endpoints included: response to IM: cytogenetic (CyR) or molecular response (MoR); resistance to IM: loss of response (LOR), treatment-failure (including primary resistance or LOR); progression to accelerated phase (AP) or blast crisis (BC), or death; and need for IM dose escalation to overcome resistance or LOR. The DNAs from peripheral blood samples were genotyped using the Sequenom MassARRAY system based on MALDI-TOF technique. The study population included 229 patients whose clinical outcomes following IM therapy were evaluated from January 2000 to January 2007 (male:female 96:133; median age at start of IM, 53 years-old; white:non-white 170:59; chronic phase:AP:BC, 199:23:3). Results: The frequencies of genotypes in 16 SNPs are summarized in Table 1. The GG allele in ABCG2 (rs2231137), AA allele in CYP3A5 (rs776746) and advanced stage were significantly associated with poor response to IM, while GG allele at HOCT1 (rs683369) and advanced stage correlated with high rate of LOR or treatment failure. The CC allele in ABCG2 (rs2231142) was also identified as an independent predictor of more frequent need for IM dose escalation. The results of multivariate analyses are summarized in Table 2. Conclusion: Using a novel, multiple candidate gene approach based on the pharmacogenetics of Imatinib mesylate, we identified several SNP candidates in patients with CML that are potential predictors of clinical response to IM (rs2231137, ABCG2 or rs776746, CYP3A5), resistance to IM (rs683369, HOCT1) and need for IM dose escalation (rs2231142, ABCG2). Further studies are warranted to validate the role of these SNPs in the early identification of individuals with CML who may not respond optimally to standard IM therapy. Table 1. The genotype frequency and clinical significance in 16 SNPs Table 2. Multivariate regression model based on Cox's proportional hazard model for each-point

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.032
GPT teacher head0.328
Teacher spread0.297 · 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 designObservational
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
Published2007
Admission routes1
Has abstractyes

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