Clinical Relevance of Vascular Endothelial Growth Factor (VEGFA) and VEGF Receptor (VEGFR2) Gene Single Nucleotide Polymorphism on the Treatment Outcomes Following Imatinib Mesylate Therapy.
Bibliographic record
Abstract
Abstract Imatinib mesylate (IM) could reverse marrow angiogenesis and decrease the plasma level of vascular endothelial growth factor (VEGF) in chronic myeloid leukemia (CML) patients. The current study investigated the impact of 4 VEGF (VEGFA) and 3 VEGF receptor (VEGFR2) gene polymorphisms on the outcomes of 228 CML patients following IM therapy (male:female 96:132; median age at start of IM, 55 years-old; chronic phase: accelerated phase: blastic crisis, 205/17/2). The DNAs from blood samples were genotyped using MALDI-TOF-based method. VEGFA genotypes such as -2578C>A (rs699947), -460T>C (rs833061), +405G>C (rs2010963) and +936 C>T (rs3025039) loci, and VEGFR2 genotypes including rs1531289, rs1870377 and rs2305948 were analyzed. In single marker analyses, strong correlations were noted between complete cytogenetic response (CCyR) and VEGFR2 genotypes (rs1531289 and rs1870377), treatment failure and VEGFR2 genotype (rs1870377), dose escalation and VEGFR2 genotype (rs1870377), complete molecular response (CMoR) and VEGFA genotype (rs3025039), progression to advanced disease stage and VEGFA genotypes (rs699947 and rs833061). Three haplotypes of VEGFR2 gene were generated as follows: GT (46.1%), AT (27.9%) and GA (25.7%). Haplotype analyses showed good correlations between VEGFR2 haplotype and CCyR, treatment failure, and dose escalation of IM. Multivariate analyses confirmed strong correlations of VEGFR2 polymorphisms (especially rs1531289, rs1870377 or VEGFR2 haplotype) with CCyR, treatment failure, dose escalation and of VEGFA genotype (rs699947) with progression to advanced disease stage. The VEGFR2 genotypes and haplotype correlate well with cytogenetic response, treatment failure and dose escalation of IM therapy in CML patients, while VEGFA genotype correlates with progression to advanced disease stage.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".