Mapping Genes that Contribute to Daunorubicin-Induced Cytotoxicity
Bibliographic record
Abstract
Daunorubicin is an anthracycline antibiotic agent used in the treatment of hematopoietic malignancies. Toxicities associated with this agent include myelosuppression and cardiotoxicity; however, the genes or genetic determinants that contribute to these toxicities are unknown. We present an unbiased genome-wide approach that incorporates heritability, whole-genome linkage analysis, and linkage-directed association to uncover genetic variants contributing to the sensitivity to daunorubicin-induced cytotoxicity. Cell growth inhibition in 324 Centre d' Etude du Polymorphisme Humain lymphoblastoid cell lines (24 pedigrees) was evaluated following treatment with daunorubicin for 72 h. Heritability analysis showed a significant genetic component contributing to the cytotoxic phenotypes (h2 = 0.18-0.63 at 0.0125, 0.025, 0.05, 0.1, 0.2, and 1.0 mumol/L daunorubicin and at the IC50, the dose required to inhibit 50% cell growth). Whole-genome linkage scans at all drug concentrations and IC50 uncovered 11 regions with moderate peak LOD scores (> 1.5), including 4q28.2 to 4q32.3 with a maximum LOD score of 3.18. The quantitative transmission disequilibrium tests were done using 31,312 high-frequency single-nucleotide polymorphisms (SNP) located in the 1 LOD confidence interval of these 11 regions. Thirty genes were identified as significantly associated with daunorubicin-induced cytotoxicity (P < or = 2.0 x 10(-4), false discovery rate < or = 0.1). Pathway and functional gene ontology analysis showed that these genes were overrepresented in the phosphatidylinositol signaling system, axon guidance pathway, and GPI-anchored proteins family. Our findings suggest that a proportion of susceptibility to daunorubicin-induced cytotoxicity may be controlled by genetic determinants and that analysis using linkage-directed association studies with dense SNP markers can be used to identify the genetic variants contributing to cytotoxicity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".