Pharmacogenomics of vincristine‐induced neurotoxicity in pediatric cancer patients
Bibliographic record
Abstract
The use of the anticancer agent vincristine is limited by debilitating neurotoxicity (VIN), for which a genetic basis has been suggested. Here, we investigated variation in 315 genes involved in drug absorption, metabolism, distribution, excretion (ADME) and toxicity in the context of VIN in children with cancer. We genotyped 4536 ADME variants in a pilot cohort of 140 children with Wilms tumor and rhabdomyosarcoma. To replicate findings, known variants in CYP3A5 and ABCB1 as well as variants with evidence for association (p<0.01) in the pilot cohort were analyzed in 422 patients with acute lymphoblastic leukemia (ALL). No association of variants in CYP3A5 or ABCB1 with VIN was observed. Of the candidates identified in the pilot cohort, the association of a variant in ABCA4 with VIN was replicated in patients with ALL (combined cohort OR: 1.7; P = 2.9×10 −4 ). This study was the first to broadly screen genetic variation in the context of VIN in pediatric cancer patients and serves as a starting point for further study of the genetic basis of VIN, providing novel hypotheses for mechanisms underlying the susceptibility to VIN and the possibility for predictive genetic testing to identify patients at risk. Funded by CIHR/CFI
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 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".