Genome‐wide study identifies novel genes associated with bone toxicities in children with acute lymphoblastic leukaemia
Bibliographic record
Abstract
Summary Bone toxicities are common among paediatric patients treated for acute lymphoblastic leukaemia (ALL) with potentially major negative impact on patients' quality of life. To identify the underlying genetic contributors, we conducted a genome‐wide association study (GWAS) and a transcriptome‐wide association study (TWAS) in 260 patients of European‐descent from the DFCI 05–001 ALL trial, with validation in 101 patients of European‐descent from the DFCI 11–001 ALL trial. We identified a significant association between rs844882 on chromosome 20 and bone toxicities in the DFCI 05–001 trial (p = 1.7 × 10−8). In DFCI 11–001 trial, we observed a consistent trend of this variant with fracture. The variant was an eQTL for two nearby genes, CD93 and THBD. In TWAS, genetically predicted ACAD9 expression was associated with an increased risk of bone toxicities, which was confirmed by meta‐analysis of the two cohorts (meta‐p = 2.4 × 10−6). In addition, a polygenic risk score of heel quantitative ultrasound speed of sound was associated with fracture risk in both cohorts (meta‐p = 2.3 × 10−3). Our findings highlight the genetic influence on treatment‐related bone toxicities in this patient population. The genes we identified in our study provide new biological insights into the development of bone adverse events related to ALL treatment.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".