Genetic ancestry and skeletal toxicities among childhood acute lymphoblastic leukemia patients in the DFCI 05-001 cohort
Bibliographic record
Abstract
Hispanic children have a higher incidence of acute lymphoblastic leukemia (ALL) and inferior treatment outcomes relative to non-Hispanic White children. We previously reported that Hispanic children with ALL had lower risk of fracture and osteonecrosis. To unravel the genetic root of such ethnic differences, we genotyped 449 patients from the DFCI 05-001 cohort and analyzed their ancestry. Patients with discordant clinical and genetic ancestral groups were reclassified, and those with unknown ancestry were reassigned on the basis of genetic estimates. Both clinical and genetic ancestries were analyzed in relation to risk of bone toxicities and survival outcomes. Consistent with clinically reported race/ethnicity, genetically defined Hispanic and Black patients had significantly lower risk of fracture (Hispanic: subdistribution hazard ratio [SHR], 0.42; 95% confidence interval [CI], 0.22-0.81; P = .01; Black: SHR, 0.28; 95% CI, 0.10-0.75; P = .01), and osteonecrosis (Hispanic: SHR, 0.12; 95% CI, 0.02-0.93; P = .04; Black: SHR, 0.24; 95% CI, 0.08-0.78; P = .02). The lower risk was driven by African but not Native American or Asian ancestry. In addition, patients with a higher percentage of Native American ancestry had significantly poorer overall survival and event-free survival. Our study revealed that the lower risk of bone toxicities among Black and Hispanic children treated for ALL was attributed, in part, to the percentage of African ancestry in their genetic admixture. The findings provide suggestive evidence for the protective effects of genetic factors associated with African decent against bone damage caused by ALL treatment and clues for future studies to identify underlying biological mechanisms.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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".