Impact of Genetic Ancestry on Genomics and Survival Outcomes in T-cell Acute Lymphoblastic Leukemia
Bibliographic record
Abstract
The influence of genetic ancestry on genomics in T-cell acute lymphoblastic leukemia (T-ALL) has not been fully explored. We examined the impact of genetic ancestry on multiomic alterations, survival outcomes, and risk stratification. Among 1,309 children and young adults with T-ALL treated on the Children's Oncology Group trial AALL0434, the prognostic value of five commonly altered T-ALL genes varied by ancestry-including NOTCH1, which was associated with superior overall survival for patients of European ancestry but was nonprognostic among patients of African ancestry. Integrating genetic ancestry with published T-ALL risk classifiers, we identified that an X01 penalized Cox regression classifier stratified patients regardless of ancestry, whereas a European multigene classifier misclassified patients of certain ancestries. Overall, 80% of patients harbored a genomic alteration in at least one gene with differential prognostic impact in an ancestry-specific manner. These data demonstrate the importance of incorporating genetic ancestry into genomic risk classification. SIGNIFICANCE: There is a lack of studies examining the prognostic significance of genomic features by genetic ancestry in T-ALL, especially in non-European ancestral groups. In this study, we demonstrate how the prognostic value of individual alterations differs by genetic ancestry, warranting future studies to identify germline alleles affecting these associations. See related commentary by de Smith, p. xxx.
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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 | 0.006 | 0.026 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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".