Genomic determinants of outcome in acute lymphoblastic leukemia: A Children’s Oncology Group study.
Bibliographic record
Abstract
10015 Background: While cure rates for childhood acute lymphoblastic leukemia (ALL) exceed 90%, half of relapses arise in those originally classified with standard risk (SR) disease. Methods: We performed genome/transcriptome sequencing of diagnostic and germline samples of children with SR (n=1381) B-ALL or high-risk (HR) B-ALL with favorable cytogenetics ( ETV6: RUNX1 or double trisomy (DT) of chromosomes (chr) 4+10; n=115) to identify predictors of relapse. We used a case-control study to analyze 439 patients who relapsed and 1057 who remained in complete remission for > 5 years. Results: Genomic subtype was associated with relapse. Unbalanced ETV6:RUNX1 translocations were more common than balanced in relapse patients (OR=2.01, CI=1.25-3.20, P=0.002). Conversely, balanced TCF3:PBX1 translocations were more often associated with relapse than unbalanced in TCF3:PBX1 ALL (OR=0.11, CI=0.01-0.50, P=0.003). A striking finding was the high relapse rate in PAX5 altered ALL (57 of 116 cases (49%); OR=3.29, CI=2.16-5.01, P=3.49x10-8). The nature of the heterogeneous PAX5 driver alterations of this subtype influenced relapse risk, with internal PAX5 amplifications and biallelic PAX5 alterations associated with the highest risk. Specific chr gains influenced outcome in hyperdiploid ALL, with gain of chr 10 and disomy of chr 7 associated with favorable outcome (OR=0.27, CI=0.17-0.42, P=8.02x10-10, St Jude Children’s Research Hospital (SJCRH) validation cohort: OR=0.22, CI=0.05-0.80, P=0.009), while disomy of chr 10 and 17 and gain of chr 6 were enriched in patients that relapsed (OR=7.16, CI=2.63-21.51, P=2.19x10-5; SJCRH cohort: OR=21.32, CI=3.62-119.30, P=0.0004). Genomic alterations were also associated with relapse in a subtype-dependent manner, including alterations of INO80 in ETV6:RUNX1, IKZF1 and CREBBP in hyperdiploid, and FHIT in Ph-like ALL. Conclusions: Genetic subtype, aneuploidy patterns, and secondary genomic alterations influence risk of relapse in children otherwise classified with SR ALL, or HR ALL with favorable genetics. Comprehensive genomic analysis is required for optimal risk stratification and treatment allocation, and particularly to study reduction of therapy in the lowest risk patients. [Table: see text]
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".