Characterisation of the genomic landscape of <i>CRLF2</i>‐rearranged acute lymphoblastic leukemia
Bibliographic record
Abstract
Deregulated expression of the type I cytokine receptor, CRLF2, is observed in 5-15% of precursor B-cell acute lymphoblastic leukaemia (B-ALL). We aimed to determine the clinical and genetic landscape of those with IGH-CRLF2 or P2RY8-CRLF2 (CRLF2-r) using multiple genomic approaches. Clinical and demographic features of CRLF2-r patients were characteristic of B-ALL. Patients with IGH-CRLF2 were older (14 y vs. 4 y, P < .001), while the incidence of CRLF2-r among Down syndrome patients was high (50/161, 31%). CRLF2-r co-occurred with primary chromosomal rearrangements but the majority (111/161, 69%) had B-other ALL. Copy number alteration (CNA) profiles were similar to B-other ALL, although CRLF2-r patients harbored higher frequencies of IKZF1 (60/138, 43% vs. 77/1351, 24%) and BTG1 deletions (20/138, 15% vs. 3/1351, 1%). There were significant differences in CNA profiles between IGH-CRLF2 and P2RY8-CRLF2 patients: IKZF1 (25/35, 71% vs. 36/108, 33%, P < .001), BTG1 (11/35, 31% vs. 10/108, 9%, P =.004), and ADD3 deletions (9/19, 47% vs. 5/38, 13%, P =.008). A novel gene fusion, USP9X-DDX3X, was discovered in 10/54 (19%) of patients. Pathway analysis of the mutational profile revealed novel involvement for focal adhesion. Although the functional relevance of many of these abnormalities are unknown, they likely activate additional pathways, which may represent novel therapeutic targets.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".