High-risk Molecular Features Eclipse Genomic Complexity in Predicting CLL Patient Outcomes; Insights from the UK CLL4, ARCTIC and ADMIRE Trials
Bibliographic record
Abstract
Abstract High genomic complexity is linked to poor prognosis in chronic lymphocytic leukaemia (CLL), but its independent prognostic value remains uncertain amid emerging biomarkers. We analysed copy number alterations (CNA) in 495 treatment-naïve patients from three randomized trials (CLL4, ADMIRE, ARCTIC), incorporating IGHV status, telomere length (TL), targeted sequencing, and DNA-methylation subtypes. Patients harboured low (LGC, 0–2 CNAs; n=334), intermediate (IGC, 3–4 CNAs; n=97), or high (HGC, ≥5 CNAs; n=64) genomic complexity. U-CLL (81%, p<0.001) and short TL (61%, p<0.05) were enriched in HGC, and TL inversely correlated with CNA burden (τ = –0.147, p<0.001). 62% of HGC patients were n-CLL. TP53 dysfunction was associated with HGC (36%, p<0.001). Trisomy 12 and NOTCH1 mutations, were enriched in LGC (p<0.001). HGC predicted shorter progression-free and overall survival in all univariate models but only remained independently prognostic for OS only in CLL4 (HR=1.61, p=0.02). Of 64 HGC patients, 23 had TP53 dysfunction; 92% of TP53 wild-type cases had other high-risk features (TL-S, U-CLL, or n- CLL). HGC is associated with adverse outcomes but may reflect underlying biological risk rather than serve as an independent biomarker. Its interplay with telomere attrition, immunogenetics, and epigenetic subtype warrants further validation in targeted therapy-treated cohorts.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".