High-risk molecular features may eclipse genomic complexity in predicting chronic lymphocytic leukemia outcomes; UK clinical trial insights
High genomic complexity (HGC) is linked to poor prognosis in CLL, but its independent prognostic value remains uncertain amid emerging biomarkers.
High genomic complexity (HGC) is linked to poor prognosis in CLL, but its independent prognostic value remains uncertain amid emerging biomarkers. We analysed copy number alterations (CNA) in 495 untreated patients from (immuno)chemotherapy trials (CLL4, ADMIRE, ARCTIC), incorporating IGHV status, telomere length (TL), targeted sequencing and DNA‑methylation subtypes. Patients harboured low (LGC, ≤2 CNAs; n = 334), intermediate (IGC, 3–4 CNAs; n = 97), or high (HGC, ≥5 CNAs; n = 64) genomic complexity. HGC associated with U‑CLL (81%, p < 0.001), TP53‑aberration (36%, p < 0.001), short TL (TL‑S; 61%, p < 0.05), del13q (50%, p < 0.001) and del11q (22%, p < 0.05). IGC was enriched for biallelic ATM disruption and BIRC3 deletions (p < 0.001). Trisomy 12 and NOTCH1 mutations were enriched in LGC (p < 0.001). HGC associated with shorter progression‑free and overall survival in univariate models but only remained independent for OS in CLL4 (HR = 1.61, p = 0.02). Independent prognostic factors included TP53 aberration, U‑CLL, TL‑S and n‑CLL. Of 64 HGC patients, 23 had TP53‑aberration; 92% of TP53 wild‑type cases had other high‑risk features (TL‑S, U‑CLL, or n‑CLL). HGC may reflect a convergence of high‑risk features rather than represent an independent biomarker. The interplay of telomere attrition, IGHV status and DNA methylation subtype necessitates further validation in targeted therapy cohorts to enhance risk assessment in prognostic models.
The study shows that HGC, traditionally considered a key prognostic marker in chronic lymphocytic leukemia (CLL), may not independently predict outcomes when high‑risk clinical and molecular features are taken into account. Identifying patients with TP53‑aberrations, unmutated IGHV, short telomeres or naive‑like methylation subtypes can refine risk stratification, informing treatment decisions and potentially guiding the use of targeted therapies such as BTK inhibitors, venetoclax or PI3K inhibitors in clinical trials.
Evidence level: Sejtvonalas. Laboratóriumi sejtekben vizsgálták.
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