The landscape of genetic mutations in patients with chronic lymphocytic leukaemia and complex karyotype

High karyotypic complexity is an independent prognostic factor in patients with CLL treated with venetoclax combinations

Complex karyotypes have been associated with inferior outcomes in chronic lymphocytic leukemia (CLL) treated with chemoimmunotherapy (CIT), whereas their prognostic impact in the context of venetoclax-based treatments is still debated. In this prospective analysis on karyotype complexity in CLL, we evaluated the impact of complex (≥3 chromosomal aberrations [CAs], CKTs) and highly complex karyotypes (≥5 CAs; hCKTs) as well as specific aberrations in previously untreated patients without TP53 aberrations undergoing either CIT or time-limited venetoclax-based therapies in the phase 3 GAIA/CLL13 trial. Karyotype analyses were available for 895 of 926 patients (96.7%), of whom 153 (17%) had a CKT and 43 (5%) hCKT. In the CIT arm, CKT was associated with shorter progression-free survival (PFS) (hazard ratio [HR] 2.58; 95% confidence interval [95% CI], 1.54-4.32; P < .001) and overall survival (HR, 3.25; 95% CI, 1.03-10.26; P = .044). In the pooled venetoclax arms, a multivariable analysis identified hCKTs (HR, 1.96; 95% CI, 1.03-3.72; P = .041), but not CKTs, as independent adverse prognosticators for PFS. The presence of translocations (unbalanced and/or balanced) was also independently associated with shorter PFSs in the venetoclax arms. CIT led to the acquisition of additional CAs (mean CAs, 2.0-3.4; from baseline to CLL progression), whereas karyotype complexity remained stable after venetoclax-based treatments (2.0, both time points). This analysis establishes highly complex karyotypes and translocations as adverse prognostic factors in the context of venetoclax-based combination treatments. The findings of this study support the incorporation of karyotyping into the standard diagnostic workup of CLL, because it identifies patients at high risk of poor treatment outcomes and thereby improves prognostication. This trial was registered at www.clinicaltrials.gov as #NCT02950051.

Personally Tailored Survival Prediction of Patients With Follicular Lymphoma Using Machine Learning Transcriptome-Based Models

Follicular Lymphoma (FL) has a 10-year mortality rate of 20%, and this is mostly related to lymphoma progression and transformation to higher grades. In the era of personalized medicine it has become increasingly important to provide patients with an optimal prediction about their expected outcomes. The objective of this work was to apply machine learning (ML) tools on gene expression data in order to create individualized predictions about survival in patients with FL. Using data from two different studies, we were able to create a model which achieved good prediction accuracies in both cohorts (c-indexes of 0.793 and 0.662 in the training and test sets). Integration of this model with m7-FLIPI and age rendered high prediction accuracies in the test set (cox c-index 0.79), and a simplified approach identified 4 groups with remarkably different outcomes in terms of survival. Importantly, one of the groups comprised 27.35% of patients and had a median survival of 4.64 years. In summary, we have created a gene expression-based individualized predictor of overall survival in FL that can improve the predictions of the m7-FLIPI score.

Tumor mutational load predicts time to first treatment in chronic lymphocytic leukemia (CLL) and monoclonal B-cell lymphocytosis beyond the CLL international prognostic index

Next-generation sequencing identified about 60 genes recurrently mutated in chronic lymphocytic leukemia (CLL). We examined the additive prognostic value of the total number of recurrently mutated CLL genes (i.e., tumor mutational load [TML]) or the individually mutated genes beyond the CLL international prognostic index (CLL-IPI) in newly diagnosed CLL and high-count monoclonal B-cell lymphocytosis (HC MBL). We sequenced 59 genes among 557 individuals (112 HC MBL/445 CLL) in a multi-stage design, to estimate hazard ratios (HR) and 95% confidence intervals (CI) for time-to-first treatment (TTT), adjusted for CLL-IPI and sex. TML was associated with shorter TTT in the discovery and validation cohorts, with a combined estimate of continuous HR = 1.27 (CI:1.17-1.39, P = 2.6 × 10^-8 ; c-statistic = 0.76). When stratified by CLL-IPI, the association of TML with TTT was stronger and validated within low/intermediate risk (combined HR = 1.54, CI:1.37-1.72, P = 7.0 × 10^-14 ). Overall, 80% of low/intermediate CLL-IPI cases with two or more mutated genes progressed to require therapy within 5 years, compared to 24% among those without mutations. TML was also associated with shorter TTT in the HC MBL cohort (HR = 1.53, CI:1.12-2.07, P = .007; c-statistic = 0.71). TML is a strong prognostic factor for TTT independent of CLL-IPI, especially among low/intermediate CLL-IPI risk, and a better predictor than any single gene. Mutational screening at early stages may improve risk stratification and better predict TTT.