The Number of Signaling Pathways Altered by Driver Mutations in Chronic Lymphocytic Leukemia Impacts Disease Outcome

NFKBIE mutations are selected by the tumor microenvironment and contribute to immune escape in chronic lymphocytic leukemia

Loss-of-function mutations in NFKBIE, which encodes for the NF-κB inhibitor IκBε, are frequent in chronic lymphocytic leukemia (CLL) and certain other B-cell malignancies and have been associated with accelerated disease progression and inferior responses to chemotherapy. Using in vitro and in vivo murine models and primary patient samples, we now show that NFKBIE-mutated CLL cells are selected by microenvironmental signals that activate the NF-κB pathway and induce alterations within the tumor microenvironment that can allow for immune escape, including expansion of CD8+ T-cells with an exhausted phenotype and increased PD-L1 expression on the malignant B-cells. Consistent with the latter observations, we find increased expression of exhaustion markers on T-cells from patients with NFKBIE-mutated CLL. In addition, we show that NFKBIE-mutated murine CLL cells display selective resistance to ibrutinib and report inferior outcomes to ibrutinib treatment in NFKBIE-mutated CLL patients. These findings suggest that NFKBIE mutations can contribute to CLL progression through multiple mechanisms, including a bidirectional crosstalk with the microenvironment and reduced sensitivity to BTK inhibitor treatment.

Different prognostic impact of recurrent gene mutations in chronic lymphocytic leukemia depending on IGHV gene somatic hypermutation status: a study by ERIC in HARMONY

Recent evidence suggests that the prognostic impact of gene mutations in patients with chronic lymphocytic leukemia (CLL) may differ depending on the immunoglobulin heavy variable (IGHV) gene somatic hypermutation (SHM) status. In this study, we assessed the impact of nine recurrently mutated genes (BIRC3, EGR2, MYD88, NFKBIE, NOTCH1, POT1, SF3B1, TP53, and XPO1) in pre-treatment samples from 4580 patients with CLL, using time-to-first-treatment (TTFT) as the primary end-point in relation to IGHV gene SHM status. Mutations were detected in 1588 (34.7%) patients at frequencies ranging from 2.3-9.8% with mutations in NOTCH1 being the most frequent. In both univariate and multivariate analyses, mutations in all genes except MYD88 were associated with a significantly shorter TTFT. In multivariate analysis of Binet stage A patients, performed separately for IGHV-mutated (M-CLL) and unmutated CLL (U-CLL), a different spectrum of gene alterations independently predicted short TTFT within the two subgroups. While SF3B1 and XPO1 mutations were independent prognostic variables in both U-CLL and M-CLL, TP53, BIRC3 and EGR2 aberrations were significant predictors only in U-CLL, and NOTCH1 and NFKBIE only in M-CLL. Our findings underscore the need for a compartmentalized approach to identify high-risk patients, particularly among M-CLL patients, with potential implications for stratified management.

Precision diagnostics in chronic lymphocytic leukemia: Past, present and future

Genetic diagnostics of hematological malignancies has evolved dramatically over the years, from chromosomal banding analysis to next-generation sequencing, with a corresponding increased capacity to detect clinically relevant prognostic and predictive biomarkers. In diagnostics of patients with chronic lymphocytic leukemia (CLL), we currently apply fluorescence in situ hybridization (FISH)-based analysis to detect recurrent chromosomal aberrations (del(11q), del(13q), del(17p) and trisomy 12) as well as targeted sequencing (IGHV and TP53 mutational status) for risk-stratifying purposes. These analyses are performed before start of any line of treatment and assist in clinical decision-making including selection of targeted therapy (BTK and BCL2 inhibitors). Here, we present the current view on the genomic landscape of CLL, including an update on recent advances with potential for clinical translation. We discuss different state-of-the-art technologies that are applied to enable precision diagnostics in CLL and highlight important genomic markers with current prognostic and/or predictive impact as well as those of prospective clinical relevance. In the coming years, it will be important to develop more comprehensive genomic analyses that can capture all types of relevant genetic aberrations, but also to develop highly sensitive assays to detect minor mutations that affect therapy response or confer resistance to targeted therapies. Finally, we will bring up the potential of new technologies and multi-omics analysis to further subclassify the disease and facilitate implementation of precision medicine approaches in this still incurable disease.

Prediction of clinical outcome in CLL based on recurrent gene mutations, CLL-IPI variables, and (para)clinical data

Baseline data (but not genetics) added to CLL-IPI variables improve predictive performance of ML models in CLL.

Risk factors predictive of death within a 5-year outlook are mostly similar to risk factors predictive of infection within a 2-year outlook.

A highly variable clinical course, immune dysfunction, and a complex genetic blueprint pose challenges for treatment decisions and the management of risk of infection in patients with chronic lymphocytic leukemia (CLL). In recent years, the use of machine learning (ML) technologies has made it possible to attempt to untangle such heterogeneous disease entities. In this study, using 3 classes of variables (international prognostic index for CLL [CLL-IPI] variables, baseline [para]clinical data, and data on recurrent gene mutations), we built ML predictive models to identify the individual risk of 4 clinical outcomes: death, treatment, infection, and the combined outcome of treatment or infection. Using the predictive models, we assessed to what extent the different classes of variables are predictive of the 4 different outcomes, within both a short-term 2-year outlook and a long-term 5-year outlook after CLL diagnosis. By adding the baseline (para)clinical data to CLL-IPI variables, predictive performance was improved, whereas no further improvement was observed when including the data on recurrent genetic mutations. We discovered 2 main clusters of variables predictive of treatment and infection. Further emphasizing the high mortality resulting from infection in CLL, we found a close similarity between variables predictive of infection in the short-term outlook and those predictive of death in the long-term outlook. We conclude that at the time of CLL diagnosis, routine (para)clinical data are more predictive of patient outcome than recurrent mutations. Future studies on modeling genetics and clinical outcome should always consider the inclusion of several (para)clinical data to improve performance.

Precision diagnostics in lymphomas - Recent developments and future directions

Genetics is an integral part of the clinical diagnostics of lymphomas that improves disease subclassification and patient risk-stratification. With the introduction of high-throughput sequencing technologies, a rapid, in-depth portrayal of the genomic landscape in major lymphoma entities was achieved. Whilst a few lymphoma entities were characterized by a predominant gene mutation (e.g. Waldenström's macroglobulinemia and hairy cell leukemia), the vast majority demonstrated a very diverse genetic landscape with a high number of recurrent gene mutations (e.g. chronic lymphocytic leukemia and diffuse large B cell lymphoma), indeed reflecting the great clinical heterogeneity among lymphomas. These studies have allowed better understanding of the ontogeny and evolution of different lymphomas, while also identifying new genetic markers that can complement lymphoma diagnostics and improve prognostication. However, despite these efforts, there is still a limited number of gene mutations with predictive impact that can guide treatment selection. In this review, we will highlight clinically relevant diagnostic, prognostic and predictive markers in lymphomas that are used today in routine diagnostics. We will also discuss how comprehensive genomic characterization using broad sequencing panels, allowing for the simultaneous detection of different types of genetic aberrations, may aid future development of precision diagnostics in lymphomas. This may in turn pave the way for the implementation of tailored precision therapy strategies at the individual patient level.

Biological significance of monoallelic and biallelic BIRC3 loss in del(11q) chronic lymphocytic leukemia progression

BIRC3 is monoallelically deleted in up to 80% of chronic lymphocytic leukemia (CLL) cases harboring del(11q). In addition, truncating mutations in the remaining allele of this gene can lead to BIRC3 biallelic inactivation, which has been shown to be a marker for reduced survival in CLL. Nevertheless, the biological mechanisms by which these lesions could contribute to del(11q) CLL pathogenesis and progression are partially unexplored. We implemented the CRISPR/Cas9-editing system to generate isogenic CLL cell lines harboring del(11q) and/or BIRC3 mutations, modeling monoallelic and biallelic BIRC3 loss. Our results reveal that monoallelic BIRC3 deletion in del(11q) cells promotes non-canonical NF-κB signaling activation via RelB-p52 nuclear translocation, being these effects allelic dose-dependent and therefore further enhanced in del(11q) cells with biallelic BIRC3 loss. Moreover, we demonstrate ex vivo in primary cells that del(11q) cases including BIRC3 within their deleted region show evidence of non-canonical NF-κB activation which correlates with high BCL2 levels and enhanced sensitivity to venetoclax. Furthermore, our results show that BIRC3 mutations in del(11q) cells promote clonal advantage in vitro and accelerate leukemic progression in an in vivo xenograft model. Altogether, this work highlights the biological bases underlying disease progression of del(11q) CLL patients harboring BIRC3 deletion and mutation.

Proteomics and Drug Repurposing in CLL towards Precision Medicine

Simple Summary

Despite continued efforts, the current status of knowledge in CLL molecular pathobiology, diagnosis, prognosis and treatment remains elusive and imprecise. Proteomics approaches combined with advanced bioinformatics and drug repurposing promise to shed light on the complex proteome heterogeneity of CLL patients and mitigate, improve, or even eliminate the knowledge stagnation. In relation to this concept, this review presents a brief overview of all the available proteomics and drug repurposing studies in CLL and suggests the way such studies can be exploited to find effective therapeutic options combined with drug repurposing strategies to adopt and accost a more “precision medicine” spectrum.

Abstract

CLL is a hematological malignancy considered as the most frequent lymphoproliferative disease in the western world. It is characterized by high molecular heterogeneity and despite the available therapeutic options, there are many patient subgroups showing the insufficient effectiveness of disease treatment. The challenge is to investigate the individual molecular characteristics and heterogeneity of these patients. Proteomics analysis is a powerful approach that monitors the constant state of flux operators of genetic information and can unravel the proteome heterogeneity and rewiring into protein pathways in CLL patients. This review essences all the available proteomics studies in CLL and suggests the way these studies can be exploited to find effective therapeutic options combined with drug repurposing approaches. Drug repurposing utilizes all the existing knowledge of the safety and efficacy of FDA-approved or investigational drugs and anticipates drug alignment to crucial CLL therapeutic targets, leading to a better disease outcome. The drug repurposing studies in CLL are also discussed in this review. The next goal involves the integration of proteomics-based drug repurposing in precision medicine, as well as the application of this procedure into clinical practice to predict the most appropriate drugs combination that could ensure therapy and the long-term survival of each CLL patient.

The Evolving Landscape of Chronic Lymphocytic Leukemia on Diagnosis, Prognosis and Treatment

The knowledge of chronic lymphocytic leukemia (CLL) has progressively deepened during the last forty years. Research activities and clinical studies have been remarkably fruitful in novel findings elucidating multiple aspects of the pathogenesis of the disease, improving CLL diagnosis, prognosis and treatment. Whereas the diagnostic criteria for CLL have not substantially changed over time, prognostication has experienced an expansion with the identification of new biological and genetic biomarkers. Thanks to next-generation sequencing (NGS), an unprecedented number of gene mutations were identified with potential prognostic and predictive value in the 2010s, although significant work on their validation is still required before they can be used in a routine clinical setting. In terms of treatment, there has been an impressive explosion of new approaches based on targeted therapies for CLL patients during the last decade. In this current chemotherapy-free era, BCR and BCL2 inhibitors have changed the management of CLL patients and clearly improved their prognosis and quality of life. In this review, we provide an overview of these novel advances, as well as point out questions that should be further addressed to continue improving the outcomes of patients.

EXPRESSION OF LIPOPROTEIN LIPASE AND c-MYC ONCOGENE IN PATIENTS WITH CHRONIC LYMPHOCYTIC LEUKEMIA AFFECTED BY THE CHORNOBYL ACCIDENT

OBJECTIVE

to determine the association between the expression of lipoprotein lipase (LPL) and c-MYC genes inperipheral blood cells of chronic lymphocytic leukemia (CLL) patients affected by the Chornobyl catastrophedepending on the mutational status of IGHV genes.

METHODS

Analysis was performed in the group of 69 CLL patients irradiated due to the Chornobyl NPP accident (58clean-up workers of 1986 year, 6 inhabitants of radionuclide contaminated areas, and 5 evacuees). The IGHV genemutational status was studied by polymerase chain reaction (PCR) followed by direct sequencing. LPL and c-MYCexpression was evaluated by Quantitative Real-time PCR. Data were analyzed with the SPSS software package, version 20.0.

RESULTS

Relative LPL expression levels in CLL samples ranged from 0 to 1663.5 (mean 138.47 ± 30.69, median 26.1).A strong correlation between individual LPL expression levels and IGHV mutational status was found (r = 0.684;p < 0.0001). The average relative c-MYC expression level was 5.7 ± 0.87 (median 2.86; range 0-48.5). No association between c-MYC expression and IGHV mutational status was found. Among unmutated IGHV cases, a correlationbetween LPL and c-MYC gene expression levels was identified: r = 0.351; p = 0.013.

CONCLUSIONS

Our data confirm the dominant concept that unmutated IGHV CLL cases are more sensitive to the actionof proliferative stimuli compared to mutated IGHV CLL cases. This is manifested by an increase in the expression ofa functionally significant LPL gene, is one for the strongest negative prognostic markers in CLL.

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.