An update on: molecular genetics of high-risk chronic lymphocytic leukemia

ABSTRACTIntroduction: During the past few years, new genomic approaches have elucidated the molecular genetics of chronic lymphocytic leukemia (CLL) to a large extent. As a consequence, specific hi..

Assessing prognosis of chronic lymphocytic leukemia using biomarkers and genetics

Chronic lymphocytic leukemia (CLL) is a clinically and genetically heterogenous disease. Genomic studies have deciphered the pathogenesis of CLL and has allowed the identification of prognostic and..

Liquid biopsy in hematological malignancies: current and future applications

The assessment of the cancer mutational profile is crucial for patient management, stratification, and therapeutic decisions. At present, in hematological malignancies with a solid mass, such as lymphomas, tumor genomic profiling is generally performed on the tissue biopsy, but the tumor may harbor genetic lesions that are unique to other anatomical compartments. The analysis of circulating tumor DNA (ctDNA) on the liquid biopsy is an emerging approach that allows genotyping and monitoring of the disease during therapy and follow-up. This review presents the different methods for ctDNA analysis and describes the application of liquid biopsy in different hematological malignancies. In diffuse large B-cell lymphoma (DLBCL) and Hodgkin lymphoma (HL), ctDNA analysis on the liquid biopsy recapitulates the mutational profile of the tissue biopsy and can identify mutations otherwise absent on the tissue biopsy. In addition, changes in the ctDNA amount after one or two courses of chemotherapy significantly predict patient outcomes. ctDNA analysis has also been tested in myeloid neoplasms with promising results. In addition to mutational analysis, liquid biopsy also carries potential future applications of ctDNA, including the analysis of ctDNA fragmentation and epigenetic patterns. On these grounds, several clinical trials aiming at incorporating ctDNA analysis for treatment tailoring are currently ongoing in hematological malignancies

Targeting p53 in chronic lymphocytic leukemia.

Genomic studies have allowed to identify molecular predictors for chronic lymphocytic leukemia (CLL) treatment tailoring.The review covers the p53 biological pathway, its genetic alterations and clinical implications in CLL, and its druggable targets. The potential therapeutic options forThe key approach to improve CLL outcome is treatment tailoring in individual patients. BCR and BCL2 inhibitors have significantly improved CLL survival, howeve

A leukemia-protective germline variant mediates chromatin module formation via transcription factor nucleation

Non-coding variants coordinate transcription factor (TF) binding and chromatin mark enrichment changes over regions spanning >100 kb. These molecularly coordinated regions are named "variable chromatin modules" (VCMs), providing a conceptual framework of how regulatory variation might shape complex traits. To better understand the molecular mechanisms underlying VCM formation, here, we mechanistically dissect a VCM-modulating noncoding variant that is associated with reduced chronic lymphocytic leukemia (CLL) predisposition and disease progression. This common, germline variant constitutes a 5-bp indel that controls the activity of an AXIN2 gene-linked VCM by creating a MEF2 binding site, which, upon binding, activates a super-enhancer-like regulatory element. This triggers a large change in TF binding activity and chromatin state at an enhancer cluster spanning >150 kb, coinciding with subtle, long-range chromatin compaction and robust AXIN2 up-regulation. Our results support a model in which the indel acts as an AXIN2 VCM-activating TF nucleation event, which modulates CLL pathology

Stiffer Spleen Predicts Higher Bone Marrow Fibrosis and Higher JAK2 Allele Burden in Patients With Myeloproliferative Neoplasms

A total of 63 myeloproliferative neoplasms [MPN; 9 polycythemia vera (PV), 32 essential thrombocythemia (ET), and 22 myelofibrosis (MF)] underwent spleen stiffness (SS) measurement by vibration-controlled transient elastography equipped with a novel spleen-dedicated module. Higher SS values significantly correlated with grade 2-3 bone marrow (BM) fibrosis (p=0.035), with hemoglobin level <10 g/dl (p=0.014) and with white blood cells 6510,000/ml (p=0.008). Median SS was significantly higher in MF patients compared to ET and PV (p=0.015). SS also correlated with higher JAK2 variant allele frequency (p=0.02). This study identifies SS as a potential noninvasive tool that reflects BM fibrosis and the mutational burden in MPN

A Clinical Prognostic Model Based on Machine Learning from the Fondazione Italiana Linfomi (FIL) MCL0208 Phase III Trial

BACKGROUND Multicenter clinical trials are producing growing amounts of clinical data. Machine Learning (ML) might facilitate the discovery of novel tools for prognostication and disease-stratification. Taking advantage of a systematic collection of multiple variables, we developed a model derived from data collected on 300 patients with mantle cell lymphoma (MCL) from the Fondazione Italiana Linfomi-MCL0208 phase III trial (NCT02354313). METHODS We developed a score with a clustering algorithm applied to clinical variables. The candidate score was correlated to overall survival (OS) and validated in two independent data series from the European MCL Network (NCT00209222, NCT00209209); Results: Three groups of patients were significantly discriminated: Low, Intermediate (Int), and High risk (High). Seven discriminants were identified by a feature reduction approach: albumin, Ki-67, lactate dehydrogenase, lymphocytes, platelets, bone marrow infiltration, and B-symptoms. Accordingly, patients in the Int and High groups had shorter OS rates than those in the Low and Int groups, respectively (Int→Low, HR: 3.1, 95% CI: 1.0-9.6; High→Int, HR: 2.3, 95% CI: 1.5-4.7). Based on the 7 markers, we defined the engineered MCL international prognostic index (eMIPI), which was validated and confirmed in two independent cohorts; Conclusions: We developed and validated a ML-based prognostic model for MCL. Even when currently limited to baseline predictors, our approach has high scalability potential

A Clinical Prognostic Model Based on Machine Learning from the Fondazione Italiana Linfomi (FIL) MCL0208 Phase III Trial

SIMPLE SUMMARY: The interest in using Machine-Learning (ML) techniques in clinical research is growing. We applied ML to build up a novel prognostic model from patients affected with Mantle Cell Lymphoma (MCL) enrolled in a phase III open-labeled, randomized clinical trial from the Fondazione Italiana Linfomi (FIL)—MCL0208. This is the first application of ML in a prospective clinical trial on MCL lymphoma. We applied a novel ML pipeline to a large cohort of patients for which several clinical variables have been collected at baseline, and assessed their prognostic value based on overall survival. We validated it on two independent data series provided by European MCL Network. Due to its flexibility, we believe that ML would be of tremendous help in the development of a novel MCL prognostic score aimed at re-defining risk stratification. ABSTRACT: Background: Multicenter clinical trials are producing growing amounts of clinical data. Machine Learning (ML) might facilitate the discovery of novel tools for prognostication and disease-stratification. Taking advantage of a systematic collection of multiple variables, we developed a model derived from data collected on 300 patients with mantle cell lymphoma (MCL) from the Fondazione Italiana Linfomi-MCL0208 phase III trial (NCT02354313). Methods: We developed a score with a clustering algorithm applied to clinical variables. The candidate score was correlated to overall survival (OS) and validated in two independent data series from the European MCL Network (NCT00209222, NCT00209209); Results: Three groups of patients were significantly discriminated: Low, Intermediate (Int), and High risk (High). Seven discriminants were identified by a feature reduction approach: albumin, Ki-67, lactate dehydrogenase, lymphocytes, platelets, bone marrow infiltration, and B-symptoms. Accordingly, patients in the Int and High groups had shorter OS rates than those in the Low and Int groups, respectively (Int→Low, HR: 3.1, 95% CI: 1.0–9.6; High→Int, HR: 2.3, 95% CI: 1.5–4.7). Based on the 7 markers, we defined the engineered MCL international prognostic index (eMIPI), which was validated and confirmed in two independent cohorts; Conclusions: We developed and validated a ML-based prognostic model for MCL. Even when currently limited to baseline predictors, our approach has high scalability potential

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

Prognostication in chronic lymphocytic leukemia

: Chronic lymphocytic leukemia (CLL) is the most common type of leukemia in Western countries. CLL is a highly heterogeneous disease: some patients may never require therapy and others relapse several times after different therapeutic strategies. Therefore, in CLL, prognostic markers are essential to capture high-risk patients for different clinical endpoints including early treatment requirement, early progression after BTK or BCL2 inhibitors and Richter transformation. In early stage CLL, different biological and clinical biomarkers have been identified to predict time to treatment requirement that could be used to identify the most appropriate population for early intervention clinical trial. However, at the moment, the standard of care for early stage CLL remains watch & wait since no survival benefit has been identified in clinical trials with chemoimmunotherapy and with BTK inhibitors. In patients requiring treatment TP53 disruptions identify high-risk patients who benefit the most from long-term continuous therapy with BTKi. On the opposite side of the spectrum, IGHV mutated patients devoid of TP53 disruption benefit the most from fixed-duration therapy with venetoclax-obinutuzumab. In between, the highly heterogenous subgroup of patients with IGHV unmutated genes represents the group in which further efforts are needed to identify additional prognostic biomarkers aimed at selecting patients who can benefit from fixed-duration and patients who can benefit from long term BTKi therapy. In the context of the aggressive transformation of CLL, namely Richter syndrome, the clonal relationship to the CLL counterpart represents the strongest prognostic biomarker. Clonally related Richter syndrome still represents an unmet clinical need which requires further efforts to identify new therapeutic strategies