Chronic Lymphocytic Leukemia (CLL) is a heterogeneous malignancy and recurrent gene mutations typically occur at low frequencies. Large patient cohorts - accessed via the HARMONY Data Platform - are needed to pinpoint the role of these genetic mutations in CLL pathobiology, in particular the impact on disease progression and prognosis.
CLL is a heterogeneous B-cell malignancy, both from a molecular and clinical perspective. Patients follow very different disease courses, with some surviving for long periods of time and others presenting with aggressive, progressive disease from the outset.
Currently, the genetic mutations that underpin this molecular and biological heterogeneity in CLL are not fully understood. Although next-generation sequencing (NGS) has helped to decipher the genetic landscape of CLL –more than 3000 genes have been found to be mutated in the disease – these recurrent mutations typically occur at very low frequencies. Large patient cohorts are therefore needed to uncover the true impact of gene mutations on the underlying mechanisms driving the pathogenesis of CLL. The HARMONY Big Data Platform delivers high quality, robust data which will aid in answering these critical questions in CLL.
This multicenter study is retrospectively collecting mutational and clinical data from ~4.000 CLL patients who will be added to the HARMONY Big Data Platform. Importantly, the data is being gathered from treatment-naïve patients – before exposure to any therapy – thereby avoiding any potential confounding influence of treatment on the clonal evolution of the disease. The study is not using exome sequencing, but instead will analyze a targeted panel that includes 11 of the most frequently mutated genes in CLL. A strict template for patient data collection is being employed and it is a stipulation of the study that specific molecular parameters and set clinical characteristics are included for every patient, to ensure all conclusions drawn are robust and reproducible.
By evaluating mutational status in a large and well annotated CLL patient series, this study will shed light on the prevalence, prognostic impact and clinical relevance of 11 genes known to be mutated in CLL and associated with a more aggressive disease. It may also prove possible to identify distinct patterns of association and occurrence between recurrent mutations and other clinical and biological features of CLL. The final step will then be to develop and validate a robust and refined prognostic model for use in clinical practice. Currently, prognostication in CLL is based largely on the four recurrent FISH aberrations. Building on this prognostication model with the inclusion of gene mutations should enable better stratification of patients, a key step toward the attainment of personalized medicine. Looking to the longer-term, insights gathered from this study may also have applications for the therapeutic targeting of patients for treatment with novel agents and inhibitors under active development in the CLL arena.
Improvement of existing prognostication models with the inclusion of key gene mutations able to guide clinical decision-making, including targeted therapy selection and response prediction.
Lesley Ann Sutton, Karolinska Institute, Sweden and European Research Initiative on CLL (ERIC): “Big data offers us the opportunity to enhance our understanding of the mutations that drive disease development and progression in CLL.”
Paolo Ghia, European Research Initiative on CLL (ERIC)
CEITEC Masaryk University, Czech Republic; MLL GmbH, Germany; Vall d’Hebron Institut de Recerca (VHIR), Spain; University of Eastern Piedmont and Oncology Institute of Southern Switzerland, Switzerland; Hospital del Mar, Barcelona, Spain; Hospital 12 de Octubre, Spain; Royal Bournemouth Hospital, United Kingdom; Belfast Trust - QUB, Ireland; Centro de Investigación del Cáncer de Salamanca, Spain; Institute of Applied Biosciences / CERTH, Greece; University of Southampton, United Kingdom; Rigshospitalet, Denmark; Azienda Ospedaliero Universitaria di Ferrara, Italy; Karolinska Institute, Sweden.