The CLL-map project: from multiomics to precision medicine for a common leukemia
2Cancer Program, Broad Institute of MIT and Harvard, USA
3Department of Medical Oncology, Dana-Farber Cancer Institute, USA
4IDIBAPS, Institut d’Investigacions Biomèdiques August Pi i Sunyer, Spain
5CIBERONC, Centro de Investigación Biomédica en Red de Cáncer, Spain
6Department of Data Science, Dana-Farber Cancer Institute, USA
7Department of Internal Medicine III, Ulm University, Germany
8Servicio de Hematología, Hospital Clínic, IDIBAPS, Spain
9Center for Cancer Research, Massachusetts General Hospital, USA
10Departamento de Bioquímica y Biología Molecular, Instituto Universitario de Oncología, Universidad de Oviedo, Spain
11Department of Genome Regulation, Max Planck Institute for Molecular Genetics, Germany
12Memorial Sloan Kettering Cancer Center, Computational Oncology Service, Department of Pediatrics, USA
13Laboratory of Lymphoid Malignancies, National Heart, Lung, and Blood Institute, National Institutes of Health
14Department of Leukemia, The University of Texas, MD Anderson Cancer Center, USA
15Moores Cancer Center, University of California San Diego, USA
16Translational Immunogenomics Laboratory, Dana-Farber Cancer Institute, USA
17IBM Research, IBM, USA
18Department of Stem Cell and Regenerative Biology, Harvard University, USA
19Harvard Medical School, Harvard University, USA
20Center for Molecular Medicine, University of Cologne, Germany
21Department I of Internal Medicine, Center for Integrated Oncology Aachen Bonn Cologne Duesseldorf and German CLL Study Group, University of Cologne, Germany
22ICREA, Institució Catalana de Recerca i Estudis Avançats, Spain
23Departament de Fonaments Clinics, Facultat de Medicina, Universitat de Barcelona, Spain
24Department of Medicine, Brigham and Women’s Hospital, USA
25Hematopathology Section, Laboratory of Pathology, Hospital Clinic of Barcelona, Spain
26Department of Pathology, Massachusetts General Hospital, USA
Chronic lymphocytic leukemia (CLL), the most common leukemia in adults, has consistently been at the forefront of genomic discovery. The two major subtypes of CLL are distinguished by the level of mutation in their immunoglobulin IGHV gene, which reflects the maturation level of their B-cell of origin. Despite prior advances, we lack a comprehensive molecular map of CLL that could explain the high variability in patient outcome and provide a roadmap towards precision medicine. To that end, we devised the ‘CLL-map’ project - an international endeavor to harmonize and analyze genomic, transcriptomic, and epigenomic data from 1148 patients.
The large genomic cohort, which was two-fold larger than previous studies, facilitated novel discovery. With greater statistical power, we identified 202 candidate genetic drivers of CLL (109 novel) and refined the characterization of the two IGHV subtypes, which revealed distinct genomic landscapes and predictors of patient outcome. Using 600 CLL RNA-seqs, we discovered 8 new gene expression subtypes that proved to be independent prognostic factors. Through CLL drug-screens and a machine learning classifier, we show the expression subtypes’ potential in precision medicine. Integrative multiomic analysis shows that clinical outcomes are associated with a combination of genetic, epigenetic, and gene expression features, further advancing our prognostic paradigm for CLL.
Overall, this work reveals fresh insights into CLL oncogenesis and prognostication and advances us towards improved precision medicine for CLL patients. In the current post-TCGA project era, our findings motivate the initiation of cancer multiomics projects at larger scale for additional tumor types.