Genomic landscape and chronological reconstruction of driver events in multiple myeloma.
Francesco MauraNiccoló BolliNicos AngelopoulosKevin J DawsonDaniel LeongamornlertInigo MartincorenaThomas J MitchellAnthony FullamSantiago GonzalezRaphael SzalatFederico AbascalBernardo Rodriguez-MartinMehmet Kemal SamurDominik GłodzikMarco RoncadorMariateresa FulcinitiYu Tzu TaiStephane MinvielleFlorence MagrangeasPhilippe MoreauPaolo CorradiniKenneth C AndersonJose M C TubioDavid C WedgeMoritz GerstungHervé Avet-LoiseauNikhil MunshiPeter J CampbellPublished in: Nature communications (2019)
The multiple myeloma (MM) genome is heterogeneous and evolves through preclinical and post-diagnosis phases. Here we report a catalog and hierarchy of driver lesions using sequences from 67 MM genomes serially collected from 30 patients together with public exome datasets. Bayesian clustering defines at least 7 genomic subgroups with distinct sets of co-operating events. Focusing on whole genome sequencing data, complex structural events emerge as major drivers, including chromothripsis and a novel replication-based mechanism of templated insertions, which typically occur early. Hyperdiploidy also occurs early, with individual trisomies often acquired in different chronological windows during evolution, and with a preferred order of acquisition. Conversely, positively selected point mutations, whole genome duplication and chromoplexy events occur in later disease phases. Thus, initiating driver events, drawn from a limited repertoire of structural and numerical chromosomal changes, shape preferred trajectories of evolution that are biologically relevant but heterogeneous across patients.
Keyphrases
- multiple myeloma
- end stage renal disease
- ejection fraction
- newly diagnosed
- copy number
- chronic kidney disease
- prognostic factors
- healthcare
- emergency department
- machine learning
- stem cells
- gene expression
- depressive symptoms
- single cell
- mesenchymal stem cells
- electronic health record
- rna seq
- genome wide
- deep learning
- data analysis