Mutations driving CLL and their evolution in progression and relapse

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Mutations driving CLL and their evolution in progression and relapse

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Title: Mutations driving CLL and their evolution in progression and relapse
Author: Landau, Dan A.; Tausch, Eugen; Taylor-Weiner, Amaro N; Stewart, Chip; Reiter, Johannes G.; Bahlo, Jasmin; Kluth, Sandra; Bozic, Ivana; Lawrence, Mike; Böttcher, Sebastian; Carter, Scott L.; Cibulskis, Kristian; Mertens, Daniel; Sougnez, Carrie; Rosenberg, Mara; Hess, Julian M.; Edelmann, Jennifer; Kless, Sabrina; Kneba, Michael; Ritgen, Matthias; Fink, Anna; Fischer, Kirsten; Gabriel, Stacey; Lander, Eric; Nowak, Martin A.; Döhner, Hartmut; Hallek, Michael; Neuberg, Donna; Getz, Gad; Stilgenbauer, Stephan; Wu, Catherine J.

Note: Order does not necessarily reflect citation order of authors.

Citation: Landau, D. A., E. Tausch, A. N. Taylor-Weiner, C. Stewart, J. G. Reiter, J. Bahlo, S. Kluth, et al. 2015. “Mutations driving CLL and their evolution in progression and relapse.” Nature 526 (7574): 525-530. doi:10.1038/nature15395.
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Abstract: SUMMARY Which genetic alterations drive tumorigenesis and how they evolve over the course of disease and therapy are central questions in cancer biology. We identify 44 recurrently mutated genes and 11 recurrent somatic copy number variations through whole-exome sequencing of 538 chronic lymphocytic leukemia (CLL) and matched germline DNA samples, 278 of which were collected in a prospective clinical trial. These include previously unrecognized cancer drivers (RPS15, IKZF3) and collectively identify RNA processing and export, MYC activity and MAPK signaling as central pathways involved in CLL. Clonality analysis of this large dataset further enabled reconstruction of temporal relationships between driver events. Direct comparison between matched pre-treatment and relapse samples from 59 patients demonstrated highly frequent clonal evolution. Thus, large sequencing datasets of clinically informative samples enable the discovery of novel cancer genes and the network of relationships between the driver events and their impact on disease relapse and clinical outcome.
Published Version: doi:10.1038/nature15395
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