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Mutations driving CLL and their evolution in progression and relapse.

Landau DA, Tausch E, Taylor-Weiner AN, Stewart C, Reiter JG, Bahlo J, Kluth S, Bozic I, Lawrence M, Böttcher S, Carter SL, Cibulskis K, Mertens D, Sougnez CL, Rosenberg M, Hess JM, Edelmann J, Kless S, Kneba M, Ritgen M, Fink A, Fischer K, Gabriel S, Lander ES, Nowak MA, Döhner H, Hallek M, Neuberg D, Getz G, Stilgenbauer S, Wu CJ - Nature (2015)

Bottom Line: Which genetic alterations drive tumorigenesis and how they evolve over the course of disease and therapy are central questions in cancer biology.Direct comparison between matched pre-treatment and relapse samples from 59 patients demonstrated highly frequent clonal evolution.Thus, large sequencing data sets of clinically informative samples enable the discovery of novel genes associated with cancer, the network of relationships between the driver events, and their impact on disease relapse and clinical outcome.

View Article: PubMed Central - PubMed

Affiliation: Broad Institute of Harvard and MIT, Cambridge, Massachusetts 02142, USA.

ABSTRACT
Which genetic alterations drive tumorigenesis and how they evolve over the course of disease and therapy are central questions in cancer biology. Here we identify 44 recurrently mutated genes and 11 recurrent somatic copy number variations through whole-exome sequencing of 538 chronic lymphocytic leukaemia (CLL) and matched germline DNA samples, 278 of which were collected in a prospective clinical trial. These include previously unrecognized putative cancer drivers (RPS15, IKZF3), and collectively identify RNA processing and export, MYC activity, and MAPK signalling as central pathways involved in CLL. Clonality analysis of this large data set 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 data sets of clinically informative samples enable the discovery of novel genes associated with cancer, the network of relationships between the driver events, and their impact on disease relapse and clinical outcome.

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The landscape of putative driver gene mutations and recurrent somatic copy number variations in CLLSomatic mutation information is shown across the 55 putative driver genes and recurrent sCNVs (rows) for 538 primary patient samples (from CLL8 [green], Spanish ICGC [red], DFCI/Broad [blue]) that underwent WES (columns). Blue labels- recurrent sCNVs; Bold labels- putative CLL cancer genes previously identified in Landau et al.3); asterisked labels- additional cancer genes identified in this study. Samples were annotated for IGHV status (black-mutated; white-unmutated; red-unknown), and for exposure to therapy prior to sampling (black-prior therapy; white – no prior therapy; red-unknown prior treatment status).
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Figure 1: The landscape of putative driver gene mutations and recurrent somatic copy number variations in CLLSomatic mutation information is shown across the 55 putative driver genes and recurrent sCNVs (rows) for 538 primary patient samples (from CLL8 [green], Spanish ICGC [red], DFCI/Broad [blue]) that underwent WES (columns). Blue labels- recurrent sCNVs; Bold labels- putative CLL cancer genes previously identified in Landau et al.3); asterisked labels- additional cancer genes identified in this study. Samples were annotated for IGHV status (black-mutated; white-unmutated; red-unknown), and for exposure to therapy prior to sampling (black-prior therapy; white – no prior therapy; red-unknown prior treatment status).

Mentions: We detected 44 putative CLL driver genes, including 18 CLL mutated drivers that we previously identified3, as well as 26 additional putative CLL genes (Fig. 1-2, Extended Data Fig. 1-2). In total, 33.5% of CLLs harbored mutation in at least one of these 26 additional genes. Targeted DNA sequencing as well as variant allele expression by RNAseq demonstrated high rates of orthogonal validation (Extended Data Fig. 3).


Mutations driving CLL and their evolution in progression and relapse.

Landau DA, Tausch E, Taylor-Weiner AN, Stewart C, Reiter JG, Bahlo J, Kluth S, Bozic I, Lawrence M, Böttcher S, Carter SL, Cibulskis K, Mertens D, Sougnez CL, Rosenberg M, Hess JM, Edelmann J, Kless S, Kneba M, Ritgen M, Fink A, Fischer K, Gabriel S, Lander ES, Nowak MA, Döhner H, Hallek M, Neuberg D, Getz G, Stilgenbauer S, Wu CJ - Nature (2015)

The landscape of putative driver gene mutations and recurrent somatic copy number variations in CLLSomatic mutation information is shown across the 55 putative driver genes and recurrent sCNVs (rows) for 538 primary patient samples (from CLL8 [green], Spanish ICGC [red], DFCI/Broad [blue]) that underwent WES (columns). Blue labels- recurrent sCNVs; Bold labels- putative CLL cancer genes previously identified in Landau et al.3); asterisked labels- additional cancer genes identified in this study. Samples were annotated for IGHV status (black-mutated; white-unmutated; red-unknown), and for exposure to therapy prior to sampling (black-prior therapy; white – no prior therapy; red-unknown prior treatment status).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4815041&req=5

Figure 1: The landscape of putative driver gene mutations and recurrent somatic copy number variations in CLLSomatic mutation information is shown across the 55 putative driver genes and recurrent sCNVs (rows) for 538 primary patient samples (from CLL8 [green], Spanish ICGC [red], DFCI/Broad [blue]) that underwent WES (columns). Blue labels- recurrent sCNVs; Bold labels- putative CLL cancer genes previously identified in Landau et al.3); asterisked labels- additional cancer genes identified in this study. Samples were annotated for IGHV status (black-mutated; white-unmutated; red-unknown), and for exposure to therapy prior to sampling (black-prior therapy; white – no prior therapy; red-unknown prior treatment status).
Mentions: We detected 44 putative CLL driver genes, including 18 CLL mutated drivers that we previously identified3, as well as 26 additional putative CLL genes (Fig. 1-2, Extended Data Fig. 1-2). In total, 33.5% of CLLs harbored mutation in at least one of these 26 additional genes. Targeted DNA sequencing as well as variant allele expression by RNAseq demonstrated high rates of orthogonal validation (Extended Data Fig. 3).

Bottom Line: Which genetic alterations drive tumorigenesis and how they evolve over the course of disease and therapy are central questions in cancer biology.Direct comparison between matched pre-treatment and relapse samples from 59 patients demonstrated highly frequent clonal evolution.Thus, large sequencing data sets of clinically informative samples enable the discovery of novel genes associated with cancer, the network of relationships between the driver events, and their impact on disease relapse and clinical outcome.

View Article: PubMed Central - PubMed

Affiliation: Broad Institute of Harvard and MIT, Cambridge, Massachusetts 02142, USA.

ABSTRACT
Which genetic alterations drive tumorigenesis and how they evolve over the course of disease and therapy are central questions in cancer biology. Here we identify 44 recurrently mutated genes and 11 recurrent somatic copy number variations through whole-exome sequencing of 538 chronic lymphocytic leukaemia (CLL) and matched germline DNA samples, 278 of which were collected in a prospective clinical trial. These include previously unrecognized putative cancer drivers (RPS15, IKZF3), and collectively identify RNA processing and export, MYC activity, and MAPK signalling as central pathways involved in CLL. Clonality analysis of this large data set 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 data sets of clinically informative samples enable the discovery of novel genes associated with cancer, the network of relationships between the driver events, and their impact on disease relapse and clinical outcome.

Show MeSH
Related in: MedlinePlus