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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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Related in: MedlinePlus

Gene mutation maps for candidate CLL genesIndividual gene mutation maps are shown for all newly identified candidate CLL cancer genes not included in Fig. 2. The plots show mutation subtype (e.g., missense, nonsense etc) and position along the gene.
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Figure 9: Gene mutation maps for candidate CLL genesIndividual gene mutation maps are shown for all newly identified candidate CLL cancer genes not included in Fig. 2. The plots show mutation subtype (e.g., missense, nonsense etc) and position along the gene.

Mentions: Of the newly identified putative cancer genes, some were previously suggested as CLL drivers in studies using other detection platforms. For example, the suppressor of MYC MGA (n=17, 3.2%), which we detected as recurrently inactivated by insertions and nonsense mutations, was previously found to be inactivated through deletions8 and truncating mutations8,9 in high-risk CLL (Extended Data Fig. 4). A gene set enrichment analysis of matched RNAseq data revealed down-regulation of genes that are suppressed upon MYC activation in B-cells10 (Supplementary Table 4). In addition to MGA, we report two additional candidate driver genes that likely modulate MYC activity (PTPN1111 [n=7, 1.3%] and FUBP112 [n=9, 1.7%]), highlighting MYC-related proteins as drivers of CLL.


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)

Gene mutation maps for candidate CLL genesIndividual gene mutation maps are shown for all newly identified candidate CLL cancer genes not included in Fig. 2. The plots show mutation subtype (e.g., missense, nonsense etc) and position along the gene.
© Copyright Policy
Related In: Results  -  Collection

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

Figure 9: Gene mutation maps for candidate CLL genesIndividual gene mutation maps are shown for all newly identified candidate CLL cancer genes not included in Fig. 2. The plots show mutation subtype (e.g., missense, nonsense etc) and position along the gene.
Mentions: Of the newly identified putative cancer genes, some were previously suggested as CLL drivers in studies using other detection platforms. For example, the suppressor of MYC MGA (n=17, 3.2%), which we detected as recurrently inactivated by insertions and nonsense mutations, was previously found to be inactivated through deletions8 and truncating mutations8,9 in high-risk CLL (Extended Data Fig. 4). A gene set enrichment analysis of matched RNAseq data revealed down-regulation of genes that are suppressed upon MYC activation in B-cells10 (Supplementary Table 4). In addition to MGA, we report two additional candidate driver genes that likely modulate MYC activity (PTPN1111 [n=7, 1.3%] and FUBP112 [n=9, 1.7%]), highlighting MYC-related proteins as drivers of CLL.

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