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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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Candidate CLL cancer genes discovered in the combined cohort of 538 primary CLL samplesSignificantly mutated genes identified in 538 primary CLL. Top panel: the rate of coding mutations (mutations per megabase) per sample. Center panel: Detection of individual gene found to be mutated (sSNVs or sINDELs) in each of the 538 patient samples (columns), color-coded by type of mutation. Only one mutation per gene is shown if multiple mutations from the same gene were found in a sample. Right panel: Q-values (red: Q<0.1; purple dashed: Q<0.05) and Hugo Symbol gene identification. New candidate CLL genes are marked with asterisks (*) Left panel: The percentages of samples affected with mutations (sSNVs and sINDELs) in each gene. Bottom panel: plots showing allelic fractions and the spectrum of mutations (sSNVs and sINDELs) for each sample.
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Figure 6: Candidate CLL cancer genes discovered in the combined cohort of 538 primary CLL samplesSignificantly mutated genes identified in 538 primary CLL. Top panel: the rate of coding mutations (mutations per megabase) per sample. Center panel: Detection of individual gene found to be mutated (sSNVs or sINDELs) in each of the 538 patient samples (columns), color-coded by type of mutation. Only one mutation per gene is shown if multiple mutations from the same gene were found in a sample. Right panel: Q-values (red: Q<0.1; purple dashed: Q<0.05) and Hugo Symbol gene identification. New candidate CLL genes are marked with asterisks (*) Left panel: The percentages of samples affected with mutations (sSNVs and sINDELs) in each gene. Bottom panel: plots showing allelic fractions and the spectrum of mutations (sSNVs and sINDELs) for each sample.

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)

Candidate CLL cancer genes discovered in the combined cohort of 538 primary CLL samplesSignificantly mutated genes identified in 538 primary CLL. Top panel: the rate of coding mutations (mutations per megabase) per sample. Center panel: Detection of individual gene found to be mutated (sSNVs or sINDELs) in each of the 538 patient samples (columns), color-coded by type of mutation. Only one mutation per gene is shown if multiple mutations from the same gene were found in a sample. Right panel: Q-values (red: Q<0.1; purple dashed: Q<0.05) and Hugo Symbol gene identification. New candidate CLL genes are marked with asterisks (*) Left panel: The percentages of samples affected with mutations (sSNVs and sINDELs) in each gene. Bottom panel: plots showing allelic fractions and the spectrum of mutations (sSNVs and sINDELs) for each sample.
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Figure 6: Candidate CLL cancer genes discovered in the combined cohort of 538 primary CLL samplesSignificantly mutated genes identified in 538 primary CLL. Top panel: the rate of coding mutations (mutations per megabase) per sample. Center panel: Detection of individual gene found to be mutated (sSNVs or sINDELs) in each of the 538 patient samples (columns), color-coded by type of mutation. Only one mutation per gene is shown if multiple mutations from the same gene were found in a sample. Right panel: Q-values (red: Q<0.1; purple dashed: Q<0.05) and Hugo Symbol gene identification. New candidate CLL genes are marked with asterisks (*) Left panel: The percentages of samples affected with mutations (sSNVs and sINDELs) in each gene. Bottom panel: plots showing allelic fractions and the spectrum of mutations (sSNVs and sINDELs) for each sample.
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