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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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Matched pre-treatment and relapse samples reveal patterns of clonal evolution in relation to therapyA. The number and proportion of the patterns of clonal evolution of CLLs studied at pre-treatment and at relapse. B. Selected plots of 2D clustering of pre-treatment and relapse cancer cell fraction (CCF) demonstrating clonal stability of tri(12) (CLL case: GCLL115), concordant increase in CCFs of TP53 and del(17p) (GCLL27), clonal shifts in ATM sSNVs in a sample with clonally stable monoallelic deletion of ATM (GCLL307). Red coloring was added when greater than half of the CCF probability indicated >0.1 increase in CCF at relapse. C. Clonal evolution of CLL drivers. Left panel – for each driver with at least 4 instances detected across the 59 CLLs, the proportion of instances where the CCF increased (red), decreased (blue) or remained stable (grey) over time is shown (see Methods for details of the statistical analysis). The driver events were distributed to three main groups: predominately stable events (top); predominately increasing CCF (middle), and all other patterns (bottom). Right panel - Comparison (modal CCF with 95%CI) between pre-treatment and relapse samples for select CLL drivers (see Extended Data Fig. 10 for the remaining driver events from the cohort of 59 CLLs).
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Figure 5: Matched pre-treatment and relapse samples reveal patterns of clonal evolution in relation to therapyA. The number and proportion of the patterns of clonal evolution of CLLs studied at pre-treatment and at relapse. B. Selected plots of 2D clustering of pre-treatment and relapse cancer cell fraction (CCF) demonstrating clonal stability of tri(12) (CLL case: GCLL115), concordant increase in CCFs of TP53 and del(17p) (GCLL27), clonal shifts in ATM sSNVs in a sample with clonally stable monoallelic deletion of ATM (GCLL307). Red coloring was added when greater than half of the CCF probability indicated >0.1 increase in CCF at relapse. C. Clonal evolution of CLL drivers. Left panel – for each driver with at least 4 instances detected across the 59 CLLs, the proportion of instances where the CCF increased (red), decreased (blue) or remained stable (grey) over time is shown (see Methods for details of the statistical analysis). The driver events were distributed to three main groups: predominately stable events (top); predominately increasing CCF (middle), and all other patterns (bottom). Right panel - Comparison (modal CCF with 95%CI) between pre-treatment and relapse samples for select CLL drivers (see Extended Data Fig. 10 for the remaining driver events from the cohort of 59 CLLs).

Mentions: To define clonal evolution in disease relapse, we performed WES on matched samples collected at the time of relapse from 59 of 278 CLL8 subjects (Supplementary Tables 9 & 10). We observed large clonal shifts between pre-treatment and relapse samples in the majority of cases (57 of 59), thus demonstrating that CLL evolution after therapy is the rule rather than the exception (Fig. 5A). The relapse clone was already detectable in pre-treatment WES in 18 of 59 (30%) cases, demonstrating that the study of pre-treatment diversity anticipates the future evolutionary trajectories of the relapsed disease34. By targeted deep sequencing, we detected relapse drivers in 11 of the 41 of pre-treatment samples in which WES did not detect the relapse driver. In 7 of these 11 CLLs, at least one relapse driver was detected in the pretreatment sample (Supplementary Table 10).


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)

Matched pre-treatment and relapse samples reveal patterns of clonal evolution in relation to therapyA. The number and proportion of the patterns of clonal evolution of CLLs studied at pre-treatment and at relapse. B. Selected plots of 2D clustering of pre-treatment and relapse cancer cell fraction (CCF) demonstrating clonal stability of tri(12) (CLL case: GCLL115), concordant increase in CCFs of TP53 and del(17p) (GCLL27), clonal shifts in ATM sSNVs in a sample with clonally stable monoallelic deletion of ATM (GCLL307). Red coloring was added when greater than half of the CCF probability indicated >0.1 increase in CCF at relapse. C. Clonal evolution of CLL drivers. Left panel – for each driver with at least 4 instances detected across the 59 CLLs, the proportion of instances where the CCF increased (red), decreased (blue) or remained stable (grey) over time is shown (see Methods for details of the statistical analysis). The driver events were distributed to three main groups: predominately stable events (top); predominately increasing CCF (middle), and all other patterns (bottom). Right panel - Comparison (modal CCF with 95%CI) between pre-treatment and relapse samples for select CLL drivers (see Extended Data Fig. 10 for the remaining driver events from the cohort of 59 CLLs).
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Figure 5: Matched pre-treatment and relapse samples reveal patterns of clonal evolution in relation to therapyA. The number and proportion of the patterns of clonal evolution of CLLs studied at pre-treatment and at relapse. B. Selected plots of 2D clustering of pre-treatment and relapse cancer cell fraction (CCF) demonstrating clonal stability of tri(12) (CLL case: GCLL115), concordant increase in CCFs of TP53 and del(17p) (GCLL27), clonal shifts in ATM sSNVs in a sample with clonally stable monoallelic deletion of ATM (GCLL307). Red coloring was added when greater than half of the CCF probability indicated >0.1 increase in CCF at relapse. C. Clonal evolution of CLL drivers. Left panel – for each driver with at least 4 instances detected across the 59 CLLs, the proportion of instances where the CCF increased (red), decreased (blue) or remained stable (grey) over time is shown (see Methods for details of the statistical analysis). The driver events were distributed to three main groups: predominately stable events (top); predominately increasing CCF (middle), and all other patterns (bottom). Right panel - Comparison (modal CCF with 95%CI) between pre-treatment and relapse samples for select CLL drivers (see Extended Data Fig. 10 for the remaining driver events from the cohort of 59 CLLs).
Mentions: To define clonal evolution in disease relapse, we performed WES on matched samples collected at the time of relapse from 59 of 278 CLL8 subjects (Supplementary Tables 9 & 10). We observed large clonal shifts between pre-treatment and relapse samples in the majority of cases (57 of 59), thus demonstrating that CLL evolution after therapy is the rule rather than the exception (Fig. 5A). The relapse clone was already detectable in pre-treatment WES in 18 of 59 (30%) cases, demonstrating that the study of pre-treatment diversity anticipates the future evolutionary trajectories of the relapsed disease34. By targeted deep sequencing, we detected relapse drivers in 11 of the 41 of pre-treatment samples in which WES did not detect the relapse driver. In 7 of these 11 CLLs, at least one relapse driver was detected in the pretreatment sample (Supplementary Table 10).

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