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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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Inferred evolutionary history of CLLA. The proportion in which a recurrent driver is found as clonal or subclonal across the 538 samples is provided (top), along with the individual cancer cell fraction (CCF) values for each sample affected by a driver (tested for each driver with a Fisher's exact test, comparing to the cumulative proportions of clonal and subclonal drivers excluding the driver evaluated). Median CCF values are shown (bottom, bars represent the median and IQR for each driver). B. Temporally direct edges are drawn when two drivers are found in the same sample, one in clonal and the other in subclonal frequency. These edges are used to infer the temporal sequences in CLL evolution, leading from early, through intermediate to late drivers. Note that only driver pairs with at least 5 connecting edges were tested for statistical significance and only drivers connected by at least one statistically significant edge are displayed (see Methods, and Supplementary Tables 6 & 7).
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Figure 3: Inferred evolutionary history of CLLA. The proportion in which a recurrent driver is found as clonal or subclonal across the 538 samples is provided (top), along with the individual cancer cell fraction (CCF) values for each sample affected by a driver (tested for each driver with a Fisher's exact test, comparing to the cumulative proportions of clonal and subclonal drivers excluding the driver evaluated). Median CCF values are shown (bottom, bars represent the median and IQR for each driver). B. Temporally direct edges are drawn when two drivers are found in the same sample, one in clonal and the other in subclonal frequency. These edges are used to infer the temporal sequences in CLL evolution, leading from early, through intermediate to late drivers. Note that only driver pairs with at least 5 connecting edges were tested for statistical significance and only drivers connected by at least one statistically significant edge are displayed (see Methods, and Supplementary Tables 6 & 7).

Mentions: We first classified driver events likely acquired earlier or later in the disease course based on the proportion of cases in which the driver was found as clonal (Fig. 3A). This large dataset further enabled the inference of temporal relationships between pairs of drivers. We systematically identified instances in which a clonal driver was found together with a subclonal driver within the same sample, as these pairs reflect the acquisition of one lesion (clonal) followed by another (subclonal), providing a temporal ‘edge’ leading from the former to the latter28,29. For each driver, we calculated the relative enrichment of out-going edges compared to in-going edges to define early, late and intermediary drivers (Supplementary Table 7). For 23 pairs connected by at least 5 edges, we further established the temporal relationship between the two drivers in each pair, and thereby constructed a temporal map of the evolutionary trajectories of CLL (Supplementary Table 8, Fig. 3B). This network highlights sCNVs as the earliest events with two distinct points of departure involving del(13q) and tri(12). It further demonstrates an early convergence towards del(11q) and substantial diversity in late drivers. Finally, this analysis suggests that in the case of the tumor suppressor genes ATM and BIRC3, copy loss precedes sSNVs and sINDELs in biallelic inactivation.


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)

Inferred evolutionary history of CLLA. The proportion in which a recurrent driver is found as clonal or subclonal across the 538 samples is provided (top), along with the individual cancer cell fraction (CCF) values for each sample affected by a driver (tested for each driver with a Fisher's exact test, comparing to the cumulative proportions of clonal and subclonal drivers excluding the driver evaluated). Median CCF values are shown (bottom, bars represent the median and IQR for each driver). B. Temporally direct edges are drawn when two drivers are found in the same sample, one in clonal and the other in subclonal frequency. These edges are used to infer the temporal sequences in CLL evolution, leading from early, through intermediate to late drivers. Note that only driver pairs with at least 5 connecting edges were tested for statistical significance and only drivers connected by at least one statistically significant edge are displayed (see Methods, and Supplementary Tables 6 & 7).
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Related In: Results  -  Collection

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Figure 3: Inferred evolutionary history of CLLA. The proportion in which a recurrent driver is found as clonal or subclonal across the 538 samples is provided (top), along with the individual cancer cell fraction (CCF) values for each sample affected by a driver (tested for each driver with a Fisher's exact test, comparing to the cumulative proportions of clonal and subclonal drivers excluding the driver evaluated). Median CCF values are shown (bottom, bars represent the median and IQR for each driver). B. Temporally direct edges are drawn when two drivers are found in the same sample, one in clonal and the other in subclonal frequency. These edges are used to infer the temporal sequences in CLL evolution, leading from early, through intermediate to late drivers. Note that only driver pairs with at least 5 connecting edges were tested for statistical significance and only drivers connected by at least one statistically significant edge are displayed (see Methods, and Supplementary Tables 6 & 7).
Mentions: We first classified driver events likely acquired earlier or later in the disease course based on the proportion of cases in which the driver was found as clonal (Fig. 3A). This large dataset further enabled the inference of temporal relationships between pairs of drivers. We systematically identified instances in which a clonal driver was found together with a subclonal driver within the same sample, as these pairs reflect the acquisition of one lesion (clonal) followed by another (subclonal), providing a temporal ‘edge’ leading from the former to the latter28,29. For each driver, we calculated the relative enrichment of out-going edges compared to in-going edges to define early, late and intermediary drivers (Supplementary Table 7). For 23 pairs connected by at least 5 edges, we further established the temporal relationship between the two drivers in each pair, and thereby constructed a temporal map of the evolutionary trajectories of CLL (Supplementary Table 8, Fig. 3B). This network highlights sCNVs as the earliest events with two distinct points of departure involving del(13q) and tri(12). It further demonstrates an early convergence towards del(11q) and substantial diversity in late drivers. Finally, this analysis suggests that in the case of the tumor suppressor genes ATM and BIRC3, copy loss precedes sSNVs and sINDELs in biallelic inactivation.

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