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Tumor evolutionary directed graphs and the history of chronic lymphocytic leukemia.

Wang J, Khiabanian H, Rossi D, Fabbri G, Gattei V, Forconi F, Laurenti L, Marasca R, Del Poeta G, Foà R, Pasqualucci L, Gaidano G, Rabadan R - Elife (2014)

Bottom Line: Cancer is a clonal evolutionary process, caused by successive accumulation of genetic alterations providing milestones of tumor initiation, progression, dissemination, and/or resistance to certain therapeutic regimes.To unravel these milestones we propose a framework, tumor evolutionary directed graphs (TEDG), which is able to characterize the history of genetic alterations by integrating longitudinal and cross-sectional genomic data.Our results suggest that TEDG may constitute an effective framework to recapitulate the evolutionary history of tumors.

View Article: PubMed Central - PubMed

Affiliation: Department of Biomedical Informatics, Columbia University, New York, United States.

ABSTRACT
Cancer is a clonal evolutionary process, caused by successive accumulation of genetic alterations providing milestones of tumor initiation, progression, dissemination, and/or resistance to certain therapeutic regimes. To unravel these milestones we propose a framework, tumor evolutionary directed graphs (TEDG), which is able to characterize the history of genetic alterations by integrating longitudinal and cross-sectional genomic data. We applied TEDG to a chronic lymphocytic leukemia (CLL) cohort of 70 patients spanning 12 years and show that: (a) the evolution of CLL follows a time-ordered process represented as a global flow in TEDG that proceeds from initiating events to late events; (b) there are two distinct and mutually exclusive evolutionary paths of CLL evolution; (c) higher fitness clones are present in later stages of the disease, indicating a progressive clonal replacement with more aggressive clones. Our results suggest that TEDG may constitute an effective framework to recapitulate the evolutionary history of tumors.

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Related in: MedlinePlus

The comparison between TEDG (B) and phylogenetic tree method (A).The two methods are compared in terms of the order of mutations and the TEDG networks. * indicates p-value<0.05, and ** indicates p-value<0.01. p-value in (C) is calculated by Fisher’s exact test.
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fig14: The comparison between TEDG (B) and phylogenetic tree method (A).The two methods are compared in terms of the order of mutations and the TEDG networks. * indicates p-value<0.05, and ** indicates p-value<0.01. p-value in (C) is calculated by Fisher’s exact test.

Mentions: To compare the results between the phylogenetic trees method and TEDG, we have applied both methods in our CLL data. The results show that all significantly early or late events with p-value<0.01 (**) reported by TEDG (before or after adjustment of MAF) are still significant (Author response image 8A). NOTCH1, reported significant early in TEDG, is very early (not significant due to limited number of samples). Furthermore, we rank all events based on fold change between indegree and outdegree by phylogenetic tree method and TEDG separately. Strikingly, the rank of all events based on those two methods is significantly related with Pearson correlation more than 0.9 (Author response image 8C). In the TEDG network analysis, the backbone of CLL evolution is slightly changed, but two main branches are the same, indicating two major types of CLL patients suffering T12 and del13q independently (Author response image 8). The actual topologies of the phylogeny inferred network and TEDG are very similar (six out of ten edges are in common and share directionality, p-value<0.0001 by Fisher’s exact test). It is interesting to observe that the differences are in the order of del17p and TP53 mutations and the indirect association of SF3B1.Author response image 8.The comparison between TEDG (B) and phylogenetic tree method (A).


Tumor evolutionary directed graphs and the history of chronic lymphocytic leukemia.

Wang J, Khiabanian H, Rossi D, Fabbri G, Gattei V, Forconi F, Laurenti L, Marasca R, Del Poeta G, Foà R, Pasqualucci L, Gaidano G, Rabadan R - Elife (2014)

The comparison between TEDG (B) and phylogenetic tree method (A).The two methods are compared in terms of the order of mutations and the TEDG networks. * indicates p-value<0.05, and ** indicates p-value<0.01. p-value in (C) is calculated by Fisher’s exact test.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4308685&req=5

fig14: The comparison between TEDG (B) and phylogenetic tree method (A).The two methods are compared in terms of the order of mutations and the TEDG networks. * indicates p-value<0.05, and ** indicates p-value<0.01. p-value in (C) is calculated by Fisher’s exact test.
Mentions: To compare the results between the phylogenetic trees method and TEDG, we have applied both methods in our CLL data. The results show that all significantly early or late events with p-value<0.01 (**) reported by TEDG (before or after adjustment of MAF) are still significant (Author response image 8A). NOTCH1, reported significant early in TEDG, is very early (not significant due to limited number of samples). Furthermore, we rank all events based on fold change between indegree and outdegree by phylogenetic tree method and TEDG separately. Strikingly, the rank of all events based on those two methods is significantly related with Pearson correlation more than 0.9 (Author response image 8C). In the TEDG network analysis, the backbone of CLL evolution is slightly changed, but two main branches are the same, indicating two major types of CLL patients suffering T12 and del13q independently (Author response image 8). The actual topologies of the phylogeny inferred network and TEDG are very similar (six out of ten edges are in common and share directionality, p-value<0.0001 by Fisher’s exact test). It is interesting to observe that the differences are in the order of del17p and TP53 mutations and the indirect association of SF3B1.Author response image 8.The comparison between TEDG (B) and phylogenetic tree method (A).

Bottom Line: Cancer is a clonal evolutionary process, caused by successive accumulation of genetic alterations providing milestones of tumor initiation, progression, dissemination, and/or resistance to certain therapeutic regimes.To unravel these milestones we propose a framework, tumor evolutionary directed graphs (TEDG), which is able to characterize the history of genetic alterations by integrating longitudinal and cross-sectional genomic data.Our results suggest that TEDG may constitute an effective framework to recapitulate the evolutionary history of tumors.

View Article: PubMed Central - PubMed

Affiliation: Department of Biomedical Informatics, Columbia University, New York, United States.

ABSTRACT
Cancer is a clonal evolutionary process, caused by successive accumulation of genetic alterations providing milestones of tumor initiation, progression, dissemination, and/or resistance to certain therapeutic regimes. To unravel these milestones we propose a framework, tumor evolutionary directed graphs (TEDG), which is able to characterize the history of genetic alterations by integrating longitudinal and cross-sectional genomic data. We applied TEDG to a chronic lymphocytic leukemia (CLL) cohort of 70 patients spanning 12 years and show that: (a) the evolution of CLL follows a time-ordered process represented as a global flow in TEDG that proceeds from initiating events to late events; (b) there are two distinct and mutually exclusive evolutionary paths of CLL evolution; (c) higher fitness clones are present in later stages of the disease, indicating a progressive clonal replacement with more aggressive clones. Our results suggest that TEDG may constitute an effective framework to recapitulate the evolutionary history of tumors.

Show MeSH
Related in: MedlinePlus