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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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Summary of longitudinal data in 70 patients.(A) The 70 patients are selected from a large cohort of 1403 CLL patients with no-bias screening. (B) The 70 patients are ranked according to their minimal cell frequency of all available genetic lesions at diagnosis. Patients with minimal cell frequency less than 20 are in red, the others are in green.DOI:http://dx.doi.org/10.7554/eLife.02869.008
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fig3s1: Summary of longitudinal data in 70 patients.(A) The 70 patients are selected from a large cohort of 1403 CLL patients with no-bias screening. (B) The 70 patients are ranked according to their minimal cell frequency of all available genetic lesions at diagnosis. Patients with minimal cell frequency less than 20 are in red, the others are in green.DOI:http://dx.doi.org/10.7554/eLife.02869.008

Mentions: In order to investigate the evolutionary history of CLL, we apply the TEDG framework to the driver genetic lesions of this leukemia. We study the most common alterations of 164 temporally sequential samples from 70 patients by high-depth next generation sequencing (NGS) and fluorescence in situ hybridization (FISH) analysis (‘Materials and methods’). Half (35 out of 70) of the patients have at least one subclonal genetic lesion with mutation frequency less than 20% at diagnosis (Figure 3—figure supplement 1). We firstly build sequential networks for each patient based on this longitudinal genetic data. Then, we pool all sequential networks to construct the ISN of CLL. We ask whether the genetic lesions in CLL are temporally ordered or randomly accumulated and reason that if the genetic alterations driving CLL progression follow a preferential order, there exists a well ordered directed flow in ISN. Figure 3A represents a hierarchical layout of ISN, which depicts an ordered structure of lesions in genes represented by sources (nodes with more outgoing arrows) and sinks (nodes with more incoming arrows) (p-value < 0.0001, chi square distribution).10.7554/eLife.02869.007Figure 3.Evolutionary network analysis of CLL genetic lesions.


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)

Summary of longitudinal data in 70 patients.(A) The 70 patients are selected from a large cohort of 1403 CLL patients with no-bias screening. (B) The 70 patients are ranked according to their minimal cell frequency of all available genetic lesions at diagnosis. Patients with minimal cell frequency less than 20 are in red, the others are in green.DOI:http://dx.doi.org/10.7554/eLife.02869.008
© Copyright Policy
Related In: Results  -  Collection

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

fig3s1: Summary of longitudinal data in 70 patients.(A) The 70 patients are selected from a large cohort of 1403 CLL patients with no-bias screening. (B) The 70 patients are ranked according to their minimal cell frequency of all available genetic lesions at diagnosis. Patients with minimal cell frequency less than 20 are in red, the others are in green.DOI:http://dx.doi.org/10.7554/eLife.02869.008
Mentions: In order to investigate the evolutionary history of CLL, we apply the TEDG framework to the driver genetic lesions of this leukemia. We study the most common alterations of 164 temporally sequential samples from 70 patients by high-depth next generation sequencing (NGS) and fluorescence in situ hybridization (FISH) analysis (‘Materials and methods’). Half (35 out of 70) of the patients have at least one subclonal genetic lesion with mutation frequency less than 20% at diagnosis (Figure 3—figure supplement 1). We firstly build sequential networks for each patient based on this longitudinal genetic data. Then, we pool all sequential networks to construct the ISN of CLL. We ask whether the genetic lesions in CLL are temporally ordered or randomly accumulated and reason that if the genetic alterations driving CLL progression follow a preferential order, there exists a well ordered directed flow in ISN. Figure 3A represents a hierarchical layout of ISN, which depicts an ordered structure of lesions in genes represented by sources (nodes with more outgoing arrows) and sinks (nodes with more incoming arrows) (p-value < 0.0001, chi square distribution).10.7554/eLife.02869.007Figure 3.Evolutionary network analysis of CLL genetic lesions.

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