Identification of conserved evolutionary trajectories in tumors
Bioinformatics, 2020•academic.oup.com
Motivation As multi-region, time-series and single-cell sequencing data become more widely
available; it is becoming clear that certain tumors share evolutionary characteristics with
others. In the last few years, several computational methods have been developed with the
goal of inferring the subclonal composition and evolutionary history of tumors from tumor
biopsy sequencing data. However, the phylogenetic trees that they report differ significantly
between tumors (even those with similar characteristics). Results In this article, we present a …
available; it is becoming clear that certain tumors share evolutionary characteristics with
others. In the last few years, several computational methods have been developed with the
goal of inferring the subclonal composition and evolutionary history of tumors from tumor
biopsy sequencing data. However, the phylogenetic trees that they report differ significantly
between tumors (even those with similar characteristics). Results In this article, we present a …
Motivation
As multi-region, time-series and single-cell sequencing data become more widely available; it is becoming clear that certain tumors share evolutionary characteristics with others. In the last few years, several computational methods have been developed with the goal of inferring the subclonal composition and evolutionary history of tumors from tumor biopsy sequencing data. However, the phylogenetic trees that they report differ significantly between tumors (even those with similar characteristics).
Results
In this article, we present a novel combinatorial optimization method, CONETT, for detection of recurrent tumor evolution trajectories. Our method constructs a consensus tree of conserved evolutionary trajectories based on the information about temporal order of alteration events in a set of tumors. We apply our method to previously published datasets of 100 clear-cell renal cell carcinoma and 99 non-small-cell lung cancer patients and identify both conserved trajectories that were reported in the original studies, as well as new trajectories.
Availability and implementation
CONETT is implemented in C++ and available at https://github.com/ehodzic/CONETT.
Supplementary information
Supplementary data are available at Bioinformatics online.
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