Ermin Hodzic1, Raunak Shrestha2, Salem Malikic3, Colin C Collins4,5, Kevin Litchfield6, Samra Turajlic6,7, S Cenk Sahinalp8. 1. Department of Computing Science, Simon Fraser University, Burnaby, BC, Canada. 2. Department of Radiation Oncology, University of California San Francisco, San Francisco, CA, USA. 3. Department of Computer Science, Indiana University Bloomington, Bloomington, IN, USA. 4. Department of Urologic Sciences, University of British Columbia, Vancouver, BC, Canada. 5. aboratory for Advanced Genome Analysis, Vancouver Prostate Centre, Vancouver, BC, Canada. 6. Cancer Dynamics Laboratory, the Francis Crick institute, Genome Instability Laboratory, Francis Crick Institute, London, UK. 7. Skin and Renal Units, The royal Marsden NHS Foundation Trust, London, UK. 8. Cancer Data Science Lab., National Cancer Institute, NIH, Bethesda, MD, USA.
Abstract
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. Published by Oxford University Press 2020.
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 cancerpatients 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. Published by Oxford University Press 2020.
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