gpps: an ILP-based approach for inferring cancer progression with mutation losses from single cell data
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RESEARCH
Open Access
gpps: an ILP-based approach for inferring cancer progression with mutation losses from single cell data Simone Ciccolella1* , Mauricio Soto Gomez1 , Murray D. Patterson1,3 , Gianluca Della Vedova1 , Iman Hajirasouliha2,4 and Paola Bonizzoni1 From 8th IEEE International Conference on Computational Advances in Bio and medical Sciences (ICCABS 2018) Las Vegas, NV, USA. 18–20 October 2018 *Correspondence: [email protected] 1 Department of Informatics, Systems, and Communication, University of Milano - Bicocca, Milan, Italy Full list of author information is available at the end of the article
Abstract Background: Cancer progression reconstruction is an important development stemming from the phylogenetics field. In this context, the reconstruction of the phylogeny representing the evolutionary history presents some peculiar aspects that depend on the technology used to obtain the data to analyze: Single Cell DNA Sequencing data have great specificity, but are affected by moderate false negative and missing value rates. Moreover, there has been some recent evidence of back mutations in cancer: this phenomenon is currently widely ignored. Results: We present a new tool, gpps, that reconstructs a tumor phylogeny from Single Cell Sequencing data, allowing each mutation to be lost at most a fixed number of times. The General Parsimony Phylogeny from Single cell (gpps) tool is open source and available at https://github.com/AlgoLab/gpps. Conclusions: gpps provides new insights to the analysis of intra-tumor heterogeneity by proposing a new progression model to the field of cancer phylogeny reconstruction on Single Cell data. Keywords: Integer linear programming, Hill climbing, Phylogeny, Single cell sequencing
Background Phylogenetics is the field which studies how to reconstruct the evolutionary histories of species, and it has a rich literature [1]. However, phylogenetics has focused on inferring histories from data coming from extant species or individuals, under the assumption that data for ancestor species/individuals are impossible or difficult to obtain.
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