Search

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: Despite the remarkable advances in sequencing and computational techniques, noise in the data and complexity of the underlying biological mechanisms render deconvolution of the phylogenetic relationships between cancer mutations difficult. Besides that, the majority of the existing datasets consist of bulk sequencing data of single tumor sample of an individual. Accurate inference of the phylogenetic order of mutations is particularly challenging in these cases and the existing methods are faced with several theoretical limitations. To overcome these limitations, new methods are required for integrating and harnessing the full potential of the existing data., Results: We introduce a method called Hintra for intra-tumor heterogeneity detection. Hintra integrates sequencing data for a cohort of tumors and infers tumor phylogeny for each individual based on the evolutionary information shared between different tumors. Through an iterative process, Hintra learns the repeating evolutionary patterns and uses this information for resolving the phylogenetic ambiguities of individual tumors. The results of synthetic experiments show an improved performance compared to two state-of-the-art methods. The experimental results with a recent Breast Cancer dataset are consistent with the existing knowledge and provide potentially interesting findings., Availability and Implementation: The source code for Hintra is available at https://github.com/sahandk/HINTRA., (© The Author(s) 2019. Published by Oxford University Press.)

Motivation: Intra-tumor heterogeneity presents itself through the evolution of subclones during cancer progression. Although recent research suggests that this heterogeneity has clinical implications, in silico determination of the clonal subpopulations remains a challenge., Results: We address this problem through a novel combinatorial method, named clonality inference in tumors using phylogeny (CITUP), that infers clonal populations and their frequencies while satisfying phylogenetic constraints and is able to exploit data from multiple samples. Using simulated datasets and deep sequencing data from two cancer studies, we show that CITUP predicts clonal frequencies and the underlying phylogeny with high accuracy., Availability and Implementation: CITUP is freely available at: http://sourceforge.net/projects/citup/., Contact: cenk@sfu.ca, Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)