Imperial College London
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DrLeonidChindelevitch

Faculty of Medicine, School of Public Health

Lecturer in Infectious Disease Epidemiology
 
 
 
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Contact

 

l.chindelevitch Website

 
 
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Location

 

Sir Michael Uren HubWhite City Campus

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Summary

 

Publications

Citation

BibTex format

@article{Khakabimamaghami:2019:bioinformatics/btz355,
author = {Khakabimamaghami, S and Malikic, S and Tang, J and Ding, D and Morin, R and Chindelevitch, L and Ester, M},
doi = {bioinformatics/btz355},
journal = {Bioinformatics},
pages = {i379--i388},
title = {Collaborative intra-tumor heterogeneity detection},
url = {http://dx.doi.org/10.1093/bioinformatics/btz355},
volume = {35},
year = {2019}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - MotivationDespite 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.ResultsWe 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 implementationThe source code for Hintra is available at https://github.com/sahandk/HINTRA.
AU  - Khakabimamaghami,S
AU  - Malikic,S
AU  - Tang,J
AU  - Ding,D
AU  - Morin,R
AU  - Chindelevitch,L
AU  - Ester,M
DO  - bioinformatics/btz355
EP  - 388
PY  - 2019///
SN  - 1367-4803
SP  - 379
TI  - Collaborative intra-tumor heterogeneity detection
T2  - Bioinformatics
UR  - http://dx.doi.org/10.1093/bioinformatics/btz355
UR  - http://hdl.handle.net/10044/1/86911
VL  - 35
ER  -
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