Gusnanto, AS, Tcherveniakov, P, Shuweihdi, F et al. (3 more authors) (2015) Stratifying tumour subtypes based on copy number alteration profiles using next-generation sequence data. Bioinformatics, 31 (16). 2713 - 2720. ISSN 1367-4803
Abstract
Motivation: The role of personalized medicine and target treatment in the clinical management of cancer patients has become increasingly important in recent years. This has made the task of precise histological substratification of cancers crucial. Increasingly, genomic data are being seen as a valuable classifier. Specifically, copy number alteration (CNA) profiles generated by next-generation sequencing (NGS) can become a determinant for tumours subtyping. The principle purpose of this study is to devise a model with good prediction capability for the tumours histological subtypes as a function of both the patients covariates and their genome-wide CNA profiles from NGS data. Results: We investigate a logistic regression for modelling tumour histological subtypes as a function of the patients’ covariates and their CNA profiles, in a mixed model framework. The covariates, such as age and gender, are considered as fixed predictors and the genome-wide CNA profiles are considered as random predictors. We illustrate the application of this model in lung and oral cancer datasets, and the results indicate that the tumour histological subtypes can be modelled with a good fit. Our cross-validation indicates that the logistic regression exhibits the best prediction relative to other classification methods we considered in this study. The model also exhibits the best agreement in the prediction between smooth-segmented and circular binary-segmented CNA profiles.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | (c) 2015, The Author. Published by Oxford University Press. This is a pre-copyedited, author-produced PDF of an article accepted for publication in Bioinformatics following peer review. The version of record Bioinformatics (2015) 31 (16): 2713-2720. doi: 10.1093/bioinformatics/btv191 is available online at: http://dx.doi.org/10.1093/bioinformatics/btv191 |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Mathematics (Leeds) > Statistics (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 04 Sep 2015 15:48 |
Last Modified: | 16 Nov 2016 09:55 |
Published Version: | http://dx.doi.org/10.1093/bioinformatics/btv191 |
Status: | Published |
Publisher: | Oxford University Press |
Identification Number: | 10.1093/bioinformatics/btv191 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:86119 |