ISOpureR: an R implementation of a computational purification algorithm of mixed tumour profiles. Issue 1 (December 2015)

• Record Type: Journal Article
• Authors: Anghel, Catalina; Quon, Gerald; Haider, Syed; Nguyen, Francis; Deshwar, Amit; Morris, Quaid; Boutros, Paul
• Is Part Of: BMC bioinformatics. Volume 16:Issue 1(2015)
• Page Start: 1
• Page End: 11
• Publication Date: 2015-12
• Subjects: Tumour heterogeneity -- mRNA abundance profile -- Deconvolution; Bioinformatics -- Periodicals; Computational biology -- Periodicals; 570.285
• DOI: 10.1186/s12859-015-0597-x
• Languages: English
• ISSNs: 1471-2105

Abstract

Abstract Background Tumour samples containing distinct sub-populations of cancer and normal cells present challenges in the development of reproducible biomarkers, as these biomarkers are based on bulk signals from mixed tumour profiles. ISOpure is the only mRNA computational purification method to date that does not require a paired tumour-normal sample, provides a personalized cancer profile for each patient, and has been tested on clinical data. Replacing mixed tumour profiles with ISOpure-preprocessed cancer profiles led to better prognostic gene signatures for lung and prostate cancer. Results To simplify the integration of ISOpure into standard R-based bioinformatics analysis pipelines, the algorithm has been implemented as an R package. TheISOpureR package performs analogously to the original code in estimating the fraction of cancer cells and the patient cancer mRNA abundance profile from tumour samples in four cancer datasets. Conclusions TheISOpureR package estimates the fraction of cancer cells and personalized patient cancer mRNA abundance profile from a mixed tumour profile. This open-source R implementation enables integration into existing computational pipelines, as well as easy testing, modification and extension of the model.