Abstract
BackgroundTumour 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.ResultsTo simplify the integration of ISOpure into standard R-based bioinformatics analysis pipelines, the algorithm has been implemented as an R package. The ISOpureR 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.ConclusionsThe ISOpureR 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.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-015-0597-x) contains supplementary material, which is available to authorized users.
Highlights
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
While we focus on microarray mRNA abundance data, ISOpure is generic when it comes to different species of RNA
To minimize differences due to random number generation implementations, the initial values of parameters were loaded from a file, and the extra optimizations of parameters ν, ω, and k [33] in the Cancer Profile Estimation (CPE) step, which included some random initializations, were omitted
Summary
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. Replacing mixed tumour profiles with ISOpure-preprocessed cancer profiles led to better prognostic gene signatures for lung and prostate cancer. Tumour heterogeneity provides both challenges and opportunities in the development of cancer biomarkers. Characterizing the heterogeneity of a patient’s tumour by identifying the sub-populations present, along with their proportions and molecular profiles, would provide a personalized cancer “fingerprint” that captures both cell-centred and. Even small fractions of contaminating normal cells can introduce noise in gene signatures [9,10], motivating the search for methods to deconvolve a mixed tumour profile by estimating the fraction of cancer cells and providing a personalized, purified mRNA abundance profile of the cancer cells. Computational approaches to purification of tumour molecular profiles have become increasingly important
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