Abstract
BackgroundThe amount of publicly available cancer-related “omics” data is constantly growing and can potentially be used to gain insights into the tumour biology of new cancer patients, their diagnosis and suitable treatment options. However, the integration of different datasets is not straightforward and requires specialized approaches to deal with heterogeneity at technical and biological levels.MethodsHere we present a method that can overcome technical biases, predict clinically relevant outcomes and identify tumour-related biological processes in patients using previously collected large discovery datasets. The approach is based on independent component analysis (ICA) – an unsupervised method of signal deconvolution. We developed parallel consensus ICA that robustly decomposes transcriptomics datasets into expression profiles with minimal mutual dependency.ResultsBy applying the method to a small cohort of primary melanoma and control samples combined with a large discovery melanoma dataset, we demonstrate that our method distinguishes cell-type specific signals from technical biases and allows to predict clinically relevant patient characteristics. We showed the potential of the method to predict cancer subtypes and estimate the activity of key tumour-related processes such as immune response, angiogenesis and cell proliferation. ICA-based risk score was proposed and its connection to patient survival was validated with an independent cohort of patients. Additionally, through integration of components identified for mRNA and miRNA data, the proposed method helped deducing biological functions of miRNAs, which would otherwise not be possible.ConclusionsWe present a method that can be used to map new transcriptomic data from cancer patient samples onto large discovery datasets. The method corrects technical biases, helps characterizing activity of biological processes or cell types in the new samples and provides the prognosis of patient survival.
Highlights
The amount of publicly available cancer-related “omics” data is constantly growing and can potentially be used to gain insights into the tumour biology of new cancer patients, their diagnosis and suitable treatment options
In order to distinguish independent components obtained after independent component analysis (ICA) of mRNA and miRNA data, we introduce the terms mRNA independent component (RIC) and microRNA independent component (MIC)
ICA was applied to two types of transcriptomic data: mRNA and miRNA expression
Summary
The amount of publicly available cancer-related “omics” data is constantly growing and can potentially be used to gain insights into the tumour biology of new cancer patients, their diagnosis and suitable treatment options. The question arises if these resources can be used to support clinicians in making rapid and accurate assessments leading to tailored treatments for individual cancer patients Integrating this information still poses a considerable obstacle as genomic and transcriptomic data from cancer patients are characterised by significant heterogeneity at three levels. The experimental approach to resolve the complex issue of working with heterogeneous cancer samples involves physical separation of tissue into homogeneous cell populations or even single cells (by cell sorting, single cell technologies or microdissection) before the actual measurement. This is an expensive and laborious task, which is not yet accessible routinely and which can introduce experimental errors [5, 6]
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