Abstract
BackgroundThe classic central dogma in biology is the information flow from DNA to mRNA to protein, yet complicated regulatory mechanisms underlying protein translation often lead to weak correlations between mRNA and protein abundances. This is particularly the case in cancer samples and when evaluating the same gene across multiple samples.ResultsHere, we report a method for predicting proteome from transcriptome, using a training dataset provided by NCI-CPTAC and TCGA, consisting of transcriptome and proteome data from 77 breast and 105 ovarian cancer samples. First, we establish a generic model capturing the correlation between mRNA and protein abundance of a single gene. Second, we build a gene-specific model capturing the interdependencies among multiple genes in a regulatory network. Third, we create a cross-tissue model by joint learning the information of shared regulatory networks and pathways across cancer tissues. Our method ranked first in the NCI-CPTAC DREAM Proteogenomics Challenge, and the predictive performance is close to the accuracy of experimental replicates. Key functional pathways and network modules controlling the proteomic abundance in cancers were revealed, in particular metabolism-related genes.ConclusionsWe present a method to predict proteome from transcriptome, leveraging data from different cancer tissues to build a trans-tissue model, and suggest how to integrate information from multiple cancers to provide a foundation for further research.
Highlights
The classic central dogma in biology is the information flow from DNA to mRNA to protein, yet complicated regulatory mechanisms underlying protein translation often lead to weak correlations between mRNA and protein abundances
Overview of the experimental design for protein abundance prediction In this study, we use a training dataset provided by National Cancer Institute (NCI)-Clinical Proteomic Tumor Analysis Consortium (CPTAC), which consists of the transcriptome and proteome data from 77 breast and 105 ovarian cancer samples
To unbiasedly evaluate prediction methods, a docker image system was used in the NCI-CPTAC Dialogue on Reverse Engineering Assessment and Method (DREAM) challenge for participants to submit their code and score on a held-out testing dataset of proteomic data from 108 breast and 82 ovarian cancer samples (Fig. 1 left)
Summary
The classic central dogma in biology is the information flow from DNA to mRNA to protein, yet complicated regulatory mechanisms underlying protein translation often lead to weak correlations between mRNA and protein abundances. Transcriptomic and proteomic variations across individuals are expected in diverse cancers, such as colorectal, breast, and ovarian cancers [10,11,12] These variations have important clinical consequences and implications, due to activation of different functional pathways, leading to different subtypes in the same organ, and biomarkers indicative of high- and low-risk patients in survival analysis [10,11,12]. These transcriptional and proteomic expression profiles provide invaluable information to studying cancer mechanisms. A computational model to predict protein abundance from mRNA data could help
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