Joint learning improves protein abundance prediction in cancers

• Published in: BMC biology Vol. 17; no. 1; p. 107
• Main Authors: Li, Hongyang, Siddiqui, Omer, Zhang, Hongjiu, Guan, Yuanfang
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 23-12-2019; BioMed Central; BMC
• Subjects: Analysis; Breast; Breast cancer; Breast Neoplasms - genetics; Cancer; Deoxyribonucleic acid; DNA; Female; Gene expression; Genes; Genetic research; Humans; Information flow; Information management; Information processing; Learning; Machine Learning; Messenger RNA; Metabolism; Methodology; Ovarian cancer; Ovarian Neoplasms - genetics; Performance prediction; Protein expression; Proteins; Proteome - genetics; Proteomes; Proteomics; Proteomics - methods; Regulatory mechanisms (biology); RNA; Survival analysis; Tissues; Transcriptome - genetics; Transcriptomics; Transcriptomics; Machine learning; Proteomics; Cancer
• ISSN: 1741-7007; 1741-7007
• DOI: 10.1186/s12915-019-0730-9

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. This is particularly the case in cancer samples and when evaluating the same gene across multiple samples.
Here, 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.
We 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.