Abstract
Cancer is driven by genomic alterations, but the processes causing this disease are largely performed by proteins. However, proteins are harder and more expensive to measure than genes and transcripts. To catalyze developments of methods to infer protein levels from other omics measurements, we leveraged crowdsourcing via the NCI-CPTAC DREAM proteogenomic challenge. We asked for methods to predict protein and phosphorylation levels from genomic and transcriptomic data in cancer patients. The best performance was achieved by an ensemble of models, including as predictors transcript level of the corresponding genes, interaction between genes, conservation across tumor types, and phosphosite proximity for phosphorylation prediction. Proteins from metabolic pathways and complexes were the best and worst predicted, respectively. The performance of even the best-performing model was modest, suggesting that many proteins are strongly regulated through translational control and degradation. Our results set a reference for the limitations of computational inference in proteogenomics. A record of this paper's transparent peer review process is included in the Supplemental Information.
Highlights
The central dogma of molecular biology describes the two-step process, transcription and translation, by which the information in genes flows into proteins: DNA / RNA / protein
188 Cell Systems 11, 186–195, August 26, 2020 ll predicted from protein levels, transcript levels, and gene copy number (Figure 1B; the phosphoproteomics sub-challenge)? The clinical proteomic tumor analysis consortium (CPTAC) proteogenomic challenge had multiple rounds of competition: two leaderboard rounds, a final round, and a collaborative phase
In order to assess the predictive performance of different methods, the CPTAC confirmatory data were utilized as a validation set
Summary
The central dogma of molecular biology describes the two-step process, transcription and translation, by which the information in genes flows into proteins: DNA / RNA / protein. Proteins can be further modified post-translationally to regulate cellular function. The processes of transcription and translation are regulated in numerous ways. Understanding these regulations and how they are altered in tumors, holds the promise to advance cancer research and treatment (Alfaro et al, 2014). Dysregulated protein activity—including kinase signaling and chromatin acetylation—is most directly assessed with measurements of proteins and their post-translational modifications. Proteomics holds important complementary value to the genomic and transcriptomic characterization of tumors
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