Probability-based detection of phosphoproteomic uncertainty reveals rare signaling events driven by oncogenic kinase gene fusion

Abstract

We describe a novel Bayesian method for estimating protein concentration and phosphorylation site occupancy ratios from mass spectrometry experiments. Our variance model assigns standard deviations to all quantitative ratios, even when only a single peptide is observed, increasing the number of quantifiable observations in a sample compared to conventional methods. We further demonstrate the application of this method using a dataset investigating the impact of the PRKAR1A-RET gene fusion in immortalized thyroid cells. Main textTypically, the analysis of data generated by mass spectrometry is hampered by few available replicates, therefore harsh filtering applied to ensure statistical significance results in the loss of a substan-

NODAL/TGFβ signalling mediates the self-sustained stemness induced by PIK3CAH1047R homozygosity in pluripotent stem cells

“…To assess whether transcriptional changes observed in iPSCs were mirrored in the proteome, we applied label-free proteomics to the iPSC lines used in our first study ( 5 ). Around 4,600 protein ratios were obtained for both heterozygous versus wild-type and homozygous versus wild-type iPSC comparisons, as estimated using a novel Bayesian approach based on the Markov Chain Monte Carlo (MCMC) method ( 29 ). In contrast to other algorithms, the MCMC method generates an error estimate alongside each protein concentration which permits more confident determination of proteins with the most robust differential expression.…”

“…We filtered out peptides that were associated with multiple identifications in the MaxQuant msms.txt file, had a score < 40, were identified in the reverse database or came from known contaminants. Analysis of the observed peptides passing these filters was performed using a Monte Carlo Markov Chain model as described previously ( 29 ). Briefly, the model predicted the average ratio (sample versus control) of a peptide as a function of the observed protein concentration (obtained from the MaxQuant evidence.txt file).…”

Addressing Cellular Heterogeneity in Cancer through Precision Proteomics

“…The analysis of low-input proteomic data has been supported by data analysis tools designed to improve the assignment of signal to peptides, including DART-ID and MaxQuant’s “match between runs” feature. , Given the success of such strategies, other approaches to improving ID and quantification of mass spectrometric data may prove effective . As low-input data proteomic is currently more sparse than bulk approaches, the inference of protein-level quantitation may require redesigned approaches. , …”

NODAL/TGFβ signalling mediates the self-sustained stemness induced by PIK3CAH1047R homozygosity in pluripotent stem cells

“…To assess whether transcriptional changes observed in iPSCs were mirrored in the proteome, we applied label-free proteomics to the iPSC lines used in our previous study ( Madsen et al, 2019 ). Approximately 4600 protein ratios were obtained for both heterozygous versus wild-type and homozygous versus wild-type iPSC comparisons, as estimated using a novel Bayesian approach based on the Markov Chain Monte Carlo (MCMC) method ( Robin et al, 2019 preprint). In contrast to other algorithms, the MCMC method generates an error estimate alongside each protein concentration that permits a more confident determination of proteins with the most robust differential expression.…”