Development and Optimization of a Subtraction-Normalized Immunocyte Profiling Signature for Prostate Cancer Active Surveillance Risk Stratification.

PURPOSE: Less invasive decision support tools are desperately needed to identify occult high-risk disease in men with prostate cancer (PCa) on active surveillance (AS). For a variety of reasons, many men on AS with low- or intermediate-risk disease forgo the necessary repeat surveillance biopsies needed to identify potentially higher-risk PCa. Here, we describe the development of a blood-based immunocyte transcriptomic signature to identify men harboring occult aggressive PCa. We then validate it on a biopsy-positive population with the goal of identifying men who should not be on AS and confirm those men with indolent disease who can safely remain on AS. This model uses subtraction-normalized immunocyte transcriptomic profiles to risk-stratify men with PCa who could be candidates for AS. MATERIALS AND METHODS: Men were eligible for enrollment in the study if they were determined by their physician to have a risk profile that warranted prostate biopsy. Both training (n = 1017) and validation cohort (n = 1198) populations had blood samples drawn coincident to their prostate biopsy. Purified CD2+ and CD14+ immune cells were obtained from peripheral blood mononuclear cells, and RNA was extracted and sequenced. To avoid overfitting and unnecessary complexity, a regularized regression model was built on the training cohort to predict PCa aggressiveness based on the National Comprehensive Cancer Network PCa guidelines. This model was then validated on an independent cohort of biopsy-positive men only, using National Comprehensive Cancer Network unfavorable intermediate risk and worse as an aggressiveness outcome, identifying patients who were not appropriate for AS. RESULTS: The best final model for the AS setting was obtained by combining an immunocyte transcriptomic profile based on 2 cell types with PSA density and age, reaching an AUC of 0.73 (95% CI: 0.69-0.77). The model significantly outperforms (P < .001) PSA density as a biomarker, which has an AUC of 0.69 (95% CI: 0.65-0.73). This model yields an individualized patient risk score with 90% negative predictive value and 50% positive predictive value. CONCLUSIONS: While further validation in an intended-use cohort is needed, the immunocyte transcriptomic model offers a promising tool for risk stratification of individual patients who are being considered for AS.

Precursor Intensity-Based Label-Free Quantification Software Tools for Proteomic and Multi-Omic Analysis within the Galaxy Platform.

For mass spectrometry-based peptide and protein quantification, label-free quantification (LFQ) based on precursor mass peak (MS1) intensities is considered reliable due to its dynamic range, reproducibility, and accuracy. LFQ enables peptide-level quantitation, which is useful in proteomics (analyzing peptides carrying post-translational modifications) and multi-omics studies such as metaproteomics (analyzing taxon-specific microbial peptides) and proteogenomics (analyzing non-canonical sequences). Bioinformatics workflows accessible via the Galaxy platform have proven useful for analysis of such complex multi-omic studies. However, workflows within the Galaxy platform have lacked well-tested LFQ tools. In this study, we have evaluated moFF and FlashLFQ, two open-source LFQ tools, and implemented them within the Galaxy platform to offer access and use via established workflows. Through rigorous testing and communication with the tool developers, we have optimized the performance of each tool. Software features evaluated include: (a) match-between-runs (MBR); (b) using multiple file-formats as input for improved quantification; (c) use of containers and/or conda packages; (d) parameters needed for analyzing large datasets; and (e) optimization and validation of software performance. This work establishes a process for software implementation, optimization, and validation, and offers access to two robust software tools for LFQ-based analysis within the Galaxy platform.

Simple Peptide Quantification Approach for MS-Based Proteomics Quality Control.

Despite its growing popularity and use, bottom-up proteomics remains a complex analytical methodology. Its general workflow consists of three main steps: sample preparation, liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS), and computational data analysis. Quality assessment of the different steps and components of this workflow is instrumental to identify technical flaws and avoid loss of precious measurement time and sample material. However, assessment of the extent of sample losses along with the sample preparation protocol, in particular, after proteolytic digestion, is not yet routinely implemented because of the lack of an accurate and straightforward method to quantify peptides. Here, we report on the use of a microfluidic UV/visible spectrophotometer to quantify MS-ready peptides directly in the MS-loading solvent, consuming only 2 µL of sample. We compared the performance of the microfluidic spectrophotometer with a standard device and determined the optimal sample amount for LC-MS/MS analysis on a Q Exactive HF mass spectrometer using a dilution series of a commercial K562 cell digest. A careful evaluation of selected LC and MS parameters allowed us to define 3 µg as an optimal peptide amount to be injected into this particular LC-MS/MS system. Finally, using tryptic digests from human HEK293T cells and showing that injecting equal peptide amounts, rather than approximate ones, result in less variable LC-MS/MS and protein quantification data. The obtained quality improvement together with easy implementation of the approach makes it possible to routinely quantify MS-ready peptides as a next step in daily proteomics quality control.

Update on the moFF Algorithm for Label-Free Quantitative Proteomics.

moFF is a modular and operating-system-independent tool for quantitative analysis of label-free mass-spectrometry-based proteomics data. The moFF workflow, comprising matching-between-runs and apex quantification, can be applied to any upstream search engine's output, along with the corresponding Thermo or mzML raw file. We here present moFF 2.0, with improvements in speed through multithreading, the use of a new raw file access library, and a novel filtering approach in the matching-between-runs module. This filter allows moFF to correctly identify features that are present in one run but not in another, as demonstrated using spiked-in iRT peptides. Moreover, moFF 2.0 also provides a new peptide summary export that can be used in downstream statistical analysis. moFF is open source and freely available and can be downloaded from https://github.com/compomics/moFF.

Open-Source, Platform-Independent Library and Online Scripting Environment for Accessing Thermo Scientific RAW Files.

Mass spectrometers typically output data in proprietary binary formats. While converter suites and standardized XML formats have been developed in response, these conversion steps come with non-negligible computational time and storage space overhead. As a result, simple, everyday data inspection tasks are often beyond the skills of the mass spectrometrist, who is unable to freely access the acquired data. We therefore here describe the unthermo library for convenient, platform-independent access to Thermo Scientific RAW files and the associated online playground to transform small and easily understandable scriptlets into executable programs for end-users. By fostering the provision of code examples and snippet exchange, the interested mass spectrometrist or researcher can use this playground to quickly assemble custom scripts for their particular purpose. In this way, the data in these RAW files can be mined much more readily and directly by the user, and fast, automated raw data extraction or analysis can finally become part of the daily routine of the mass spectrometrist.

Summarization vs Peptide-Based Models in Label-Free Quantitative Proteomics: Performance, Pitfalls, and Data Analysis Guidelines.

Quantitative label-free mass spectrometry is increasingly used to analyze the proteomes of complex biological samples. However, the choice of appropriate data analysis methods remains a major challenge. We therefore provide a rigorous comparison between peptide-based models and peptide-summarization-based pipelines. We show that peptide-based models outperform summarization-based pipelines in terms of sensitivity, specificity, accuracy, and precision. We also demonstrate that the predefined FDR cutoffs for the detection of differentially regulated proteins can become problematic when differentially expressed (DE) proteins are highly abundant in one or more samples. Care should therefore be taken when data are interpreted from samples with spiked-in internal controls and from samples that contain a few very highly abundant proteins. We do, however, show that specific diagnostic plots can be used for assessing differentially expressed proteins and the overall quality of the obtained fold change estimates. Finally, our study also illustrates that imputation under the "missing by low abundance" assumption is beneficial for the detection of differential expression in proteins with low abundance, but it negatively affects moderately to highly abundant proteins. Hence, imputation strategies that are commonly implemented in standard proteomics software should be used with care.

About neighborhood counting measure metric and minimum risk metric.

In a 2006 TPAMI paper, Wang proposed the Neighborhood Counting Measure, a similarity measure for the k-NN algorithm. In his paper, Wang mentioned the Minimum Risk Metric (MRM), an early distance measure based on the minimization of the risk of misclassification. Wang did not compare NCM to MRM because of its allegedly excessive computational load. In this comment paper, we complete the comparison that was missing in Wang's paper and, from our empirical evaluation, we show that MRM outperforms NCM and that its running time is not prohibitive as Wang suggested.