Data Imputation in Merged Isobaric Labeling-Based Relative Quantification Datasets

Pan-Cancer Proteomics Analysis to Identify Tumor-Enriched and Highly Expressed Cell Surface Antigens as Potential Targets for Cancer Therapeutics

The National Cancer Institute's Clinical Proteomic Tumor Analysis Consortium (CPTAC) provides unique opportunities for cancer target discovery using protein expression. Proteomics data from CPTAC tumor types have been primarily generated using a multiplex tandem mass tag (TMT) approach, which is designed to provide protein quantification relative to reference samples. However, relative protein expression data are suboptimal for prioritization of targets within a tissue type, which requires additional reprocessing of the original proteomics data to derive absolute quantitation estimation. We evaluated the feasibility of using differential protein analysis coupled with intensity-based absolute quantification (iBAQ) to identify tumor-enriched and highly expressed cell surface antigens, employing tandem mass tag (TMT) proteomics data from CPTAC. Absolute quantification derived from TMT proteomics data was highly correlated with that of label-free proteomics data from the CPTAC colon adenocarcinoma cohort, which contains proteomics data measured by both approaches. We validated the TMT-iBAQ approach by comparing the iBAQ value to the receptor density value of HER2 and TROP2 measured by flow cytometry in about 30 selected breast and lung cancer cell lines from the Cancer Cell Line Encyclopedia. Collections of these tumor-enriched and highly expressed cell surface antigens could serve as a valuable resource for the development of cancer therapeutics, including antibody-drug conjugates and immunotherapeutic agents.

Identification of Highly Sensitive Pleural Effusion Protein Biomarkers for Malignant Pleural Mesothelioma by Affinity-Based Quantitative Proteomics

Malignant pleural mesothelioma (MPM) is an asbestos-associated, highly aggressive cancer characterized by late-stage diagnosis and poor prognosis. Gold standards for diagnosis are pleural biopsy and cytology of pleural effusion (PE), both of which are limited by low sensitivity and markedly inter-observer variations. Therefore, the assessment of PE biomarkers is considered a viable and objective diagnostic tool for MPM diagnosis. We applied a novel affinity-enrichment mass spectrometry-based proteomics method for explorative analysis of pleural effusions from a prospective cohort of 84 patients referred for thoracoscopy due to clinical suspicion of MPM. Protein biomarkers with a high capability to discriminate MPM from non-MPM patients were identified, and a Random Forest algorithm was applied for building classification models. Immunohistology of pleural biopsies confirmed MPM in 40 patients and ruled out MPM in 44 patients. Proteomic analysis of pleural effusions identified panels of proteins with excellent diagnostic properties (90-100% sensitivities, 89-98% specificities, and AUC 0.97-0.99) depending on the specific protein combination. Diagnostic proteins associated with cancer growth included galactin-3 binding protein, testican-2, haptoglobin, Beta ig-h3, and protein AMBP. Moreover, we also confirmed previously reported diagnostic accuracies of the MPM markers fibulin-3 and mesothelin measured by two complementary mass spectrometry-based methods. In conclusion, a novel affinity-enrichment mass spectrometry-based proteomics identified panels of proteins in pleural effusion with extraordinary diagnostic accuracies, which are described here for the first time as biomarkers for MPM.

ProteoSushi: A Software Tool to Biologically Annotate and Quantify Modification-Specific, Peptide-Centric Proteomics Data Sets

Large-scale proteomic profiling of protein post-translational modifications has provided important insights into the regulation of cell signaling and disease. These modification-specific proteomics workflows nearly universally enrich modified peptides prior to mass spectrometry analysis, but protein-centric proteomic software tools have many limitations evaluating and interpreting these peptide-centric data sets. We, therefore, developed ProteoSushi, a software tool tailored to analysis of each modified site in peptide-centric proteomic data sets that is compatible with any post-translational modification or chemical label. ProteoSushi uses a unique approach to assign identified peptides to shared proteins and genes, minimizing redundancy by prioritizing shared assignments based on UniProt annotation score and optional user-supplied protein/gene lists. ProteoSushi simplifies quantitation by summing or averaging intensities for each modified site, merging overlapping peptide charge states, missed cleavages, spectral matches, and variable modifications into a single value. ProteoSushi also annotates each PTM site with the most up-to-date biological information available from UniProt, such as functional roles or known modifications, the protein domain in which the site resides, the protein's subcellular location and function, and more. ProteoSushi has a graphical user interface for ease of use. ProteoSushi's flexibility and combination of analysis features streamlines peptide-centric data processing and knowledge mining of large modification-specific proteomics data sets.

Comprehensive proteomic investigation of infectious and inflammatory changes in late preterm prelabour rupture of membranes

Preterm prelabour rupture of membranes beyond the 34th week of gestation (late PPROM) is frequently associated with the risk of the microbial invasion of the amniotic fluid (MIAC) and histological chorioamnionitis (HCA). Hence, we employed a Tandem Mass Tag-based approach to uncover amniotic fluid proteome response to the presence of MIAC and HCA in late PPROM. Protein dysregulation was associated with only five cases in the group of 15 women with confirmed MIAC and HCA. Altogether, 138 amniotic fluid proteins were changed in these five cases exclusively. These proteins were particularly associated with excessive neutrophil responses to infection, such as neutrophil degranulation and extracellular trap formation. We believe that the quantification of these proteins in amniotic fluid may assist in revealing women with the highest risk of excessive inflammatory response in late PPROM.

A Review of Imputation Strategies for Isobaric Labeling-Based Shotgun Proteomics

The throughput efficiency and increased depth of coverage provided by isobaric-labeled proteomics measurements have led to increased usage of these techniques. However, the structure of missing data is different than unlabeled studies, which prompts the need for this review to compare the efficacy of nine imputation methods on large isobaric-labeled proteomics data sets to guide researchers on the appropriateness of various imputation methods. Imputation methods were evaluated by accuracy, statistical hypothesis test inference, and run time. In general, expectation maximization and random forest imputation methods yielded the best performance, and constant-based methods consistently performed poorly across all data set sizes and percentages of missing values. For data sets with small sample sizes and higher percentages of missing data, results indicate that statistical inference with no imputation may be preferable. On the basis of the findings in this review, there are core imputation methods that perform better for isobaric-labeled proteomics data, but great care and consideration as to whether imputation is the optimal strategy should be given for data sets comprised of a small number of samples.