ArticleJournal of proteome research2017
Quality Assessments of Long-Term Quantitative Proteomic Analysis of Breast Cancer Xenograft Tissues.
Article in Journal of proteome research, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
14 citing papers in PubMed.
- Multi-omic analyses of the same sample using metabolomics, lipidomics, proteomics, phosphoproteomics, and glycoproteomics.Chemical communications (Cambridge, England) · 2026Article
- SPOTTER: Automated Tissue-Barcoding Platform for Spatial Proteomics and Phosphoproteomics.bioRxiv : the preprint server for biology · 2026Article
- ProteoformDB: A Built-In Application to Generate Proteoform Database.Journal of proteome research · 2026Article
- Deciphering Cancer Complexity: Integrative Proteogenomics and Proteomics Approaches for Biomarker Discovery.Methods in molecular biology (Clifton, N.J.) · 2025Review
- SPOT: spatial proteomics through on-site tissue-protein-labeling.Clinical proteomics · 2024Article
- Reference Materials for Improving Reliability of Multiomics Profiling.Phenomics (Cham, Switzerland) · 2024Review
- Toward Practical Integration of Omic and Imaging Data in Co-Clinical Trials.Tomography (Ann Arbor, Mich.) · 2023Article
- Recent Developments and Applications of Quantitative Proteomics Strategies for High-Throughput Biomolecular Analyses in Cancer Research.RSC chemical biology · 2021Article
- Large-scale site-specific mapping of the O-GalNAc glycoproteome.Nature protocols · 2020Article
- Orthogonal Proteomic Platforms and Their Implications for the Stable Classification of High-Grade Serous Ovarian Cancer Subtypes.iScience · 2020Article
- Deep Proteomics Using Two Dimensional Data Independent Acquisition Mass Spectrometry.Analytical chemistry · 2020Article
- Developing Workflow for Simultaneous Analyses of Phosphopeptides and Glycopeptides.ACS chemical biology · 2019Article
- Surfactant and Chaotropic Agent Assisted Sequential Extraction/On-Pellet Digestion (SCAD) for Enhanced Proteomics.Journal of proteome research · 2018Article
- Evaluation of NCI-7 Cell Line Panel as a Reference Material for Clinical Proteomics.Journal of proteome research · 2018Article
Corrections and comments
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Authors and funding
25 authors.
Funding
Abstract
Clinical proteomics requires large-scale analysis of human specimens to achieve statistical significance. We evaluated the long-term reproducibility of an iTRAQ (isobaric tags for relative and absolute quantification)-based quantitative proteomics strategy using one channel for reference across all samples in different iTRAQ sets. A total of 148 liquid chromatography tandem mass spectrometric (LC-MS/MS) analyses were completed, generating six 2D LC-MS/MS data sets for human-in-mouse breast cancer xenograft tissues representative of basal and luminal subtypes. Such large-scale studies require the implementation of robust metrics to assess the contributions of technical and biological variability in the qualitative and quantitative data. Accordingly, we derived a quantification confidence score based on the quality of each peptide-spectrum match to remove quantification outliers from each analysis. After combining confidence score filtering and statistical analysis, reproducible protein identification and quantitative results were achieved from LC-MS/MS data sets collected over a 7-month period. This study provides the first quality assessment on long-term stability and technical considerations for study design of a large-scale clinical proteomics project.
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Registered trials
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