Improved identification and quantification of peptides in mass spectrometry data via chemical and random additive noise elimination (CRANE)

Proteomic-based stratification of intermediate-risk prostate cancer patients

Gleason grading is an important prognostic indicator for prostate adenocarcinoma and is crucial for patient treatment decisions. However, intermediate-risk patients diagnosed in the Gleason grade group (GG) 2 and GG3 can harbour either aggressive or non-aggressive disease, resulting in under- or overtreatment of a significant number of patients. Here, we performed proteomic, differential expression, machine learning, and survival analyses for 1,348 matched tumour and benign sample runs from 278 patients. Three proteins (F5, TMEM126B, and EARS2) were identified as candidate biomarkers in patients with biochemical recurrence. Multivariate Cox regression yielded 18 proteins, from which a risk score was constructed to dichotomize prostate cancer patients into low- and high-risk groups. This 18-protein signature is prognostic for the risk of biochemical recurrence and completely independent of the intermediate GG. Our results suggest that markers generated by computational proteomic profiling have the potential for clinical applications including integration into prostate cancer management.

Opportunities for pharmacoproteomics in biomarker discovery

Proteomic data are a uniquely valuable resource for drug response prediction and biomarker discovery because most drugs interact directly with proteins in target cells rather than with DNA or RNA. Recent advances in mass spectrometry and associated processing methods have enabled the generation of large-scale proteomic datasets. Here we review the significant opportunities that currently exist to combine large-scale proteomic data with drug-related research, a field termed pharmacoproteomics. We describe successful applications of drug response prediction using molecular data, with an emphasis on oncology. We focus on technical advances in data-independent acquisition mass spectrometry (DIA-MS) that can facilitate the discovery of protein biomarkers for drug responses, alongside the increased availability of big biomedical data. We spotlight new opportunities for machine learning in pharmacoproteomics, driven by the combination of these large datasets and improved high-performance computing. Finally, we explore the value of pre-clinical models for pharmacoproteomic studies and the accompanying challenges of clinical validation. We propose that pharmacoproteomics offers the potential for novel discovery and innovation within the cancer landscape. This article is protected by copyright. All rights reserved.

Diagnostic potential and future directions of matrix metalloproteinases as biomarkers in gingival crevicular fluid of oral and systemic diseases

Gingival crevicular fluid (GCF) is a physiological fluid and an inflammatory serum exudate derived from the gingival plexus of blood vessels and mixed with host tissues and subgingival plaque flows. In addition to proteins, GCF contains a diverse population of cells, including desquamated epithelial cells, cytokines, electrolytes, and bacteria from adjacent plaques. Recently, matrix metalloproteinases(MMPs), which are endopeptidases that are active against extracellular macromolecules, in GCF have been revealed as potential utility biomarkers for the diagnosis and follow-up of oral and systemic diseases, thereby facilitating the early evaluation of malignancy risk and the monitoring of disease progression and treatment response. Tissue inhibitors of metalloproteinases (TIMPs) are specific inhibitors of matrixins that participate in the regulation of local activities of MMPs in tissues. This review provides an overview of the latest findings on the diagnostic and prognostic values of MMPs and TIMPs in GCF of oral and systemic diseases, including periodontal disease, pulpitis, peri-implantitis and cardiovascular disease as well as the extraction, detection and analytical methods for GCF.