Early symptoms and sensations as predictors of lung cancer: a machine learning multivariate model.

The aim of this study was to identify a combination of early predictive symptoms/sensations attributable to primary lung cancer (LC). An interactive e-questionnaire comprised of pre-diagnostic descriptors of first symptoms/sensations was administered to patients referred for suspected LC. Respondents were included in the present analysis only if they later received a primary LC diagnosis or had no cancer; and inclusion of each descriptor required ≥4 observations. Fully-completed data from 506/670 individuals later diagnosed with primary LC (n = 311) or no cancer (n = 195) were modelled with orthogonal projections to latent structures (OPLS). After analysing 145/285 descriptors, meeting inclusion criteria, through randomised seven-fold cross-validation (six-fold training set: n = 433; test set: n = 73), 63 provided best LC prediction. The most-significant LC-positive descriptors included a cough that varied over the day, back pain/aches/discomfort, early satiety, appetite loss, and having less strength. Upon combining the descriptors with the background variables current smoking, a cold/flu or pneumonia within the past two years, female sex, older age, a history of COPD (positive LC-association); antibiotics within the past two years, and a history of pneumonia (negative LC-association); the resulting 70-variable model had accurate cross-validated test set performance: area under the ROC curve = 0.767 (descriptors only: 0.736/background predictors only: 0.652), sensitivity = 84.8% (73.9/76.1%, respectively), specificity = 55.6% (66.7/51.9%, respectively). In conclusion, accurate prediction of LC was found through 63 early symptoms/sensations and seven background factors. Further research and precision in this model may lead to a tool for referral and LC diagnostic decision-making

Glioblastoma stem cells express non-canonical proteins and exclusive mesenchymal-like or non-mesenchymal-like protein signatures

Glioblastoma (GBM) cancer stem cells (GSCs) contribute to GBM's origin, recurrence, and resistance to treatment. However, the understanding of how mRNA expression patterns of GBM subtypes are reflected at global proteome level in GSCs is limited. To characterize protein expression in GSCs, we performed in-depth proteogenomic analysis of patient-derived GSCs by RNA-sequencing and mass-spectrometry. We quantified > 10 000 proteins in two independent GSC panels and propose a GSC-associated proteomic signature characterizing two distinct phenotypic conditions; one defined by proteins upregulated in proneural and classical GSCs (GPC-like), and another by proteins upregulated in mesenchymal GSCs (GM-like). The GM-like protein set in GBM tissue was associated with necrosis, recurrence, and worse overall survival. Through proteogenomics, we discovered 252 non-canonical peptides in the GSCs, i.e., protein sequences that are variant or derive from genome regions previously considered non-protein-coding, including variants of the heterogeneous ribonucleoproteins implicated in RNA splicing. In summary, GSCs express two protein sets that have an inverse association with clinical outcomes in GBM. The discovery of non-canonical protein sequences questions existing gene models and pinpoints new protein targets for research in GBM

Evaluation of Spin Columns for Human Plasma Depletion to Facilitate MS-Based Proteomics Analysis of Plasma

High abundant protein depletion is a common strategy applied to increase analytical depth in global plasma proteomics experiment setups. The standard strategies for depletion of the highest abundant proteins currently rely on multiple-use HPLC columns or multiple-use spin columns. Here we evaluate the performance of single-use spin columns for plasma depletion and show that the single-use spin reduces handling time by allowing parallelization and is easily adapted to a nonspecialized lab environment without reducing the high plasma proteome coverage and reproducibility. In addition, we evaluate the effect of viral heat inactivation on the plasma proteome, an additional step in the plasma preparation workflow that allows the sample preparation of SARS-Cov2-infected samples to be performed in a BSL3 laboratory, and report the advantage of performing the heat inactivation postdepletion. We further show the possibility of expanding the use of the depletion column cross-species to macaque plasma samples. In conclusion, we report that single-use spin columns for high abundant protein depletion meet the requirements for reproducibly in in-depth plasma proteomics and can be applied on a common animal model while also reducing the sample handling time

Quantitative iTRAQ-Based Proteomic Identification of Candidate Biomarkers for Diabetic Nephropathy in Plasma of Type 1 Diabetic Patients

# The Author(s) 2010. This article is published with open access at Springerlink.com Introduction As part of a clinical proteomics programme focused on diabetes and its complications, it was our goal to investigate the proteome of plasma in order to find improved candidate biomarkers to predict diabetic nephropathy. Methods Proteins derived from plasma from a crosssectiona

High Abundance Proteins Depletion vs Low Abundance Proteins Enrichment: Comparison of Methods to Reduce the Plasma Proteome Complexity

BACKGROUND: To date, the complexity of the plasma proteome exceeds the analytical capacity of conventional approaches to isolate lower abundance proteins that may prove to be informative biomarkers. Only complex multistep separation strategies have been able to detect a substantial number of low abundance proteins (<100 ng/ml). The first step of these protocols is generally the depletion of high abundance proteins by the use of immunoaffinity columns or, alternatively, the enrichment of by the use of solid phase hexapeptides ligand libraries. METHODOLOGY/PRINCIPAL FINDINGS: Here we present a direct comparison of these two approaches. Following either approach, the plasma sample was further fractionated by SCX chromatography and analyzed by RP-LC-MS/MS with a Q-TOF mass spectrometer. The depletion of the 20 most abundant plasma proteins allowed the identification of about 25% more proteins than those detectable following low abundance proteins enrichment. The two datasets are partially overlapping and the identified proteins belong to the same order of magnitude in terms of plasma concentration. CONCLUSIONS/SIGNIFICANCE: Our results show that the two approaches give complementary results. However, the enrichment of low abundance proteins has the great advantage of obtaining much larger amount of material that can be used for further fractionations and analyses and emerges also as a cheaper and technically simpler approach. Collectively, these data indicate that the enrichment approach seems more suitable as the first stage of a complex multi-step fractionation protocol