Comparison of Supervised Classification Methods for Protein Profiling in Cancer Diagnosis

A key challenge in clinical proteomics of cancer is the identification of biomarkers that could allow detection, diagnosis and prognosis of the diseases. Recent advances in mass spectrometry and proteomic instrumentations offer unique chance to rapidly identify these markers. These advances pose considerable challenges, similar to those created by microarray-based investigation, for the discovery of pattern of markers from high-dimensional data, specific to each pathologic state (e.g. normal vs cancer). We propose a three-step strategy to select important markers from high-dimensional mass spectrometry data using surface enhanced laser desorption/ionization (SELDI) technology. The first two steps are the selection of the most discriminating biomarkers with a construction of different classifiers. Finally, we compare and validate their performance and robustness using different supervised classification methods such as Support Vector Machine, Linear Discriminant Analysis, Quadratic Discriminant Analysis, Neural Networks, Classification Trees and Boosting Trees. We show that the proposed method is suitable for analysing high-throughput proteomics data and that the combination of logistic regression and Linear Discriminant Analysis outperform other methods tested

Serum Proteomic Profiling of Lung Cancer in High-Risk Groups and Determination of Clinical Outcomes

HypothesisLung cancer remains the leading cause of cancer-related mortality worldwide. Currently known serum markers do not efficiently diagnose lung cancer at early stage.MethodsIn the present study, we developed a serum proteomic fingerprinting approach coupled with a three-step classification method to address two important clinical questions: (i) to determine whether or not proteomic profiling differs between lung cancer and benign lung diseases in a population of smokers and (ii) to assess the prognostic impact of this profiling in lung cancer. Proteomic spectra were obtained from 170 pathologically confirmed lung cancer or smoking patients with benign chronic lung disease serum samples.ResultsAmong the 228 protein peaks differentially expressed in the whole population, 88 differed significantly between lung cancer patients and benign lung disease, with area under the curve diagnostic values ranging from 0.63 to 0.84. Multiprotein classifiers based on differentially expressed peaks allowed the classification of lung cancer and benign disease with an area under the curve ranging from 0.991 to 0.994. Using a cross-validation methodology, diagnostic accuracy was 93.1% (sensitivity 94.3%, specificity 85.9%), and more than 90% of the stage I/II lung cancers were correctly classified. Finally, in the prognosis part of the study, a 4628 Da protein was found to be significantly and independently associated with prognosis in advanced stage non-small cell lung cancer patients (p = 0.0005).ConclusionsThe potential markers that we identified through proteomic fingerprinting could accurately classify lung cancers in a high-risk population and predict survival in a non-small cell lung cancer population

Anti-PrP antibodies block PrPSc replication in prion-infected cell cultures by accelerating PrPC degradation.

manuscript received October 15, 2003; revised manuscript received December 15, 2003; accepted December 16, 2003. We thanks P. Rondard, O Bischof, J.-L. Laplanche and J.-P. Pin for their fruitful discussions. we are grateful to S. barrère for her assistance in the statistical analysis of the data and H. McMahon for her assistance in reading the manuscript

HDL Proteome in Hemodialysis Patients: A Quantitative Nanoflow Liquid Chromatography-Tandem Mass Spectrometry Approach

Aside from a decrease in the high-density lipoprotein (HDL) cholesterol levels, qualitative abnormalities of HDL can contribute to an increase in cardiovascular (CV) risk in end-stage renal disease (ESRD) patients undergoing chronic hemodialysis (HD). Dysfunctional HDL leads to an alteration of reverse cholesterol transport and the antioxidant and anti-inflammatory properties of HDL. In this study, a quantitative proteomics approach, based on iTRAQ labeling and nanoflow liquid chromatography mass spectrometry analysis, was used to generate detailed data on HDL-associated proteins. The HDL composition was compared between seven chronic HD patients and a pool of seven healthy controls. To confirm the proteomics results, specific biochemical assays were then performed in triplicate in the 14 samples as well as 46 sex-matched independent chronic HD patients and healthy volunteers. Of the 122 proteins identified in the HDL fraction, 40 were differentially expressed between the healthy volunteers and the HD patients. These proteins are involved in many HDL functions, including lipid metabolism, the acute inflammatory response, complement activation, the regulation of lipoprotein oxidation, and metal cation homeostasis. Among the identified proteins, apolipoprotein C-II and apolipoprotein C-III were significantly increased in the HDL fraction of HD patients whereas serotransferrin was decreased. In this study, we identified new markers of potential relevance to the pathways linked to HDL dysfunction in HD. Proteomic analysis of the HDL fraction provides an efficient method to identify new and uncharacterized candidate biomarkers of CV risk in HD patients

Développement de modèles cellulaires humain et bovin des maladies à prion, approches thérapeutiques expérimentales et étude d'un nouvel agent de décontamination

MONTPELLIER-BU Médecine UPM (341722108) / SudocMONTPELLIER-BU Médecine (341722104) / SudocSudocFranceF

Protein interactions study through proximity-labeling

International audienc

Use of autoantibodies to detect the onset of breast cancer

International audienceThe widespread use of screening mammography has resulted in increased detection of early-stage breast disease, particularly for in situ carcinoma and early-stage breast cancer. However, the majority of women with abnormalities noted on screening mammograms are not diagnosed with cancer because of several factors, including radiologist assessment, patient age, breast density, malpractice concerns, and quality control procedures. Although magnetic resonance imaging is a highly sensitive detection tool that has become standard for women at very high risk of developing breast cancer, it lacks sufficient specificity and costeffectiveness for use as a general screening tool. Therefore, there is an important need to improve screening and diagnosis of early-invasive and noninvasive tumors, that is, in situ carcinoma. The great potential for molecular tools to improve breast cancer outcomes based on early diagnosis has driven the search for diagnostic biomarkers. Identification of tumor-specific markers capable of eliciting an immune response in the early stages of tumor development seems to provide an effective approach for early diagnosis. The aim of this review is to describe several autoantibodies identified during breast cancer diagnosis. We will focus on these molecules highlighted in the past two years and discuss the potential future use of autoantibodies as biomarkers of early-stage breast cancer

NOUVELLE APPROCHE DE QUANTIFICATION DU GENOME DE EPSTEIN-BARR VIRUS PAR LA TECHNIQUE DE PCR EN TEMPS REEL (APPLICATION AUX LYMPHOMES APRES TRANSPLANTATION)

CLERMONT FD-BCIU-Santé (631132104) / SudocPARIS-BIUM (751062103) / SudocSudocFranceF

Mise en évidence et identification des auto-anticorps dans le cancer du sein par approche protéomique

MONTPELLIER-BU Pharmacie (341722105) / SudocSudocFranceF