Proteomic profile determination of autosomal aneuploidies by mass spectrometry on amniotic fluids

<p>Abstract</p> <p>Background</p> <p>Prenatal diagnosis of chromosomal abnormalities by cytogenetic analysis is time-consuming, expensive, and requires highly qualified technicians. Rapid diagnosis of aneuploidies followed by reassurance of women with normal results can be performed by molecular analysis of uncultured foetal cells. In the present study, we developed a proteomic fingerprinting approach coupled with a statistical classification method to improve diagnosis of aneuploidies, including trisomies 13, 18, and 21, in amniotic fluid samples.</p> <p>Results</p> <p>The proteomic spectra obtained from 52 pregnant women were compiled, normalized, and mass peaks with mass-to-charge ratios between 2.5 and 50 kDa identified. Peak information was combined together and analysed using univariate statistics. Among the 208 expressed protein peaks, 40 differed significantly between aneuploid and non aneuploid samples, with AUC diagnostic values ranging from 0.71 to 0.91. Hierarchical clustering, principal component analysis and support vector machine (SVM) analysis were performed. Two class predictor models were defined from the training set, which resulted in a prediction accuracy of 92.3% and 96.43%, respectively. Using an external and independent validation set, diagnostic accuracies were maintained at 87.5% and 91.67%, respectively.</p> <p>Conclusion</p> <p>This pilot study demonstrates the potential interest of protein expression signature in the identification of new potential biological markers that might be helpful for the rapid clinical management of high-risk pregnancies.</p

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

Evaluating ZNF217 mRNA Expression Levels as a Predictor of Response to Endocrine Therapy in ER+ Breast Cancer

ZNF217 is a candidate oncogene with a wide variety of deleterious functions in breast cancer. Here, we aimed at investigating in a pilot prospective study the association between ZNF217 mRNA expression levels and the clinical response to neoadjuvant endocrine therapy (ET) in postmenopausal ER-positive (ER C) breast cancer patients. Core surgical biopsy samples before treatment initiation and post-treatment were obtained from 68 patients, and Ki-67 values measured by immunohistochemistry (IHC) were used to identify responders (n = 59) and non-responders (n = 9) after 4 months of ET. We report for the first time that high ZNF217 mRNA expression level measured by RT-qPCR in the initial tumor samples (pre-treatment) is associated with poor response to neoadjuvant ET. Indeed, the clinical positive response rate in patients with low ZNF217 expression levels was significantly higher than that in those with high ZNF217 expression levels (P = 0.027). Additionally, a retrospective analysis evaluating ZNF217 expression levels in primary breast tumor of ER+/HER2-/LNO breast cancer patients treated with adjuvant ET enabled the identification of poorer responders prone to earlier relapse (P = 0.013), while ZNF217 did not retain any prognostic value in the ER+/HER2-/LNO breast cancer patients who did not receive any treatment. Altogether, these data suggest that ZNF217 expression might be predictive of clinical response to ET

KRAS Mutation Detection in Paired Frozen and Formalin-Fixed Paraffin-Embedded (FFPE) Colorectal Cancer Tissues

KRAS mutation has been unambiguously identified as a marker of resistance to cetuximab-based treatment in metastatic colorectal cancer (mCRC) patients. However, most studies of KRAS mutation analysis have been performed using homogenously archived CRC specimens, and studies that compare freshly frozen specimens and formalin-fixed paraffin-embedded (FFPE) specimens of CRC are lacking. The aim of the present study was to evaluate the impact of tissue preservation on the determination of KRAS mutational status. A series of 131 mCRC fresh-frozen tissues were first analyzed using both high-resolution melting (HRM) and direct sequencing. KRAS mutations were found in 47/131 (35.8%) using both approaches. Out of the 47 samples that were positive for KRAS mutations, 33 had available matched FFPE specimens. Using HRM, 2/33 (6%) demonstrated suboptimal template amplification, and 2/33 (6%) expressed an erroneous wild-type KRAS profile. Using direct sequencing, 6/33 (18.1%) displayed a wild-type KRAS status, and 3/33 (9.1%) showed discordant mutations. Finally, the detection of KRAS mutations was lower among the FFPE samples compared with the freshly frozen samples, demonstrating that tissue processing clearly impacts the accuracy of KRAS genotyping

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

Gyneco-obs actualite 2002 Paris, 31 janvier - 2 fevrier 2002, resumes

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Traitement hormonal substitutif, ses alternatives et cancer du sein

MONTPELLIER-BU Médecine UPM (341722108) / SudocPARIS-BIUM (751062103) / SudocMONTPELLIER-BU Médecine (341722104) / SudocSudocFranceF

Rôle des phyto-oestrogènes sur la fertilité humaine (résultats d'une étude préliminaire)

MONTPELLIER-BU Médecine UPM (341722108) / SudocPARIS-BIUM (751062103) / SudocMONTPELLIER-BU Médecine (341722104) / SudocSudocFranceF