Biomedical subjects

Ivar Siccama

Publications and source records attributed to Ivar Siccama.

5 recordsLinked to original sources

PubMed2006

A database application for pre-processing, storage and comparison of mass spectra derived from patients and controls.

BACKGROUND: Statistical comparison of peptide profiles in biomarker discovery requires fast, user-friendly software for high throughput data analysis. Important features are flexibility in changing input variables and statistical analysis of peptides that are differentially expressed between patient and control groups. In addition, integration the mass spectrometry data with the results of other experiments, such as microarray analysis, and information from other databases requires a central storage of the profile matrix, where protein id's can be added to peptide masses of interest. RESULTS: A new database application is presented, to detect and identify significantly differentially expressed peptides in peptide profiles obtained from body fluids of patient and control groups. The presented modular software is capable of central storage of mass spectra and results in fast analysis. The software architecture consists of 4 pillars, 1) a Graphical User Interface written in Java, 2) a MySQL database, which contains all metadata, such as experiment numbers and sample codes, 3) a FTP (File Transport Protocol) server to store all raw mass spectrometry files and processed data, and 4) the software package R, which is used for modular statistical calculations, such as the Wilcoxon-Mann-Whitney rank sum test. Statistic analysis by the Wilcoxon-Mann-Whitney test in R demonstrates that peptide-profiles of two patient groups 1) breast cancer patients with leptomeningeal metastases and 2) prostate cancer patients in end stage disease can be distinguished from those of control groups. CONCLUSION: The database application is capable to distinguish patient Matrix Assisted Laser Desorption Ionization (MALDI-TOF) peptide profiles from control groups using large size datasets. The modular architecture of the application makes it possible to adapt the application to handle also large sized data from MS/MS- and Fourier Transform Ion Cyclotron Resonance (FT-ICR) mass spectrometry experiments. It is expected that the higher resolution and mass accuracy of the FT-ICR mass spectrometry prevents the clustering of peaks of different peptides and allows the identification of differentially expressed proteins from the peptide profiles.

Algorithms↗

PubMed2005

MALDI-TOF mass spectrometry analysis of cerebrospinal fluid tryptic peptide profiles to diagnose leptomeningeal metastases in patients with breast cancer.

Leptomeningeal metastasis (LM) is a devastating complication that occurs in 5% of patients with breast cancer. Early diagnosis and initiation of treatment are essential to prevent neurological deterioration. However, early diagnosis of LM remains challenging because 25% of cerebrospinal fluid (CSF) samples produce false-negative results at first cytological examination. We developed a new, MS-based method to investigate the protein expression patterns present in the CSF from patients with breast cancer with and without LM. CSF samples from 106 patients with active breast cancer (54 with LM and 52 without LM) and 45 control subjects were digested with trypsin. The resulting peptides were measured by MALDI-TOF MS. Then, the mass spectra were analyzed and compared between patient groups using newly developed bioinformatics tools. A total of 895 possible peak positions was detected, and 164 of these peaks discriminated between the patient groups (Kruskal-Wallis, p<0.01). The discriminatory masses were clustered, and a classifier was built to distinguish patients with breast cancer with and without LM. After bootstrap validation, the classifier had a maximum accuracy of 77% with a sensitivity of 79% and a specificity of 76%. Direct MALDI-TOF analysis of tryptic digests of CSF gives reproducible peptide profiles that can assist in diagnosing LM in patients with breast cancer. The same method can be used to develop diagnostic assays for other neurological disorders.

Adult↗

PubMed2005

A new method to analyze matrix-assisted laser desorption/ionization time-of-flight peptide profiling mass spectra.

In protein and peptide mass spectrometry in which profiling of peaks is involved, their masses and intensities are important characteristics. Because of the relative low reproducibility of peak intensities associated with complex samples in matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOFMS), it is difficult to accurately assess the number of peaks and their intensities. In this study we evaluate these two characteristics for tryptic digests of cerebro-spinal fluid. We observed that the reproducibility of peak intensities was relatively poor (CV = 42%) and that additional normalization or spiking did not lead to a large improvement (CV = 30%). Moreover, at least seven mass spectra per sample were required to obtain a reliable peak list. An improvement of the sensitivity (i.e., eventually more peaks are detected) is observed if more replicates per sample are measured. We conclude that the reproducibility and sensitivity of peptide profiling can be significantly improved by a combination of measuring at least seven spectra per sample with a dichotomous scoring of the intensities. This approach will aid the analysis of large numbers of mass spectra of patient samples in a reproducible way for the detection and validation of candidate biomarkers.

Humans↗

PubMed2004

Genetic programming outperformed multivariable logistic regression in diagnosing pulmonary embolism.

OBJECTIVE: Genetic programming is a search method that can be used to solve complex associations between large numbers of variables. It has been used, for example, for myoelectrical signal recognition, but its value for medical prediction as in diagnostic and prognostic settings, has not been documented. STUDY DESIGN AND SETTING: We compared genetic programming and the commonly used logistic regression technique in the development of a prediction model using empirical data from a study on diagnosis of pulmonary embolism. Using part (67%) of the data, we developed and internally validated (using bootstrapping techniques) a diagnostic prediction model by genetic programming and by logistic regression, and compared both on their predictive ability in the remaining data (validation set). RESULTS: In the validation set, the area under the ROC curve of the genetic programming model was significantly larger (0.73; 95%CI: 0.64-0.82) than that of the logistic regression model (0.68; 0.59-0.77). The calibration of both models was similar, indicating a similar amount of overoptimism. CONCLUSION: Although the interpretation of a genetic programming model is less intuitive and this is the first empirical study quantifying its value for medical prediction, genetic programming seems a promising technique to develop prediction rules for diagnostic and prognostic purposes.

Adult↗

PubMed2003

Genomic profiling of gastric cancer predicts lymph node status and survival.

Gastric carcinogenesis is driven by an accumulation of genetic changes that to a large extent occur at the chromosomal level. We analysed the patterns of chromosomal instability in 35 gastric carcinomas and their clinical correlations. With microarray competitive genomic hybridization, genomewide chromosomal copy number changes can be studied with high resolution and sensitivity. A genomewide scanning array with 2275 BAC and P1 clones spotted in triplicate was used. This array provided an average resolution of 1.4 Mb across the genome. Patterns of chromosomal aberrations were analysed by hierarchical cluster analysis of the normalized log(2) tumour to normal fluorescence ratios of all clones, and cluster membership was correlated to clinicopathological data including survival. Hierarchical cluster analysis revealed three groups with different genomic profiles that correlated significantly with lymph node status (P=0.02). Moreover, gastric cancer cases from cluster 3 showed a significantly better prognosis than those from clusters 1 and 2 (P=0.02). Genomic profiling of gastric adenocarcinomas based on microarray analysis of chromosomal copy number changes predicted lymph node status and survival. The possibility to discriminate between patients with a high risk of lymph node metastasis could clinically be helpful for selecting patients for extended lymph node resection.

Animals↗