Biomedical subjects

Dominik Driesch

Publications and source records attributed to Dominik Driesch.

5 recordsLinked to original sources

PubMed2006

Prediction of renal allograft rejection by urinary protein analysis using ProteinChip Arrays (surface-enhanced laser desorption/ionization time-of-flight mass spectrometry).

OBJECTIVES: To develop a noninvasive method for the detection of renal transplant rejection using ProteinChip Arrays (surface-enhanced laser desorption/ionization time-of-flight mass spectrometry). METHODS: A total of 23 urine samples were collected from 13 patients showing biopsy-proven renal allograft rejection and from 10 patients without histologic signs of rejection. All 23 patients had clinical symptoms and signs of acute allograft rejection and underwent renal biopsy. Samples were centrifuged, and supernatants were directly spotted onto the ProteinChip arrays with different chromatographic surfaces. The obtained spectra in a range from 2 to 200 kDa were subjected to bioinformatic analysis using the method of Fuzzy c-means, followed by the establishment of rule bases and evaluation using the relevance index according to Kiendl. RESULTS: Several protein peaks were identified allowing differentiation between rejection and no rejection. Using two different ProteinChip surfaces, we found two biomarkers at 25.71 kDa and 28.13 kDa that gave a diagnostic sensitivity of 90% and 93% and a specificity of 80% (SAX2) and 85% (CM10), respectively. CONCLUSIONS: Surface-enhanced laser desorption/ionization time-of-flight mass spectrometry appears to be a promising new diagnostic tool for distinguishing renal transplant patients with no rejection from those with acute rejection.

Graft Rejection↗

PubMed2005

Identification of HNP3 as a tumour marker in CD4+ and CD4- lymphocytes of patients with cutaneous T-cell lymphoma.

Cutaneous T-cell lymphomas (CTCL) are characterized by malignant proliferation of skin homing T-cells. Although prognosis is generally good, reliable markers are needed to identify patients at risk for a more aggressive course. ProteinChip (SELDI) technology was used as a tool for the discovery of protein patterns in lymphocytes from patients with CTCL (n=25) and unaffected controls (n=25). Lymphocytes were separated in CD4+ and CD4- fractions by magnetic cell sorting (MACS). Each whole protein extract was analysed by ProteinChip technology. The resulting protein profiles were submitted for bioinformatic analysis including a clustering algorithm, a rule extraction, a rating and a rule-base construction step. For the generated combined rule base for the CD4- cell fraction, both the sensitivity and specificity for the prediction of CTCL reached 96%, while for the CD4+ fraction they were 92% and 84%, respectively, for sensitivity and specificity. The most significant peak at 3489Da could be identified as HNP3, an alpha-defensin, by immunocapturing. These results open up both the possibility for the use of this protein signature, especially HNP3, to more effectively monitor and screen CTCL, and the avenue to identify the other relevant peaks for a better understanding of the development of this tumour.

Biomarkers, Tumor↗

PubMed2005

Proteome analysis of maternal serum samples for trisomy 21 pregnancies using ProteinChip arrays and bioinformatics.

A surface-enhanced laser desorption/ionization time of flight (SELDI-TOF)-based ProteinChip System was used as a tool for rapid discovery and identification of protein patterns in serum that discriminate between trisomy 21 and unaffected pregnancies. We analyzed 24 serum samples from women carrying a trisomy 21 pregnancy and 32 with an unaffected pregnancy, ranging from 10.0 to 14.0 weeks of gestation. The resulting protein profiles were submitted to a clustering algorithm, a rule extraction, a rating, and a rule base construction step. For the generated combined rule base, the specificity and sensitivity for the prediction of a trisomy 21 pregnancy reach 97% and 91%, respectively.

Computational Biology↗

PubMed2005

Identification of protein pattern in kidney cancer using ProteinChip arrays and bioinformatics.

Tumor biology of renal cell carcinoma (RCC) is not very well understood, although many studies on molecular and cellular biology have been performed. It is accepted now that cancer research has to be performed also with proteomic tools, because proteins are the real actors in the genesis and progression of cancer. Therefore, we used a ProteinChip System(R) (SELDI) which is able to detect minute amounts of protein and moreover to analyze a complex protein pattern. We analyzed 37 cases of clear cell RCC as a training set including corresponding normal tissue. From all samples protein lysates were made and spotted directly on different chip surfaces (SAX2, WCX). After a washing procedure the arrays were analyzed in the ProteinChip Reader. All profiles were subjected to a bioinformatical analysis including normalization, clustering, rule extraction and rating. Defined rules (markers) were evaluated using a test set of 24 samples (13 tumor tissues and 11 normal kidney tissues). The generated rule base for the SAX2 surface showed a sensitivity of 100% and a specificity of 97.3%. For the WCX arrays the optimal rule base showed worse results. A combined rule base for SAX2 and WCX did not result in a higher sensitivity or specificity. Using the optimal rule base for the SAX2 chip in the test set, sensitivity and specificity reached 76.9% and 100%, respectively. The ProteinChip System represents a key technology for the rapid detection of cancer specific proteomic patterns. It is possible to identify clear cell renal cancer with high sensitivity and specificity from minimal amounts of cells.

Cell Line, Tumor↗

PubMed2004

Proteomic profiling in microdissected hepatocellular carcinoma tissue using ProteinChip technology.

At present, the molecular mechanisms of hepatocellular carcinogenesis are not well understood. It is known, however, that cancer development and progression are accompanied by profound changes at the cellular and subcellular level, involving RNA/DNA and protein structure and function. Therefore, high-throughput, proteomic techniques targeting these biological molecules may provide novel insights into HCC genesis and prognosis. We characterized tissue protein profiles from 10 HCC patients using ProteinChip technology (SELDI) which is able to detect minute amounts of proteins and moreover to analyze complex protein pattern. Therefore, after histopathological examination, proteins from kryostat sections of non-tumorous hepatic tissue as well as from central and peripheral tumor areas were isolated from complete histological sections or from selected and microdissected tissue areas. Analysis on the SAX and WCX ProteinChip Arrays revealed 14-26, and 25-29 differentially expressed peaks respectively, which characterized non-tumorous and tumor tissue (p< or =0.05). One feature which allows differentiation between central tumor and peripheral tumor regions could only be detected in microdissected tissue. Using ProteinChip technology in combination with tissue microdissection it is possible to investigate complex changes at the protein level in hepatocellular cancer associated with tumor development and progression.

Biomarkers, Tumor↗