P12 SELDI-TOF MS serum protein profiling predicts poor prognosis renal cancer patients

2007 ◽  
Vol 5 (8) ◽  
pp. 29
Author(s):  
N. Mehra ◽  
J. Engwegen ◽  
C. van Gils ◽  
J. Haanen ◽  
J. Bonfrer ◽  
...  
Keyword(s):  
Cancer Patients ◽  
Serum Protein ◽  
Renal Cancer ◽  
Poor Prognosis ◽  
Protein Profiling ◽  
Tof Ms

Serum pharmacoproteomics of erlotinib-treated advanced prostate cancer patients: A SELDI-TOF mass spectrometry analysis

2006 ◽  
Vol 24 (18_suppl) ◽  
pp. 14603-14603
Author(s):  
A. Gonçalves ◽  
B. Taylor ◽  
Y. Toiron ◽  
B. Esterni ◽  
N. Salem ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Mass Spectrometry ◽  
Cancer Patients ◽  
Serum Protein ◽  
Advanced Prostate Cancer ◽  
Protein Profiling ◽  
Mass Spectrometry Analysis ◽  
Protein Profiles ◽  
Protein Biomarkers ◽  
Tof Ms

14603 Background: EGFR (Epidermal Growth Factor Receptor) is a promising target in various epithelial cancers. Erlotinib is an orally active small molecule tyrosine kinase inhibitor (TKI) targeting EGFR, under evaluation in prostate cancer. To identify protein biomarkers associated with EGFR TKI treatment we performed serum protein profiling in advanced prostate cancer patients receiving erlotinib, using Surface Enhanced Laser Desorption/Ionization-Time of Flight Mass Spectrometry (SELDI-TOF MS). Method: Serums from 23 advanced or metastatic prostate cancer patients enrolled in a phase II study of erlotinib as single agent were collected before treatment, on D28 and D56 and kept frozen in liquid nitrogen until analysis. Serum samples from each patients and each time points were first urea treated and then incubated with 3 different ProteinChip arrays (Ciphergen Biosystems): IMAC-Cu, CM10 and H50 using a fully automated platform (Tecan). After adding SPA matrix, arrays were analysed using a PBSIIc ProteinChip reader (Ciphergen Biosystems). After noise reduction, baseline substraction, and data normalisation, protein peaks were detected using the Biomarker Wizard tool integrated to the ProteinChip Software 3.1. Numeric data were then exported to excel files that were used for further biostatistic processing. Results: Combining protein profiles resolved from each experimental condition, several hundreds of protein peaks were obtained. Protein profiles from untreated and D28 and D56-treated patients were compared using non-parametric statistical methods and several protein peaks appeared differentially expressed between pre and post-treatment samples. Supervised methods identified protein peaks correlating with specific erlotinib toxicity on day 28. Since only minimal activity was noted in this trial, no protein profile correlating with anti-tumor effect was identified. Purification and identification of proteins modulated by treatment and associated with toxicity are ongoing. Conclusion: Serum protein profiling using SELDI-TOF MS is a promising method to characterize pharmacoproteomics of innovative compounds under development and to identify protein biomarkers potentially associated with drug effects. No significant financial relationships to disclose.

scholarly journals Serum protein profiling by SELDI mass spectrometry: detection of multiple variants of serum amyloid alpha in renal cancer patients

2004 ◽  
Vol 84 (7) ◽  
pp. 845-856 ◽  
Author(s):  
Jonathan Tolson ◽  
Ralf Bogumil ◽  
Elke Brunst ◽  
Hermann Beck ◽  
Raimund Elsner ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Cancer Patients ◽  
Serum Protein ◽  
Renal Cancer ◽  
Protein Profiling ◽  
Serum Amyloid ◽  
Mass Spectrometry Detection ◽  
Multiple Variants

scholarly journals Comparing the old and new generation SELDI-TOF MS: implications for serum protein profiling

2008 ◽  
Vol 1 (1) ◽  
Author(s):  
Marie-Christine W Gast ◽  
Judith YMN Engwegen ◽  
Jan HM Schellens ◽  
Jos H Beijnen
Keyword(s):  
Serum Protein ◽  
Protein Profiling ◽  
New Generation ◽  
Tof Ms

Application of serum proteomic patterns by ProteinChip SELDI system in diagnosis of gastric cancer

2007 ◽  
Vol 25 (18_suppl) ◽  
pp. 10559-10559
Author(s):  
J. Lim ◽  
J. Cho ◽  
Y. Paik ◽  
Y. Chang ◽  
H. Kim
Keyword(s):  
Gastric Cancer ◽  
Decision Tree ◽  
Cancer Patients ◽  
Expression Profiles ◽  
Protein Profiling ◽  
Healthy Individuals ◽  
Protein Biomarker ◽  
Biomarker Pattern ◽  
Gastric Cancer Patients ◽  
Tof Ms

10559 Background: Gastric cancer is one of the most common malignancy in the world and one of the leading causes of cancer related death in Korea. Most treatments for advanced gastric cancer have limited efficacy. So early detection of gastric cancer could have profound impact on the successful treatment. Application of multiple biomarkers may improve the diagnostic prediction to distinguish cancer from non-cancer. ProteinChip Surface-Enhanced Laser Desorption/Ionization Time-of-flight Mass Spectrometry (SELDI-TOF-MS) system is one of the currently used techniques to identify biomarkers for cancers. In this study, we have explored whether the serum proteomic patterns by ProteinChip SELDI system can differentiate gastric cancers from non-cancer cohorts. Methods: We have screened protein profiles of 100 serum samples obtained from 60 gastric cancer patients and 40 healthy individuals. Protein expression profiles were expressed on ProteinChip Array and analyzed by PreoteinChip Reader. Peak intensities were normalized by total ion currency and analyzed by the Biomarker Wizard Software to identify the peaks showing significantly different intensities between normal and cancer groups. Classification analysis and construction of decision trees were done with the Biomarker Pattern Software. Results: SELDI -TOF-MS by averaging 50 laser spots collected at a laser intensity setting of 160, a detector sensitivity of 6, and mean mass range of 30 kDa. Seventeen protein peaks shown significant differences between two groups were chosen to make a protein biomarker pattern. The decision tree which gives the highest discrimination included four peaks at 5,919, 8,583, 10,286, and 13,758 as splitters. The sensitivity and the specificity for classification of with the decision tree giving the highest discrimination were 96.7% (58/60) and 97.5% (39/40), respectively. When the protein biomarker pattern was tested with the blinded test set including 30 gastric cancer patients and 20 healthy individuals and, it yielded a sensitivity of 93.3% (28/30) and a specificity of 90% (18/20). Conclusions: These results suggest that serum-protein profiling pattern by SELDI system may distinguish gastric cancer patients from normal counterparts with relatively high sensitivity and specificity. No significant financial relationships to disclose.

Use of surface-enhanced laser desorption/ionisation time-of-flight mass spectrometry (SELDI-TOF-MS) to detect breast cancer markers in serum

2009 ◽  
Vol 27 (15_suppl) ◽  
pp. e22133-e22133
Author(s):  
D. Boehm ◽  
A. Lebrecht ◽  
R. Schwirz ◽  
K. Keller ◽  
M. Schmidt ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Patients ◽  
Protein Profiling ◽  
Breast Cancer Patients ◽  
Screening Programs ◽  
Healthy Women ◽  
Metastatic Relapse ◽  
Protein Chips ◽  
Detect Breast Cancer ◽  
Tof Ms

e22133 Background: Breast cancer is one of the most frequent and deadly cancers worldwide. Although the survival of patients has increased over the last decades, many patients die from metastatic relapse. Progresses in screening or early diagnosis will improve survival of breast cancer. Breast cancer has never had any good serum tumor markers. Therefore, we developed and evaluated a proteomics approach to search for new biomarkers in serum of breast cancer patients. Methods: Blood samples of 50 women with breast cancer (CA) and 50 healthy women (CTRL), matched to the age, were drawn prior to surgery. We used SELDI-TOF-MS for protein profiling with three different active surfaces of the protein chips: cationic exchanger (CM-10), hydrophobic surface (H50) and a strong anion exchange surface (Q10) with different binding properties. Data were analyzed by multivariate statistical techniques and artificial neural networks. Results: SELDI-TOF- MS could discriminate between serum of breast cancer patients and healthy women. We could generate a statistic significant (p<0.001) panel with 15 biomarkers resulting of multiple peaks with different molecular weights. The diagnostic pattern could differentiate CA from CRTL with specificity of 77% and sensitivity of 85% in serum. Conclusions: In this study we could exemplify SELDI-TOF-MS as a potential screening method to detect breast cancer patients by serum analysis. The protein chip technology could greatly facilitate the discovery of new and better biomarkers in breast cancer patients. This promising approach provides a high sensitivity and specificity by a less invasive method similar to mammography that is used in screening programs. No significant financial relationships to disclose.

Sign in / Sign up

Export Citation Format

Share Document