Body Fluids as a Source of Diagnostic Biomarkers: Prostate

Abbreviated Name
Body Fluids as a Source of Diagnostic Biomarkers: Prostate
Lead Investigator
Semmes, John — Eastern Virginia Medical School
Coordinating Investigator
Semmes, John — Eastern Virginia Medical School
Involved Investigators

Abstract

Recent advances in high-throughput protein expression profiling of bodily fluids has generated great enthusiasm and hope for this approach as a potent diagnostic tool. At the center of these efforts is the application of SELDI-TOF-MS and artificial intelligence algorithms by the EDRN BDL site at Eastern Virginia Medical School and the DMCC respectively. When the expression profiling process was applied to sera from individuals with prostate cancer (N=197), BPH (N=92) or from otherwise healthy donors (N=97) we achieved an overall misclassification rate of <10%. The sensitivity was 83% and the specificity was 100% with an overall positive predictive value of 91.15%. This result is a significant improvement over PSA which presents as only 25% specificity and >90% sensitivity. Since this represents a noticeable improvement in current clinical approach we are proposing to embark upon a validation process. The described studies are designed to address validation issues and include three phases. Phase 1; Synchronization of SELDI Output within the EDRN-Prostate-SELDI Investigational Collaboration (EPSIC); addressing portability (A) Synchronize SELDI instrumentation and robotic sample processing across the EPSIC using pooled serum(QC); (B) Establish the portability and reproducibility of the SELDI protein profiling approach within the EPSIC using normal and prostate cancer patient’s serum from a single site; (C) Establish robustness of the approach toward geographic, sample collection and processing differences within EPSIC using case and control serum from five different sites. Phase 2; Population Validation Establish geographic variability and robustness in a large cross-sectional study among different sample population. Phase 3; Clinical Validation; validate the serum protein expression profiling coupled with a learning algorithm as a means for early detection of prostate cancer using longitudinal PCPT samples. We have assembled a cohesive multi-institutional team for completing these studies in a timely and efficient manner. The team consists of five EDRN laboratories, DMCC and CBI and the proposed budget reflects the total involvement.

Aims

The specific aims are elaborated in the Validation protocol submitted earlier.

Analytic Method

SELDI-TOF, MALDI-TOF/TOF, SELDI-QStar

Comments

The three article that emerged from this study have been well referenced and represent the new standard for validation of high-throughput proteomics data. The last two mansucripts were published back to back in Clinical Chemistry with three invited editorials to comment on the research.

Outcome

Recent advances in high-throughput protein expression profiling of bodily fluids has generated great enthusiasm and hope for this approach as a potent diagnostic tool. At the center of these efforts is the application of SELDI-TOF-MS and artificial intelligence algorithms by the EDRN BDL site at Eastern Virginia Medical School and the DMCC respectively. When the expression profiling process was applied to sera from individuals with prostate cancer (N=197), BPH (N=92) or from otherwise healthy donors (N=97) we achieved an overall misclassification rate of <10%. The sensitivity was 83% and the specificity was 100% with an overall positive predictive value of 91.15%. This result is a significant improvement over PSA which presents as only 25% specificity and >90% sensitivity. Since this represents a noticeable improvement in current clinical approach we are proposing to embark upon a validation process. The described studies are designed to address validation issues and include three phases. Phase 1; Synchronization of SELDI Output within the EDRN-Prostate-SELDI Investigational Collaboration (EPSIC); addressing portability (A) Synchronize SELDI instrumentation and robotic sample processing across the EPSIC using pooled serum(QC); (B) Establish the portability and reproducibility of the SELDI protein profiling approach within the EPSIC using normal and prostate cancer patient’s serum from a single site; (C) Establish robustness of the approach toward geographic, sample collection and processing differences within EPSIC using case and control serum from five different sites. Phase 2; Population Validation Establish geographic variability and robustness in a large cross-sectional study among different sample population. Phase 3; Clinical Validation; validate the serum protein expression profiling coupled with a learning algorithm as a means for early detection of prostate cancer using longitudinal PCPT samples. We have assembled a cohesive multi-institutional team for completing these studies in a timely and efficient manner. The team consists of five EDRN laboratories, DMCC and CBI and the proposed budget reflects the total involvement.

Publications

Biomarkers

  • No biomarkers available at this time for this protocol.

Data Collections

  • No data collections available at this time for this protocol.
Finish Date
Jan 1 2007
Protocol ID
67
Protocol Type
Collaboration
Fields of Research
  • Proteomics
Collaborative Group
Prostate and Urologic Cancers Research Group
Cancer Types
  • Malignant neoplasm of prostate

Associated Forms