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Surface-enhanced Resonance Raman Scattering Nanoprobe Ratiometry for Detecting Microscopic Ovarian Cancer via Folate Receptor Targeting
Published on: March 25, 2019
High-resolution serum proteomic features for ovarian cancer detection
T P Conrads1, V A Fusaro, S Ross
1National Cancer Institute Biomedical Proteomics Program, Laboratory of Proteomics and Analytical Technologies, SAIC-Frederick, Inc., National Cancer Institute at Frederick, Frederick, MD 21702, USA.
Endocrine-Related Cancer
|May 28, 2004
Summary
High-resolution mass spectrometry (MS) offers superior diagnostic patterns for ovarian cancer compared to low-resolution MS. Quality assurance procedures ensure reliable proteomic pattern analysis for accurate biomarker discovery.
Area of Science:
- Proteomics
- Biomarker Discovery
- Mass Spectrometry
Background:
- Serum proteomic pattern diagnostics is an emerging field using mass spectrometry (MS) for biomarker classification.
- Ovarian cancer diagnosis can be improved with sensitive and specific proteomic biomarkers.
Purpose of the Study:
- To compare the diagnostic performance of high-resolution MS versus low-resolution MS for ovarian cancer serum proteomic patterns.
- To develop and implement quality assurance procedures for reliable proteomic data acquisition and analysis.
Main Methods:
- Utilized a controlled ovarian cancer serum study set for comparative analysis.
- Employed high-resolution and low-resolution mass spectrometry platforms for spectral data acquisition.
- Developed and applied quality-assurance and control (QA/QC) procedures to identify and manage spectral variability.
Main Results:
- High-resolution MS yielded significantly superior diagnostic patterns (P<0.00001) compared to low-resolution MS.
- Four distinct proteomic patterns from high-resolution MS achieved 100% sensitivity and specificity in diagnosing ovarian cancer.
- QA/QC procedures identified and excluded 32 outlying spectra, with 216 high-quality spectra used for analysis, confirming 100% accuracy in validation.
Conclusions:
- High-resolution MS provides superior classification patterns for ovarian cancer diagnostics over low-resolution MS.
- Quality assurance and control statistical procedures are crucial for monitoring and reducing spectral variability in proteomic analysis.
- Distinct proteomic patterns detected by high-resolution MS show promise for accurate ovarian cancer detection, warranting further clinical validation.
