Rapid mass spectrometric metabolic profiling of blood sera detects ovarian cancer with high accuracy.
BACKGROUND
Ovarian cancer diagnosis is problematic because the disease is typically asymptomatic, especially at the early stages of progression and/or recurrence. We report here the integration of a new mass spectrometric technology with a novel support vector machine computational method for use in cancer diagnostics, and describe the application of the method to ovarian cancer.


METHODS
We coupled a high-throughput ambient ionization technique for mass spectrometry (direct analysis in real-time mass spectrometry) to profile relative metabolite levels in sera from 44 women diagnosed with serous papillary ovarian cancer (stages I-IV) and 50 healthy women or women with benign conditions. The profiles were input to a customized functional support vector machine-based machine-learning algorithm for diagnostic classification. Performance was evaluated through a 64-30 split validation test and with a stringent series of leave-one-out cross-validations.


RESULTS
The assay distinguished between the cancer and control groups with an unprecedented 99% to 100% accuracy (100% sensitivity and 100% specificity by the 64-30 split validation test; 100% sensitivity and 98% specificity by leave-one-out cross-validations).


CONCLUSIONS
The method has significant clinical potential as a cancer diagnostic tool. Because of the extremely low prevalence of ovarian cancer in the general population (approximately 0.04%), extensive prospective testing will be required to evaluate the test's potential utility in general screening applications. However, more immediate applications might be as a diagnostic tool in higher-risk groups or to monitor cancer recurrence after therapeutic treatment.


CONCLUSIONS
The ability to accurately and inexpensively diagnose ovarian cancer will have a significant positive effect on ovarian cancer treatment and outcome.
Zhou
Manshui
M
Georgia Institute of Technology, Atlanta, Georgia 30332-0363, USA.
Guan
Wei
W
Walker
L DeEtte
LD
Mezencev
Roman
R
Benigno
Benedict B
BB
Gray
Alexander
A
Fernández
Facundo M
FM
McDonald
John F
JF
D000328:Adult; D000368:Aged; D014408:Biomarkers, Tumor / Q000097:blood*; D016022:Case-Control Studies; D005260:Female; D006801:Humans; D013058:Mass Spectrometry / Q000379:methods*; D055442:Metabolome; D008875:Middle Aged; D010051:Ovarian Neoplasms / Q000097:blood* / Q000175:diagnosis / Q000473:pathology
D014408:Biomarkers, Tumor