Ovarian cancer detection by logical analysis of proteomic data.
A new type of efficient and accurate proteomic ovarian cancer diagnosis systems is proposed. The system is developed using the combinatorics and optimization-based methodology of logical analysis of data (LAD) to the Ovarian Dataset 8-7-02 (http://clinicalproteomics.steem.com), which updates the one used by Petricoin et al. in The Lancet 2002, 359, 572-577. This mass spectroscopy-generated dataset contains expression profiles of 15 154 peptides defined by their mass/charge ratios (m/z) in serum of 162 ovarian cancer and 91 control cases. Several fully reproducible models using only 7-9 of the 15 154 peptides were constructed, and shown in multiple cross-validation tests (k-folding and leave-one-out) to provide sensitivities and specificities of up to 100%. A special diagnostic system for stage I ovarian cancer patients is shown to have similarly high accuracy. Other results: (i) expressions of peptides with relatively low m/z values in the dataset are shown to be better at distinguishing ovarian cancer cases from controls than those with higher m/z values; (ii) two large groups of patients with a high degree of similarities among their formal (mathematical) profiles are detected; (iii) several peptides with a blocking or promoting effect on ovarian cancer are identified.
Alexe
Gabriela
G
Rutgers Center for Operations Research, Rutgers University, Piscataway, NJ 08854, USA.
Alexe
Sorin
S
Liotta
Lance A
LA
Petricoin
Emanuel
E
Reiss
Michael
M
Hammer
Peter L
PL
D000465:Algorithms; D014408:Biomarkers, Tumor; D002138:Calibration; D019992:Databases as Topic; D005260:Female; D006801:Humans; D013058:Mass Spectrometry; D008962:Models, Theoretical; D010051:Ovarian Neoplasms / Q000175:diagnosis* / Q000378:metabolism*; D010455:Peptides / Q000737:chemistry; D020543:Proteome; D040901:Proteomics / Q000379:methods*; D012984:Software; D013997:Time Factors
D014408:Biomarkers, Tumor; D010455:Peptides; D020543:Proteome