Rapid mass spectrometric metabolic profiling of blood sera detects ovarian cancer with high accuracy

Cancer Epidemiology Biomarkers and Prevention

Volumn 19, Issue 9, 2010, Pages 2262-2271

  Zhou, Manshui ^a   Guan, Wei ^b   Walker, L DeEtte ^a,c   Mezencev, Roman ^a,c   Benigno, Benedict B ^c   Gray, Alexander ^b   Fernández, Facundo M ^a   McDonald, John F ^a,c  

^a GEORGIA INSTITUTE OF TECHNOLOGY   (United States)

^b Georgia Institute of Technology   (United States)

^c Ovarian Cancer Institute   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ADULT; AGED; ARTICLE; CANCER DIAGNOSIS; CANCER RECURRENCE; CANCER STAGING; CLINICAL ARTICLE; CONTROLLED STUDY; DIAGNOSTIC ACCURACY; DIRECT ANALYSIS IN REAL TIME MASS SPECTROMETRY; FEMALE; HIGH RISK POPULATION; HISTOPATHOLOGY; HUMAN; LEARNING ALGORITHM; MASS SPECTROMETRY; METABOLIC CAPACITY; OUTCOME ASSESSMENT; OVARY CANCER; PAPILLARY CARCINOMA; PRIORITY JOURNAL; SENSITIVITY AND SPECIFICITY; SERUM; SUPPORT VECTOR MACHINE; VALIDITY;

ADULT; AGED; CASE-CONTROL STUDIES; FEMALE; HUMANS; MASS SPECTROMETRY; METABOLOME; MIDDLE AGED; OVARIAN NEOPLASMS; TUMOR MARKERS, BIOLOGICAL;

EID: 77956508105     PISSN: 10559965     EISSN: None     Source Type: Journal    
DOI: 10.1158/1055-9965.EPI-10-0126     Document Type: Article

References (50)

1. American Cancer Society. Cancer facts & figures 2009. Atlanta: American Cancer Society; 2009.
2. Horner MJ, Ries LAG, Krapcho M, et al, editors. SEER Cancer Statistics Review, 1975-2006. Bethesda, MD: National Cancer Institute; 2009, Available from: http://seer.cancer.gov/csr/1975-2006/, based on November 2008 SEER data submission, posted to the SEER web site.
3. Jacobs IJ, Menon U. Progress and challenges in screening for early detection of ovarian cancer. Mol Cell Proteomics 2004;3:355-66.
4. Visintin I, Feng Z, Longton G, et al. Diagnostic markers for early detection of ovarian cancer. Clin Cancer Res 2008;14:1065-72. (Pubitemid 351302552)
5. Greene MH, Feng ZD, Gail MH. The importance of test positive predictive value in ovarian cancer screening. Clin Cancer Res 2008;14:7574-5.
6. Cody R, Laramee J, Durst H. Versatile new ion source for the analysis of materials in open air under ambient conditions. Anal Chem 2005;77:2297-302. (Pubitemid 40562727)
7. Harris GA, Nyadong L, Fernández FM. Recent developments in ambient ionization techniques for high-throughput mass spectrometry. Analyst 2008;133:1297-301.
8. Pan Z, Gu H, Talaty N, et al. Principal component analysis of urine metabolites detected by NMR and DESI-MS in patients with inborn errors of metabolism. Anal Bioanal Chem 2007;387:539-49. (Pubitemid 46067471)
9. Zhou M, McDonald JF, Fernández FM. Optimization of a direct analysis in real time/time-of-flight mass spectrometry method for rapid serum metabolomic fingerprinting. J Am Soc Mass Spectrom 2010;21:68-75.
10. Available from: http://www.mathworks.com/products/bioinfo/.
11. See http://web.chemistry.gatech.edu/~fernandez/DART-dataset.mat.
12. Vapnik VN. The nature of statistical learning theory. New York: Springer-Verlag; 2000.
13. Chang C-C, Lin C-J. LIBSVM: a library for support vector machines. 2001, (software available at http://www.csie.ntu.edu.tw/cjlin/libsvm).
14. Available from: http://www.psych.mcgill.ca/misc/fda/software.html (2007).
15. Li L, Tang H, Wu Z, et al. Data mining techniques for cancer detection using serum proteomic profiling. Artif Intell Med 2004;32:71-83. (Pubitemid 39201347)
16. Weston J, Elisseeff A, Scholkopf B, Tipping M, Kaelbling P. Use of the zero-norm with linear models and kernel methods. J Mach Learn Res 2003;3:1439-61.
17. Bradley PS, Mangasarian OL. Feature selection via concave minimization and support vector machines. In: Shavlik JW, editor. Machine learning. Proceedings of the Fifteenth International Conference. 1998, p. 82-90.
18. Mangasarian OL. Exact 1-norm support vector machines via unconstrained convex differentiable minimization. J Mach Learn Res 2007;7:1517-30.
19. Biau G, Bunea F, Wegkamp M. Functional classification in Hilbert spaces. Information Theory, IEEE Transactions 2005;51:2163-72.
20. Ramsay J, Silverman B. Functional data analysis. New York: Springer; 2005.
21. Rossi F, Villa N. Support vector machine for functional data classification. Neurocomputing 2006;69:730-42. (Pubitemid 43230378)
22. Villa N, Rossi F. Recent advances in the use of svm for functional data classification. In: Ferraty F, Dabo-Niang S, editors. Proceedings of 1st International Workshop on Functional and Operatorial Statistics (IWFOS 2008), Toulouse, France. 2008, p. 1-4.
23. Jobson JD. Applied Multivariate Data Analysis. New York: Springer-Verlag; 1992.
24. Johnson RA, Wichern DW. Applied multivariate statistical analysis. Upper Saddle River, NJ: Prentice Hall; 1998.
25. Available from: http://www.hmdb.ca/.
26. Guan W, Zhou M, Hampton CY, et al. Ovarian cancer detection from metabolomic liquid chromatography/mass spectrometry data by support vector machines. BMC Bioinformatics 2009;10:259.
27. Guyon I, Weston J, Barnhill S, Vapnik VN. Gene selection for cancer classification using support vector machines. Mach Learn 2002;46:389-422. (Pubitemid 34129977)
28. Weston J, Mukherjee S, Chapelle O, et al. Feature selection for SVMs. In: Leen TK, Dietterich TG, Tresp V, editors. Neural information processing systems 13. Cambridge, MA: The MIT Press; 2001, p. 668-74.
29. Barker M, Rayens W. Partial least squares for discrimination. J Chemo 2003;17:166-73.
30. Wu BL, Abbott T, Fishman D, et al. Comparison of statistical methods for classification of ovarian cancer using mass spectrometry data. Bioinformatics 2003;19:1636-43. (Pubitemid 37121151)
31. Breiman L. Heuristics of instability and stabilization in model selection. Ann Stat 1996;24:2350-83. (Pubitemid 127035045)
32. Braga-Neto UM, Dougherty ER. Is cross-validation valid for small-sample microarray classification? Bioinformatics 2004;20:374-80.
33. See http://web.chemistry.gatech.edu/~fernandez/suppl-X1.xls.
34. Available from: http://www.genego.com.
35. Sieja K, Stanosz S, von Mach-Szczypinski J, Olewniczak S, Stanosz M. Concentration of histamine in serum and tissues of the primary ductal breast cancers in women. Breast 2005;14:236-41. (Pubitemid 40740437)
36. Morris DL, Adams WJ. Cimetidine and colorectal cancer - old drug, new use? Nat Med 1995;1:1243-44.
37. Medina MA, Quesada AR, Nunez de Castro I, Sanchez-Jimenez F. Histamine, polyamines, and cancer. Biochem Pharmacol 1999;57:1341-4.
38. Denkert C, Budczies J, Kind T, et al. Mass spectrometry-based metabolic profiling reveals different metabolite patterns in invasive ovarian carcinomas and ovarian borderline tumors. Cancer Res 2006;66:10795-804. (Pubitemid 44876975)
39. Sreekumar A, Poisson LM, Rajendiran TM, et al. Metabolomic profiles delineate potential role for sarcosine in prostate cancer progression. Nature 2009;457:910-4.
40. Lee JC, Chen MJ, Chang CH, et al. Plasma amino acid levels in patients with colorectal cancers and liver cirrhosis with hepatocellular carcinoma. Hepatogastroenterology 2003;50:1269-73.
41. Cascino A, Muscaritoli M, Cangiano C, et al. Plasma amino acid imbalance in patients with lung and breast cancer. Anticancer Res 1995;15:507-10.
42. Bowen NJ, Walker LD, Matyunina LV, et al. Gene expression profiling supports the hypothesis that human ovarian surface epithelia are pluripotent and capable of serving as ovarian cancer initiating cells. BMC Med Genomics 2009;2:71.
43. Rajapakse JC, Duan KB, Yeo WK. Proteomic cancer classification with mass spectrometry data. Am J Pharmacogenomics 2005;5:281-92.
44. Anderson NL, Anderson NG. The human plasma proteome - history, character, and diagnostic prospects. Mol Cell Proteomics 2002;1: 845-67.
45. Pearson H. Meet the human metabolome. Nature 2007;446:8.
46. Cody RB. Observation of molecular ions and analysis of nonpolar compounds with the direct analysis in real time ion source. Anal Chem 2009;81:1101-7.
47. Schwartz PE, Taylor KJW. Is early detection of ovarian cancer possible? Ann Med 1995;27:519-28.
48. Butterworth A. Family history as a risk factor for a common complex disease. An independent, epidemiologic assessment of the evidence for familial risk of disease. Cambridge, UK: Public Health Genetics Foundation; 2007.
49. Szkio M, Nieto FJ. Epidemiology. Sudbury, MA: Jones & Bartlett; 2007.
50. U.S. Preventive Services Task Force. Genetic risk assessment and BRCA mutation testing for breast and ovarian cancer susceptibility: recommendation statement (http://www.ahrq.gov/clinic/uspstf/uspsbrgen.htm).