Early detection of recurrent breast cancer using metabolite profiling

Cancer Research

Volumn 70, Issue 21, 2010, Pages 8309-8318

  Asiago, Vincent M ^a   Alvarado, Leiddy Z ^a   Shanaiah, Narasimhamurthy ^b   Gowda, G A Nagana ^a   Owusu Sarfo, Kwadwo ^a   Ballas, Robert A ^c   Raftery, Daniel ^a,b  

^a PURDUE UNIVERSITY   (United States)

^b Matrix Bio Inc   (United States)

^c Biomarker Associates Inc   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

3 HYDROXY 2 METHYLBUTYRIC ACID; 3 HYDROXYBUTYRIC ACID; ACETURIC ACID; AZELAIC ACID; CA 125 ANTIGEN; CA 27-29 ANTIGEN; CARCINOEMBRYONIC ANTIGEN; CHOLINE; ESTROGEN RECEPTOR; FORMIC ACID; GLUTAMIC ACID; GLYCINE DERIVATIVE; HISTIDINE; LACTIC ACID; PROGESTERONE RECEPTOR; PROLINE; TUMOR MARKER; TYROSINE; UNCLASSIFIED DRUG;

ADULT; AGED; ARTICLE; BONE; BRAIN; BREAST; BREAST CANCER; BREAST METASTASIS; CANCER LOCALIZATION; CANCER PATIENT; CANCER RECURRENCE; CANCER STAGING; CONTROLLED STUDY; DIAGNOSTIC ACCURACY; DISCRIMINANT ANALYSIS; DISEASE MARKER; EARLY DIAGNOSIS; FEMALE; HUMAN; LIVER; LUNG; LYMPH; MAJOR CLINICAL STUDY; MASS FRAGMENTOGRAPHY; METABOLITE; METABOLITE PROFILING; METABOLOMICS; MULTIVARIATE ANALYSIS; PRIORITY JOURNAL; PROTON NUCLEAR MAGNETIC RESONANCE; RECEIVER OPERATING CHARACTERISTIC; RECURRENT CANCER; SIGNAL TRANSDUCTION; SKIN;

ADULT; AGED; BREAST NEOPLASMS; EARLY DIAGNOSIS; FEMALE; FOLLOW-UP STUDIES; GAS CHROMATOGRAPHY-MASS SPECTROMETRY; HUMANS; LONGITUDINAL STUDIES; MAGNETIC RESONANCE SPECTROSCOPY; METABOLOME; METABOLOMICS; MIDDLE AGED; NEOPLASM RECURRENCE, LOCAL; PROGNOSIS; RETROSPECTIVE STUDIES; REVERSE TRANSCRIPTASE POLYMERASE CHAIN REACTION; RNA, MESSENGER; SENSITIVITY AND SPECIFICITY; SURVIVAL RATE; TUMOR MARKERS, BIOLOGICAL;

EID: 78449285472     PISSN: 00085472     EISSN: 15387445     Source Type: Journal    
DOI: 10.1158/0008-5472.CAN-10-1319     Document Type: Article

References (46)

1. American Cancer Society. Breast cancer facts and figures 2009-2010. Atlanta (GA): American Cancer Society; 2009.
2. Jemal A, Siegel R, Ward E, Hao Y, Xu J, Thun MJ. Cancer statistics, 2009. CA Cancer J Clin 2009;59:225-49.
3. Brewster AM, Hortobagyi GN, Broglio KR, et al. Residual risk of breast cancer recurrence 5 years after adjuvant therapy. J Natl Cancer Inst 2008;100:1179-83.
4. Megha T, Neri A, Malagnino V, et al. Traditional and new prognosticators in breast cancer: Nottingham index, Mib-1 and estrogen receptor signaling remain the best predictors of relapse and survival in a series of 289 cases. Cancer Biol Ther 2010;9:266-73.
5. Lu WL, Jansen L, Post WJ, Bonnema J, Van de Velde JC, De Bock GH. Impact on survival of early detection of isolated breast recurrences after the primary treatment for breast cancer: a meta-analysis. Breast Cancer Res Treat 2009;14:403-12.
6. Houssami N, Ciatto S, Martinelli F, Bonardi R, Duffy SW. Early detection of second breast cancers improves prognosis in breast cancer survivors. Ann Oncol 2009;20:1505-10.
7. Pivot X, Asmar L, Hortobagyi GN, Theriault R, Pastorini F, Buzdar A. A retrospective study of first indicators of breast cancer recurrence. Oncology 2000;58:185-90.
8. Ebeling FG, Stieber P, Untch M, et al. Serum CEA and CA 15-3 as prognostic factors in primary breast cancer. Br J Cancer 2002;86:1217-22.
9. Dhafir A, Gabrielle K, Eddie M, et al. CA 15-3 is predictive of response and disease recurrence following treatment in locally advanced breast cancer. BMC Cancer 2006;6:220.
10. Gion M, Mione R, Leon AE, Dittadi R. Comparison of the diagnostic accuracy of CA 27.29 and CA15.3 in primary breast cancer. Clin Chem 1999;45:630-7.
11. Duffy MJ. Serum tumor markers in breast cancer: are they of clinical value? Clin Chem 2006;52:345-51.
12. Lumachi F, Basso SMM, Basso U. Breast cancer recurrence: role of serum tumor markers CEA and CA 15-3. In: Hayat MA, editor. Methods of cancer diagnosis, therapy and prognosis. Netherlands: Springer; 2008, p. 109-15.
13. Lumachi F, Ermani M, Brandes AA, Basso S, Basso U, Boccagni P. Predictive value of different prognostic factors in breast cancer recurrences: multivariate analysis using a logistic regression model. Anticancer Res 2001;6:4105-8.
14. Nicholson JK, Lindon JC, Holmes E. "Metabonomics": understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data. Xenobiotica 1999;29:1181-9.
15. 1H-NMR-based metabonomics. Int J Cancer 2005;113:782-8.
16. Gowda GAN, Zhang S, Gu H, Asiago V, Shanaiah N, Raftery D. Metabolomics-based methods for early disease diagnostics: a review. Expert Rev Mol Diagn 2008;8:617-33.
17. Pan Z, Raftery D. Combining NMR spectroscopy and mass spectrometry in metabolomics. Anal Bioanal Chem 2007;387:525-7.
18. Holmes E, Wilson ID, Nicholson JK. Metabolic phenotyping in health and disease. Cell 2008;134:714-7.
19. Clayton TA, Lindon JC, Cloarec O, et al. Pharmaco-metabonomic phenotyping and personalized drug treatment. Nature 2006;440:1073-7.
20. 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.
21. Sreekumar A, Poisson LM, Rajendiran TM, et al. Metabolomic profiles delineate potential role for sarcosine in prostate cancer progression. Nature 2009;457:910-4.
22. Shanaiah N, Zhang S, Desilva MA, Raftery D. NMR-based metabolomics for biomarker discovery. In: Wang F, editor. Methods in pharmacology and toxicology: biomarker methods in drug discovery and development. Totowa (NJ): Humana Press; 2008, p. 341-68.
23. Bursac Z, Gauss CH, Williams DK, Hosmer DW. Purposeful selection of variables in logistic regression. Source Code Biol Med 2008;3:17.
24. Kiezun A, Lee ITA, Shomron N. Evaluation of optimization techniques for variable selection in logistic regression applied to diagnosis of myocardial infarction. Bioinformation 2009;3:311-3.
25. Chen MH, Dey DK. Variable selection for multivariate logistic regression models. J Stat Plan Inference 2003;111:37-55.
26. Markley JL, Anderson ME, Cui Q, et al. New bioinformatics resources for metabolomics. Pacific Symp Biocomp 2007;12:157-68.
27. Wishart DS, Tzur D, Knox C, et al. HMDB: the human metabolome database. Nucleic Acids Res 2007;35:D521-6.
28. Chan DW, Beveridge RA, Muss H, et al. Use of Truquant BR radioimmunoassay for early detection of breast cancer recurrence in patients with stage II and stage III disease. J Clin Oncol 1997;15:2322-8.
29. Cheung KL, Graves CR, Robertson JF. Tumor marker measurements in the diagnosis and monitoring of breast cancer. Cancer Treat Rev 2000;26:91-102.
30. Harris L, Fritsche H, Mennel R, et al. American Society of Clinical Oncology 2007 update of recommendations for the use of tumor markers in breast cancer. J Clin Oncol 2007;25:5287-312.
31. Giskeødegård GF, Grinde MT, Sitter B, et al. Multivariate modeling and prediction of breast cancer prognostic factors using MR metabolomics. J Proteome Res 2010;9:972-9.
32. 1H nuclear magnetic resonance visible polyunsaturated fatty acids in BT4C gliomas undergoing ganciclovir-thymidine kinase gene therapy-induced programmed cell death. Cancer Res 2003;63:3195-201.
33. 1H nuclear magnetic resonance diffusion spectroscopy of BT4C rat glioma during thymidine kinase-mediated gene therapy in vivo: identification of apoptotic response. Cancer Res 1998;58:3791-9.
34. Griffiths JR, McSheehy PM, Robinson SP, et al. Metabolic changes detected by in vivo magnetic resonance studies of HEPA-1 wild-type tumors and tumors deficient in hypoxia-inducible factor-1h (HIF-1h): evidence of an anabolic role for the HIF-1 pathway. Cancer Res 2002;62:688-95.
35. Florian CL, Preece NE, Bhakoo KK, Williams SR, Noble MD. Cell type-specific fingerprinting of meningioma and meningeal cells by proton nuclear magnetic resonance spectroscopy. Cancer Res 1995;55:420-7.
36. Gribbestad IS, Sitter B, Lundgren S, Krane J, Axelson D. Metabolite composition in breast tumors examined by proton nuclear magnetic resonance spectroscopy. Anticancer Res 1999;19:1737-46.
37. Sitter B, Lundgren S, Bathen TF, Halgunset J, Fjosne HE, Gribbestad IS. Comparison of HR MAS MR spectroscopic profiles of breast cancer tissue with clinical parameters. NMR Biomed 2006;19:30-40.
38. Sitter B, Sonnewald U, Spraul M, Fjösne HE, Gribbestad IS. High-resolution magic angle spinning MRS of breast cancer tissue. NMR Biomed 2002;15:327-37.
39. 1H NMR metabonomics to breast cancer characterization and detection. Prog NMR Spectrosc 2005;47:165-74.
40. Cheng LL, Chang IW, Smith BL, Gonzalez RG. Evaluating human breast ductal carcinomas with high-resolution magic-angle spinning proton magnetic resonance spectroscopy. J Magn Reson 1998;35:194-202.
41. Bathen TF, Jensen LR, Sitter B, et al. MR-determined metabolic phenotype of breast cancer in prediction of lymphatic spread, grade, and hormone status. Breast Cancer Res Treat 2006;104:181-9.
42. Yang C, Richardson AD, Smith JW, Osterman A. Comparative metabolomics of breast cancer. Pac Symp Biocomput 2007;12:181-92.
43. 1H MRS of normal breast and breast tumors using a dedicated double breast coil. J Magn Reson Imaging 1998;8:1191-7.
44. Lai HS, Lee JC, Lee PH, Wang ST, Chen WJ. Plasma free amino acid profile in cancer patients. Semin Cancer Biol 2005;15:267-76.
45. Wanatabe A, Higashi T, Sakata T, Nagashima H. Serum amino acid levels in patients with hepatocellular carcinoma. Cancer 1984;54:1875-82.
46. Kubota A, Meguid MM, Hitch DC. Amino acid profiles correlate diagnostically with organ site in three kinds of malignant tumors. Cancer 1992;69:2343-8.