BACKGROUND: Diagnosis of nasopharyngeal carcinoma (NPC) at an early disease stage is important for successful treatment and improving the outcome of patients. The use of serum protein profiles and a classification tree algorithm were explored to distinguish NPC from noncancer. METHODS: Serum samples were applied to metal affinity protein chips to generate mass spectra by surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS). Protein peak identification and clustering were performed using the Biomarker Wizard software. Proteomic spectra of serum samples from 50 NPC patients and 54 noncancer controls were used as a training set and a classification tree with 6 distinct protein masses was generated by using Biomarker Pattern software. The validity of the classification tree was then challenged with a blind test set including another 20 NPC patients and 25 noncancer controls. RESULTS: The software identified an average of 93 mass peaks/spectrum and 6 of the identified peaks were used to construct the classification tree. The classification tree correctly determined 83% (123 of 149) of the test samples with 83% (58 of 70) of the NPC samples and 82% (65 of 79) of the noncancer samples. In a combination of the serum protein profiles with Epstein-Barr (EBV) nuclear antigen 1 (EBNA1 IgA) test, the diagnostic sensitivity and specificity were increased to 99% and 96%, respectively. CONCLUSIONS: The results suggest that SELDI-TOF-MS serum protein profiles could discriminate NPC from noncancer. The combination of serum protein profiles with an EBV antibody serology test could further improve the accuracy of NPC screening. Copyright 2006 American Cancer Society.
BACKGROUND: Diagnosis of nasopharyngeal carcinoma (NPC) at an early disease stage is important for successful treatment and improving the outcome of patients. The use of serum protein profiles and a classification tree algorithm were explored to distinguish NPC from noncancer. METHODS: Serum samples were applied to metal affinity protein chips to generate mass spectra by surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS). Protein peak identification and clustering were performed using the Biomarker Wizard software. Proteomic spectra of serum samples from 50 NPC patients and 54 noncancer controls were used as a training set and a classification tree with 6 distinct protein masses was generated by using Biomarker Pattern software. The validity of the classification tree was then challenged with a blind test set including another 20 NPC patients and 25 noncancer controls. RESULTS: The software identified an average of 93 mass peaks/spectrum and 6 of the identified peaks were used to construct the classification tree. The classification tree correctly determined 83% (123 of 149) of the test samples with 83% (58 of 70) of the NPC samples and 82% (65 of 79) of the noncancer samples. In a combination of the serum protein profiles with Epstein-Barr (EBV) nuclear antigen 1 (EBNA1 IgA) test, the diagnostic sensitivity and specificity were increased to 99% and 96%, respectively. CONCLUSIONS: The results suggest that SELDI-TOF-MS serum protein profiles could discriminate NPC from noncancer. The combination of serum protein profiles with an EBV antibody serology test could further improve the accuracy of NPC screening. Copyright 2006 American Cancer Society.
Authors: Lei Chen; David W Y Ho; Nikki P Y Lee; Stella Sun; Brian Lam; Kwong-Fai Wong; Xin Yi; George K Lau; Eddy W Y Ng; Terence C W Poon; Paul B S Lai; Zongwei Cai; Jirun Peng; Xisheng Leng; Ronnie T P Poon; John M Luk Journal: Ann Surg Oncol Date: 2010-03-31 Impact factor: 5.344
Authors: Chandrika J Piyathilake; Denise K Oelschlager; Sreelatha Meleth; Edward E Partridge; William E Grizzle Journal: Cancer Inform Date: 2007-02-27