PURPOSE AND EXPERIMENTAL DESIGN: Cancer sera contain antibodies which react with a unique group of autologous cellular antigens called tumor-associated antigens (TAAs), but the low frequency of positive reactions against any individual antigen has precluded use of autoantibodies as useful diagnostic markers. With enzyme immunoassay, we examined antibody frequencies to a panel of seven TAAs, c-myc, cyclin B1, IMP1, Koc, p53, p62, and survivin, in 527 cancer patients (64 breast cancer patients, 45 colorectal cancers, 91 gastric cancers, 65 hepatocellular carcinomas, 56 lung cancers, and 206 prostate cancers), and 346 normals. We used recursive partitioning to assess whether we could accurately classify individuals as either cancer patients or normals on the basis of the profile of antibody reactivity to the seven TAAs for each individual. RESULTS: Recursive partitioning resulted in the selection of subsets of the seven-panel TAA, which differentiated between tumors and controls, and these subsets were unique to each cancer cohort. The classification trees had sensitivities ranging from 0.77 to 0.92 and specificities ranging from 0.85 to 0.91 in the cancer cohorts when normal means +2 SDs were used as standard cutoffs for immunoassay positivity. Antibody to cyclin B1 was the initial discriminating node for gastric and lung cancers, and for hepatocellular carcinoma, and was a subsequent discriminating node in all of the other cancer cohorts. c-myc was the initial discriminating node in breast cancer, p62 in prostate cancer, and IMP1 in colon cancer. Recursive partitioning demonstrated that no more than three of the seven TAAs were needed for any cancer cohort to arrive at these levels of sensitivity and specificity. CONCLUSIONS: This initial study shows that multiple antigen miniarrays can provide accurate and valuable tools for cancer detection and diagnosis. Performance of the miniarrays might be enhanced by other combinations of TAAs appropriately selected for different cancer cohorts.
PURPOSE AND EXPERIMENTAL DESIGN:Cancer sera contain antibodies which react with a unique group of autologous cellular antigens called tumor-associated antigens (TAAs), but the low frequency of positive reactions against any individual antigen has precluded use of autoantibodies as useful diagnostic markers. With enzyme immunoassay, we examined antibody frequencies to a panel of seven TAAs, c-myc, cyclin B1, IMP1, Koc, p53, p62, and survivin, in 527 cancerpatients (64 breast cancerpatients, 45 colorectal cancers, 91 gastric cancers, 65 hepatocellular carcinomas, 56 lung cancers, and 206 prostate cancers), and 346 normals. We used recursive partitioning to assess whether we could accurately classify individuals as either cancerpatients or normals on the basis of the profile of antibody reactivity to the seven TAAs for each individual. RESULTS: Recursive partitioning resulted in the selection of subsets of the seven-panel TAA, which differentiated between tumors and controls, and these subsets were unique to each cancer cohort. The classification trees had sensitivities ranging from 0.77 to 0.92 and specificities ranging from 0.85 to 0.91 in the cancer cohorts when normal means +2 SDs were used as standard cutoffs for immunoassay positivity. Antibody to cyclin B1 was the initial discriminating node for gastric and lung cancers, and for hepatocellular carcinoma, and was a subsequent discriminating node in all of the other cancer cohorts. c-myc was the initial discriminating node in breast cancer, p62 in prostate cancer, and IMP1 in colon cancer. Recursive partitioning demonstrated that no more than three of the seven TAAs were needed for any cancer cohort to arrive at these levels of sensitivity and specificity. CONCLUSIONS: This initial study shows that multiple antigen miniarrays can provide accurate and valuable tools for cancer detection and diagnosis. Performance of the miniarrays might be enhanced by other combinations of TAAs appropriately selected for different cancer cohorts.
Authors: Karen S Anderson; Sahar Sibani; Garrick Wallstrom; Ji Qiu; Eliseo A Mendoza; Jacob Raphael; Eugenie Hainsworth; Wagner R Montor; Jessica Wong; Jin G Park; Naa Lokko; Tanya Logvinenko; Niroshan Ramachandran; Andrew K Godwin; Jeffrey Marks; Paul Engstrom; Joshua Labaer Journal: J Proteome Res Date: 2010-11-23 Impact factor: 4.466
Authors: James A Koziol; Anne C Feng; Zhenyu Jia; Yipeng Wang; Seven Goodison; Michael McClelland; Dan Mercola Journal: Bioinformatics Date: 2008-07-15 Impact factor: 6.937
Authors: William N Rom; Judith D Goldberg; Doreen Addrizzo-Harris; Heather N Watson; Michael Khilkin; Alissa K Greenberg; David P Naidich; Bernard Crawford; Ellen Eylers; Daorong Liu; Eng M Tan Journal: BMC Cancer Date: 2010-05-26 Impact factor: 4.430