RATIONALE: Phenotypic and genotypic heterogeneity of lung cancer likely precludes the identification of a single predictive marker and suggests the importance of identifying and measuring multiple markers. OBJECTIVES: We describe the use of a fluorescent protein microarray to identify and measure multiple non-small cell lung cancer-associated antibodies and show how simultaneous measurements can be combined into a single diagnostic assay. METHODS: T7 phage cDNA libraries of non-small cell lung cancer were first biopanned with plasma samples from normal subjects and patients with non-small cell lung cancer to enrich the component of tumor-associated proteins, and then applied to microarray slides. Two hundred twelve immunogenic phage-expressed proteins were identified from roughly 4,000 clones, using high-throughput screening with patient plasmas and assayed with 40 cancer and 41 normal plasma samples. Twenty patient and 21 normal plasma samples were randomly chosen and used for statistical determination of the predictive value of each putative marker. Statistical analysis identified antibody reactivity to seven unique phage-expressed proteins that were significantly different (p < 0.01) between patient and normal groups. The remaining 20 patient and 20 normal plasma samples were used as an independent test of the predictive ability of the selected markers. MAIN RESULTS: Measurements of the 5 most predictive phage proteins were combined in a logistic regression model that achieved 90% sensitivity and 95% specificity in prediction of patient samples, whereas leave-one-out statistical analysis achieved 88.9% diagnostic accuracy among all 81 samples. CONCLUSION: Our data indicate that antibody profiling is a promising approach that could achieve high diagnostic accuracy for non-small cell lung cancer.
RATIONALE: Phenotypic and genotypic heterogeneity of lung cancer likely precludes the identification of a single predictive marker and suggests the importance of identifying and measuring multiple markers. OBJECTIVES: We describe the use of a fluorescent protein microarray to identify and measure multiple non-small cell lung cancer-associated antibodies and show how simultaneous measurements can be combined into a single diagnostic assay. METHODS: T7 phage cDNA libraries of non-small cell lung cancer were first biopanned with plasma samples from normal subjects and patients with non-small cell lung cancer to enrich the component of tumor-associated proteins, and then applied to microarray slides. Two hundred twelve immunogenic phage-expressed proteins were identified from roughly 4,000 clones, using high-throughput screening with patient plasmas and assayed with 40 cancer and 41 normal plasma samples. Twenty patient and 21 normal plasma samples were randomly chosen and used for statistical determination of the predictive value of each putative marker. Statistical analysis identified antibody reactivity to seven unique phage-expressed proteins that were significantly different (p < 0.01) between patient and normal groups. The remaining 20 patient and 20 normal plasma samples were used as an independent test of the predictive ability of the selected markers. MAIN RESULTS: Measurements of the 5 most predictive phage proteins were combined in a logistic regression model that achieved 90% sensitivity and 95% specificity in prediction of patient samples, whereas leave-one-out statistical analysis achieved 88.9% diagnostic accuracy among all 81 samples. CONCLUSION: Our data indicate that antibody profiling is a promising approach that could achieve high diagnostic accuracy for non-small cell lung cancer.
Authors: P Foa; M Fornier; R Miceli; E Seregni; L Santambrogio; M Nosotti; I Cataldo; M Sala; S Caldiera; E Bombardieri Journal: Anticancer Res Date: 1999 Jul-Aug Impact factor: 2.480
Authors: J Pei; B R Balsara; W Li; S Litwin; E Gabrielson; M Feder; J Jen; J R Testa Journal: Genes Chromosomes Cancer Date: 2001-07 Impact factor: 5.006
Authors: F M Brichory; D E Misek; A M Yim; M C Krause; T J Giordano; D G Beer; S M Hanash Journal: Proc Natl Acad Sci U S A Date: 2001-08-14 Impact factor: 11.205
Authors: D Jäger; E Stockert; A O Güre; M J Scanlan; J Karbach; E Jäger; A Knuth; L J Old; Y T Chen Journal: Cancer Res Date: 2001-03-01 Impact factor: 12.701
Authors: Christina Backes; Nicole Ludwig; Petra Leidinger; Christian Harz; Jana Hoffmann; Andreas Keller; Eckart Meese; Hans-Peter Lenhof Journal: BMC Genomics Date: 2011-07-04 Impact factor: 3.969
Authors: Eda Çelik; Anne A Ollis; Yi Lasanajak; Adam C Fisher; Göksu Gür; David F Smith; Matthew P DeLisa Journal: Biotechnol J Date: 2014-10-31 Impact factor: 4.677
Authors: Petra Leidinger; Andreas Keller; Sabrina Heisel; Nicole Ludwig; Stefanie Rheinheimer; Veronika Klein; Claudia Andres; Andrea Staratschek-Jox; Jürgen Wolf; Erich Stoelben; Bernhard Stephan; Ingo Stehle; Jürg Hamacher; Hanno Huwer; Hans-Peter Lenhof; Eckart Meese Journal: Respir Res Date: 2010-02-10
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
Authors: Rachel M Ostroff; William L Bigbee; Wilbur Franklin; Larry Gold; Mike Mehan; York E Miller; Harvey I Pass; William N Rom; Jill M Siegfried; Alex Stewart; Jeffrey J Walker; Joel L Weissfeld; Stephen Williams; Dom Zichi; Edward N Brody Journal: PLoS One Date: 2010-12-07 Impact factor: 3.240