Background: Protein expression profiling for differences indicative of early cancer has promise for improving diagnostics. This report describes the first stage of a National Cancer Institute/Early Detection Research Network-sponsored multiinstitutional evaluation and validation of this approach for detection of prostate cancer. Methods: Two sequential experimental phases were conducted to establish interlaboratory calibration and standardization of the surface-enhanced laser desorption (SELDI) instrumental and assay platform output.

Background: The low specificity of the prostate-specific antigen (PSA) test makes it a poor biomarker for early detection of prostate cancer (PCA). Because single biomarkers most likely will not be found that are expressed by all genetic forms of PCA, we evaluated and developed a proteomic approach for the simultaneous detection and analysis of multiple proteins for the differentiation of PCA from noncancer patients. Methods: Serum samples from 386 men [197 with PCA, 92 with benign prostatic hyperplasia (BPH), and 96 healthy individuals], randomly divided into training (n = 326) and test (n = 60) sets, were analyzed by surface-enhanced laser desorption/ionization (SELDI) mass spectrometry. The 124 peaks detected by computer analyses were analyzed in the training set by a boosting tree algorithm to develop a classifier for separating PCA from the noncancer groups. The classifier was then challenged with the test set (30 PCA samples, 15 BPH samples, 15 samples from healthy men) to determine the validity and accuracy of the classification system. Results: Two classifiers were developed. The AdaBoost classifier completely separated the PCA from the noncancer samples, achieving 100% sensitivity and specificity. The second classifier, the Boosted Decision Stump Feature Selection classifier, was easier to interpret and used only 21 (compared with 74) peaks and a combination of 21 (vs 500) base classifiers to achieve a sensitivity and specificity of 97% for the test set. Conclusions: The high sensitivity and specificity achieved in this study provides support of the potential for SELDI, coupled with a bioinformatics learning algorithm, to improve the early detection/diagnosis of PCA.

Epstein‐Barr virus (EBV) immortalizes resting human B cells very efficiently in vitro. The EBV nuclear protein EBNA2 is absolutely required for this process. It also activates transcription of cellular, as well as viral, genes. It is assumed that EBNA2 contributes to B cell immortalization by its transactivating potential, since its transforming and transactivating functions could not be separated. Mutational analysis of the 80 bp EBNA2 responsive cis‐element within the viral bidirectional LMP/TP2 promoter region identified two sequence elements, which are both essential for transactivation by EBNA2. These sequences harbour putative consensus binding sites for Spi‐1 oncoprotein and recombination signal binding protein RBP‐J kappa, the homologue of Drosophila Suppressor of Hairless. Electrophoretic mobility shift assays demonstrated the high affinity binding of Spi‐1 and Spi‐B, both members of the Ets family of transcription factors, to one sequence element. The other element bound RBP‐J kappa with low affinity. In addition, co‐transfections showed that the replacement of the Spi‐1/Spi‐B binding site in the bi‐directional LMP/TP2 promoter by the analogous SV40 Spi‐1 responsive element did not impair its function on EBNA2‐mediated transactivation. It is concluded that the transcriptional regulators Spi‐1 and Spi‐B as well as RBP‐J kappa play an essential role in transactivating the LMP/TP2 promoter by EBNA2 and therefore in the immortalization of B cells by EBV.

Proteomic profiling of serum is an emerging technique to identify new biomarkers indicative of disease severity and progression. The objective of our study was to assess the use of surfaceenhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS) to identify multiple serum protein biomarkers for detection of liver disease progression to hepatocellular carcinoma (HCC). A cohort of 170 serum samples obtained from subjects in the United States with no liver disease (n ‫؍‬ 39), liver diseases not associated with cirrhosis (n ‫؍‬ 36), cirrhosis (n ‫؍‬ 38), or HCC (n ‫؍‬ 57) were applied to metal affinity protein chips for protein profiling by SELDI-TOF MS. Across the four test groups, 38 differentially expressed proteins were used to generate multiple decision classification trees to distinguish the known disease states. Analysis of a subset of samples with only hepatitis C virus (HCV)-related disease was emphasized. The serum protein profiles of control patients were readily distinguished from each HCV-associated disease state. Two-way comparisons of chronic hepatitis C, HCV cirrhosis, or HCV-HCC versus healthy had a sensitivity/specificity range of 74% to 95%. For distinguishing chronic HCV from HCV-HCC, a sensitivity of 61% and a specificity of 76% were obtained. However, when the values of known serum markers ␣ fetoprotein, des-gamma carboxyprothrombin, and GP73 were combined with the SELDI peak values, the sensitivity and specifity improved to 75% and 92%, respectively. In conclusion, SELDI-TOF MS serum profiling is able to distinguish HCC from liver disease before cirrhosis as well as cirrhosis, especially in patients with HCV infection compared with other etiologies. (HEPATOLOGY 2005;41:634-642.) T he incidence of hepatocellular carcinoma (HCC) continues to increase in the United States, 1 while, unfortunately, patient survival with HCC has only marginally improved over the last 20 years. Between 1981 and 1998, the 5-year survival rate only rose from 2% to 5%. 2 The poor survival rate is in part related to the diagnosis of HCC at advanced stages, where effective therapies are lacking. 3 Surveillance of patients at the highest risk for developing HCC (i.e., patients with cirrhosis) is an important strategy that can potentially decrease the cancer-related mortality rate. Although HCC meets the criteria of a tumor that would benefit from a surveillance program, the poor sensitivity and specificity of currently available tools has prevented widespread implementation of HCC surveillance. For example, ␣ fetoprotein (AFP) has been the serum marker that is most widely used for diagnosis as well as surveillance of HCC. 4,5 However, AFP levels may be normal in up to 40% of patients with HCC, particularly during the early stages (low sensitivity). 6 Furthermore, elevated AFP levels may be seen in patients with cirrhosis or exacerbations of chronic hepatitis (low specificity). 7 Prospective studies evaluating the performance characteristics of AFP for HCC surveillance