Ovarian cancer is usually found at a late stage when the prognosis is often bad. Relative survival rates decrease with tumor stage or grade, and the 5-year survival rate for women with carcinoma is only 38%. Thus, there is a great need to find biomarkers that can be used to carry out routine screening, especially in high-risk patient groups. Here, we present a large-scale study of 64 tissue samples taken from patients at all stages and show that we can identify statistically valid markers using nonsupervised methods that distinguish between normal, benign, borderline, and malignant tissue. We have identified 217 of the significantly changing protein spots. We are expressing and raising antibodies to 35 of these. Currently, we have validated 5 of these antibodies for use in immunohistochemical analysis using tissue microarrays of healthy and diseased ovarian, as well as other, human tissues.

Two-dimensional SDS-PAGE gel electrophoresis using post-run staining is widely used to measure the abundances of thousands of protein spots simultaneously. Usually, the protein abundances of two or more biological groups are compared using biological and technical replicates. After gel separation and staining, the spots are detected, spot volumes are quantified, and spots are matched across gels. There are almost always many missing values in the resulting data set. The missing values arise either because the corresponding proteins have very low abundances (or are absent) or because of experimental errors such as incomplete/over focusing in the first dimension or varying run times in the second dimension as well as faulty spot detection and matching. In this study, we show that the probability for a spot to be missing can be modeled by a logistic regression function of the logarithm of the volume. Furthermore, we present an algorithm that takes a set of gels with technical and biological replicates as input and estimates the average protein abundances in the biological groups from the number of missing spots and measured volumes of the present spots using a maximum likelihood approach. Confidence intervals for abundances and p-values for differential expression between two groups are calculated using bootstrap sampling. The algorithm is compared to two standard approaches, one that discards missing values and one that sets all missing values to zero. We have evaluated this approach in two different gel data sets of different biological origin. An R-program, implementing the algorithm, is freely available at http://bioinfo.thep .lu.se/MissingValues2Dgels.html.

Abstract. Some clinical results indicate that somatostatin (sms) might be useful in the treatment of advanced prostate cancer (HRPC). Because of its transient in vivo half-life only more stable derivatives of sms are of interest in this context. Recent studies have shown that natural sms can be conjugated to a carbohydrate (smsdx) with preservation of sms-like effects on the prostatic tumor cell proteome. The present study identifies some of the affected proteins in an effort to elucidate pathways and proteins that might be of importance for the potential usefulness of sms treatment in HRPC. After incubating the LNCaP cell-line with sms14/smsdx, comparative proteomics was used for analysing and identifying affected proteins. Protein expression patterns were analysed with two-dimensional polyacrylamide gel electrophoresis and mass spectrometry. Catalytic mitochondrial and mitochondrial-associated proteins were significantly affected (fold change ~2 or higher) and they were in general up-regulated. Apoptosis-related proteins were both up-regulated (VDAC1, VDAC2) and down-regulated (PRDX2, TCTP). The fold change was >2 for PRDX2 and <2 for the others. There was a strong agreement between sms and smsdx on the up-and down-regulation of proteins. Sms/smsdx triggered up-regulation of catalytic mitochondrial proteins and seemed to affect apoptosis-related proteins. This could indicate important pathways on which smsdx might be able to curb the progression of HRPC. The results encourage a pending clinical phase II study.