AACR Annual Meeting-- Apr 12-16, 2008; San Diego, CA 4435 Prostate cancer (PCA) is the most frequent tumor type in males and a major cause of death due to malignancy. Although the diagnosis of prostate cancer using serum based PSA assays has improved during the past decades, surprisingly little is currently known about early alterations in prostate cancer development. It is highly likely, that currently detectable morphological features of non-invasive neoplasia are preceded by specific molecular processes and that the volume of molecularly altered areas is substantially larger than the histologically manifest cancer or even the PIN area therein. The identification of such molecular changes could lead to novel strategies in the clinical management of cancer. We performed large-scale gene expression analysis on tissue samples, followed by real-time PCR validation on 114 prostate biopsy samples. We identified six genes, which displayed significant differential expression between morphologically normal prostate biopsy tissues from three groups of individuals differing by their risks of having prostate cancer. These results were independent of age, PSA (prostate specific antigen), frequency and timing of previous prostate biopsies, tissue composition (gland vs. stroma) and tumor stage or grade. In univariate logistic regression analyses the transcription levels of these genes were found to be highly discriminative markers to estimate the presence of prostate cancer based on the analysis of histologically tumor-free prostate biopsies. Our results suggest a measurable molecular cancer phenotype in histologically normal prostate tissue indicating the presence of prostate cancer elsewhere in the organ.

The matter of concern are algorithms for the discrimination of direct from indirect regulatory effects from an interaction graph built up by error-prone measurements. Many of these algorithms can be cast as a rule for the removal of a single edge of the graph, such that the remaining graph is still consistent with the data. A set of mild conditions is given under which iterated application of such a rule leads to a unique minimal consistent graph. We show that three of the common methods for direct interactions search fulfill these conditions, thus providing a justification of their use. The main issues a reconstruction algorithm has to deal with, are the noise in the data, the presence of regulatory cycles, and the direction of the regulatory effects. We introduce a novel rule that, in contrast to the previously mentioned methods, simultaneously takes into account all these aspects. An efficient algorithm for the computation of the minimal graph is given, whose time complexity is cubic in the number of vertices of the graph. Finally, we demonstrate the utility of our method in a simulation study.

Sunitinib, pazopanib, sorafenib, axitinib and bevacizumab are the five recommended antiangiogenic agents in first-line therapy for metastatic renal cell carcinoma (mRCC). Because these drugs underwent simultaneous clinical development, no direct efficacy and safety comparison was ever conducted, thus preventing optimal therapy choices.We performed a traditional and network meta-analysis to evaluate the efficacy and safety of mRCC-recommended first-line antiangiogenic agents. After a systematic review of Medline and Embase up to July 2014, we identified randomized clinical trials (RCTs) evaluating the outcomes of mRCC patients treated with sunitinib, pazopanib, sorafenib, axitinib and bevacizumab as first-line treatment. Endpoints of interest were response rate, progression-free survival (PFS), overall survival (OS), and safety.We screened 769 abstracts and included nine RCTs with a total of 4282 patients. In the weighted pooled analysis, first-line antiangiogenic agents showed significant improvement in PFS (HR = 0.6; 95% IC, 0.51–0.72) and OS (HR = 0.85; 95% IC, 0.78–0.93) compared to control (placebo or interferon-alpha2a (INF)). Network meta-analysis showed no significant differences among antiangiogenic drugs in 6-month PFS, 1-year OS, disease control rate and drug-related safety for all-grade hypertension, diarrhea, weight-loss, nausea or anorexia. However, pazopanib showed a lower incidence of fatigue, anemia and hand foot skin reaction.This meta-analysis confirms the benefits of first-line antiangiogenic therapy in mRCC, with an improvement in OS. Sunitinib, pazopanib, axitinib and bevacizumab + INF offer similar efficacy but different safety profiles which can help clinicians to better personalize treatment decisions in patients with mRCC.

Human MOB1 (hMOB1) is a recently isolated gene that is a human homologue of the Schizosaccharomyces mitotic checkpoint gene MOB1. The loss of checkpoint control in mammalian cells results in genomic instability, leading to the amplification, rearrangement, or loss of chromosomes, events associated with tumor progression. We hypothesized that hMOB1 might be expressed in non–small-cell lung cancer (NSCLC).We attempted to determine the influence of hMOB1 expression on clinicopathologic features in patients with NSCLC who had undergone surgery. Expression of hMOB1 messenger RNA (mRNA) was evaluated by reverse transcription-polymerase chain reaction in 60 NSCLCs and adjacent histologic normal lung samples using LightCycler®.Human MOB1/glyseraldehyde-3-phosphate dehydrogenase (GAPDH) mRNA expression was significantly decreased in the tumor of lung cancer tissue (3.347 ± 4.306) compared with normal lung tissue (4.833 ± 4.306; P = 0.0437), although 22 of 60 lung cancer tissue samples had > 1 tumor-normal ratio of MOB1/GAPDH mRNA expression. There was no relationship between hMOB1 gene expression and age, sex, pathologic stages, or pN status. However, decreased hMOB1/GAPDH expression was especially seen in pT1 lung cancer (tumor-normal ratio; 0.318 ± 0.328) when compared with pT4 lung cancer (1.915 ± 1.895; P = 0.0362).The decreased expression of hMOB1 mRNA might be the early phase phenomenon for tumor invasion from NSCLC. Alternatively, loss of mitotic checkpoint might play a role in oncogenesis for lung cancer.

OBJECTIVE:To characterize the gene expression profile and determine potential diagnostic markers and therapeutic targets in pigmented villonodular synovitis (PVNS).METHODS:Gene expression patterns in 11 patients with PVNS, 18 patients with rheumatoid arthritis (RA), and 19 patients with osteoarthritis (OA) were investigated using genome-wide complementary DNA microarrays. Validation of differentially expressed genes was performed by real-time quantitative polymerase chain reaction and immunohistochemical analysis on tissue arrays (80 patients with PVNS, 51 patients with RA, and 20 patients with OA).RESULTS:The gene expression profile in PVNS was clearly distinct from those in RA and OA. One hundred forty-one up-regulated genes and 47 down-regulated genes were found in PVNS compared with RA, and 153 up-regulated genes and 89 down-regulated genes were found in PVNS compared with OA (fold change > or = 1.5; Q < or = 0.001). Genes differentially expressed in PVNS were involved in apoptosis regulation, matrix degradation, and inflammation (ALOX5AP, ATP6V1B2, CD53, CHI3L1, CTSL, CXCR4, HSPA8, HSPCA, LAPTM5, MMP9, MOAP1, and SPP1).CONCLUSION:The gene expression signature in PVNS is similar to that of activated macrophages and is consistent with the local destructive course of the disease. The gene and protein expression patterns suggest that the ongoing proliferation in PVNS is sustained by apoptosis resistance. This result suggests the possibility of a potential novel therapeutic intervention against PVNS.

To process large numbers of samples in parallel is one potential of protein microarrays for research and diagnostics. However, the application of protein arrays is currently hampered by the lack of comprehensive technological knowledge about the suitability of 2-D and 3-D slide surface coatings. We have performed a systematic study to analyze how both surface types perform in combination with different fluorescent dyes to generate significant and reproducible data. In total, we analyzed more than 100 slides containing 1152 spots each. Slides were probed against different monoclonal antibodies (mAbs) and recombinant fusion proteins. We found two surface coatings to be most suitable for protein and antibody (Ab) immobilization. These were further subjected to quantitative analyses by evaluating intraslide and slide-to-slide reproducibilities, and the linear range of target detection. In summary, we demonstrate that only suitable combinations of surface and fluorescent dyes allow the generation of highly reproducible data.

UNLABELLED:arrayMagic is a software package for quality control and preprocessing of two-colour cDNA microarray data. The automated analysis pipeline comprises data import, normalization, replica merging, quality diagnostics and data export. The script-based processing combines reproducibility and flexibility at high-throughput and provides quality-assured and preprocessed microarray data to high-level follow-up analysis.AVAILABILITY:The R package arrayMagic is available with BSD license at http://www.bioconductor.orgCONTACT:a.buness@dkfz.deSUPPLEMENTARY INFORMATION:The package contains documentation in the form of manual pages and a vignette with a guided tour of a typical workflow.

Current diagnosis of renal cancer consists of histopathologic examination of tissue sections and classification into tumor stages and grades of malignancy. Until recently, molecular differences between tumor types were largely unknown. To examine such differences, we did gene expression measurements of 112 renal cell carcinoma and normal kidney samples on renal cell carcinoma-specific cDNA microarrays containing 4,207 genes and expressed sequence tags. The gene expression patterns showed deregulation of complete biological pathways in the tumors. Many of the molecular changes corresponded well to the histopathologic tumor types, and a set of 80 genes was sufficient to classify tumors with a very low error rate. Distinct gene expression signatures were associated with chromosomal abnormalities of tumor cells, metastasis. formation, and patient survival. The data highlight the benefit of microarrays to detect novel tumor classes and to identify genes that are associated with patient variables and tumor properties.