Finding consistent disease subnetworks across microarray datasets.

BACKGROUND: While contemporary methods of microarray analysis are excellent tools for studying individual microarray datasets, they have a tendency to produce different results from different datasets of the same disease. We aim to solve this reproducibility problem by introducing a technique (SNet). SNet provides both quantitative and descriptive analysis of microarray datasets by identifying specific connected portions of pathways that are significant. We term such portions within pathways as "subnetworks". RESULTS: We tested SNet on independent datasets of several diseases, including childhood ALL, DMD and lung cancer. For each of these diseases, we obtained two independent microarray datasets produced by distinct labs on distinct platforms. In each case, our technique consistently produced almost the same list of significant nontrivial subnetworks from two independent sets of microarray data. The gene-level agreement of these significant subnetworks was between 51.18% to 93.01%. In contrast, when the same pairs of microarray datasets were analysed using GSEA, t-test and SAM, this percentage fell between 2.38% to 28.90% for GSEA, 49.60% tp 73.01% for t-test, and 49.96% to 81.25% for SAM. Furthermore, the genes selected using these existing methods did not form subnetworks of substantial size. Thus it is more probable that the subnetworks selected by our technique can provide the researcher with more descriptive information on the portions of the pathway actually affected by the disease. CONCLUSIONS: These results clearly demonstrate that our technique generates significant subnetworks and genes that are more consistent and reproducible across datasets compared to the other popular methods available (GSEA, t-test and SAM). The large size of subnetworks which we generate indicates that they are generally more biologically significant (less likely to be spurious). In addition, we have chosen two sample subnetworks and validated them with references from biological literature. This shows that our algorithm is capable of generating descriptive biologically conclusions.

Consistency, comprehensiveness, and compatibility of pathway databases.

BACKGROUND: It is necessary to analyze microarray experiments together with biological information to make better biological inferences. We investigate the adequacy of current biological databases to address this need. DESCRIPTION: Our results show a low level of consistency, comprehensiveness and compatibility among three popular pathway databases (KEGG, Ingenuity and Wikipathways). The level of consistency for genes in similar pathways across databases ranges from 0% to 88%. The corresponding level of consistency for interacting genes pairs is 0%-61%. These three original sources can be assumed to be reliable in the sense that the interacting gene pairs reported in them are correct because they are curated. However, the lack of concordance between these databases suggests each source has missed out many genes and interacting gene pairs. CONCLUSIONS: Researchers will hence find it challenging to obtain consistent pathway information out of these diverse data sources. It is therefore critical to enable them to access these sources via a consistent, comprehensive and unified pathway API. We accumulated sufficient data to create such an aggregated resource with the convenience of an API to access its information. This unified resource can be accessed at http://www.pathwayapi.com.

Pharmacodynamic effects of seliciclib, an orally administered cell cycle modulator, in undifferentiated nasopharyngeal cancer.

PURPOSE: Cell cycle dysregulation resulting in expression of antiapoptotic genes and uncontrolled proliferation is a feature of undifferentiated nasopharyngeal carcinoma. The pharmacodynamic effects of seliciclib, a cyclin-dependent kinase (CDK) inhibitor, were studied in patients with nasopharyngeal carcinoma. EXPERIMENTAL DESIGN: Patients with treatment-naïve locally advanced nasopharyngeal carcinoma received seliciclib at 800 mg or 400 mg twice daily on days 1 to 3 and 8 to 12. Paired tumor samples obtained at baseline and on day 13 were assessed by light microscopy, immunohistochemistry, and transcriptional profiling using real-time PCR low-density array consisting of a panel of 380 genes related to cell cycle inhibition, apoptosis, signal transduction, and cell proliferation. RESULTS: At 800 mg bd, one patient experienced grade 3 liver toxicity and another had grade 2 vomiting; no significant toxicities were experienced in 13 patients treated at 400 mg bd. Seven of fourteen evaluable patients had clinical evidence of tumor reduction. Some of these responses were associated with increased tumor apoptosis, necrosis, and decreases in plasma EBV DNA posttreatment. Reduced protein expression of Mcl-1, cyclin D1, phosphorylated retinoblastoma protein pRB (T821), and significant transcriptional down-regulation of genes related to cellular proliferation and survival were shown in some patients posttreatment, indicative of cell cycle modulation by seliciclib, more specifically inhibition of cdk2/cyclin E, cdk7/cyclin H, and cdk9/cyclin T. CONCLUSIONS: Brief treatment with this regimen of seliciclib in patients with nasopharyngeal carcinoma is tolerable at 400 mg bd and associated with tumor pharmacodynamic changes consistent with cdk inhibition, and warrants further efficacy studies in this tumor.