Effective Response Metric: a novel tool to predict relapse in childhood acute lymphoblastic leukaemia using time-series gene expression profiling.

Accurate risk assignment in childhood acute lymphoblastic leukaemia is essential to avoid under- or over-treatment. We hypothesized that time-series gene expression profiles (GEPs) of bone marrow samples during remission-induction therapy can measure the response and be used for relapse prediction. We computed the time-series changes from diagnosis to Day 8 of remission-induction, termed Effective Response Metric (ERM-D8) and tested its ability to predict relapse against contemporary risk assignment methods, including National Cancer Institutes (NCI) criteria, genetics and minimal residual disease (MRD). ERM-D8 was trained on a set of 131 patients and validated on an independent set of 79 patients. In the independent blinded test set, unfavourable ERM-D8 patients had >3-fold increased risk of relapse compared to favourable ERM-D8 (5-year cumulative incidence of relapse 38·1% vs. 10·6%; P = 2·5 × 10-3 ). ERM-D8 remained predictive of relapse [P = 0·05; Hazard ratio 4·09, 95% confidence interval (CI) 1·03-16·23] after adjusting for NCI criteria, genetics, Day 8 peripheral response and Day 33 MRD. ERM-D8 improved risk stratification in favourable genetics subgroups (P = 0·01) and Day 33 MRD positive patients (P = 1·7 × 10-3 ). We conclude that our novel metric - ERM-D8 - based on time-series GEP after 8 days of remission-induction therapy can independently predict relapse even after adjusting for NCI risk, genetics, Day 8 peripheral blood response and MRD.

A database of annotated promoters of genes associated with common respiratory and related diseases.

Many genes have been implicated in the pathogenesis of common respiratory and related diseases (RRDs), yet the underlying mechanisms are largely unknown. Differential gene expression patterns in diseased and healthy individuals suggest that RRDs affect or are affected by modified transcription regulation programs. It is thus crucial to characterize implicated genes in terms of transcriptional regulation. For this purpose, we conducted a promoter analysis of genes associated with 11 common RRDs including allergic rhinitis, asthma, bronchiectasis, bronchiolitis, bronchitis, chronic obstructive pulmonary disease, cystic fibrosis, emphysema, eczema, psoriasis, and urticaria, many of which are thought to be genetically related. The objective of the present study was to obtain deeper insight into the transcriptional regulation of these disease-associated genes by annotating their promoter regions with transcription factors (TFs) and TF binding sites (TFBSs). We discovered many TFs that are significantly enriched in the target disease groups including associations that have been documented in the literature. We also identified a number of putative TFs/TFBSs that appear to be novel. The results of our analysis are provided in an online database that is freely accessible to researchers at http://www.respiratorygenomics.com. Promoter-associated TFBS information and related genomic features, such as histone modification sites, microsatellites, CpG islands, and SNPs, are graphically summarized in the database. Users can compare and contrast underlying mechanisms of specific RRDs relative to candidate genes, TFs, gene ontology terms, micro-RNAs, and biological pathways for the conduct of metaanalyses. This database represents a novel, useful resource for RRD researchers.

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.

Network-based pipeline for analyzing MS data: an application toward liver cancer.

Current limitations in proteome analysis by high-throughput mass spectrometry (MS) approaches have sometimes led to incomplete (or inconclusive) data sets being published or unpublished. In this work, we used an iTRAQ reference data on hepatocellular carcinoma (HCC) to design a two-stage functional analysis pipeline to widen and improve the proteome coverage and, subsequently, to unveil the molecular changes that occur during HCC progression in human tumorous tissue. The first involved functional cluster analysis by incorporating an expansion step on a cleaned integrated network. The second used an in-house developed pathway database where recovery of shared neighbors was followed by pathway enrichment analysis. In the original MS data set, over 500 proteins were detected from the tumors of 12 male patients, but in this paper we reported an additional 1000 proteins after application of our bioinformatics pipeline. Through an integrative effort of network cleaning, community finding methods, and network analysis, we also uncovered several biologically interesting clusters implicated in HCC. We established that HCC transition from a moderate to poor stage involved densely connected clusters that comprised of PCNA, XRCC5, XRCC6, PARP1, PRKDC, and WRN. From our pathway enrichment analyses, it appeared that the HCC moderate stage, unlike the poor stage, is enriched in proteins involved in immune responses, thus suggesting the acquisition of immuno-evasion. Our strategy illustrates how an original oncoproteome could be expanded to one of a larger dynamic range where current technology limitations prevent/limit comprehensive proteome characterization.

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.

Low cytosine triphosphate synthase 2 expression renders resistance to 5-fluorouracil in colorectal cancer.

Understanding the determinants of resistance of 5-fluorouracil (5FU) is of significant value to optimising administration of the drug, and introducing novel agents and treatment strategies. Here, the expression of 92 genes involved in 5FU transport, metabolism, co-factor (folate) metabolism and downstream effects was measured by real-time PCR low density arrays in 14 patient-derived colorectal cancer xenografts characterised for 5FU resistance. Candidate gene function was tested by siRNA and uridine modulation, and immunoblotting, apoptosis and cell cycle analysis. Predictive significance was tested by immunohistochemistry of tumours from 125 stage III colorectal cancer patients treated with and without 5FU. Of 8 genes significantly differentially expressed between 5FU sensitive and resistant xenograft tumours, CTPS2 was the gene with the highest probability of differential expression (p=0.008). Reduction of CTPS2 expression by siRNA increased the resistance of colorectal cancer cell lines DLD1 and LS174T to 5FU and its analogue, FUDR. CTPS2 siRNA significantly reduced cell S-phase accumulation and apoptosis following 5FU treatment. Exposure of cells to uridine, a precursor to the CTPS2 substrate uridine triphosphate, also increased 5FU resistance. Patients with low CTPS2 did not gain a survival benefit from 5FU treatment (p=0.072), while those with high expression did (p=0.003). Low CTPS2 expression may be a rationally-based determinant of 5FU resistance.

Genome-wide analysis of regions similar to promoters of histone genes.

BACKGROUND: The purpose of this study is to: i) develop a computational model of promoters of human histone-encoding genes (shortly histone genes), an important class of genes that participate in various critical cellular processes, ii) use the model so developed to identify regions across the human genome that have similar structure as promoters of histone genes; such regions could represent potential genomic regulatory regions, e.g. promoters, of genes that may be coregulated with histone genes, and iii/ identify in this way genes that have high likelihood of being coregulated with the histone genes. RESULTS: We successfully developed a histone promoter model using a comprehensive collection of histone genes. Based on leave-one-out cross-validation test, the model produced good prediction accuracy (94.1% sensitivity, 92.6% specificity, and 92.8% positive predictive value). We used this model to predict across the genome a number of genes that shared similar promoter structures with the histone gene promoters. We thus hypothesize that these predicted genes could be coregulated with histone genes. This hypothesis matches well with the available gene expression, gene ontology, and pathways data. Jointly with promoters of the above-mentioned genes, we found a large number of intergenic regions with similar structure as histone promoters. CONCLUSIONS: This study represents one of the most comprehensive computational analyses conducted thus far on a genome-wide scale of promoters of human histone genes. Our analysis suggests a number of other human genes that share a high similarity of promoter structure with the histone genes and thus are highly likely to be coregulated, and consequently coexpressed, with the histone genes. We also found that there are a large number of intergenic regions across the genome with their structures similar to promoters of histone genes. These regions may be promoters of yet unidentified genes, or may represent remote control regions that participate in regulation of histone and histone-coregulated gene transcription initiation. While these hypotheses still remain to be verified, we believe that these form a useful resource for researchers to further explore regulation of human histone genes and human genome. It is worthwhile to note that the regulatory regions of the human genome remain largely un-annotated even today and this study is an attempt to supplement our understanding of histone regulatory regions.