High Prediction Scores Research Articles

Abstract 4912: Systematic, comparative network analysis on non-small cell lung cancer

Abstract BACKGROUND: Most cancers lack any effective early disease markers, prognostic and predictive signatures. We fail treating cancer due to multiple ways cancer initiates and develops treatment resistance. While drug modes of action are complex and poorly understood, using Comparative Toxicogenomics Database is an effective way to identify drug combinations. Integrating signatures with deregulated network information may lead to identifying novel treatment option for individual patients. APPROACH: A systematic graph analysis was used to extract network structure differences between normal and tumor patient samples in non-small cell lung cancer. Three gene expression datasets with 27 squamous cell carcinoma, 129 adenocarcinoma, 20 large cell carcinoma and 141 normal samples, and 18 prognostic non-small cell lung cancer gene signatures were used to construct normal and tumor co-expression graphs. RESULTS: We enumerated all 5-node graphlets in normal and tumor graphs, and separated them into 3 categories: unique for normal graph, unique for tumor graph, or present in both. We further focused on subgraphs with the same membership across all 3 datasets and unique to tumor graph. Using gene enrichment analysis with a hypergeometric test we identified 9 subgraphs significantly enriched in the term “regulation of lymphocyte activation” (p<0.05), and genes related to chemokine receptors (CCR2, CCR7), interleukin (IL16), interleukin receptor (IL7R), interferon regulatory factor (IRF4), and T cells or B cells (PTPRCAP, SH2D1A, LCK, BTK, MS4A1). Importantly, this analysis identified protein interaction deregulated in tumors. Using the Comparative Toxicogenomics Database, we identified putative compounds that may “repair” the wiring of these subnetworks in tumor samples. 7 out of 7 identified compounds for the significantly up-regulated genes (p<0.05), and 20 out of 25 significantly down-regulated genes (p<0.05) are associated with non-small cell lung cancer. The other 5 identified compounds are known for other types of cancer or neoplasms, including lung neoplasms. Importantly, 13/38 edges have known/predicted protein interaction evidence, with a high prediction scores >0.9 (11 interactions) and 0.8 (2 interactions). CONCLUSIONS: Systematic integration and network analysis of non-small cell lung signatures identifies potential treatment options and insights to the difference in the underlying wiring related to immune system, an emerging hallmark of cancer. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 4912. doi:1538-7445.AM2012-4912

Speech Recognition Performance under Noisy Conditions of Children with Hearing Loss

ObjectivesIn order to understand the communicative abilities of hearing impaired children in noisy situations and their communication problems, this study was undertaken to examine speech recognition at different background noise levels, and to compare how context cues in noisy situations affect speech recognition.MethodsThirty-four children with severe/profound hearing impairment were enrolled. Fifteen children had cochlear implants (CIs) and 19 used hearing aids (HAs). The Mandarin Speech Perception in Noise (SPIN) test was performed under two levels of background noise, signal-to-noise ratio (SNR) 10 dB and SNR 0 dB (high and low levels, respectively). High predictive (HP) and low predictive (LP) sentences SPIN test scores were recorded to test the effect of context cues on speech recognition.ResultsPerformance was significantly better in children with CIs (SNR 10: mean, 49.44, standard deviation [SD], 13.90; SNR 0: mean, 31.95, SD, 15.72) than in children with HAs (SNR 10: mean, 33.33, SD, 9.72; SNR 0: mean, 19.52, SD, 6.67; P<0.05) in both noise backgrounds, but no significant interaction was found between devices and background noise level. Hearing-impaired children performed better at SNR 10 dB (mean, 40.44; SD, 14.12) than at SNR 0 dB (mean, 25.0; SD, 12.98), significantly (P<0.001). Performance for HP sentences (mean, 38.6; SD, 12.66) was significantly (P<0.001) better than that for LP sentences (mean, 25.25; SD, 12.93). An interaction was found to between background noise level and contextual cues in sentences (F=8.47, P<0.01).ConclusionThe study shows that SNR conditions significantly influence speech recognition performance in children with severe/profound hearing impairment. Under better SNR listening situations, children have better speech recognition when listening to sentences with contextual cues. Children with CIs perform better than children with HAs at both noise levels.

Identification of a Highly Antigenic Linear B Cell Epitope within Plasmodium vivax Apical Membrane Antigen 1 (AMA-1)

Apical membrane antigen 1 (AMA-1) is considered to be a major candidate antigen for a malaria vaccine. Previous immunoepidemiological studies of naturally acquired immunity to Plasmodium vivax AMA-1 (PvAMA-1) have shown a higher prevalence of specific antibodies to domain II (DII) of AMA-1. In the present study, we confirmed that specific antibody responses from naturally infected individuals were highly reactive to both full-length AMA-1 and DII. Also, we demonstrated a strong association between AMA-1 and DII IgG and IgG subclass responses. We analyzed the primary sequence of PvAMA-1 for B cell linear epitopes co-occurring with intrinsically unstructured/disordered regions (IURs). The B cell epitope comprising the amino acid sequence 290–307 of PvAMA-1 (SASDQPTQYEEEMTDYQK), with the highest prediction scores, was identified in domain II and further selected for chemical synthesis and immunological testing. The antigenicity of the synthetic peptide was identified by serological analysis using sera from P. vivax-infected individuals who were knowingly reactive to the PvAMA-1 ectodomain only, domain II only, or reactive to both antigens. Although the synthetic peptide was recognized by all serum samples specific to domain II, serum with reactivity only to the full-length protein presented 58.3% positivity. Moreover, IgG reactivity against PvAMA-1 and domain II after depletion of specific synthetic peptide antibodies was reduced by 18% and 33% (P = 0.0001 for both), respectively. These results suggest that the linear epitope SASDQPTQYEEEMTDYQK is highly antigenic during natural human infections and is an important antigenic region of the domain II of PvAMA-1, suggesting its possible future use in pre-clinical studies.

Acute on Chronic Gastrointestinal Bleeding

G A A b st ra ct s mean overall quality rating was 16.9 (SD 4.1, range 9-23). None of the prediction scores was of high quality, not even the frequently used Rockall (23) and Blatchford (23) score. Moreover, the quality of different scores did not improve over the years. Major methodological shortcomings were the absence of validation or absence of impact analyses. Internal and external validation was performed in 4 (27%) and 7 (47%) studies, respectively, whereas in 4 (27%) studies no validation was performed at all. Furthermore, only 5/15 (30%) of the scores was ‘easy to use’ and 5 (30%) reported some kind of action based on the results. Conclusion: None of the identified prediction scores was of an undisputed methodological quality, which was due to factors that potentially could have been remedied. This emphasizes the need for new high quality prediction scores on the outcome of GIB.

Prolactin induces Jak2 phosphorylation of RUSHY195

Jak2/RUSH-mediated prolactin signaling culminates in RUSH-1α-DNA-binding. Heretofore, Jak2-specific phosphorylation residues in RUSH were unknown. Genpathway’s discovery approaches correlated RUSH-DNA binding (−126/−121) in uteroglobin’s proximal promoter with recruitment of the transcriptional machinery. NetPhos 2.0 server found a single tyrosine phosphorylation site in RUSH’s minimal DNA-binding domain. Y195 had identical context and prediction scores (0.52) for rabbit and human (HLTF) orthologs. The mouse ortholog (Hltf) had a higher prediction score (0.897). Affinity purified RUSHY195ph antibodies recognized native tyrosine phosphorylated RUSH protein immunoprecipitated from nuclear extracts. When R5020-treated HRE-H9 cells ± the Jak2 inhibitor, Tyrene CR4, were stimulated with prolactin, confocal immunofluorescence images provided conclusive evidence that Jak2 mediated the availability of phosphorylated RUSHY195 in nucleus and cytoplasm. Catalytically active Jak2 is ipso facto a RUSH site-specific tyrosine kinase. Immunoprecipitation/Western blotting revealed both phosphorylation at Y195 and the physical interaction between p-Jak2/RUSH/HLTF/Hltf are evolutionarily conserved across three mammalian (rabbit, human, mouse) orthologs.

Predictive factors related to progression toward rheumatoid arthritis in Korean patients with undifferentiated arthritis

Some patients with undifferentiated arthritis (UA) experience spontaneous remission; however, one-third of patients progress to rheumatoid arthritis (RA) in the final process of the disease. This study evaluated clinical variables in order to find a prediction model that could predict the development of RA in patients with UA. The medical records of 164 patients, who were initially diagnosed with undifferentiated arthritis in Yonsei University Medical Center from January 2004 to December 2007, were retrospectively reviewed. They were followed up for at least 6months. The clinical variables related to the development of RA were identified by univariate analyses. Using logistic regression analysis, the prediction model was made and the diagnostic performance of the model was evaluated. Thirty-two patients of the 164 total patients progressed to RA during the follow-up period. The prediction model was composed of clinical factors including the duration of morning stiffness, the number of tender joints, the number of swollen joints, C-reactive protein level, rheumatoid factor, anti-cyclic citrullinated peptide antibody, and erosive change on baseline X-ray. The prediction score ranged from 0 to 10. All of the patients with a higher prediction score greater than five experienced RA progression. The area under the curve value for the prediction rule was 0.976. The prediction model could predict progression to RA in patients with UA. It especially helps the clinician to decide on a management plan for patients with a high prediction score.

Human stanniocalcin-1 interacts with nuclear and cytoplasmic proteins and acts as a SUMO E3 ligase

Human stanniocalcin-1 (STC1) is a glycoprotein that has been implicated in different physiological process, including angiogenesis, apoptosis and carcinogenesis. Here we identified STC1 as a putative molecular marker for the leukemic bone marrow microenvironment and identified new interacting protein partners for STC1. Seven selected interactions retrieved from yeast two-hybrid screens were confirmed by GST-pull down assays in vitro. The N-terminal region was mapped to be the region that mediates the interaction with cytoplasmic, mitochondrial and nuclear proteins. STC1 interacts with SUMO-1 and several proteins that have been shown to be SUMOylated and localized to SUMOylation related nuclear bodies. Although STC1 interacts with SUMO-1 and has a high theoretical prediction score for a SUMOylation site, endogenous co-immunoprecipitation and in vitro SUMOylation assays with the purified recombinant protein could not detect STC1 SUMOylation. However, when we tested STC1 for SUMO E3 ligase activity, we found in an in vitro assay, that it significantly increases the SUMOylation of two other proteins. Confocal microscopic subcellular localization studies using both transfected cells and specific antibodies for endogenous STC1 revealed a cytoplasmic and nuclear deposition, the latter in the form of some specific dot-like substructure resembling SUMOylation related nuclear bodies. Together, these findings suggest a new role for STC1 in SUMOylation pathways, in nuclear bodies.

A race through the maze of genomic evidence

A race through the maze of genomic evidence

Prediction of chronic hepatitis B, liver cirrhosis and hepatocellular carcinoma by SELDI-based serum decision tree classification

To screen potential serological biomarkers and develop decision tree classifications of chronic hepatitis B, liver cirrhosis (LC) and hepatocellular carcinoma (HCC), respectively, with high prediction score for improving diagnosis of liver diseases. The total serum samples were randomly divided into three training sets (41 HBV and 35 health; 36 LC and 35 health; 39 HCC and 35 health) and three testing groups (34 HBV and 38 health; 18 LC and 52 health; 42 HCC and 47 health). Selected WCX2 protein chip capture followed by SELDI-TOF-MS analysis was applied to generate the serum protein profiles. Subsequently serum protein spectra were normalized and aligned by Ciphergen SELDI Software 3.1.1 with Biomarker Wizard including baseline subtraction, mass accuracy calibration, automatic peak detection. Once the intensities of selected significant peaks from the training data set were transferred to further BPS analysis, an optimized classification tree with sequence-decision was established to divide training data set into disease group and control group successfully. A double blind test was employed to determine the clinical sensitivity and clinical specificity of three models. After comparative analysis of SELDI based serum protein profile between the cases of disease and healthy, a HCC decision tree classification with sensitivity of 94.872% and specificity of 94.286%; a LC decision tree classification with sensitivity of 91.667% and specificity of 94.286% and a HBV decision tree classification with sensitivity of 95.122% and specificity of 94.286% were produced by BPS respectively. When three decision tree models were challenged by the double-blind test samples, clinical sensitivity and clinical specificity of these models were predicted in diagnosis of three liver diseases (HCC: 90.48 and 89.36%; cirrhosis: 100 and 86.5%; HBV: 85.29 and 84.21%). SELDI-based decision tree classifications showed great advantages over conventional serological biomarkers in the diagnosis of chronic hepatitis B, LC as well as HCC.

Geographical classification of crude oils by Kohonen self-organizing maps

In the analysis of an environmental disaster caused by spillage of crude oil, limitation of the possible sources to a few geographical origins can help in the identification of the polluting vessel from a group of potential candidates. In this paper we show that Kohonen self-organizing maps (or Kohonen neural networks) can classify samples of crude oils on the basis of gas chromatography–mass spectrometry (GC–MS) descriptors, in terms of geographical origin, with a high degree of accuracy. Two data sets were investigated – one from Instituto Hidrográfico (Lisbon, Portugal) with 188 samples from 20 geographical origins, and another from EUROCRUDE ® with 374 samples. After training the Kohonen self-organizing maps with a training set, predictions were obtained for an independent test set. Correct predictions were obtained for 70% and 60% of the test sets for the two studies, respectively. Ensembles of networks were highly interesting for the calculation of a prediction score, which can be used as a measure of the reliability of the prediction. For the samples with high prediction scores, the percentage of correct predictions jumped to 94–96%. The ability of the maps to identify a given origin is very much dependent on the availability of samples from that class in the training set. Equally good predictions were obtained for a small test set of weathered samples. This investigation adds value to the GC–MS descriptors already in use for practical analytical work, suggesting new ways to ferret out useful knowledge from them.

Development of IgHV family specific nucleic acid vaccine against lymphoma by construction of fusion gene of immunoglobulin heavy chain variable region and cytokine

To construct the gene co-expression vector of immunoglobulin heavy chain variable region (IgHV) gene and GM-CSF or IL-2 as IgHV family specific nucleic acid vaccine to lymphoma, and study the immune response of the immunized mice against lymphoma. Six clones with significantly different length in gene fragments were selected from a gene bank constructed from normal fetal umbilical cord blood. These gene fragments in the clones were sequenced. The sequences were translated into IgHV peptides. T cell epitopes in the IgHV were predicted by bioinformatics. Meanwhile 6 clones of IgHV1 constructed before were analyzed. The most typical clone of IgHV1 and IgHV3 containing most of the T cell epitopes were selected. The IgHV gene fragments whose complementary determining region 3 (CDR3) was cut out and composed mainly of framework region were cloned into eukaryotic expression vector. The gene fragments of framework region of IgHV linking with gene of GM-CSF or IL-2 were cloned into pcDNA3.0 to form fusion genes of IgHV (FR)-GM-CSF/IL-2. Then they were transected into COS cells, African green monkey renal cells, by Lipofectin and their transient expression product was detected by ELISA. Twenty male Balb/c mice were randomly divided into 5 groups of 4 mice to be injected with different vectors at the weeks 0, 2, and 4: with IgHV3 (FR)/pcDNA3.0, with IgHV3 (FR)-IL-2/pcDNA 3.0, with blank vector pcDNA3.0 as control, with IgHV1 (FR)/pcDNA3.0, and with IgHV1 (FR)-IL-2/pcDNA3.0. At the weeks 0, 2, 4, 6, and 8 serum was collected from the mice to undergo indirect immunofluorescence examination to detect the antibodies against Namalwa cells, lymphoma cells from Africa green monkey, and normal lymphocytes. ELISA was used to examine the serum interferon (IFN)-gamma. About 30 of T cell epitopes existed in each IgHV peptides predicted by bioinformatics. Among the bioinformatics prediction, about 90% of the T cell epitopes were in the framework region (FR) of IgHV, and the first 10% with higher prediction score were in FR. The CDR3 of two typical IgHV sequences were cut down and the remaining sequences, mainly composed of FR, were used to construct the IgHV (FR)/pcDNA3.0 expression vectors. The 3' end of IgHV1 (FR) and IgHV3 (FR) were linked to GM-CSF or IL-2 gene successfully. The expression of GM-CSF in the group transfected with IgHV (FR)-GM-CSF/pcDNA3.0 were 200 times higher than in the group transfected with control pcDNA3.0. The expression of IL-2 in the group transfected with IgHV (FR)-IL-2/pcDNA3.0 were 60 times higher than in the group transfected with control pcDNA3.0. The antibody against IgHV1 family lymphoma cell line Namalwa could be detected since the second week in the mice immunized with IgHV1 (FR)-IL-2/pcDNA3.0. The antibody could be detected since the fourth week in the mice immunized with IgHV1 (FR). The antibody against lymphocyte line of IgHV3 family could be detected since the second week in the mice immunized with IgHV3 (FR)-IL-2/pcDNA3.0. The antibody could be detected since the fourth week in the mice immunized with IgHV3 (FR)/pcDNA3.0. The contents of serum IFN-gamma the mice immunized with IgHV1 (FR)-IL-2/pcDNA3.0 and with IgHV3 (FR)-IL-2/pcDNA3.0 was significantly higher than those of the mice immunized with IgHV (FR)/pcDNA3.0 or pcDNA3.0 (all P < 0.01). The gene fragments of IgHV (FR) can be used to construct IgHV family specific nucleic acid vaccine that induces anti-lymphoma immune response in mice by muscle injection. The expressing vectors of IgHV (FR)-GM-CSF/IL-2 induces stronger immunoreaction.