Relationship Between Rapid Skin Clearance and Quality of Life Benefit: Post Hoc Analysis of Japanese Patients with Moderate-to-Severe Psoriasis Treated with Ixekizumab (UNCOVER-J).

INTRODUCTION: Ixekizumab has demonstrated rapid onset of action, high levels of skin clearance, and improvements in quality of life in patients with moderate-to-severe psoriasis, including plaque, erythrodermic, or generalized pustular psoriasis. METHODS: This was a post hoc analysis of UNCOVER-J, a phase 3, multicenter, single-arm, open-label study of ixekizumab for treatment of Japanese patients with psoriasis. The objective was to assess the proportion of patients who achieved Dermatology Life Quality Index (DLQI) (0,1) and Itch Numeric Rating Scale (NRS) (0) at weeks 4 and 12 according to Psoriasis Area and Severity Index (PASI) percentage improvement levels. All intent-to-treat patients with plaque, erythrodermic, or generalized pustular psoriasis were analyzed. RESULTS: A total of 91 patients were treated with ixekizumab and included in the analysis. Rapid improvements in PASI at weeks 4 and 12 were associated with improvements in DLQI (0,1) response at week 4 and at week 12. Complete skin clearance (PASI 100) achieved either at week 4 or week 12 was associated with a higher Itch NRS (0) response at week 12. CONCLUSIONS: Patients with rapid improvement in clinical symptoms of psoriasis had better patient outcomes than those with slower responses. These findings highlight the clinical importance of achieving a fast response in patients with psoriasis, which may lead to better treatment outcomes. TRIAL REGISTRATION: ClinicalTrials.gov identifier, NCT01624233.

SiBIC: A Tool for Generating a Network of Biclusters Captured by Maximal Frequent Itemset Mining.

Biclustering extracts coexpressed genes under certain experimental conditions, providing more precise insight into the genetic behaviors than one-dimensional clustering. For understanding the biological features of genes in a single bicluster, visualizations such as heatmaps or parallel coordinate plots and tools for enrichment analysis are widely used. However, simultaneously handling many biclusters still remains a challenge. Thus, we developed a web service named SiBIC, which, using maximal frequent itemset mining, exhaustively discovers significant biclusters, which turn into networks of overlapping biclusters, where nodes are gene sets and edges show their overlaps in the detected biclusters. SiBIC provides a graphical user interface for manipulating a gene set network, where users can find target gene sets based on the enriched network. This chapter provides a user guide/instruction of SiBIC with background of having developed this software. SiBIC is available at http://utrecht.kuicr.kyoto-u.ac.jp:8080/sibic/faces/index.jsp .

Computational recognition for long non-coding RNA (lncRNA): Software and databases.

Since the completion of the Human Genome Project, it has been widely established that most DNA is not transcribed into proteins. These non-protein-coding regions are believed to be moderators within transcriptional and post-transcriptional processes, which play key roles in the onset of diseases. Long non-coding RNAs (lncRNAs) are generally lacking in conserved motifs typically used for detection and thus hard to identify, but nonetheless present certain characteristic features that can be exploited by bioinformatics methods. By combining lncRNA detection with known miRNA, RNA-binding protein and chromatin interaction, current tools are able to recognize and functionally annotate large number of lncRNAs. This review discusses databases and platforms dedicated to cataloging and annotating lncRNAs, as well as tools geared at discovering novel sequences. We emphasize the issues posed by the diversity of lncRNAs and their complex interaction mechanisms, as well as technical issues such as lack of unified nomenclature. We hope that this wide overview of existing platforms and databases might help guide biologists toward the tools they need to analyze their experimental data, while our discussion of limitations and of current lncRNA-related methods may assist in the development of new computational tools.

Exploring phenotype patterns of breast cancer within somatic mutations: a modicum in the intrinsic code.

Triple-negative (TN) breast cancer (BC) patients have limited treatment options and poor prognosis even after extant treatments and standard chemotherapeutic regimens. Linking TN patients to clinically known phenotypes with appropriate treatments is vital. Location-specific sequence variants are expected to be useful for this purpose by identifying subgroups within a disease population. Single gene mutational signatures have been widely reported, with related phenotypes in literature. We thoroughly survey currently available mutations (and mutated genes), linked to BC phenotypes, to demonstrate their limited performance as sole predictors/biomarkers to assign phenotypes to patients. We then explore mutational combinations, as a pilot study, using The Cancer Genome Atlas Research Network mutational data of BC and three machine learning methods: association rules (limitless arity multiple procedure), decision tree and hierarchical disjoint clustering. The study results in a patient classification scheme through combinatorial mutations in Phosphatidylinositol-4,5-Bisphosphate 3-Kinase and tumor protein 53, being consistent with all three methods, implying its validity from a diverse viewpoint. However, it would warrant further research to select multi-gene signatures to identify phenotypes specifically and be clinically used routinely.

CellTree: an R/bioconductor package to infer the hierarchical structure of cell populations from single-cell RNA-seq data.

BACKGROUND: Single-cell RNA sequencing is fast becoming one the standard method for gene expression measurement, providing unique insights into cellular processes. A number of methods, based on general dimensionality reduction techniques, have been suggested to help infer and visualise the underlying structure of cell populations from single-cell expression levels, yet their models generally lack proper biological grounding and struggle at identifying complex differentiation paths. RESULTS: Here we introduce cellTree: an R/Bioconductor package that uses a novel statistical approach, based on document analysis techniques, to produce tree structures outlining the hierarchical relationship between single-cell samples, while identifying latent groups of genes that can provide biological insights. CONCLUSIONS: With cellTree, we provide experimentalists with an easy-to-use tool, based on statistically and biologically-sound algorithms, to efficiently explore and visualise single-cell RNA data. The cellTree package is publicly available in the online Bionconductor repository at: http://bioconductor.org/packages/cellTree/ .

Evaluation of serum-based cancer biomarkers: a brief review from a clinical and computational viewpoint.

Biomarkers are vital to detect diseases in various clinical stages. A variety of cancer serum biomarkers are already known, while for more accurate cancer-type detection, there required more rigorous evaluation manners, especially computational evaluation measures, for biomarkers. In this review, we first show three typical pitfalls in finding biomarkers and their examples, after briefly presenting standard five clinical biomarker screening phases by National Cancer Institute. We then introduce current computational biomarker evaluation measures, including current, standard methods with their intrinsic features. We further show an up-to-date list of existing cancer serum biomarkers, pointing out several issues, being caused by the limitations of current biomarker evaluation approaches. Finally we discuss the current attempts to develop new, statistically robust, computational serum-based biomarker measures in terms of specificity to each of various cancer types.

DIFFERENTIAL SERUM LEVEL OF SPECIFIC HAPTOGLOBIN ISOFORMS IN SMALL CELL LUNG CANCER.

Lung cancer is the leading cause of cancer death for both men and women in the United States, and similar trends are seen world wide. The lack of early diagnosis is one of the primary reasons for the high mortality rate. A number of biomarkers have been evaluated in lung cancer patients, however, their specificity and early stage diagnostic values are limited. Using traditional protein chemistry and proteomics tool we have demonstrated higher serum haptoglobin levels in small cell lung cancer (SCLC). Similar findings have been reported for other cancers including ovarian cancer and glioblastoma. Haptoglobin is an acute phase protein with at least six possible phenotypes. The six phenotypes, in combination with two post translational modifications, glycosylation and deamidation, lead to large numbers of possible haptoglobin isoforms. Recent studies indicate a possible correlation between specific haptoglobin glycosylation and particular disease conditions. In our current study, we have fractionated control and SCLC patient serum by 2-D gel electrophoresis to identify differentially expressed haptoglobin isoforms in SCLC serum samples.