Hydra: A mixture modeling framework for subtyping pediatric cancer cohorts using multimodal gene expression signatures.

Precision oncology has primarily relied on coding mutations as biomarkers of response to therapies. While transcriptome analysis can provide valuable information, incorporation into workflows has been difficult. For example, the relative rather than absolute gene expression level needs to be considered, requiring differential expression analysis across samples. However, expression programs related to the cell-of-origin and tumor microenvironment effects confound the search for cancer-specific expression changes. To address these challenges, we developed an unsupervised clustering approach for discovering differential pathway expression within cancer cohorts using gene expression measurements. The hydra approach uses a Dirichlet process mixture model to automatically detect multimodally distributed genes and expression signatures without the need for matched normal tissue. We demonstrate that the hydra approach is more sensitive than widely-used gene set enrichment approaches for detecting multimodal expression signatures. Application of the hydra analysis framework to small blue round cell tumors (including rhabdomyosarcoma, synovial sarcoma, neuroblastoma, Ewing sarcoma, and osteosarcoma) identified expression signatures associated with changes in the tumor microenvironment. The hydra approach also identified an association between ATRX deletions and elevated immune marker expression in high-risk neuroblastoma. Notably, hydra analysis of all small blue round cell tumors revealed similar subtypes, characterized by changes to infiltrating immune and stromal expression signatures

Design Recombinant Protein Adhesion Molecules Target for Therapeutic Nanoparticles and Tumor Biomarkers

A wide spectrum of therapeutic nanoparticles have been investigated as delivery systems to improve the pharmacological properties of drugs or imaging agents. By utilizing the advantageous properties of nanoparticles such as high surface to volume ratio and unique optical properties, improved delivery has been shown in several different disease applications. Furthermore, nanoparticles can be associated with proteins that can provide the ability to specifically target selected areas or diseased tissues without exposing the rest of the body. In previous work, our lab engineered recombinant protein constructs containing single-chain antibodies to study nanoparticle adhesion to inflammatory molecules. The goal of this work is to advance the recombinant protein expression construct towards two specific goals: 1) site-specific modification of the recombinant proteins with small molecule chemistries using different enzyme/peptide tag systems and 2) create a new panel of adhesion receptors based on I-domains of the integrin LFA-1 for targeting the inflammatory molecule ICAM-1. Specifically, three different enzyme/tag systems are tested and compared using yeast surface display and soluble protein, including Sfp synthase/S6 tag, and Sortase A/LPETG tag, and Lipoic Acid Ligase (LplA)/LAP2 tag. We also insert wild type and mutant I-domains obtained from the integrin Lymphocyte function-associated antigen-1 (LFA-1) into the plasmid vector and transformed into yeast. The establishment of I-domain targeting panel and enzymatic conjugation method will provide a solid foundation for further optimization of adhesion therapeutic nanoparticles

A New Method to Analyze the Tsunami Incitement Process and Site-selection for Tsunami Observations in China's Eastern Sea

In this paper, we present a CONTROL volume model for tsunami incitement process by combining the Navier-Stokes equation, the jet theory and relative velocity model. We conclude that the initial condition for tsunami propagation simulation is equivalent to the static near-field seismic displacement of earthquake that induces the tsunami. The error analyzed from this method is only about 1 percent for a common seafloor earthquake, and it is consistent with the result of Ansys/Ls-dyna numerical analysis. EDGRN/EDCMP and COMCOT program provide some new acquirement for the tsunami studies. In the second part of the paper, we develop a site-selection method for anchor-grounded tsunami observation in Chinese eastern sea

Single-cell signatures identify microenvironment factors in tumors associated with patient outcomes.

The cellular components of tumors and their microenvironment play pivotal roles in tumor progression, patient survival, and the response to cancer treatments. Unveiling a comprehensive cellular profile within bulk tumors via single-cell RNA sequencing (scRNA-seq) data is crucial, as it unveils intrinsic tumor cellular traits that elude identification through conventional cancer subtyping methods. Our contribution, scBeacon, is a tool that derives cell-type signatures by integrating and clustering multiple scRNA-seq datasets to extract signatures for deconvolving unrelated tumor datasets on bulk samples. Through the employment of scBeacon on the The Cancer Genome Atlas (TCGA) cohort, we find cellular and molecular attributes within specific tumor categories, many with patient outcome relevance. We developed a tumor cell-type map to visually depict the relationships among TCGA samples based on the cell-type inferences

Backstepping controller design for a class of stochastic nonlinear systems with Markovian switching

A more general class of stochastic nonlinear systems with irreducible homogenous Markovian switching are considered in this paper. As preliminaries, the stability criteria and the existence theorem of strong solutions are first presented by using the inequality of mathematic expectation of a Lyapunov function. The state-feedback controller is designed by regarding Markovian switching as constant such that the closed-loop system has a unique solution, and the equilibrium is asymptotically stable in probability in the large. The output-feedback controller is designed based on a quadratic-plus-quartic-form Lyapunov function such that the closed-loop system has a unique solution with the equilibrium being asymptotically stable in probability in the large in the unbiased case and has a unique bounded-in-probability solution in the biased case.

A New Generation of Lineage Tracing Dynamically Records Cell Fate Choices

Reconstructing the development of lineage relationships and cell fate mapping has been a fundamental problem in biology. Using advanced molecular biology and single-cell RNA sequencing, we have profiled transcriptomes at the single-cell level and mapped cell fates during development. Recently, CRISPR/Cas9 barcode editing for large-scale lineage tracing has been used to reconstruct the pseudotime trajectory of cells and improve lineage tracing accuracy. This review presents the progress of the latest CbLT (CRISPR-based Lineage Tracing) and discusses the current limitations and potential technical pitfalls in their application and other emerging concepts

Prioritizing transcriptional factors in gene regulatory networks with PageRank.

Biological states are controlled by orchestrated transcriptional factors (TFs) within gene regulatory networks. Here we show TFs responsible for the dynamic changes of biological states can be prioritized with temporal PageRank. We further show such TF prioritization can be extended by integrating gene regulatory networks reverse engineered from multi-omics profiles, e.g. gene expression, chromatin accessibility, and chromosome conformation assays, using multiplex PageRank

Prioritizing transcriptional factors in gene regulatory networks with PageRank.

Biological states are controlled by orchestrated transcriptional factors (TFs) within gene regulatory networks. Here we show TFs responsible for the dynamic changes of biological states can be prioritized with temporal PageRank. We further show such TF prioritization can be extended by integrating gene regulatory networks reverse engineered from multi-omics profiles, e.g. gene expression, chromatin accessibility, and chromosome conformation assays, using multiplex PageRank

Reduced expression of microRNA-100 confers unfavorable prognosis in patients with bladder cancer

Abstract Objective MicroRNA-100 (miR-100) has been demonstrated to be downregulated in bladder cancer tissues, and enforced expression of this miRNA may inhibit cell growth and colony formation of human bladder cancer 5637 cells in vitro. However, the clinical significance of miR-100 in human bladder cancer has not yet been elucidated. Thus, the aim of this study was to investigate the diagnostic and prognostic values of miR-100 in this disease. Methods Expression levels of miR-100 in 126 pairs of bladder cancer and adjacent normal tissues were detected by TaqMan real-time quantitative RT-PCR assay. In order to determine its prognostic value, overall survival (OS) and progression-free survival (PFS) were evaluated using the Kaplan-Meier method, and multivariate analysis was performed using the Cox proportional hazard analysis. Results Expression levels of miR-100 in bladder cancer tissues were significantly lower than those in adjacent normal tissues (mean expression level: 2.6 ± 1.2 vs. 3.9 ± 1.5, P  Conclusion Our data offer the convincing evidence that miR-100 may play an important role in the progression of bladder cancer and that the reduced expression of this miRNA may be independently associated with shorter PFS and OS of patients, suggesting that miR-100 might be a potential marker for further risk stratification in the treatment of this cancer. Virtual slides The virtual slides’ for this article can be found here: http://www.diagnosticpathology.diagnomx.eu/vs/1105483419841671</p