Motivation

Tumor tissue samples often contain an unknown fraction of stromal cells. This problem is widely known as tumor purity heterogeneity (TPH) was recently recognized as a severe issue in omics studies. Specifically, if TPH is ignored when inferring co-expression networks, edges are likely to be estimated among genes with mean shift between non-tumor- and tumor cells rather than among gene pairs interacting with each other in tumor cells. To address this issue, we propose Tumor Specific Net (TSNet), a new method which constructs tumor-cell specific gene/protein co-expression networks based on gene/protein expression profiles of tumor tissues. TSNet treats the observed expression profile as a mixture of expressions from different cell types and explicitly models tumor purity percentage in each tumor sample.

Results

Using extensive synthetic data experiments, we demonstrate that TSNet outperforms a standard graphical model which does not account for TPH. We then apply TSNet to estimate tumor specific gene co-expression networks based on TCGA ovarian cancer RNAseq data. We identify novel co-expression modules and hub structure specific to tumor cells.

Availability and implementation

R codes can be found at https://github.com/petraf01/TSNet.

Supplementary information

Supplementary data are available at Bioinformatics online.

Functional expression of alpha-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptors in cerebellar granule cells requires stargazin, a member of a large family of four-pass transmembrane proteins. Here, we define a family of transmembrane AMPA receptor regulatory proteins (TARPs), which comprise stargazin, gamma-3, gamma-4, and gamma-8, but not related proteins, that mediate surface expression of AMPA receptors. TARPs exhibit discrete and complementary patterns of expression in both neurons and glia in the developing and mature central nervous system. In brain regions that express multiple isoforms, such as cerebral cortex, TARP-AMPA receptor complexes are strictly segregated, suggesting distinct roles for TARP isoforms. TARPs interact with AMPA receptors at the postsynaptic density, and surface expression of mature AMPA receptors requires a TARP. These studies indicate a general role for TARPs in controlling synaptic AMPA receptors throughout the central nervous system.

Purpose

Spectral-domain optical coherence tomography (SD-OCT) was used to characterize the retinal phenotype, natural history, and treatment responses in a mouse model of X-linked retinoschisis (Rs1-KO) and to identify new structural markers of AAV8-mediated gene therapy outcome.

Methods

Optical coherence tomography scans were performed on wild-type and Rs1-KO mouse retinas between 1 and 12 months of age and on Rs1-KO mice after intravitreal injection of AAV8-scRS/IRBPhRS (AAV8-RS1). Cavities and photoreceptor outer nuclear layer (ONL) thickness were measured, and outer retina reflective band (ORRB) morphology was examined with age and after AAV8-RS1 treatment. Outer retina reflective band morphology was compared to immunohistochemical staining of the outer limiting membrane (OLM) and photoreceptor inner segment (IS) mitochondria and to electron microscopy (EM) images of IS.

Results

Retinal cavity size in Rs1-KO mice increased between 1 and 4 months and decreased thereafter, while ONL thickness declined steadily, comparable to previous histologic studies. Wild-type retina had four ORRBs. In Rs1-KO, ORRB1was fragmented from 1 month, but was normal after 8 months; ORRB2 and ORRB3 were merged at all ages. Outer retina reflective band morphology returned to normal after AAV-RS1 therapy, paralleling the recovery of the OLM and IS mitochondria as indicated by anti-β-catenin and anti-COX4 labeling, respectively, and EM.

Conclusions

Spectral-domain OCT is a sensitive, noninvasive tool to monitor subtle changes in retinal morphology, disease progression, and effects of therapies in mouse models. The ORRBs may be useful to assess the outcome of gene therapy in the treatment of X-linked retinoschisis patients.

To improve the understanding of chemo-refractory high-grade serous ovarian cancers (HGSOCs), we characterized the proteogenomic landscape of 242 (refractory and sensitive) HGSOCs, representing one discovery and two validation cohorts across two biospecimen types (formalin-fixed paraffin-embedded and frozen). We identified a 64-protein signature that predicts with high specificity a subset of HGSOCs refractory to initial platinum-based therapy and is validated in two independent patient cohorts. We detected significant association between lack of Ch17 loss of heterozygosity (LOH) and chemo-refractoriness. Based on pathway protein expression, we identified 5 clusters of HGSOC, which validated across two independent patient cohorts and patient-derived xenograft (PDX) models. These clusters may represent different mechanisms of refractoriness and implicate putative therapeutic vulnerabilities.

Cancer is driven by genomic alterations, but the processes causing this disease are largely performed by proteins. However, proteins are harder and more expensive to measure than genes and transcripts. To catalyze developments of methods to infer protein levels from other omics measurements, we leveraged crowdsourcing via the NCI-CPTAC DREAM proteogenomic challenge. We asked for methods to predict protein and phosphorylation levels from genomic and transcriptomic data in cancer patients. The best performance was achieved by an ensemble of models, including as predictors transcript level of the corresponding genes, interaction between genes, conservation across tumor types, and phosphosite proximity for phosphorylation prediction. Proteins from metabolic pathways and complexes were the best and worst predicted, respectively. The performance of even the best-performing model was modest, suggesting that many proteins are strongly regulated through translational control and degradation. Our results set a reference for the limitations of computational inference in proteogenomics. A record of this paper's transparent peer review process is included in the Supplemental Information.

This paper describes the scientific aims and potentials as well as the preliminary technical design of IRIDE, an innovative tool for multi-disciplinary investigations in a wide field of scientific, technological and industrial applications. IRIDE will be a high intensity "particles factory", based on a combination of high duty cycle radio-frequency superconducting electron linacs and of high energy lasers. Conceived to provide unique research possibilities for particle physics, for condensed matter physics, chemistry and material science, for structural biology and industrial applications, IRIDE will open completely new research possibilities and advance our knowledge in many branches of science and technology. IRIDE is also supposed to be realized in subsequent stages of development depending on the assigned priorities. © 2013 Elsevier B.V.