TIN: An R Package for Transcriptome Instability Analysis
We present the R package TIN, available from Bioconductor, which implements a set of methods for TIN analysis based on exon-level microarray expression profiles. TIN provides tools for estimating aberrant exon usage across samples and for analyzing correlation patterns between TIN and splicing factor expression levels. (Source: Cancer Informatics)
Source: Cancer Informatics - September 20, 2015 Category: Cancer & Oncology Authors: Bjarne JohannessenAnita Sveenand Rolf I. Skotheim Source Type: research
Alview: Portable Software for Viewing Sequence Reads in BAM Formatted Files
The name Alview is a contraction of the term Alignment Viewer. Alview is a compiled to native architecture software tool for visualizing the alignment of sequencing data. Inputs are files of short-read sequences aligned to a reference genome in the SAM/BAM format and files containing reference genome data. Outputs are visualizations of these aligned short reads. Alview is written in portable C with optional graphical user interface (GUI) code written in C, C++, and Objective-C. The application can run in three different ways: as a web server, as a command line tool, or as a native, GUI program. Alview is compatible with Mi...
Source: Cancer Informatics - September 13, 2015 Category: Cancer & Oncology Authors: Richard P. FinneyQing-Rong ChenCu V. NguyenChih Hao HsuChunhua YanYing HuMassih AbawiXiaopeng BianDaoud M. Meerzaman Source Type: research
An Application of Sequential Meta-Analysis to Gene Expression Studies
Most of the discoveries from gene expression data are driven by a study claiming an optimal subset of genes that play a key role in a specific disease. Meta-analysis of the available datasets can help in getting concordant results so that a real-life application may be more successful. Sequential meta-analysis (SMA) is an approach for combining studies in chronological order while preserving the type I error and pre-specifying the statistical power to detect a given effect size. We focus on the application of SMA to find gene expression signatures across experiments in acute myeloid leukemia. SMA of seven raw datasets is u...
Source: Cancer Informatics - September 10, 2015 Category: Cancer & Oncology Authors: Putri W. NoviantiIngeborg van der TweelVictor L. JongKit C. B. RoesMarinus J. C. Eijkemans Source Type: research
The Importance of Neighborhood Scheme Selection in Agent-based Tumor Growth Modeling
Modeling tumor growth has proven a very challenging problem, mainly due to the fact that tumors are highly complex systems that involve dynamic interactions spanning multiple scales both in time and space. The desire to describe interactions in various scales has given rise to modeling approaches that use both continuous and discrete variables, known as hybrid approaches. This work refers to a hybrid model on a 2D square lattice focusing on cell movement dynamics as they play an important role in tumor morphology, invasion and metastasis and are considered as indicators for the stage of malignancy used for early prognosis ...
Source: Cancer Informatics - September 7, 2015 Category: Cancer & Oncology Authors: Georgios TzedakisEleftheria TzamaliKostas MariasVangelis Sakkalis Source Type: research
CoGNaC: A Chaste Plugin for the Multiscale Simulation of Gene Regulatory Networks Driving the Spatial Dynamics of Tissues and Cancer
We introduce a Chaste plugin for the generation and the simulation of Gene Regulatory Networks (GRNs) in multiscale models of multicellular systems. Chaste is a widely used and versatile computational framework for the multiscale modeling and simulation of multicellular biological systems. The plugin, named CoGNaC (Chaste and Gene Networks for Cancer), allows the linking of the regulatory dynamics to key properties of the cell cycle and of the differentiation process in populations of cells, which can subsequently be modeled using different spatial modeling scenarios. The approach of CoGNaC focuses on the emergent dynamica...
Source: Cancer Informatics - September 1, 2015 Category: Cancer & Oncology Authors: Simone RubinacciAlex GraudenziGiulio CaravagnaGiancarlo MauriJames OsborneJoe Pitt-FrancisMarco Antoniotti Source Type: research
Introductory Editorial: Computer Simulation, Bioinformatics, and Statistical Analysis of Cancer Data and Processes
No abstract supplied. (Source: Cancer Informatics)
Source: Cancer Informatics - September 1, 2015 Category: Cancer & Oncology Authors: Kellie J. ArcherKevin DobbinSwati BiswasRoger S. DayDavid C. WheelerHao Wu Source Type: research
Assessing Treatment Response Through Generalized Pharmacokinetic Modeling of DCE-MRI Data
Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) enables the quantification of contrast leakage from the vascular tissue by using pharmacokinetic (PK) models. Such quantitative analysis of DCE-MRI data provides physiological parameters that are able to provide information of tumor pathophysiology and therapeutic outcome. Several assumptive PK models have been proposed to characterize microcirculation in the tumoral tissue. In this paper, we present a comparative study between the well-known extended Tofts model (ETM) and the more recent gamma capillary transit time (GCTT) model, with the latter showing initia...
Source: Cancer Informatics - August 12, 2015 Category: Cancer & Oncology Authors: Eleftherios KontopodisGeorgia KanliGeorgios C. ManikisSofie Van CauterKostas Marias Source Type: research
In Silico Neuro-Oncology: Brownian Motion-Based Mathematical Treatment as a Potential Platform for Modeling the Infiltration of Glioma Cells into Normal Brain Tissue
Intensive glioma tumor infiltration into the surrounding normal brain tissues is one of the most critical causes of glioma treatment failure. To quantitatively understand and mathematically simulate this phenomenon, several diffusion-based mathematical models have appeared in the literature. The majority of them ignore the anisotropic character of diffusion of glioma cells since availability of pertinent truly exploitable tomographic imaging data is limited. Aiming at enriching the anisotropy-enhanced glioma model weaponry so as to increase the potential of exploiting available tomographic imaging data, we propose a Browni...
Source: Cancer Informatics - August 10, 2015 Category: Cancer & Oncology Authors: Markos AntonopoulosGeorgios Stamatakos Source Type: research
HMPL: A Pipeline for Identifying Hemimethylation Patterns by Comparing Two Samples
DNA methylation (the addition of a methyl group to a cytosine) is an important epigenetic event in mammalian cells because it plays a key role in regulating gene expression. Most previous methylation studies assume that DNA methylation occurs on both positive and negative strands. However, a few studies have reported that in some genes, methylation occurs only on one strand (ie, hemimethylation) and has clustering patterns. These studies report that hemimethylation occurs on individual genes. It is unclear whether hemimethylation occurs genome-wide and whether there are hemimethylation differences between cancerous and non...
Source: Cancer Informatics - August 9, 2015 Category: Cancer & Oncology Authors: Shuying SunPeng Li Source Type: research
Secondary Data Analytics of Aquaporin Expression Levels in Glioblastoma Stem-Like Cells
Glioblastoma is the most common brain tumor in adults in which recurrence has been attributed to the presence of cancer stem cells in a hypoxic microenvironment. On the basis of tumor formation in vivo and growth type in vitro, two published microarray gene expression profiling studies grouped nine glioblastoma stem-like (GS) cell lines into one of two groups: full (GSf) or restricted (GSr) stem-like phenotypes. Aquaporin-1 (AQP1) and aquaporin-4 (AQP4) are water transport proteins that are highly expressed in primary glial-derived tumors. However, the expression levels of AQP1 and AQP4 have not been previously described i...
Source: Cancer Informatics - July 30, 2015 Category: Cancer & Oncology Authors: Raphael D. IsokpehiKatharina C. Wollenberg ValeroBarbara E. GrahamMaricica PacurariJennifer N. SimsUdensi K. UdensiKenneth Ndebele Source Type: research
The Impact of Microenvironmental Heterogeneity on the Evolution of Drug Resistance in Cancer Cells
Therapeutic resistance arises as a result of evolutionary processes driven by dynamic feedback between a heterogeneous cell population and environmental selective pressures. Previous studies have suggested that mutations conferring resistance to epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKI) in non-small-cell lung cancer (NSCLC) cells lower the fitness of resistant cells relative to drug-sensitive cells in a drug-free environment. Here, we hypothesize that the local tumor microenvironment could influence the magnitude and directionality of the selective effect, both in the presence and absence of ...
Source: Cancer Informatics - July 15, 2015 Category: Cancer & Oncology Authors: Shannon M. MumenthalerJasmine FooNathan C. ChoiNicholas HeiseKevin LederDavid B. AgusWilliam PaoFranziska Michorand Parag Mallick Source Type: research
Identification of Genetic Mutations in Human Lung Cancer by Targeted Sequencing
This study demonstrates the feasibility of using the Ion Torrent sequencing to efficiently identify genetic mutations in individual tumors for targeted lung cancer therapy. (Source: Cancer Informatics)
Source: Cancer Informatics - June 29, 2015 Category: Cancer & Oncology Authors: Hongxiang FengXiaowei WangZhenrong ZhangChuanning TangHua YeLindsey JonesFeng LouDandan ZhangShouwen JiangHong SunHaichao DongGuangchun ZhangZhiyuan LiuZhishou DongBaishuai GuoHe YanChaowei YanLu WangZiyi SuYangyang LiVijayalakshmi NandakumarXue F. HuangS Source Type: research
Time-Frequency Analysis of Peptide Microarray Data: Application to Brain Cancer Immunosignatures
One of the gravest dangers facing cancer patients is an extended symptom-free lull between tumor initiation and the first diagnosis. Detection of tumors is critical for effective intervention. Using the body’s immune system to detect and amplify tumor-specific signals may enable detection of cancer using an inexpensive immunoassay. Immunosignatures are one such assay: they provide a map of antibody interactions with random-sequence peptides. They enable detection of disease-specific patterns using classic train/test methods. However, to date, very little effort has gone into extracting information from the sequence of pe...
Source: Cancer Informatics - June 18, 2015 Category: Cancer & Oncology Authors: Brian O’DonnellAlexander MaurerAntonia Papandreou-SuppappolaPhillip Stafford Source Type: research
A Proposed Paradigm Shift in Initializing Cancer Predictive Models with DCE-MRI Based PK Parameters: A Feasibility Study
Glioblastoma multiforme is the most aggressive type of glioma and the most common malignant primary intra-axial brain tumor. In an effort to predict the evolution of the disease and optimize therapeutical decisions, several models have been proposed for simulating the growth pattern of glioma. One of the latest models incorporates cell proliferation and invasion, angiogenic net rates, oxygen consumption, and vasculature. These factors, particularly oxygenation levels, are considered fundamental factors of tumor heterogeneity and compartmentalization. This paper focuses on the initialization of the cancer cell populations a...
Source: Cancer Informatics - June 10, 2015 Category: Cancer & Oncology Authors: Alexandros RoniotisMariam-Eleni OraiopoulouEleftheria TzamaliEleftherios KontopodisSofie Van CauterVangelis SakkalisKostas Marias Source Type: research
Gene Expression Signatures Based on Variability can Robustly Predict Tumor Progression and Prognosis
Gene expression signatures are commonly used to create cancer prognosis and diagnosis methods, yet only a small number of them are successfully deployed in the clinic since many fail to replicate performance on subsequent validation. A primary reason for this lack of reproducibility is the fact that these signatures attempt to model the highly variable and unstable genomic behavior of cancer. Our group recently introduced gene expression anti-profiles as a robust methodology to derive gene expression signatures based on the observation that while gene expression measurements are highly heterogeneous across tumors of a spec...
Source: Cancer Informatics - June 7, 2015 Category: Cancer & Oncology Authors: Wikum DinalankaraHéctor Corrada Bravo Source Type: research
