Gene-Set Reduction for Analysis of Major and Minor Gleason Scores Based on Differential Gene-Set Expressions and Biological Pathways in Prostate Cancer
This article applies Significance Analysis of Microarray for Gene-Set Reduction to a real microarray study of patients with prostate cancer and identifies 13 core genes differentially expressed between patients with a major GS of 3 and a minor GS of 4, or (3,4), vs patients with a combination of (4,3), starting from a less aggressive GS combination of (3,3), and moving toward a more aggressive one of (4,4) via gray areas of (3,4) and (4,3). The resulting core genes may improve understanding of prostate cancer in patients with a total GS of 7, the most common grade and most challenging with respect to prognosis. (Source: Cancer Informatics)
Source: Cancer Informatics - September 11, 2017 Category: Cancer & Oncology Authors: Irina Dinu Surya Poudel Saumyadipta Pyne Source Type: research

xSyn: A Software Tool for Identifying Sophisticated 3-Way Interactions From Cancer Expression Data
Conclusions: xSyn is a useful tool for decoding the complex relationships among gene expression profiles. xSyn is available at http://www.bdxconsult.com/xSyn.html. (Source: Cancer Informatics)
Source: Cancer Informatics - August 28, 2017 Category: Cancer & Oncology Authors: Baishali Bandyopadhyay Veda Chanda Yupeng Wang Source Type: research

Adaptive Multiview Nonnegative Matrix Factorization Algorithm for Integration of Multimodal Biomedical Data
We report rigorous evaluation of the method on large-scale quantitative protein and phosphoprotein tumor data from the Clinical Proteomic Tumor Analysis Consortium (CPTAC) acquired using state-of-the-art liquid chromatography mass spectrometry. Exome sequencing and RNA-Seq data were also available from The Cancer Genome Atlas for the same tumors. For unimodal data, in case of breast cancer, transcript levels were most predictive of estrogen and progesterone receptor status and copy number variation of human epidermal growth factor receptor 2 status. For ovarian and colon cancers, phosphoprotein and protein levels were most...
Source: Cancer Informatics - August 18, 2017 Category: Cancer & Oncology Authors: Bisakha Ray Wenke Liu David Feny ö Source Type: research

Immuno-Oncology Integrative Networks: Elucidating the Influences of Osteosarcoma Phenotypes
In vivo and in vitro functional phenotyping characterization was recently obtained with reference to an experimental pan-cancer study of 22 osteosarcoma (OS) cell lines. Here, differentially expressed gene (DEG) profiles were recomputed from the publicly available data to conduct network inference on the immune system regulatory activity across the characterized OS phenotypes. Based on such DEG profiles, and for each phenotype that was analyzed, we obtained coexpression networks and bio-annotations for them. Then, we described the immune-modulated influences in phenotype-specific networks’ integrating pathway, transcript...
Source: Cancer Informatics - July 26, 2017 Category: Cancer & Oncology Authors: Ankush Sharma Enrico Capobianco Source Type: research

Prediction With Dimension Reduction of Multiple Molecular Data Sources for Patient Survival
Predictive modeling from high-dimensional genomic data is often preceded by a dimension reduction step, such as principal component analysis (PCA). However, the application of PCA is not straightforward for multisource data, wherein multiple sources of ‘omics data measure different but related biological components. In this article, we use recent advances in the dimension reduction of multisource data for predictive modeling. In particular, we apply exploratory results from Joint and Individual Variation Explained (JIVE), an extension of PCA for multisource data, for prediction of differing response types. We conduct ill...
Source: Cancer Informatics - July 11, 2017 Category: Cancer & Oncology Authors: Adam Kaplan Eric F Lock Source Type: research

The Impact of Collisions on the Ability to Detect Rare Mutant Alleles Using Barcode-Type Next-Generation Sequencing Techniques
Barcoding techniques are used to reduce error from next-generation sequencing, with applications ranging from understanding tumor subclone populations to detecting circulating tumor DNA. Collisions occur when more than one sample molecule is tagged by the same unique identifier (UID) and can result in failure to detect very-low-frequency mutations and error in estimating mutation frequency. Here, we created computer models of barcoding technique, with and without amplification bias introduced by the UID, and analyzed the effect of collisions for a range of mutant allele frequencies (1e−6 to 0.2), number of sample molecul...
Source: Cancer Informatics - July 10, 2017 Category: Cancer & Oncology Authors: Jenna VanLiere Canzoniero Karen Cravero Ben Ho Park Source Type: research

An Assessment of Database-Validated microRNA Target Genes in Normal Colonic Mucosa: Implications for Pathway Analysis
Conclusions: Our data suggest that miRNA target gene databases are incomplete; pathways derived from these databases have similar deficiencies. Although we know a lot about several miRNAs, little is known about other miRNAs in terms of their targeted genes. We encourage others to use their data to continue to further identify and validate miRNA-targeted genes. (Source: Cancer Informatics)
Source: Cancer Informatics - June 23, 2017 Category: Cancer & Oncology Authors: Martha L Slattery Jennifer S Herrick John R Stevens Roger K Wolff Lila E Mullany Source Type: research

A Software Application for Mining and Presenting Relevant Cancer Clinical Trials per Cancer Mutation
We present CTMine, a system that mines ClinicalTrials.org for clinical trials per cancer mutation and displays the trials in a user-friendly Web application. The system currently lists clinical trials for 6 common genes (ALK, BRAF, ERBB2, EGFR, KIT, and KRAS). The current machine learning model used to identify relevant clinical trials focusing on the above gene mutations had an average 88% precision/recall. As part of this analysis, we compared human versus machine and found that oncologists were unable to reach a consensus on whether a clinical trial mined by CTMine was “relevant” per gene mutation, a finding that hi...
Source: Cancer Informatics - June 22, 2017 Category: Cancer & Oncology Authors: Lisa M Gandy Jordan Gumm Amanda L Blackford Elana J Fertig Luis A Diaz Source Type: research

The Model-Based Study of the Effectiveness of Reporting Lists of Small Feature Sets Using RNA-Seq Data
In this study, we compare the performance of ranking feature sets derived from a model of RNA-Seq data with that of a multivariate normal model of gene concentrations using 3 measures: (1) ranking power, (2) length of extensions, and (3) Bayes features. This is the model-based study to examine the effectiveness of reporting lists of small feature sets using RNA-Seq data and the effects of different model parameters and error estimators. The results demonstrate that the general trends of the parameter effects on the ranking power of the underlying gene concentrations are preserved in the RNA-Seq data, whereas the power of f...
Source: Cancer Informatics - June 12, 2017 Category: Cancer & Oncology Authors: Eunji Kim Ivan Ivanov Jianping Hua Johanna W Lampe Meredith AJ Hullar Robert S Chapkin Edward R Dougherty Source Type: research

Tumor RAS Gene Expression Levels Are Influenced by the Mutational Status of RAS Genes and Both Upstream and Downstream RAS Pathway Genes
In this report, we examined data from The Cancer Genome Atlas to investigate the relationship between RAS gene mutational status and messenger RNA expression. We show that all 3 RAS genes exhibit increased expression when they are mutated in a context-dependent manner. In the case of KRAS, this increase is manifested by a larger proportional increase in KRAS4A than KRAS4B, although both increase significantly. In addition, the mutational status of RAS genes can be associated with expression changes in other RAS genes, with most of these cases showing decreased expression. The mutational status associations with expression ...
Source: Cancer Informatics - June 8, 2017 Category: Cancer & Oncology Authors: Robert M Stephens Ming Yi Bailey Kessing Dwight V Nissley Frank McCormick Source Type: research

Lung Cancer Pathological Image Analysis Using a Hidden Potts Model
Nowadays, many biological data are acquired via images. In this article, we study the pathological images scanned from 205 patients with lung cancer with the goal to find out the relationship between the survival time and the spatial distribution of different types of cells, including lymphocyte, stroma, and tumor cells. Toward this goal, we model the spatial distribution of different types of cells using a modified Potts model for which the parameters represent interactions between different types of cells and estimate the parameters of the Potts model using the double Metropolis-Hastings algorithm. The double Metropolis-...
Source: Cancer Informatics - June 5, 2017 Category: Cancer & Oncology Authors: Qianyun Li Faliu Yi Tao Wang Guanghua Xiao Faming Liang Source Type: research

Immune Checkpoint Inhibition and the Prevalence of Autoimmune Disorders Among Patients With Lung and Renal Cancer
Conclusions: This population presents a dilemma to physicians when deciding to treat with immune checkpoint inhibitors and risk immune-related adverse events. Future evaluation of real-world use of immune checkpoint inhibitors in patients with cancer with autoimmune diseases will be needed. (Source: Cancer Informatics)
Source: Cancer Informatics - June 1, 2017 Category: Cancer & Oncology Authors: Sherif M El-Refai Joshua D Brown Esther P Black Jeffery C Talbert Source Type: research

Sequence Analysis and Phylogenetic Studies of Hypoxia-Inducible Factor-1 α
In this study, we have aimed at the evolutionary investigation of the protein HIF-1α across different species to decipher their sequence variations/mutations and look into the probable causes and abnormal behaviour of this molecule under exotic conditions. In total, 16 homologous sequences for HIF-1α were retrieved from the National Center for Biotechnology Information. Sequence identity was performed using the Needle program. Multiple aligned sequences were used to construct the phylogeny using the neighbour-joining method. Most of the changes were observed in oxygen-dependent degradation domain and inhibitory domain. S...
Source: Cancer Informatics - May 31, 2017 Category: Cancer & Oncology Authors: Jagadeesha Poyya Chandrashekhar G Joshi D Jagadeesha Kumar HG Nagendra Source Type: research

Quantitative Study of Thermal Disturbances Due to Nonuniformly Perfused Tumors in Peripheral Regions of Women ’s Breast
Conclusions: The proposed model was successfully used to study the impact of different sizes and shapes of nonuniformly perfused tumor on thermograms in peripheral regions of ellipse-shaped woman’s breast. The proposed model is more realistic in terms of shape and size of tumors and woman’s breast in comparison with earlier models reported in the literature. The finite element discretization of breast into large number of triangular ring elements effectively models the heterogeneity of region. The changes in slope of the thermal curves at the junctions of various peripheral and tumor layers are due to the nonhomogeneou...
Source: Cancer Informatics - May 15, 2017 Category: Cancer & Oncology Authors: Akshara Makrariya Neeru Adlakha Source Type: research

Time-Based Switching Control of Genetic Regulatory Networks: Toward Sequential Drug Intake for Cancer Therapy
This study therefore examines the feasibility of such an approach from a switched system control perspective. Particularly, we study how genetic regulatory systems respond to sequential (switched) drug inputs using the time-based switching mechanism. The design of the time-driven drug switching function guarantees the stability of the genetic regulatory system and the repression of the diseased genes. Simulation results using proof-of-concept models and the proliferation and survival pathways with sequential drug inputs show the effectiveness of the proposed approach. (Source: Cancer Informatics)
Source: Cancer Informatics - May 10, 2017 Category: Cancer & Oncology Authors: Wasiu Opeyemi Oduola Xiangfang Li Chang Duan Lijun Qian Fen Wu Edward R Dougherty Source Type: research