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Cancer Informatics

Abdallah K Alameddine, Frederick Conlin, Brian Binnall
Background: Frequently occurring in cancer are the aberrant alterations of regulatory onco-metabolites, various oncogenes/epigenetic stochasticity, and suppressor genes, as well as the deficient mismatch repair mechanism, chronic inflammation, or those deviations belonging to the other cancer characteristics. How these aberrations that evolve overtime determine the global phenotype of malignant tumors remains to be completely understood. Dynamic analysis may have potential to reveal the mechanism of carcinogenesis and can offer new therapeutic intervention...
2018: Cancer Informatics
Mahdi Imani, Roozbeh Dehghannasiri, Ulisses M Braga-Neto, Edward R Dougherty
Scientists are attempting to use models of ever-increasing complexity, especially in medicine, where gene-based diseases such as cancer require better modeling of cell regulation. Complex models suffer from uncertainty and experiments are needed to reduce this uncertainty. Because experiments can be costly and time-consuming, it is desirable to determine experiments providing the most useful information. If a sequence of experiments is to be performed, experimental design is needed to determine the order. A classical approach is to maximally reduce the overall uncertainty in the model, meaning maximal entropy reduction...
2018: Cancer Informatics
Wasiu Opeyemi Oduola, Xiangfang Li
Effective cancer treatment strategy requires an understanding of cancer behavior and development across multiple temporal and spatial scales. This has resulted into a growing interest in developing multiscale mathematical models that can simulate cancer growth, development, and response to drug treatments. This study thus investigates multiscale tumor modeling that integrates drug pharmacokinetic and pharmacodynamic (PK/PD) information using stochastic hybrid system modeling framework. Specifically, (1) pathways modeled by differential equations are adopted for gene regulations at the molecular level; (2) cellular automata (CA) model is proposed for the cellular and multicellular scales...
2018: Cancer Informatics
Kashyap Nagaraja, Ulisses Braga-Neto
Selected reaction monitoring (SRM) has become one of the main methods for low-mass-range-targeted proteomics by mass spectrometry (MS). However, in most SRM-MS biomarker validation studies, the sample size is very small, and in particular smaller than the number of proteins measured in the experiment. Moreover, the data can be noisy due to a low number of ions detected per peptide by the instrument. In this article, those issues are addressed by a model-based Bayesian method for classification of SRM-MS data...
2018: Cancer Informatics
Gokhan Kirlik, Rao Gullapalli, Warren D'Souza, Gazi Md Daud Iqbal, Michael Naslund, Jade Wong, John Papadimitrou, Steve Roys, Nilesh Mistry, Hao Zhang
Prostate cancer is the most frequently diagnosed cancer in men in the United States. The current main methods for diagnosing prostate cancer include prostate-specific antigen test and transrectal biopsy. Prostate-specific antigen screening has been criticized for overdiagnosis and unnecessary treatment, and transrectal biopsy is an invasive procedure with low sensitivity for diagnosis. We provided a quantitative tool using supervised learning with multiparametric imaging to be able to accurately detect cancer foci and its aggressiveness...
2018: Cancer Informatics
Shinuk Kim
Motivation: Uncovering the relationship between micro-RNAs (miRNAs) and their target messenger RNAs (mRNAs) can provide critical information regarding the mechanisms underlying certain types of cancers. In this context, we have proposed a computational method, referred to as prediction analysis by optimization method (PAOM), to predict miRNA-mRNA relations using data from normal and cancer tissues, and then applying the relevant algorithms to colon and breast cancers. Specifically, we used 26 miRNAs and 26 mRNAs with 676 (= 26 × 26) relationships to be recovered as unknown parameters...
2018: Cancer Informatics
Souptik Barua, Luisa Solis, Edwin Roger Parra, Naohiro Uraoka, Mei Jiang, Huamin Wang, Jaime Rodriguez-Canales, Ignacio Wistuba, Anirban Maitra, Subrata Sen, Arvind Rao
Intraductal papillary mucinous neoplasms (IPMNs), critical precursors of the devastating tumor pancreatic ductal adenocarcinoma (PDAC), are poorly understood in the pancreatic cancer community. Researchers have shown that IPMN patients with high-grade dysplasia have a greater risk of subsequent development of PDAC in the remnant pancreas than do patients with low-grade dysplasia. In this study, we built a computational prediction model that encapsulates the spatial cellular interactions in IPMNs that play key roles in the transformation of low-grade IPMN cysts to high-grade cysts en route to PDAC...
2018: Cancer Informatics
Richard Finney, Daoud Meerzaman
Chromatic is a novel web-browser tool that enables researchers to visually inspect genomic variations identified through next-generation sequencing of cancer data sets to determine whether such calls are, in fact, valid. It is the first cancer bioinformatics tool developed using WebAssembly technology, which comprises a portable, low-level byte code format that provides for the rapid execution of programs within supported web browsers. It has been designed expressly for ease of use by scientists without extensive expertise in bioinformatics...
2018: Cancer Informatics
Chao Sima, Jianping Hua, Michael L Bittner, Seungchan Kim, Edward R Dougherty
Features for standard expression microarray and RNA-Seq classification are expression averages over collections of cells. Single cell provides expression measurements for individual cells in a collection of cells from a particular tissue sample. Hence, it can yield feature vectors consisting of higher order and mixed moments. This article demonstrates the advantage of using these expression moments in cancer-related classification. We use synthetic data generated from 2 real networks, the mammalian cell cycle network and a melanoma-related pathway network, and real single-cell data generated via fluorescent protein reporters from 2 cell lines, HT-29 and HCT-116...
2018: Cancer Informatics
Min Wang, Steven M Kornblau, Kevin R Coombes
Principal component analysis (PCA) is one of the most common techniques in the analysis of biological data sets, but applying PCA raises 2 challenges. First, one must determine the number of significant principal components (PCs). Second, because each PC is a linear combination of genes, it rarely has a biological interpretation. Existing methods to determine the number of PCs are either subjective or computationally extensive. We review several methods and describe a new R package, PCDimension, that implements additional methods, the most important being an algorithm that extends and automates a graphical Bayesian method...
2018: Cancer Informatics
Yi Yang, Saonli Basu, Lisa Mirabello, Logan Spector, Lin Zhang
Osteosarcoma is considered to be the most common primary malignant bone cancer among children and young adults. Previous studies suggest growth spurts and height to be risk factors for osteosarcoma. However, studies on the genetic cause are still limited given the rare occurrence of the disease. In this study, we investigated in a family trio data set that is composed of 209 patients and their unaffected parents and conducted a genome-wide association study (GWAS) to identify genetic risk factors for osteosarcoma...
2018: Cancer Informatics
Martha L Slattery, Lila E Mullany, Lori C Sakoda, Roger K Wolff, Wade S Samowitz, Jennifer S Herrick
Mitogen-activated protein kinase (MAPK) pathways regulate many cellular functions including cell proliferation and apoptosis. We examined associations of differential gene and microRNA (miRNA) expression between carcinoma and paired normal mucosa for 241 genes in the KEGG-identified MAPK-signaling pathway among 217 colorectal cancer (CRC) cases. Gene expression data (RNA-Seq) and miRNA expression data (Agilent Human miRNA Microarray V19.0; Agilent Technologies Inc., Santa Clara, CA, USA) were analyzed. We first identified genes most strongly associated with CRC using a fold change (FC) of >1...
2018: Cancer Informatics
Mohamed Abdulaziz Al Dawish, Asirvatham Alwin Robert, Mohammed A Thabet, Rim Braham
Background/objectives: Thyroid nodule (TN) is a common thyroid disorder globally, and the incidence has been increasing in recent decades. The objective of this study was to determine the contribution of thyroid-stimulating hormone (TSH), ultrasound (US), and cytological classification system for predicting malignancy among the surgically excised nodules. Design and methods: A retrospective analysis was performed between January 2012 and December 2014, using data drawn from 1188 patients (15-90 years), who had 1433 TN and fine-needle aspiration in Prince Sultan Military Medical City, Saudi Arabia...
2018: Cancer Informatics
Edward R Dougherty, Anne-Laure Boulesteix, Lori A Dalton, Michelle Zhang
Cancer is a systems disease involving mutations and altered regulation. This supplement treats cancer research as it pertains to 3 systems issues of an inherently statistical nature: regulatory modeling and information processing, diagnostic classification, and therapeutic intervention and control. Topics of interest include (but are not limited to) multiscale modeling, gene/protein transcriptional regulation, dynamical systems, pharmacokinetic/pharmacodynamic modeling, compensatory regulation, feedback, apoptotic and proliferative control, copy number-expression interaction, integration of different feature types, error estimation, and reproducibility...
2018: Cancer Informatics
Adel Aloraini, Karim M ElSawy
Understanding gene-gene interaction and its causal relationship to protein-protein interaction is a viable route for understanding drug action at the genetic level, which is largely hindered by inability to robustly map gene regulatory networks. Here, we use biological prior knowledge of family-to-family gene interactions available in the KEGG database to reveal individual gene-to-gene interaction networks that underlie the gene expression profiles of 2 cell line data sets, sensitive and resistive to neoadjuvant docetaxel breast anticancer drug...
2018: Cancer Informatics
Yanina Natanzon, Madalene Earp, Julie M Cunningham, Kimberly R Kalli, Chen Wang, Sebastian M Armasu, Melissa C Larson, David Dl Bowtell, Dale W Garsed, Brooke L Fridley, Stacey J Winham, Ellen L Goode
High-grade serous ovarian cancer (HGSOC) is a complex disease in which initiation and progression have been associated with copy number alterations, epigenetic processes, and, to a lesser extent, germline variation. We hypothesized that, when summarized at the gene level, tumor methylation and germline genetic variation, alone or in combination, influence tumor gene expression in HGSOC. We used Elastic Net (ENET) penalized regression method to evaluate these associations and adjust for somatic copy number in 3 independent data sets comprising tumors from more than 470 patients...
2018: Cancer Informatics
Lei Song, Krithika Bhuvaneshwar, Yue Wang, Yuanjian Feng, Ie-Ming Shih, Subha Madhavan, Yuriy Gusev
The CINdex Bioconductor package addresses an important area of high-throughput genomic analysis. It calculates the chromosome instability (CIN) index, a novel measurement that quantitatively characterizes genome-wide copy number alterations (CNAs) as a measure of CIN. The advantage of this package is an ability to compare CIN index values between several groups for patients (case and control groups), which is a typical use case in translational research. The differentially changed cytobands or chromosomes can then be linked to genes located in the affected genomic regions, as well as pathways...
2017: Cancer Informatics
Jian Wang, Rajesh Talluri, Sanjay Shete
To address the complexity of the X-chromosome inactivation (XCI) process, we previously developed a unified approach for the association test for X-chromosomal single-nucleotide polymorphisms (SNPs) and the disease of interest, accounting for different biological possibilities of XCI: random, skewed, and escaping XCI. In the original study, we focused on the SNP-disease association test but did not provide knowledge regarding the underlying XCI models. One can use the highest likelihood ratio (LLR) to select XCI models (max-LLR approach)...
2017: Cancer Informatics
Julia Krushkal, Yingdong Zhao, Curtis Hose, Anne Monks, James H Doroshow, Richard Simon
Cellular glycosylation processes are vital to cell functioning. In malignant cells, they are profoundly altered. We used time-course gene expression data from the NCI-60 cancer cell lines treated with 11 antitumor agents to analyze expression changes of genes involved in glycosylation pathways, genes encoding glycosylation targets or regulators, and members of cancer pathways affected by glycosylation. We also identified glycosylation genes for which pretreatment expression levels or changes after treatment were correlated with drug sensitivity...
2017: Cancer Informatics
Nick Kinney, Robin T Varghese, Ramu Anandakrishnan, Harold R Skip Garner
African American woman are 43% more likely to die from breast cancer than white women and have increased the risk of tumor recurrence despite lower incidence. We investigate variations in microsatellite genomic regions-a type of repetitive DNA-and possible links to the breast cancer mortality gap. We screen 33 854 microsatellites in germline DNA of African American women with and without breast cancer: 4 are statistically significant. These are located in the 3' UTR (untranslated region) of gene ZDHHC3 , an intron of transcribed pseudogene INTS4L1 , an intron of ribosomal gene RNA5-8S5 , and an intergenic region of chromosome 16...
2017: Cancer Informatics
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