Determining the optimal method for zygoma fracture reduction is a common challenge. Numerous methods for treating zygomatic arch fractures have been suggested. However, a substantial gap exists between suggested treatment strategies and real-world practice. A general consensus of classification and treatment guidelines for zygomatic arch reduction has not yet been established. We reviewed our cases and propose a new classification of zygomatic arch fracture and a treatment algorithm for successful reduction based on the injury vectors.
Rapidly evolving advances in the understanding of theorized unique driver mutations within individual patient's cancers, as well as dramatic reduction in the cost of genomic profiling, have stimulated major interest in the role of such testing in routine clinical practice. The aim of this study was to report our initial experience with genomic testing in heavily pretreated breast cancer patients.
Identifying differential features between conditions is a popular approach to understanding molecular features and their mechanisms underlying a biological process of particular interest. Although many tests for identifying differential expression of gene or gene sets have been proposed, there was limited success in developing methods for differential interactions of genes between conditions because of its computational complexity. We present a method for Evaluation of Dependency DifferentialitY (EDDY), which is a statistical test for differential dependencies of a set of genes between two conditions. Unlike previous methods focused on differential expression of individual genes or correlation changes of individual gene-gene interactions, EDDY compares two conditions by evaluating the probability distributions of dependency networks from genes. The method has been evaluated and compared with other methods through simulation studies, and application to glioblastoma multiforme data resulted in informative cancer and glioblastoma multiforme subtype-related findings. The comparison with Gene Set Enrichment Analysis, a differential expression-based method, revealed that EDDY identifies the gene sets that are complementary to those identified by Gene Set Enrichment Analysis. EDDY also showed much lower false positives than Gene Set Co-expression Analysis, a method based on correlation changes of individual gene-gene interactions, thus providing more informative results. The Java implementation of the algorithm is freely available to noncommercial users. Download from: http://biocomputing.tgen.org/software/EDDY.
Muscle-invasive bladder cancers (MIBCs) are biologically heterogeneous and have widely variable clinical outcomes and responses to conventional chemotherapy. We discovered three molecular subtypes of MIBC that resembled established molecular subtypes of breast cancer. Basal MIBCs shared biomarkers with basal breast cancers and were characterized by p63 activation, squamous differentiation, and more aggressive disease at presentation. Luminal MIBCs contained features of active PPAR? and estrogen receptor transcription and were enriched with activating FGFR3 mutations and potential FGFR inhibitor sensitivity. p53-like MIBCs were consistently resistant to neoadjuvant methotrexate, vinblastine, doxorubicin and cisplatin chemotherapy, and all chemoresistant tumors adopted a p53-like phenotype after therapy. Our observations have important implications for prognostication, the future clinical development of targeted agents, and disease management with conventional chemotherapy.
Activity of GFR/PI3K/AKT pathway inhibitors in glioblastoma clinical trials has not been robust. We hypothesized variations in the pathway between tumors contribute to poor response. We clustered GBM based on AKT pathway genes and discovered new subtypes then characterized their clinical and molecular features. There are at least 5 GBM AKT subtypes having distinct DNA copy number alterations, enrichment in oncogenes and tumor suppressor genes and patterns of expression for PI3K/AKT/mTOR signaling components. Gene Ontology terms indicate a different cell of origin or dominant phenotype for each subgroup. Evidence suggests one subtype is very sensitive to BCNU or CCNU (median survival 5.8 vs. 1.5 years; BCNU/CCNU vs other treatments; respectively). AKT subtyping advances previous approaches by revealing additional subgroups with unique clinical and molecular features. Evidence indicates it is a predictive marker for response to BCNU or CCNU and PI3K/AKT/mTOR pathway inhibitors. We anticipate Akt subtyping may help stratify patients for clinical trials and augment discovery of class-specific therapeutic targets.
Molecularly-guided trials (i.e. PMed) now seek to aid clinical decision-making by matching cancer targets with therapeutic options. Progress has been hampered by the lack of cancer models that account for individual-to-individual heterogeneity within and across cancer types. Naturally occurring cancers in pet animals are heterogeneous and thus provide an opportunity to answer questions about these PMed strategies and optimize translation to human patients. In order to realize this opportunity, it is now necessary to demonstrate the feasibility of conducting molecularly-guided analysis of tumors from dogs with naturally occurring cancer in a clinically relevant setting.
There has been a growing interest in using next-generation sequencing (NGS) to profile extracellular small RNAs from the blood and cerebrospinal fluid (CSF) of patients with neurological diseases, CNS tumors, or traumatic brain injury for biomarker discovery. Small sample volumes and samples with low RNA abundance create challenges for downstream small RNA sequencing assays. Plasma, serum, and CSF contain low amounts of total RNA, of which small RNAs make up a fraction. The purpose of this study was to maximize RNA isolation from RNA-limited samples and apply these methods to profile the miRNA in human CSF by small RNA deep sequencing. We systematically tested RNA isolation efficiency using ten commercially available kits and compared their performance on human plasma samples. We used RiboGreen to quantify total RNA yield and custom TaqMan assays to determine the efficiency of small RNA isolation for each of the kits. We significantly increased the recovery of small RNA by repeating the aqueous extraction during the phenol-chloroform purification in the top performing kits. We subsequently used the methods with the highest small RNA yield to purify RNA from CSF and serum samples from the same individual. We then prepared small RNA sequencing libraries using Illuminas TruSeq sample preparation kit and sequenced the samples on the HiSeq 2000. Not surprisingly, we found that the miRNA expression profile of CSF is substantially different from that of serum. To our knowledge, this is the first time that the small RNA fraction from CSF has been profiled using next-generation sequencing.
Epidemiological data have suggested that African American (AA) persons are twice as likely to be diagnosed with multiple myeloma (MM) compared with European American (EA) persons. Here, we have analyzed a set of cytogenetic and genomic data derived from AA and EA MM patients. We have compared the frequency of IgH translocations in a series of data from 115 AA patients from 3 studies and 353 EA patients from the Eastern Cooperative Oncology Group (ECOG) studies E4A03 and E9487. We have also interrogated tumors from 45 AA and 196 EA MM patients for somatic copy number abnormalities associated with poor outcome. In addition, 35 AA and 178 EA patients were investigated for a transcriptional profile associated with high-risk disease. Overall, based on this cohort, genetic profiles were similar except for a significantly lower frequency of IgH translocations (40% vs 52%; P = .032) in AA patients. Frequency differences of somatic copy number aberrations were not significant after correction for multiple testing. There was also no significant difference in the frequency of high-risk disease based on gene expression profiling. Our study represents the first comprehensive comparisons of the frequency and distribution of molecular alterations in MM tumors between AA and EA patients. ECOG E4A03 is registered with ClinicalTrials.gov, number NCT00098475. ECOG E9487 is a companion validation set to the ECOG study E9486 and is registered with the National Institutes of Health, National Cancer Institute, Clinical Trials (PDQ), number EST-9486.
Identifying similarities and differences in the molecular constitutions of various types of cancer is one of the key challenges in cancer research. The appearances of a cancer depend on complex molecular interactions, including gene regulatory networks and gene-environment interactions. This complexity makes it challenging to decipher the molecular origin of the cancer. In recent years, many studies reported methods to uncover heterogeneous depictions of complex cancers, which are often categorized into different subtypes. The challenge is to identify diverse molecular contexts within a cancer, to relate them to different subtypes, and to learn underlying molecular interactions specific to molecular contexts so that we can recommend context-specific treatment to patients.
MicroRNAs influence cell physiology; alteration in miRNA regulation can be implicated in carcinogenesis and disease progression. Generally, one miRNA is predicted to regulate several hundred genes, and as a result, miRNAs could serve as a better classifier than gene expression. We combine validated miRNA expression values with imaging features to classify NSCLC brain mets from non-brain mets and identify possible biomarkers of brain mets. This research involves comprehensive miRNA expression profiling, evaluation of normalisation techniques and combination of miRNA with imaging features FDG-PET/CT and CT Scan. The biomarkers were validated on an independent data set to predict potential brain mets.
A grand challenge in the modeling of biological systems is the identification of key variables which can act as targets for intervention. Good intervention targets are the "key players" in a system and have significant influence over other variables; in other words, in the context of diseases such as cancer, targeting these variables with treatments and interventions will provide the greatest effects because of their direct and indirect control over other parts of the system. Boolean networks are among the simplest of models, yet they have been shown to adequately model many of the complex dynamics of biological systems. Often ignored in the Boolean network model, however, are the so called basins of attraction. As the attractor states alone have been shown to correspond to cellular phenotypes, it is logical to ask which variables are most responsible for triggering a path through a basin to a particular attractor.
Breast cancer is a highly heterogeneous disease with respect to molecular alterations and cellular composition making therapeutic and clinical outcome unpredictable. This diversity creates a significant challenge in developing tumor classifications that are clinically reliable with respect to prognosis prediction.
Brain metastasis (BM) can affect ? 25% of nonsmall cell lung cancer (NSCLC) patients during their lifetime. Efforts to characterize patients that will develop BM have been disappointing. microRNAs (miRNAs) regulate the expression of target mRNAs. miRNAs play a role in regulating a variety of targets and, consequently, multiple pathways, which make them a powerful tool for early detection of disease, risk assessment, and prognosis. We investigated miRNAs that may serve as biomarkers to differentiate between NSCLC patients with and without BM. miRNA microarray profiling was performed on samples from clinically matched NSCLC from seven patients with BM (BM+) and six without BM (BM-). Using t-test and further qRT-PCR validation, eight miRNAs were confirmed to be significantly differentially expressed. Of these, expression of miR-328 and miR-330-3p were able to correctly classify BM+ vs. BM- patients. This classifier was used on a validation cohort (n = 15), and it correctly classified 12/15 patients. Gene expression analysis comparing A549 parental and A549 cells stably transfected to over-express miR-328 (A549-328) identified several significantly differentially expressed genes. PRKCA was one of the genes over-expressed in A549-328 cells. Additionally, A549-328 cells had significantly increased cell migration compared to A549 cells, which was significantly reduced upon PRKCA knockdown. In summary, miR-328 has a role in conferring migratory potential to NSCLC cells working in part through PRKCA and with further corroboration in additional independent cohorts, these miRNAs may be incorporated into clinical treatment decision making to stratify NSCLC patients at higher risk for developing BM.
Magnetic fields may delay the rate of cell cycle progression, and there are reports that magnetic fields induce neurite outgrowth in cultured neuronal cells. To demonstrate whether magnetic field also effects on myoblast cells in cell growth, C2C12 cell lines were cultured and 2000G static magnetic field was applied. After 48 h of incubation, both the WST-1 assay (0.01 < P < 0.025, t-test) and direct cell counting (P < 0.0005, t-test) showed that static magnetic fields inhibit the proliferation of cultured C2C12 cells. Immunocytochemistry for alpha and tubulin gamma complex protein (TUBA and GCP3) was made and applying a static magnetic field-dispersed tubulin GCP3 formation, a intracellular apparatus for tubulin structuring in cell division. This protein expression was not altered by western blot. This study indicates that applying a static magnetic field alters the subcellular localizing of GCP3, and may delay the cell growth in cultured C2C12 myoblast cells.
We propose a novel semi-supervised clustering method called GO Fuzzy c-means, which enables the simultaneous use of biological knowledge and gene expression data in a probabilistic clustering algorithm. Our method is based on the fuzzy c-means clustering algorithm and utilizes the Gene Ontology annotations as prior knowledge to guide the process of grouping functionally related genes. Unlike traditional clustering methods, our method is capable of assigning genes to multiple clusters, which is a more appropriate representation of the behavior of genes. Two datasets of yeast (Saccharomyces cerevisiae) expression profiles were applied to compare our method with other state-of-the-art clustering methods. Our experiments show that our method can produce far better biologically meaningful clusters even with the use of a small percentage of Gene Ontology annotations. In addition, our experiments further indicate that the utilization of prior knowledge in our method can predict gene functions effectively. The source code is freely available at http://sysbio.fulton.asu.edu/gofuzzy/.
The two important problems of computational biology are the modeling of gene regulatory networks and the study of the network structure of complex biological systems. There is an increased use of mathematical and computational theory techniques to solve both these problems. The Boolean circuit model is one of the most popular modeling paradigms used to model gene regulatory networks. In this paper we try to make use of the properties of threshold logic (an alternative to Boolean logic to design digital circuits) to determine the network structure of gene systems. This approach uses the gene-expression data from microarray experiments as input. The proposed method was first used to build the gene network for a set of genes, proteins, and other molecular components based on in silico data. Then, the method was applied to a biological dataset to build the gene regulatory network for a core set of genes associated with melanoma. Some of the interactions found could be verified by earlier biological experiments reported in published literature. Other interactions that could not be validated by existing biological knowledge can provide insights into the investigation of bio-chemical pathways associated with melanoma development.
Gene regulation modeling is one of the most active research topics in systems biology. The aim of modeling gene regulation is to understand how individual genes function and interact with each other to create complex biological phenomena. In this paper we propose a novel gene regulatory model based on threshold logic. The approach is developed by a combination of threshold logic properties and perceptron learning techniques. This work does not focus on determination of the pair-wise interactions among genes. Instead, the objective of this work is to generate a model that will describe and predict phenomena associated with a biological system. The utility of the approach is demonstrated by modeling a cellular system of 50 genes. The model could effectively replicate both the steady state and the transient behavior of genes.
Learning or inferring networks of genomic regulation specific to a cellular state, such as a subtype of tumor, can yield insight above and beyond that resulting from network-learning techniques which do not acknowledge the adaptive nature of the cellular system. In this study we show that Cellular Context Mining, which is based on a mathematical model of contextual genomic regulation, produces gene regulatory networks (GRNs) from steady-state expression microarray data which are specific to the varying cellular contexts hidden in the data; we show that these GRNs not only model gene interactions, but that they are also readily annotated with context-specific genomic information. We propose that these context-specific GRNs provide advantages over other techniques, such as clustering and Bayesian networks, when applied to gene expression data of cancer patients.
Previous studies have now demonstrated that both genic and global hypomethylation characterizes the multiple myeloma (MM) epigenome. Whether these methylation changes are associated with global and corresponding increases (or decreases) in transcriptional activity are poorly understood. The purpose of our current study was to correlate DNA methylation levels in MM to gene expression. We analyzed matching datasets generated by the GoldenGate methylation BeadArray and Affymetrix gene expression platforms in 193 MM samples. We subsequently utilized two independent statistical approaches to identify methylation-expression correlations. In the first approach, we used a linear correlation parameter by computing a Pearson correlation coefficient. In the second approach, we discretized samples into low and high methylation groups and then compared the gene expression differences between the groups. Only methylation of 2.1% and 25.3% of CpG sites on the methylation array correlated to gene expression by Pearson correlation or the discretization method, respectively. Among the genes with methylation-expression correlations were IGF1R, DLC1, p16, and IL17RB. In conclusion, DNA methylation may directly regulate relatively few genes and suggests that additional studies are needed to determine the effects of genome-wide methylation changes in MM.
Glioblastoma (GB) is the most common and lethal type of primary brain tumor. Clinical outcome remains poor and is essentially palliative due to the highly invasive nature of the disease. A more thorough understanding of the molecular mechanisms that drive glioma invasion is required to limit dispersion of malignant glioma cells.
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