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Carter H.,Institute for Computational Medicine | Samayoa J.,Institute for Computational Medicine | Hruban R.H.,Sol Goldman Pancreatic Cancer Research Center | Karchin R.,Institute for Computational Medicine
Cancer Biology and Therapy | Year: 2010

Over 20,000 genes were recently sequenced in a series of 24 pancreatic cancers. We applied CHASM (Cancer-specific High-throughput Annotation of Somatic Mutations) to 963 of the missense somatic missense mutations discovered in these 24 cancers. CHASM identified putative driver mutations (false discovery rate ≤0.3) in three known pancreatic cancer driver genes (P53, SMAD4, CDKN2A). An additional 15 genes with putative driver mutations include genes coding for kinases (PIK3CG, DGKA, STK33, TTK and PRKCG), for cell cycle related proteins (NEK8), and for proteins involved in cell adhesion (CMAS, PCDHB2). These and other mutations identified by CHASM point to potential "driver genes" in pancreatic cancer that should be prioritized for additional follow-up. © 2010 Landes Bioscience.

PubMed | Baltimore., Institute for Computational Medicine, Johns Hopkins University, Foundation Medicine and University of Chicago
Type: Journal Article | Journal: Annals of oncology : official journal of the European Society for Medical Oncology | Year: 2015

To determine genomic alterations in head and neck squamous cell carcinoma (HNSCC) using formalin-fixed, paraffin-embedded (FFPE) tumors obtained through routine clinical practice, selected cancer-related genes were evaluated and compared with alterations seen in frozen tumors obtained through research studies.DNA samples obtained from 252 FFPE HNSCC were analyzed using next-generation sequencing-based (NGS) clinical assay to determine sequence and copy number variations in 236 cancer-related genes plus 47 introns from 19 genes frequently rearranged in cancer. Human papillomavirus (HPV) status was determined by presence of the HPV DNA sequence in all samples and corroborated with high-risk HPV in situ hybridization (ISH) and p16 immunohistochemical (IHC) staining in a subset of tumors. Sequencing data from 399 frozen tumors in The Cancer Genome Atlas and University of Chicago public datasets were analyzed for comparison.Among 252 FFPE HNSCC, 84 (33%) were HPV positive and 168 (67%) were HPV negative by sequencing. A subset of 40 tumors with HPV ISH and p16 IHC results showed complete concordance with NGS-derived HPV status. The most common genes with genomic alterations were PIK3CA and PTEN in HPV-positive tumors and TP53 and CDKN2A/B in HPV-negative tumors. In the pathway analysis, the PI3K pathway in HPV-positive tumors and DNA repair-p53 and cell cycle pathways in HPV-negative tumors were frequently altered. The HPV-positive oropharynx and HPV-positive nasal cavity/paranasal sinus carcinoma shared similar mutational profiles.The genomic profile of FFPE HNSCC tumors obtained through routine clinical practice is comparable with frozen tumors studied in research setting, demonstrating the feasibility of comprehensive genomic profiling in a clinical setting. However, the clinical significance of these genomic alterations requires further investigation through application of these genomic profiles as integral biomarkers in clinical trials.

News Article | December 16, 2016

In their search for new ways to treat cancer, many scientists are using a high-tech process called genome sequencing to hunt for genetic mutations that encourage tumor cells to thrive. To aid in this search, some researchers have developed new bioinformatics methods that each claim to help pinpoint the cancer-friendly mutants. But a stubborn question remains: Among the numerous new tactics that aim to spotlight the so-called cancer driver genes, which produce the most accurate results? To help solve this puzzle, a team of Johns Hopkins computational scientists and cancer experts have devised their own bioinformatics software to evaluate how well the current strategies identify cancer-promoting mutations and distinguish them from benign mutations in cancer cells. The team's paper, titled "Evaluating the evaluation of cancer driver genes," was published recently in the Early Edition of Proceedings of the National Academy of Sciences. The scholars say it is important to assess how well these genetic research methods work because of their potential value in developing treatments to keep cancer in check. "Identifying the genes that cause cancer when altered is often challenging, but is critical for directing research along the most fruitful course," said Bert Vogelstein, a member of the Ludwig Center at the Johns Hopkins Kimmel Cancer Center and one of the journal article's co-authors. "This paper establishes novel ways to judge the techniques used to identify true cancer-causing genes and should considerably facilitate advances in this field in the future." The lead author of the article was Collin J. Tokheim, a doctoral student in the Institute of Computational Medicine and the Department of Biomedical Engineering, which are both shared by the university's Whiting School of Engineering and its School of Medicine. He was supervised by his doctoral adviser, Rachel Karchin, an associate professor of biomedical engineering and oncology, the William R. Brody Faculty Scholar and a core faculty member of the university's Institute for Computational Medicine. Karchin, who also is a member of the Kimmel Cancer Center, was the senior author of the journal article. Tokheim said one of the challenges the team faced was the lack of a widely accepted consensus on what qualifies as a cancer driver gene. "People have lists of what they consider to be cancer driver genes, but there's no official reference guide, no gold standard," he said. Nevertheless, Tokheim and his colleagues were able to develop a machine-learning-based method for driver gene prediction and a framework for evaluating and comparing other prediction methods. For the study, this evaluation tool was applied to eight existing cancer driver gene prediction methods. The results were not entirely reassuring. "Our conclusion," Tokheim said, "is that these methods still need to get better. We're sharing our methodology publicly, and it should help others to improve their systems for identifying cancer driver genes." The other co-authors of the paper were Nickolas Papadopoulos and Kenneth Kinzler, both affiliated with of the Ludwig Center at the Johns Hopkins Kimmel Cancer Center. This research was funded by National Cancer Institute grants F31CA200266, 5U01CA180956-03, 1U24CA204817-01 and P50-CA62924; The Virginia and D. K. Ludwig Fund for Cancer Research; Lustgarten Foundation for Pancreatic Cancer Research; and The Sol Goldman Center for Pancreatic Cancer Research.

News Article | September 14, 2016

Using high-tech human heart models and mouse experiments, scientists at Johns Hopkins and Germany’s University of Bonn have shown that beams of light could replace electric shocks in patients reeling from a deadly heart rhythm disorder. The findings, published online Sept. 12 in the October 2016 edition of The Journal of Clinical Investigation, could pave the way for a new type of implantable defibrillators. Current devices deliver pulses of electricity that are extremely painful and can damage heart tissue. Light-based treatment, the Johns Hopkins and Bonn researchers say, should provide a safer and gentler remedy for patients at high risk of arrhythmia, an irregular heartbeat that can cause sudden cardiac death within minutes. This idea springs from advances in the field of optogenetics, in which light-sensitive proteins are embedded in living tissue, enabling the use of light sources to modify electrical activity in cells. “We are working towards optical defibrillation of the heart, where light will be given to a patient who is experiencing cardiac arrest, and we will be able to restore the normal functioning of the heart in a gentle and painless manner,” said Natalia Trayanova, who supervised the research at Johns Hopkins. Trayanova is the Murray B. Sachs Professor in the Department of Biomedical Engineering and is a core faculty member in the university’s Institute for Computational Medicine. To move the new heart treatment closer to reality, the scientists at the University of Bonn and Johns Hopkins focused on two different types of research. The Bonn team conducted tests on beating mouse hearts whose cells had been genetically engineered to express proteins that react to light and alter electrical activity within the organ. When the Bonn researchers triggered ventricular fibrillation in the mouse heart, a light pulse of one second applied to the heart was enough to restore normal rhythm. “This is a very important result,” said Tobias Bruegmann, one of the lead authors of the journal article. “It shows for the first time experimentally that light can be used for defibrillation of cardiac arrhythmia.” To find out if this technique could help human patients, Trayanova’s team at Johns Hopkins performed an analogous experiment within a detailed computer model of a human heart, one derived from MRI scans taken of a patient who had experienced a heart attack and was now at risk of arrhythmia. “Our simulations show that a light pulse to the heart could stop the cardiac arrhythmia in this patient,” said Patrick M. Boyle, a Johns Hopkins biomedical engineering research professor who was also a lead author of the journal article. To do so, however, the method from the University of Bonn had to be tweaked for the human heart by using red light to stimulate the heart cells, instead of the blue light used in mice. Boyle, who is a member of Trayanova’s lab team, explained that the blue light used in the much smaller mouse hearts was not powerful enough to fully penetrate human heart tissue. The red light, which has a longer wavelength, was more effective in the virtual human tests. “In addition to demonstrating the feasibility of optogenetic defibrillation in a virtual heart of a patient, the simulations revealed the precise ways in which light alters the collective electrical behavior of the cells in the heart to achieve the desired arrhythmia termination,” Trayanova said. Boyle added that this aspect of the study highlighted the important role that computational modeling can play in guiding and accelerating the development of therapeutic applications for cardiac optogenetics, a technology that is still in its infancy. Junior Professor Philipp Sasse of the Institute of Physiology I at the University of Bonn, who is corresponding author of the study and supervised the project in Germany, agreed that the promising light treatment will require much more time and research before it can become a commonplace medical procedure. “The new method is still in the stage of basic research,” Sasse said. “Until implantable optical defibrillators can be developed for the treatment of patients, it will still take at least five to ten years.”

Santaniello S.,Institute for Computational Medicine | Santaniello S.,University of Connecticut | McCarthy M.M.,Boston University | Montgomery E.B.,Greenville Neuromodulation Center | And 5 more authors.
Proceedings of the National Academy of Sciences of the United States of America | Year: 2015

High-frequency deep brain stimulation (HFS) is clinically recognized to treat parkinsonian movement disorders, but its mechanisms remain elusive. Current hypotheses suggest that the therapeutic merit of HFS stems from increasing the regularity of the firing patterns in the basal ganglia (BG). Although this is consistent with experiments in humans and animal models of Parkinsonism, it is unclear how the pattern regularization would originate from HFS. To address this question, we built a computational model of the cortico-BG-thalamo-cortical loop in normal and parkinsonian conditions. We simulated the effects of subthalamic deep brain stimulation both proximally to the stimulation site and distally through orthodromic and antidromic mechanisms for several stimulation frequencies (20-180 Hz) and, correspondingly, we studied the evolution of the firing patterns in the loop. The model closely reproduced experimental evidence for each structure in the loop and showed that neither the proximal effects nor the distal effects individually account for the observed pattern changes, whereas the combined impact of these effects increases with the stimulation frequency and becomes significant for HFS. Perturbations evoked proximally and distally propagate along the loop, rendezvous in the striatum, and, for HFS, positively overlap (reinforcement), thus causing larger poststimulus activation and more regular patterns in striatum. Reinforcement is maximal for the clinically relevant 130-Hz stimulation and restores a more normal activity in the nuclei downstream. These results suggest that reinforcement may be pivotal to achieve pattern regularization and restore the neural activity in the nuclei downstream and may stem from frequency-selective resonant properties of the loop.

Wei A.-C.,Institute for Computational Medicine | Aon M.A.,Johns Hopkins University | O'Rourke B.,Johns Hopkins University | Winslow R.L.,Institute for Computational Medicine | And 2 more authors.
Biophysical Journal | Year: 2011

We developed a computational model of mitochondrial energetics that includes Ca 2+, proton, Na +, and phosphate dynamics. The model accounts for distinct respiratory fluxes from substrates of complex I and complex II, pH effects on equilibrium constants and enzyme kinetics, and the acid-base equilibrium distributions of energy intermediaries. We experimentally determined NADH and Δψ m in guinea pig mitochondria during transitions from de-energized to energized, or during state 2/4 to state 3 respiration, or into hypoxia and uncoupling, and compared the results with those obtained in model simulations. The model quantitatively reproduces the experimentally observed magnitude of Δψ m, the range of NADH levels, respiratory fluxes, and respiratory control ratio upon transitions elicited by sequential additions of substrate and ADP. Simulation results are also able to mimic the change in Δψ m upon addition of phosphate to state 4 mitochondria, leading to matrix acidification and Δψ m polarization. The steady-state behavior of the integrated mitochondrial model qualitatively simulates the dependence of respiration on the proton motive force, and the expected flux-force relationships existing between respiratory and ATP synthesis fluxes versus redox and phosphorylation potentials. This upgraded mitochondrial model provides what we believe are new opportunities for simulating mitochondrial physiological behavior during dysfunctional states involving changes in pH and ion dynamics. © 2011 by the Biophysical Society.

Vadakkumpadan F.,Johns Hopkins University | Arevalo H.,Johns Hopkins University | Ceritoglu C.,Johns Hopkins University | Miller M.,Institute for Computational Medicine | And 2 more authors.
IEEE Transactions on Medical Imaging | Year: 2012

Technological limitations pose a major challenge to acquisition of myocardial fiber orientations for patient-specific modeling of cardiac (dys)function and assessment of therapy. The objective of this project was to develop a methodology to estimate cardiac fiber orientations from in vivo images of patient heart geometries. An accurate representation of ventricular geometry and fiber orientations was reconstructed, respectively, from high-resolution ex vivo structural magnetic resonance (MR) and diffusion tensor (DT) MR images of a normal human heart, referred to as the atlas. Ventricular geometry of a patient heart was extracted, via semiautomatic segmentation, from an in vivo computed tomography (CT) image. Using image transformation algorithms, the atlas ventricular geometry was deformed to match that of the patient. Finally, the deformation field was applied to the atlas fiber orientations to obtain an estimate of patient fiber orientations. The accuracy of the fiber estimates was assessed using six normal and three failing canine hearts. The mean absolute difference between inclination angles of acquired and estimated fiber orientations was 15.4°. Computational simulations of ventricular activation maps and pseudo-ECGs in sinus rhythm and ventricular tachycardia indicated that there are no significant differences between estimated and acquired fiber orientations at a clinically observable level. © 2012 IEEE.

Kembro J.M.,Johns Hopkins University | Aon M.A.,Johns Hopkins University | Winslow R.L.,Institute for Computational Medicine | O'Rourke B.,Johns Hopkins University | And 2 more authors.
Biophysical Journal | Year: 2013

To understand the mechanisms involved in the control and regulation of mitochondrial reactive oxygen species (ROS) levels, a two-compartment computational mitochondrial energetic-redox (ME-R) model accounting for energetic, redox, and ROS metabolisms is presented. The ME-R model incorporates four main redox couples (NADH/NAD+, NADPH/NADP+, GSH/GSSG, Trx(SH)2/TrxSS). Scavenging systems - glutathione, thioredoxin, superoxide dismutase, catalase - are distributed in mitochondrial matrix and extra-matrix compartments, and transport between compartments of ROS species (superoxide: O2ṡ-, hydrogen peroxide: H 2O2), and GSH is also taken into account. Model simulations are compared with experimental data obtained from isolated heart mitochondria. The ME-R model is able to simulate: i), the shape and order of magnitude of H2O2 emission and dose-response kinetics observed after treatment with inhibitors of the GSH or Trx scavenging systems and ii), steady and transient behavior of ΔΨm and NADH after single or repetitive pulses of substrate- or uncoupler-elicited energetic-redox transitions. The dynamics of the redox environment in both compartments is analyzed with the model following substrate addition. The ME-R model represents a useful computational tool for exploring ROS dynamics, the role of compartmentation in the modulation of the redox environment, and how redox regulation participates in the control of mitochondrial function. © 2013 Biophysical Society.

Chen L.,Intel Corporation | Chen L.,Johns Hopkins University | Shen C.,Johns Hopkins University | Vogelstein J.T.,Institute for Computational Medicine | Priebe C.E.,Johns Hopkins University
IEEE Transactions on Pattern Analysis and Machine Intelligence | Year: 2016

For random graphs distributed according to stochastic blockmodels, a special case of latent position graphs, adjacency spectral embedding followed by appropriate vertex classification is asymptotically Bayes optimal; but this approach requires knowledge of and critically depends on the model dimension. In this paper, we propose a sparse representation vertex classifier which does not require information about the model dimension. This classifier represents a test vertex as a sparse combination of the vertices in the training set and uses the recovered coefficients to classify the test vertex. We prove consistency of our proposed classifier for stochastic blockmodels, and demonstrate that the sparse representation classifier can predict vertex labels with higher accuracy than adjacency spectral embedding approaches via both simulation studies and real data experiments. Our results demonstrate the robustness and effectiveness of our proposed vertex classifier when the model dimension is unknown. © 1979-2012 IEEE.

Gauthier L.D.,Institute for Computational Medicine | Greenstein J.L.,Institute for Computational Medicine | Cortassa S.,Johns Hopkins University | Cortassa S.,Institute for Computational Medicine | And 2 more authors.
Biophysical Journal | Year: 2013

Elevated levels of reactive oxygen species (ROS) play a critical role in cardiac myocyte signaling in both healthy and diseased cells. Mitochondria represent the predominant cellular source of ROS, specifically the activity of complexes I and III. The model presented here explores the modulation of electron transport chain ROS production for state 3 and state 4 respiration and the role of substrates and respiratory inhibitors. Model simulations show that ROS production from complex III increases exponentially with membrane potential (ΔΨm) when in state 4. Complex I ROS release in the model can occur in the presence of NADH and succinate (reverse electron flow), leading to a highly reduced ubiquinone pool, displaying the highest ROS production flux in state 4. In the presence of ample ROS scavenging, total ROS production is moderate in state 3 and increases substantially under state 4 conditions. The ROS production model was extended by combining it with a minimal model of ROS scavenging. When the mitochondrial redox status was oxidized by increasing the proton permeability of the inner mitochondrial membrane, simulations with the combined model show that ROS levels initially decline as production drops off with decreasing ΔΨm and then increase as scavenging capacity is exhausted. Hence, this mechanistic model of ROS production demonstrates how ROS levels are controlled by mitochondrial redox balance. © 2013 Biophysical Society.

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