Meric-Bernstam F.,Khalifa University |
Johnson A.,Khalifa University |
Holla V.,Khalifa University |
Bailey A.M.,Khalifa University |
And 16 more authors.
Journal of the National Cancer Institute | Year: 2015
Rapidly improving understanding of molecular oncology, emerging novel therapeutics, and increasingly available and affordable next-generation sequencing have created an opportunity for delivering genomically informed personalized cancer therapy. However, to implement genomically informed therapy requires that a clinician interpret the patient's molecular profile, including molecular characterization of the tumor and the patient's germline DNA. In this Commentary, we review existing data and tools for precision oncology and present a framework for reviewing the available biomedical literature on therapeutic implications of genomic alterations. Genomic alterations, including mutations, insertions/deletions, fusions, and copy number changes, need to be curated in terms of the likelihood that they alter the function of a "cancer gene" at the level of a specific variant in order to discriminate so-called "drivers" from "passengers." Alterations that are targetable either directly or indirectly with approved or investigational therapies are potentially "actionable." At this time, evidence linking predictive biomarkers to therapies is strong for only a few genomic markers in the context of specific cancer types. For these genomic alterations in other diseases and for other genomic alterations, the clinical data are either absent or insufficient to support routine clinical implementation of biomarker-based therapy. However, there is great interest in optimally matching patients to early-phase clinical trials. Thus, we need accessible, comprehensive, and frequently updated knowledge bases that describe genomic changes and their clinical implications, as well as continued education of clinicians and patients. © 2015 The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: email@example.com. Source
Fong R.,Structural Biology |
Mukund S.,Structural Biology |
Tam C.,Structural Biology |
Zilberleyb I.,Structural Biology |
And 7 more authors.
Journal of Biological Chemistry | Year: 2013
Background: The effect of HDAC inhibitor kinetic properties on biological function is currently unknown. Results: The kinetic rate constants of HDAC inhibitors differentially affect histone acetylation, cell viability, and gene expression. Conclusion: Evaluating HDAC inhibitor properties using histone acetylation is not predictive of their function on cellular activity. Significance: Characterizing the biological effect of different HDAC inhibitors will help to evaluate their clinical utility. © 2013 by The American Society for Biochemistry and Molecular Biology, Inc. Source
Akbani R.,Bioinformatics and Computational Biology |
Korkmaz T.,University of Texas at San Antonio |
Raju G.V.,University of Texas at San Antonio
Ad Hoc Networks | Year: 2012
Many mission critical networks including MANETs for military communications and disaster relief communications rely on node cooperation. If malicious nodes gain access to such networks they can easily launch attacks, such as spreading viruses or spam, or attacking known vulnerabilities. One way to defend against malicious nodes is to use Reputation Systems (RS) that try to predict future behavior of nodes by observing their past behavior. In this paper, we propose a Machine Learning (ML) based RS that defends against many patterns of attacks. We specifically consider the proposed RS in the context of MANETs. After introducing a basic RS, we propose further enhancements to it to improve its performance and to deal with some of the more challenging aspects of MANETs. For instance, we consider digital signature based mechanisms that do not require trusted third parties, or servers that are always online. Another enhancement uses an algorithm called Fading Memories that allows us to look back at longer histories using fewer features. Finally, we introduce a new technique, called Dynamic Thresholds, to improve accuracies even further. We compare the performance of our RS with another RS found in the literature, called TrustGuard, and perform detailed evaluations against a variety of attacks. The results show that our RS significantly outperforms TrustGuard, even when the proportion of malicious nodes in the network is high. We also show that our scheme has very low bandwidth and computation overhead. In contrast to existing RSs designed to detect specific attacks, ML based RSs can be retrained to detect new attack patterns as well. © 2011 Elsevier B.V. All rights reserved. Source
Blake P.M.,Sophic Systems Alliance, Inc. |
Decker D.A.,Florida Hospital Cancer Institute |
Decker D.A.,University of Central Florida |
Glennon T.M.,Sophic Systems Alliance, Inc. |
And 5 more authors.
Cancer Journal | Year: 2011
Around the world, teams of researchers continue to develop a wide range of systems to capture, store, and analyze data including treatment, patient outcomes, tumor registries, next-generation sequencing, single-nucleotide polymorphism, copy number, gene expression, drug chemistry, drug safety, and toxicity. Scientists mine, curate, and manually annotate growing mountains of data to produce high-quality databases, while clinical information is aggregated in distant systems. Databases are currently scattered, and relationships between variables coded in disparate datasets are frequently invisible. The challenge is to evolve oncology informatics from a "systems" orientation of standalone platforms and silos into an "integrated knowledge environments" that will connect "knowable" research data with patient clinical information. The aim of this article is to review progress toward an integrated knowledge environment to support modern oncology with a focus on supporting scientific discovery and improving cancer care. Copyright © 2011 by Lippincott Williams & Wilkins. Source
Zand B.,University of Texas M. D. Anderson Cancer Center |
Previs R.A.,University of Texas M. D. Anderson Cancer Center |
Rupaimoole R.,University of Texas M. D. Anderson Cancer Center |
Mitamura T.,University of Texas M. D. Anderson Cancer Center |
And 29 more authors.
Journal of the National Cancer Institute | Year: 2016
Background: The clinical and biological effects of metabolic alterations in cancer are not fully understood. Methods: In high-grade serous ovarian cancer (HGSOC) samples (n = 101), over 170 metabolites were profiled and compared with normal ovarian tissues (n = 15). To determine NAT8L gene expression across different cancer types, we analyzed the RNA expression of cancer types using RNASeqV2 data available from the open access The Cancer Genome Atlas (TCGA) website (http://www.cbioportal.org/public-portal/). Using NAT8L siRNA, molecular techniques and histological analysis, we determined cancer cell viability, proliferation, apoptosis, and tumor growth in in vitro and in vivo (n = 6-10 mice/group) settings. Data were analyzed with the Student's t test and Kaplan-Meier analysis. Statistical tests were two-sided. Results: Patients with high levels of tumoral NAA and its biosynthetic enzyme, aspartate N-acetyltransferase (NAT8L), had worse overall survival than patients with low levels of NAA and NAT8L. The overall survival duration of patients with higher-than-median NAA levels (3.6 years) was lower than that of patients with lower-than-median NAA levels (5.1 years, P =. 03). High NAT8L gene expression in other cancers (melanoma, renal cell, breast, colon, and uterine cancers) was associated with worse overall survival. NAT8L silencing reduced cancer cell viability (HEYA8: control siRNA 90.61%±2.53, NAT8L siRNA 39.43%±3.00, P <. 001; A2780: control siRNA 90.59%±2.53, NAT8L siRNA 7.44%±1.71, P <. 001) and proliferation (HEYA8: control siRNA 74.83%±0.92, NAT8L siRNA 55.70%±1.54, P <. 001; A2780: control siRNA 50.17%±4.13, NAT8L siRNA 26.52%±3.70, P <. 001), which was rescued by addition of NAA. In orthotopic mouse models (ovarian cancer and melanoma), NAT8L silencing reduced tumor growth statistically significantly (A2780: control siRNA 0.52 g±0.15, NAT8L siRNA 0.08 g±0.17, P <. 001; HEYA8: control siRNA 0.79 g±0.42, NAT8L siRNA 0.24 g±0.18, P =. 008, A375-SM: control siRNA 0.55 g±0.22, NAT8L siRNA 0.21 g±0.17g, P =. 001). NAT8L silencing downregulated the anti-apoptotic pathway, which was mediated through FOXM1. Conclusion: These findings indicate that the NAA pathway has a prominent role in promoting tumor growth and represents a valuable target for anticancer therapy. Altered energy metabolism is a hallmark of cancer (1). Proliferating cancer cells have much greater metabolic requirements than nonproliferating differentiated cells (2,3). Moreover, altered cancer metabolism elevates unique metabolic intermediates, which can promote cancer survival and progression (4,5). Furthermore, emerging evidence suggests that proliferating cancer cells exploit alternative metabolic pathways to meet their high demand for energy and to accumulate biomass (6-8). © 2016 The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: firstname.lastname@example.org. Source