Exogen Biotechnology Inc.

Berkeley, CA, United States

Exogen Biotechnology Inc.

Berkeley, CA, United States

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Sridharan D.M.,Lawrence Berkeley National Laboratory | Asaithamby A.,Southwestern Medical Center | Blattnig S.R.,Langley Research Center | Costes S.V.,Lawrence Berkeley National Laboratory | And 13 more authors.
Life Sciences in Space Research | Year: 2016

Robust predictive models are essential to manage the risk of radiation-induced carcinogenesis. Chronic exposure to cosmic rays in the context of the complex deep space environment may place astronauts at high cancer risk. To estimate this risk, it is critical to understand how radiation-induced cellular stress impacts cell fate decisions and how this in turn alters the risk of carcinogenesis. Exposure to the heavy ion component of cosmic rays triggers a multitude of cellular changes, depending on the rate of exposure, the type of damage incurred and individual susceptibility. Heterogeneity in dose, dose rate, radiation quality, energy and particle flux contribute to the complexity of risk assessment. To unravel the impact of each of these factors, it is critical to identify sensitive biomarkers that can serve as inputs for robust modeling of individual risk of cancer or other long-term health consequences of exposure. Limitations in sensitivity of biomarkers to dose and dose rate, and the complexity of longitudinal monitoring, are some of the factors that increase uncertainties in the output from risk prediction models. Here, we critically evaluate candidate early and late biomarkers of radiation exposure and discuss their usefulness in predicting cell fate decisions. Some of the biomarkers we have reviewed include complex clustered DNA damage, persistent DNA repair foci, reactive oxygen species, chromosome aberrations and inflammation. Other biomarkers discussed, often assayed for at longer points post exposure, include mutations, chromosome aberrations, reactive oxygen species and telomere length changes. We discuss the relationship of biomarkers to different potential cell fates, including proliferation, apoptosis, senescence, and loss of stemness, which can propagate genomic instability and alter tissue composition and the underlying mRNA signatures that contribute to cell fate decisions. Our goal is to highlight factors that are important in choosing biomarkers and to evaluate the potential for biomarkers to inform models of post exposure cancer risk. Because cellular stress response pathways to space radiation and environmental carcinogens share common nodes, biomarker-driven risk models may be broadly applicable for estimating risks for other carcinogens. © 2016 The Committee on Space Research (COSPAR)


Chiolo I.,University of Southern California | Tang J.,Lawrence Berkeley National Laboratory | Tang J.,Exogen Biotechnology Inc. | Georgescu W.,Lawrence Berkeley National Laboratory | And 2 more authors.
Mutation Research - Fundamental and Molecular Mechanisms of Mutagenesis | Year: 2013

Repair of double strand breaks (DSBs) is essential for cell survival and genome integrity. While much is known about the molecular mechanisms involved in DSB repair and checkpoint activation, the roles of nuclear dynamics of radiation-induced foci (RIF) in DNA repair are just beginning to emerge. Here, we summarize results from recent studies that point to distinct features of these dynamics in two different chromatin environments: heterochromatin and euchromatin. We also discuss how nuclear architecture and chromatin components might control these dynamics, and the need of novel quantification methods for a better description and interpretation of these phenomena. These studies are expected to provide new biomarkers for radiation risk and new strategies for cancer detection and treatment. © 2013 Elsevier B.V.


Heuskin A.C.,Lawrence Berkeley National Laboratory | Heuskin A.C.,University of Namur | Osseiran A.I.,Lawrence Berkeley National Laboratory | Tang J.,Exogen Biotechnology Inc. | Costes S.V.,Lawrence Berkeley National Laboratory
Radiation Research | Year: 2016

Estimating cancer risk from space radiation has been an ongoing challenge for decades primarily because most of the reported epidemiological data on radiation-induced risks are derived from studies of atomic bomb survivors who were exposed to an acute dose of gamma rays instead of chronic high-LET cosmic radiation. In this study, we introduce a formalism using cellular automata to model the long-term effects of ionizing radiation in human breast for different radiation qualities. We first validated and tuned parameters for an automata-based two-stage clonal expansion model simulating the age dependence of spontaneous breast cancer incidence in an unexposed U.S. population. We then tested the impact of radiation perturbation in the model by modifying parameters to reflect both targeted and nontargeted radiation effects. Targeted effects (TE) reflect the immediate impact of radiation on a cell's DNA with classic end points being gene mutations and cell death. They are well known and are directly derived from experimental data. In contrast, nontargeted effects (NTE) are persistent and affect both damaged and undamaged cells, are nonlinear with dose and are not well characterized in the literature. In this study, we introduced TE in our model and compared predictions against epidemiologic data of the atomic bomb survivor cohort. TE alone are not sufficient for inducing enough cancer. NTE independent of dose and lasting ∼100 days postirradiation need to be added to accurately predict dose dependence of breast cancer induced by gamma rays. Finally, by integrating experimental relative biological effectiveness (RBE) for TE and keeping NTE (i.e., radiation-induced genomic instability) constant with dose and LET, the model predicts that RBE for breast cancer induced by cosmic radiation would be maximum at 220 keV/μm. This approach lays the groundwork for further investigation into the impact of chronic low-dose exposure, inter-individual variation and more complex space radiation scenarios. © 2016 by Radiation Research Society.


Patent
The Regents Of The University Of California and Exogen Biotechnology Inc. | Date: 2015-02-27

Kits, methods and systems for providing a service to provide a subject with information regarding the state of a subjects DNA damage. Collection, processing and analysis of samples are also described.


Tang J.,Exogen Biotechnology Inc | Georgescu W.,Lawrence Berkeley National Laboratory | Deschamps T.,Exogen Biotechnology Inc | Yannone S.M.,Exogen Biotechnology Inc | And 3 more authors.
Cancer Metastasis - Biology and Treatment | Year: 2015

The constant damage of DNA in human cells is considered the main cause of aging and cancer. In this review, we discuss the most lethal form of DNA damage, the DNA double strand break (DSB), and how it relates to cancer. DSB sensor proteins in the nucleus detect DNA breaks within minutes following damage. These proteins are now routinely labeled by immunocytochemistry, and access to high throughput fluorescence microscopy and robotics open the door to rapid measurement of DSB levels in individuals. This method, often referred as the DSB foci assay, leads to images showing small bright spots at the site of each damage in the nucleus. We first discuss how energy consumption in the cell leads to detectable baseline levels of foci per cell measured in peripheral blood lymphocytes. Mathematical kinetics are then described to infer both genetic defects in DNA repair and environmental factors influencing these levels. We emphasize ionizing radiation, which is the principal environmental factor that increases DSB levels. Mathematical models associating a mutation probability for each DSB have been used to explain the dose dependence of cancer incidence observed after exposure to high doses of radiation. The main assumption in these models is that high mutation frequency can eventually lead to tumor suppressor gene deletion or oncogene amplification. We conclude by suggesting that the growing stream of genetic and phenotypic measurements related to DNA repair and DNA damage will lead to more accurate predictive tools for cancer risk and individualized cancer prevention. © Springer International Publishing Switzerland 2015

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