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      Straightening Beta: Overdispersion of Lethal Chromosome Aberrations following Radiotherapeutic Doses Leads to Terminal Linearity in the Alpha–Beta Model

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          Abstract

          Recent technological advances allow precise radiation delivery to tumor targets. As opposed to more conventional radiotherapy—where multiple small fractions are given—in some cases, the preferred course of treatment may involve only a few (or even one) large dose(s) per fraction. Under these conditions, the choice of appropriate radiobiological model complicates the tasks of predicting radiotherapy outcomes and designing new treatment regimens. The most commonly used model for this purpose is the venerable linear-quadratic (LQ) formalism as it applies to cell survival. However, predictions based on the LQ model are frequently at odds with data following very high acute doses. In particular, although the LQ predicts a continuously bending dose–response relationship for the logarithm of cell survival, empirical evidence over the high-dose region suggests that the survival response is instead log-linear with dose. Here, we show that the distribution of lethal chromosomal lesions among individual human cells (lymphocytes and fibroblasts) exposed to gamma rays and X rays is somewhat overdispersed, compared with the Poisson distribution. Further, we show that such overdispersion affects the predicted dose response for cell survival (the fraction of cells with zero lethal lesions). This causes the dose response to approximate log-linear behavior at high doses, even when the mean number of lethal lesions per cell is well fitted by the continuously curving LQ model. Accounting for overdispersion of lethal lesions provides a novel, mechanistically based explanation for the observed shapes of cell survival dose responses that, in principle, may offer a tractable and clinically useful approach for modeling the effects of high doses per fraction.

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          Using observation-level random effects to model overdispersion in count data in ecology and evolution

          Overdispersion is common in models of count data in ecology and evolutionary biology, and can occur due to missing covariates, non-independent (aggregated) data, or an excess frequency of zeroes (zero-inflation). Accounting for overdispersion in such models is vital, as failing to do so can lead to biased parameter estimates, and false conclusions regarding hypotheses of interest. Observation-level random effects (OLRE), where each data point receives a unique level of a random effect that models the extra-Poisson variation present in the data, are commonly employed to cope with overdispersion in count data. However studies investigating the efficacy of observation-level random effects as a means to deal with overdispersion are scarce. Here I use simulations to show that in cases where overdispersion is caused by random extra-Poisson noise, or aggregation in the count data, observation-level random effects yield more accurate parameter estimates compared to when overdispersion is simply ignored. Conversely, OLRE fail to reduce bias in zero-inflated data, and in some cases increase bias at high levels of overdispersion. There was a positive relationship between the magnitude of overdispersion and the degree of bias in parameter estimates. Critically, the simulations reveal that failing to account for overdispersion in mixed models can erroneously inflate measures of explained variance (r 2), which may lead to researchers overestimating the predictive power of variables of interest. This work suggests use of observation-level random effects provides a simple and robust means to account for overdispersion in count data, but also that their ability to minimise bias is not uniform across all types of overdispersion and must be applied judiciously.
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            Universal survival curve and single fraction equivalent dose: useful tools in understanding potency of ablative radiotherapy.

            Overprediction of the potency and toxicity of high-dose ablative radiotherapy such as stereotactic body radiotherapy (SBRT) by the linear quadratic (LQ) model led to many clinicians' hesitating to adopt this efficacious and well-tolerated therapeutic option. The aim of this study was to offer an alternative method of analyzing the effect of SBRT by constructing a universal survival curve (USC) that provides superior approximation of the experimentally measured survival curves in the ablative, high-dose range without losing the strengths of the LQ model around the shoulder. The USC was constructed by hybridizing two classic radiobiologic models: the LQ model and the multitarget model. We have assumed that the LQ model gives a good description for conventionally fractionated radiotherapy (CFRT) for the dose to the shoulder. For ablative doses beyond the shoulder, the survival curve is better described as a straight line as predicted by the multitarget model. The USC smoothly interpolates from a parabola predicted by the LQ model to the terminal asymptote of the multitarget model in the high-dose region. From the USC, we derived two equivalence functions, the biologically effective dose and the single fraction equivalent dose for both CFRT and SBRT. The validity of the USC was tested by using previously published parameters of the LQ and multitarget models for non-small-cell lung cancer cell lines. A comparison of the goodness-of-fit of the LQ and USC models was made to a high-dose survival curve of the H460 non-small-cell lung cancer cell line. The USC can be used to compare the dose fractionation schemes of both CFRT and SBRT. The USC provides an empirically and a clinically well-justified rationale for SBRT while preserving the strengths of the LQ model for CFRT.
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              Alternative end-joining repair pathways are the ultimate backup for abrogated classical non-homologous end-joining and homologous recombination repair: Implications for the formation of chromosome translocations.

              DNA double strand breaks (DSB) are the most deleterious lesions for the integrity of the genome, as their misrepair can lead to the formation of chromosome translocations. Cells have evolved two main repair pathways to suppress the formation of these genotoxic lesions: homology-dependent, error-free homologous recombination repair (HRR), and potentially error-prone, classical, DNA-PK-dependent non-homologous end-joining (c-NHEJ). The most salient feature of c-NHEJ, speed, will largely suppress chromosome translocation formation, while sequence alterations at the junction remain possible. It is now widely accepted that when c-NHEJ is inactivated, globally or locally, an alternative form of end-joining (alt-EJ) removes DSBs. Alt-EJ operates with speed and fidelity markedly lower than c-NHEJ, causing thus with higher probability chromosome translocations, and generating more extensive sequence alterations at the junction. Our working hypothesis is that alt-EJ operates as a backup to c-NHEJ. Recent results show that alt-EJ can also backup abrogated HRR in G2 phase cells, again at the cost of elevated formation of chromosome translocations. These observations raise alt-EJ to a global rescuing mechanism operating on ends that have lost their chromatin context in ways that compromise processing by HRR or c-NHEJ. While responsible for eliminating from the genome highly cytotoxic DNA ends, alt-EJ provides this function at the price of increased translocation formation. Here, we analyze recent literature on the mechanisms of chromosome translocation formation and propose a functional hierarchy among DSB processing pathways that makes alt-EJ the global backup pathway. We discuss possible ramifications of this model in cellular DSB management and pathway choice, and analyze its implications in radiation carcinogenesis and the design of novel therapeutic approaches.
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                Author and article information

                Contributors
                URI : http://frontiersin.org/people/u/315013
                URI : http://frontiersin.org/people/u/317133
                URI : http://frontiersin.org/people/u/262531
                Journal
                Front Oncol
                Front Oncol
                Front. Oncol.
                Frontiers in Oncology
                Frontiers Media S.A.
                2234-943X
                21 December 2017
                2017
                : 7
                : 318
                Affiliations
                [1] 1Center for Radiological Research, Columbia University , New York, United States
                [2] 2Department of Radiation Oncology, University of Texas Medical Branch , Galveston, TX, United States
                Author notes

                Edited by: Anatoly Dritschilo, Georgetown University, United States

                Reviewed by: Dalong Pang, Georgetown University, United States; Vinay Sharma, University of the Witwatersrand, South Africa; Timothy John Jorgensen, Georgetown University, United States

                *Correspondence: Igor Shuryak, is144@ 123456cumc.columbia.edu

                Specialty section: This article was submitted to Radiation Oncology, a section of the journal Frontiers in Oncology

                Article
                10.3389/fonc.2017.00318
                5742594
                4b735adf-d0a4-446b-a6ba-7f6d48c6294a
                Copyright © 2017 Shuryak, Loucas and Cornforth.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 05 September 2017
                : 07 December 2017
                Page count
                Figures: 4, Tables: 0, Equations: 12, References: 33, Pages: 9, Words: 6047
                Funding
                Funded by: National Institute of Allergy and Infectious Diseases 10.13039/100000060
                Award ID: U19AI067773
                Funded by: National Aeronautics and Space Administration 10.13039/100000104
                Award ID: NNX15AG74G, NNX14AC76G
                Categories
                Oncology
                Original Research

                Oncology & Radiotherapy
                linear-quadratic model,stereotactic radiotherapy,radiation,chromosomal aberrations,lethal lesions,survival curve,overdispersion

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