Hatim Fakir

Clusters of DNA double-strand breaks induced by different doses of nitrogen ions for various LETs: experimental measurements and theoretical analyses.

Abstract Fakir, H., Sachs, R. K., Stenerlöw, B. and Hofmann, W. Clusters of DNA Double-Strand Breaks Induced by Different Doses of Nitrogen Ions for Various LETs: Experimental Measurements and Theoretical Analyses. Radiat. Res. 166, 917– 927 (2006). The yields and clustering of DNA double-strand breaks (DSBs) were investigated in normal human skin fibroblasts exposed to γ rays or to a wide range of doses of nitrogen ions with various linear energy transfers (LETs). Data obtained by pulsed-field gel electrophoresis on the dose and LET dependence of DNA fragmentation were analyzed with the randomly located clusters (RLC) formalism. The formalism considers stochastic clustering of DSBs along a chromosome due to chromatin structure, particle track structure, and multitrack action. The relative biological effectiveness (RBE) for the total DSB yield did not depend strongly on LET, but particles with higher LET produced higher fractions of small DNA fragments, corresponding in the formalism to an increase in the average number of DSBs per DSB cluster. The results are consistent with the idea that DSB clustering along chromosomes is what leads to large RBEs of high-LET radiations for major biological end points. At a given dose, large fragments are less affected by the variability in LET than small fragments, suggesting that the two free ends in large fragments are often produced by two different tracks. The formalism successfully described an extra increase in small DNA fragments as dose increases and a related decrease in large fragments, mainly due to interlacing of DSB clusters produced along a chromosome by different tracks, since interlacing cuts larger DNA fragments into smaller ones.

Stochastic Population Dynamic Effects for Lung Cancer Progression

Abstract The multistage paradigm is widely used in quantitative analyses of radiation-influenced carcinogenesis. Steps such as initiation, promotion and transformation have been investigated in detail. However, progression, a later step during which malignant cells produced in the earlier steps can develop into clinical cancer, has received less attention in computational radiobiology; it has often been approximated deterministically as a fixed, comparatively short, lag time. This approach overlooks important mechanisms in progression, including stochastic extinction, possible radiation effects on tumor growth, immune suppression and angiogenic bottlenecks. Here we analyze tumor progression in background and in radiation-induced lung cancers, emphasizing tumor latent times and the stochastic extinction of malignant lesions. A Monte Carlo cell population dynamics formalism is developed by supplementing the standard two-stage clonal expansion (TSCE) model with a stochastic birth-death model for proliferation of malignant cells. Simulation results for small cell lung cancers and lung adenocarcinomas show that the effects of stochastic malignant cell extinction broaden progression time distributions drastically. We suggest that fully stochastic cancer progression models incorporating malignant cell kinetics, dormancy (a phase in which tumors remain asymptomatic), escape from dormancy, and invasiveness, with radiation able to act directly on each phase, need to be considered for a better assessment of radiation-induced lung cancer risks.

Biology Based Lung Cancer Model for Chronic Low Radon Exposures

Low dose effects of alpha particles at the tissue level are characterized by the interaction of single alpha particles, affecting only a small fraction of the cells within that tissue. Alpha particle intersections of bronchial target cells during a given exposure period were simulated by an initiation‐promotion model, formulated in terms of cellular hits within the cycle time of the cell (dose‐rate) and then integrated over the whole exposure period (dose). For a given average number of cellular hits during the lifetime of bronchial cells, the actual number of single and multiple hits was selected from a Poisson distribution. While oncogenic transformation is interpreted as the primary initiation step, stimulated mitosis by killing adjacent cells is assumed to be the primary radiological promotion event. Analytical initiation and promotion functions were derived from experimental in vitro data on oncogenic transformation and cellular survival.To investigate the shape of the lung cancer risk function at ch...

The effect of non-targeted cellular mechanisms on lung cancer risk for chronic, low level radon exposures

Abstract Purpose: The goal of the present study was to investigate the effect of non-targeted mechanisms on the shape of the lung cancer risk function at chronic, low level radon exposures relative to direct cellular radiation effects. This includes detrimental and protective bystander effects, radio-adaptive bystander response, genomic instability and induction of apoptosis by surrounding cells. Methods: To quantify the dependence of these mechanisms on dose, analytical functions were derived from the experimental evidence presently available. Alpha particle intersections of bronchial target cells during a given exposure period were simulated by a Transformation Frequency-Tissue Response (TF-TR) model, formulated in terms of cellular hits within the cycle time of the cell and then integrated over the whole exposure period. Results: In general, non-targeted effects like genomic instability and bystander effects amplify the biological effectiveness of a given radiation dose, while induction of apoptosis and adaptive response will decrease the risk values. While these observations are related to the absolute number of lung cancer cases, normalization to the epidemiologically observed risk at 0.675 Gy suggests that the effect of such mechanisms on the shape of the dose-response relationship may be different. Indeed, genomic instability and adaptive response cause a substantial reduction of the risk at low doses, while induction of apoptosis and detrimental bystander effects slightly increase the risk. Conclusions: Predictions of lung cancer risk, including these mechanisms, exhibit a distinct sublinear dose-response relationship at low exposures, particularly for very low exposure rates. However, the relatively large error bars of the epidemiological data do not currently allow the prediction of a statistically significant deviation from the Linear – No Threshold (LNT) assumption.