When ionizing radiation is used in cancer therapy it can induce second cancers in nearby organs. Mainly due to longer patient survival times, these second cancers have become of increasing concern. Estimating the risk of solid second cancers involves modeling: because of long latency times, available data is usually for older, obsolescent treatment regimens. Moreover, modeling second cancers gives unique insights into human carcinogenesis, since the therapy involves administering well characterized doses of a well studied carcinogen, followed by long-term monitoring.In addition to putative radiation initiation that produces pre-malignant cells, inactivation (i.e. cell killing), and subsequent cell repopulation by proliferation can be important at the doses relevant to second cancer situations. A recent initiation/inactivation/proliferation (IIP) model characterized quantitatively the observed occurrence of second breast and lung cancers, using a deterministic cell population dynamics approach. To analyze if radiation-initiated pre-malignant clones become extinct before full repopulation can occur, we here give a stochastic version of this IIP model. Combining Monte Carlo simulations with standard solutions for time-inhomogeneous birth-death equations, we show that repeated cycles of inactivation and repopulation, as occur during fractionated radiation therapy, can lead to distributions of pre-malignant cells per patient with variance ≫ mean, even when pre-malignant clones are Poisson-distributed. Thus fewer patients would be affected, but with a higher probability, than a deterministic model, tracking average pre-malignant cell numbers, would predict. Our results are applied to data on breast cancers after radiotherapy for Hodgkin disease. The stochastic IIP analysis, unlike the deterministic one, indicates: a) initiated, pre-malignant cells can have a growth advantage during repopulation, not just during the longer tumor latency period that follows; b) weekend treatment gaps during radiotherapy, apart from decreasing the probability of eradicating the primary cancer, substantially increase the risk of later second cancers.
We propose a mechanistic model for radiation cell killing and carcinogenesis-related end points that combines direct and bystander responses. The model describes the bystander component as a sequence of two distinct processes: triggering of signal emission from irradiated cells and response of nonirradiated recipient cells; in principle it can incorporate microdosimetric information as well as the random aspects of signal triggering and recipient response. Late effects are modeled using a one-stage model based on the concepts of inactivation and initiation, which allows for the proliferation of normal and initiated cells; proliferation of initiated cells is analyzed using a stochastic, birth-death approach. The model emphasizes the dependence of bystander effects on dose, which is important for the assessment of low-dose cancer induction by extrapolations of risk from high-dose exposures. The results obtained show adequate agreement with different in vitro bystander experiments involving ultrasoft X rays and alpha particles and correctly reflect the main features observed for several end points. Our results suggest signal transmission through the medium rather than gap junctions. We suggest that for many such experiments, a moderate increase in medium volume should have about the same effect as a moderate decrease in the fraction of irradiated cells.
A Monte Carlo code, initially developed for the calculation of microdosimetric spectra for alpha particles in cylindrical airways, has been extended to allow the computation of microdosimetric parameters for multiple source-target configurations in bronchial airway bifurcations. The objective of the present study was to investigate the effects of uniform and non-uniform radon progeny surface activity distributions in symmetric and asymmetric bronchial airway bifurcations on absorbed dose, hit frequency, lineal energy, single hit specific energy and LET spectra. In order to assess the effects of multiple hits, dose-dependent specific energy spectra were calculated by solving the compound Poisson process by iterative convolution. While the simulations showed significant differences of cellular dose quantities at different cell locations for uniformly distributed surface activities, even higher variations, as high as several orders of magnitude, were observed for non-uniform surface activity distributions, depending on the location of the cell and the local activity distribution.
The yields and clustering of DNA double-strand breaks (DSBs) were investigated in normal human skin fibroblasts exposed to gamma 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.
The predicted efficacy of moderate boosts depends sensitively on α. Presumably, the larger values of α are the ones appropriate for individualized treatment protocols, with the smaller values relevant only to protocols for a heterogeneous patient population. On that assumption, boosting is predicted to be highly effective. Front boosting, apart from practical advantages and a possible advantage as regards iatrogenic second cancers, also probably gives a slightly higher TCP than back boosting. If the total number of SLCC at the start of treatment can be measured even roughly, it will provide a highly sensitive way of discriminating between various models and parameter choices. Updated mathematical methods for calculating repopulation allow credible generalizations of earlier results.
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