Synergy theory for murine Harderian gland tumours after irradiation by mixtures of high-energy ionized atomic nuclei.

Experimental studies reporting murine Harderian gland (HG) tumourigenesis have been a NASA concern for many years. Studies used particle accelerators to produce beams that, on beam entry, consist of a single isotope also present in the galactic cosmic ray (GCR) spectrum. In this paper synergy theory is described, potentially applicable to corresponding mixed-field experiments, in progress, planned, or hypothetical. The "obvious" simple effect additivity (SEA) approach of comparing an observed mixture dose-effect relationship (DER) to the sum of the components' DERs is known from other fields of biology to be unreliable when the components' DERs are highly curvilinear. Such curvilinearity may be present at low fluxes such as those used in the one-ion HG experiments due to non-targeted ('bystander') effects, in which case a replacement for SEA synergy theory is needed. This paper comprises in silico modeling of published experimental data using a recently introduced, arguably optimal, replacement for SEA: incremental effect additivity (IEA). Customized open-source software is used. IEA is based on computer numerical integration of non-linear ordinary differential equations. To illustrate IEA synergy theory, possible rapidly-sequential-beam mixture experiments are discussed, including tight 95% confidence intervals calculated by Monte-Carlo sampling from variance-covariance matrices. The importance of having matched one-ion and mixed-beam experiments is emphasized. Arguments are presented against NASA over-emphasizing accelerator experiments with mixed beams whose dosing protocols are standardized rather than being adjustable to take biological variability into account. It is currently unknown whether mixed GCR beams sometimes have statistically significant synergy for the carcinogenesis endpoint. Synergy would increase risks for prolonged astronaut voyages in interplanetary space.

Quantitative modeling of chronic myeloid leukemia: insights from radiobiology.

Mathematical models of chronic myeloid leukemia (CML) cell population dynamics are being developed to improve CML understanding and treatment. We review such models in light of relevant findings from radiobiology, emphasizing 3 points. First, the CML models almost all assert that the latency time, from CML initiation to diagnosis, is at most ∼10 years. Meanwhile, current radiobiologic estimates, based on Japanese atomic bomb survivor data, indicate a substantially higher maximum, suggesting longer-term relapses and extra resistance mutations. Second, different CML models assume different numbers, between 400 and 10(6), of normal HSCs. Radiobiologic estimates favor values>10(6) for the number of normal cells (often assumed to be the HSCs) that are at risk for a CML-initiating BCR-ABL translocation. Moreover, there is some evidence for an HSC dead-band hypothesis, consistent with HSC numbers being very different across different healthy adults. Third, radiobiologists have found that sporadic (background, age-driven) chromosome translocation incidence increases with age during adulthood. BCR-ABL translocation incidence increasing with age would provide a hitherto underanalyzed contribution to observed background adult-onset CML incidence acceleration with age, and would cast some doubt on stage-number inferences from multistage carcinogenesis models in general.

Etiology and treatment of hematological neoplasms: stochastic mathematical models.

Leukemias are driven by stemlike cancer cells (SLCC), whose initiation, growth, response to treatment, and posttreatment behavior are often "stochastic", i.e., differ substantially even among very similar patients for reasons not observable with present techniques. We review the probabilistic mathematical methods used to analyze stochastics and give two specific examples. The first example concerns a treatment protocol, e.g., for acute myeloid leukemia (AML), where intermittent cytotoxic drug dosing (e.g., once each weekday) is used with intent to cure. We argue mathematically that, if independent SLCC are growing stochastically during prolonged treatment, then, other things being equal, front-loading doses are more effective for tumor eradication than back loading. We also argue that the interacting SLCC dynamics during treatment is often best modeled by considering SLCC in microenvironmental niches, with SLCC-SLCC interactions occurring only among SLCC within the same niche, and we present a stochastic dynamics formalism, involving "Poissonization," applicable in such situations. Interactions at a distance due to partial control of total cell numbers are also considered. The second half of this chapter concerns chromosomal aberrations, lesions known to cause some leukemias. A specific example is the induction of a Philadelphia chromosome by ionizing radiation, subsequent development of chronic myeloid leukemia (CML), CML treatment, and treatment outcome. This time evolution involves a coordinated sequence of > 10 steps, each stochastic in its own way, at the subatomic, molecular, macromolecular, cellular, tissue, and population scales, with corresponding time scales ranging from picoseconds to decades. We discuss models of these steps and progress in integrating models across scales.

Sex differences in the incidence of chronic myeloid leukemia.

The incidence of chronic myeloid leukemia (CML), which is caused by BCR/ABL chimeric oncogene formation in a pluripotent hematopoietic stem cell (HSC), increases with age and exposure to ionizing radiation. CML is a comparatively well-characterized neoplasm, important for its own sake and useful for insights into other neoplasms. Here, Surveillance, Epidemiology and End Results (SEER) CML data are analyzed after considering possible misclassification of chronic myelo-monocytic leukemia as CML. For people older than 25 years, plots of male and female CML log incidences versus age at diagnosis are approximately parallel straight lines with males either above or to the left of females. This is consistent with males having a higher risk of developing CML or a shorter latency from initiation to diagnosis of CML. These distinct mechanisms cannot be distinguished using SEER data alone. Therefore, CML risks among male and female Japanese A-bomb survivors are also analyzed. The present analyses suggest that sex differences in CML incidence more likely result from differences in risk than in latency. The simplest but not the sole interpretation of this is that males have more target cells at risk to develop CML. Comprehensive mathematical models of CML could lead to a better understanding of the role of HSCs in CML and other preleukemias that can progress to acute leukemia.

Quantitative modeling of carcinogenesis induced by single beams or mixtures of space radiations using targeted and non-targeted effects.

Ionizing radiations encountered by astronauts on deep space missions produce biological damage by two main mechanisms: (1) Targeted effects (TE) due to direct traversals of cells by ionizing tracks. (2) Non-targeted effects (NTE) caused by release of signals from directly hit cells. The combination of these mechanisms generates non-linear dose response shapes, which need to be modeled quantitatively to predict health risks from space exploration. Here we used a TE + NTE model to analyze data on APC(1638N/+) mouse tumorigenesis induced by space-relevant doses of protons, 4He, 12C, 16O, 28Si or 56Fe ions, or γ rays. A customized weighted Negative Binomial distribution was used to describe the radiation type- and dose-dependent data variability. This approach allowed detailed quantification of dose-response shapes, NTE- and TE-related model parameters, and radiation quality metrics (relative biological effectiveness, RBE, and radiation effects ratio, RER, relative to γ rays) for each radiation type. Based on the modeled responses for each radiation type, we predicted the tumor yield for a Mars-mission-relevant mixture of these radiations, using the recently-developed incremental effect additivity (IEA) synergy theory. The proposed modeling approach can enhance current knowledge about quantification of space radiation quality effects, dose response shapes, and ultimately the health risks for astronauts.

A rapid-mutation approximation for cell population dynamics.

Carcinogenesis and cancer progression are often modeled using population dynamics equations for a diverse somatic cell population undergoing mutations or other alterations that alter the fitness of a cell and its progeny. Usually it is then assumed, paralleling standard mathematical approaches to evolution, that such alterations are slow compared to selection, i.e., compared to subpopulation frequency changes induced by unequal subpopulation proliferation rates. However, the alterations can be rapid in some cases. For example, results in our lab on in vitro analogues of transformation and progression in carcinogenesis suggest there could be periods where rapid alterations triggered by horizontal intercellular transfer of genetic material occur and quickly result in marked changes of cell population structure.We here initiate a mathematical study of situations where alterations are rapid compared to selection. A classic selection-mutation formalism is generalized to obtain a "proliferation-alteration" system of ordinary differential equations, which we analyze using a rapid-alteration approximation. A system-theoretical estimate of the total-population net growth rate emerges. This rate characterizes the diverse, interacting cell population acting as a single system; it is a weighted average of subpopulation rates, the weights being components of the Perron-Frobenius eigenvector for an ergodic Markov-process matrix that describes alterations by themselves. We give a detailed numerical example to illustrate the rapid-alteration approximation, suggest a possible interpretation of the fact that average aneuploidy during cancer progression often appears to be comparatively stable in time, and briefly discuss possible generalizations as well as weaknesses of our approach.

Modeling progression in radiation-induced lung adenocarcinomas.

Quantitative multistage carcinogenesis models are used in radiobiology to estimate cancer risks and latency periods (time from exposure to clinical cancer). Steps such as initiation, promotion and transformation have been modeled in detail. However, progression, a later step during which malignant cells can develop into clinical symptomatic cancer, has often been approximated simply as a fixed lag time. This approach discounts important stochastic mechanisms in progression and evidence on the high prevalence of dormant tumors. Modeling progression more accurately is therefore important for risk assessment. Unlike models of earlier steps, progression models can readily utilize not only experimental and epidemiological data but also clinical data such as the results of modern screening and imaging. Here, a stochastic progression model is presented. We describe, with minimal parameterization: the initial growth or extinction of a malignant clone after formation of a malignant cell; the likely dormancy caused, for example, by nutrient and oxygen deprivation; and possible escape from dormancy resulting in a clinical cancer. It is shown, using cohort simulations with parameters appropriate for lung adenocarcinomas, that incorporating such processes can dramatically lengthen predicted latency periods. Such long latency periods together with data on timing of radiation-induced cancers suggest that radiation may influence progression itself.

Simulating galactic cosmic ray effects: Synergy modeling of murine tumor prevalence after exposure to two one-ion beams in rapid sequence.

Health risks from galactic cosmic rays (GCR) in space travel above low earth orbit remain a concern. For many years accelerator experiments investigating space radiation induced prevalence of murine Harderian gland (HG) tumorigenesis have been performed to help estimate GCR risks. Most studies used acute, relatively low fluence, exposures. Results on a broad spectrum of individual ions and linear energy transfers (LETs) have become available. However, in space, the crew are exposed simultaneously to many different GCR. Recent upgrades at the Brookhaven NASA Space Radiation Laboratory (NSRL) now allow mixtures in the form of different one-ion beams delivered in rapid sequence. This paper uses the results of three two-ion mixture experiments to illustrate conceptual, mathematical, computational, and statistical aspects of synergy analyses and also acts as an interim report on the mixture experiments' results. The results were interpreted using the following: (a) accumulated data from HG one-ion accelerator experiments; (b) incremental effect additivity synergy theory rather than simple effect additivity synergy theory; (c) parsimonious models for one-ion dose-effect relations; and (d), computer-implemented numerical methods encapsulated in freely available open source customized software. The main conclusions are the following. As yet, the murine HG tumorigenesis experimental studies show synergy in only one case out of three. Moreover, some theoretical arguments suggest GCR-simulating mixed beams are not likely to be synergistic. However, more studies relevant to possible synergy are needed by various groups that are studying various endpoints. Especially important is the possibility of synergy among high-LET radiations, since individual high-LET ions have large relative biological effectiveness for many endpoints. Selected terminology, symbols, and abbreviations. DER - dose-effect relation; E(d) - DER of a one-ion beam, where d is dose; HG prevalence p - in this paper, p is the number of mice with at least one Harderian gland tumor divided by the number of mice that are at risk of developing Harderian gland tumors (so that in this paper prevalence p can never, conceptually speaking, be greater than 1); IEA - incremental effect additivity synergy theory; synergy level - a specification, exemplified in Fig. 5, of how clear-cut an observed synergy is; mixmix principle - a consistency condition on a synergy theory which insures that the synergy theory treats mixtures of agent mixtures in a mathematically self-consistent way; NTE - non-targeted effect(s); NSNA - neither synergy nor antagonism; SEA - simple effect additivity synergy theory; TE - targeted effect(s); ß* - ion speed relative to the speed of light, with 0 < ß* < 1; SLI - swift light ion(s).

Triggering-response model for radiation-induced bystander effects.

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.

Stochastic population dynamic effects for lung cancer progression.

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.

Chromosomes are predominantly located randomly with respect to each other in interphase human cells.

To test quantitatively whether there are systematic chromosome-chromosome associations within human interphase nuclei, interchanges between all possible heterologous pairs of chromosomes were measured with 24-color whole-chromosome painting (multiplex FISH), after damage to interphase lymphocytes by sparsely ionizing radiation in vitro. An excess of interchanges for a specific chromosome pair would indicate spatial proximity between the chromosomes comprising that pair. The experimental design was such that quite small deviations from randomness (extra pairwise interchanges within a group of chromosomes) would be detectable. The only statistically significant chromosome cluster was a group of five chromosomes previously observed to be preferentially located near the center of the nucleus. However, quantitatively, the overall deviation from randomness within the whole genome was small. Thus, whereas some chromosome-chromosome associations are clearly present, at the whole-chromosomal level, the predominant overall pattern appears to be spatially random.

A new view of radiation-induced cancer: integrating short- and long-term processes. Part II: second cancer risk estimation.

As the number of cancer survivors grows, prediction of radiotherapy-induced second cancer risks becomes increasingly important. Because the latency period for solid tumors is long, the risks of recently introduced radiotherapy protocols are not yet directly measurable. In the accompanying article, we presented a new biologically based mathematical model, which, in principle, can estimate second cancer risks for any protocol. The novelty of the model is that it integrates, into a single formalism, mechanistic analyses of pre-malignant cell dynamics on two different time scales: short-term during radiotherapy and recovery; long-term during the entire life span. Here, we apply the model to nine solid cancer types (stomach, lung, colon, rectal, pancreatic, bladder, breast, central nervous system, and thyroid) using data on radiotherapy-induced second malignancies, on Japanese atomic bomb survivors, and on background US cancer incidence. Potentially, the model can be incorporated into radiotherapy treatment planning algorithms, adding second cancer risk as an optimization criterion.

A new view of radiation-induced cancer: integrating short- and long-term processes. Part I: approach.

Mathematical models of radiation carcinogenesis are important for understanding mechanisms and for interpreting or extrapolating risk. There are two classes of such models: (1) long-term formalisms that track pre-malignant cell numbers throughout an entire lifetime but treat initial radiation dose-response simplistically and (2) short-term formalisms that provide a detailed initial dose-response even for complicated radiation protocols, but address its modulation during the subsequent cancer latency period only indirectly. We argue that integrating short- and long-term models is needed. As an example of this novel approach, we integrate a stochastic short-term initiation/inactivation/repopulation model with a deterministic two-stage long-term model. Within this new formalism, the following assumptions are implemented: radiation initiates, promotes, or kills pre-malignant cells; a pre-malignant cell generates a clone, which, if it survives, quickly reaches a size limitation; the clone subsequently grows more slowly and can eventually generate a malignant cell; the carcinogenic potential of pre-malignant cells decreases with age.

Synergy Theory in Radiobiology.

Customized open-source software is used to characterize, exemplify, compare and critically evaluate mathematical/computational synergy analysis methods currently used in biology, and used or potentially applicable in radiobiology. As examples, we reanalyze some published results on murine Harderian gland tumors and on in vitro chromosome aberrations induced by exposure to single-ion radiations that simulate components of the galactic cosmic ray field. Baseline no-synergy/no-antagonism-mixture dose-effect relationships are calculated for corresponding mixed fields. No new experimental results are presented. Synergy analysis of effects due to a mixed radiation field whose components' individual dose-effect relationships are highly curvilinear should not consist of simply comparing to the sum of the components' effects. Such curvilinearity must often be allowed for in current radiobiology, especially when studying possible non-targeted ("bystander") effects. Consequently, many different synergy analysis theories are currently used in biology to replace simple effect additivity. We give evidence that for most synergy experiments and observations, incremental effect additivity is the most appropriate replacement. It has a large domain of applicability, being useful even when pronounced individual dose-effect relationship curvilinearity is a confounding factor. It allows calculation of 95% confidence intervals for baseline mixture dose-effect relationships taking into account parameter correlations; if non-targeted effects are important this gives much tighter intervals than neglecting the correlations. It always obeys two consistency conditions that simple effect additivity usually fails to obey: a "mixture of mixtures principle" and the standard "sham mixture principle". The mixture of mixtures principle is important in radiobiology because even nominally single-ion radiations are usually mixtures when they strike the biological target, due to intervening material. It is not yet clear whether mixing galactic cosmic ray components sometimes leads to statistically significant synergy for animal tumorigenesis. The substantial limitations of synergy theories are sometimes overlooked, and they warrant further study.

Biophysical models of radiation bystander effects: 1. Spatial effects in three-dimensional tissues.

Non-targeted (bystander) effects of ionizing radiation are caused by intercellular signaling; they include production of DNA damage and alterations in cell fate (i.e. apoptosis, differentiation, senescence or proliferation). Biophysical models capable of quantifying these effects may improve cancer risk estimation at radiation doses below the epidemiological detection threshold. Understanding the spatial patterns of bystander responses is important, because it provides estimates of how many bystander cells are affected per irradiated cell. In a first approach to modeling of bystander spatial effects in a three-dimensional artificial tissue, we assume the following: (1) The bystander phenomenon results from signaling molecules (S) that rapidly propagate from irradiated cells and decrease in concentration (exponentially in the case of planar symmetry) as distance increases. (2) These signals can convert cells to a long-lived epigenetically activated state, e.g. a state of oxidative stress; cells in this state are more prone to DNA damage and behavior alterations than normal and therefore exhibit an increased response (R) for many end points (e.g. apoptosis, differentiation, micronucleation). These assumptions are implemented by a mathematical formalism and computational algorithms. The model adequately describes data on bystander responses in the 3D system using a small number of adjustable parameters.

Scaling Human Cancer Risks from Low LET to High LET when Dose-Effect Relationships are Complex.

Health risks from space radiations, particularly from densely ionizing radiations, represent an important challenge for long-ranged manned space missions. Reliable methods are needed for scaling low-LET to high-LET radiation risks for humans, based on animal or in vitro studies comparing these radiations. The current standard metric, relative biological effectiveness (RBE) compares iso-effect doses of two radiations. By contrast, a proposed new metric, radiation effects ratio (RER), compares effects of two radiations at the same dose. This definition of RER allows direct scaling of low-LET to high-LET radiation risks in humans at the dose or doses of interest. By contrast to RBE, RER can be used without need for detailed information about dose response shapes for compared radiations. This property of RER allows animal carcinogenesis experiments to be simplified by reducing the number of tested radiation doses. For simple linear dose-effect relationships, RBE = RER. However, for more complex dose-effect relationships, such as those with nontargeted effects at low doses, RER can be lower than RBE. We estimated RBE and RER values and uncertainties using heavy ion (12C, 28Si, 56Fe) and gamma-ray-induced tumors in a mouse model for intestinal cancer (APC1638N/+), and used both RBE and RER to estimate low-LET to high-LET risk scaling factors. The data showed clear evidence of nontargeted effects at low doses. In situations, such as the ones discussed here where nontargeted effects dominate at low doses, RER was lower than RBE by factors around 2.8-3.5 at 0.03 Gy and 1.3-1.4 at 0.3 Gy. It follows that low-dose high-LET human cancer risks scaled from low-LET human risks using RBE may be correspondingly overestimated.

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

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.

A robust procedure for removing background damage in assays of radiation-induced DNA fragment distributions.

The non-random distribution of DNA breakage in PFGE (pulsed-field gel electrophoresis) experiments poses a problem of proper subtraction of the background DNA damage to obtain a fragment-size distribution due to radiation only. A naive bin-to-bin subtraction of the background signal will not result in the right DNA mass distribution histogram. This problem could become more pronounced for high-LET (linear energy transfer) radiation, because the fragment-size distribution manifests a higher frequency of smaller fragments. Previous systematic subtraction methods have been based on random breakage, appropriate for low-LET radiation. Moreover, an investigation is needed to determine whether the background breakage is itself random or non-random. We consider two limiting cases: (1) the background damage is present in all cells, and (2) it is present in only a small subset of cells, while other cells are not contributing to the background DNA fragmentation. We give a generalized formalism based on stochastic processes for the subtraction of the background damage in PFGE experiments for any LET and apply it to two sets of PFGE data for iron ions.

Mixed Beam Murine Harderian Gland Tumorigenesis: Predicted Dose-Effect Relationships if neither Synergism nor Antagonism Occurs.

Complex mixed radiation fields exist in interplanetary space, and little is known about their late effects on space travelers. In silico synergy analysis default predictions are useful when planning relevant mixed-ion-beam experiments and interpreting their results. These predictions are based on individual dose-effect relationships (IDER) for each component of the mixed-ion beam, assuming no synergy or antagonism. For example, a default hypothesis of simple effect additivity has often been used throughout the study of biology. However, for more than a century pharmacologists interested in mixtures of therapeutic drugs have analyzed conceptual, mathematical and practical questions similar to those that arise when analyzing mixed radiation fields, and have shown that simple effect additivity often gives unreasonable predictions when the IDER are curvilinear. Various alternatives to simple effect additivity proposed in radiobiology, pharmacometrics, toxicology and other fields are also known to have important limitations. In this work, we analyze upcoming murine Harderian gland (HG) tumor prevalence mixed-beam experiments, using customized open-source software and published IDER from past single-ion experiments. The upcoming experiments will use acute irradiation and the mixed beam will include components of high atomic number and energy (HZE). We introduce a new alternative to simple effect additivity, "incremental effect additivity", which is more suitable for the HG analysis and perhaps for other end points. We use incremental effect additivity to calculate default predictions for mixture dose-effect relationships, including 95% confidence intervals. We have drawn three main conclusions from this work. 1. It is important to supplement mixed-beam experiments with single-ion experiments, with matching end point(s), shielding and dose timing. 2. For HG tumorigenesis due to a mixed beam, simple effect additivity and incremental effect additivity sometimes give default predictions that are numerically close. However, if nontargeted effects are important and the mixed beam includes a number of different HZE components, simple effect additivity becomes unusable and another method is needed such as incremental effect additivity. 3. Eventually, synergy analysis default predictions of the effects of mixed radiation fields will be replaced by more mechanistic, biophysically-based predictions. However, optimizing synergy analyses is an important first step. If mixed-beam experiments indicate little synergy or antagonism, plans by NASA for further experiments and possible missions beyond low earth orbit will be substantially simplified.

Ionizing radiation exposures in treatments of solid neoplasms are not associated with subsequent increased risks of chronic lymphocytic leukemia.

Exposure to ionizing radiation is not thought to cause chronic lymphocytic leukemia (CLL). Challenging this notion are recent data suggesting CLL incidence may be increased by radiation exposure from the atomic bombs (after many decades), uranium mining and nuclear power facility accidents. To assess the effects of therapeutic ionizing radiation for the treatment of solid neoplasms we studied CLL risks in data from the Surveillance, Epidemiology, and End Results (SEER) Program. Specifically, we compared the risks of developing CLL in persons with a 1(st) non-hematologic cancer treated with or without ionizing radiation. We controlled for early detection effects on CLL risk induced by surveillance after 1(st) cancer diagnoses by forming all-time cumulative CLL relative risks (RR). We estimate such CLL RR to be 1.20 (95% confidence interval, 1.17, 1.23) for persons whose 1(st) cancer was not treated with ionizing radiation and 1.00 (0.96, 1.05) for persons whose 1(st) cancer was treated with ionizing radiations. These results imply that diagnosis of a solid neoplasm is associated with an increased risk of developing CLL only in persons whose 1(st) cancer was not treated with radiation therapy.