A multi-compartment cell repopulation model allowing for inter-compartmental migration following radiation exposure, applied to leukaemia

The risk of cancer following high, and very high, doses of ionising radiation

It is established that moderate-to-high doses of ionising radiation increase the risk of subsequent cancer in the exposed individual, but the question arises as to the risk of cancer from higher doses, such as those delivered during radiotherapy, accidents, or deliberate acts of malice. In general, the cumulative dose received during a course of radiation treatment is sufficiently high that it would kill a person if delivered as a single dose to the whole body, but therapeutic doses are carefully fractionated and high/very high doses are generally limited to a small tissue volume under controlled conditions. The very high cumulative doses delivered as fractions during radiation treatment are designed to inactivate diseased cells, but inevitably some healthy cells will also receive high/very high doses. How the doses (ranging from <1 Gy to tens of Gy) received by healthy tissues during radiotherapy affect the risk of second primary cancer is an increasingly important issue to address as more cancer patients survive the disease. Studies show that, except for a turndown for thyroid cancer, a linear dose-response for second primary solid cancers seems to exist over a cumulative gamma radiation dose range of tens of gray, but with a gradient of excess relative risk per Gy that varies with the type of second cancer, and which is notably shallower than that found in the Japanese atomic bomb survivors receiving a single moderate-to-high acute dose. The risk of second primary cancer consequent to high/very high doses of radiation is likely to be due to repopulation of heavily irradiated tissues by surviving stem cells, some of which will have been malignantly transformed by radiation exposure, although the exact mechanism is not known, and various models have been proposed. It is important to understand the mechanisms that lead to the raised risk of second primary cancers consequent to the receipt of high/very high doses, in particular so that the risks associated with novel radiation treatment regimens-for example, intensity modulated radiotherapy and volumetric modulated arc therapy that deliver high doses to the target volume while exposing relatively large volumes of healthy tissue to low/moderate doses, and treatments using protons or heavy ions rather than photons-may be properly assessed.

Application of stem cells in tissue engineering for defense medicine

The dynamic nature of modern warfare, including threats and injuries faced by soldiers, necessitates the development of countermeasures that address a wide variety of injuries. Tissue engineering has emerged as a field with the potential to provide contemporary solutions. In this review, discussions focus on the applications of stem cells in tissue engineering to address health risks frequently faced by combatants at war. Human development depends intimately on stem cells, the mysterious precursor to every kind of cell in the body that, with proper instruction, can grow and differentiate into any new tissue or organ. Recent reports have suggested the greater therapeutic effects of the anti-inflammatory, trophic, paracrine and immune-modulatory functions associated with these cells, which induce them to restore normal healing and tissue regeneration by modulating immune reactions, regulating inflammation, and suppressing fibrosis. Therefore, the use of stem cells holds significant promise for the treatment of many battlefield injuries and their complications. These applications include the treatment of injuries to the skin, sensory organs, nervous system tissues, the musculoskeletal system, circulatory/pulmonary tissues and genitals/testicles and of acute radiation syndrome and the development of novel biosensors. The new research developments in these areas suggest that solutions are being developed to reduce critical consequences of wounds and exposures suffered in warfare. Current military applications of stem cell-based therapies are already saving the lives of soldiers who would have died in previous conflicts. Injuries that would have resulted in deaths previously now result in wounds today; similarly, today's permanent wounds may be reduced to tomorrow's bad memories with further advances in stem cell-based therapies.

A radiobiological model of radiotherapy response and its correlation with prognostic imaging variables

Radiobiological models of tumour control probability (TCP) can be personalized using imaging data. We propose an extension to a voxel-level radiobiological TCP model in order to describe patient-specific differences and intra-tumour heterogeneity. In the proposed model, tumour shrinkage is described by means of a novel kinetic Monte Carlo method for inter-voxel cell migration and tumour deformation. The model captures the spatiotemporal evolution of the tumour at the voxel level, and is designed to take imaging data as input. To test the performance of the model, three image-derived variables found to be predictive of outcome in the literature have been identified and calculated using the model's own parameters. Simulating multiple tumours with different initial conditions makes it possible to perform an in silico study of the correlation of these variables with the dose for 50% tumour control ([Formula: see text]) calculated by the model. We find that the three simulated variables correlate with the calculated [Formula: see text]. In addition, we find that different variables have different levels of sensitivity to the spatial distribution of hypoxia within the tumour, as well as to the dynamics of the migration mechanism. Finally, based on our results, we observe that an adequate combination of the variables may potentially result in higher predictive power.

Minimizing second cancer risk following radiotherapy: current perspectives

Secondary cancer risk following radiotherapy is an increasingly important topic in clinical oncology with impact on treatment decision making and on patient management. Much of the evidence that underlies our understanding of secondary cancer risks and our risk estimates are derived from large epidemiologic studies and predictive models of earlier decades with large uncertainties. The modern era is characterized by more conformal radiotherapy technologies, molecular and genetic marker approaches, genome-wide studies and risk stratifications, and sophisticated biologically based predictive models of the carcinogenesis process. Four key areas that have strong evidence toward affecting secondary cancer risks are 1) the patient age at time of radiation treatment, 2) genetic risk factors, 3) the organ and tissue site receiving radiation, and 4) the dose and volume of tissue being irradiated by a particular radiation technology. This review attempts to summarize our current understanding on the impact on secondary cancer risks for each of these known risk factors. We review the recent advances in genetic studies and carcinogenesis models that are providing insight into the biologic processes that occur from tissue irradiation to the development of a secondary malignancy. Finally, we discuss current approaches toward minimizing the risk of radiation-associated secondary malignancies, an important goal of clinical radiation oncology.

Second solid cancers after radiation therapy: a systematic review of the epidemiologic studies of the radiation dose-response relationship

Rapid innovations in radiation therapy techniques have resulted in an urgent need for risk projection models for second cancer risks from high-dose radiation exposure, because direct observation of the late effects of newer treatments will require patient follow-up for a decade or more. However, the patterns of cancer risk after fractionated high-dose radiation are much less well understood than those after lower-dose exposures (0.1-5 Gy). In particular, there is uncertainty about the shape of the dose-response curve at high doses and about the magnitude of the second cancer risk per unit dose. We reviewed the available evidence from epidemiologic studies of second solid cancers in organs that received high-dose exposure (>5 Gy) from radiation therapy where dose-response curves were estimated from individual organ-specific doses. We included 28 eligible studies with 3434 second cancer patients across 11 second solid cancers. Overall, there was little evidence that the dose-response curve was nonlinear in the direction of a downturn in risk, even at organ doses of ≥60 Gy. Thyroid cancer was the only exception, with evidence of a downturn after 20 Gy. Generally the excess relative risk per Gray, taking account of age and sex, was 5 to 10 times lower than the risk from acute exposures of <2 Gy among the Japanese atomic bomb survivors. However, the magnitude of the reduction in risk varied according to the second cancer. The results of our review provide insights into radiation carcinogenesis from fractionated high-dose exposures and are generally consistent with current theoretical models. The results can be used to refine the development of second solid cancer risk projection models for novel radiation therapy techniques.

A reanalysis of curvature in the dose response for cancer and modifications by age at exposure following radiation therapy for benign disease

PURPOSE

To assess the shape of the dose response for various cancer endpoints and modifiers by age and time.

METHODS AND MATERIALS

Reanalysis of the US peptic ulcer data testing for heterogeneity of radiogenic risk by cancer endpoint (stomach, pancreas, lung, leukemia, all other).

RESULTS

There are statistically significant (P<.05) excess risks for all cancer and for lung cancer and borderline statistically significant risks for stomach cancer (P=.07), and leukemia (P=.06), with excess relative risks Gy(-1) of 0.024 (95% confidence interval [CI] 0.011, 0.039), 0.559 (95% CI 0.221, 1.021), 0.042 (95% CI -0.002, 0.119), and 1.087 (95% CI -0.018, 4.925), respectively. There is statistically significant (P=.007) excess risk of pancreatic cancer when adjusted for dose-response curvature. General downward curvature is apparent in the dose response, statistically significant (P<.05) for all cancers, pancreatic cancer, and all other cancers (ie, other than stomach, pancreas, lung, leukemia). There are indications of reduction in relative risk with increasing age at exposure (for all cancers, pancreatic cancer), but no evidence for quadratic variations in relative risk with age at exposure. If a linear-exponential dose response is used, there is no significant heterogeneity in the dose response among the 5 endpoints considered or in the speed of variation of relative risk with age at exposure. The risks are generally consistent with those observed in the Japanese atomic bomb survivors and in groups of nuclear workers.

CONCLUSIONS

There are excess risks for various malignancies in this data set. Generally there is a marked downward curvature in the dose response and significant reduction in relative risk with increasing age at exposure. The consistency of risks with those observed in the Japanese atomic bomb survivors and in groups of nuclear workers implies that there may be little sparing effect of fractionation of dose or low-dose-rate exposure.

Stem cell applications in military medicine

There are many similarities between health issues affecting military and civilian patient populations, with the exception of the relatively small but vital segment of active soldiers who experience high-energy blast injuries during combat. A rising incidence of major injuries from explosive devices in recent campaigns has further complicated treatment and recovery, highlighting the need for tissue regenerative options and intensifying interest in the possible role of stem cells for military medicine. In this review we outline the array of tissue-specific injuries typically seen in modern combat - as well as address a few complications unique to soldiers - and discuss the state of current stem cell research in addressing each area. Embryonic, induced-pluripotent and adult stem cell sources are defined, along with advantages and disadvantages unique to each cell type. More detailed stem cell sources are described in the context of each tissue of interest, including neural, cardiopulmonary, musculoskeletal and sensory tissues, with brief discussion of their potential role in regenerative medicine moving forward. Additional commentary is given to military stem cell applications aside from regenerative medicine, such as blood pharming, immunomodulation and drug screening, with an overview of stem cell banking and the unique opportunity provided by the military and civilian overlap of stem cell research.

A new view of radiation-induced cancer

Biologically motivated mathematical models are important for understanding the mechanisms of radiation-induced carcinogenesis. Existing models fall into two categories: (1) short-term formalisms, which focus on the processes taking place during and shortly after irradiation (effects of dose, radiation quality, dose rate and fractionation), and (2) long-term formalisms, which track background cancer risks throughout the entire lifetime (effects of age at exposure and time since exposure) but make relatively simplistic assumptions about radiation effects. Grafting long-term mechanisms on to short-term models is badly needed for modelling radiogenic cancer. A combined formalism was developed and applied to cancer risk data in atomic bomb survivors and radiotherapy patients and to background cancer incidence. The data for nine cancer types were described adequately with a set of biologically meaningful parameters for each cancer. These results suggest that the combined short-long-term approach is a potentially promising method for predicting radiogenic cancer risks and interpreting the underlying biological mechanisms.

Quantitative modeling of tumor dynamics and radiotherapy

Cancer is a complex disease, necessitating research on many different levels; at the subcellular level to identify genes, proteins and signaling pathways associated with the disease; at the cellular level to identify, for example, cell-cell adhesion and communication mechanisms; at the tissue level to investigate disruption of homeostasis and interaction with the tissue of origin or settlement of metastasis; and finally at the systems level to explore its global impact, e.g. through the mechanism of cachexia. Mathematical models have been proposed to identify key mechanisms that underlie dynamics and events at every scale of interest, and increasing effort is now being paid to multi-scale models that bridge the different scales. With more biological data becoming available and with increased interdisciplinary efforts, theoretical models are rendering suitable tools to predict the origin and course of the disease. The ultimate aims of cancer models, however, are to enlighten our concept of the carcinogenesis process and to assist in the designing of treatment protocols that can reduce mortality and improve patient quality of life. Conventional treatment of cancer is surgery combined with radiotherapy or chemotherapy for localized tumors or systemic treatment of advanced cancers, respectively. Although radiation is widely used as treatment, most scheduling is based on empirical knowledge and less on the predictions of sophisticated growth dynamical models of treatment response. Part of the failure to translate modeling research to the clinic may stem from language barriers, exacerbated by often esoteric model renderings with inaccessible parameterization. Here we discuss some ideas for combining tractable dynamical tumor growth models with radiation response models using biologically accessible parameters to provide a more intuitive and exploitable framework for understanding the complexity of radiotherapy treatment and failure.

Influence of infection rate and migration on extinction of disease in spatial epidemics

Extinction of disease can be explained by the patterns of epidemic spreading, yet the underlying causes of extinction are far from being well understood. To reveal a mechanism of disease extinction, a cellular automata model with both birth, death rate and migration is presented. We find that, in single patch, when the infection rate is small or large enough, the disease will disappear for a long time. When the invasion form is in the coexistence of stable spiral and turbulent wave state, the disease will persist. Also, we find that the migration has dual effects on the epidemic spreading. On one hand, in the extinction region of single patch, if the migration rate is large enough, there is a phase transition from the disease free to endemic state in two patches. On the other hand, migration will induce extinction in the regime, which can ensure the persistence of the disease in single patch, due to emergence of anti-phase synchrony. The results obtained well reveal the effect of infection rate and migration on the extinction of the disease, which enriches the finding in the filed of epidemiology and may provide some new ideas to control the disease in the real world.

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.

New models for evaluation of radiation-induced lifetime cancer risk and its uncertainty employed in the UNSCEAR 2006 report

Generalized relative and absolute risk models are fitted to the latest Japanese atomic bomb survivor solid cancer and leukemia mortality data (through 2000), with the latest (DS02) dosimetry, by classical (regression calibration) and Bayesian techniques, taking account of errors in dose estimates and other uncertainties. Linear-quadratic and linear-quadratic-exponential models are fitted and used to assess risks for contemporary populations of China, Japan, Puerto Rico, the U.S. and the UK. Many of these models are the same as or very similar to models used in the UNSCEAR 2006 report. For a test dose of 0.1 Sv, the solid cancer mortality for a UK population using the generalized linear-quadratic relative risk model is estimated as 5.4% Sv(-1) [90% Bayesian credible interval (BCI) 3.1, 8.0]. At 0.1 Sv, leukemia mortality for a UK population using the generalized linear-quadratic relative risk model is estimated as 0.50% Sv(-1) (90% BCI 0.11, 0.97). Risk estimates varied little between populations; at 0.1 Sv the central estimates ranged from 3.7 to 5.4% Sv(-1) for solid cancers and from 0.4 to 0.6% Sv(-1) for leukemia. Analyses using regression calibration techniques yield central estimates of risk very similar to those for the Bayesian approach. The central estimates of population risk were similar for the generalized absolute risk model and the relative risk model. Linear-quadratic-exponential models predict lower risks (at least at low test doses) and appear to fit as well, although for other (theoretical) reasons we favor the simpler linear-quadratic models.

Individualized estimates of second cancer risks after contemporary radiation therapy for Hodgkin lymphoma

BACKGROUND

Estimates of radiation-related second cancer risk among Hodgkin lymphoma survivors are largely based on radiation therapy (RT) fields and doses no longer in use, and these estimates do not account for differences in normal tissue dose among individual patients. This study gives individualized estimates for the risks of lung and female breast cancer expected with contemporary involved-field RT and low-dose (20 Gy) RT for mediastinal Hodgkin lymphoma.

METHODS

Three RT plans were constructed for 37 consecutive patients with mediastinal Hodgkin lymphoma: 35 Gy mantle RT, 35 Gy involved-field RT (IFRT), and 20 Gy IFRT. For each of the 111 RT plans, individual-level dosimetry data were incorporated into a cell initiation/inactivation/proliferation model to estimate the excess relative risk (ERR) and cumulative incidence of radiation-induced second cancer.

RESULTS

ERR estimates were compatible with results of epidemiological studies. Compared with 35 Gy mantle radiation therapy, 35 Gy IFRT was predicted to reduce the 20-year ERRs of breast and lung cancer by 63% and 21%, respectively, primarily because of lower normal tissue doses with the omission of axillary RT. Low-dose (20 Gy) IFRT was associated with a 77% and 57% decrease in these ERRs. Patient-specific differences in normal tissue dose with IFRT led to 11-fold and 3.6-fold variations among individual's estimates of breast and lung cancer ERR, respectively.

CONCLUSIONS

Contemporary IFRT is predicted to substantially reduce risk of secondary breast and lung cancer compared with mantle RT, with considerable variation in risk among individuals. Individualized prospective risk estimates could facilitate patient-specific counseling and the development of more effective RT techniques.

Leukaemia following childhood radiation exposure in the Japanese atomic bomb survivors and in medically exposed groups

Incidence and mortality risks of radiation-associated leukaemia are surveyed in the Japanese atomic bomb (A-bomb) survivors exposed in early childhood and in utero. Leukaemia incidence and mortality risks are also surveyed in 16 other studies of persons who received appreciable doses of ionizing radiation in the course of treatment in childhood and for whom there is adequate dosimetry and cancer incidence or mortality follow-up. Relative risks tend to be lower in the medical series than in the Japanese A-bomb survivors. The relative risks in the medical studies tend to diminish with increasing average therapy dose. After taking account of cell sterilisation and dose fractionation, the apparent differences between the relative risks for leukaemia in the Japanese A-bomb survivors and in the medical series largely disappear. This suggests that cell sterilisation largely accounts for the discrepancy between the relative risks in the Japanese data and the medical studies. Excess absolute risk has also been assessed in four studies, and there is found to be more variability in this measure than in excess relative risk. In particular, there is a substantial difference between the absolute risk in the Japanese atomic bomb survivor data and those in three other (European) populations. In summary, the relative risks of leukaemia in studies of persons exposed to appreciable doses of ionizing radiation in the course of treatment for a variety of malignant and non-malignant conditions in childhood are generally less than those in the Japanese A-bomb survivor data. The effects of cell sterilisation can largely explain the discrepancy between the Japanese and the medical series.

Second cancers after fractionated radiotherapy: stochastic population dynamics effects

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.

Radiation-induced leukemia at doses relevant to radiation therapy: modeling mechanisms and estimating risks

BACKGROUND

Because many cancer patients are diagnosed earlier and live longer than in the past, second cancers induced by radiation therapy have become a clinically significant issue. An earlier biologically based model that was designed to estimate risks of high-dose radiation-induced solid cancers included initiation of stem cells to a premalignant state, inactivation of stem cells at high radiation doses, and proliferation of stem cells during cellular repopulation after inactivation. This earlier model predicted the risks of solid tumors induced by radiation therapy but overestimated the corresponding leukemia risks.

METHODS

To extend the model to radiation-induced leukemias, we analyzed--in addition to cellular initiation, inactivation, and proliferation--a repopulation mechanism specific to the hematopoietic system: long-range migration through the blood stream of hematopoietic stem cells (HSCs) from distant locations. Parameters for the model were derived from HSC biologic data in the literature and from leukemia risks among atomic bomb survivors who were subjected to much lower radiation doses.

RESULTS

Proliferating HSCs that migrate from sites distant from the high-dose region include few preleukemic HSCs, thus decreasing the high-dose leukemia risk. The extended model for leukemia provides risk estimates that are consistent with epidemiologic data for leukemia risk associated with radiation therapy over a wide dose range. For example, when applied to an earlier case-control study of 110,000 women undergoing radiotherapy for uterine cancer, the model predicted an excess relative risk (ERR) of 1.9 for leukemia among women who received a large inhomogeneous fractionated external beam dose to the bone marrow (mean = 14.9 Gy), consistent with the measured ERR (2.0, 95% confidence interval [CI] = 0.2 to 6.4; from 3.6 cases expected and 11 cases observed). As a corresponding example for brachytherapy, the predicted ERR of 0.80 among women who received an inhomogeneous low-dose-rate dose to the bone marrow (mean = 2.5 Gy) was consistent with the measured ERR (0.62, 95% CI = -0.2 to 1.9).

CONCLUSIONS

An extended, biologically based model for leukemia that includes HSC initiation, inactivation, proliferation, and, uniquely for leukemia, long-range HSC migration predicts, with reasonable accuracy, risks for radiation-induced leukemia associated with exposure to therapeutic doses of radiation.