Solid tumor risks after high doses of ionizing radiation

Abstract

There is increasing concern regarding radiation-related secondcancer risks in long-term radiotherapy survivors and a corresponding need to be able to predict cancer risks at high radiation doses. Although cancer risks at moderately low radiation doses are reasonably understood from atomic bomb survivor studies, there is much more uncertainty at the high doses used in radiotherapy. It has generally been assumed that cancer induction decreases rapidly at high doses due to cell killing. However, recent studies of radiation-induced second cancers in the lung and breast, covering a very wide range of doses, contradict this assumption. A likely resolution of this disagreement comes from considering cellular repopulation during and after radiation exposure. Such repopulation tends to counteract cell killing and accounts for the large discrepancies between the current standard model for cancer induction at high doses and recent second-cancer data. We describe and apply a biologically based minimally parameterized model of dose-dependent cancer risks, incorporating carcinogenic effects, cell killing, and, additionally, proliferation͞repopulation effects. Including stem-cell repopulation leads to risk estimates consistent with high-dose second-cancer data. A simplified version of the model provides a practical and parameter-free approach to predicting high-dose cancer risks, based only on data for atomic bomb survivors (who were exposed to lower total doses) and the demographic variables of the population of interest. Incorporating repopulation effects provides both a mechanistic understanding of cancer risks at high doses and a practical methodology for predicting cancer risks in organs exposed to high radiation doses, such as during radiotherapy.second cancer risks ͉ radiotherapy T here is increasing concern regarding radiation-related second-cancer risks in long-term radiotherapy survivors (1, 2) and a corresponding need to be able to understand and predict cancer risks at high radiation doses (3). Cancer risks after acute exposure to ionizing radiation at intermediate doses, up to approximately 3 Gy, are reasonably well understood, based mainly on data from atomic bomb (A-bomb) survivors; however, there is considerably more uncertainty about the effects of higher doses (3). Because of the high doses inevitably given to organs close to a tumor during radiotherapy, the issue of radiation-induced second cancers at high doses is increasingly important (1), particularly in light of the large number of cancer patients undergoing high-dose (65-80 Gy) radiotherapy, at younger ages (4), and with increasing survival times (5).It has usually been assumed that cancer induction decreases rapidly at higher doses due to cell killing, in that dead cells cannot give rise to a malignancy, and cell survival decreases exponentially or even faster with increasing dose (6, 7). A standard quantitative implementation of this ''initiation ϩ killing'' mechanism, in which the high dose risks are dominated solely by a balance between carcinogeni...

Second cancers after fractionated radiotherapy: Stochastic population dynamics effects

“…(3) is so small at total doses above about 15 Gy that the predicted number of second cancers is negligible -essentially, the prediction is that there is no carcinogenesis because no pre-malignant cells survive. However, recent data indicates that in fact substantial second carcinogenesis can occur at high total doses, such as those used in radiotherapy [reviewed in (Sachs and Brenner, 2005;Schneider and Kaser-Hotz, 2005)]. A likely source of this discrepancy is that Eq.…”

“…(1) and (3). The resulting initiation/ inactivation/proliferation (IIP) model (Sachs and Brenner, 2005) was based on the following equations : a) Eq. (2) to describe initiation in each dose-fraction; b) standard LQ equations to describe inactivation of normal and of radiation-initiated pre-malignant stem cells in each dosefraction (Appendix B); and c), a system of two nonlinear ordinary differential equations to describe cell repopulation dynamics between dose-fractions or after the last dose-fraction (Appendix B).…”

“…An explicit analytic solution for a relevant time-inhomogeneous birth-death process with additional fractionated cell killing has been found [ (Zaider and Minerbo, 2000); (Hanin, 2004) and references there]. Our calculation here differs, mainly because we include initiation, but it is known (Little, 2007;Sachs and Brenner, 2005;Shuryak et al, 2006;Tucker and Taylor, 1996) that in either situation, for fractionated radiation with large total doses, repeated cycles of inactivation and proliferation can lead to statistical distributions of cell numbers where the ratio of variance to mean is much larger than the value 1 that a Poisson distribution would imply ["overdispersion";compare (Boucher et al, 1998)]. Correspondingly, the zero class probability, interpreted in our setting as the fraction of patients who do not have any radiationinitiated, pre-malignant cells, can be much larger than anticipated from the mean number of radiation-initiated cells per patient.…”

“…The deterministic IIP model is a special case of a somewhat more general model (Appendix A). To calculate m final we previously assumed that, when m is negligible compared to n, repopulation between dose fractions (or during the recovery period following the last dose fraction) is described by the following differential equations (Sachs and Brenner, 2005):…”

“…As in the deterministic IIP model (Sachs and Brenner, 2005), the ERR is calculated for comparatively low doses using atomic bomb survivor data and standard methods for "translating" the results from a Japanese to a Western cohort (Land et al, 2003). Because B is dose-independent and all our final estimates involve the product AB, not either factor separately, the following steps then suffice to give ERRs at higher doses.…”