title: Estimating dose painting effects in radiotherapy: a mathematical model.
abstract: Tumor heterogeneity is widely considered to be a determinant factor in tumor progression and in particular in its recurrence after therapy. Unfortunately, current medical techniques are unable to deduce clinically relevant information about tumor heterogeneity by means of non-invasive methods. As a consequence, when radiotherapy is used as a treatment of choice, radiation dosimetries are prescribed under the assumption that the malignancy targeted is of a homogeneous nature. In this work we discuss the effects of different radiation dose distributions on heterogeneous tumors by means of an individual cell-based model. To that end, a case is considered where two tumor cell phenotypes are present, which we assume to strongly differ in their respective cell cycle duration and radiosensitivity properties. We show herein that, as a result of such differences, the spatial distribution of the corresponding phenotypes, whence the resulting tumor heterogeneity can be predicted as growth proceeds. In particular, we show that if we start from a situation where a majority of ordinary cancer cells (CCs) and a minority of cancer stem cells (CSCs) are randomly distributed, and we assume that the length of CSC cycle is significantly longer than that of CCs, then CSCs become concentrated at an inner region as tumor grows. As a consequence we obtain that if CSCs are assumed to be more resistant to radiation than CCs, heterogeneous dosimetries can be selected to enhance tumor control by boosting radiation in the region occupied by the more radioresistant tumor cell phenotype. It is also shown that, when compared with homogeneous dose distributions as those being currently delivered in clinical practice, such heterogeneous radiation dosimetries fare always better than their homogeneous counterparts. Finally, limitations to our assumptions and their resulting clinical implications will be discussed.
authors.affiliation: Department of Applied Mathematics, Faculty of Mathematics, Universidad Complutense de Madrid, Madrid, Spain.
authors.forename: Juan Carlos López
authors.initials: JC
authors.lastname: Alfonso
authors.affiliation: Institut National de Recherche en Informatique et en Automatique (INRIA), Domaine de Voluceau - Rocquencourt, Paris, France ; Institute of Computational Biology, Helmholtz Center Munich, German Research Center for Environmental Health, Neuherberg, Germany.
authors.forename: Nick
authors.initials: N
authors.lastname: Jagiella
authors.affiliation: Radiophysics Department, Hospital Universitario Puerta de Hierro, Majadahonda, Madrid, Spain.
authors.forename: Luis
authors.initials: L
authors.lastname: Núñez
authors.affiliation: Department of Applied Mathematics, Faculty of Mathematics, Universidad Complutense de Madrid, Madrid, Spain.
authors.forename: Miguel A
authors.initials: MA
authors.lastname: Herrero
authors.affiliation: Institut National de Recherche en Informatique et en Automatique (INRIA), Domaine de Voluceau - Rocquencourt, Paris, France ; University of Paris 6 (UPMC), CNRS UMR 7598, Laboratoire Jacques-Louis Lions, Paris, France ; Interdisciplinary Center for Bioinformatics (IZBI), University of Leipzig, Leipzig, Germany.
authors.forename: Dirk
authors.initials: D
authors.lastname: Drasdo
mesh_terms: D006801:Humans; D008962:Models, Theoretical; D009369:Neoplasms / Q000473:pathology / Q000532:radiotherapy; D014411:Neoplastic Stem Cells / Q000473:pathology; D011879:Radiotherapy Dosage; D013269:Stochastic Processes