Combined therapies of antithrombotics and antioxidants delay in silico brain tumor progression

Alicia Martínez-González, Mario Durán-Prado, Gabriel F. Calvo, Francisco J. Alcaín, Luis A. Pérez-Romasanta, Víctor M. Pérez-García

(Submitted on 10 Jan 2014)

Glioblastoma multiforme, the most frequent type of primary brain tumor, is a rapidly evolving and spatially heterogeneous high-grade astrocytoma that presents areas of necrosis, hypercellularity and microvascular hyperplasia. The aberrant vasculature leads to hypoxic areas and results in an increase of the oxidative stress selecting for more invasive tumor cell phenotypes. In our study we assay in silico different therapeutic approaches which combine antithrombotics, antioxidants and standard radiotherapy. To do so, we have developed a biocomputational model of glioblastoma multiforme that incorporates the spatio-temporal interplay among two glioma cell phenotypes corresponding to oxygenated and hypoxic cells, a necrotic core and the local vasculature whose response evolves with tumor progression. Our numerical simulations predict that suitable combinations of antithrombotics and antioxidants may diminish, in a synergetic way, oxidative stress and the subsequent hypoxic response. This novel therapeutical strategy, with potentially low or no toxicity, might reduce tumor invasion and further sensitize glioblastoma multiforme to conventional radiotherapy or other cytotoxic agents, hopefully increasing median patient overall survival time.

http://arxiv.org/abs/1401.2397

Effects of space structure and combination therapies on phenotypic heterogeneity and drug resistance in solid tumors

Alexander Lorz, Tommaso Lorenzi, Jean Clairambault, Alexandre Escargueil, Benoît Perthame

(Submitted on 21 Dec 2013)

Histopathological evidence supports the idea that the emergence of phenotypic heterogeneity and resistance to cytotoxic drugs can be considered as a process of adaptation, or evolution, in tumor cell populations. In this framework, can we explain intra-tumor heterogeneity in terms of cell adaptation to local conditions? How do anti-cancer therapies affect the outcome of cell competition for nutrients within solid tumors? Can we overcome the emergence of resistance and favor the eradication of cancer cells by using combination therapies? Bearing these questions in mind, we develop a model describing cell dynamics inside a tumor spheroid under the effects of cytotoxic and cytostatic drugs. Cancer cells are assumed to be structured as a population by two real variables standing for space position and the expression level of a cytotoxic resistant phenotype. The model takes explicitly into account the dynamics of resources and anti-cancer drugs as well as their interactions with the cell population under treatment. We analyze the effects of space structure and combination therapies on phenotypic heterogeneity and chemotherapeutic resistance. Furthermore, we study the efficacy of combined therapy protocols based on constant infusion and/or bang-bang delivery of cytotoxic and cytostatic drugs.

http://arxiv.org/abs/1312.6237 

Minimizing Metastatic Risk in Radiotherapy Fractionation Schedules

Kevin Leder, Jagdish Ramakrishnan

(Submitted on 27 Dec 2013)

Metastasis is the process by which cells from a primary tumor disperse and form new tumors at distant anatomical locations. The treatment and prevention of metastatic cancer remains an extremely challenging problem. In this work, we consider the problem of developing fractionated irradiation schedules that minimize production of metastatic cancer cells. Interestingly we observe that the resulting fractionation schedules are significantly different than those that result from more standard objectives such as minimization of final primary tumor volume. Hypo-fractionation is suggested even in cases when the α/β value of the tumor is large. This work introduces a novel biologically motivated objective function to the radiation optimization community that takes into account metastatic risk instead of the status of the primary tumor.

http://arxiv.org/abs/1312.7337

Tumour Control Probability in Cancer Stem Cells Hypothesis

Tumour Control Probability in Cancer Stem 

Cells Hypothesis

A. Dhawan, M. Kohandel, R. P. Hill, S. Sivaloganathan

(Submitted on 19 Dec 2013)

The tumour control probability (TCP) is a formalism derived to compare various treatment regimens of radiation therapy, defined as the probability that given a prescribed dose of radiation, a tumour has been eradicated or controlled. In the traditional view of cancer, all cells share the ability to divide without limit and thus have the potential to generate a malignant tumour. However, an emerging notion is that only a sub-population of cells, the so-called cancer stem cells (CSCs), are responsible for the initiation and maintenance of the tumour. A key implication of the CSC hypothesis is that these cells must be eradicated to achieve cures, thus we define TCP_S as the probability of eradicating CSCs for a given dose of radiation. A cell surface protein expression profile, such as CD44high/CD24low for breast cancer, is often used as a biomarker to monitor CSCs enrichment. However, it is increasingly recognized that not all cells bearing this expression profile are necessarily CSCs, and in particular early generations of progenitor cells may share the same phenotype. Thus, due to the lack of a perfect biomarker for CSCs, we also define a novel measurable TCP_CD+, that is the probability of eliminating or controlling biomarker positive cells. Based on these definitions, we use stochastic methods and numerical simulations to compare the theoretical TCP_S and the measurable TCP_CD+. We also use the measurable TCP to compare the effect of various radiation protocols.

link: http://arxiv.org/abs/1312.5582

Radiotherapy planning for glioblastoma based on a tumor growth model: Improving target volume delineation

Jan Unkelbach, Bjoern H. Menze, Ender Konukoglu, Florian Dittmann, Matthieu Le, Nicholas Ayache, Helen A. Shih

Glioblastoma di ffer from many other tumors in the sense that they grow in filtratively into the brain tissue instead of forming a solid tumor mass with a de fined boundary. Only the part of the tumor with high tumor cell density can be localized through imaging directly. In contrast, brain tissue infi ltrated by tumor cells at low density appears normal on current imaging modalities. In current clinical practice, a uniform margin, typically two centimeters, is applied to account for microscopic spread of disease that is not directly assessable through imaging. The current treatment planning procedure can potentially be improved by accounting for the anisotropy of tumor growth, which arises from di fferent factors: Anatomical barriers such as the falx cerebri represent boundaries for migrating tumor cells. In addition, tumor cells primarily spread in white matter and in lfiltrate gray matter at lower rate. We investigate the use of a phenomenological tumor growth model for treatment planning. The model is based on the Fisher-Kolmogorov equation, which formalizes these growth characteristics and estimates the spatial distribution of tumor cells in normal appearing regions of the brain. The target volume for radiotherapy planning can be defi ned as an isoline of the simulated tumor cell density. This paper analyzes the model with respect to implications for target volume de finition and identifi es its most critical components. A retrospective study involving 10 glioblastoma patients treated at our institution has been performed.

To illustrate the main fi ndings of the study, a detailed case study is presented for a glioblastoma located close to the falx. In this situation, the falx represents a boundary for migrating tumor cells, whereas the corpus callosum provides a route for the tumor to spread to the contralateral hemisphere. We further discuss the sensitivity of the model with respect to the input parameters. Correct segmentation of the brain appears to be the most crucial model input. We conclude that the tumor growth model provides a method to account for anisotropic growth patterns of glioma, and may therefore provide a tool to make target delineation more objective and automated.

Link: http://arxiv.org/pdf/1311.5902.pdf

Inferring causal models of cancer progression with a shrinkage estimator and probability raising

Olde Loohuis Loes, Caravagna Giulio, Graudenzi Alex, Ramazzotti Daniele, Mauri Giancarlo, Antoniotti Marco, Mishra Bud

(Submitted on 25 Nov 2013)

Existing techniques to reconstruct tree models of progression for accumulative processes such as cancer, seek to estimate causation by combining correlation and a frequentist notion of temporal priority. In this paper we define a novel theoretical framework to reconstruct such models based on the probabilistic notion of causation defined by Suppes, which differ fundamentally from that based on correlation. We consider a general reconstruction setting complicated by the presence of noise in the data, owing to the intrinsic variability of biological processes as well as experimental or measurement errors. To gain immunity to noise in the reconstruction performance we use a shrinkage estimator. On synthetic data, we show that our approach outperforms the state-of-the-art and, for some real cancer datasets, we highlight biologically significant differences revealed by the reconstructed progressions. Finally, we show that our method is efficient even with a relatively low number of samples and its performance quickly converges to its asymptote as the number of samples increases. Our analysis suggests the applicability of the method on small datasets of real patients.

http://arxiv.org/abs/1311.6293

Most viewed abstracts: inception through November

Taking the idea from Haldane's Sieve I've decided to start a monthly 'most viewed' post.  To start though, I'll list the top 5 viewed posts to date, from our first post on June 19th, 2013.



The number one post actually comprises two abstracts:

A recent physical approach to angiogenesis modeling: mixture models

http://mathematicaloncology.blogspot.com/2013/06/a-recent-physical-approach-to.html

Modeling the Dichotomy of the Immune Response to Cancer: Cytotoxic Effects and Tumor-Promoting Inflammation

http://mathematicaloncology.blogspot.com/2013/05/modeling-dichotomy-of-immune-response.html

A deterministic model for the occurrence and dynamics of multiple mutations in hierarchically organized tissues

http://mathematicaloncology.blogspot.com/2013/06/a-deterministic-model-for-occurrence.html

Cooperation and competition in the dynamics of tissue architecture during homeostasis and tumorigenesis

http://mathematicaloncology.blogspot.com/2013/06/cooperation-and-competition-in-dynamics.html

Cancer initiation with epistatic interactions between driver and passenger mutations

http://mathematicaloncology.blogspot.com/2013/10/cancer-initiation-with-epistatic.html

Please, keep spreading the word, and let us know if you find any preprints that we've missed. With +PeerJ, the +bioRxiv, +F1000 and the +arXiv there are more and more out there!