Spatial model predicts dispersal and cell turnover cause reduced intra-tumor heterogeneity

Spatial model predicts dispersal and cell turnover cause reduced intra-tumor heterogeneity

Bartlomiej Waclaw , Ivana Bozic , Meredith E Pittman , Ralph H Hruban , Bert Vogelstein , Martin A Nowak

 

Most cancers in humans are large, measuring centimeters in diameter, composed of many billions of cells. An equivalent mass of normal cells would be highly heterogeneous as a result of the mutations that occur during each cell division. What is remarkable about cancers is their homogeneity - virtually every neoplastic cell within a large cancer contains the same core set of genetic alterations, with heterogeneity confined to mutations that have emerged after the last clonal expansions. How such clones expand within the spatially-constrained three dimensional architecture of a tumor, and come to dominate a large, pre-existing lesion, has never been explained. We here describe a model for tumor evolution that shows how short-range migration and cell turnover can account for rapid cell mixing inside the tumor. With it, we show that even a small selective advantage of a single cell within a large tumor allows the descendants of that cell to replace the precursor mass in a clinically relevant time frame. We also demonstrate that the same mechanisms can be responsible for the rapid onset of resistance to chemotherapy. Our model not only provides novel insights into spatial and temporal aspects of tumor growth but also suggests that targeting short range cellular migratory activity could have dramatic effects on tumor growth rates.

 

BioRXiv: link 

The overshoot and phenotypic equilibrium in characterizing cancer dynamics of reversible phenotypic plasticity

Xiufang Chen, Yue Wang, Tianquan Feng, Ming Yi, Xingan Zhang, Da Zhou

(Submitted on 16 Mar 2015)

The paradigm of phenotypic plasticity indicates reversible relations of different cancer cell phenotypes, which challenges the cellular hierarchy proposed by the conventional cancer stem cell (CSC) theory. Since the validity of the reversible model versus the hierarchical model of cancer cells is still experimentally debated, it is worthwhile to theoretically explore the dynamic behavior characterizing the reversible model in comparison of the hierarchical model. By comparing the two models in predicting the cell-state dynamics observed in biological experiments, our results imply that the reversible model has advantages over the hierarchical model in predicting both long-term stable and short-term transient dynamics of cancer cells. In particular, it is found that i) the reversible model can predict the phenotypic equilibrium better than the hierarchical model, namely, the stability of the phenotypic mixture of cancer cells is more rooted in the reversible model; ii) the reversible model can perform various types of overshoot behavior, whereas the hierarchical model can never predict the overshoot of CSCs proportion. These also indicate that the phenotypic equilibrium and overshoot can be good candidates to characterize the models with the reversible phenotypic plasticity.

http://arxiv.org/abs/1503.04558

Phase i trials in melanoma: A framework to translate preclinical findings to the clinic

Eunjung Kim , Vito W. Rebecca , Keiran S.M. Smalley , Alexander R.A. Anderson

doi: http://dx.doi.org/10.1101/015925 

Abstract

The combination of chemotherapy and an AKT inhibitor in patients with metastatic solid tumors including melanomas was tested in a recently completed phase 1 clinical trial. Our experiments showed that such regimens differentially induce autophagy in melanoma cells and autophagy modulates the response to treatment. Motivated by these observations, we formulated a mathematical model comprised of a system of ordinary differential equations that explains the dynamics of the response of melanoma cells to different mono and combination therapies. Model parameters were estimated using an optimization algorithm that minimizes differences between predicted cell populations and experimentally measured cell numbers. The model predicts that the combination therapy treatment protocol used in the trial is effective in short term tumor control but that the treatment will eventually fail, although smarter schedules can be applied to extend response. To move this model forward into a more clinically relevant setting, we implemented a phase i trial (a virtual/imaginary yet informed clinical trial), where a genetic algorithm was used to generate a cohort of virtual patients that captured the diversity of disease response observed in a comparable clinical trial. Simulated clinical trials with the cohort and sensitivity analysis defined parameters that discriminated virtual patients having more favorable versus less favorable outcomes. These analyses established the relevance of selecting patients based on rates of tumor growth and autophagic flux. Finally, the model predicts optimal therapeutic approaches across all virtual patients, laying the foundation for phase i-informed clinical melanoma trials. The specific melanoma model developed here is just one example of the much broader potential of the phase i framework, which can be applied to almost any parameterized cancer model.  

Link: http://biorxiv.org/content/early/2015/03/02/015925