Cancer evolution simulation identifies possible principles underlying intratumor heterogeneity

Atsushi Niida, Satoshi Ito, Georg Tremmel, Seiya Imoto, Ryutaro Uchi, Yusuke Takahashi, Yusuke Takahashi, Koshi Mimori, Satoru Miyano

Cancer arises from accumulation of somatic mutations and accompanying evolutionary selection for growth advantage. During the evolutionary process, an ancestor clone branches into multiple clones, yielding intratumor heterogeneity. However, principles underlying intratumor heterogeneity have been poorly understood. Here, to explore the principles, we built a cellular automaton model, termed the BEP model, which can reproduce the branching cancer evolution in silico. We then extensively searched for conditions leading to high intratumor heterogeneity by performing simulations with various parameter settings on a supercomputer. Our result suggests that multiple driver genes of moderate strength can shape subclonal structures by positive natural selection. Moreover, we found that high mutation rate and a stem cell hierarchy can contribute to extremely high intratumor heterogeneity, which is characterized by fractal patterns, through neutral evolution. Collectively, This study identified the possible principles underlying intratumor heterogeneity, which provide novel insights into the origin of cancer robustness and evolvability.

http://biorxiv.org/content/early/2015/07/17/022806

 

Gibbs Free Energy of Protein-Protein Interactions Reflects Tumor Stage

 Gibbs Free Energy of Protein-Protein Interactions reflects tumor stage 

Edward A. Rietman1 , Alex Bloemendal2 , John Platig3 , Jack A. Tuszynski4,5, Giannoula Lakka Klement1,6*

1. Molecular Oncology Research Institute, Tufts Medical Center, Boston, 02111 2. Mathematics Department, Harvard University, Cambridge, MA 3. Dana-Farber Cancer Institute, Boston, MA 4. Department of Oncology, Faculty of Medicine & Dentistry, University of Alberta, Edmonton, Alberta, Canada T6G 1Z2 5. Department of Physics, University of Alberta, Edmonton, Alberta, Canada T6G 2E1 6. Pediatric Hematology Oncology, Floating Hospital for Children at Tufts Medical Center, Boston, MA 

Abstract The sequential changes occurring with cancer progression are now being harnessed with therapeutic intent. Yet, there is no understanding of the chemical thermodynamics of proteomic changes associated with cancer progression/ cancer stage. This manuscript reveals a strong correlation of a chemical thermodynamic measure (known as Gibbs free energy) of protein-protein interaction networks for several cancer types and 5-year overall survival and stage in patients with cancer. Earlier studies have linked degree entropy of signaling networks to patient survival data, but not with stage. It appears that Gibbs free energy is a more general metric and accounts better for the underlying energetic landscape of protein expression in cells, thus correlating with stage as well as survival.

This is an especially timely finding because of improved ability to obtain and analyze genomic/ proteomic information from individual patients. Yet, at least at present, only candidate gene imaging (FISH or immunohistochemistry) can be used for entropy computations. With continually expanding use of genomic information in clinical medicine, there is an ever-increasing need to understand the thermodynamics of protein-protein interaction networks.