Stress-Induced Mutagenesis and Complex Adaptation

Yoav Ram, Lilach Hadany

doi: http://dx.doi.org/10.1101/007096

 

Abstract:

Because mutations are mostly deleterious, mutation rates should be reduced by natural selection. However, mutations also provide the raw material for adaptation. Therefore, evolutionary theory suggests that the mutation rate must balance between adaptability - the ability to adapt - and adaptedness - the ability to remain adapted. We model an asexual population crossing a fitness valley and analyze the rate of complex adaptation with and without stress-induced mutagenesis - the increase of mutation rates in response to stress or maladaptation. We show that stress-induced mutagenesis increases the rate of complex adaptation without reducing the population mean fitness, thus breaking the evolutionary trade-off between adaptability and adaptedness. Our theoretical results support the hypothesis that stress-induced mutagenesis promotes adaptation and provide quantitative predictions of the rate of complex adaptation with different mutational strategies.

 

Link: http://www.biorxiv.org/content/early/2014/07/21/007096.abstract?%3Fcollection=

 

Keywords: Adaptation, stress, evolution

Not strictly cancer but the topic is very relevant to many of us trying to understand the emergence of invasiveness.

The Role of Migration in the Evolution of Phenotypic Switching

Oana Carja, Robert E Furrow, Marc W Feldman

 

Stochastic switching is an example of phenotypic bet-hedging, where an individual can switch between different phenotypic states in a fluctuating environment. Although the evolution of stochastic switching has been studied when the environment varies temporally, there has been little theoretical work on the evolution of phenotypic switching under both spatially and temporally fluctuating selection pressures. Here we use a population genetic model to explore the interaction of temporal and spatial variation in the evolutionary dynamics of phenotypic switching. We find that spatial variation in selection is important; when selection pressures are similar across space, migration can decrease the rate of switching, but when selection pressures differ spatially, increasing migration between demes can facilitate the evolution of higher rates of switching. These results may help explain the diverse array of non-genetic contributions to phenotypic variability and phenotypic inheritance observed in both wild and experimental populations.

 

Source: bioRxiv

 

Investigating the development of chemotherapeutic drug resistance in cancer: A multiscale computational study

Investigating the development of chemotherapeutic drug resistance in cancer: A multiscale computational study

 By: Gibin G Powathil, Mark AJ Chaplain, Maciej Swat

Chemotherapy is one of the most important therapeutic options used to treat human cancers, either alone or in combination with radiation therapy and surgery. Recent studies have indicated that intra-tumoural heterogeneity has a significant role in driving resistance to chemotherapy in many human malignancies. Multiple factors including the internal cell-cycle dynamics and the external microenvironement contribute to the intra-tumoural heterogeneity. In this paper we present a hybrid, multiscale, individual-based mathematical model, incorporating internal cell-cycle dynamics and changes in oxygen concentration, to study the effects of delivery of several different chemotherapeutic drugs on the heterogeneous subpopulations of cancer cells with varying cell-cycle dynamics. The computational simulation results from the multiscale model are in good agreement with available experimental data and support the hypothesis that slow-cycling sub-populations of tumour cells within a growing tumour mass can induce drug resistance to chemotherapy and thus the use of conventional chemotherapy may actually result in the emergence of dominant, therapy-resistant, slow-cycling subpopulations of tumour cells. Our results indicate that the appearance of this chemotherapeutic resistance is mainly due to the inability of the administered drug to target all cancer cells irrespective of the stage in the cell-cycle they are in i.e. most chemotherapeutic drugs target cells in a particular phase/phases of the cell-cycle, and hence always spare some cancer cells that are not in the targeted cell-cycle phase/phases. The results also suggest that this cell-cycle-mediated drug resistance may be overcome by using multiple doses of cell-cycle, phase-specific chemotherapy that targets cells in all phases and its appropriate sequencing and scheduling.   

http://arxiv.org/abs/1407.0865

Spatial evolutionary games with small selection coefficients

Spatial evolutionary games with small selection coefficients

Rick Durrett May 5, 2014

Abstract
Here we will use results of Cox, Durrett, and Perkins [56] for voter model perturba- tions to study spatial evolutionary games on Zd, d ≥ 3 when the interaction kernel is finite range, symmetric, and has covariance matrix σ2I. The games we consider have payoff matrices of the form 1 + wG where 1 is matrix of all 1’s and w is small and positive. Since our population size N = ∞, we call our selection small rather than weak which usually means w = O(1/N). We prove that the effect of space is equiv- alent to replacing the replicator ODE by a related PDE where the reaction term is the replicator equation for a game matrix with some of the entries changed. The first idea is well known in the theory of stochastic spatial processes [58, 16, 62, 63]. The second is inspired by work of Ohtsuki and Nowak [28] (for the pair approximation). A remarkable aspect of our result is that the modifications of the game matrix depend on the interaction kernel only through the values of two simple probabilities for an associated coalescing random walk. 

link: http://www.math.duke.edu/~rtd/evog/spaceg.pdf

Classical Mathematical Models for Description and Prediction of Experimental Tumor Growth

Sébastien Benzekry, Clare Lamont, Afshin Beheshti, Amanda Tracz, John M.L. Ebos, Lynn Hlatky, Philip Hahnfeldt

(Submitted on 5 Jun 2014)

Despite internal complexity, tumor growth kinetics follow relatively simple macroscopic laws that have been quantified by mathematical models. To resolve this further, quantitative and discriminant analyses were performed for the purpose of comparing alternative models for their abilities to describe and predict tumor growth. For this we used two in vivo experimental systems, an ectopic syngeneic tumor (Lewis lung carcinoma) and an orthotopically xenografted human breast carcinoma. The goals were threefold: to 1) determine a statistical model for description of the volume measurement error, 2) establish the descriptive power of each model, using several goodness-of-fit metrics and a study of parametric identifiability, and 3) assess the models ability to forecast future tumor growth.
Nine models were compared that included the exponential, power law, Gompertz and (generalized) logistic formalisms. The Gompertz and power law provided the most parsimonious and parametrically identifiable description of the lung data, whereas the breast data were best captured by the Gompertz and exponential-linear models. The latter also exhibited the highest predictive power for the breast tumor growth curves, with excellent prediction scores (greater than 80%) extending out as far as 12 days. In contrast, for the lung data, none of the models were able to achieve substantial prediction rates (greater than 70%) further than the next day data point. In this context, adjunction of a priori information on the parameter distribution led to considerable improvement of predictions.
These results not only have important implications for biological theories of tumor growth and the use of mathematical modeling in preclinical anti-cancer drug investigations, but also may assist in defining how mathematical models could serve as potential prognostic tools in the clinical setting.

link: http://arxiv.org/abs/1406.1446

Cooperation between Noncanonical Ras Network Mutations in Cancer

Edward C Stites, Paul C Trampont, Lisa B Haney, Scott F Walk, Kodi S Ravichandran

doi: http://dx.doi.org/10.1101/004846

Cancer develops after the acquisition of a collection of mutations that together create the ‘cancer phenotype’. How collections of mutations work together within a cell, and whether there is selection for certain combinations of mutations, are not well understood. Using a Ras signaling network mathematical model we tested potential synergistic combinations within the Ras network. Intriguingly, our modeling, including a “computational random mutagenesis” approach, and subsequent experiments revealed that mutations of the tumor suppressor gene NF1 can amplify the effects of mutations in multiple other components of the Ras pathway, including weakly activating, noncanonical, Ras mutants. Since conventional wisdom holds that mutations within the same pathway do not co-occur, it was surprising that modeling and experiments both suggested a functional benefit for co-occurring Ras pathway mutations. Furthermore, we analyzed >3900 sequenced cancer specimens from the Cancer Cell Line Encyclopedia (CCLE) and The Cancer Genome Atlas (TCGA) and we uncovered an increased rate of co-occurrence between mutations the model predicted could display synergy. Overall, these data suggest that selective combinations of Ras pathway mutations could serve the role of cancer “driver”. More generally, this work presents a mechanism by which the context created by one mutation influences the evolutionary trajectories of cancer development, and this work suggests that mutations that result in “network instability” may promote cancer in a manner analogous to genomic instability.

Link: http://www.biorxiv.org/content/early/2014/05/06/004846

Bridging scales in cancer progression: Mapping genotype to phenotype using neural networks

Philip Gerlee, Eunjung Kim, Alexander R.A. Anderson

(Submitted on 28 Apr 2014)

In this review we summarize our recent efforts in trying to understand the role of heterogeneity in cancer progression by using neural networks to characterise different aspects of the mapping from a cancer cells genotype and environment to its phenotype. Our central premise is that cancer is an evolving system subject to mutation and selection, and the primary conduit for these processes to occur is the cancer cell whose behaviour is regulated on multiple biological scales. The selection pressure is mainly driven by the microenvironment that the tumour is growing in and this acts directly upon the cell phenotype. In turn, the phenotype is driven by the intracellular pathways that are regulated by the genotype. Integrating all of these processes is a massive undertaking and requires bridging many biological scales (i.e. genotype, pathway, phenotype and environment) that we will only scratch the surface of in this review. We will focus on models that use neural networks as a means of connecting these different biological scales, since they allow us to easily create heterogeneity for selection to act upon and importantly this heterogeneity can be implemented at different biological scales. More specifically, we consider three different neural networks that bridge different aspects of these scales and the dialogue with the micro-environment, (i) the impact of the micro-environment on evolutionary dynamics, (ii) the mapping from genotype to phenotype under drug-induced perturbations and (iii) pathway activity in both normal and cancer cells under different micro-environmental conditions.

link: http://arxiv.org/abs/1404.7108