Biology subjects

Scott, J. G.

Publications and source records attributed to Scott, J. G..

8 recordsLinked to original sources

bioRxiv2018

Inferring Tumour Proliferative Organisation from Phylogenetic Tree Measures in a Computational Model

We use a computational modelling approach to explore whether it is possible to infer a tumours cell proliferative hierarchy, under the assumptions of the cancer stem cell hypothesis and neutral evolution. We focus on inferring the symmetric division probability for cancer stem cells in our model, as this is believed to be a key driving parameter of tumour progression and therapeutic response. Given the advent of multi-region sampling, and the opportunities offered by them to understand tumour evolutionary history, we focus on a suite of statistical measures of the phylogenetic trees resulting from the tumours evolution in different regions of parameter space and through time. We find strikingly different patterns in these measures for changing symmetric division probability which hinge on the inclusion of spatial constraints. These results give us a starting point to begin stratifying tumours by this biological parameter and also generate a number of actionable clinical and biological hypotheses including changes during therapy, and through tumour evolution.

cancer biology↗

bioRxiv2018

egtplot: A Python Package for 3-Strategy Evolutionary Games

1. Introduction 1. Introduction 2. Function Arguments 3. Illustrative Examples 4. Parameter Sweeps 5. Animations References Evolutionary game theory is a reformulation of classical game theory wherein the players of the game are members of a population. These members do not choose a strategy, but instead are born with their strategy ingrained--they cannot change strategy during the game. In biological terms, the strategies might represent discrete species or genotypes, hawks and doves being a classic example. Payoffs the players gain or lose based on their interactions with other players increase or decrease their fitness, thereby influencing the number or proportion of members playing that strategy in the next generation. As such, the populations of strategies can wax and wane as they outcompete or are outcompete ...

evolutionary biology↗

bioRxiv2018

Endogenous miRNA sponges mediate the generation of oscillatory dynamics for a non-coding RNA network

Oscillations are crucial to the sustenance of living organisms, across a wide variety of biological processes. In eukaryotes, oscillatory dynamics are thought to arise from interactions at the protein and RNA levels; however, the role of non-coding RNA in regulating these dynamics remains understudied. In this work, using a mathematical model, we show how non-coding RNA acting as microRNA (miRNA) sponges in a conserved miRNA - transcription factor feedback motif, can give rise to oscillatory behaviour. Control of these non-coding RNA can dynamically create oscillations or stability, and we show how this behaviour predisposes to oscillations in the stochastic limit. These results, supported by emerging evidence for the role of miRNA sponges in development, point towards key roles of different species of miRNA sponges, such as circular RNA, potentially in the maintenance of yet unexplained oscillatory behaviour. These results help to provide a paradigm for understanding functional differences between the many redundant, but distinct RNA species thought to act as miRNA sponges in nature, such as long non-coding RNA, pseudogenes, competing mRNA, circular RNA, and 3 UTRs.\n\nAuthor summaryWe analyze the effects of a newly discovered species of non-coding RNA, acting as microRNA (miRNA) sponges, on intracellular signalling dynamics. We show that oscillatory behaviour can arise in a time-varying manner in an over-represented transcriptional feedback network. These results point towards novel hypotheses for the roles of different species of miRNA sponges, such as their increasingly understood role in neural development.

systems biology↗

bioRxiv2017

Pan-cancer characterisation of microRNA with hallmarks of cancer reveals role of microRNA-mediated downregulation of tumour suppressor genes

microRNA are key regulators of the human transcriptome across a number of diverse biological processes, such as development, aging, and cancer, where particular miRNA have been identified as tumour suppressive and oncogenic. In this work, we sought to elucidate, in a comprehensive manner, across 15 epithelial cancer types comprising 7,316 clinical samples from the Cancer Genome Atlas, the association of miRNA expression and target regulation with the pheno-typic hallmarks of cancer. Utilising penalized regression techniques to integrate transcriptomic, methylation and mutation data, we find evidence for a complex map of interactions underlying the relationship of miRNA regulation and the hallmarks of cancer. This highlighted high redundancy for the oncomiR-1 cluster of oncogenic miRNAs, in particular hsa-miR-17-5p. In addition, we reveal extensive miRNA regulation of tumour suppressor genes such as PTEN, FAT4, and CDK12, uncovering an alternative mechanism of repression in the absence of mutation, methylation or copy number changes.

genomics↗

bioRxiv2017

Cancer associated fibroblasts and alectinib switch the evolutionary games that non-small cell lung cancer plays

Heterogeneity in strategies for survival and proliferation among the cells which constitute a tumour is a driving force behind the evolution of resistance to cancer therapy. The rules mapping the tumours strategy distribution to the fitness of individual strategies can be represented as an evolutionary game. We develop a game assay to measure effective evolutionary games in co-cultures of non-small cell lung cancer cells which are sensitive and resistant to the anaplastic lymphoma kinase inhibitor Alectinib. The games are not only quantitatively different between different environments, but targeted therapy and cancer associated fibroblasts qualitatively switch the type of game being played by the in-vitro population from Leader to Deadlock. This observation provides empirical confirmation of a central theoretical postulate of evolutionary game theory in oncology: we can treat not only the player, but also the game. Although we concentrate on measuring games played by cancer cells, the measurement methodology we develop can be used to advance the study of games in other microscopic systems by providing a quantitative description of non-cell-autonomous effects.

cancer biology↗

bioRxiv2017

Recasting the cancer stem cell hypothesis: unification using a continuum model of microenvironmental forces

Since the first evidence for cancer stem cells in leukemia, experimentalists have sought to identify tumorigenic subpopulations in solid tumors. In parallel, scientists have argued over the implications of the existence of this subpopulation. On one side, the cancer stem cell hypothesis posits that a small subset of cells within a tumor are responsible for tumorigenesis and are capable of recapitulating the entire tumor on their own. Under this hypothesis, a tumor may be conceptualized as a series of coupled compartments, representing populations of progressively differentiated cell types, starting from stem cells. The allure of this model is that it elegantly explains our therapeutic failures: we have been targeting the wrong cells. Alternatively, the stochastic model states that all cells in a tumor can have stem-like properties, and have an equally small capability of forming a tumor. As tumors are, by nature, heterogeneous, there is ample evidence to support both hypotheses. We propose a mechanistic mathematical description that integrates these two theories, settling the dissonance between the schools of thought and providing a road map for integrating disparate experimental results into a single theoretical framework. We present experimental results from clonogenic assays that demonstrate the importance of defining this novel formulation, and the clarity that is provided when interpreting these results through the lens of this formulation.

cancer biology↗

bioRxiv2017

Evolutionary Dynamics Of Incubation Periods

The incubation period of a disease is the time between an initiating pathologic event and the onset of symptoms1. For typhoid fever2,3, polio4, measles5, leukemia6 and many other diseases7-10, the incubation period is highly variable. Some affected people take much longer than average to show symptoms, leading to a distribution of incubation periods that is right skewed and often approximately lognormal8-10. Although this statistical pattern was discovered more than sixty years ago8, it remains an open question to explain its ubiquity11. Here we propose an explanation based on evolutionary dynamics on graphs12-18. For simple models of a mutant or pathogen invading a network-structured population of healthy cells, we show that skewed distributions of incubation periods emerge for a wide range of assumptions about invader fitness, competition dynamics, and network structure. The skewness stems from stochastic mechanisms associated with two classic problems in probability theory: the coupon collector and the random walk19,20. Unlike previous explanations11,21 that rely crucially on heterogeneity, our results hold even for homogeneous populations. Thus, we predict that two equally healthy individuals subjected to equal doses of equally pathogenic agents may, by chance alone, show remarkably different time courses of disease.

evolutionary biology↗

bioRxiv2017

Takeover times for a simple model of network infection

We study a stochastic model of infection spreading on a network. At each time step a node is chosen at random, along with one of its neighbors. If the node is infected and the neighbor is susceptible, the neighbor becomes infected. How many time steps T does it take to completely infect a network of N nodes, starting from a single infected node? An analogy to the classic \"coupon collector\" problem of probability theory reveals that the takeover time T is dominated by extremal behavior, either when there are only a few infected nodes near the start of the process or a few susceptible nodes near the end. We show that for N >> 1, the takeover time T is distributed as a Gumbel for the star graph; as the sum of two Gumbels for a complete graph and an Erd[o]s-Renyi random graph; as a normal for a one-dimensional ring and a two-dimensional lattice; and as a family of intermediate skewed distributions for d-dimensional lattices with d [≥] 3 (these distributions approach the sum of two Gumbels as d approaches infinity). Connections to evolutionary dynamics, cancer, incubation periods of infectious diseases, first-passage percolation, and other spreading phenomena in biology and physics are discussed.

evolutionary biology↗