Biology subjects

Jacob G Scott

Publications and source records attributed to Jacob G Scott.

7 recordsLinked to original sources

bioRxiv2016

Cancer treatment scheduling and dynamic heterogeneity in social dilemmas of tumour acidity and vasculature

BackgroundTumours are diverse ecosystems with persistent heterogeneity in various cancer hallmarks like self-sufficiency of growth factor production for angiogenesis and reprogramming of energy-metabolism for aerobic glycolysis. This heterogeneity has consequences for diagnosis, treatment, and disease progression.\n\nMethodsWe introduce the double goods game to study the dynamics of these traits using evolutionary game theory. We model glycolytic acid production as a public good for all tumour cells and oxygen from vascularization via VEGF production as a club good benefiting non-glycolytic tumour cells. This results in three viable phenotypic strategies: glycolytic, angiogenic, and aerobic non-angiogenic.\n\nResultsWe classify the dynamics into three qualitatively distinct regimes: (1) fully glycolytic, (2) fully angiogenic, or (3) polyclonal in all three cell types. The third regime allows for dynamic heterogeneity even with linear goods, something that was not possible in prior public good models that considered glycolysis or growth-factor production in isolation.\n\nConclusionThe cyclic dynamics of the polyclonal regime stress the importance of timing for antiglycolysis treatments like lonidamine. The existence of qualitatively different dynamic regimes highlights the order effects of treatments. In particular, we consider the potential of vascular renormalization as a neoadjuvant therapy before follow up with interventions like buffer therapy.

Cancer Biology↗

bioRxiv2015

Spatial metrics of tumour vascular organisation predict radiation efficacy in a computational model

Intratumoural heterogeneity is known to contribute to poor therapeutic response. Variations in oxygen tension in particular have been correlated with changes in radiation response in vitro and at the clinical scale with overall survival. Heterogeneity at the microscopic scale in tumour blood vessel architecture has been described, and is one source of the underlying variations in oxygen tension. We endeavour to determine whether histologic scale measures of the erratic distribution of blood vessels within a tumour can be used to predict differing radiation response. Using a two-dimensional hybrid cellular automaton model of tumour growth, we evaluate the effect of vessel distribution on cell survival outcomes of simulated radiation therapy. Using the standard equations for the oxygen enhancement ratio for cell survival probability under differing oxygen tensions, we calculate average radiation effect over a range of different vessel densities and organisations. We go on to quantify the vessel distribution heterogeneity and measure spatial organization using Ripleys L function, a measure designed to detect deviations from spatial homogeneity. We find that under differing regimes of vessel density the correlation coefficient between the measure of spatial organization and radiation effect changes sign. This provides not only a useful way to understand the differences seen in radiation effect for tissues based on vessel architecture, but also an alternate explanation for the vessel normalization hypothesis.\n\nAuthor SummaryIn this paper we use a mathematical model, called a hybrid cellular automaton, to study the effect of different vessel distributions on radiation therapy outcomes at the cellular level. We show that the correlation between radiation outcome and spatial organization of vessels changes signs between relatively low and high vessel density. Specifically, that for relatively low vessel density, radiation efficacy is decreased when vessels are more homogeneously distributed, and the opposite is true, that radiation efficacy is improved, when vessel organisation is normalised in high densities. This result suggests an alteration to the vessel normalization hypothesis which states that normalisation of vascular beds should improve radio- and chemo-therapeutic response, but has failed to be validated in clinical studies. In this alteration, we provide a metric that differentiates between vascular architectures in different density regimes in which the hypothesis holds and does not and that could be used for quantitative histologic analysis of tumours, and for radiation dose personalisation.\n\nAuthor ContributionsConceived and designed the experiments: JGS, AGF, ARAA, PKM\n\nPerformed the experiments: JGS\n\nAnalyzed the data: JGS, AGF, ARAA, PKM\n\nWrote the paper: JGS, AGF, ARAA, PKM

Cancer Biology↗

bioRxiv2015

Passenger DNA alterations reduce cancer fitness in cell culture and mouse models

Genomic instability causes cancers to acquire hundreds to thousands of mutations and chromosomal alterations during their somatic evolution. Most of these mutations and alterations are termed passengers because they do not confer cancer phenotypes. Evolutionary simulations and cancer genomic studies suggested that mildly-deleterious passengers accumulate, collectively slow cancer progression, reduce the fitness of cancer cells and enhance the effects of therapeutics. However, these effects of passengers and their impact on clinical variables remain limited to genomic analysis. Here, to assess passengers effect on cell fitness and cancer, we specifically introduced increasing passenger loads into human cell lines and mouse models. We found that passenger load dramatically reduced cancer cells fitness in every model investigated. Passengers average fitness cost of [~]3% per MB, indicates that genomic instability in cancer in patients can slow tumor growth and prevent metastatic progression. We conclude that genomic instability in cancer is a double-edged sword: it accelerates the accumulation of adaptive drivers, yet incurs a harmful passenger load that can outweigh drivers benefit. Passenger load could be a useful biomarker for tumor aggressiveness and response to mutagenic or passenger-exacerbating therapies, including anti-tumor immunity.

Cancer Biology↗

bioRxiv2015

Edge effects in game theoretic dynamics of spatially structured tumours

BackgroundAnalysing tumour architecture for metastatic potential usually focuses on phenotypic differences due to cellular morphology or specific genetic mutations, but often ignore the cells position within the heterogeneous substructure. Similar disregard for local neighborhood structure is common in mathematical models.\n\nMethodsWe view the dynamics of disease progression as an evolutionary game between cellular phenotypes. A typical assumption in this modeling paradigm is that the probability of a given phenotypic strategy interacting with another depends exclusively on the abundance of those strategies without regard local heterogeneities. We address this limitation by using the Ohtsuki-Nowak transform to introduce spatial structure to the go vs. grow game.\n\nResultsWe show that spatial structure can promote the invasive (go) strategy. By considering the change in neighbourhood size at a static boundary - such as a blood-vessel, organ capsule, or basement membrane - we show an edge effect that allows a tumour without invasive phenotypes in the bulk to have a polyclonal boundary with invasive cells. We present an example of this promotion of invasive (EMT positive) cells in a metastatic colony of prostate adenocarcinoma in bone marrow.\n\nInterpretationPathologic analyses that do not distinguish between cells in the bulk and cells at a static edge of a tumour can underestimate the number of invasive cells. We expect our approach to extend to other evolutionary game models where interaction neighborhoods change at fixed system boundaries.

Cancer Biology↗

bioRxiv2015

Estimating stem cell fractions in hierarchically organized tumors

Cancers arise as a result of genetic and epigenetic alterations. These accumulate in cells during the processes of tissue development, homeostasis and repair. Many tumor types are hierarchically organized and driven by a sub-population of cells often called cancer stem cells. Cancer stem cells are uniquely capable of recapitulating the tumor and can be highly resistant to radio-and chemotherapy treatment. We investigate tumor growth patterns from a theoretical standpoint and show how significant changes in pre-and post-therapy tumor dynamics are tied to the dynamics of cancer stem cells. We identify two characteristic growth regimes of a tumor population that can be leveraged to estimate cancer stem cell fractions in vivo using simple linear regression. Our method is a mathematically exact result, parameter free and does not require any microscopic knowledge of the tumor properties. A more accurate quantification of the direct link between the sub-population driving tumor growth and treatment response promises new ways to individualize treatment strategies.\n\nSignificance StatementUnder the cancer stem cell hypothesis a tumor population is driven by a fraction of self-renewing cancer stem cells. Absolute and relative size of this population in human cancers at any stage of the disease remains unknown. We formulate a mathematical model that describes the tumor cell populations growth dynamics and response to therapy. This allows to estimate cancer stem cell fraction from longitudinal measurements of tumor size (often available from imaging). Such estimates are critical because treatment outcome and risk of relapse depend on the tumors capacity to self-renew. Ideally, by tailoring patient treatment strategies based on the relative abundance of cancer stem cells could lead to radically different therapeutic regime and to the successful eradication of the disease.

Cancer Biology↗

bioRxiv2014

Exploiting evolutionary non-commutativity to prevent the emergence of bacterial antibiotic resistance

The increasing rate of antibiotic resistance and slowing discovery of novel antibiotic treatments presents a growing threat to public health. Here, we develop a Markov Chain model of evolution in asexually reproducing populations which is an encoding of the Strong Selection Weak Mutation model of evolution on fitness landscapes. This model associates the global properties of the fitness landscape with the algebraic properties of the Markov Chain transition matrix and allows us to derive general results on the non-commutativity and irreversibility of natural selection as well as antibiotic cycling strategies. Utilizing this formalism, we analyse 15 empirical fitness landscapes of E. coli under selection by di.erent beta-lactam antibiotics and demonstrate that the emergence of resistance to a given antibiotic can be both hindered and promoted by di.erent sequences of drug application. Further, we derive optimal drug application sequences with which we can probabilistically steer the population through genotype space to avoid the emergence of resistance. This suggests a new strategy in the war against antibiotic.therapy.resistant organisms: drug sequencing to shepherd evolution through genotype space to states from which resistance cannot emerge and by which to maximize the chance of successful therapy. BackgroundThe increasing rate of antibiotic resistance and slowing discovery of novel antibiotic treatments presents a growing threat to public health. Previous studies of bacterial evolutionary dynamics have shown that populations evolving on fitness landscapes follow predictable paths. In this article, we develop a general mathematical model of evolution and hypothesise that it can be used to understand, and avoid, the emergence of antibiotic resistance. Methods and FindingsWe develop a Markov Chain model of evolution in asexually reproducing populations which is an encoding of the Strong Selection Weak Mutation model of evolution on fitness landscapes. This model associates the global properties of the fitness landscape with the algebraic properties of the Markov Chain transition matrix and allows us to derive general results on the non-commutativity and irreversibility of natural selection as well as antibiotic cycling strategies. Utilizing this formalism, we analyse 15 empirical fitness landscapes of E. coli under selection by different {beta}-lactam antibiotics and demonstrate that the emergence of resistance to a given antibiotic can be both hindered and promoted by different sequences of drug application. We show that resistance to a given antibiotic is promoted in 61.4%, 68.6% and 70.3% of possible orderings of single, pair or triple prior drug administrations, respectively. Further, we derive optimal drug application sequences with which we can probabilistically steer the population through genotype space to avoid the emergence of resistance. ConclusionsOur model provides generalisable results of interest to theorists studying evolution as well as providing specific, testable predictions for experimentalists to validate our methods. Further, these results suggest a new strategy in the war against antibiotic-therapy-resistant organisms: drug sequencing to shepherd an evolving population through genotype space to states from which resistance cannot emerge and from which we can maximize the likelihood of successful therapy using existing drugs. While our results are derived using a specific class of antibiotics, the method is generalisable to other situations, including the emergence of resistance to targeted therapy in cancer and how species could change secondary to changing climate or geographical movement.

Evolutionary Biology↗

bioRxiv2013

A filter-flow perspective of hematogenous metastasis offers a non-genetic paradigm for personalized cancer therapy

Translational RelevanceSince the discovery of circulating tumor cells (CTC), we have struggled for ways to use them to inform treatment. The only currently accepted method for this is a more is worse paradigm by which clinicians measure CTC burden before and after treatment to assess efficacy. Research efforts are currently focused almost entirely on genetic classification of these cells, which has yet to bear any fruit translationally. We suggest that we should shift the focus of our investigation to one driven by a physical sciences perspective. Specifically, by understanding the vascular system as a network of interconnected organs and capillary beds as filters that capture CTCs. By ascertaining the distribution of CTCs in this network for individual patients, information about the existence of subclinical metastatic disease, and therefore metastatic propensity, will come to light, and allow for better staging, prognostication and rational use of organ-directed therapy in the setting of oligometastatic disease.\n\nAbstractO_ST_ABSPurposeC_ST_ABSResearch into mechanisms of hematogenous metastasis has largely become genetic in focus, attempting to understand the molecular basis of seed-soil relationships. However, preceding this biological mechanism is the physical process of dissemination of circulating tumour cells (CTCs) in the circulatory network. We utilize a novel, network perspective of hematogenous metastasis and a large dataset on metastatic patterns to shed new light on this process.\n\nExperimental DesignThe metastatic efficiency index (MEI), previously suggested by Weiss, quantifies the process of hematogenous metastasis by taking the ratio of metastatic incidence for a given primary-target organ pair and the relative blood flow between the two sites. In this paper we extend the methodology by taking into account the reduction in CTC number that occurs in capillary beds and a novel network model of CTC flow.\n\nResultsBy applying this model to a dataset of metastatic incidence, we show that the MEI depends strongly on the assumptions of micrometastatic lesions in the lung and liver. Utilizing this framework we can represent different configurations of metastatic disease and offer a rational method for identifying patients with oligometastatic disease for inclusion in future trials.\n\nConclusionsWe show that our understanding of the dynamics of CTC flow is significantly lacking, and that this specifically precludes our ability to predict metastatic patterns in individual patients. Our formalism suggests an opportunity to go a step further in metastatic disease characterization by including the distribution of CTCs at staging, offering a rational method of trial design for oligometastatic disease.

Cancer Biology↗