Spatial structure governs the mode of tumour evolution

AbstractCharacterizing the mode—the way, manner or pattern—of evolution in tumours is important for clinical forecasting and optimizing cancer treatment. Sequencing studies have inferred various modes, including branching, punctuated and neutral evolution, but it is unclear why a particular pattern predominates in any given tumour. Here we propose that tumour architecture is key to explaining the variety of observed genetic patterns. We examine this hypothesis using spatially explicit population genetics models and demonstrate that, within biologically relevant parameter ranges, different spatial structures can generate four tumour evolutionary modes: rapid clonal expansion, progressive diversification, branching evolution and effectively almost neutral evolution. Quantitative indices for describing and classifying these evolutionary modes are presented. Using these indices, we show that our model predictions are consistent with empirical observations for cancer types with corresponding spatial structures. The manner of cell dispersal and the range of cell–cell interactions are found to be essential factors in accurately characterizing, forecasting and controlling tumour evolution.

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Spatial structure governs the mode of tumour evolution

10.1101/586735 ◽

2019 ◽

Cited By ~ 8

Author(s):

Robert Noble ◽

Dominik Burri ◽

Jakob Nikolas Kather ◽

Niko Beerenwinkel

Keyword(s):

Early Stage ◽

Mechanistic Explanation ◽

Cell Interaction ◽

Colorectal Adenomas ◽

Neutral Evolution ◽

Relevant Parameter ◽

Biologically Relevant ◽

Wide Range ◽

Sequencing Studies ◽

Parameter Ranges

AbstractCharacterizing the mode – the way, manner, or pattern – of evolution in tumours is important for clinical forecasting and optimizing cancer treatment. DNA sequencing studies have inferred various modes, including branching, punctuated and neutral evolution, but it is unclear why a particular pattern predominates in any given tumour.1, 2Here we propose that differences in tumour architecture alone can explain the variety of observed patterns. We examine this hypothesis using spatially explicit population genetic models and demonstrate that, within biologically relevant parameter ranges, human tumours are expected to exhibit four distinct onco-evolutionary modes (oncoevotypes): rapid clonal expansion (predicted in leukaemia); progressive diversification (in colorectal adenomas and early-stage colorectal carcinomas); branching evolution (in invasive glandular tumours); and effectively almost neutral evolution (in certain non-glandular and poorly differentiated solid tumours). We thus provide a simple, mechanistic explanation for a wide range of empirical observations. Oncoevotypes are governed by the mode of cell dispersal and the range of cell-cell interaction, which we show are essential factors in accurately characterizing, forecasting and controlling tumour evolution.

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Human-interpretable image features derived from densely mapped cancer pathology slides predict diverse molecular phenotypes

Nature Communications ◽

10.1038/s41467-021-21896-9 ◽

2021 ◽

Vol 12 (1) ◽

Cited By ~ 1

Author(s):

James A. Diao ◽

Jason K. Wang ◽

Wan Fung Chui ◽

Victoria Mountain ◽

Sai Chowdary Gullapally ◽

...

Keyword(s):

Tumor Microenvironment ◽

Image Features ◽

Tissue Type ◽

Type Model ◽

Biologically Relevant ◽

Cancer Pathology ◽

Substantial Progress ◽

Cancer Types ◽

Molecular Phenotypes ◽

Immune Checkpoint Proteins

AbstractComputational methods have made substantial progress in improving the accuracy and throughput of pathology workflows for diagnostic, prognostic, and genomic prediction. Still, lack of interpretability remains a significant barrier to clinical integration. We present an approach for predicting clinically-relevant molecular phenotypes from whole-slide histopathology images using human-interpretable image features (HIFs). Our method leverages >1.6 million annotations from board-certified pathologists across >5700 samples to train deep learning models for cell and tissue classification that can exhaustively map whole-slide images at two and four micron-resolution. Cell- and tissue-type model outputs are combined into 607 HIFs that quantify specific and biologically-relevant characteristics across five cancer types. We demonstrate that these HIFs correlate with well-known markers of the tumor microenvironment and can predict diverse molecular signatures (AUROC 0.601–0.864), including expression of four immune checkpoint proteins and homologous recombination deficiency, with performance comparable to ‘black-box’ methods. Our HIF-based approach provides a comprehensive, quantitative, and interpretable window into the composition and spatial architecture of the tumor microenvironment.

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Two dimensionless parameters and a mechanical analogue for the HKB model of motor coordination

Biological Cybernetics ◽

10.1007/s00422-021-00879-5 ◽

2021 ◽

Author(s):

J. F. Cass ◽

S. J. Hogan

Keyword(s):

Motor Coordination ◽

Nonlinear Damping ◽

Numerical Continuation ◽

Arbitrary Parameter ◽

Relevant Parameter ◽

Coupling Coefficients ◽

Dimensionless Parameters ◽

Biologically Relevant ◽

Mechanical Analogue ◽

Partner Interaction

AbstractThe widely cited Haken–Kelso–Bunz (HKB) model of motor coordination is used in an enormous range of applications. In this paper, we show analytically that the weakly damped, weakly coupled HKB model of two oscillators depends on only two dimensionless parameters; the ratio of the linear damping coefficient and the linear coupling coefficient and the ratio of the combined nonlinear damping coefficients and the combined nonlinear coupling coefficients. We illustrate our results with a mechanical analogue. We use our analytic results to predict behaviours in arbitrary parameter regimes and show how this led us to explain and extend recent numerical continuation results of the full HKB model. The key finding is that the HKB model contains a significant amount of behaviour in biologically relevant parameter regimes not yet observed in experiments or numerical simulations. This observation has implications for the development of virtual partner interaction and the human dynamic clamp, and potentially for the HKB model itself.

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Experimental and Numerical Results of LIFUS5/Mod3 Series E Test on In-Box LOCA Transient for WCLL-BB

Energies ◽

10.3390/en14248527 ◽

2021 ◽

Vol 14 (24) ◽

pp. 8527

Author(s):

Marica Eboli ◽

Francesco Galleni ◽

Nicola Forgione ◽

Nicolò Badodi ◽

Antonio Cammi ◽

...

Keyword(s):

Experimental Data ◽

Safety Concern ◽

Computational Cost ◽

Water System ◽

Test Facility ◽

Relevant Parameter ◽

Research Activities ◽

Loss Of Coolant Accident ◽

Parameter Ranges ◽

Physical Phenomena

The in-box LOCA (Loss of Coolant Accident) represents a major safety concern to be addressed in the design of the WCLL-BB (water-cooled lead-lithium breeding blanket). Research activities are ongoing to master the phenomena and processes that occur during the postulated accident, to enhance the predictive capability and reliability of numerical tools, and to validate computer models, codes, and procedures for their applications. Following these objectives, ENEA designed and built the new separate effects test facility LIFUS5/Mod3. Two experimental campaigns (Series D and Series E) were executed by injecting water at high pressure into a pool of PbLi in WCLL-BB-relevant parameter ranges. The obtained experimental data were used to check the capabilities of the RELAP5 system code to reproduce the pressure transient of a water system, to validate the chemical model of PbLi/water reactions implemented in the modified version of SIMMER codes for fusion application, to investigate the dynamic effects of energy release on the structures, and to provide relevant feedback for the follow-up experimental campaigns. This work presents the experimental data and the numerical simulations of Test E4.1. The results of the test are presented and critically discussed. The code simulations highlight that SIMMER code is able to reproduce the phenomena connected to PbLi/water interaction, and the relevant test parameters are in agreement with the acquired experimental signals. Moreover, the results obtained by the first approach to SIMMER-RELAP5 code-coupling demonstrate its capability of and strength for predicting the transient scenario in complex geometries, considering multiple physical phenomena and minimizing the computational cost.

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Fluid dynamics in heart development: effects of hematocrit and trabeculation

Mathematical Medicine and Biology A Journal of the IMA ◽

10.1093/imammb/dqx018 ◽

2017 ◽

Vol 35 (4) ◽

pp. 493-516 ◽

Cited By ~ 7

Author(s):

Nicholas A Battista ◽

Andrea N Lane ◽

Jiandong Liu ◽

Laura A Miller

Keyword(s):

Heart Development ◽

Genetic Regulatory Networks ◽

Vortical Flow ◽

Immersed Boundary ◽

Relevant Parameter ◽

Womersley Number ◽

Biologically Relevant ◽

Morphogen Gradients ◽

Stage Of Development

Abstract Recent in vivo experiments have illustrated the importance of understanding the haemodynamics of heart morphogenesis. In particular, ventricular trabeculation is governed by a delicate interaction between haemodynamic forces, myocardial activity, and morphogen gradients, all of which are coupled to genetic regulatory networks. The underlying haemodynamics at the stage of development in which the trabeculae form is particularly complex, given the balance between inertial and viscous forces. Small perturbations in the geometry, scale, and steadiness of the flow can lead to changes in the overall flow structures and chemical morphogen gradients, including the local direction of flow, the transport of morphogens, and the formation of vortices. The immersed boundary method was used to solve the two-dimensional fluid-structure interaction problem of fluid flow moving through a two chambered heart of a zebrafish (Danio rerio), with a trabeculated ventricle, at 96 hours post fertilization (hpf). Trabeculae heights and hematocrit were varied, and simulations were conducted for two orders of magnitude of Womersley number, extending beyond the biologically relevant range (0.2–12.0). Both intracardial and intertrabecular vortices formed in the ventricle for biologically relevant parameter values. The bifurcation from smooth streaming flow to vortical flow depends upon the trabeculae geometry, hematocrit, and Womersley number, $Wo$. This work shows the importance of hematocrit and geometry in determining the bulk flow patterns in the heart at this stage of development.

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Bursting noise in gene expression dynamics: linking microscopic and mesoscopic models

Journal of The Royal Society Interface ◽

10.1098/rsif.2015.0772 ◽

2016 ◽

Vol 13 (114) ◽

pp. 20150772 ◽

Cited By ~ 22

Author(s):

Yen Ting Lin ◽

Tobias Galla

Keyword(s):

Regulatory Networks ◽

Coarse Grained ◽

Relevant Parameter ◽

Piecewise Deterministic Markov Process ◽

Biologically Relevant ◽

Coarse Grained Model ◽

Mesoscopic Models ◽

Gene Expression Dynamics ◽

Gene Regulatory ◽

Noise In Gene Expression

The dynamics of short-lived mRNA results in bursts of protein production in gene regulatory networks. We investigate the propagation of bursting noise between different levels of mathematical modelling and demonstrate that conventional approaches based on diffusion approximations can fail to capture bursting noise. An alternative coarse-grained model, the so-called piecewise deterministic Markov process (PDMP), is seen to outperform the diffusion approximation in biologically relevant parameter regimes. We provide a systematic embedding of the PDMP model into the landscape of existing approaches, and we present analytical methods to calculate its stationary distribution and switching frequencies.

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Dense, high-resolution mapping of cells and tissues from pathology images for the interpretable prediction of molecular phenotypes in cancer

10.1101/2020.08.02.233197 ◽

2020 ◽

Author(s):

James A. Diao ◽

Wan Fung Chui ◽

Jason K. Wang ◽

Richard N. Mitchell ◽

Sudha K. Rao ◽

...

Keyword(s):

High Resolution ◽

Image Features ◽

Tissue Type ◽

Cell Detection ◽

Biologically Relevant ◽

Novel Approach ◽

Substantial Progress ◽

Resolution Mapping ◽

Cancer Types ◽

Molecular Phenotypes

While computational methods have made substantial progress in improving the accuracy and throughput of pathology workflows for diagnostic, prognostic, and genomic prediction, lack of interpretability remains a significant barrier to clinical integration. In this study, we present a novel approach for predicting clinically-relevant molecular phenotypes from histopathology whole-slide images (WSIs) using human-interpretable image features (HIFs). Our method leverages >1.6 million annotations from board-certified pathologists across >5,700 WSIs to train deep learning models for high-resolution tissue classification and cell detection across entire WSIs in five cancer types. Combining cell- and tissue-type models enables computation of 607 HIFs that comprehensively capture specific and biologically-relevant characteristics of multiple tumors. We demonstrate that these HIFs correlate with well-known markers of the tumor microenvironment (TME) and can predict diverse molecular signatures, including immune checkpoint protein expression and homologous recombination deficiency (HRD). Our HIF-based approach provides a novel, quantitative, and interpretable window into the composition and spatial architecture of the TME.

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Mutation Rates, Mutation Frequencies, and Proofreading-Repair Activities in RNA Virus Genetics

Viruses ◽

10.3390/v13091882 ◽

2021 ◽

Vol 13 (9) ◽

pp. 1882 ◽

Cited By ~ 1

Author(s):

Esteban Domingo ◽

Carlos García-Crespo ◽

Rebeca Lobo-Vega ◽

Celia Perales

Keyword(s):

Error Rate ◽

Rna Virus ◽

Error Rates ◽

Viral Rna ◽

Mutation Rates ◽

Relevant Parameter ◽

Dna Viruses ◽

Biologically Relevant ◽

Host Interactions ◽

The Stability

The error rate displayed during template copying to produce viral RNA progeny is a biologically relevant parameter of the replication complexes of viruses. It has consequences for virus–host interactions, and it represents the first step in the diversification of viruses in nature. Measurements during infections and with purified viral polymerases indicate that mutation rates for RNA viruses are in the range of 10−3 to 10−6 copying errors per nucleotide incorporated into the nascent RNA product. Although viruses are thought to exploit high error rates for adaptation to changing environments, some of them possess misincorporation correcting activities. One of them is a proofreading-repair 3′ to 5′ exonuclease present in coronaviruses that may decrease the error rate during replication. Here we review experimental evidence and models of information maintenance that explain why elevated mutation rates have been preserved during the evolution of RNA (and some DNA) viruses. The models also offer an interpretation of why error correction mechanisms have evolved to maintain the stability of genetic information carried out by large viral RNA genomes such as the coronaviruses.

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How sequence populations persist inside bacterial genomes

Genetics ◽

10.1093/genetics/iyab027 ◽

2021 ◽

Vol 217 (4) ◽

Author(s):

Hye Jin Park ◽

Chaitanya S Gokhale ◽

Frederic Bertels

Keyword(s):

Gene Transfer ◽

Horizontal Gene Transfer ◽

Population Size ◽

Repetitive Sequences ◽

Host Genome ◽

Parameter Range ◽

Relevant Parameter ◽

Bacterial Genomes ◽

Bacterial Host ◽

Biologically Relevant

AbstractCompared to their eukaryotic counterparts, bacterial genomes are small and contain extremely tightly packed genes. Repetitive sequences are rare but not completely absent. One of the most common repeat families is REPINs. REPINs can replicate in the host genome and form populations that persist for millions of years. Here, we model the interactions of these intragenomic sequence populations with the bacterial host. We first confirm well-established results, in the presence and absence of horizontal gene transfer (hgt) sequence populations either expand until they drive the host to extinction or the sequence population gets purged from the genome. We then show that a sequence population can be stably maintained, when each individual sequence provides a benefit that decreases with increasing sequence population size. Maintaining a sequence population of stable size also requires the replication of the sequence population to be costly to the host, otherwise the sequence population size will increase indefinitely. Surprisingly, in regimes with high hgt rates, the benefit conferred by the sequence population does not have to exceed the damage it causes to its host. Our analyses provide a plausible scenario for the persistence of sequence populations in bacterial genomes. We also hypothesize a limited biologically relevant parameter range for the provided benefit, which can be tested in future experiments.

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On measuring selection in cancer from subclonal mutation frequencies

10.1101/529396 ◽

2019 ◽

Cited By ~ 2

Author(s):

Ivana Bozic ◽

Chay Paterson ◽

Bartlomiej Waclaw

Keyword(s):

Cancer Progression ◽

Treatment Strategies ◽

Intermediate Frequency ◽

Neutral Evolution ◽

Relevant Parameter ◽

Cancer Evolution ◽

Sequencing Data ◽

Novel Treatment Strategies ◽

Subclonal Mutation ◽

Expanding Population

ABSTRACTRecently available cancer sequencing data have revealed a complex view of the cancer genome containing a multitude of mutations, including drivers responsible for cancer progression and neutral passengers. Measuring selection in cancer and distinguishing drivers from passengers have important implications for development of novel treatment strategies. It has recently been argued that a third of cancers are evolving neutrally, as their mutational frequency spectrum follows a 1/f power law expected from neutral evolution in a particular intermediate frequency range. We study a stochastic model of cancer evolution and derive a formula for the probability distribution of the cancer cell frequency of a subclonal driver, demonstrating that driver frequency is biased towards 0 and 1. We show that it is difficult to capture a driver mutation at an intermediate frequency, and thus the calling of neutrality due to a lack of such driver will significantly overestimate the number of neutrally evolving tumors. Our approach provides precise quantification of the validity of the 1/f statistic across the entire range of all relevant parameter values. Our results are also applicable to the question of distinguishing driver and passenger mutations in a general exponentially expanding population.

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