scholarly journals Cancer evolution simulation identifies possible principles underlying intratumor heterogeneity

2015 ◽  
Author(s):  
Atsushi Niida ◽  
Satoshi Ito ◽  
Georg Tremmel ◽  
Seiya Imoto ◽  
Ryutaro Uchi ◽  
...  
Keyword(s):  
Evolutionary Process ◽  
Neutral Evolution ◽  
Intratumor Heterogeneity ◽  
Cancer Evolution ◽  
Driver Genes ◽  
Evolutionary Selection ◽  
Evolution Simulation ◽  
Fractal Patterns ◽  
Selection For ◽  
Stem Cell Hierarchy

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.

scholarly journals Early and multiple origins of metastatic lineages within primary tumors

2016 ◽  
Vol 113 (8) ◽  
pp. 2140-2145 ◽  
Author(s):  
Zi-Ming Zhao ◽  
Bixiao Zhao ◽  
Yalai Bai ◽  
Atila Iamarino ◽  
Stephen G. Gaffney ◽  
...  
Keyword(s):  
Cancer Progression ◽  
Cancer Biology ◽  
Tumor Development ◽  
Evolutionary Process ◽  
Driver Mutations ◽  
Cancer Evolution ◽  
Driver Genes ◽  
Genetic Changes ◽  
Primary Tumors ◽  
Multiple Origins

Many aspects of the evolutionary process of tumorigenesis that are fundamental to cancer biology and targeted treatment have been challenging to reveal, such as the divergence times and genetic clonality of metastatic lineages. To address these challenges, we performed tumor phylogenetics using molecular evolutionary models, reconstructed ancestral states of somatic mutations, and inferred cancer chronograms to yield three conclusions. First, in contrast to a linear model of cancer progression, metastases can originate from divergent lineages within primary tumors. Evolved genetic changes in cancer lineages likely affect only the proclivity toward metastasis. Single genetic changes are unlikely to be necessary or sufficient for metastasis. Second, metastatic lineages can arise early in tumor development, sometimes long before diagnosis. The early genetic divergence of some metastatic lineages directs attention toward research on driver genes that are mutated early in cancer evolution. Last, the temporal order of occurrence of driver mutations can be inferred from phylogenetic analysis of cancer chronograms, guiding development of targeted therapeutics effective against primary tumors and metastases.

scholarly journals A unified simulation model for understanding the diversity of cancer evolution

2019 ◽  
Author(s):  
Atsushi Niida ◽  
Takanori Hasegawa ◽  
Hideki Innan ◽  
Tatsuhiro Shibata ◽  
Koshi Mimori ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Simulation Model ◽  
Mutation Rate ◽  
Evolutionary Dynamics ◽  
Intratumor Heterogeneity ◽  
Cancer Evolution ◽  
Evolutionary Processes ◽  
Stem Cell Hierarchy ◽  
Neutral Mutations ◽  
Short Time

ABSTRACTBecause cancer evolution underlies the therapeutic difficulties of cancer, it is clinically important to understand the evolutionary dynamics of cancer. Thus far, a number of evolutionary processes have been proposed to be working in cancer evolution. However, there exists no simulation model that can describe the different evolutionary processes in a unified manner. In this study, we constructed a unified simulation model for describing the different evolutionary processes and performed sensitivity analysis on the model to determine the conditions in which cancer growth is driven by each of the different evolutionary processes. Our sensitivity analysis has successfully provided a series of novel insights into the evolutionary dynamics of cancer. For example, we found that, while a high neutral mutation rate shapes neutral intratumor heterogeneity (ITH) characterized by a fractal-like pattern, a stem cell hierarchy can also contribute to shaping neutral ITH by apparently increasing the mutation rate. Although It has been reported that the evolutionary principle shaping ITH shifts from selection to accumulation of neutral mutations during colorectal tumorigenesis, our simulation revealed the possibility that this evolutionary shift is triggered by drastic evolutionary events that occur in a a short time and confer a marked fitness increase on one or a few cells. This result helps us understand that each process works not separately but simultaneously and continuously as a series of phases of cancer evolution. Collectively, this study serves as a basis to understand in greater depth the diversity of cancer evolution.

scholarly journals Cancer spatial evolution in a changing microenvironment

2017 ◽  
Author(s):  
Xiaowei Jiang ◽  
Ian PM Tomlinson
Keyword(s):  
Complex Dynamics ◽  
Personalised Medicine ◽  
Critical Role ◽  
Evolutionary Process ◽  
Tumour Microenvironment ◽  
Neutral Evolution ◽  
Limiting Factor ◽  
Spatial Evolution ◽  
Cancer Evolution ◽  
Genetic Population

Cancer development as an ecological and evolutionary process is poorly understood, which includes early cancer evolution, malignancy and metastasis. It was hypothesised that the tumour microenvironment (TME) plays a critical role in this process. Unfortunately, in most cancer modelling studies the TME is ignored or considered static and different cancers are often studied in isolation. There is a lack of a general theory of cancer adaptive evolution (CAE). Here I establish a genetic and phenotypic model of cancer three-dimensional (3D) spatial evolution in a changing TME. With 3D individual-based simulations I show how cancer cells adapt to diverse changing TME conditions and selection intensities. I am able to capture key histological characteristics of various cancer forms including complex dynamics of spatial-temporal heterogeneity of subclonal fitness and subclonal mixing, ball-like and non-ball-like subclonal structures. Moreover, I identify key evolutionary and phylogenetic patterns of CAE under various combinations of phenotypic, genetic, population genetic and changing TME conditions. I show classical drivers, mini drivers, Darwinian and neutral/nearly neutral evolution and cost of complexity. I demonstrate the importance of ecology in CAE. I show that there are fundamental differences in the mode of CAE when the TME is changing, which is the limiting factor of CAE. Finally, I discuss important implications for cancer evolution theories and cancer personalised medicine.

scholarly journals A unified simulation model for understanding the diversity of cancer evolution

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e8842 ◽  
Author(s):  
Atsushi Niida ◽  
Takanori Hasegawa ◽  
Hideki Innan ◽  
Tatsuhiro Shibata ◽  
Koshi Mimori ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Simulation Model ◽  
Mutation Rate ◽  
Evolutionary Dynamics ◽  
Intratumor Heterogeneity ◽  
Cancer Evolution ◽  
Evolutionary Processes ◽  
Stem Cell Hierarchy ◽  
Neutral Mutations ◽  
Short Time

Because cancer evolution underlies the therapeutic difficulties of cancer, it is clinically important to understand the evolutionary dynamics of cancer. Thus far, a number of evolutionary processes have been proposed to be working in cancer evolution. However, there exists no simulation model that can describe the different evolutionary processes in a unified manner. In this study, we constructed a unified simulation model for describing the different evolutionary processes and performed sensitivity analysis on the model to determine the conditions in which cancer growth is driven by each of the different evolutionary processes. Our sensitivity analysis has successfully provided a series of novel insights into the evolutionary dynamics of cancer. For example, we found that, while a high neutral mutation rate shapes neutral intratumor heterogeneity (ITH) characterized by a fractal-like pattern, a stem cell hierarchy can also contribute to shaping neutral ITH by apparently increasing the mutation rate. Although It has been reported that the evolutionary principle shaping ITH shifts from selection to accumulation of neutral mutations during colorectal tumorigenesis, our simulation revealed the possibility that this evolutionary shift is triggered by drastic evolutionary events that occur in a short time and confer a marked fitness increase on one or a few cells. This result helps us understand that each process works not separately but simultaneously and continuously as a series of phases of cancer evolution. Collectively, this study serves as a basis to understand in greater depth the diversity of cancer evolution.

scholarly journals Cancer spatial evolution in a changing microenvironment

2017 ◽  
Author(s):  
Xiaowei Jiang ◽  
Ian PM Tomlinson
Keyword(s):  
Complex Dynamics ◽  
Personalised Medicine ◽  
Critical Role ◽  
Evolutionary Process ◽  
Tumour Microenvironment ◽  
Neutral Evolution ◽  
Limiting Factor ◽  
Spatial Evolution ◽  
Cancer Evolution ◽  
Genetic Population

Cancer development as an ecological and evolutionary process is poorly understood, which includes early cancer evolution, malignancy and metastasis. It was hypothesised that the tumour microenvironment (TME) plays a critical role in this process. Unfortunately, in most cancer modelling studies the TME is ignored or considered static and different cancers are often studied in isolation. There is a lack of a general theory of cancer adaptive evolution (CAE). Here I establish a genetic and phenotypic model of cancer three-dimensional (3D) spatial evolution in a changing TME. With 3D individual-based simulations I show how cancer cells adapt to diverse changing TME conditions and selection intensities. I am able to capture key histological characteristics of various cancer forms including complex dynamics of spatial-temporal heterogeneity of subclonal fitness and subclonal mixing, ball-like and non-ball-like subclonal structures. Moreover, I identify key evolutionary and phylogenetic patterns of CAE under various combinations of phenotypic, genetic, population genetic and changing TME conditions. I show classical drivers, mini drivers, Darwinian and neutral/nearly neutral evolution and cost of complexity. I demonstrate the importance of ecology in CAE. I show that there are fundamental differences in the mode of CAE when the TME is changing, which is the limiting factor of CAE. Finally, I discuss important implications for cancer evolution theories and cancer personalised medicine.

scholarly journals Mutational landscape of primary, metastatic, and recurrent ovarian cancer reveals c-MYC gains as potential target for BET inhibitors

2018 ◽  
Vol 116 (2) ◽  
pp. 619-624 ◽  
Author(s):  
Charles Li ◽  
Elena Bonazzoli ◽  
Stefania Bellone ◽  
Jungmin Choi ◽  
Weilai Dong ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Somatic Mutations ◽  
Driver Mutations ◽  
Cancer Evolution ◽  
Metastatic Tumors ◽  
Driver Genes ◽  
Primary Tumors ◽  
Mutational Landscape ◽  
Recurrent Tumors ◽  
Bet Inhibitors

Ovarian cancer remains the most lethal gynecologic malignancy. We analyzed the mutational landscape of 64 primary, 41 metastatic, and 17 recurrent fresh-frozen tumors from 77 patients along with matched normal DNA, by whole-exome sequencing (WES). We also sequenced 13 pairs of synchronous bilateral ovarian cancer (SBOC) to evaluate the evolutionary history. Lastly, to search for therapeutic targets, we evaluated the activity of the Bromodomain and Extra-Terminal motif (BET) inhibitor GS-626510 on primary tumors and xenografts harboring c-MYC amplifications. In line with previous studies, the large majority of germline and somatic mutations were found in BRCA1/2 (21%) and TP53 (86%) genes, respectively. Among mutations in known cancer driver genes, 77% were transmitted from primary tumors to metastatic tumors, and 80% from primary to recurrent tumors, indicating that driver mutations are commonly retained during ovarian cancer evolution. Importantly, the number, mutation spectra, and signatures in matched primary–metastatic tumors were extremely similar, suggesting transcoelomic metastases as an early dissemination process using preexisting metastatic ability rather than an evolution model. Similarly, comparison of SBOC showed extensive sharing of somatic mutations, unequivocally indicating a common ancestry in all cases. Among the 17 patients with matched tumors, four patients gained PIK3CA amplifications and two patients gained c-MYC amplifications in the recurrent tumors, with no loss of amplification or gain of deletions. Primary cell lines and xenografts derived from chemotherapy-resistant tumors demonstrated sensitivity to JQ1 and GS-626510 (P = 0.01), suggesting that oral BET inhibitors represent a class of personalized therapeutics in patients harboring recurrent/chemotherapy-resistant disease.

scholarly journals Stationary frequencies and mixing times for neutral drift processes with spatial structure

2018 ◽  
Vol 474 (2218) ◽  
pp. 20180238
Author(s):  
Alex McAvoy ◽  
Ben Adlam ◽  
Benjamin Allen ◽  
Martin A. Nowak
Keyword(s):  
Spatial Structure ◽  
Evolutionary Process ◽  
General Setting ◽  
Rates Of Convergence ◽  
Demographic Variables ◽  
Neutral Evolution ◽  
Mixing Times ◽  
Mutation Process ◽  
Neutral Drift ◽  
Genetic Types

We study a general setting of neutral evolution in which the population is of finite, constant size and can have spatial structure. Mutation leads to different genetic types (traits), which can be discrete or continuous. Under minimal assumptions, we show that the marginal trait distributions of the evolutionary process, which specify the probability that any given individual has a certain trait, all converge to the stationary distribution of the mutation process. In particular, the stationary frequencies of traits in the population are independent of its size, spatial structure and evolutionary update rule, and these frequencies can be calculated by evaluating a simple stochastic process describing a population of size one (i.e. the mutation process itself). We conclude by analysing mixing times, which characterize rates of convergence of the mutation process along the lineages, in terms of demographic variables of the evolutionary process.

Opposing Evolutionary Pressures Drive Clonal Evolution and Health Outcomes in the Aging Blood System

Blood ◽  
2020 ◽  
Vol 136 (Supplement 1) ◽  
pp. 37-37
Author(s):  
Kimberly Skead ◽  
Armande Ang Houle ◽  
Sagi Abelson ◽  
Marie-Julie Fave ◽  
Boxi Lin ◽  
...  
Keyword(s):  
Gene Disruption ◽  
Large Scale ◽  
Evolutionary Dynamics ◽  
Clonal Evolution ◽  
Neutral Evolution ◽  
Driver Mutations ◽  
Cancer Evolution ◽  
Sequencing Data ◽  
High Coverage ◽  
Passenger Mutations

The age-associated accumulation of somatic mutations and large-scale structural variants (SVs) in the early hematopoietic hierarchy have been linked to premalignant stages for cancer and cardiovascular disease (CVD). However, only a small proportion of individuals harboring these mutations progress to disease, and mechanisms driving the transformation to malignancy remains unclear. Hematopoietic evolution, and cancer evolution more broadly, has largely been studied through a lens of adaptive evolution and the contribution of functionally neutral or mildly damaging mutations to early disease-associated clonal expansions has not been well characterised despite comprising the majority of the mutational burden in healthy or tumoural tissues. Through combining deep learning with population genetics, we interrogate the hematopoietic system to capture signatures of selection acting in healthy and pre-cancerous blood populations. Here, we leverage high-coverage sequencing data from healthy and pre-cancerous individuals from the European Prospective Investigation into Cancer and Nutrition Study (n=477) and dense genotyping from the Canadian Partnership for Tomorrow's Health (n=5,000) to show that blood rejects the paradigm of strictly adaptive or neutral evolution and is subject to pervasive negative selection. We observe clear age associations across hematopoietic populations and the dominant class of selection driving evolutionary dynamics acting at an individual level. We find that both the location and ratio of passenger to driver mutations are critical in determining if positive selection acting on driver mutations is able to overwhelm regulated hematopoiesis and allow clones harbouring disease-predisposing mutations to rise to dominance. Certain genes are enriched for passenger mutations in healthy individuals fitting purifying models of evolution, suggesting that the presence of passenger mutations in a subset of genes might confer a protective role against disease-predisposing clonal expansions. Finally, we find that the density of gene disruption events with known pathogenic associations in somatic SVs impacts the frequency at which the SV segregates in the population with variants displaying higher gene disruption density segregating at lower frequencies. Understanding how blood evolves towards malignancy will allow us to capture cancer in its earliest stages and identify events initiating departures from healthy blood evolution. Further, as the majority of mutations are passengers, studying their contribution to tumorigenesis, will unveil novel therapeutic targets thus enabling us to better understand patterns of clonal evolution in order to diagnose and treat disease in its infancy. Disclosures Dick: Bristol-Myers Squibb/Celgene: Research Funding.

Evolutionary selection for travel agencies under COVID-19 adversity through the lens of life history theory

2021 ◽  
Vol 38 (9) ◽  
pp. 917-934
Author(s):  
GuoQiongIvanka Huang ◽  
Shuru Zhong ◽  
IpKin Anthony Wong ◽  
Zhiwei (CJ) Lin
Keyword(s):  
Life History ◽  
Life History Theory ◽  
Evolutionary Selection ◽  
Travel Agencies ◽  
Selection For

Stability indicatrices of nonnegative matrices and some of their applications in problems of biology and epidemiology

2019 ◽  
Vol 34 (5) ◽  
pp. 269-275
Author(s):  
Valery N. Razzhevaikin
Keyword(s):  
Nonnegative Matrix ◽  
Host Population ◽  
Nonnegative Matrices ◽  
Biological Communities ◽  
Evolutionary Selection ◽  
Evolutionary Optimality ◽  
Selection For ◽  
The Stability ◽  
Series Of Functions

Abstract The method of constructing a stability indicatrix of a nonnegative matrix having the form of a polynomial of its coefficients is presented. The algorithm of construction and conditions of its applicability are specified. The applicability of the algorithm is illustrated on examples of constructing the stability indicatrix for a series of functions widely used in simulation of the dynamics of discrete biological communities, for solving evolutionary optimality problems arising in biological problems of evolutionary selection, for identification of the conditions of the pandemic in a distributed host population.

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