scholarly journals Tracing the evolution of aneuploid cancers from multiregional sequencing

2020 ◽  
Author(s):  
Subhayan Chattopadhyay ◽  
Jenny Karlsson ◽  
Anders Valind ◽  
Natalie Andersson ◽  
David Gisselsson

AbstractTo understand the evolutionary dynamics of cancer, clonal deconvolution of mutational landscapes across multiple biopsies from the same patient is crucial. However, the frequencies of mutated alleles are often distorted by variation in copy number of mutated loci as well as the purity across samples. We present a semi-supervised algorithm that normalizes for purity and incorporates allelic composition with bulk sequencing to reliably segregate clonal/subclonal variants even at low sequencing depth (∼50x). In presence of at least one tumor sample with >70% purity, it deconvolves samples down to ∼40% purity, allowing robust tracking of mutated cell populations through cancer evolution.

2020 ◽  
Vol 15 ◽  
pp. 14 ◽  
Author(s):  
Rebecca E.A. Stace ◽  
Thomas Stiehl ◽  
Mark A.J. Chaplain ◽  
Anna Marciniak-Czochra ◽  
Tommaso Lorenzi

We present a stochastic individual-based model for the phenotypic evolution of cancer cell populations under chemotherapy. In particular, we consider the case of combination cancer therapy whereby a chemotherapeutic agent is administered as the primary treatment and an epigenetic drug is used as an adjuvant treatment. The cell population is structured by the expression level of a gene that controls cell proliferation and chemoresistance. In order to obtain an analytical description of evolutionary dynamics, we formally derive a deterministic continuum counterpart of this discrete model, which is given by a nonlocal parabolic equation for the cell population density function. Integrating computational simulations of the individual-based model with analysis of the corresponding continuum model, we perform a complete exploration of the model parameter space. We show that harsher environmental conditions and higher probabilities of spontaneous epimutation can lead to more effective chemotherapy, and we demonstrate the existence of an inverse relationship between the efficacy of the epigenetic drug and the probability of spontaneous epimutation. Taken together, the outcomes of the model provide theoretical ground for the development of anticancer protocols that use lower concentrations of chemotherapeutic agents in combination with epigenetic drugs capable of promoting the re-expression of epigenetically regulated genes.


2021 ◽  
Author(s):  
Eszter Lakatos ◽  
Helen Hockings ◽  
Maximilian Mossner ◽  
Michelle Lockley ◽  
Trevor A. Graham

AbstractCell-free DNA (cfDNA) measured via liquid biopsies provides a way for minimally-invasive monitoring of tumour evolutionary dynamics during therapy. Here we present liquidCNA, a method to track subclonal evolution from longitudinally collected cfDNA samples based on somatic copy number alterations (SCNAs). LiquidCNA utilises SCNA profiles derived through cost-effective low-pass whole genome sequencing to automatically and simultaneously genotype and quantify the size of the dominant subclone without requiring prior knowledge of the genetic identity of the emerging clone. We demonstrate the accuracy of liquidCNA in synthetically generated sample sets and in vitro and in silico mixtures of cancer cell lines. Application in vivo in patients with metastatic lung cancer reveals the progressive emergence of a novel tumour sub-population. LiquidCNA is straightforward to use, computationally inexpensive and enables continuous monitoring of subclonal evolution to understand and control therapy-induced resistance.


Blood ◽  
2020 ◽  
Vol 136 (Supplement 1) ◽  
pp. 37-37
Author(s):  
Kimberly Skead ◽  
Armande Ang Houle ◽  
Sagi Abelson ◽  
Marie-Julie Fave ◽  
Boxi Lin ◽  
...  

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.


2019 ◽  
Vol 37 (2) ◽  
pp. 320-326 ◽  
Author(s):  
Jason A Somarelli ◽  
Heather Gardner ◽  
Vincent L Cannataro ◽  
Ella F Gunady ◽  
Amy M Boddy ◽  
...  

Abstract Cancer progression is an evolutionary process. During this process, evolving cancer cell populations encounter restrictive ecological niches within the body, such as the primary tumor, circulatory system, and diverse metastatic sites. Efforts to prevent or delay cancer evolution—and progression—require a deep understanding of the underlying molecular evolutionary processes. Herein we discuss a suite of concepts and tools from evolutionary and ecological theory that can inform cancer biology in new and meaningful ways. We also highlight current challenges to applying these concepts, and propose ways in which incorporating these concepts could identify new therapeutic modes and vulnerabilities in cancer.


2018 ◽  
Author(s):  
Adriana Salcedo ◽  
Maxime Tarabichi ◽  
Shadrielle Melijah G. Espiritu ◽  
Amit G. Deshwar ◽  
Matei David ◽  
...  

AbstractTumours evolve through time and space. Computational techniques have been developed to infer their evolutionary dynamics from DNA sequencing data. A growing number of studies have used these approaches to link molecular cancer evolution to clinical progression and response to therapy. There has not yet been a systematic evaluation of methods for reconstructing tumour subclonality, in part due to the underlying mathematical and biological complexity and to difficulties in creating gold-standards. To fill this gap, we systematically elucidated the key algorithmic problems in subclonal reconstruction and developed mathematically valid quantitative metrics for evaluating them. We then created approaches to simulate realistic tumour genomes, harbouring all known mutation types and processes both clonally and subclonally. We then simulated 580 tumour genomes for reconstruction, varying tumour read-depth and benchmarking somatic variant detection and subclonal reconstruction strategies. The inference of tumour phylogenies is rapidly becoming standard practice in cancer genome analysis; this study creates a baseline for its evaluation.


2017 ◽  
Author(s):  
Philip Gerlee ◽  
Philipp M. Altrock

AbstractCancer evolution and progression are shaped by cellular interactions and Darwinian selection. Evolutionary game theory incorporates both of these principles, and has been proposed as a framework to understand tumor cell population dynamics. A cornerstone of evolutionary dynamics is the replicator equation, which describes changes in the relative abundance of different cell types, and is able to predict evolutionary equilibria. Typically, the replicator equation focuses on differences in relative fitness. We here show that this framework might not be sufficient under all circumstances, as it neglects important aspects of population growth. Standard replicator dynamics might miss critical differences in the time it takes to reach an equilibrium, as this time also depends on cellular turnover in growing but bounded populations. As the system reaches a stable manifold, the time to reach equilibrium depends on cellular death and birth rates. These rates shape the timescales, in particular in co-evolutionary dynamics of growth factor producers and free-riders. Replicator dynamics might be an appropriate framework only when birth and death rates are of similar magnitude. Otherwise, population growth effects cannot be neglected when predicting the time to reach an equilibrium, and cell type specific rates have to be accounted for explicitly.


2019 ◽  
Author(s):  
Marc J Williams ◽  
Luiz Zapata ◽  
Benjamin Werner ◽  
Chris Barnes ◽  
Andrea Sottoriva ◽  
...  

AbstractThe distribution of fitness effects (DFE) defines how new mutations spread through an evolving population. The ratio of non-synonymous to synonymous mutations (dN/dS) has become a popular method to detect selection in somatic cells, however the link, in somatic evolution, between dN/dS values and fitness coefficients is missing. Here we present a quantitative model of somatic evolutionary dynamics that yields the selective coefficients from individual driver mutations from dN/dS estimates, and then measure the DFE for somatic mutant clones in ostensibly normal oesophagus and skin. We reveal a broad distribution of fitness effects, with the largest fitness increases found for TP53 and NOTCH1 mutants (proliferative bias 1-5%). Accurate measurement of the per-gene DFE in cancer evolution is precluded by the quality of currently available sequencing data. This study provides the theoretical link between dN/dS values and selective coefficients in somatic evolution, and reveals the DFE for mutations in human tissues.


2019 ◽  
Vol 20 (18) ◽  
pp. 4652 ◽  
Author(s):  
Daniele Filippo Condorelli ◽  
Anna Provvidenza Privitera ◽  
Vincenza Barresi

Broad Copy Number Gains (BCNGs) are copy-number increases of chromosomes or large segments of chromosomal arms. Publicly-available single-nucleotide polymorphism (SNP) array and RNA-Seq data of colon adenocarcinoma (COAD) samples from The Cancer Genome Atlas (TCGA) consortium allowed us to design better control groups in order to identify changes in expression due to highly recurrent BCNGs (in chromosomes 20, 8, 7, 13). We identified: (1) Overexpressed Transcripts (OverT), transcripts whose expression increases in “COAD groups bearing a specific BCNG” in comparison to “control COAD groups” not bearing it, and (2) up-regulated/down-regulated transcripts, transcripts whose expression increases/decreases in COAD groups in comparison to normal colon tissue. An analysis of gene expression reveals a correlation between the density of up-regulated genes per selected chromosome and the recurrence rate of their BCNGs. We report an enrichment of gained enhancer activity and of cancer fitness genes among OverT genes. These results support the hypothesis that the chromosomal density of overexpressed cancer fitness genes might play a significant role in the selection of gained chromosomes during cancer evolution. Analysis of functional pathways associated with OverT suggest that some multi-subunit protein complexes (eIF2, eIF3, CSTF and CPSF) are candidate targets for silencing transcriptional therapy.


2017 ◽  
Vol 14 (134) ◽  
pp. 20170342 ◽  
Author(s):  
Philip Gerlee ◽  
Philipp M. Altrock

Cancer evolution and progression are shaped by cellular interactions and Darwinian selection. Evolutionary game theory incorporates both of these principles, and has been proposed as a framework to understand tumour cell population dynamics. A cornerstone of evolutionary dynamics is the replicator equation, which describes changes in the relative abundance of different cell types, and is able to predict evolutionary equilibria. Typically, the replicator equation focuses on differences in relative fitness. We here show that this framework might not be sufficient under all circumstances, as it neglects important aspects of population growth. Standard replicator dynamics might miss critical differences in the time it takes to reach an equilibrium, as this time also depends on cellular turnover in growing but bounded populations. As the system reaches a stable manifold, the time to reach equilibrium depends on cellular death and birth rates. These rates shape the time scales, in particular, in coevolutionary dynamics of growth factor producers and free-riders. Replicator dynamics might be an appropriate framework only when birth and death rates are of similar magnitude. Otherwise, population growth effects cannot be neglected when predicting the time to reach an equilibrium, and cell-type-specific rates have to be accounted for explicitly.


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