scholarly journals A tug-of-war between driver and passenger mutations in cancer and other adaptive processes

2014 ◽  
Author(s):  
Christopher Dennis McFarland ◽  
Leonid A Mirny ◽  
Kirill S Korolev
Keyword(s):  
Mutation Rate ◽  
Cancer Progression ◽  
Evolutionary Dynamics ◽  
Cancer Genomics ◽  
Treatment Strategies ◽  
Driver Mutations ◽  
Fitness Advantage ◽  
Adaptive Processes ◽  
Passenger Mutations ◽  
Stochastic Population Model

Cancer progression is an example of a rapid adaptive process where evolving new traits is essential for survival and requires a high mutation rate. Precancerous cells acquire a few key mutations that drive rapid population growth and carcinogenesis. Cancer genomics demonstrates that these few ‘driver’ mutations occur alongside thousands of random ‘passenger’ mutations––a natural consequence of cancer's elevated mutation rate. Some passengers can be deleterious to cancer cells, yet have been largely ignored in cancer research. In population genetics, however, the accumulation of mildly deleterious mutations has been shown to cause population meltdown. Here we develop a stochastic population model where beneficial drivers engage in a tug-of-war with frequent mildly deleterious passengers. These passengers present a barrier to cancer progression that is described by a critical population size, below which most lesions fail to progress, and a critical mutation rate, above which cancers meltdown. We find support for the model in cancer age-incidence and cancer genomics data that also allow us to estimate the fitness advantage of drivers and fitness costs of passengers. We identify two regimes of adaptive evolutionary dynamics and use these regimes to rationalize successes and failures of different treatment strategies. We find that a tumor’s load of deleterious passengers can explain previously paradoxical treatment outcomes and suggest that it could potentially serve as a biomarker of response to mutagenic therapies. The collective deleterious effect of passengers is currently an unexploited therapeutic target. We discuss how their effects might be exacerbated by both current and future therapies.

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.

Approaches for the identification of driver mutations in cancer: A tutorial from a computational perspective

2020 ◽  
Vol 18 (03) ◽  
pp. 2050016 ◽  
Author(s):  
Jorge Francisco Cutigi ◽  
Adriane Feijo Evangelista ◽  
Adenilso Simao
Keyword(s):  
Computational Methods ◽  
Complex Disease ◽  
Cancer Genomics ◽  
Genetic Alterations ◽  
Driver Mutations ◽  
Easy Access ◽  
Computational Perspective ◽  
High Level ◽  
Passenger Mutations ◽  
Cancer Bioinformatics

Cancer is a complex disease caused by the accumulation of genetic alterations during the individual’s life. Such alterations are called genetic mutations and can be divided into two groups: (1) Passenger mutations, which are not responsible for cancer and (2) Driver mutations, which are significant for cancer and responsible for its initiation and progression. Cancer cells undergo a large number of mutations, of which most are passengers, and few are drivers. The identification of driver mutations is a key point and one of the biggest challenges in Cancer Genomics. Many computational methods for such a purpose have been developed in Cancer Bioinformatics. Such computational methods are complex and are usually described in a high level of abstraction. This tutorial details some classical computational methods, from a computational perspective, with the transcription in an algorithmic format towards an easy access by researchers.

scholarly journals Interplay of Driver, Mini-Driver, and Deleterious Passenger Mutations on Cancer Progression

2016 ◽  
Author(s):  
Xin Li ◽  
D. Thirumalai
Keyword(s):  
Tumor Growth ◽  
Cancer Progression ◽  
Evolutionary Model ◽  
Mutation Rates ◽  
Fitness Advantage ◽  
Dissipative Dynamical Systems ◽  
Passenger Mutations ◽  
The Relationship ◽  
Surprising Finding ◽  
Stochastic Evolutionary Model

Cancer is caused by the accumulation of a critical number of somatic mutations (drivers) that offer fitness advantage to tumor cells. Moderately deleterious passengers, suppressing cancer progression, and mini-drivers, mildly beneficial to tumors, can profoundly alter the cancer evolutionary landscape. This observation prompted us to develop a stochastic evolutionary model intended to probe the interplay of drivers, mini-drivers and deleterious passengers in tumor growth over a broad range of fitness values and mutation rates. Below a (small) threshold number of drivers tumor growth exhibits a plateau (dormancy) with large burst occurring when a driver achieves fixation, reminiscent of intermittency in dissipative dynamical systems. The predictions of the model, in particular the relationship between the average number of passenger mutations versus drivers in a tumor, is in accord with clinical data on several cancers. When deleterious drivers are included, we predict a non-monotonic growth of tumors as the mutation rate is varied with shrinkage and even reversal occurring at very large mutation rates. This surprising finding explains the paradoxical observation that high chromosomal instability (CIN) correlates with improved prognosis in a number of cancers compared with intermediate CIN.

scholarly journals Sequence Neighborhoods Enable Reliable Prediction of Pathogenic Mutations in Cancer Genomes

Cancers ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 2366
Author(s):  
Shayantan Banerjee ◽  
Karthik Raman ◽  
Balaraman Ravindran
Keyword(s):  
Cancer Progression ◽  
Nucleotide Sequences ◽  
Feature Representation ◽  
Estimation Methods ◽  
Driver Mutations ◽  
Cancer Mutation ◽  
Evolutionary Features ◽  
Cancer Genomes ◽  
Passenger Mutations ◽  
Pan Cancer

Identifying cancer-causing mutations from sequenced cancer genomes hold much promise for targeted therapy and precision medicine. “Driver” mutations are primarily responsible for cancer progression, while “passengers” are functionally neutral. Although several computational approaches have been developed for distinguishing between driver and passenger mutations, very few have concentrated on using the raw nucleotide sequences surrounding a particular mutation as potential features for building predictive models. Using experimentally validated cancer mutation data in this study, we explored various string-based feature representation techniques to incorporate information on the neighborhood bases immediately 5′ and 3′ from each mutated position. Density estimation methods showed significant distributional differences between the neighborhood bases surrounding driver and passenger mutations. Binary classification models derived using repeated cross-validation experiments provided comparable performances across all window sizes. Integrating sequence features derived from raw nucleotide sequences with other genomic, structural, and evolutionary features resulted in the development of a pan-cancer mutation effect prediction tool, NBDriver, which was highly efficient in identifying pathogenic variants from five independent validation datasets. An ensemble predictor obtained by combining the predictions from NBDriver with three other commonly used driver prediction tools (FATHMM (cancer), CONDEL, and MutationTaster) significantly outperformed existing pan-cancer models in prioritizing a literature-curated list of driver and passenger mutations. Using the list of true positive mutation predictions derived from NBDriver, we identified a list of 138 known driver genes with functional evidence from various sources. Overall, our study underscores the efficacy of using raw nucleotide sequences as features to distinguish between driver and passenger mutations from sequenced cancer genomes.

scholarly journals Genealogies in Growing Solid Tumors

2018 ◽  
Author(s):  
Duke U. Rick Durrett
Keyword(s):  
Evolutionary Dynamics ◽  
Big Bang ◽  
Two Dimensions ◽  
Three Dimensions ◽  
Driver Mutations ◽  
Tumor Evolution ◽  
Alternative Theory ◽  
The Past ◽  
Fitness Advantage ◽  
Expanding Population

AbstractOver the past two decades, the theory of tumor evolution has largely focused on the selective sweeps model. According to this theory, tumors evolve by a succession of clonal expansions that are initiated by driver mutations that have a fitness advantage over the resident types. A 2015 study of colon cancer [44] has suggested an alternative theory of tumor evolution, the so-called Big Bang model, in which all of the necessary driver mutations are acquired before expansion began, and the evolutionary dynamics within the expanding population are predominantly neutral. In this paper, we will describe a simple mathematical model inspired by work of Hallatschek and Nelson [25] that makes quantitative predictions about spatial patterns of genetic variability. While this model has some success in matching observed patterns in two dimensions, it fails miserably in three dimensions. Despite this failure, we think the model analyzed here will be a useful first step in building an accurate model.

scholarly journals Sequence neighborhoods enable reliable prediction of pathogenic mutations in cancer genomes

2021 ◽  
Author(s):  
Shayantan Banerjee ◽  
Karthik Raman ◽  
Balaraman Ravindran
Keyword(s):  
Cancer Progression ◽  
Nucleotide Sequences ◽  
Feature Representation ◽  
Estimation Methods ◽  
Driver Mutations ◽  
Cancer Mutation ◽  
Evolutionary Features ◽  
Cancer Genomes ◽  
Passenger Mutations ◽  
Pan Cancer

AbstractIdentifying cancer-causing mutations from sequenced cancer genomes hold much promise for targeted therapy and precision medicine. “Driver” mutations are primarily responsible for cancer progression, while “passengers” are functionally neutral. Although several computational approaches have been developed for distinguishing between driver and passenger mutations, very few have concentrated on utilizing the raw nucleotide sequences surrounding a particular mutation as potential features for building predictive models. Using experimentally validated cancer mutation data in this study, we explored various string-based feature representation techniques to incorporate information on the neighborhood bases immediately 5’ and 3’ from each mutated position. Density estimation methods showed significant distributional differences between the neighborhood bases surrounding driver and passenger mutations. Binary classification models derived using repeated cross-validation experiments gave comparable performances across all window sizes. Integrating sequence features derived from raw nucleotide sequences with other genomic, structural and evolutionary features resulted in the development of a pan-cancer mutation effect prediction tool, NBDriver, which was highly efficient in identifying pathogenic variants from five independent validation datasets. An ensemble predictor obtained by combining the predictions from NBDriver with two other commonly used driver prediction tools (CONDEL and Mutation Taster) outperformed existing pan-cancer models in prioritizing a literature-curated list of driver and passenger mutations. Using the list of true positive mutation predictions derived from NBDriver, we identified a list of 138 known driver genes with functional evidence from various sources. Overall, our study underscores the efficacy of utilizing raw nucleotide sequences as features to distinguish between driver and passenger mutations from sequenced cancer genomes.

scholarly journals Mutational likeliness and entropy help to identify driver mutations and their functional role in cancer

2018 ◽  
Author(s):  
Giorgio Mattiuz ◽  
Salvatore Di Giorgio ◽  
Lorenzo Tofani ◽  
Antonio Frandi ◽  
Francesco Donati ◽  
...  
Keyword(s):  
Cancer Progression ◽  
Somatic Mutations ◽  
Driver Mutations ◽  
Cancer Evolution ◽  
Loss Of Function ◽  
Driver Genes ◽  
Cancer Driver ◽  
Cancer Genomes ◽  
Passenger Mutations ◽  
Mutational Processes

AbstractAlterations in cancer genomes originate from mutational processes taking place throughout oncogenesis and cancer progression. We show that likeliness and entropy are two properties of somatic mutations crucial in cancer evolution, as cancer-driver mutations stand out, with respect to both of these properties, as being distinct from the bulk of passenger mutations. Our analysis can identify novel cancer driver genes and differentiate between gain and loss of function mutations.

scholarly journals Passenger mutations in 2500 cancer genomes: Overall molecular functional impact and consequences

2018 ◽  
Author(s):  
Sushant Kumar ◽  
Jonathan Warrell ◽  
Shantao Li ◽  
Patrick D. McGillivray ◽  
William Meyerson ◽  
...  
Keyword(s):  
Cancer Progression ◽  
Complex Trait ◽  
Driver Mutations ◽  
Additive Variance ◽  
Functional Impact ◽  
Cancer Genomes ◽  
Classical Models ◽  
Cancer Phenotypes ◽  
Passenger Mutations ◽  
The Impact

AbstractThe Pan-cancer Analysis of Whole Genomes (PCAWG) project provides an unprecedented opportunity to comprehensively characterize a vast set of uniformly annotated coding and non-coding mutations present in thousands of cancer genomes. Classical models of cancer progression posit that only a small number of these mutations strongly drive tumor progression and that the remaining ones (termed “putative passengers”) are inconsequential for tumorigenesis. In this study, we leveraged the comprehensive variant data from PCAWG to ascertain the molecular functional impact of each variant. The impact distribution of PCAWG mutations shows that, in addition to high- and low-impact mutations, there is a group of medium-impact putative passengers predicted to influence gene activity. Moreover, the predicted impact relates to the underlying mutational signature: different signatures confer divergent impact, differentially affecting distinct regulatory subsystems and gene categories. We also find that impact varies based on subclonal architecture (i.e., early vs. late mutations) and can be related to patient survival. Finally, we note that insufficient power due to limited cohort sizes precludes identification of weak drivers using standard recurrence-based approaches. To address this, we adapted an additive effects model derived from complex trait studies to show that aggregating the impact of putative passenger variants (i.e. including yet undetected weak drivers) provides significant predictability for cancer phenotypes beyond the PCAWG identified driver mutations (12.5% additive variance). Furthermore, this framework allowed us to estimate the frequency of potential weak driver mutations in the subset of PCAWG samples lacking well-characterized driver alterations.

scholarly journals Revisiting the tumorigenesis timeline with a data-driven generative model

2019 ◽  
Vol 117 (2) ◽  
pp. 857-864 ◽  
Author(s):  
Kamel Lahouel ◽  
Laurent Younes ◽  
Ludmila Danilova ◽  
Francis M. Giardiello ◽  
Ralph H. Hruban ◽  
...  
Keyword(s):  
Evolutionary Dynamics ◽  
Mechanistic Explanation ◽  
Driver Mutations ◽  
Tumor Evolution ◽  
Early Event ◽  
Driver Gene ◽  
Driver Genes ◽  
Fitness Advantage ◽  
Sequencing Studies ◽  
Cancer Types

Cancer is driven by the sequential accumulation of genetic and epigenetic changes in oncogenes and tumor suppressor genes. The timing of these events is not well understood. Moreover, it is currently unknown why the same driver gene change appears as an early event in some cancer types and as a later event, or not at all, in others. These questions have become even more topical with the recent progress brought by genome-wide sequencing studies of cancer. Focusing on mutational events, we provide a mathematical model of the full process of tumor evolution that includes different types of fitness advantages for driver genes and carrying-capacity considerations. The model is able to recapitulate a substantial proportion of the observed cancer incidence in several cancer types (colorectal, pancreatic, and leukemia) and inherited conditions (Lynch and familial adenomatous polyposis), by changing only 2 tissue-specific parameters: the number of stem cells in a tissue and its cell division frequency. The model sheds light on the evolutionary dynamics of cancer by suggesting a generalized early onset of tumorigenesis followed by slow mutational waves, in contrast to previous conclusions. Formulas and estimates are provided for the fitness increases induced by driver mutations, often much larger than previously described, and highly tissue dependent. Our results suggest a mechanistic explanation for why the selective fitness advantage introduced by specific driver genes is tissue dependent.

scholarly journals Tumour heterogeneity and evolutionary dynamics in colorectal cancer

Oncogenesis ◽  
2021 ◽  
Vol 10 (7) ◽  
Author(s):  
Dedrick Kok Hong Chan ◽  
Simon James Alexander Buczacki
Keyword(s):  
Colorectal Cancer ◽  
Cancer Progression ◽  
Evolutionary Dynamics ◽  
Big Bang ◽  
Complex Model ◽  
Neutral Evolution ◽  
Driver Mutations ◽  
Current Evidence ◽  
Tumour Heterogeneity ◽  
Driver Genes

AbstractColorectal cancer (CRC) has a global burden of disease. Our current understanding of CRC has progressed from initial discoveries which focused on the stepwise accumulation of key driver mutations, as encapsulated in the Vogelstein model, to one in which marked heterogeneity leads to a complex interplay between clonal populations. Current evidence suggests that an initial explosion, or “Big Bang”, of genetic diversity is followed by a period of neutral dynamics. A thorough understanding of this interplay between clonal populations during neutral evolution gives insights into the roles in which driver genes may participate in the progress from normal colonic epithelium to adenoma and carcinoma. Recent advances have focused not only on genetics, transcriptomics, and proteomics but have also investigated the ecological and evolutionary processes which transform normal cells into cancer. This review first describes the role which driver mutations play in the Vogelstein model and subsequently demonstrates the evidence which supports a more complex model. This article also aims to underscore the significance of tumour heterogeneity and diverse clonal populations in cancer progression.

Sign in / Sign up

Export Citation Format

Share Document