scholarly journals Interpreting dN/dS under different selective regimes in cancer evolution

2021 ◽  
Author(s):  
Andrés Pérez-Figueroa ◽  
David Posada
Keyword(s):  
Negative Selection ◽  
Mutation Detection ◽  
Cancer Genomics ◽  
Detection Threshold ◽  
Cancer Genes ◽  
Selection Coefficient ◽  
Cancer Evolution ◽  
Synonymous Mutations ◽  
Somatic Evolution ◽  
The Relationship

The standard relationship between the dN/dS statistic and the selection coefficient is contingent upon the computation of the rate of fixation of non-synonymous and synonymous mutations among divergent lineages (substitutions). In cancer genomics, however, dN/dS is typically calculated by including mutations that are still segregating in the cell population. The interpretation of dN/dS within sexual populations has been shown to be problematic. Here we used a simple model of somatic evolution to study the relationship between dN/dS and the selection coefficient in the presence of deleterious, neutral, and beneficial mutations in cancer. We found that dN/dS can be used to distinguish cancer genes under positive or negative selection, but it is not always informative about the magnitude of the selection coefficient. In particular, under the asexual scenario simulated, dN/dS is insensitive to negative selection strength. Furthermore, the relationship between dN/dS and the positive selection coefficient depends on the mutation detection threshold, and, in particular scenarios, it can become non-linear. Our results warn about the necessary caution when interpreting the results drawn from dN/dS estimates in cancer.

scholarly journals Use of signals of positive and negative selection to distinguish cancer genes and passenger genes

2020 ◽  
Author(s):  
László Bányai ◽  
Mária Trexler ◽  
Krisztina Kerekes ◽  
Orsolya Csuka ◽  
László Patthy
Keyword(s):  
Negative Selection ◽  
Cancer Genomics ◽  
Tumor Evolution ◽  
Cancer Genes ◽  
Cancer Evolution ◽  
Human Genes ◽  
Strong Negative Selection ◽  
Selection For ◽  
Inactivating Mutations

AbstractA major goal of cancer genomics is to identify all genes that play critical roles in carcinogenesis. Most approaches focused on genes that are positively selected for mutations that drive carcinogenesis and neglected the role of negative selection. Some studies have actually concluded that negative selection has no role in cancer evolution. In the present work we have re-examined the role of negative selection in tumor evolution through the analysis of the patterns of somatic mutations affecting the coding sequences of human genes. Our analyses have confirmed that tumor suppressor genes are positively selected for inactivating mutations. Oncogenes, however, were found to display signals of both negative selection for inactivating mutations and positive selection for activating mutations. Significantly, we have identified numerous human genes that show signs of strong negative selection during tumor evolution, suggesting that their functional integrity is essential for the growth and survival of tumor cells.

scholarly journals Use of signals of positive and negative selection to distinguish cancer genes and passenger genes

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
László Bányai ◽  
Maria Trexler ◽  
Krisztina Kerekes ◽  
Orsolya Csuka ◽  
László Patthy
Keyword(s):  
Negative Selection ◽  
Cancer Genomics ◽  
Tumor Evolution ◽  
Cancer Genes ◽  
Cancer Evolution ◽  
Human Genes ◽  
Strong Negative Selection ◽  
Selection For ◽  
Inactivating Mutations

A major goal of cancer genomics is to identify all genes that play critical roles in carcinogenesis. Most approaches focused on genes positively selected for mutations that drive carcinogenesis and neglected the role of negative selection. Some studies have actually concluded that negative selection has no role in cancer evolution. We have re-examined the role of negative selection in tumor evolution through the analysis of the patterns of somatic mutations affecting the coding sequences of human genes. Our analyses have confirmed that tumor suppressor genes are positively selected for inactivating mutations, oncogenes, however, were found to display signals of both negative selection for inactivating mutations and positive selection for activating mutations. Significantly, we have identified numerous human genes that show signs of strong negative selection during tumor evolution, suggesting that their functional integrity is essential for the growth and survival of tumor cells.

scholarly journals Measuring the distribution of fitness effects in somatic evolution by combining clonal dynamics with dN/dS ratios

2019 ◽  
Author(s):  
Marc J Williams ◽  
Luiz Zapata ◽  
Benjamin Werner ◽  
Chris Barnes ◽  
Andrea Sottoriva ◽  
...  
Keyword(s):  
Evolutionary Dynamics ◽  
Quantitative Model ◽  
Driver Mutations ◽  
Cancer Evolution ◽  
Sequencing Data ◽  
Synonymous Mutations ◽  
Fitness Effects ◽  
Somatic Evolution ◽  
Normal Oesophagus

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.

scholarly journals Whole Genome Doubling mitigates Muller’s Ratchet in Cancer Evolution

2019 ◽  
Author(s):  
Saioa López ◽  
Emilia Lim ◽  
Ariana Huebner ◽  
Michelle Dietzen ◽  
Thanos Mourikis ◽  
...  
Keyword(s):  
Negative Selection ◽  
Chromosome Complement ◽  
Essential Genes ◽  
Tumour Suppressor Genes ◽  
Whole Genome ◽  
Cancer Genes ◽  
Cancer Evolution ◽  
Genome Doubling ◽  
Whole Genome Doubling ◽  
Strong Negative Selection

AbstractWhole genome doubling (WGD) is a prevalent macro-evolutionary event in cancer, involving a doubling of the entire chromosome complement. However, despite its prevalence and clinical prognostic relevance, the evolutionary selection pressures for WGD have not been investigated. Here, we explored whether WGD may act to mitigate the irreversible, inexorable ratchet-like, accumulation of deleterious mutations in essential genes. Utilizing 1050 tumor regions from 816 non-small cell lung cancers (NSCLC), we temporally dissect mutations to determine their temporal acquisition in relation to WGD. We find evidence for strong negative selection against homozygous loss of essential cancer genes prior to WGD. However, mutations in essential genes occurring after duplication were not subject to significant negative selection, consistent with WGD providing a buffering effect, decreasing the likelihood of homozygous loss. Finally, we demonstrate that loss of heterozygosity and temporal dissection of mutations can be exploited to identify signals of positive selection in lung, breast, colorectal cancer and other cancer types, enabling the elucidation of novel tumour suppressor genes and a deeper characterization of known cancer genes.

scholarly journals Universal patterns of selection in cancer and somatic tissues

2017 ◽  
Author(s):  
Iñigo Martincorena ◽  
Keiran M. Raine ◽  
Moritz Gerstung ◽  
Kevin J. Dawson ◽  
Kerstin Haase ◽  
...  
Keyword(s):  
Positive Selection ◽  
Negative Selection ◽  
Clonal Selection ◽  
Purifying Selection ◽  
Driver Mutations ◽  
Base Substitution ◽  
List Type ◽  
Cancer Genes ◽  
Cancer Evolution ◽  
Mutation Burden

ABSTRACTCancer develops as a result of somatic mutation and clonal selection, but quantitative measures of selection in cancer evolution are lacking. We applied methods from evolutionary genomics to 7,664 human cancers across 29 tumor types. Unlike species evolution, positive selection outweighs negative selection during cancer development. On average, <1 coding base substitution/tumor is lost through negative selection, with purifying selection only detected for truncating mutations in essential genes in haploid regions. This allows exome-wide enumeration of all driver mutations, including outside known cancer genes. On average, tumors carry ∼4 coding substitutions under positive selection, ranging from <1/tumor in thyroid and testicular cancers to >10/tumor in endometrial and colorectal cancers. Half of driver substitutions occur in yet-to-be-discovered cancer genes. With increasing mutation burden, numbers of driver mutations increase, but not linearly. We identify novel cancer genes and show that genes vary extensively in what proportion of mutations are drivers versus passengers.HIGHLIGHTSUnlike the germline, somatic cells evolve predominantly by positive selectionNearly all (∼99%) coding mutations are tolerated and escape negative selectionFirst exome-wide estimates of the total number of driver coding mutations per tumor1-10 coding driver mutations per tumor; half occurring outside known cancer genes

scholarly journals Ranking cancer drivers via betweenness-based outlier detection and random walks

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Cesim Erten ◽  
Aissa Houdjedj ◽  
Hilal Kazan
Keyword(s):  
Cancer Genomics ◽  
Interaction Network ◽  
Molecular Data ◽  
Alternative Methods ◽  
Patient Specific ◽  
Cancer Genes ◽  
Driver Genes ◽  
Cancer Driver ◽  
Protein Protein Interaction ◽  
Genomic Studies

Abstract Background Recent cancer genomic studies have generated detailed molecular data on a large number of cancer patients. A key remaining problem in cancer genomics is the identification of driver genes. Results We propose BetweenNet, a computational approach that integrates genomic data with a protein-protein interaction network to identify cancer driver genes. BetweenNet utilizes a measure based on betweenness centrality on patient specific networks to identify the so-called outlier genes that correspond to dysregulated genes for each patient. Setting up the relationship between the mutated genes and the outliers through a bipartite graph, it employs a random-walk process on the graph, which provides the final prioritization of the mutated genes. We compare BetweenNet against state-of-the art cancer gene prioritization methods on lung, breast, and pan-cancer datasets. Conclusions Our evaluations show that BetweenNet is better at recovering known cancer genes based on multiple reference databases. Additionally, we show that the GO terms and the reference pathways enriched in BetweenNet ranked genes and those that are enriched in known cancer genes overlap significantly when compared to the overlaps achieved by the rankings of the alternative methods.

scholarly journals A Statistical Framework for Evolutionary Analysis of Recurrent Somatic Mutations in Cancers

2020 ◽  
Author(s):  
Xun Gu
Keyword(s):  
Somatic Mutations ◽  
Cancer Genomics ◽  
Computational Procedure ◽  
The Cancer Genome Atlas ◽  
Component Model ◽  
Evolutionary Analysis ◽  
Cancer Genes ◽  
Component Mixture ◽  
Two Component ◽  
Empirical Bayesian Method

AbstractCurrent cancer genomics databases have accumulated millions of somatic mutations that remain to be further explored, faciltating enormous high throuput analyses to explore the underlying mechanisms that may contribute to malignant initiation or progression. In the context of over-dominant passenger mutations (unrelated to cancers), the challenge is to identify somatic mutations that are cancer-driving. Under the notion that carcinogenesis is a form of somatic-cell evolution, we developed a two-component mixture model that enables to accomplish the following analyses. (i) We formulated a quasi-likelihood approach to test whether the two-component model is significantly better than a single-component model, which can be used for new cancer gene predicting. (ii) We implemented an empirical Bayesian method to calculate the posterior probabilities of a site to be cancer-driving for all sites of a gene, which can be used for new driving site predicting. (iii) We developed a computational procedure to calculate the somatic selection intensity at driver sites and passenger sites, respectively, as well as site-specific profiles for all sites. Using these newly-developed methods, we comprehensively analyzed 294 known cancer genes based on The Cancer Genome Atlas (TCGA) database.

scholarly journals Discovering the drivers of clonal hematopoiesis

2020 ◽  
Author(s):  
Oriol Pich ◽  
Iker Reyes-Salazar ◽  
Abel Gonzalez-Perez ◽  
Nuria Lopez-Bigas
Keyword(s):  
Positive Selection ◽  
Molecular Mechanisms ◽  
Somatic Mutations ◽  
Cancer Genomics ◽  
Variant Calling ◽  
Selective Advantage ◽  
Cancer Genes ◽  
Driver Genes ◽  
Hematopoietic Stem ◽  
Clonal Hematopoiesis

AbstractMutations in genes that confer a selective advantage to hematopoietic stem cells (HSCs) in certain conditions drive clonal hematopoiesis (CH). While some CH drivers have been identified experimentally or through epidemiological studies, the compendium of all genes able to drive CH upon mutations in HSCs is far from complete. We propose that identifying signals of positive selection in blood somatic mutations may be an effective way to identify CH driver genes, similarly as done to identify cancer genes. Using a reverse somatic variant calling approach, we repurposed whole-genome and whole-exome blood/tumor paired samples of more than 12,000 donors from two large cancer genomics cohorts to identify blood somatic mutations. The application of IntOGen, a robust driver discovery pipeline, to blood somatic mutations across both cohorts, and more than 24,000 targeted sequenced samples yielded a list of close to 70 genes with signals of positive selection in CH, available at http://www.intogen.org/ch. This approach recovers all known CH genes, and discovers novel candidates. Generating this compendium is an essential step to understand the molecular mechanisms of CH and to accurately detect individuals with CH to ascertain their risk to develop related diseases.

scholarly journals Somatic evolution in non-neoplastic IBD-affected colon

2019 ◽  
Author(s):  
Sigurgeir Olafsson ◽  
Rebecca E. McIntyre ◽  
Tim Coorens ◽  
Timothy Butler ◽  
Hyunchul Jung ◽  
...  
Keyword(s):  
Chronic Inflammatory Disease ◽  
Interleukin 17 ◽  
Normal Colon ◽  
Synonymous Mutations ◽  
Somatic Evolution ◽  
Colonic Crypts ◽  
Increased Risk ◽  
Inflammatory Bowel ◽  
Selection Mechanisms ◽  
Mutational Processes

Summary paragraphInflammatory bowel disease (IBD) is a chronic inflammatory disease associated with increased risk of gastrointestinal cancers. Here, we whole-genome sequenced 447 colonic crypts from 46 IBD patients, and compared these to 412 crypts from 41 non-IBD controls. The average mutation rate of affected colonic epithelial cells is 2.4-fold that of healthy colon and this increase is mostly driven by acceleration of mutational processes ubiquitously observed in normal colon. In contrast to the normal colon, where clonal expansions outside the confines of the crypt are rare, we observed widespread millimeter-scale clonal expansions. We discovered non-synonymous mutations in ARID1A, FBXW7, PIGR and ZC3H12A, and genes in the interleukin 17 and Toll-like receptor pathways, under positive selection in IBD. These results suggest distinct selection mechanisms in the colitis-affected colon and that somatic mutations potentially play a causal role in IBD pathogenesis.

Relationship between the Differences of Odor Detection Threshold Values Measured by Different Methods and some of the Physicochemical Properties

2013 ◽  
Vol 807-809 ◽  
pp. 451-455 ◽  
Author(s):  
Di Fang ◽  
Jie Min Liu ◽  
Qin Yi
Keyword(s):  
Physicochemical Properties ◽  
Functional Group ◽  
Saturated Vapor ◽  
Detection Threshold ◽  
Threshold Values ◽  
Odor Detection ◽  
Linear Relationships ◽  
Odor Detection Threshold ◽  
The Difference ◽  
The Relationship

The amount of sample can enter the nasal cavity depends on the physiochemical characteristics such as distribution, volatility and solubility. It can be suspected that the difference of odor detection threshold (ODT) measured by different methods is related to the physicochemical properties of compounds. To investigate the relationship between ODT differences and the physicochemical properties of compounds, ODT values of four series of organic compounds were measured by triangle odor bag method and gas chromatography and olfactometry method; the results were compared and the absolute differences were calculated. Relationship between ODT differences and the type of functional group and some of the physicochemical properties of compounds was analyzed. The results showed the type of functional group had significant effect on the differences. Certain linear relationships between the logarithmic value of differences and the logarithmic values of saturated vapor pressure and molecular weight were observed.

Sign in / Sign up

Export Citation Format

Share Document