scholarly journals Inference of clonal selection in cancer populations using single-cell sequencing data

2018 ◽  
Author(s):  
Pavel Skums ◽  
Vyacheslau Tsivina ◽  
Alex Zelikovsky
Keyword(s):  
Single Cell ◽  
Cancer Progression ◽  
Tumor Heterogeneity ◽  
Evolutionary Dynamics ◽  
Clonal Selection ◽  
Fitness Landscapes ◽  
Sequencing Data ◽  
Evolutionary Mechanisms ◽  
Single Cell Sequencing ◽  
Insight Into

AbstractIntra-tumor heterogeneity is one of the major factors influencing cancer progression and treatment outcome. However, evolutionary dynamics of cancer clone populations remain poorly understood. Quantification of clonal selection and inference of fitness landscapes of tumors is a key step to understanding evolutionary mechanisms driving cancer. These problems could be addressed using single cell sequencing, which provides an unprecedented insight into intra-tumor heterogeneity allowing to study and quantify selective advantages of individual clones. Here we present SCIFIL, a computational tool for inference of fitness landscapes of heterogeneous cancer clone populations from single cell sequencing data. SCIFIL allows to estimate maximum likelihood fitnesses of clone variants, measure their selective advantages and order of appearance by fitting an evolutionary model into the tumor phylogeny. We demonstrate the accuracy and utility of our approach on simulated and experimental data. SCIFIL can be used to provide new insight into the evolutionary dynamics of cancer. Its source code is available at https://github.com/compbel/SCIFIL

scholarly journals Inference of clonal selection in cancer populations using single-cell sequencing data

2019 ◽  
Vol 35 (14) ◽  
pp. i398-i407 ◽  
Author(s):  
Pavel Skums ◽  
Viachaslau Tsyvina ◽  
Alex Zelikovsky
Keyword(s):  
Single Cell ◽  
Cancer Progression ◽  
Tumor Heterogeneity ◽  
Evolutionary Dynamics ◽  
Fitness Landscape ◽  
Clonal Selection ◽  
Fitness Landscapes ◽  
Sequencing Data ◽  
Single Cell Sequencing ◽  
Insight Into

Abstract Summary Intra-tumor heterogeneity is one of the major factors influencing cancer progression and treatment outcome. However, evolutionary dynamics of cancer clone populations remain poorly understood. Quantification of clonal selection and inference of fitness landscapes of tumors is a key step to understanding evolutionary mechanisms driving cancer. These problems could be addressed using single-cell sequencing (scSeq), which provides an unprecedented insight into intra-tumor heterogeneity allowing to study and quantify selective advantages of individual clones. Here, we present Single Cell Inference of FItness Landscape (SCIFIL), a computational tool for inference of fitness landscapes of heterogeneous cancer clone populations from scSeq data. SCIFIL allows to estimate maximum likelihood fitnesses of clone variants, measure their selective advantages and order of appearance by fitting an evolutionary model into the tumor phylogeny. We demonstrate the accuracy our approach, and show how it could be applied to experimental tumor data to study clonal selection and infer evolutionary history. SCIFIL can be used to provide new insight into the evolutionary dynamics of cancer. Availability and implementation Its source code is available at https://github.com/compbel/SCIFIL.

scholarly journals A Phylogenetic Approach to Inferring the Order in Which Mutations Arise during Cancer Progression

2020 ◽  
Author(s):  
Yuan Gao ◽  
Jeff Gaither ◽  
Julia Chifman ◽  
Laura Kubatko
Keyword(s):  
Single Cell ◽  
Cancer Progression ◽  
Somatic Mutations ◽  
Temporal Order ◽  
Sequencing Data ◽  
Evolutionary Mechanisms ◽  
Single Cell Sequencing ◽  
Data Collection Process ◽  
Number Of Cells

Although the role of evolutionary processes in cancer progression is widely accepted, increasing attention is being given to evolutionary mechanisms that can lead to differences in clinical outcome. Recent studies suggest that the temporal order in which somatic mutations accumulate during cancer progression is important. Single-cell sequencing provides a unique opportunity to examine the mutation order during cancer progression. However, the errors associated with single-cell sequencing complicate this task. We propose a new method for inferring the order in which somatic mutations arise within a tumor using noisy single-cell sequencing data that incorporates the errors that arise from the data collection process. Using simulation, we show that our method outperforms existing methods for identifying mutation order in most cases, especially when the number of cells is large. Our method also provides a means to quantify the uncertainty in the inferred mutation order along a fixed phylogeny. We apply our method to empirical data for colorectal and prostate cancer.

scholarly journals Conifer: Clonal Tree Inference for Tumor Heterogeneity With Single-cell and Bulk Sequencing Data

2021 ◽  
Author(s):  
Leila Baghaarabani ◽  
Sama Goliaei ◽  
Mohammad-Hadi Foroughmand-Araabi ◽  
Seyed Peyman Shariatpanahi ◽  
Bahram Goliaei
Keyword(s):  
Single Cell ◽  
Allele Frequency ◽  
Tumor Heterogeneity ◽  
Variant Allele ◽  
Evolutionary Relationships ◽  
Sequencing Data ◽  
Variant Allele Frequency ◽  
Similar Frequency ◽  
Single Cell Sequencing ◽  
Tree Inference

Abstract Background: An important and effective step in cancer treatment is understanding the clonal evolution of cancer tumors. Clones are cell populations with different genotypes, resulting from the differences in the somatic mutations that occur and accumulate during cancer development. An appropriate approach for better understanding a tumor population is determining the variant allele frequency with which the mutation occurs in the entire population. Bulk sequencing data can be used to provide that information, but the frequencies are not informative enough in identifying different clones and their evolutionary relationships. On the other hand, single-cell sequencing data provides valuable information about branching events in the evolution of a cancerous tumor. However, in the single-cell sequencing data, the total population of sequenced cells is naturally much smaller than bulk sequencing so it is not precise enough for calculating cell prevalence.Result: In this study, a new method called Conifer (ClONal tree Inference For hEterogeneity of tumoR) is proposed which combines aggregated variant allele frequency from bulk sequencing data with branch evolution information from single-cell sequencing data, in order to better understand clones and their evolutionary relationships. It is proven that the accuracy of clone identification is increased by using Conifer compared to other existing methods in both real and simulated data. Also, it is shown that the approach of Conifer in using single-cell sequencing data together with bulk sequencing data has reduced the possibility of cloning mutations with similar frequency but belonging to different clones.Conclusions: In this study, we provided an accurate and robust method to identify clones of tumor heterogeneity and their evolutionary history by combining single-cell and bulk sequencing data.

scholarly journals Conifer: clonal tree inference for tumor heterogeneity with single-cell and bulk sequencing data

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Leila Baghaarabani ◽  
Sama Goliaei ◽  
Mohammad-Hadi Foroughmand-Araabi ◽  
Seyed Peyman Shariatpanahi ◽  
Bahram Goliaei
Keyword(s):  
Single Cell ◽  
Tumor Heterogeneity ◽  
Temporal Order ◽  
Variant Allele ◽  
Evolutionary Relationships ◽  
Sequencing Data ◽  
Variant Allele Frequency ◽  
Single Cell Sequencing ◽  
Tree Inference ◽  
Cell Data

Abstract Background Genetic heterogeneity of a cancer tumor that develops during clonal evolution is one of the reasons for cancer treatment failure, by increasing the chance of drug resistance. Clones are cell populations with different genotypes, resulting from differences in somatic mutations that occur and accumulate during cancer development. An appropriate approach for identifying clones is determining the variant allele frequency of mutations that occurred in the tumor. Although bulk sequencing data can be used to provide that information, the frequencies are not informative enough for identifying different clones with the same prevalence and their evolutionary relationships. On the other hand, single-cell sequencing data provides valuable information about branching events in the evolution of a cancerous tumor. However, the temporal order of mutations may be determined with ambiguities using only single-cell data, while variant allele frequencies from bulk sequencing data can provide beneficial information for inferring the temporal order of mutations with fewer ambiguities. Result In this study, a new method called Conifer (ClONal tree Inference For hEterogeneity of tumoR) is proposed which combines aggregated variant allele frequency from bulk sequencing data with branching event information from single-cell sequencing data to more accurately identify clones and their evolutionary relationships. It is proven that the accuracy of clone identification and clonal tree inference is increased by using Conifer compared to other existing methods on various sets of simulated data. In addition, it is discussed that the evolutionary tree provided by Conifer on real cancer data sets is highly consistent with information in both bulk and single-cell data. Conclusions In this study, we have provided an accurate and robust method to identify clones of tumor heterogeneity and their evolutionary history by combining single-cell and bulk sequencing data.

scholarly journals Echidna: integrated simulations of single-cell immune receptor repertoires and transcriptomes

2021 ◽  
Author(s):  
Jiami Han ◽  
Raphael Kuhn ◽  
Chrysa Papadopoulou ◽  
Andreas Agrafiotis ◽  
Victor Kreiner ◽  
...  
Keyword(s):  
Gene Expression ◽  
B Cell ◽  
Single Cell ◽  
Learning Strategies ◽  
Clonal Selection ◽  
Cell Receptor ◽  
Sequencing Data ◽  
Immune Receptor ◽  
Single Cell Sequencing ◽  
Wide Range

Single-cell sequencing now enables the recovery of full-length immune repertoires [B cell receptor (BCR) and T cell receptor (TCR) repertoires], in addition to gene expression information. The feature-rich datasets produced from such experiments require extensive and diverse computational analyses, each of which can significantly influence the downstream immunological interpretations, such as clonal selection and expansion. Simulations produce validated standard datasets, where the underlying generative model can be precisely defined and furthermore perturbed to investigate specific questions of interest. Currently, there is no tool that can be used to simulate a comprehensive ground truth single-cell dataset that incorporates both immune receptor repertoires and gene expression. Therefore, we developed Echidna, an R package that simulates immune receptors and transcriptomes at single-cell resolution. Our simulation tool generates annotated single-cell sequencing data with user-tunable parameters controlling a wide range of features such as clonal expansion, germline gene usage, somatic hypermutation, and transcriptional phenotypes. Echidna can additionally simulate time-resolved B cell evolution, producing mutational networks with complex selection histories incorporating class-switching and B cell subtype information. Finally, we demonstrate the benchmarking potential of Echidna by simulating clonal lineages and comparing the known simulated networks with those inferred from only the BCR sequences as input. Together, Echidna provides a framework that can incorporate experimental data to simulate single-cell immune repertoires to aid software development and bioinformatic benchmarking of clonotyping, phylogenetics, transcriptomics and machine learning strategies.

scholarly journals SiFit: A Method for Inferring Tumor Trees from Single-Cell Sequencing Data under Finite-site Models

2016 ◽  
Author(s):  
Hamim Zafar ◽  
Anthony Tzen ◽  
Nicholas Navin ◽  
Ken Chen ◽  
Luay Nakhleh
Keyword(s):  
Single Cell ◽  
Tumor Heterogeneity ◽  
Sequencing Data ◽  
Inference Method ◽  
Single Cell Sequencing ◽  
Assumption Violations ◽  
Evolutionary Trajectories ◽  
Colorectal Cancer Patients ◽  
Inference Methods ◽  
Evolutionary Lineages

AbstractSingle-cell sequencing (SCS) enables the inference of tumor phylogenies that provide insights on intra-tumor heterogeneity and evolutionary trajectories. Recently introduced methods perform this task under the infinite-sites assumption, violations of which, due to chromosomal deletions and loss of heterozygosity, necessitate the development of inference methods that utilize finite-site models. We propose a statistical inference method for tumor phylogenies from noisy SCS data under a finite-sites model. The performance of our method on synthetic and experimental datasets from two colorectal cancer patients to trace evolutionary lineages in primary and metastatic tumors suggest that employing a finite-sites model leads to improved inference of tumor phylogenies.

484 Bioturing browser: interactively explore public single cell sequencing data

2020 ◽  
Vol 8 (Suppl 3) ◽  
pp. A520-A520
Author(s):  
Son Pham ◽  
Tri Le ◽  
Tan Phan ◽  
Minh Pham ◽  
Huy Nguyen ◽  
...  
Keyword(s):  
Single Cell ◽  
Immune Cell ◽  
Expression Profiles ◽  
Meta Analysis ◽  
Cell Types ◽  
Sequencing Data ◽  
Single Cell Sequencing ◽  
Data Formats ◽  
Cancer Types ◽  
Cell Data

BackgroundSingle-cell sequencing technology has opened an unprecedented ability to interrogate cancer. It reveals significant insights into the intratumoral heterogeneity, metastasis, therapeutic resistance, which facilitates target discovery and validation in cancer treatment. With rapid advancements in throughput and strategies, a particular immuno-oncology study can produce multi-omics profiles for several thousands of individual cells. This overflow of single-cell data poses formidable challenges, including standardizing data formats across studies, performing reanalysis for individual datasets and meta-analysis.MethodsN/AResultsWe present BioTuring Browser, an interactive platform for accessing and reanalyzing published single-cell omics data. The platform is currently hosting a curated database of more than 10 million cells from 247 projects, covering more than 120 immune cell types and subtypes, and 15 different cancer types. All data are processed and annotated with standardized labels of cell types, diseases, therapeutic responses, etc. to be instantly accessed and explored in a uniform visualization and analytics interface. Based on this massive curated database, BioTuring Browser supports searching similar expression profiles, querying a target across datasets and automatic cell type annotation. The platform supports single-cell RNA-seq, CITE-seq and TCR-seq data. BioTuring Browser is now available for download at www.bioturing.com.ConclusionsN/A

scholarly journals Abstract 4697: Expression variation analysis for tumor heterogeneity in single-cell RNA-sequencing data

2019 ◽  
Author(s):  
Emily F. Davis-Marcisak ◽  
Pranay Orugunta ◽  
Genevieve Stein-O'Brien ◽  
Sidharth V. Puram ◽  
Evanthia Roussos Torres ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Tumor Heterogeneity ◽  
Variation Analysis ◽  
Sequencing Data ◽  
Expression Variation ◽  
Single Cell Rna Sequencing
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