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 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 PhyDOSE: Design of Follow-up Single-cell Sequencing Experiments of Tumors

2020 ◽  
Author(s):  
Leah Weber ◽  
Nuraini Aguse ◽  
Nicholas Chia ◽  
Mohammed El-Kebir
Keyword(s):  
Single Cell ◽  
Retrospective Analysis ◽  
High Fidelity ◽  
Sequencing Data ◽  
Single Cell Sequencing ◽  
Bulk Data ◽  
Sequencing Experiment ◽  
Tumor Phylogeny ◽  
Number Of Cells

AbstractThe combination of bulk and single-cell DNA sequencing data of the same tumor enables the inference of high-fidelity phylogenies that form the input to many important downstream analyses in cancer genomics. While many studies simultaneously perform bulk and single-cell sequencing, some studies have analyzed initial bulk data to identify which mutations to target in a follow-up single-cell sequencing experiment, thereby decreasing cost. Bulk data provide an additional untapped source of valuable information, composed of candidate phylogenies and associated clonal prevalence. Here, we introduce PhyDOSE, a method that uses this information to strategically optimize the design of follow-up single cell experiments. Underpinning our method is the observation that only a small number of clones uniquely distinguish one candidate tree from all other trees. We incorporate distinguishing features into a probabilistic model that infers the number of cells to sequence so as to confidently reconstruct the phylogeny of the tumor. We validate PhyDOSE using simulations and a retrospective analysis of a leukemia patient, concluding that PhyDOSE’s computed number of cells resolves tree ambiguity even in the presence of typical single-cell sequencing errors. We also conduct a retrospective analysis on an acute myeloid leukemia cohort, demonstrating the potential to achieve similar results with a significant reduction in the number of cells sequenced. In a prospective analysis, we demonstrate that only a small number of cells suffice to disambiguate the solution space of trees in a recent lung cancer cohort. In summary, PhyDOSE proposes cost-efficient single-cell sequencing experiments that yield high-fidelity phylogenies, which will improve downstream analyses aimed at deepening our understanding of cancer biology.Author summaryCancer development in a patient can be explained using a phylogeny — a tree that describes the evolutionary history of a tumor and has therapeutic implications. A tumor phylogeny is constructed from sequencing data, commonly obtained using either bulk or single-cell DNA sequencing technology. The accuracy of tumor phylogeny inference increases when both types of data are used, but single-cell sequencing may become prohibitively costly with increasing number of cells. Here, we propose a method that uses bulk sequencing data to guide the design of a follow-up single-cell sequencing experiment. Our results suggest that PhyDOSE provides a significant decrease in the number of cells to sequence compared to the number of cells sequenced in existing studies. The ability to make informed decisions based on prior data can help reduce the cost of follow-up single cell sequencing experiments of tumors, improving accuracy of tumor phylogeny inference and ultimately getting us closer to understanding and treating cancer.

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 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 Redox Regulator GLRX Is Associated With Tumor Immunity in Glioma

2020 ◽  
Vol 11 ◽  
Author(s):  
Yuanhao Chang ◽  
Guanzhang Li ◽  
You Zhai ◽  
Lijie Huang ◽  
Yuemei Feng ◽  
...  
Keyword(s):  
Single Cell ◽  
Prognostic Value ◽  
Malignant Gliomas ◽  
Enrichment Score ◽  
Immune Checkpoints ◽  
Inflammatory Factors ◽  
Sequencing Data ◽  
Single Cell Sequencing ◽  
Potential Marker

Glutaredoxin is central to cellular redox chemistry and regulates redox homeostasis and malignant progression of many cancers. In glioma, the role of its coding gene (GLRX) remains unclear. We aimed to elucidate the role of glutaredoxin at the transcriptome level and its clinical prognostic value in glioma. In total, we evaluated 1,717 glioma samples with transcriptome data and corresponding clinical data as well as single-cell sequencing data from 6 glioma patients from publicly available databases. Gene set variation analysis and gene ontology analysis were performed to reveal the biological function of GLRX. The immune cell enrichment score was calculated by GSVA analysis. Single-cell sequencing data was visualized by t-distributed stochastic neighbor embedding analysis. The prognostic value of GLRX in glioma was verified by the Kaplan-Meier curve and multivariate COX analysis. GLRX was found to be highly enriched in gliomas of higher grades with wild-type IDH, without 1p/19q co-deletion, and with a methylated MGMT promoter. Moreover, GLRX could be a potential marker for the mesenchymal molecular subtype of gliomas. The expression of GLRX was closely related to the tumor immune process, immune checkpoints, and inflammatory factors with GLRX being specifically expressed in M0 macrophages. GLRX is also shown to be an independent prognostic factor in glioma. Altogether, our study outcomes show that GLRX is highly enriched in malignant gliomas and is closely related to the tumor immune microenvironment. Therefore, GLRX-targeted cell redox regulatory therapy may enhance the efficacy of glioma immunotherapy.

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 Effective clustering for single cell sequencing cancer data

2019 ◽  
Author(s):  
Simone Ciccolella ◽  
Murray Patterson ◽  
Paola Bonizzoni ◽  
Gianluca Della Vedova
Keyword(s):  
Single Cell ◽  
Categorical Data ◽  
Euclidean Distance ◽  
Missing Values ◽  
False Negative ◽  
Ground Truth ◽  
Sequencing Data ◽  
Large Space ◽  
Cancer Data ◽  
Single Cell Sequencing

AbstractBackgroundSingle cell sequencing (SCS) technologies provide a level of resolution that makes it indispensable for inferring from a sequenced tumor, evolutionary trees or phylogenies representing an accumulation of cancerous mutations. A drawback of SCS is elevated false negative and missing value rates, resulting in a large space of possible solutions, which in turn makes infeasible using some approaches and tools. While this has not inhibited the development of methods for inferring phylogenies from SCS data, the continuing increase in size and resolution of these data begin to put a strain on such methods.One possible solution is to reduce the size of an SCS instance — usually represented as a matrix of presence, absence and missing values of the mutations found in the different sequenced cells — and infer the tree from this reduced-size instance. Previous approaches have used k-means to this end, clustering groups of mutations and/or cells, and using these means as the reduced instance. Such an approach typically uses the Euclidean distance for computing means. However, since the values in these matrices are of a categorical nature (having the three categories: present, absent and missing), we explore techniques for clustering categorical data — commonly used in data mining and machine learning — to SCS data, with this goal in mind.ResultsIn this work, we present a new clustering procedure aimed at clustering categorical vector, or matrix data — here representing SCS instances, called celluloid. We demonstrate that celluloid clusters mutations with high precision: never pairing too many mutations that are unrelated in the ground truth, but also obtains accurate results in terms of the phylogeny inferred downstream from the reduced instance produced by this method.Finally, we demonstrate the usefulness of a clustering step by applying the entire pipeline (clustering + inference method) to a real dataset, showing a significant reduction in the runtime, raising considerably the upper bound on the size of SCS instances which can be solved in practice.AvailabilityOur approach, celluloid: clustering single cell sequencing data around centroids is available at https://github.com/AlgoLab/celluloid/ under an MIT license.

scholarly journals K-mer counting with low memory consumption enables fast clustering of single-cell sequencing data without read alignment

2019 ◽  
Author(s):  
Christina Huan Shi ◽  
Kevin Y. Yip
Keyword(s):  
Single Cell ◽  
State Of The Art ◽  
Rna Seq ◽  
Sequencing Data ◽  
Memory Consumption ◽  
Analysis Pipeline ◽  
Cell Clusters ◽  
Single Cell Sequencing ◽  
Sequencing Errors ◽  
Full Analysis

AbstractK-mer counting has many applications in sequencing data processing and analysis. However, sequencing errors can produce many false k-mers that substantially increase the memory requirement during counting. We propose a fast k-mer counting method, CQF-deNoise, which has a novel component for dynamically identifying and removing false k-mers while preserving counting accuracy. Compared with four state-of-the-art k-mer counting methods, CQF-deNoise consumed 49-76% less memory than the second best method, but still ran competitively fast. The k-mer counts from CQF-deNoise produced cell clusters from single-cell RNA-seq data highly consistent with CellRanger but required only 5% of the running time at the same memory consumption, suggesting that CQF-deNoise can be used for a preview of cell clusters for an early detection of potential data problems, before running a much more time-consuming full analysis pipeline.

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