scholarly journals PhISCS - A Combinatorial Approach for Sub-perfect Tumor Phylogeny Reconstruction via Integrative use of Single Cell and Bulk Sequencing Data

2018 ◽  
Author(s):  
Salem Malikic ◽  
Simone Ciccolella ◽  
Farid Rashidi Mehrabadi ◽  
Camir Ricketts ◽  
Khaledur Rahman ◽  
...  
Keyword(s):  
Single Cell ◽  
Phylogeny Reconstruction ◽  
Tumor Evolution ◽  
Sequencing Data ◽  
Perfect Phylogeny ◽  
Sequence Coverage ◽  
Allele Dropout ◽  
Single Cell Sequencing ◽  
First Time ◽  
Tumor Phylogeny

AbstractRecent technological advances in single cell sequencing (SCS) provide high resolution data for studying intra-tumor heterogeneity and tumor evolution. Available computational methods for tumor phylogeny inference via SCS typically aim to identify the most likelyperfect phylogeny treesatisfyinginfinite sites assumption(ISA). However limitations of SCS technologies such as frequent allele dropout or highly variable sequence coverage, commonly result in mutational call errors and prohibit a perfect phylogeny. In addition, ISA violations are commonly observed in tumor phylogenies due to the loss of heterozygosity, deletions and convergent evolution. In order to address such limitations, we, for the first time, introduce a new combinatorial formulation that integrates single cell sequencing data with matching bulk sequencing data, with the objective of minimizing a linear combination of (i) potential false negatives (due to e.g. allele dropout or variance in sequence coverage) and (ii) potential false positives (due to e.g. read errors) among mutation calls, as well as (iii) the number of mutations that violate ISA - to define theoptimal sub-perfect phylogeny.Our formulation ensures that several lineage constraints imposed by the use of variant allele frequencies (VAFs, derived from bulk sequence data) are satisfied. We express our formulation both in the form of an integer linear program (ILP) and - for the first time in the context of tumor phylogeny reconstruction - a boolean constraint satisfaction problem (CSP) and solve them by leveraging state-of-the-art ILP/CSP solvers. The resulting method, which we name PhISCS, is the first to integrate SCS and bulk sequencing data under the finite sites model. Using several simulated and real SCS data sets, we demonstrate that PhISCS is not only more general but also more accurate than the alternative tumor phylogeny inference tools. PhISCS is very fast especially when its CSP based variant is used returns the optimal solution, except in rare instances for which it provides an optimality gap. PhISCS is available athttps://github.com/haghshenas/PhISCS.

scholarly journals Profiles of expressed mutations in single cells reveal patterns of tumor evolution and therapeutic impact of intratumor heterogeneity

2021 ◽  
Author(s):  
Farid Rashidi Mehrabadi ◽  
Kerrie L. Marie ◽  
Eva Perez-Guijarro ◽  
Salem Malikic ◽  
Erfan Sadeqi Azer ◽  
...  
Keyword(s):  
Single Cell ◽  
Expression Profiles ◽  
Single Cells ◽  
Phylogeny Reconstruction ◽  
Intratumor Heterogeneity ◽  
Tumor Evolution ◽  
Sequencing Data ◽  
Genomic Alterations ◽  
Reconstruction Methods ◽  
Tumor Phylogeny

Advances in single cell RNA sequencing (scRNAseq) technologies uncovered an unexpected complexity in solid tumors, underlining the relevance of intratumor heterogeneity for cancer progression and therapeutic resistance. Heterogeneity in the mutational composition of cancer cells is well captured by tumor phylogenies, which demonstrate how distinct cell populations evolve, and, e.g. develop metastatic potential or resistance to specific treatments. Unfortunately, because of their low read coverage per cell, mutation calls that can be made from scRNAseq data are very sparse and noisy. Additionally, available tumor phylogeny reconstruction methods cannot computationally handle a large number of cells and mutations present in typical scRNAseq datasets. Finally, there are no principled methods to assess distinct subclones observed in inferred tumor phylogenies and the genomic alterations that seed them. Here we present Trisicell, a computational toolkit for scalable tumor phylogeny reconstruction and evaluation from scRNAseq as well as single cell genome or exome sequencing data. Trisicell allows the identification of reliable subtrees of a tumor phylogeny, offering the ability to focus on the most important subclones and the genomic alterations that are associated with subclonal proliferation. We comprehensively assessed Trisicell on a melanoma model by comparing the phylogeny it builds using scRNAseq data, to those using matching bulk whole exome (bWES) and transcriptome (bWTS) sequencing data from clonal sublines derived from single cells. Our results demonstrate that tumor phylogenies based on mutation calls from scRNAseq data can be robustly inferred and evaluated by Trisicell. We also applied Trisicell to reconstruct and evaluate the phylogeny it builds using scRNAseq data from melanomas of the same mouse model after treatment with immune checkpoint blockade (ICB). After integratively analyzing our cell-specific mutation calls with their expression profiles, we observed that each subclone with a distinct set of novel somatic mutations is strongly associated with a distinct developmental status. Moreover, each subclone had developed a specific ICB-resistance mechanism. These results demonstrate that Trisicell can robustly utilize scRNAseq data to delineate intratumoral heterogeneity and tumor evolution.

scholarly journals Tumor Phylogeny Topology Inference via Deep Learning

2020 ◽  
Author(s):  
Erfan Sadeqi Azer ◽  
Mohammad Haghir Ebrahimabadi ◽  
Salem Malikić ◽  
Roni Khardon ◽  
S. Cenk Sahinalp
Keyword(s):  
Deep Learning ◽  
Single Cells ◽  
Linear Chain ◽  
Optimization Techniques ◽  
Phylogeny Reconstruction ◽  
Tumor Evolution ◽  
Perfect Phylogeny ◽  
Single Cell Sequencing ◽  
Topological Features ◽  
Tumor Phylogeny

SummaryPrincipled computational approaches for tumor phylogeny reconstruction via single-cell sequencing typically aim to build the most likely perfect phylogeny tree from the noisy genotype matrix - which represents genotype calls of single-cells. This problem is NP-hard, and as a result, existing approaches aim to solve relatively small instances of it through combinatorial optimization techniques or Bayesian inference. As expected, even when the goal is to infer basic topological features of the tumor phylogeny - rather than reconstructing the topology entirely, these approaches could be prohibitively slow. In this paper, we introduce fast deep-learning solutions to the problems of inferring whether the most likely tree has a linear (chain) or branching topology and whether a perfect phylogeny is feasible from a given genotype matrix. We also present a reinforcement learning approach for reconstructing the most likely tumor phylogeny. This preliminary work demonstrates that data-driven approaches can reconstruct key features of tumor evolution.

scholarly journals gpps: an ILP-based approach for inferring cancer progression with mutation losses from single cell data

2020 ◽  
Vol 21 (S1) ◽  
Author(s):  
Simone Ciccolella ◽  
Mauricio Soto Gomez ◽  
Murray D. Patterson ◽  
Gianluca Della Vedova ◽  
Iman Hajirasouliha ◽  
...  
Keyword(s):  
Single Cell ◽  
Cancer Progression ◽  
False Negative ◽  
Fixed Number ◽  
Phylogeny Reconstruction ◽  
Sequencing Data ◽  
Progression Model ◽  
Great Specificity ◽  
Cell Data ◽  
Tumor Phylogeny

Abstract Background Cancer progression reconstruction is an important development stemming from the phylogenetics field. In this context, the reconstruction of the phylogeny representing the evolutionary history presents some peculiar aspects that depend on the technology used to obtain the data to analyze: Single Cell DNA Sequencing data have great specificity, but are affected by moderate false negative and missing value rates. Moreover, there has been some recent evidence of back mutations in cancer: this phenomenon is currently widely ignored. Results We present a new tool, , that reconstructs a tumor phylogeny from Single Cell Sequencing data, allowing each mutation to be lost at most a fixed number of times. The General Parsimony Phylogeny from Single cell () tool is open source and available at https://github.com/AlgoLab/gpps. Conclusions provides new insights to the analysis of intra-tumor heterogeneity by proposing a new progression model to the field of cancer phylogeny reconstruction on Single Cell data.

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 A statistical test on single-cell data reveals widespread recurrent mutations in tumor evolution

2016 ◽  
Author(s):  
Jack Kuipers ◽  
Katharina Jahn ◽  
Benjamin J. Raphael ◽  
Niko Beerenwinkel
Keyword(s):  
Single Cell ◽  
Large Scale ◽  
Tumor Evolution ◽  
Sequencing Data ◽  
General Validity ◽  
Genomic Deletions ◽  
Single Cell Sequencing ◽  
Statistical Framework ◽  
Recurrent Mutations ◽  
Complex Models

The infinite sites assumption, which states that every genomic position mutates at most once over the lifetime of a tumor, is central to current approaches for reconstructing mutation histories of tumors, but has never been tested explicitly. We developed a rigorous statistical framework to test the assumption with single-cell sequencing data. The framework accounts for the high noise and contamination present in such data. We found strong evidence for recurrent mutations at the same site in 8 out of 9 single-cell sequencing datasets from human tumors. Six cases involved the loss of earlier mutations, five of which occurred at sites unaffected by large scale genomic deletions. Two cases exhibited parallel mutation, including the dataset with the strongest evidence of recurrence. Our results refute the general validity of the infinite sites assumption and indicate that more complex models are needed to adequately quantify intra-tumor heterogeneity.

scholarly journals Studying the History of Tumor Evolution from Single-Cell Sequencing Data by Exploring the Space of Binary Matrices

2021 ◽  
Author(s):  
Salem Malikić ◽  
Farid Rashidi Mehrabadi ◽  
Erfan Sadeqi Azer ◽  
Mohammad Haghir Ebrahimabadi ◽  
Suleyman Cenk Sahinalp
Keyword(s):  
Single Cell ◽  
Tumor Evolution ◽  
Sequencing Data ◽  
Single Cell Sequencing ◽  
History Of ◽  
Binary Matrices

scholarly journals Studying the history of tumor evolution from single-cell sequencing data by exploring the space of binary matrices

2020 ◽  
Author(s):  
Salem Malikić ◽  
Farid Rashidi Mehrabadi ◽  
Erfan Sadeqi Azer ◽  
Mohammad Haghir Ebrahimabadi ◽  
S. Cenk Sahinalp
Keyword(s):  
Single Cell ◽  
Evolutionary History ◽  
Tumor Evolution ◽  
Sequencing Data ◽  
Single Cell Sequencing ◽  
Linear Programming Formulation ◽  
History Of ◽  
Binary Matrices ◽  
Constraint Satisfaction Programming ◽  
Integer Linear Programming Formulation

AbstractSingle-cell sequencing data has great potential in reconstructing the evolutionary history of tumors. Rapid advances in single-cell sequencing technology in the past decade were followed by the design of various computational methods for inferring trees of tumor evolution. Some of the earliest of these methods were based on the direct search in the space of trees. However, it can be shown that instead of this tree search strategy we can perform a search in the space of binary matrices and obtain the most likely tree directly from the most likely among the candidate binary matrices. The search in the space of binary matrices can be expressed as an instance of integer linear or constraint satisfaction programming and solved by some of the available solvers, which typically provide a guarantee of optimality of the reported solution. In this review, we first describe one convenient tree representation of tumor evolutionary history and present tree scoring model that is most commonly used in the available methods. We then provide proof showing that the most likely tree of tumor evolution can be obtained directly from the most likely matrix from the space of candidate binary matrices. Next, we provide integer linear programming formulation to search for such matrix and summarize the existing methods based on this formulation or its extensions. Lastly, we present one use-case which illustrates how binary matrices can be used as a basis for developing a fast deep learning method for inferring some topological properties of the most likely tree of tumor evolution.

scholarly journals Tumor Copy Number Deconvolution Integrating Bulk and Single-Cell Sequencing Data

2019 ◽  
Author(s):  
Haoyun Lei ◽  
Bochuan Lyu ◽  
E. Michael Gertz ◽  
Alejandro A. Schäffer ◽  
Xulian Shi ◽  
...  
Keyword(s):  
Single Cell ◽  
Copy Number ◽  
Simulated Data ◽  
Mixed Integer ◽  
Intratumor Heterogeneity ◽  
Tumor Evolution ◽  
Sequencing Data ◽  
Minimum Evolution ◽  
Promising Alternative ◽  
Single Cell Sequencing

AbstractCharacterizing intratumor heterogeneity (ITH) is crucial to understanding cancer development, but it is hampered by limits of available data sources. Bulk DNA sequencing is the most common technology to assess ITH, but mixes many genetically distinct cells in each sample, which must then be computationally deconvolved. Single-cell sequencing (SCS) is a promising alternative, but its limitations — e.g., high noise, difficulty scaling to large populations, technical artifacts, and large data sets — have so far made it impractical for studying cohorts of sufficient size to identify statistically robust features of tumor evolution. We have developed strategies for deconvolution and tumor phylogenetics combining limited amounts of bulk and single-cell data to gain some advantages of single-cell resolution with much lower cost, with specific focus on deconvolving genomic copy number data. We developed a mixed membership model for clonal deconvolution via non-negative matrix factorization (NMF) balancing deconvolution quality with similarity to single-cell samples via an associated efficient coordinate descent algorithm. We then improve on that algorithm by integrating deconvolution with clonal phylogeny inference, using a mixed integer linear programming (MILP) model to incorporate a minimum evolution phylogenetic tree cost in the problem objective. We demonstrate the effectiveness of these methods on semi-simulated data of known ground truth, showing improved deconvolution accuracy relative to bulk data alone.

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