Computational approaches for inferring tumor evolution from single-cell genomic data

Abstract Genomic heterogeneity in tumors results from mutations and selection of high-fitness single cells, the operational components of evolution. Precise knowledge about mutational heterogeneity and evolutionary trajectory of a tumor can provide useful insights into predicting cancer progression and designing personalized treatment. The rapidly advancing field of single-cell genomics provides an opportunity to study tumor heterogeneity and evolution at the ultimate level of resolution. In this review, we present an overview of the state-of-the-art single-cell DNA sequencing methods, technical errors that are inherent in the resulting large-scale datasets, and computational methods to overcome these errors. Finally, we discuss the computational and mathematical approaches for understanding intratumor heterogeneity and cancer evolution at the resolution of a single cell.

[1]  A. Bashashati,et al.  Integrative analysis of genome-wide loss of heterozygosity and monoallelic expression at nucleotide resolution reveals disrupted pathways in triple-negative breast cancer , 2012, Genome research.

[2]  A. Bouchard-Côté,et al.  PyClone: statistical inference of clonal population structure in cancer , 2014, Nature Methods.

[3]  C. Maley,et al.  Cancer is a disease of clonal evolution within the body1–3. This has profound clinical implications for neoplastic progression, cancer prevention and cancer therapy. Although the idea of cancer as an evolutionary problem , 2006 .

[4]  Miles A. Miller,et al.  Imaging of anticancer drug action in single cells , 2017, Nature Reviews Cancer.

[5]  Sijia Lu,et al.  Single-Cell Whole-Genome Amplification and Sequencing: Methodology and Applications. , 2015, Annual review of genomics and human genetics.

[6]  Peter Van Loo,et al.  Single cell analysis of cancer genomes. , 2014, Current opinion in genetics & development.

[7]  Russell Schwartz,et al.  Reference-free inference of tumor phylogenies from single-cell sequencing data , 2014, ICCABS.

[8]  Peter J. Campbell,et al.  Evolution of the cancer genome , 2012, Nature Reviews Genetics.

[9]  Michael C. Hout,et al.  Multidimensional Scaling , 2003, Encyclopedic Dictionary of Archaeology.

[10]  Nevenka Dimitrova,et al.  Optimizing sparse sequencing of single cells for highly multiplex copy number profiling , 2015, Genome research.

[11]  Joshua F. McMichael,et al.  Visualizing tumor evolution with the fishplot package for R , 2016, bioRxiv.

[12]  Klaus Peter Schliep,et al.  phangorn: phylogenetic analysis in R , 2010, Bioinform..

[13]  N. Navin,et al.  Clonal Evolution in Breast Cancer Revealed by Single Nucleus Genome Sequencing , 2014, Nature.

[14]  Susan Done,et al.  Whole-Genome Amplification by Degenerate Oligonucleotide Primed PCR (DOP-PCR). , 2008, CSH protocols.

[15]  Meng Zhang,et al.  Quantitative assessment of single-cell whole genome amplification methods for detecting copy number variation using hippocampal neurons , 2015, Scientific Reports.

[16]  Ting Wang,et al.  Comparison of variations detection between whole-genome amplification methods used in single-cell resequencing , 2015, GigaScience.

[17]  Michael C. Schatz,et al.  Interactive analysis and assessment of single-cell copy-number variations , 2015, Nature Methods.

[18]  Benjamin J. Raphael,et al.  A statistical test on single-cell data reveals widespread recurrent mutations in tumor evolution , 2016, bioRxiv.

[19]  X. Xie,et al.  Single-cell whole-genome analyses by Linear Amplification via Transposon Insertion (LIANTI) , 2017, Science.

[20]  Krishnendu Chatterjee,et al.  Reconstructing metastatic seeding patterns of human cancers , 2017, Nature Communications.

[21]  Christopher J. Lee,et al.  Wagner and Dollo: a stochastic duet by composing two parsimonious solos. , 2008, Systematic biology.

[22]  Alexander Davis,et al.  Computing tumor trees from single cells , 2016, Genome Biology.

[23]  Carlo C. Maley,et al.  Clonal evolution in cancer , 2012, Nature.

[24]  Dan Gusfield.  Algorithms on Strings, Trees, and Sequences - Computer Science and Computational Biology , 1997 .

[25]  G. Nolan,et al.  Mass Cytometry: Single Cells, Many Features , 2016, Cell.

[26]  N. Saitou,et al.  The neighbor-joining method: a new method for reconstructing phylogenetic trees. , 1987, Molecular biology and evolution.

[27]  Xuemei Lu,et al.  Extremely high genetic diversity in a single tumor points to prevalence of non-Darwinian cell evolution , 2015, Proceedings of the National Academy of Sciences.

[28]  Samuel Aparicio,et al.  Scalable whole-genome single-cell library preparation without preamplification , 2017, Nature Methods.

[29]  Masahito Hosokawa,et al.  Massively parallel whole genome amplification for single-cell sequencing using droplet microfluidics , 2017, Scientific Reports.

[30]  N. Carter,et al.  Degenerate oligonucleotide-primed PCR: general amplification of target DNA by a single degenerate primer. , 1992, Genomics.

[31]  J. Troge,et al.  Tumour evolution inferred by single-cell sequencing , 2011, Nature.

[32]  Florian Markowetz,et al.  OncoNEM: inferring tumor evolution from single-cell sequencing data , 2016, Genome Biology.

[33]  Huanming Yang,et al.  Single-Cell Exome Sequencing and Monoclonal Evolution of a JAK2-Negative Myeloproliferative Neoplasm , 2012, Cell.

[34]  R. Gillies,et al.  Evolutionary dynamics of carcinogenesis and why targeted therapy does not work , 2012, Nature Reviews Cancer.

[35]  Irmtraud M. Meyer,et al.  The clonal and mutational evolution spectrum of primary triple-negative breast cancers , 2012, Nature.

[36]  Jeff Gore,et al.  Turning ecology and evolution against cancer , 2014, Nature Reviews Cancer.

[37]  W. Koh,et al.  Dissecting the clonal origins of childhood acute lymphoblastic leukemia by single-cell genomics , 2014, Proceedings of the National Academy of Sciences.

[38]  Christopher A. Miller,et al.  Clonal Architecture of Secondary Acute Myeloid Leukemia Defined by Single-Cell Sequencing , 2014, PLoS genetics.

[39]  S. Weissman,et al.  Evolution and heterogeneity of non-hereditary colorectal cancer revealed by single-cell exome sequencing , 2017, Oncogene.

[40]  W. Koh,et al.  Single-cell genome sequencing: current state of the science , 2016, Nature Reviews Genetics.

[41]  Ali Bashashati,et al.  Divergent modes of clonal spread and intraperitoneal mixing in high-grade serous ovarian cancer , 2016, Nature Genetics.

[42]  Martin A. Nowak,et al.  Mutations driving CLL and their evolution in progression and relapse , 2015, Nature.

[43]  Siddharth S. Dey,et al.  Integrated genome and transcriptome sequencing from the same cell , 2014, Nature Biotechnology.

[44]  Gyan Bhanot,et al.  Single Cell Profiling of Circulating Tumor Cells: Transcriptional Heterogeneity and Diversity from Breast Cancer Cell Lines , 2012, PloS one.

[45]  Benjamin J. Raphael,et al.  Inferring the Mutational History of a Tumor Using Multi-state Perfect Phylogeny Mixtures. , 2016, Cell systems.

[46]  Michael Wigler,et al.  Genome-wide copy number analysis of single cells , 2012, Nature Protocols.

[47]  Nancy R. Zhang,et al.  Assessing intratumor heterogeneity and tracking longitudinal and spatial clonal evolutionary history by next-generation sequencing , 2016, Proceedings of the National Academy of Sciences.

[48]  Jian Wang,et al.  Discovery of biclonal origin and a novel oncogene SLC12A5 in colon cancer by single-cell sequencing , 2014, Cell Research.

[49]  Jeff E. Mold,et al.  Comparison of whole genome amplification techniques for human single cell exome sequencing , 2017, PloS one.

[50]  Kun Zhang,et al.  Massively parallel polymerase cloning and genome sequencing of single cells using nanoliter microwells , 2013, Nature Biotechnology.

[51]  Ali Bashashati,et al.  Robust high-performance nanoliter-volume single-cell multiple displacement amplification on planar substrates , 2016, Proceedings of the National Academy of Sciences.

[52]  Huanming Yang,et al.  Single-Cell Exome Sequencing Reveals Single-Nucleotide Mutation Characteristics of a Kidney Tumor , 2012, Cell.

[53]  Angus M. Sidore,et al.  Enhanced sequencing coverage with digital droplet multiple displacement amplification , 2015, Nucleic acids research.

[54]  Giulio Caravagna,et al.  Learning mutational graphs of individual tumor evolution from multi-sample sequencing data , 2017, bioRxiv.

[55]  N. McGranahan,et al.  The causes and consequences of genetic heterogeneity in cancer evolution , 2013, Nature.

[56]  Tae-Min Kim,et al.  Subclonal Genomic Architectures of Primary and Metastatic Colorectal Cancer Based on Intratumoral Genetic Heterogeneity , 2015, Clinical Cancer Research.

[57]  Geoff K. Nicholls,et al.  Missing data in a stochastic Dollo model for binary trait data, and its application to the dating of Proto‐Indo‐European , 2011 .

[58]  N. Navin,et al.  SNES: single nucleus exome sequencing , 2015, Genome Biology.

[59]  S. Kingsmore,et al.  Comprehensive human genome amplification using multiple displacement amplification , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[60]  M. Wigler,et al.  Circular binary segmentation for the analysis of array-based DNA copy number data. , 2004, Biostatistics.

[61]  Robert R. Sokal,et al.  A statistical method for evaluating systematic relationships , 1958 .

[62]  G Medoro,et al.  Levitation and movement of human tumor cells using a printed circuit board device based on software-controlled dielectrophoresis. , 2003, Biotechnology and bioengineering.

[63]  M. DePristo,et al.  A framework for variation discovery and genotyping using next-generation DNA sequencing data , 2011, Nature Genetics.

[64]  David Y. Zhang,et al.  Ramification Amplification: A Novel Isothermal DNA Amplification Method , 2001, Molecular Diagnosis.

[65]  Kevin A. Pelphrey,et al.  Genome-Wide Detection of Single-Nucleotide and Copy-Number Variations of a Single Human Cell , 2012 .

[66]  Yu Cao,et al.  Intratumor heterogeneity in localized lung adenocarcinomas delineated by multiregion sequencing , 2014, Science.

[67]  Andrew C. Adey,et al.  Sequencing thousands of single-cell genomes with combinatorial indexing , 2017 .

[68]  C. Ponting,et al.  Single-Cell Multiomics: Multiple Measurements from Single Cells , 2017, Trends in genetics : TIG.

[69]  Alison Stopeck,et al.  Circulating tumor cells, disease progression, and survival in metastatic breast cancer. , 2004, The New England journal of medicine.

[70]  Benjamin J. Raphael,et al.  Mutational landscape and significance across 12 major cancer types , 2013, Nature.

[71]  Yong Wang,et al.  Single-cell DNA sequencing reveals a late-dissemination model in metastatic colorectal cancer , 2017, Genome research.

[72]  M. Glickman,et al.  Converting Cancer Therapies into Cures: Lessons from Infectious Diseases , 2012, Cell.

[73]  P. Campbell,et al.  Single-cell mutational profiling and clonal phylogeny in cancer , 2013, Genome research.

[74]  Richard Simon,et al.  Using single cell sequencing data to model the evolutionary history of a tumor , 2014, BMC Bioinformatics.

[75]  Sijia Lu,et al.  Uniform and accurate single-cell sequencing based on emulsion whole-genome amplification , 2015, Proceedings of the National Academy of Sciences.

[76]  F. Tang,et al.  Single-cell multi-omics sequencing of mouse early embryos and embryonic stem cells , 2017, Cell Research.

[77]  Andrew Menzies,et al.  Subclonal diversification of primary breast cancer revealed by multiregion sequencing , 2015, Nature Medicine.

[78]  James Hicks,et al.  Unravelling biology and shifting paradigms in cancer with single-cell sequencing , 2017, Nature Reviews Cancer.

[79]  M. Tomasson.  Cancer stem cells: A guide for skeptics , 2009, Journal of cellular biochemistry.

[80]  Michael I. Jordan,et al.  Tree-Structured Stick Breaking for Hierarchical Data , 2010, NIPS.

[81]  B. Vogelstein,et al.  A genetic model for colorectal tumorigenesis , 1990, Cell.

[82]  C. Curtis,et al.  A Big Bang model of human colorectal tumor growth , 2015, Nature Genetics.

[83]  P. Nowell.  The clonal evolution of tumor cell populations. , 1976, Science.

[84]  N. Navin.  Cancer genomics: one cell at a time , 2014, Genome Biology.

[85]  D. Posada,et al.  Multiregional Tumor Trees Are Not Phylogenies , 2017, Trends in cancer.

[86]  K. Kinzler,et al.  Cancer Genome Landscapes , 2013, Science.

[87]  P. A. Futreal,et al.  Genomic architecture and evolution of clear cell renal cell carcinomas defined by multiregion sequencing , 2014, Nature Genetics.

[88]  Xun Xu,et al.  A Single Cell Level Based Method for Copy Number Variation Analysis by Low Coverage Massively Parallel Sequencing , 2013, PloS one.

[89]  Nicholas Navin,et al.  Tumor evolution: Linear, branching, neutral or punctuated? , 2017, Biochimica et biophysica acta. Reviews on cancer.

[90]  Sohrab P. Shah,et al.  Dynamics of genomic clones in breast cancer patient xenografts at single-cell resolution , 2014, Nature.

[91]  Ken Chen,et al.  SiFit: inferring tumor trees from single-cell sequencing data under finite-sites models , 2017, Genome Biology.

[92]  J. Uhm.  Comprehensive genomic characterization defines human glioblastoma genes and core pathways , 2009 .

[93]  M. Stratton,et al.  The cancer genome , 2009, Nature.

[94]  Zhigang Xue,et al.  Simultaneous profiling of transcriptome and DNA methylome from a single cell , 2016, Genome Biology.

[95]  Shankar Vembu,et al.  PhyloWGS: Reconstructing subclonal composition and evolution from whole-genome sequencing of tumors , 2015, Genome Biology.

[96]  R. Arceci.  Intratumor Heterogeneity and Branched Evolution Revealed by Multiregion Sequencing , 2012 .

[97]  C. Ponting,et al.  Parallel single-cell sequencing links transcriptional and epigenetic heterogeneity , 2015, Nature Methods.

[98]  X. Qian,et al.  Laser capture microdissection for analysis of single cells. , 2007, Methods in molecular medicine.

[99]  Sergey I. Nikolenko,et al.  BayesHammer: Bayesian clustering for error correction in single-cell sequencing , 2012, BMC Genomics.

[100]  Y. Moreau,et al.  Single-cell copy number variation detection , 2011, Genome Biology.

[101]  N. Navin,et al.  Highly multiplexed targeted DNA sequencing from single nuclei , 2016, Nature Protocols.

[102]  Charles Gawad,et al.  A Quantitative Comparison of Single-Cell Whole Genome Amplification Methods , 2014, PloS one.

[103]  F. Dean,et al.  Rapid amplification of plasmid and phage DNA using Phi 29 DNA polymerase and multiply-primed rolling circle amplification. , 2001, Genome research.

[104]  Donna Neuberg,et al.  Integrated single-cell genetic and transcriptional analysis suggests novel drivers of chronic lymphocytic leukemia , 2017, Genome research.

[105]  Niko Beerenwinkel,et al.  BitPhylogeny: a probabilistic framework for reconstructing intra-tumor phylogenies , 2015, Genome Biology.

[106]  Evan Z. Macosko,et al.  Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets , 2015, Cell.

[107]  A. Sivachenko,et al.  Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples , 2013, Nature Biotechnology.

[108]  Angelika Amon,et al.  Assessment of megabase-scale somatic copy number variation using single-cell sequencing , 2016, Genome research.

[109]  Alexandre Bouchard-Côté,et al.  ddClone: joint statistical inference of clonal populations from single cell and bulk tumour sequencing data , 2017, Genome Biology.

[110]  R G HAM,et al.  CLONAL GROWTH OF MAMMALIAN CELLS IN A CHEMICALLY DEFINED, SYNTHETIC MEDIUM. , 1965, Proceedings of the National Academy of Sciences of the United States of America.

[111]  Huanming Yang,et al.  Single-cell sequencing analysis characterizes common and cell-lineage-specific mutations in a muscle-invasive bladder cancer , 2012, GigaScience.

[112]  Ian T. Jolliffe,et al.  Principal Component Analysis , 2002, International Encyclopedia of Statistical Science.

[113]  Marc J. Williams,et al.  Identification of neutral tumor evolution across cancer types , 2016, Nature Genetics.

[114]  Ning Zhang,et al.  Single-cell sequencing deciphers a convergent evolution of copy number alterations from primary to circulating tumor cells , 2017, Genome research.

[115]  N. Beerenwinkel,et al.  Advances in understanding tumour evolution through single-cell sequencing* , 2017, Biochimica et biophysica acta. Reviews on cancer.

[116]  C. Ponting,et al.  G&T-seq: parallel sequencing of single-cell genomes and transcriptomes , 2015, Nature Methods.

[117]  Obi L. Griffith,et al.  SciClone: Inferring Clonal Architecture and Tracking the Spatial and Temporal Patterns of Tumor Evolution , 2014, PLoS Comput. Biol..

[118]  N. Navin,et al.  Advances and applications of single-cell sequencing technologies. , 2015, Molecular cell.

[119]  Mehmet Toner,et al.  Circulating tumor cells: approaches to isolation and characterization , 2011, The Journal of cell biology.

[120]  J Christopher Love,et al.  Development and optimization of a process for automated recovery of single cells identified by microengraving , 2010, Biotechnology progress.

[121]  Maxim Teslenko,et al.  MrBayes 3.2: Efficient Bayesian Phylogenetic Inference and Model Choice Across a Large Model Space , 2012, Systematic biology.

[122]  Alexandre Bouchard-Côté,et al.  Clonal genotype and population structure inference from single-cell tumor sequencing , 2016, Nature Methods.

[123]  Gouri Nanjangud,et al.  Whole-genome single-cell copy number profiling from formalin-fixed paraffin-embedded samples , 2017, Nature Medicine.

[124]  Benjamin J. Raphael,et al.  Reconstruction of clonal trees and tumor composition from multi-sample sequencing data , 2015, Bioinform..

[125]  Heng Li,et al.  A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data , 2011, Bioinform..

[126]  Ken Chen,et al.  Monovar: single nucleotide variant detection in single cells , 2016, Nature Methods.

[127]  Kenric Leung,et al.  The Life History of 21 Breast Cancers , 2015, Cell.

[128]  Funda Meric-Bernstam,et al.  Punctuated Copy Number Evolution and Clonal Stasis in Triple-Negative Breast Cancer , 2016, Nature Genetics.

[129]  Jack Kuipers,et al.  Tree inference for single-cell data , 2016 .

[130]  Matthew Meyerson,et al.  Calibrating genomic and allelic coverage bias in single-cell sequencing , 2015, Nature Communications.