Cancer represents an evolutionary process through which growing malignant populations genetically diversify, leading to tumour progression, relapse and resistance to therapy. In addition to genetic diversity, the cell-to-cell variation that fuels evolutionary selection also manifests in cellular states, epigenetic profiles, spatial distributions and interactions with the microenvironment. Therefore, the study of cancer requires the integration of multiple heritable dimensions at the resolution of the single cell — the atomic unit of somatic evolution. In this Review, we discuss emerging analytic and experimental technologies for single-cell multi-omics that enable the capture and integration of multiple data modalities to inform the study of cancer evolution. These data show that cancer results from a complex interplay between genetic and non-genetic determinants of somatic evolution.
Subscribe to Journal
Get full journal access for 1 year
only $21.58 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Rent or Buy article
Get time limited or full article access on ReadCube.
All prices are NET prices.
Greaves, M. & Maley, C. C. Clonal evolution in cancer. Nature 481, 306–313 (2012).
Duffy, T. P. Portraits of an illness. Trans. Am. Clin. Climatol. Assoc. 120, 209–225 (2009).
Turajlic, S., Sottoriva, A., Graham, T. & Swanton, C. Resolving genetic heterogeneity in cancer. Nat. Rev. Genet. 20, 404–416 (2019).
Martincorena, I. et al. Somatic mutant clones colonize the human esophagus with age. Science 362, 911–917 (2018).
Yizhak, K. et al. RNA sequence analysis reveals macroscopic somatic clonal expansion across normal tissues. Science 364, eaaw0726 (2019).
Yokoyama, A. et al. Age-related remodelling of oesophageal epithelia by mutated cancer drivers. Nature 565, 312–317 (2019).
Martincorena, I. et al. Tumor evolution. High burden and pervasive positive selection of somatic mutations in normal human skin. Science 348, 880–886 (2015). This study demonstrated the ubiquitous nature and positive selection of somatic mutations in cancer driver genes in normal skin tissue.
Yoshida, K. et al. Tobacco smoking and somatic mutations in human bronchial epithelium. Nature 578, 266–272 (2020).
Teixeira, V. H. et al. Deciphering the genomic, epigenomic, and transcriptomic landscapes of pre-invasive lung cancer lesions. Nat. Med. 25, 517–525 (2019).
Kaufman, C. K. et al. A zebrafish melanoma model reveals emergence of neural crest identity during melanoma initiation. Science 351, aad2197 (2016). This work showed that somatic drivers of melanoma, when superimposed on a progenitor cell state, induced malignant melanoma, highlighting the significance of cell state for tumorigenesis.
Shaffer, S. M. et al. Rare cell variability and drug-induced reprogramming as a mode of cancer drug resistance. Nature 546, 431–435 (2017). This study identified a transient transcriptional state in melanoma cells that leads to stable drug resistance, underscoring cell state heterogeneity as a key mediator of tumour evolution.
Hata, A. N. et al. Tumor cells can follow distinct evolutionary paths to become resistant to epidermal growth factor receptor inhibition. Nat. Med. 22, 262–269 (2016).
Stuart, T. & Satija, R. Integrative single-cell analysis. Nat. Rev. Genet. 20, 257–272 (2019).
Landau, D. A. et al. Evolution and impact of subclonal mutations in chronic lymphocytic leukemia. Cell 152, 714–726 (2013).
Gerstung, M. et al. The evolutionary history of 2,658 cancers. Nature 578, 122–128 (2020).
Papaemmanuil, E. et al. Genomic classification and prognosis in acute myeloid leukemia. N. Engl. J. Med. 374, 2209–2221 (2016).
Landau, D. A. et al. Mutations driving CLL and their evolution in progression and relapse. Nature 526, 525–530 (2015).
Turajlic, S. et al. Tracking cancer evolution reveals constrained routes to metastases: TRACERx renal. Cell 173, 581–594.e12 (2018).
Turajlic, S. et al. Deterministic evolutionary trajectories influence primary tumor growth: TRACERx renal. Cell 173, 595–610.e11 (2018).
Jamal-Hanjani, M. et al. Tracking the evolution of non-small-cell lung cancer. N. Engl. J. Med. 376, 2109–2121 (2017).
Gruber, M. et al. Growth dynamics in naturally progressing chronic lymphocytic leukaemia. Nature 570, 474–479 (2019). This work demonstrated the ability to define patterns of subclonal growth rates of CLL through dense temporal sequencing.
Leshchiner, I. et al. Comprehensive analysis of tumour initiation, spatial and temporal progression under multiple lines of treatment. Preprint at bioRxiv https://doi.org/10.1101/508127 (2018).
Burger, J. A. et al. Clonal evolution in patients with chronic lymphocytic leukaemia developing resistance to BTK inhibition. Nat. Commun. 7, 11589 (2016).
Landau, D. A. et al. The evolutionary landscape of chronic lymphocytic leukemia treated with ibrutinib targeted therapy. Nat. Commun. 8, 2185 (2017).
Siravegna, G. et al. Clonal evolution and resistance to EGFR blockade in the blood of colorectal cancer patients. Nat. Med. 21, 795–801 (2015).
Abbosh, C. et al. Phylogenetic ctDNA analysis depicts early-stage lung cancer evolution. Nature 545, 446–451 (2017).
Bolan, P. O. et al. Genotype-fitness maps of EGFR-mutant lung adenocarcinoma chart the evolutionary landscape of resistance for combination therapy optimization. Cell Syst. 10, 52–65.e7 (2020).
Wedge, D. C. et al. Sequencing of prostate cancers identifies new cancer genes, routes of progression and drug targets. Nat. Genet. 50, 682–692 (2018).
Shih, D. J. H. et al. Genomic characterization of human brain metastases identifies drivers of metastatic lung adenocarcinoma. Nat. Genet. 52, 371–377 (2020).
Tsao, J. L. et al. Genetic reconstruction of individual colorectal tumor histories. Proc. Natl Acad. Sci. USA 97, 1236–1241 (2000).
Tsao, J. L., Davis, S. D., Baker, S. M., Liskay, R. M. & Shibata, D. Intestinal stem cell division and genetic diversity. A computer and experimental analysis. Am. J. Pathol. 151, 573–579 (1997).
Naxerova, K. et al. Hypermutable DNA chronicles the evolution of human colon cancer. Proc. Natl Acad. Sci. USA 111, E1889–E1898 (2014).
Reiter, J. G. et al. Lymph node metastases develop through a wider evolutionary bottleneck than distant metastases. Nat. Genet. 52, 692–700 (2020).
Gerlinger, M. et al. Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N. Engl. J. Med. 366, 883–892 (2012).
El-Kebir, M., Satas, G. & Raphael, B. J. Inferring parsimonious migration histories for metastatic cancers. Nat. Genet. 50, 718–726 (2018).
Snippert, H. J. et al. Intestinal crypt homeostasis results from neutral competition between symmetrically dividing Lgr5 stem cells. Cell 143, 134–144 (2010).
Sutherland, K. D. et al. Cell of origin of small cell lung cancer: inactivation of Trp53 and Rb1 in distinct cell types of adult mouse lung. Cancer Cell 19, 754–764 (2011).
Quintana, E. et al. Efficient tumour formation by single human melanoma cells. Nature 456, 593–598 (2008).
Bhang, H. E. et al. Studying clonal dynamics in response to cancer therapy using high-complexity barcoding. Nat. Med. 21, 440–448 (2015).
Eirew, P. et al. Dynamics of genomic clones in breast cancer patient xenografts at single-cell resolution. Nature 518, 422–426 (2015).
Nguyen, L. V. et al. DNA barcoding reveals diverse growth kinetics of human breast tumour subclones in serially passaged xenografts. Nat. Commun. 5, 5871 (2014).
Hwang, B. et al. Lineage tracing using a Cas9-deaminase barcoding system targeting endogenous L1 elements. Nat. Commun. 10, 1234 (2019).
Frieda, K. L. et al. Synthetic recording and in situ readout of lineage information in single cells. Nature 541, 107–111 (2017).
Spanjaard, B. et al. Simultaneous lineage tracing and cell-type identification using CRISPR-Cas9-induced genetic scars. Nat. Biotechnol. 36, 469–473 (2018).
Raj, B. et al. Simultaneous single-cell profiling of lineages and cell types in the vertebrate brain. Nat. Biotechnol. 36, 442–450 (2018).
McKenna, A. et al. Whole-organism lineage tracing by combinatorial and cumulative genome editing. Science 353, aaf7907 (2016).
Alemany, A., Florescu, M., Baron, C. S., Peterson-Maduro, J. & van Oudenaarden, A. Whole-organism clone tracing using single-cell sequencing. Nature 556, 108–112 (2018).
Kalhor, R. et al. Developmental barcoding of whole mouse via homing CRISPR. Science 361, eaat9804 (2018).
Woodworth, M. B., Girskis, K. M. & Walsh, C. A. Building a lineage from single cells: genetic techniques for cell lineage tracking. Nat. Rev. Genet. 18, 230–244 (2017).
Kester, L. & van Oudenaarden, A. Single-cell transcriptomics meets lineage tracing. Cell Stem Cell 23, 166–179 (2018).
Baron, C. S. & van Oudenaarden, A. Unravelling cellular relationships during development and regeneration using genetic lineage tracing. Nat. Rev. Mol. Cell Biol. 20, 753–765 (2019).
Pellegrino, M. et al. High-throughput single-cell DNA sequencing of acute myeloid leukemia tumors with droplet microfluidics. Genome Res. 28, 1345–1352 (2018).
Smith, M. A. et al. E-scape: interactive visualization of single-cell phylogenetics and cancer evolution. Nat. Methods 14, 549–550 (2017).
Navin, N. et al. Tumour evolution inferred by single-cell sequencing. Nature 472, 90–94 (2011).
Kim, C. et al. Chemoresistance evolution in triple-negative breast cancer delineated by single-cell sequencing. Cell 173, 879–893.e13 (2018).
Laks, E. et al. Clonal decomposition and DNA replication states defined by scaled single-cell genome sequencing. Cell 179, 1207–1221.e22 (2019).
Kuipers, J., Jahn, K., Raphael, B. J. & Beerenwinkel, N. Single-cell sequencing data reveal widespread recurrence and loss of mutational hits in the life histories of tumors. Genome Res. 27, 1885–1894 (2017).
Zafar, H., Tzen, A., Navin, N., Chen, K. & Nakhleh, L. SiFit: inferring tumor trees from single-cell sequencing data under finite-sites models. Genome Biol. 18, 178 (2017).
Schwartz, R. & Schaffer, A. A. The evolution of tumour phylogenetics: principles and practice. Nat. Rev. Genet. 18, 213–229 (2017).
Jahn, K., Kuipers, J. & Beerenwinkel, N. Tree inference for single-cell data. Genome Biol. 17, 86 (2016).
Davis, A. & Navin, N. E. Computing tumor trees from single cells. Genome Biol. 17, 113 (2016).
Roerink, S. F. et al. Intra-tumour diversification in colorectal cancer at the single-cell level. Nature 556, 457–462 (2018). This study utilized clonal organoids of colorectal carcinoma to chart mutational and DNAme lineage trees within tumours.
Lee-Six, H. et al. Population dynamics of normal human blood inferred from somatic mutations. Nature 561, 473–478 (2018).
Nieto, M. A., Huang, R. Y., Jackson, R. A. & Thiery, J. P. Emt: 2016. Cell 166, 21–45 (2016).
Pastushenko, I. et al. Identification of the tumour transition states occurring during EMT. Nature 556, 463–468 (2018).
McKenzie, M. D. et al. Interconversion between tumorigenic and differentiated states in acute myeloid leukemia. Cell Stem Cell 25, 258–272.e9 (2019).
Bonnet, D. & Dick, J. E. Human acute myeloid leukemia is organized as a hierarchy that originates from a primitive hematopoietic cell. Nat. Med. 3, 730–737 (1997).
Tirosh, I. et al. Single-cell RNA-seq supports a developmental hierarchy in human oligodendroglioma. Nature 539, 309–313 (2016). This work demonstrated distinct stem-like and differentiated cell states within oligodendrogliomas through scRNA-seq.
de Sousa e Melo, F. et al. A distinct role for Lgr5+ stem cells in primary and metastatic colon cancer. Nature 543, 676–680 (2017).
Shimokawa, M. et al. Visualization and targeting of LGR5+ human colon cancer stem cells. Nature 545, 187–192 (2017).
Kreso, A. & Dick, J. E. Evolution of the cancer stem cell model. Cell Stem Cell 14, 275–291 (2014).
Bell, C. C. et al. Targeting enhancer switching overcomes non-genetic drug resistance in acute myeloid leukaemia. Nat. Commun. 10, 2723 (2019).
Beltran, H. et al. The role of lineage plasticity in prostate cancer therapy resistance. Clin. Cancer Res. 25, 6916–6924 (2019).
Oser, M. G., Niederst, M. J., Sequist, L. V. & Engelman, J. A. Transformation from non-small-cell lung cancer to small-cell lung cancer: molecular drivers and cells of origin. Lancet Oncol. 16, e165–e172 (2015).
Fabbri, G. et al. Genetic lesions associated with chronic lymphocytic leukemia transformation to Richter syndrome. J. Exp. Med. 210, 2273–2288 (2013).
Tirosh, I. et al. Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq. Science 352, 189–196 (2016).
Neftel, C. et al. An integrative model of cellular states, plasticity, and genetics for glioblastoma. Cell 178, 835–849.e21 (2019).
Patel, A. P. et al. Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma. Science 344, 1396–1401 (2014).
Batlle, E. & Clevers, H. Cancer stem cells revisited. Nat. Med. 23, 1124–1134 (2017).
Ledergor, G. et al. Single cell dissection of plasma cell heterogeneity in symptomatic and asymptomatic myeloma. Nat. Med. 24, 1867–1876 (2018).
Trapnell, C. et al. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat. Biotechnol. 32, 381–386 (2014).
La Manno, G. et al. RNA velocity of single cells. Nature 560, 494–498 (2018).
Han, K. Y. et al. SIDR: simultaneous isolation and parallel sequencing of genomic DNA and total RNA from single cells. Genome Res. 28, 75–87 (2018).
Yin, Y. et al. High-throughput single-cell sequencing with linear amplification. Mol. Cell 76, 676–690.e10 (2019).
Macaulay, I. C. et al. G&T-seq: parallel sequencing of single-cell genomes and transcriptomes. Nat. Methods 12, 519–522 (2015).
Fan, J. et al. Linking transcriptional and genetic tumor heterogeneity through allele analysis of single-cell RNA-seq data. Genome Res. 28, 1217–1227 (2018).
Filbin, M. G. et al. Developmental and oncogenic programs in H3K27M gliomas dissected by single-cell RNA-seq. Science 360, 331–335 (2018).
Giustacchini, A. et al. Single-cell transcriptomics uncovers distinct molecular signatures of stem cells in chronic myeloid leukemia. Nat. Med. 23, 692–702 (2017).
Rodriguez-Meira, A. et al. Unravelling intratumoral heterogeneity through high-sensitivity single-cell mutational analysis and parallel RNA sequencing. Mol. Cell 73, 1292–1305.e8 (2019). This paper describes a novel method to capture highly sensitive somatic genotyping in plate-based scRNA-seq by targeted amplification of both genomic DNA and cDNA.
Petti, A. A. et al. A general approach for detecting expressed mutations in AML cells using single cell RNA-sequencing. Nat. Commun. 10, 3660 (2019).
van Galen, P. et al. Single-cell RNA-Seq reveals AML hierarchies relevant to disease progression and immunity. Cell 176, 1265–1281.e24 (2019).
Nam, A. S. et al. Somatic mutations and cell identity linked by genotyping of transcriptomes. Nature 571, 355–360 (2019). This work demonstrated that the transcriptional impact of somatic mutations in myeloid neoplasms varies as a function of cell state by developing a method for highly sensitive somatic genotyping in high-throughput scRNA-seq.
Izzo, F. et al. DNA methylation disruption reshapes the hematopoietic differentiation landscape. Nat. Genet. 52, 378–387 (2020).
Jaiswal, S. et al. Age-related clonal hematopoiesis associated with adverse outcomes. N. Engl. J. Med. 371, 2488–2498 (2014).
Desai, P. et al. Somatic mutations precede acute myeloid leukemia years before diagnosis. Nat. Med. 24, 1015–1023 (2018).
Jaiswal, S., Natarajan, P. & Ebert, B. L. Clonal hematopoiesis and atherosclerosis. N. Engl. J. Med. 377, 1401–1402 (2017).
Ludwig, L. S. et al. Lineage tracing in humans enabled by mitochondrial mutations and single-cell genomics. Cell 176, 1325–1339.e22 (2019). This work performed single-cell lineage tracing in human samples by interrogating mitochondrial DNA mutations within single-cell chromatin accessibility and RNA-seq data.
Stewart, J. B. & Chinnery, P. F. The dynamics of mitochondrial DNA heteroplasmy: implications for human health and disease. Nat. Rev. Genet. 16, 530–542 (2015).
Xu, J. et al. Single-cell lineage tracing by endogenous mutations enriched in transposase accessible mitochondrial DNA. eLife 8, e45105 (2019).
Shlush, L. I. et al. Cell lineage analysis of acute leukemia relapse uncovers the role of replication-rate heterogeneity and microsatellite instability. Blood 120, 603–612 (2012).
Latil, M. et al. Cell-type-specific chromatin states differentially prime squamous cell carcinoma tumor-initiating cells for epithelial to mesenchymal transition. Cell Stem Cell 20, 191–204.e5 (2017).
Hinohara, K. et al. KDM5 histone demethylase activity links cellular transcriptomic heterogeneity to therapeutic resistance. Cancer Cell 34, 939–953.e9 (2018).
Yu, V. W. C. et al. Epigenetic memory underlies cell-autonomous heterogeneous behavior of hematopoietic stem cells. Cell 167, 1310–1322.e17 (2016).
Catania, S. et al. Evolutionary persistence of DNA methylation for millions of years after ancient loss of a de novo methyltransferase. Cell 180, 263–277.e20 (2020).
Lappalainen, T. & Greally, J. M. Associating cellular epigenetic models with human phenotypes. Nat. Rev. Genet. 18, 441–451 (2017).
Cancer Genome Atlas Research Network. et al. Genomic and epigenomic landscapes of adult de novo acute myeloid leukemia. N. Engl. J. Med. 368, 2059–2074 (2013).
St Pierre, R. & Kadoch, C. Mammalian SWI/SNF complexes in cancer: emerging therapeutic opportunities. Curr. Opin. Genet. Dev. 42, 56–67 (2017).
Liau, B. B. et al. Adaptive chromatin remodeling drives glioblastoma stem cell plasticity and drug tolerance. Cell Stem Cell 20, 233–246.e7 (2017).
Beekman, R. et al. The reference epigenome and regulatory chromatin landscape of chronic lymphocytic leukemia. Nat. Med. 24, 868–880 (2018).
Martin-Subero, J. I. & Oakes, C. C. Charting the dynamic epigenome during B-cell development. Semin. Cancer Biol. 51, 139–148 (2018).
Capper, D. et al. DNA methylation-based classification of central nervous system tumours. Nature 555, 469–474 (2018).
Landan, G. et al. Epigenetic polymorphism and the stochastic formation of differentially methylated regions in normal and cancerous tissues. Nat. Genet. 44, 1207–1214 (2012).
Brocks, D. et al. Intratumor DNA methylation heterogeneity reflects clonal evolution in aggressive prostate cancer. Cell Rep. 8, 798–806 (2014).
Landau, D. A. et al. Locally disordered methylation forms the basis of intratumor methylome variation in chronic lymphocytic leukemia. Cancer Cell 26, 813–825 (2014).
Pastore, A. et al. Corrupted coordination of epigenetic modifications leads to diverging chromatin states and transcriptional heterogeneity in CLL. Nat. Commun. 10, 1874 (2019).
Flavahan, W. A. et al. Insulator dysfunction and oncogene activation in IDH mutant gliomas. Nature 529, 110–114 (2016).
Flavahan, W. A., Gaskell, E. & Bernstein, B. E. Epigenetic plasticity and the hallmarks of cancer. Science 357, eaal2380 (2017).
McCabe, M. T. et al. EZH2 inhibition as a therapeutic strategy for lymphoma with EZH2-activating mutations. Nature 492, 108–112 (2012).
Beguelin, W. et al. EZH2 is required for germinal center formation and somatic EZH2 mutations promote lymphoid transformation. Cancer Cell 23, 677–692 (2013).
Lieberman, E., Hauert, C. & Nowak, M. A. Evolutionary dynamics on graphs. Nature 433, 312–316 (2005).
Gaiti, F. et al. Epigenetic evolution and lineage histories of chronic lymphocytic leukaemia. Nature 569, 576–580 (2019). This study performed single-cell multi-omics to capture whole transcriptomic, DNAme and somatic genotyping information from the same cells and performed lineage tracing through DNAme data onto which genetic and transcriptional identities were overlayed.
Stoeckius, M. et al. Simultaneous epitope and transcriptome measurement in single cells. Nat. Methods 14, 865–868 (2017). This group developed a method to capture targeted protein expression levels within droplet-based scRNA-seq platforms.
Peterson, V. M. et al. Multiplexed quantification of proteins and transcripts in single cells. Nat. Biotechnol. 35, 936–939 (2017).
Granja, J. M. et al. Single-cell multiomic analysis identifies regulatory programs in mixed-phenotype acute leukemia. Nat. Biotechnol. 37, 1458–1465 (2019).
Cao, J. et al. Joint profiling of chromatin accessibility and gene expression in thousands of single cells. Science 361, 1380–1385 (2018).
Hou, Y. et al. Single-cell triple omics sequencing reveals genetic, epigenetic, and transcriptomic heterogeneity in hepatocellular carcinomas. Cell Res. 26, 304–319 (2016).
Bian, S. et al. Single-cell multiomics sequencing and analyses of human colorectal cancer. Science 362, 1060–1063 (2018).
Clark, S. J. et al. scNMT-seq enables joint profiling of chromatin accessibility DNA methylation and transcription in single cells. Nat. Commun. 9, 781 (2018).
Angermueller, C. et al. Parallel single-cell sequencing links transcriptional and epigenetic heterogeneity. Nat. Methods 13, 229–232 (2016).
Yatabe, Y., Tavare, S. & Shibata, D. Investigating stem cells in human colon by using methylation patterns. Proc. Natl Acad. Sci. USA 98, 10839–10844 (2001).
Shibata, D. Inferring human stem cell behaviour from epigenetic drift. J. Pathol. 217, 199–205 (2009).
Siegmund, K. D., Marjoram, P., Tavare, S. & Shibata, D. Many colorectal cancers are “flat” clonal expansions. Cell Cycle 8, 2187–2193 (2009).
Shibata, D. Mutation and epigenetic molecular clocks in cancer. Carcinogenesis 32, 123–128 (2011).
Sottoriva, A., Spiteri, I., Shibata, D., Curtis, C. & Tavare, S. Single-molecule genomic data delineate patient-specific tumor profiles and cancer stem cell organization. Cancer Res. 73, 41–49 (2013).
Pott, S. Simultaneous measurement of chromatin accessibility, DNA methylation, and nucleosome phasing in single cells. eLife 6, e23203 (2017).
Guo, F. et al. Single-cell multi-omics sequencing of mouse early embryos and embryonic stem cells. Cell Res. 27, 967–988 (2017).
Sottoriva, A. et al. A big bang model of human colorectal tumor growth. Nat. Genet. 47, 209–216 (2015).
Gong, P., Wang, Y., Liu, G., Zhang, J. & Wang, Z. New insight into Ki67 expression at the invasive front in breast cancer. PLoS ONE 8, e54912 (2013).
Chkhaidze, K. et al. Spatially constrained tumour growth affects the patterns of clonal selection and neutral drift in cancer genomic data. PLoS Comput. Biol. 15, e1007243 (2019).
Sun, R. et al. Between-region genetic divergence reflects the mode and tempo of tumor evolution. Nat. Genet. 49, 1015–1024 (2017).
Ryser, M. D., Min, B. H., Siegmund, K. D. & Shibata, D. Spatial mutation patterns as markers of early colorectal tumor cell mobility. Proc. Natl Acad. Sci. USA 115, 5774–5779 (2018).
Waclaw, B. et al. A spatial model predicts that dispersal and cell turnover limit intratumour heterogeneity. Nature 525, 261–264 (2015).
Tricot, G., De Wolf-Peeters, C., Vlietinck, R. & Verwilghen, R. L. Bone marrow histology in myelodysplastic syndromes. II. Prognostic value of abnormal localization of immature precursors in MDS. Br. J. Haematol. 58, 217–225 (1984).
Thorsson, V. et al. The immune landscape of cancer. Immunity 48, 812–830.e14 (2018).
Dong, L. et al. Leukaemogenic effects of Ptpn11 activating mutations in the stem cell microenvironment. Nature 539, 304–308 (2016).
Kode, A. et al. Leukaemogenesis induced by an activating beta-catenin mutation in osteoblasts. Nature 506, 240–244 (2014).
Rosenthal, R. et al. Neoantigen-directed immune escape in lung cancer evolution. Nature 567, 479–485 (2019).
Zhang, A. W. et al. Interfaces of malignant and immunologic clonal dynamics in ovarian cancer. Cell 173, 1755–1769.e22 (2018).
Puram, S. V. et al. Single-cell transcriptomic analysis of primary and metastatic tumor ecosystems in head and neck cancer. Cell 171, 1611–1624.e24 (2017).
Chen, H., Ye, F. & Guo, G. Revolutionizing immunology with single-cell RNA sequencing. Cell Mol. Immunol. 16, 242–249 (2019).
Finotello, F., Rieder, D., Hackl, H. & Trajanoski, Z. Next-generation computational tools for interrogating cancer immunity. Nat. Rev. Genet. 20, 724–746 (2019).
Vento-Tormo, R. et al. Single-cell reconstruction of the early maternal-fetal interface in humans. Nature 563, 347–353 (2018).
Kumar, M. P. et al. Analysis of single-cell RNA-Seq identifies cell-cell communication associated with tumor characteristics. Cell Rep. 25, 1458–1468.e4 (2018).
Nitzan, M., Karaiskos, N., Friedman, N. & Rajewsky, N. Gene expression cartography. Nature 576, 132–137 (2019). This work inferred spatial mapping of single-cells through scRNA-seq data obtained from dissociated cells.
Edsgard, D., Johnsson, P. & Sandberg, R. Identification of spatial expression trends in single-cell gene expression data. Nat. Methods 15, 339–342 (2018).
Giesen, C. et al. Highly multiplexed imaging of tumor tissues with subcellular resolution by mass cytometry. Nat. Methods 11, 417–422 (2014).
Angelo, M. et al. Multiplexed ion beam imaging of human breast tumors. Nat. Med. 20, 436–442 (2014).
Gerdes, M. J. et al. Highly multiplexed single-cell analysis of formalin-fixed, paraffin-embedded cancer tissue. Proc. Natl Acad. Sci. USA 110, 11982–11987 (2013).
Lin, J. R., Fallahi-Sichani, M. & Sorger, P. K. Highly multiplexed imaging of single cells using a high-throughput cyclic immunofluorescence method. Nat. Commun. 6, 8390 (2015).
Chen, K. H., Boettiger, A. N., Moffitt, J. R., Wang, S. & Zhuang, X. RNA imaging. spatially resolved, highly multiplexed RNA profiling in single cells. Science 348, aaa6090 (2015).
Shah, S., Lubeck, E., Zhou, W. & Cai, L. seqFISH accurately detects transcripts in single cells and reveals robust spatial organization in the hippocampus. Neuron 94, 752–758.e1 (2017).
Eng, C.-H. L. et al. Transcriptome-scale super-resolved imaging in tissues by RNA seqFISH. Nature 568, 235–239 (2019).
Lee, J. H. et al. Highly multiplexed subcellular RNA sequencing in situ. Science 343, 1360–1363 (2014).
Wang, X. et al. Three-dimensional intact-tissue sequencing of single-cell transcriptional states. Science 361, eaat5691 (2018).
Stahl, P. L. et al. Visualization and analysis of gene expression in tissue sections by spatial transcriptomics. Science 353, 78–82 (2016).
Rodriques, S. G. et al. Slide-seq: a scalable technology for measuring genome-wide expression at high spatial resolution. Science 363, 1463–1467 (2019).
Satija, R., Farrell, J. A., Gennert, D., Schier, A. F. & Regev, A. Spatial reconstruction of single-cell gene expression data. Nat. Biotechnol. 33, 495–502 (2015). This paper presented the Seurat algorithm for computationally mapping spatial locations of single cells from scRNA-seq derived from dissociated zebra embryos by integrating in situ hybridization data for landmark genes.
Achim, K. et al. High-throughput spatial mapping of single-cell RNA-seq data to tissue of origin. Nat. Biotechnol. 33, 503–509 (2015).
Schulz, D. et al. Simultaneous multiplexed imaging of mRNA and proteins with subcellular resolution in breast cancer tissue samples by mass cytometry. Cell Syst. 6, 25–36.e5 (2018).
Svensson, V., Teichmann, S. A. & Stegle, O. SpatialDE: identification of spatially variable genes. Nat. Methods 15, 343–346 (2018).
Schapiro, D. et al. histoCAT: analysis of cell phenotypes and interactions in multiplex image cytometry data. Nat. Methods 14, 873–876 (2017).
Arnol, D., Schapiro, D., Bodenmiller, B., Saez-Rodriguez, J. & Stegle, O. Modeling cell-cell interactions from spatial molecular data with spatial variance component analysis. Cell Rep. 29, 202–211.e6 (2019).
Tomasetti, C., Marchionni, L., Nowak, M. A., Parmigiani, G. & Vogelstein, B. Only three driver gene mutations are required for the development of lung and colorectal cancers. Proc. Natl Acad. Sci. USA 112, 118–123 (2015).
Lopez-Garcia, C., Klein, A. M., Simons, B. D. & Winton, D. J. Intestinal stem cell replacement follows a pattern of neutral drift. Science 330, 822–825 (2010).
Karin, M. & Clevers, H. Reparative inflammation takes charge of tissue regeneration. Nature 529, 307–315 (2016).
Cedar, H. & Bergman, Y. Epigenetics of haematopoietic cell development. Nat. Rev. Immunol. 11, 478–488 (2011).
Schwartzentruber, J. et al. Driver mutations in histone H3.3 and chromatin remodelling genes in paediatric glioblastoma. Nature 482, 226–231 (2012).
Wu, G. et al. Somatic histone H3 alterations in pediatric diffuse intrinsic pontine gliomas and non-brainstem glioblastomas. Nat. Genet. 44, 251–253 (2012).
Li, S. et al. Distinct evolution and dynamics of epigenetic and genetic heterogeneity in acute myeloid leukemia. Nat. Med. 22, 792–799 (2016).
Lengauer, C., Kinzler, K. W. & Vogelstein, B. DNA methylation and genetic instability in colorectal cancer cells. Proc. Natl Acad. Sci. USA 94, 2545–2550 (1997).
The authors declare no competing interests.
Peer review information
Nature Reviews Genetics thanks M. L. Suvà and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
- Somatic mutations
Alterations in the DNA acquired post-conception (versus germline mutations) and able to be passed onto progeny of mutated cells. Somatic mutations can be detected by sequencing in otherwise histologically normal appearing tissue, are often associated with age and environmental exposures, and can manifest in cancer driver genes.
A population of cells with the same underlying genetic make-up (that is, with the same somatic mutations), which can beget subclonal populations that have acquired additional genetic aberrancies.
Somatic mutations that increase tumour cell fitness.
- Cell states
A cell’s phenotype, as inferred by transcriptional or protein markers, that are often transitional (for example, intermediate states within a developmental system such as haematopoiesis or epithelium).
- Single-cell multi-omics
Analytic or experimental integrations of multiple data ‘omics’ modalities in single cells.
- Variant allele frequencies
Frequencies of mutated alleles in the sequenced reads from next-generation sequencing. Variant allele frequencies reflect copy number, zygosity, tumour purity and the fraction of cancer cells that harbour the mutation (for example, clonal versus subclonal).
- Tumour purity
Per cent of a tumour mass composed of tumour cells, versus admixed non-neoplastic cells, such as tumour-infiltrating immune cells and stromal cells.
- Cancer cell fractions
(CCFs). Fractions of cells that harbour a given mutation.
- Copy number alterations
(CNAs). Changes in the number of copies of a DNA segment due to deletions or gains in the genome.
- Retrospective lineage tracing
Clonal architecture and/or phylogenetic reconstruction of primary samples through naturally accumulated heritable marks, such as copy number alterations, single nucleotide variants or DNA methylation, as opposed to prospective lineage tracing, in which lineage barcodes are artificially introduced into a model organism.
- Molecular clock
A method to deduce the temporal history (often in terms of number of divisions) of a cell or group of cells based on genetic or epigenetic changes that reflect time (or number of divisions).
Heritable stochastic errors in epigenetic marks (best described in DNA methylation).
About this article
Cite this article
Nam, A.S., Chaligne, R. & Landau, D.A. Integrating genetic and non-genetic determinants of cancer evolution by single-cell multi-omics. Nat Rev Genet (2020). https://doi.org/10.1038/s41576-020-0265-5