Cairns, J. Mutation selection and the natural history of cancer. Nature 255, 197–200 (1975).
Google Scholar
Merlo, L. M. F., Pepper, J. W., Reid, B. J. & Maley, C. C. Cancer as an evolutionary and ecological process. Nat. Rev. Cancer 6, 924–935 (2006).
Google Scholar
Anderson, A. R. A., Weaver, A. M., Cummings, P. T. & Quaranta, V. Tumor morphology and phenotypic evolution driven by selective pressure from the microenvironment. Cell 127, 905–915 (2006).
Google Scholar
Maley, C. C. & Reid, B. J. Natural selection in neoplastic progression of Barrett’s esophagus. Semin. Cancer Biol. 15, 474–483 (2005).
Google Scholar
McPherson, A. et al. Divergent modes of clonal spread and intraperitoneal mixing in high-grade serous ovarian cancer. Nat. Genet. 48, 758–767 (2016). This study describes clonal compositions of distinct tumour sites in ovarian cancer and discovers distinct modes of intraperitoneal spread.
Google Scholar
de Visser, K. E. & Joyce, J. A. The evolving tumor microenvironment: from cancer initiation to metastatic outgrowth. Cancer Cell 41, 374–403 (2023).
Google Scholar
Nowell, P. C. The clonal evolution of tumor cell populations. Science 194, 23–28 (1976).
Google Scholar
Maley, C. C. et al. Genetic clonal diversity predicts progression to esophageal adenocarcinoma. Nat. Genet. 38, 468–473 (2006).
Google Scholar
Ortmann, C. A. et al. Effect of mutation order on myeloproliferative neoplasms. N. Engl. J. Med. 372, 601–612 (2015). This seminal paper shows that the order in which mutations occur (JAK2V617F homozygosity and TET2) can affect cancer phenotypes and clinical response to targeted therapy.
Google Scholar
Benard, B. et al. Clonal architecture and variant allele frequency predict clinical outcomes and drug response in acute myeloid leukemia. Blood 136, 2 (2020).
Google Scholar
Ai, X. et al. Clonal architecture of EGFR mutation predicts the efficacy of EGFR-tyrosine kinase inhibitors in advanced NSCLC: a prospective multicenter study (NCT03059641). Clin. Cancer Res. 27, 704–712 (2021).
Google Scholar
Benard, B. A. et al. Clonal architecture predicts clinical outcomes and drug sensitivity in acute myeloid leukemia. Nat. Commun. 12, 7244 (2021).
Google Scholar
Kaur, G. et al. Correlation of changes in subclonal architecture with progression in the MMRF CoMMpass study. Transl. Oncol. 23, 101472 (2022).
Google Scholar
Takahashi, K. & Tanaka, T. Clonal evolution and hierarchy in myeloid malignancies. Trends Cancer 9, 707–715 (2023).
Google Scholar
McPherson, A. et al. Ongoing genome doubling shapes evolvability and immunity in ovarian cancer. Nature 644, 1078–1087 (2025). This paper studies whole-genome doubling-induced treatment immunity and characterizes genetic diversification post whole-genome doubling in single cells.
Google Scholar
Navin, N. et al. Tumour evolution inferred by single-cell sequencing. Nature 472, 90–94 (2011). This research demonstrates that single-cell sequencing is able to reveal clonal evolution and intra-tumour heterogeneity in cancer.
Google Scholar
Gawad, C., Koh, W. & Quake, S. R. Single-cell genome sequencing: current state of the science. Nat. Rev. Genet. 17, 175–188 (2016).
Google Scholar
Jovic, D. et al. Single-cell RNA sequencing technologies and applications: a brief overview. Clin. Transl. Med. 12, e694 (2022).
Google Scholar
Pellegrino, M. et al. High-throughput single-cell DNA sequencing of acute myeloid leukemia tumors with droplet microfluidics. Genome Res. 28, 1345–1352 (2018).
Google Scholar
Zahn, H. et al. Scalable whole-genome single-cell library preparation without preamplification. Nat. Methods 14, 167–173 (2017).
Google Scholar
Hughes, A. E. O. et al. Clonal architecture of secondary acute myeloid leukemia defined by single-cell sequencing. PLoS Genet. 10, e1004462 (2014).
Google Scholar
Schwartz, R. & Schäffer, A. A. The evolution of tumour phylogenetics: principles and practice. Nat. Rev. Genet. 18, 213–229 (2017).
Google Scholar
Kuipers, J., Jahn, K. & Beerenwinkel, N. Advances in understanding tumour evolution through single-cell sequencing. Biochim. Biophys. Acta Rev. Cancer 1867, 127–138 (2017).
Google Scholar
Li, L. et al. Resolving tumor evolution: a phylogenetic approach. J. Natl Cancer Cent. 4, 97–106 (2024).
Google Scholar
Lu, B. Cancer phylogenetic inference using copy number alterations detected from DNA sequencing data. Cancer Pathog. Ther. 3, 16–29 (2025).
Google Scholar
Su, X. et al. Accurate tumor clonal structures require single-cell analysis. Ann. N. Y. Acad. Sci. 1517, 213–224 (2022).
Google Scholar
Leung, M. L. et al. Single-cell DNA sequencing reveals a late-dissemination model in metastatic colorectal cancer. Genome Res. 27, 1287–1299 (2017).
Google Scholar
Gremmelspacher, D. et al. Phylogenetic inference reveals clonal heterogeneity in circulating tumor cell clusters. Nat. Genet. 57, 1357–1361 (2025).
Google Scholar
Noble, R. et al. Spatial structure governs the mode of tumour evolution. Nat. Ecol. Evol. 6, 207–217 (2022).
Google Scholar
Davis, A., Gao, R. & Navin, N. Tumor evolution: Linear, branching, neutral or punctuated? Biochim. Biophys. Acta Rev. Cancer 1867, 151–161 (2017).
Google Scholar
Sun, R., Hu, Z. & Curtis, C. Big bang tumor growth and clonal evolution. Cold Spring Harb. Perspect. Med. 8, a028381 (2018).
Google Scholar
Gao, R. et al. Punctuated copy number evolution and clonal stasis in triple-negative breast cancer. Nat. Genet. 48, 1119–1130 (2016). This paper demonstrates that copy number profiles at single-cell resolution can offer a better understanding of the evolutionary constraints and mechanisms underlying tumour growth.
Google Scholar
Tarabichi, M. et al. Neutral tumor evolution? Nat. Genet. 50, 1630–1633 (2018).
Google Scholar
Williams, M. J. et al. Quantification of subclonal selection in cancer from bulk sequencing data. Nat. Genet. 50, 895–903 (2018).
Google Scholar
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).
Google Scholar
Eghdami, A., Paulose, J. & Fusco, D. Branching structure of genealogies in spatially growing populations and its implications for population genetics inference. J. Phys. Condens. Matter 34, 294008 (2022).
Google Scholar
Johnson, B., Shuai, Y., Schweinsberg, J. & Curtius, K. cloneRate: fast estimation of single-cell clonal dynamics using coalescent theory. Bioinformatics 39, btad561 (2023).
Google Scholar
Skums, P., Tsyvina, V. & Zelikovsky, A. Inference of clonal selection in cancer populations using single-cell sequencing data. Bioinformatics 35, i398–i407 (2019).
Google Scholar
Poon, G. et al. Single-cell DNA sequencing reveals pervasive positive selection throughout preleukemic evolution. Cell Genom. 5, 100744 (2025).
Google Scholar
Tsyvina, V., Zelikovsky, A., Snir, S. & Skums, P. Inference of mutability landscapes of tumors from single cell sequencing data. PLoS Comput. Biol. 16, e1008454 (2020).
Google Scholar
Borgsmüller, N., Valecha, M., Kuipers, J., Beerenwinkel, N. & Posada, D. Single-cell phylogenies reveal changes in the evolutionary rate within cancer and healthy tissues. Cell Genom. 3, 100380 (2023).
Google Scholar
Tamborero, D. et al. The Molecular Tumor Board Portal supports clinical decisions and automated reporting for precision oncology. Nat. Cancer 3, 251–261 (2022).
Google Scholar
Miglino, N. et al. Feasibility of multiomics tumor profiling for guiding treatment of melanoma. Nat. Med. 31, 2430–2441 (2025).
Google Scholar
Irmisch, A. et al. The Tumor Profiler Study: integrated, multi-omic, functional tumor profiling for clinical decision support. Cancer Cell 39, 288–293 (2021).
Google Scholar
Morita, K. et al. Clonal evolution of acute myeloid leukemia revealed by high-throughput single-cell genomics. Nat. Commun. 11, 5327 (2020). This large cohort study of acute myeloid leukaemias links the clonal architecture to evolutionary resistance mechanisms.
Google Scholar
Miles, L. A. et al. Single-cell mutation analysis of clonal evolution in myeloid malignancies. Nature 587, 477–482 (2020).
Google Scholar
Jamal-Hanjani, M. et al. Tracking genomic cancer evolution for precision medicine: the lung TRACERx study. PLoS Biol. 12, e1001906 (2014).
Google Scholar
Turajlic, S. et al. Deterministic evolutionary trajectories influence primary tumor growth: TRACERx Renal. Cell 173, 595–610.e11 (2018).
Google Scholar
Christensen, S., Kim, J., Chia, N., Koyejo, O. & El-Kebir, M. Detecting evolutionary patterns of cancers using consensus trees. Bioinformatics 36, i684–i691 (2020).
Google Scholar
Baciu-Drăgan, M.-A. & Beerenwinkel, N. Oncotree2vec — a method for embedding and clustering of tumor mutation trees. Bioinformatics 40, i180–i188 (2024).
Google Scholar
Vandin, F., Upfal, E. & Raphael, B. J. De novo discovery of mutated driver pathways in cancer. Genome Res. 22, 375–385 (2012).
Google Scholar
Jerby-Arnon, L. et al. Predicting cancer-specific vulnerability via data-driven detection of synthetic lethality. Cell 158, 1199–1209 (2014).
Google Scholar
Leiserson, M. D. M., Wu, H.-T., Vandin, F. & Raphael, B. J. CoMEt: a statistical approach to identify combinations of mutually exclusive alterations in cancer. Genome Biol. 16, 160 (2015).
Google Scholar
Constantinescu, S., Szczurek, E., Mohammadi, P., Rahnenführer, J. & Beerenwinkel, N. TiMEx: a waiting time model for mutually exclusive cancer alterations. Bioinformatics 32, 968–975 (2016).
Google Scholar
Kuipers, J. et al. Statistical tests for intra-tumour clonal co-occurrence and exclusivity. PLoS Comput. Biol. 17, e1009036 (2021).
Google Scholar
Hodzic, E. et al. Identification of conserved evolutionary trajectories in tumors. Bioinformatics 36, i427–i435 (2020).
Google Scholar
Pellegrina, L. & Vandin, F. Discovering significant evolutionary trajectories in cancer phylogenies. Bioinformatics 38, ii49–ii55 (2022).
Google Scholar
Caravagna, G. et al. Detecting repeated cancer evolution from multi-region tumor sequencing data. Nat. Methods 15, 707–714 (2018).
Google Scholar
Khakabimamaghani, S. et al. Collaborative intra-tumor heterogeneity detection. Bioinformatics 35, i379–i388 (2019).
Google Scholar
Schill, R., Solbrig, S., Wettig, T. & Spang, R. Modelling cancer progression using Mutual Hazard Networks. Bioinformatics 36, 241–249 (2020).
Google Scholar
Luo, X. G., Kuipers, J. & Beerenwinkel, N. Joint inference of exclusivity patterns and recurrent trajectories from tumor mutation trees. Nat. Commun. 14, 3676 (2023). This research shows that the most probable evolutionary trajectories in cancer can be inferred and predicted from cohorts of tumour mutation trees reconstructed from single-cell sequencing data.
Google Scholar
Ivanovic, S. & El-Kebir, M. Modeling and predicting cancer clonal evolution with reinforcement learning. Genome Res. 33, 1078–1088 (2023).
Google Scholar
Aga, O. N. L. et al. HyperTraPS-CT: inference and prediction for accumulation pathways with flexible data and model structures. PLoS Comput. Biol. 20, e1012393 (2024).
Google Scholar
Luo, X. G., Kuipers, J., Rupp, K., Takahashi, K. & Beerenwinkel, N. Bayesian inference of fitness landscapes via tree-structured branching processes. Bioinformatics 41, i160–i169 (2025).
Google Scholar
Felsenstein, J. Inferring Phylogenies (Oxford Univ. Press, 2003).
Semple, C. & Steel, M. Phylogenetics (Oxford Lecture Series in Mathematics and Its Applications, 24) (Oxford Univ. Press, 2003).
Stadler, T., Pybus, O. G. & Stumpf, M. P. H. Phylodynamics for cell biologists. Science 371, eaah6266 (2021).
Google Scholar
Hadfield, J. et al. Nextstrain: real-time tracking of pathogen evolution. Bioinformatics 34, 4121–4123 (2018).
Google Scholar
O’Toole, Á et al. Assignment of epidemiological lineages in an emerging pandemic using the pangolin tool. Virus Evol. 7, veab064 (2021).
Google Scholar
Blanpain, C. & Simons, B. D. Unravelling stem cell dynamics by lineage tracing. Nat. Rev. Mol. Cell Biol. 14, 489–502 (2013).
Google Scholar
Packer, J. S. et al. A lineage-resolved molecular atlas of C. elegans embryogenesis at single-cell resolution. Science 365, eaax1971 (2019).
Google Scholar
Hoehn, K. B. & Kleinstein, S. H. B cell phylogenetics in the single cell era. Trends Immunol. 45, 62–74 (2024).
Google Scholar
Garraway, L. A. & Lander, E. S. Lessons from the cancer genome. Cell 153, 17–37 (2013).
Google Scholar
Lucek, K. et al. The impact of chromosomal rearrangements in speciation: from micro- to macroevolution. Cold Spring Harb. Perspect. Biol. 15, a041447 (2023).
Google Scholar
Au, H. M., Nong, W. & Hui, J. H. L. Whole genome duplication in the genomics era: the hidden gems in invertebrates? Genome Biol. Evol. 17, evaf073 (2025).
Google Scholar
Sankoff, D. & Zheng, C. Whole genome duplication in plants: implications for evolutionary analysis. Methods Mol. Biol. 1704, 291–315 (2018).
Google Scholar
Katju, V. & Bergthorsson, U. Copy-number changes in evolution: rates, fitness effects and adaptive significance. Front. Genet. 4, 273 (2013).
Google Scholar
Navin, N. E. Cancer genomics: one cell at a time. Genome Biol. 15, 452 (2014).
Google Scholar
Roth, A. et al. Clonal genotype and population structure inference from single-cell tumor sequencing. Nat. Methods 13, 573–576 (2016).
Google Scholar
Beerenwinkel, N., Schwarz, R. F., Gerstung, M. & Markowetz, F. Cancer evolution: mathematical models and computational inference. Syst. Biol. 64, e1–e25 (2015).
Google Scholar
Cook, G. W. et al. Structural variation and its potential impact on genome instability: novel discoveries in the EGFR landscape by long-read sequencing. PLoS ONE 15, e0226340 (2020).
Google Scholar
Ho, S. S., Urban, A. E. & Mills, R. E. Structural variation in the sequencing era. Nat. Rev. Genet. 21, 171–189 (2020).
Google Scholar
Funnell, T. et al. Single-cell genomic variation induced by mutational processes in cancer. Nature 612, 106–115 (2022).
Google Scholar
Yuan, K., Sakoparnig, T., Markowetz, F. & Beerenwinkel, N. BitPhylogeny: a probabilistic framework for reconstructing intra-tumor phylogenies. Genome Biol. 16, 36 (2015). This research uses a flexible probabilistic framework to estimate the number and composition of clones in a tumour sample and the most probable tree connecting them from scDNA-seq data.
Google Scholar
Ross, E. M. & Markowetz, F. OncoNEM: inferring tumor evolution from single-cell sequencing data. Genome Biol. 17, 69 (2016). This research uses a probabilistic model to infer the evolutionary history of SNVs from noisy single-cell DNA sequencing data.
Google Scholar
Jahn, K., Kuipers, J. & Beerenwinkel, N. Tree inference for single-cell data. Genome Biol. 17, 86 (2016). This paper uses a scalable method to reconstruct the evolutionary history of a cancer, accounting for sequencing noise and allowing uncertainty quantification.
Google Scholar
Kim, K. I. & Simon, R. Using single cell sequencing data to model the evolutionary history of a tumor. BMC Bioinform. 15, 27 (2014).
Google Scholar
Kozlov, A., Alves, J. M., Stamatakis, A. & Posada, D. CellPhy: accurate and fast probabilistic inference of single-cell phylogenies from scDNA-seq data. Genome Biol. 23, 37 (2022).
Google Scholar
Wood, L. D. et al. The genomic landscapes of human breast and colorectal cancers. Science 318, 1108–1113 (2007).
Google Scholar
Rubin, A. F. & Green, P. Mutation patterns in cancer genomes. Proc. Natl Acad. Sci. USA 106, 21766–21770 (2009).
Google Scholar
Bignell, G. R. et al. Signatures of mutation and selection in the cancer genome. Nature 463, 893–898 (2010).
Google Scholar
Salk, J. J. & Horwitz, M. S. Passenger mutations as a marker of clonal cell lineages in emerging neoplasia. Semin. Cancer Biol. 20, 294–303 (2010).
Google Scholar
Satas, G., Zaccaria, S., Mon, G. & Raphael, B. J. SCARLET: single-cell tumor phylogeny inference with copy-number constrained mutation losses. Cell Syst. 10, 323–332.e8 (2020). This research integrates both SNV and copy number data for inferring the evolutionary history of cancer.
Google Scholar
Sashittal, P., Zhang, H., Iacobuzio-Donahue, C. A. & Raphael, B. J. ConDoR: tumor phylogeny inference with a copy-number constrained mutation loss model. Genome Biol. 24, 272 (2023).
Google Scholar
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). This paper presents a phylogenetic tree inference method for tumour evolution that allows for loss and recurrence of point mutations.
Google Scholar
Sollier, E., Kuipers, J., Takahashi, K., Beerenwinkel, N. & Jahn, K. COMPASS: joint copy number and mutation phylogeny reconstruction from amplicon single-cell sequencing data. Nat. Commun. 14, 4921 (2023).
Google Scholar
Kaufmann, T. L. et al. MEDICC2: whole-genome doubling aware copy-number phylogenies for cancer evolution. Genome Biol. 23, 241 (2022). This study describes a finite-states transducer framework for efficiently computing evolutionary distances between copy number profiles in a haplotype-specific and whole-genome doubling-aware manner.
Google Scholar
Zaccaria, S. & Raphael, B. J. Characterizing allele- and haplotype-specific copy numbers in single cells with CHISEL. Nat. Biotechnol. 39, 207–214 (2021).
Google Scholar
Kimura, M. Theoretical foundation of population genetics at the molecular level. Theor. Popul. Biol. 2, 174–208 (1971).
Google Scholar
Gusfield, D. Efficient algorithms for inferring evolutionary trees. Networks 21, 19–28 (1991).
Google Scholar
Sansregret, L. & Swanton, C. The role of aneuploidy in cancer evolution. Cold Spring Harb. Perspect. Med. 7, a028373 (2017).
Google Scholar
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). This paper rigorously demonstrates mutation recurrence and loss, and hence violation of the ISA, in tumour evolution using Bayesian model comparison.
Google Scholar
Beckman, R. A. Neutral evolution of rare cancer mutations in the computer and the clinic. NPJ Syst. Biol. Appl. 10, 110 (2024).
Google Scholar
El-Kebir, M. SPhyR: tumor phylogeny estimation from single-cell sequencing data under loss and error. Bioinformatics 34, i671–i679 (2018). This study characterizes cancer evolution with constraint loss of mutations as a combinatorial optimization problem with a highly efficient solution.
Google Scholar
Ciccolella, S. et al. Inferring cancer progression from single-cell sequencing while allowing mutation losses. Bioinformatics 37, 326–333 (2021).
Google Scholar
Bonizzoni, P., Ciccolella, S., Vedova, G. D. & Soto, M. Does relaxing the infinite sites assumption give better tumor phylogenies? An ILP-based comparative approach. IEEE/ACM Trans. Comput. Biol. Bioinform. 16, 1410–1423 (2019).
Google Scholar
Markowska, M. et al. CONET: copy number event tree model of evolutionary tumor history for single-cell data. Genome Biol. 23, 128 (2022).
Google Scholar
Kuipers, J., Tuncel, M. A., Ferreira, P. F., Jahn, K. & Beerenwinkel, N. Single-cell copy number calling and event history reconstruction. Bioinformatics 41, btaf072 (2025).
Google Scholar
Zafar, H., Navin, N., Chen, K. & Nakhleh, L. SiCloneFit: Bayesian inference of population structure, genotype, and phylogeny of tumor clones from single-cell genome sequencing data. Genome Res. 29, 1847–1859 (2019).
Google Scholar
Kang, S. et al. DelSIEVE: cell phylogeny modeling of single nucleotide variants and deletions from single-cell DNA sequencing data. Genome Biol. 26, 255 (2025).
Google Scholar
Jukes, T. H. & Cantor, C. R. in Mammalian Protein Metabolism (ed. Munro, H. N.) 21–132 (Academic, 1969).
Kimura, M. A simple method for estimating evolutionary rates of base substitutions through comparative studies of nucleotide sequences. J. Mol. Evol. 16, 111–120 (1980).
Google Scholar
Borgsmüller, N. et al. DemoTape: computational demultiplexing of targeted single-cell sequencing data. Preprint at bioRxiv (2024).
Wang, Y. et al. Clonal evolution in breast cancer revealed by single nucleus genome sequencing. Nature 512, 155–160 (2014).
Google Scholar
Schwarz, R. F. et al. Phylogenetic quantification of intra-tumour heterogeneity. PLoS Comput. Biol. 10, e1003535 (2014).
Google Scholar
Cordonnier, G. & Lafond, M. Comparing copy-number profiles under multi-copy amplifications and deletions. BMC Genom. 21, 198 (2020).
Google Scholar
Zeira, R. & Raphael, B. J. Copy number evolution with weighted aberrations in cancer. Bioinformatics 36, i344–i352 (2020).
Google Scholar
Weiner, S. & Bansal, M. S. DICE: fast and accurate distance-based reconstruction of single-cell copy number phylogenies. Life Sci. Alliance 8, e202402923 (2025).
Google Scholar
El-Kebir, M. et al. Complexity and algorithms for copy-number evolution problems. Algorithms Mol. Biol. 12, 13 (2017).
Google Scholar
Subramanian, A. & Schwartz, R. Reference-free inference of tumor phylogenies from single-cell sequencing data. BMC Genom. 16, S7 (2015).
Google Scholar
Del Monte, U. Does the cell number 109 still really fit one gram of tumor tissue? Cell Cycle 8, 505–506 (2009).
Google Scholar
Wang, Y. J. et al. Comparative analysis of commercially available single-cell RNA sequencing platforms for their performance in complex human tissues. Preprint at bioRxiv (2019).
Filippov, I., Philip, C. S., Schauser, L. & Peterson, P. Comparative transcriptomic analyses of thymocytes using 10x Genomics and Parse scRNA-seq technologies. BMC Genom. 25, 1069 (2024).
Google Scholar
What is the expected doublet rate? Parse Biosciences (2020).
Xie, Y. et al. Comparative analysis of single-cell RNA sequencing methods with and without sample multiplexing. Int. J. Mol. Sci. 25, 3828 (2024).
Google Scholar
Weber, L. L., Sashittal, P. & El-Kebir, M. doubletD: detecting doublets in single-cell DNA sequencing data. Bioinformatics 37, i214–i221 (2021).
Google Scholar
Wolock, S. L., Lopez, R. & Klein, A. M. Scrublet: computational identification of cell doublets in single-cell transcriptomic data. Cell Syst. 8, 281–291.e9 (2019).
Google Scholar
Wu, Y. Accurate and efficient cell lineage tree inference from noisy single cell data: the maximum likelihood perfect phylogeny approach. Bioinformatics 36, 742–750 (2020).
Google Scholar
Deng, K. et al. ImageDoubler: image-based doublet identification in single-cell sequencing. Nat. Commun. 16, 21 (2025).
Google Scholar
Lähnemann, D. et al. Accurate and scalable variant calling from single cell DNA sequencing data with ProSolo. Nat. Commun. 12, 6744 (2021).
Google Scholar
Dean, F. B. et al. Comprehensive human genome amplification using multiple displacement amplification. Proc. Natl Acad. Sci. USA 99, 5261–5266 (2002).
Google Scholar
Pfeiffer, F. et al. Systematic evaluation of error rates and causes in short samples in next-generation sequencing. Sci. Rep. 8, 10950 (2018).
Google Scholar
Schmitt, M. W. et al. Detection of ultra-rare mutations by next-generation sequencing. Proc. Natl Acad. Sci. USA 109, 14508–14513 (2012).
Google Scholar
Xing, D., Tan, L., Chang, C.-H., Li, H. & Xie, X. S. Accurate SNV detection in single cells by transposon-based whole-genome amplification of complementary strands. Proc. Natl Acad. Sci. USA 118, e2013106118 (2021).
Google Scholar
Bae, J. H. et al. Single duplex DNA sequencing with CODEC detects mutations with high sensitivity. Nat. Genet. 55, 871–879 (2023).
Google Scholar
Chen, K., Moravec, J. C., Gavryushkin, A., Welch, D. & Drummond, A. J. Accounting for errors in data improves divergence time estimates in single-cell cancer evolution. Mol. Biol. Evol. 39, msac143 (2022).
Google Scholar
Laks, E. et al. Clonal decomposition and DNA replication states defined by scaled single-cell genome sequencing. Cell 179, 1207–1221.e22 (2019).
Google Scholar
Singer, J., Kuipers, J., Jahn, K. & Beerenwinkel, N. Single-cell mutation identification via phylogenetic inference. Nat. Commun. 9, 5144 (2018).
Google Scholar
Edrisi, M., Zafar, H. & Nakhleh, L. A combinatorial approach for single-cell variant detection via phylogenetic inference. Leibniz Int. Proc. Inform. 22, 1–13 (2019).
Kuipers, J., Singer, J. & Beerenwinkel, N. Single-cell mutation calling and phylogenetic tree reconstruction with loss and recurrence. Bioinformatics 38, 4713–4719 (2022).
Google Scholar
Edrisi, M. et al. Phylovar: toward scalable phylogeny-aware inference of single-nucleotide variations from single-cell DNA sequencing data. Bioinformatics 38, i195–i202 (2022).
Google Scholar
Malikic, S., Jahn, K., Kuipers, J., Sahinalp, S. C. & Beerenwinkel, N. Integrative inference of subclonal tumour evolution from single-cell and bulk sequencing data. Nat. Commun. 10, 2750 (2019).
Google Scholar
Malikic, S. et al. PhISCS: a combinatorial approach for subperfect tumor phylogeny reconstruction via integrative use of single-cell and bulk sequencing data. Genome Res. 29, 1860–1877 (2019).
Google Scholar
Moravec, J. C., Lanfear, R., Spector, D. L., Diermeier, S. D. & Gavryushkin, A. Testing for phylogenetic signal in single-cell RNA-seq data. J. Comput. Biol. 30, 518–537 (2023).
Google Scholar
Jiang, R., Sun, T., Song, D. & Li, J. J. Statistics or biology: the zero-inflation controversy about scRNA-seq data. Genome Biol. 23, 31 (2022).
Google Scholar
Raj, A., Peskin, C. S., Tranchina, D., Vargas, D. Y. & Tyagi, S. Stochastic mRNA synthesis in mammalian cells. PLoS Biol. 4, e309 (2006).
Google Scholar
Eckersley-Maslin, M. A. & Spector, D. L. Random monoallelic expression: regulating gene expression one allele at a time. Trends Genet. 30, 237–244 (2014).
Google Scholar
Pickrell, J. K. et al. Understanding mechanisms underlying human gene expression variation with RNA sequencing. Nature 464, 768–772 (2010).
Google Scholar
Macosko, E. Z. et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161, 1202–1214 (2015).
Google Scholar
Zheng, G. X. Y. et al. Massively parallel digital transcriptional profiling of single cells. Nat. Commun. 8, 14049 (2017).
Google Scholar
Zhou, Z., Xu, B., Minn, A. & Zhang, N. R. DENDRO: genetic heterogeneity profiling and subclone detection by single-cell RNA sequencing. Genome Biol. 21, 10 (2020).
Google Scholar
Lu, T. et al. Overcoming expressional drop-outs in lineage reconstruction from single-cell RNA-sequencing data. Cell Rep. 34, 108589 (2021).
Google Scholar
Liu, X. et al. Phylogenetic inference from single-cell RNA-seq data. Sci. Rep. 13, 12854 (2023).
Google Scholar
Bai, S., Su, X., Chen, Z. & Han, Z.-G. De novo detection of clonal structure and evolution in single-cell and spatial transcriptomes. Int. J. Mol. Sci. 26, 11428 (2025).
Google Scholar
Jun, S.-H. et al. Reconstructing clonal tree for phylo-phenotypic characterization of cancer using single-cell transcriptomics. Nat. Commun. 14, 982 (2023).
Google Scholar
Weideman, A. M. K. et al. Canopy2: tumor phylogeny inference by bulk DNA and single-cell RNA sequencing. Stat. Biosci. 18, 68–110 (2026).
Google Scholar
Zimmermann, N., Sun, X., Hård, J., Kuipers, J. & Beerenwinkel, N. Phylogenetic tree inference from single-cell RNA sequencing data with SCITE-RNA. Genome Biol. 27, 195 (2026).
Google Scholar
Ronemus, M., Bradford, D., Laster, Z. & Li, S. Exploring genome-transcriptome correlations in cancer. Biochem. Soc. Trans. 53, BST20240108 (2025).
Google Scholar
Li, S. et al. Hybrid BAG-seq: DNA and RNA from the same single nucleus reveals interactions between genomic and transcriptomic landscapes in human tumor samples. Genome Biol. 26, 314 (2025).
Google Scholar
Sashittal, P., Chen, V., Pasarkar, A. & Raphael, B. J. Joint inference of cell lineage and mitochondrial evolution from single-cell sequencing data. Bioinformatics 40, i218–i227 (2024).
Google Scholar
Seidel, S. et al. Probabilistic cell lineage inference from mitochondrial mutations. Preprint at bioRxiv (2026).
Ludwig, L. S. et al. Lineage tracing in humans enabled by mitochondrial mutations and single-cell genomics. Cell 176, 1325–1339.e22 (2019).
Google Scholar
Abyzov, A. & Vaccarino, F. M. Cell lineage tracing and cellular diversity in humans. Annu. Rev. Genom. Hum. Genet. 21, 101–116 (2020).
Google Scholar
Yu, X. et al. MitoTracer facilitates the identification of informative mitochondrial mutations for precise lineage reconstruction. PLoS Comput. Biol. 21, e1013090 (2025).
Google Scholar
Li, S., Wang, K., Wang, X. & Hu, Z. Single-cell mitochondrial lineage tracing: opportunities and challenges. Quant. Biol. 14, e70018 (2026).
Google Scholar
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).
Google Scholar
Lareau, C. A. et al. Artefacts in single-cell mtDNA analyses misinform phylogenies. Nature 656, E25–E31 (2026).
Google Scholar
Wang, X. et al. Clonal expansion dictates the efficacy of mitochondrial lineage tracing in single cells. Genome Biol. 26, 70 (2025).
Google Scholar
Clark, S. J. et al. Genome-wide base-resolution mapping of DNA methylation in single cells using single-cell bisulfite sequencing (scBS-seq). Nat. Protoc. 12, 534–547 (2017).
Google Scholar
Bianchi, A. et al. scTAM-seq enables targeted high-confidence analysis of DNA methylation in single cells. Genome Biol. 23, 229 (2022).
Google Scholar
Liu, F., Wang, Y., Gu, H. & Wang, X. Technologies and applications of single-cell DNA methylation sequencing. Theranostics 13, 2439–2454 (2023).
Google Scholar
Fu, R., Chen, M. & Wang, S.-W. DNA methylation meets lineage tracing: history, recent progress, and future directions. Quant. Biol. 14, e70017 (2026).
Google Scholar
Scherer, M. et al. Clonal tracing with somatic epimutations reveals dynamics of blood ageing. Nature 643, 478–487 (2025).
Google Scholar
Chen, M., Fu, R., Chen, Y., Li, L. & Wang, S.-W. High-resolution, noninvasive single-cell lineage tracing in mice and humans based on DNA methylation epimutations. Nat. Methods 22, 488–498 (2025).
Google Scholar
Gabbutt, C. et al. Fluctuating DNA methylation tracks cancer evolution at clinical scale. Nature 645, 764–773 (2025).
Google Scholar
Strino, F., Parisi, F., Micsinai, M. & Kluger, Y. TrAp: a tree approach for fingerprinting subclonal tumor composition. Nucleic Acids Res. 41, e165 (2013).
Google Scholar
Goh, D., Wee, F., Fincham, R. E. A., Li, R. & Yeong, J. Single-cell profiling: any scale, any size, all at once. Adv. Sci. 13, e18479 (2026).
Google Scholar
Billera, L. J., Holmes, S. P. & Vogtmann, K. Geometry of the space of phylogenetic trees. Adv. Appl. Math. 27, 733–767 (2001).
Google Scholar
Speyer, D. & Sturmfels, B. The tropical Grassmannian. Adv. Geom. 4, 389–411 (2004).
Google Scholar
Felsenstein, J. Confidence limits on phylogenies: an approach using the bootstrap. Evolution 39, 783 (1985).
Google Scholar
Lemoine, F. et al. Renewing Felsenstein’s phylogenetic bootstrap in the era of big data. Nature 556, 452–456 (2018).
Google Scholar
Yu, Z., Liu, H., Du, F. & Tang, X. GRMT: generative reconstruction of mutation tree from scratch using single-cell sequencing data. Front. Genet. 12, 692964 (2021).
Google Scholar
Izquierdo-Carrasco, F., Cazes, J., Smith, S. A. & Stamatakis, A. PUmPER: phylogenies updated perpetually. Bioinformatics 30, 1476–1477 (2014).
Google Scholar
Ye, C. et al. matOptimize: a parallel tree optimization method enables online phylogenetics for SARS-CoV-2. Bioinformatics 38, 3734–3740 (2022).
Google Scholar
Zhang, H., Zhang, Y., Gao, T. & Wu, Y. ScisTree2 enables large-scale inference of cell lineage trees and genotype calling using efficient local search. Genome Res. 35, 2781–2791 (2025).
Google Scholar
Saitou, N. & Nei, M. The neighbor-joining method: a new method for reconstructing phylogenetic trees. Mol. Biol. Evol. 4, 406–425 (1987).
Google Scholar
Michener, C. D. A statistical method for evaluating systematic relationships. Univ. Kans. Sci. Bull. 38, 1409–1438 (1958).
Schmidt, H., Sashittal, P. & Raphael, B. J. A zero-agnostic model for copy number evolution in cancer. PLoS Comput. Biol. 19, e1011590 (2023).
Google Scholar
Granville, V., Krivanek, M. & Rasson, J. P. Simulated annealing: a proof of convergence. IEEE Trans. Pattern Anal. Mach. Intell. 16, 652–656 (1994).
Google Scholar
Marchand, H., Martin, A., Weismantel, R. & Wolsey, L. Cutting planes in integer and mixed integer programming. Discret. Appl. Math. 123, 397–446 (2002).
Google Scholar
Dakin, R. J. A tree-search algorithm for mixed integer programming problems. Comput. J. 8, 250–255 (1965).
Google Scholar
Kızılkale, C. et al. Fast intratumor heterogeneity inference from single-cell sequencing data. Nat. Comput. Sci. 2, 577–583 (2022).
Google Scholar
Sadeqi Azer, E., Haghir Ebrahimabadi, M., Malikić, S., Khardon, R. & Sahinalp, S. C. Tumor phylogeny topology inference via deep learning. iScience 23, 101655 (2020).
Google Scholar
Fitch, W. M. Toward defining the course of evolution: minimum change for a specific tree topology. Syst. Zool. 20, 406 (1971).
Google Scholar
Catanzaro, D. The minimum evolution problem: overview and classification. Networks 53, 112–125 (2009).
Google Scholar
Köhn, G. Quantifying Markov Chain Monte Carlo Exploration of Tumour Progression Tree Spaces: Initialisation Strategies, Convergence Diagnostics & Multi-modalities. MSc thesis, ETH Zurich (2023).
Koptagel, H., Kviman, O., Melin, H., Safinianaini, N. & Lagergren, J. VaiPhy: a variational inference based algorithm for phylogeny. Proceedings of the 36th International Conference on Neural Information Processing Systems 1073, 14758–14770 (2022).
Zhang, C. & Matsen, F. A. IV A variational approach to Bayesian phylogenetic inference. J. Mach. Learn. Res. 25, 1 (2024).
Siepel, A., Hassett, R. & Staklinski, S. J. VINE: variational inference for scalable Bayesian reconstruction of species and cell-lineage phylogenies. Preprint at bioRxiv (2026).
Melin, H., Zampinetti, V., McPherson, A. & Lagergren, J. VICTree — a variational inference method for clonal tree reconstruction. In Research in Computational Molecular Biology Vol. 14758 (ed. Ma, J.) 429–433 (Springer Nature, 2024).
Bouchard-Côté, A., Sankararaman, S. & Jordan, M. I. Phylogenetic inference via sequential Monte Carlo. Syst. Biol. 61, 579–593 (2012).
Google Scholar
Wang, L., Bouchard-Côté, A. & Doucet, A. Bayesian phylogenetic inference using a combinatorial sequential Monte Carlo method. J. Am. Stat. Assoc. 110, 1362–1374 (2015).
Google Scholar
Fan, H. H. & Kubatko, L. S. Estimating species trees using approximate Bayesian computation. Mol. Phylogenet. Evol. 59, 354–363 (2011).
Google Scholar
Satas, G. & Raphael, B. J. Tumor phylogeny inference using tree-constrained importance sampling. Bioinformatics 33, i152–i160 (2017).
Google Scholar
Dinh, V., Darling, A. E. & Matsen Iv, F. A. Online Bayesian phylogenetic inference: theoretical foundations via sequential Monte Carlo. Syst. Biol. 67, 503–517 (2018).
Google Scholar
Blassel, L. et al. Likelihood-free inference of phylogenetic tree posterior distributions. Preprint at (2026).
He, M., Hautphenne, S. & Chan, Y.-B. Approximate Bayesian computation for Markovian binary trees in phylogenetics. J. Theor. Biol. 616, 112246 (2026).
Google Scholar
Valcz, G. et al. Small extracellular vesicle DNA-mediated horizontal gene transfer as a driving force for tumor evolution: facts and riddles. Front. Oncol. 12, 945376 (2022).
Google Scholar
Bristy, N. A., Fu, X. & Schwartz, R. Sc-TUSV-Ext: single-cell clonal lineage inference from single nucleotide variants, copy number alterations, and structural variants. J. Comput. Biol. 8, 723–737 (2025). This research provides a model for the evolutionary history of structural rearrangements.
Google Scholar
Bristy, N. A. & Schwartz, R. Deconvolution and phylogeny inference of diverse variant types integrating bulk DNA-seq with single-cell RNA-seq. Bioinform. Adv. 5, vbaf234 (2025).
Google Scholar
Luecken, M. D. et al. Defining and benchmarking open problems in single-cell analysis. Nat. Biotechnol. 43, 1035–1040 (2025).
Google Scholar
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. 22, 3–18 (2021).
Google Scholar
Campbell, K. R. et al. clonealign: statistical integration of independent single-cell RNA and DNA sequencing data from human cancers. Genome Biol. 20, 54 (2019). The paper introduces a method to assign transcriptomic measurements to genetic clones of a tumour from DNA and RNA sequencing data of cells from a heterogeneous population, allowing the clone-level association of genomic and phenotypic traits.
Google Scholar
Ferreira, P. F., Kuipers, J. & Beerenwinkel, N. Mapping single-cell transcriptomes to copy number evolutionary trees. In Research in Computational Molecular Biology RECOMB 2022 (ed. Pe’er, I.) 380–381 (Springer Nature, 2021).
Wang, K. et al. Coalescing single-cell genomes and transcriptomes to decode breast cancer progression. Cell 188, 6355–6369.e16 (2025).
Google Scholar
Olsen, T. R. et al. Scalable co-sequencing of RNA and DNA from individual nuclei. Nat. Methods 22, 477–487 (2025).
Google Scholar
Yu, L. et al. scONE-seq: a single-cell multi-omics method enables simultaneous dissection of phenotype and genotype heterogeneity from frozen tumors. Sci. Adv. 9, eabp8901 (2023).
Google Scholar
Lin, Y. et al. Normal breast tissues harbour rare populations of aneuploid epithelial cells. Nature 636, 663–670 (2024).
Google Scholar
Bian, S. et al. Single-cell multiomics sequencing and analyses of human colorectal cancer. Science 362, 1060–1063 (2018).
Google Scholar
El-Kebir, M., Satas, G. & Raphael, B. J. Inferring parsimonious migration histories for metastatic cancers. Nat. Genet. 50, 718–726 (2018).
Google Scholar
Schwede, M. et al. Mutation order in acute myeloid leukemia identifies uncommon patterns of evolution and illuminates phenotypic heterogeneity. Leukemia 38, 1501–1510 (2024).
Google Scholar
Ramazzotti, D. et al. LACE: inference of cancer evolution models from longitudinal single-cell sequencing data. J. Comput. Sci. 58, 101523 (2021).
Google Scholar
Khan, R. & Mallory, X. scLongTree: an accurate computational tool to infer the longitudinal tree for single-cell DNA sequencing data. Preprint at bioRxiv (2026).
Jakatdar, A., Sashittal, P. & Raphael, B. J. Deriving longitudinal tumor phylogenies from single-cell sequencing data. Preprint at bioRxiv (2025).
Ma, C. et al. Inferring allele-specific copy number aberrations and tumor phylogeography from spatially resolved transcriptomics. Nat. Methods 21, 2239–2247 (2024).
Google Scholar
Williams, M. J. et al. Tracking clonal evolution during treatment in ovarian cancer using cell-free DNA. Nature 647, 757–765 (2025).
Google Scholar
Gerstung, M. et al. The evolutionary history of 2,658 cancers. Nature 578, 122–128 (2020).
Google Scholar
Satas, G., Myers, M. A., McPherson, A. & Shah, S. P. Inferring active mutational processes in cancer using single cell sequencing and evolutionary constraints. Preprint at bioRxiv (2025).
Sadeqi Azer, E. et al. PhISCS-BnB: a fast branch and bound algorithm for the perfect tumor phylogeny reconstruction problem. Bioinformatics 36, i169–i176 (2020).
Google Scholar
Ciccolella, S. et al. gpps: an ILP-based approach for inferring cancer progression with mutation losses from single cell data. BMC Bioinform. 21, 413 (2020).
Google Scholar
Foroughmand-Araabi, M.-H., Goliaei, S. & McHardy, A. C. Scelestial: fast and accurate single-cell lineage tree inference based on a Steiner tree approximation algorithm. PLoS Comput. Biol. 18, e1009100 (2022).
Google Scholar
Kang, S. et al. SIEVE: joint inference of single-nucleotide variants and cell phylogeny from single-cell DNA sequencing data. Genome Biol. 23, 248 (2022).
Google Scholar
Weber, L. L., Zhang, C., Ochoa, I. & El-Kebir, M. Phertilizer: growing a clonal tree from ultra-low coverage single-cell DNA sequencing of tumors. PLoS Comput. Biol. 19, e1011544 (2023).
Google Scholar
Chen, Z., Gong, F., Wan, L. & Ma, L. BiTSC 2: Bayesian inference of tumor clonal tree by joint analysis of single-cell SNV and CNA data. Brief. Bioinform. 23, bbac092 (2022).
Google Scholar
Salehi, S. et al. Cancer phylogenetic tree inference at scale from 1000s of single cell genomes. Peer Community J. 3, e63 (2023).
Google Scholar
Zhang, L., Bass, H. W., Irianto, J. & Mallory, X. Integrating SNVs and CNAs on a phylogenetic tree from single-cell DNA sequencing data. Genome Res. 33, 2002–2017 (2023).
Google Scholar
Weber, L. L., Hart, A., Ochoa, I. & El-Kebir, M. Pharming: joint clonal tree reconstruction of single-nucleotide variant and copy number aberration evolution from single-cell DNA sequencing of tumors. J. Comput. Biol. 33, 2–18 (2025).
Google Scholar
Lei, H. et al. Tumor copy number deconvolution integrating bulk and single-cell sequencing data. J. Comput. Biol. 27, 565–598 (2020).
Google Scholar
Wang, F. et al. MEDALT: single-cell copy number lineage tracing enabling gene discovery. Genome Biol. 22, 70 (2021).
Google Scholar
Lei, H. et al. Tumor heterogeneity assessed by sequencing and fluorescence in situ hybridization (FISH) data. Bioinformatics 37, 4704–4711 (2021).
Google Scholar
Liu, Y., Edrisi, M., Yan, Z., A Ogilvie, H. & Nakhleh, L. NestedBD: Bayesian inference of phylogenetic trees from single-cell copy number profiles under a birth-death model. Algorithms Mol. Biol. 19, 18 (2024).
Google Scholar


