• Article
• Open access
• Published: 27 December 2025

Nature Communications , Article number: (2025) Cite this article

We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

Abstract

Despite its critical role in tumor evolution, a detailed quantitative understanding of the evolutionary dynamics of aneuploidy remains elusive. Here we introduce ALFA-K (Adaptive Local Fitness landscapes for Aneuploid Karyotypes), a method that infers chromosome-level karyotype fitness landscapes from longitudinal single-cell data. ALFA-K estimates fitness of thousands of karyotypes closely related to observed populations, enabling robust prediction of emergent karyotypes not yet experimentally detected. We validate ALFA-K’s performance using synthetic data from an agent-based model and empirical data from in vitro and in vivo passaged cell lines. Analysis of fitted landscapes suggests several key insights: (1) Whole genome doubling facilitates aneuploidy evolution by narrowing the spectrum of deleterious copy-number changes; (2) Environmental context and cisplatin treatment significantly modulate the fitness impact of these changes; (3) Fitness effects of copy-number changes depend on parental karyotype; and (4) Chromosome mis-segregation rates strongly influence the predominant karyotypes in evolving populations.

Data availability

The large dataset containing Agent-Based Model (ABM) simulation data used for synthetic benchmarking is available via Zenodo36https://doi.org/10.5281/zenodo.17726562. The raw experimental data originating from Salehi et al.23, as well as data required to reproduce the specific analyses presented in this work, are available in the project repository37: https://github.com/Richard-Beck/ALFA-K; https://zenodo.org/doi/10.5281/zenodo.17753550.

Code availability

The core ALFA-K method is implemented as a lightweight R package (alfakR), which is available38 at https://github.com/Richard-Beck/alfakR. Source code and scripts required to generate the results and figures presented in this paper are available37: https://github.com/Richard-Beck/ALFA-K.

References

Bakhoum, S. F. & Cantley, L. C. The multifaceted role of chromosomal instability in cancer and its microenvironment. Cell 174, 1347–1360 (2018).

Google Scholar 1.

Santaguida, S. & Amon, A. Short- and long-term effects of chromosome mis-segregation and aneuploidy. Nat. Rev. Mol. Cell Biol. 16, 473–485 (2015).

Google Scholar 1.

Sansregret, L., Vanhaesebroeck, B. & Swanton, C. Determinants and clinical implications of chromosomal instability in cancer. Nat. Rev. Clin. Oncol. 15, 139–150 (2018).

Google Scholar 1.

Ben-David, U. & Amon, A. Context is everything: aneuploidy in cancer. Nat. Rev. Genet. 21, 44–62 (2020).

Google Scholar 1.

Pavelka, N. et al. Aneuploidy confers quantitative proteome changes and phenotypic variation in budding yeast. Nature 468, 321–325 (2010).

Google Scholar 1.

Torres, E. M. et al. Effects of aneuploidy on cellular physiology and cell division in haploid yeast. Science 317, 916–924 (2007).

Google Scholar 1.

Chen, G., Rubinstein, B. & Li, R. Whole chromosome aneuploidy: big mutations drive adaptation by phenotypic leap. Bioessays 34, 893–900 (2012).

Google Scholar 1.

Shih, J. et al. Cancer aneuploidies are shaped primarily by effects on tumour fitness. Nature 619, 793–800 (2023).

Google Scholar 1.

Davoli, T. et al. Cumulative haploinsufficiency and triplosensitivity drive aneuploidy patterns and shape the cancer genome. Cell 155, 948–962 (2013).

Google Scholar 1.

Torres, E. M., Williams, B. R. & Amon, A. Aneuploidy: cells losing their balance. Genetics 179, 737–746 (2008).

Google Scholar 1.

Tang, Y.-C. & Amon, A. Gene copy-number alterations: a cost-benefit analysis. Cell 152, 394–405 (2013).

Google Scholar 1.

ICGC/TCGA Pan-Cancer Analysis of Whole Genomes Consortium Pan-cancer analysis of whole genomes. Nature 578, 82–93 (2020).

Google Scholar 1.

Nguyen, B. et al. Genomic characterization of metastatic patterns from prospective clinical sequencing of 25,000 patients. Cell 185, 563–575.e11 (2022).

Google Scholar 1.

Drews, R. M. et al. A pan-cancer compendium of chromosomal instability. Nature 606, 976–983 (2022).

Google Scholar 1.

Shukla, A. et al. Chromosome arm aneuploidies shape tumour evolution and drug response. Nat. Commun. 11, 449 (2020).

Google Scholar 1.

Karlsson, K. et al. Deterministic evolution and stringent selection during preneoplasia. Nature 618, 383–393 (2023).

Google Scholar 1.

Baslan, T. et al. Ordered and deterministic cancer genome evolution after p53 loss. Nature 608, 795–802 (2022).

Google Scholar 1.

Gusev, Y., Kagansky, V. & Dooley, W. C. A stochastic model of chromosome segregation errors with reference to cancer cells. Math. Comput. Model. 32, 97–111 (2000).

Google Scholar 1.

Gusev, Y., Kagansky, V. & Dooley, W. C. Long-term dynamics of chromosomal instability in cancer: a transition probability model. Math. Comput. Model. 33, 1253–1273 (2001).

Google Scholar 1.

Elizalde, S., Laughney, A. M. & Bakhoum, S. F. A Markov chain for numerical chromosomal instability in clonally expanding populations. PLoS Comput. Biol. 14, e1006447 (2018).

Google Scholar 1.

Laughney, A. M., Elizalde, S., Genovese, G. & Bakhoum, S. F. Dynamics of tumor heterogeneity derived from clonal karyotypic evolution. Cell Rep. 12, 809–820 (2015).

Google Scholar 1.

Lynch, A. R., Arp, N. L., Zhou, A. S., Weaver, B. A. & Burkard, M. E. Quantifying chromosomal instability from intratumoral karyotype diversity using agent-based modeling and Bayesian inference. Elife 11, e69799 (2022).

Google Scholar 1.

Salehi, S. et al. Clonal fitness inferred from time-series modelling of single-cell cancer genomes. Nature 595, 585–590 (2021).

Google Scholar 1.

Nowak, M. A. Exploring the Equations of Life. (Harvard Univ. Press: Cambridge, MA, 2006).

Google Scholar 1.

Kimmel, G. J. et al. Intra-tumor heterogeneity, turnover rate and karyotype space shape susceptibility to missegregation-induced extinction. PLoS Comput. Biol. 19, e1010815 (2023).

Google Scholar 1.

Watson, E. V. et al. Chromosome evolution screens recapitulate tissue-specific tumor aneuploidy patterns. Nat. Genet. 56, 900–912 (2024).

Google Scholar 1.

Vargas-Rondón, N., Villegas, V. E. & Rondón-Lagos, M. The role of chromosomal instability in cancer and therapeutic responses. Cancers 10, 4 (2017).

Google Scholar 1.

Gatenby, R. A., Silva, A. S., Gillies, R. J. & Frieden, B. R. Adaptive therapy. Cancer Res. 69, 4894–4903 (2009).

Google Scholar 1.

Gemble, S. et al. Genetic instability from a single S phase after whole-genome duplication. Nature 604, 146–151 (2022).

Google Scholar 1.

Bakhoum, S. F. et al. Chromosomal instability drives metastasis through a cytosolic DNA response. Nature 553, 467–472 (2018).

Google Scholar 1.

Thompson, S. L. & Compton, D. A. Examining the link between chromosomal instability and aneuploidy in human cells. J. Cell Biol. 180, 665–672 (2008).

Google Scholar 1.

Shi, Q. & King, R. W. Chromosome nondisjunction yields tetraploid rather than aneuploid cells in human cell lines. Nature 437, 1038–1042 (2005).

Google Scholar 1.

Fabre, M. A. et al. The longitudinal dynamics and natural history of clonal haematopoiesis. Nature 606, 335–342 (2022).

Google Scholar 1.

Oktriani, R. et al. Genome-wide CRISPR screen identifies genes involved in metastasis of pancreatic ductal adenocarcinoma. Cancers 16, 3684 (2024).

Google Scholar 1.

Jain, I. H. et al. Genetic screen for cell fitness in high or low oxygen highlights mitochondrial and lipid metabolism. Cell 181, 716–727.e11 (2020).

Google Scholar 1.

Beck, R. J. Processed data supporting ALFA-K. Zenodo https://doi.org/10.5281/zenodo.15809074 (2025). 1.

Beck, R. J. ALFA-K Manuscript Reproducibility Code. Zenodo https://doi.org/10.5281/zenodo.17753550 (2025). 1.

Beck, R. J. alfakR R Package. Zenodo https://doi.org/10.5281/zenodo.17753535 (2025).

Download references

Acknowledgments

We are grateful to the members of the Integrated Mathematical Oncology (IMO) department at Moffitt for their valuable feedback on this manuscript. The insightful comments and suggestions provided during numerous department meetings were instrumental in strengthening the final work. We also thank Sohrab Shah and Sohrab Salehi for sharing the scDNA-seq–derived copy number calls, along with detailed information on their generation. This work was supported by the NCI grants 1R37CA266727-01A1 (N.A), 1R21CA269415-01A1 (N.A) and 1R03CA259873-01A1 (N.A). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Author information

Authors and Affiliations

H. Lee Moffitt Cancer Center and Research Institute, Integrated Mathematical Oncology, Tampa, Florida, USA

Richard J. Beck, Tao Li & Noemi Andor

Authors

1. Richard J. Beck
2. Tao Li
3. Noemi Andor

Contributions

N.A. and R.J.B. conceived the study; R.J.B. and N.A. developed the mathematical methodology; R.J.B. implemented the ALFA-K software and performed the formal analysis; T.L. performed software and methodological validation; N.A. acquired funding and supervised the project; R.J.B. and N.A. wrote and edited the manuscript.

Corresponding author

Correspondence to Noemi Andor.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Communications thanks Ruping Sun, who co-reviewed with Chenyu Wu and the other anonymous reviewer(s) for their contribution to the peer review of this work. A peer review file is available.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Reprints and permissions

About this article

Cite this article

Beck, R.J., Li, T. & Andor, N. ALFA-K: Local adaptive mapping of karyotype fitness landscapes. Nat Commun (2025). https://doi.org/10.1038/s41467-025-67750-0

Download citation

Received: 12 July 2024

Accepted: 08 December 2025

Published: 27 December 2025

DOI: https://doi.org/10.1038/s41467-025-67750-0