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The loggerhead sea turtle was classified as a threatened species in the late twentieth century. Conservation strategies initially focused on protecting nests and safeguarding eggs, largely because these interventions were intuitive and easy to implement, but these efforts yielded limited success. Quantitative life history analysis ultimately revealed why: protecting early life stages had minimal impact on population growth, whereas reducing adult mortality would yield far greater benefits¹. These modeling insights directly informed policy, culminating in the 1994 World Trade Organization Shrimp-Turtle case, which mandated the use of turtle excluder devices on shrimp trawls to reduce bycatch². This striking ecological example underscores the power of life history theory to guide recovery strategies.

Over the last few years, we have applied similar life history theory principles to cancer, with the aim of driving cancer cell populations to extinction. It is well known that cancer cells often respond to therapy by entering different cellular states, a phenomenon known as phenotypic plasticity^3,4. We specifically focused on one such case: therapy-induced endocycling, an alternate cell cycle trajectory in which cells exit the cell cycle after S phase, bypass mitosis, and undergo whole-genome duplication. The resulting polyploid life history state seemed to be linked to therapy resistance: experimental evidence suggested that these cells persisted under harsh therapeutic conditions and gave rise to progeny that went on to seed recurrence. However, how these cell state transitions interact with genetic evolution to generate resistance remained poorly understood.

To this end, we developed a suite of mathematical tools to study eco-evolutionary dynamics in structured populations more broadly^5–8. Using these methods, we constructed models within a strong-inference framework to test various hypotheses about the role of therapy-induced endocycling: from a simple dormancy strategy to more provocative mechanisms involving self-(epi)genetic modification and cellular memory^8–10. In collaboration with empirical colleagues, we showed that the polyploid state served as a source of evolutionary rescue, providing a refuge for cancer cells from therapy and accelerating their adaptation to the stressor¹¹. Furthermore, we showed that blocking this keystone cell state transition could lead to cancer extinction (work that contributed to an approved clinical trial: NCT05574712).

However, identifying drugs to use in life history enlightened therapies was challenging. To address this challenge, we developed a novel technique called cell cycle mapping that combined multiplex immunofluorescence imaging, deep learning-based cellular segmentation, single-cell proteomics, and manifold learning to provide visual representations of the life history of cells^12–14. This interpretable machine learning framework allowed us to interrogate the molecular factors that govern transitions among phases of the cell cycle. With this approach, we identified key regulators of pathological endocycling, providing targets for ongoing preclinical validation. Notably, this hybrid approach is broadly generalizable: we have applied it to study endocycling in a variety of contexts, from breast cancer¹⁵ to liver disease¹⁶ to kidney disease.

Life history enlightened therapies hold much promise as a treatment strategy for a wide range of pathologies. Our work, which spanned basic theoretical development, new eco-evolutionary theories underlying drug resistance, and novel experimental-computational hybrid approaches to drug target identification, illustrates how basic mathematical and conceptual frameworks can translate into fundamental and far-reaching clinical impact.

References

1. Crouse, D. T., Crowder, L. B. & Caswell, H. A stage-based population model for loggerhead sea turtles and implications for conservation. Ecology 68, (1987).



2. Committee on Sea Turtle Conservation, Board on Environmental Studies and Toxicology Board on Biology & Commission on Life Sciences National Research Council. Decline of the Sea Turtles: Causes and Prevention. 1–259 (1990).



3. Emmons, M. F., Faião-Flores, F. & Smalley, K. S. M. The role of phenotypic plasticity in the escape of cancer cells from targeted therapy. Biochem. Pharmacol. 122, 1–9 (2016).



4. Gupta, P. B., Pastushenko, I., Skibinski, A., Blanpain, C. & Kuperwasser, C. Phenotypic plasticity: Driver of cancer initiation, progression, and therapy resistance. Cell Stem Cell 24, 65–78 (2019).



5. Bukkuri, A. Eco-evolutionary dynamics of structured populations in periodically fluctuating environments: a G function approach. Theory Biosci. 143, 293–299 (2024).



6. Bukkuri, A. Modeling stress-induced responses: plasticity in continuous state space and gradual clonal evolution. Theory Biosci. 143, 63–77 (2024).



7. Bukkuri, A. & Brown, J. S. Integrating eco‐evolutionary dynamics into matrix population models for structured populations: Discrete and continuous frameworks. Methods Ecol. Evol. 14, 1475–1488 (2023).



8. Bukkuri, A. et al. Stochastic models of Mendelian and reverse transcriptional inheritance in state-structured cancer populations. Sci. Rep. 12, 1–13 (2022).



9. Bukkuri, A. et al. A life history model of the ecological and evolutionary dynamics of polyaneuploid cancer cells. Sci. Rep. 12, 1–12 (2022).



10. Bukkuri, A. et al. A mathematical investigation of polyaneuploid cancer cell memory and cross-resistance in state-structured cancer populations. Sci. Rep. 13, 1–11 (2023).



11. Carroll, C. et al. Drug-resilient Cancer Cell Phenotype Is Acquired via Polyploidization Associated with Early Stress Response Coupled to HIF2α Transcriptional Regulation. Cancer Res. Commun. 4, 691–705 (2024).



12. Stallaert, W. et al. The structure of the human cell cycle. Cell Syst. 13, 230–240 (2022).



13. Stallaert, W. et al. The molecular architecture of cell cycle arrest. Mol. Syst. Biol. 18, 1–14 (2022).



14. Zikry, T. M. et al. Cell cycle plasticity underlies fractional resistance to palbociclib in ER+/HER2- breast tumor cells. Proc. Natl. Acad. Sci. U. S. A. 121, 1–12 (2024).



15. Yang, Y. et al. Distinct p21 dynamics drive alternative routes to whole-genome duplication through a common CDK4/6-dependent polyploid G0 state. bioRxiv 1–50 (2026).



16. Bukkuri, A., McLaughlin, J., Duncan, A. W. & Stallaert, W. Life history enlightened therapies: Cell cycle mapping to identify molecular targets to prevent hepatocellular carcinoma. Evol. Med. Public Health 1–20 (2026).