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Evolutionary therapy

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Evolutionary therapy is a subfield of evolutionary medicine that utilizes concepts from evolutionary biology in management of diseases caused by evolving entities such as cancer and microbial infections.[1] These evolving disease agents adapt to selective pressure introduced by treatment, allowing them to develop resistance to therapy, making it ineffective.[2]

Evolutionary therapy relies on the notion that Darwinian evolution is the main reason behind lethality of late stage cancer and multi-drug resistant bacterial infections such as methicillin-resistant Staphylococcus aureus.[3] Thus, evolutionary therapy suggests that treatment of such highly dynamic evolving diseases should be changing over time to account for changes in disease populations.[4] Adaptive treatment strategies typically cycle between different drugs or drug doses to take advantage of predictable patterns of disease evolution. This is in contrast to standardized treatment approach which is applied to all patients and equally based on their cancer type and grade. There are still numerous obstacles to the use of evolutionary therapy in clinical practice. These obstacles include high contingency of trajectory, speed of evolution, and inability to track the population state of disease over time.

Context

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Resistance to chemotherapy and molecularly targeted therapies is a major problem facing current cancer research.[5] All malignant cancers are fundamentally governed by Darwinian dynamics of the somatic evolution in cancer. Malignant cancers are dynamically evolving clades of cells living in distinct microhabitats that almost certainly ensure the emergence of therapy-resistant populations. Cytotoxic cancer therapies also impose intense evolutionary selection pressures on the surviving cells and thus increase the evolutionary rate. Importantly, the principles of Darwinian dynamics also embody fundamental principles that can illuminate strategies for the successful management of cancer.[6][7] Eradicating the large, diverse and adaptive populations found in most cancers presents a formidable challenge. One centimetre cubed of cancer contains about 10^9 transformed cells and weighs about 1 gram, which means there are more cancer cells in 10 grams of tumour than there are people on Earth. Unequal cell division and differences in genetic lineages and microenvironmental selection pressures mean that the cells within a tumour are diverse both in genetic make-up and observable characteristics.

Mechanisms

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Collateral sensitivity

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Resistance to one drug can lead to unwanted cross-resistance to some other drugs[8] and "collateral" sensitivity to yet other drugs [9][10][11] Alternative methods include incorporating analytically tractable stochastic control algorithms to direct the evolution to specific states of resistance that encode sensitivity to other drugs,[12] or machine learning based approaches like reinforcement learning.[13]

Treatment strategies

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Adaptive therapy

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The standard approach to treating cancer is giving patients the maximum tolerated amount of chemotherapy with the goal of doing the maximum possible damage to the tumor without killing the patient. This method is relatively effective, but it also causes major toxicities.[14] Adaptive therapy is an evolutionary therapy that aims to maintain or reduce tumor volume by employing minimum effective drug doses or timed drug holidays.[15][16] The timing and duration of these holidays, which relies on the ability to modulate resistant vs. sensitive populations of cancer cells through competition, is a subject which has been studied using optimal control[17] in theoretical studies based on Evolutionary game theory based models. The ability to modulate these populations secondary relies on the assumption that there is a both frequency-dependent selection, and an associated fitness cost to that resistance.

Proof of principle for adaptive therapy has also been established in a recent phase 2 clinical trial[18] [19] as well as in vivo.[14]

Double bind

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In the evolutionary double bind, one drug causes increased susceptibility of the evolving cancer to another drug. Some have found that effectiveness might be based on interactions of populations through commensalism.[20] Others imply that population control may be possible if resistance to therapy requires a substantial and costly phenotypic adaptation that reduces the organism's fitness. [21]

Extinction therapy

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Extinction therapy is inspired by mass extinction events from the Anthropocene era. [22] This treatment strategy is also sometimes referred to as first strike-second strike, where the first strike reduces the size and heterogeneity of a population so that the second strike that follows can kill the surviving, often fragmented population below a threshold by stochastic perturbations. [23]

Current state

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Although there is extensive modeling work on evolutionary therapy,[24] there are only a few completed and ongoing clinical trials that use evolutionary therapy. First one conducted in Moffitt Cancer Center on patients with metastatic castrate-resistant prostate cancer showed outcomes that "show significant improvement over published studies and a contemporaneous population."[25] This study met with some criticism.[26]

References

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  1. ^ "Evolutionary Therapy". Moffitt Cancer Center. Open Publishing. Retrieved 2022-02-25.
  2. ^ Greaves M, Maley CC (January 2012). "Clonal evolution in cancer". Nature. 481 (7381): 306–313. Bibcode:2012Natur.481..306G. doi:10.1038/nature10762. PMC 3367003. PMID 22258609.
  3. ^ Davies J, Davies D (September 2010). "Origins and evolution of antibiotic resistance". Microbiology and Molecular Biology Reviews. 74 (3): 417–433. doi:10.1128/MMBR.00016-10. PMC 2937522. PMID 20805405.
  4. ^ Gatenby RA, Brown JS (November 2020). "Integrating evolutionary dynamics into cancer therapy". Nature Reviews. Clinical Oncology. 17 (11): 675–686. doi:10.1038/s41571-020-0411-1. PMID 32699310. S2CID 220681064.
  5. ^ Holohan C, Van Schaeybroeck S, Longley DB, Johnston PG (October 2013). "Cancer drug resistance: an evolving paradigm". Nature Reviews. Cancer. 13 (10): 714–726. doi:10.1038/nrc3599. PMID 24060863. S2CID 24719097.
  6. ^ Gillies RJ, Verduzco D, Gatenby RA (June 2012). "Evolutionary dynamics of carcinogenesis and why targeted therapy does not work". Nature Reviews. Cancer. 12 (7): 487–493. doi:10.1038/nrc3298. PMC 4122506. PMID 22695393.
  7. ^ Gatenby RA (May 2009). "A change of strategy in the war on cancer". Nature. 459 (7246): 508–509. Bibcode:2009Natur.459..508G. doi:10.1038/459508a. PMID 19478766. S2CID 205046753.
  8. ^ "CROSS resistance to antibiotics". Journal of the American Medical Association. 148 (6): 470–471. February 1952. doi:10.1001/jama.1952.02930060052015. PMID 14888510.
  9. ^ Santos-Lopez, Alfonso; Marshall, Christopher W; Haas, Allison L; Turner, Caroline; Rasero, Javier; Cooper, Vaughn S (25 August 2021). "The roles of history, chance, and natural selection in the evolution of antibiotic resistance". eLife. 10: e70676. doi:10.7554/eLife.70676. PMC 8412936. PMID 34431477.
  10. ^ Maltas J, Wood KB (October 2019). "Pervasive and diverse collateral sensitivity profiles inform optimal strategies to limit antibiotic resistance". PLOS Biology. 17 (10): e3000515. doi:10.1371/journal.pbio.3000515. PMC 6834293. PMID 31652256.
  11. ^ Acar A, Nichol D, Fernandez-Mateos J, Cresswell GD, Barozzi I, Hong SP, et al. (April 2020). "Exploiting evolutionary steering to induce collateral drug sensitivity in cancer". Nature Communications. 11 (1): 1923. Bibcode:2020NatCo..11.1923A. doi:10.1038/s41467-020-15596-z. PMC 7174377. PMID 32317663.
  12. ^ Iram, S. (2021). "Controlling the speed and trajectory of evolution with counterdiabatic driving". Nature Physics. 17: 135–142. arXiv:1912.03764. doi:10.1038/s41567-020-0989-3.
  13. ^ Weaver, D. (2024). "Reinforcement Learning informs optimal treatment strategies to limit antibiotic resistance". Proceedings of the National Academy of Sciences. 121 (16): e2303165121. doi:10.1073/pnas.2303165121. PMC 11032439. PMID 38607932.
  14. ^ a b Enriquez-Navas PM, Kam Y, Das T, Hassan S, Silva A, Foroutan P, et al. (February 2016). "Exploiting evolutionary principles to prolong tumor control in preclinical models of breast cancer". Science Translational Medicine. 8 (327). 327ra24. doi:10.1126/scitranslmed.aad7842. PMC 4962860. PMID 26912903.
  15. ^ Kim E, Brown JS, Eroglu Z, Anderson AR (February 2021). "Adaptive Therapy for Metastatic Melanoma: Predictions from Patient Calibrated Mathematical Models". Cancers. 13 (4): 823. doi:10.3390/cancers13040823. PMC 7920057. PMID 33669315.
  16. ^ Gatenby RA, Silva AS, Gillies RJ, Frieden BR (June 2009). "Adaptive therapy". Cancer Research. 69 (11): 4894–4903. doi:10.1158/0008-5472.CAN-08-3658. PMC 3728826. PMID 19487300.
  17. ^ Cunningham J, Brown J, Gatenby R, Stankova K (December 2018). "Optimal control to develop therapeutic strategies for metastatic castrate resistant prostate cancer". JAMA Oncology. 459: 67–78. Bibcode:2018JThBi.459...67C. doi:10.1016/j.jtbi.2018.09.022. PMID 30243754. S2CID 52340340.
  18. ^ Zhang J, Cunningham JJ, Brown JS, Gatenby RA (November 2017). "Integrating evolutionary dynamics into treatment of metastatic castrate-resistant prostate cancer". Nature Communications. 8 (1): 1816. Bibcode:2017NatCo...8.1816Z. doi:10.1038/s41467-017-01968-5. PMC 5703947. PMID 29180633.
  19. ^ Zhang J, Cunningham JJ, Brown JS, Gatenby RA (June 2022). "Evolution-based mathematical models significantly prolong response to abiraterone in metastatic castrate-resistant prostate cancer and identify strategies to further improve outcomes". eLife. 11. doi:10.7554/eLife.76284. PMC 9239688. PMID 35762577.
  20. ^ Basanta D, Gatenby RA, Anderson AR (April 2012). "Exploiting evolution to treat drug resistance: combination therapy and the double bind". Mol Pharm. 9 (4): 914–921. doi:10.1021/mp200458e. PMC 3325107. PMID 22369188.
  21. ^ Gatenby R, Brown J, Vincent T (September 2014). "Lessons from applied ecology: cancer control using an evolutionary double bind". Cancer Research. 69 (19): 7499–7502. doi:10.1158/0008-5472.CAN-09-1354. PMID 19752088.
  22. ^ Gatenby RA, Artzy-Randrup Y, Epstein T, Reed DR, Brown JS (February 2021). "Eradicating Metastatic Cancer and the Eco-Evolutionary Dynamics of Anthropocene Extinctions". Cancer Research. 80 (3): 613–623. doi:10.1158/0008-5472.CAN-19-1941. PMC 7771333. PMID 31772037.
  23. ^ Gatenby RA, Zhang J, Brown JS (July 2019). "First Strike–Second Strike Strategies in Metastatic Cancer: Lessons from the Evolutionary Dynamics of Extinction". Cancer Research. 79 (13): 3174–3177. doi:10.1158/0008-5472.CAN-19-0807. PMC 6606376. PMID 31221821.
  24. ^ Wölfl B, Te Rietmole H, Salvioli M, Kaznatcheev A, Thuijsman F, Brown JS, et al. (2021). "The Contribution of Evolutionary Game Theory to Understanding and Treating Cancer". Dynamic Games and Applications. 12 (2): 313–342. doi:10.1007/s13235-021-00397-w. PMC 9117378. PMID 35601872. S2CID 239673089.
  25. ^ Zhang J, Cunningham JJ, Brown JS, Gatenby RA (November 2017). "Integrating evolutionary dynamics into treatment of metastatic castrate-resistant prostate cancer". Nature Communications. 8 (1): 1816. Bibcode:2017NatCo...8.1816Z. doi:10.1038/s41467-017-01968-5. PMC 5703947. PMID 29180633.
  26. ^ Mistry HB (January 2021). "On the reporting and analysis of a cancer evolutionary adaptive dosing trial". Nature Communications. 12 (1): 316. Bibcode:2021NatCo..12..316M. doi:10.1038/s41467-020-20174-4. PMC 7804309. PMID 33436546.
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