Overview:

Many obstacles complicate the ability to successfully treat cancer; chemotherapy resistance is one of the most pressing. Chemotherapy consists of many different types of chemical drugs that kill fast-growing cells, including cancer cells. Since its discovery, these drugs have been some of the most essential treatments available for cancer. Chemotherapy resistance occurs when the cancerous cells within a patient are no longer killed by a chemotherapy drug, rendering this treatment ineffective. The emergence of chemotherapy resistance is an evolutionary problem. This means that finding ways to combat resistance requires evolutionary thinking. Here, we discuss one of several proposed solutions to chemotherapy resistance – adaptive therapy.

Adaptive therapy
Chemotherapy resistance is a common occurrence. While precise measurements of its occurrence are hard to gauge, one estimate suggests that over 90% of treatment failures in patients with metastatic cancer are caused by drug resistance (1). In response to the challenge presented by chemotherapy resistance, researchers have been searching for potential solutions. One proposed solution, called adaptive therapy, has a different goal for chemotherapy. Instead of trying to kill as many cancer cells as possible, adaptive therapy aims to prevent cancer progression, avoiding selection for resistant cancer cells in the process. While adaptive therapy is not the only promising therapeutic strategy, it illustrates how evolutionary and ecological principles are applicable to a pervasive medical problem.

The logic behind adaptive therapy is based on two key principles – Somatic selection and Trade-offs.

Multiple levels of selections: somatic selection
Tumors are a population of cells undergoing natural selection (2). Cells within a tumor exhibit heritable variation (3), and this variation influences each cell’s ability to replicate. New mutations can create cancer cells with selective advantages within the tumor; for example, new mutations may result in cells that replicate faster, hog more energy, or disconnect from a primary tumor, enter the bloodstream, and colonize a secondary tumor. Cells with these sorts of selective advantages are expected to become more common over time. This process, often called somatic selection, explains why cancers become more aggressive with time. It also explains how tumors can become resistant to chemotherapy.

Traditionally, doctors administer high doses in an attempt to kill as many cancerous cells as possible. If no resistant cells are present at the start of treatment, this high-dosage treatment should, theoretically, completely eliminate a cancerous tumor. However, this is rarely the case. Cells with some level of resistance are often present before treatment ever starts and are among the surviving cancer cells after high dosage treatment finishes. This reduces the size of the tumor, but simultaneously selects for any cells with resistance to the chemotherapy that was used. Importantly, with the susceptible cells gone, the growth of the resistant cell population is no longer hindered by competition with the susceptible cells. That is, the resistant cells can experience a competitive release.

Trade-offs
To avoid this competitive release, adaptive therapy exploits a trade-off that comes with evolved resistance. While drug resistance is beneficial in the presence of chemotherapy, it can be costly in its absence. For example, imagine a mutation makes a cell resistant by upregulating the expression of a membrane pump able to remove the chemotherapy drug from the cell. In the presence of chemotherapy, this mutation provides a clear benefit. Even though this cell is diverting extra resources to this pump, this cost is worth avoiding cell death. However, in the absence of chemotherapy, the extra energy needed to express and use the pump is wasteful; the cell would be better off allocating this energy to other cellular processes, like replication. Thus, while this mutation would face positive selection in the presence of chemotherapy, it would face negative selection in its absence.

So how does an adaptive therapy strategy exploit this trade-off and avoid competitive release? Small doses of chemotherapy are administered at intervals timed according to the growth of the cancer being treated (4, 5). The dose of chemotherapy should be high enough to shrink the tumor and mitigate the harm associated with cancer, but low enough to allow chemotherapy susceptible cells to survive. If done properly, it maintains a cellular ecology where resistant cells are always around to outcompete susceptible cells. Lowering the levels of chemotherapy administered has the added benefit of mitigating some of its notoriously harsh side effects.

Early clinical uses of adaptive therapy provide proof of concept. The first human clinical study of an adaptive therapy strategy includes 11 patients with prostate cancer. Compared to outcomes using traditional high dose therapies, the use of adaptive therapy has prolonged time to progression for patients in the trial (6). As the trial is ongoing, it is still unclear just how effective this strategy can be. However, adaptive therapy presents an innovative use of evolutionary theory that has progressed to the clinic. It can similarly be used as content within evolutionary biology curriculum.

Principles this example illustrates:

Multiple levels of selection: somatic selection

Teaching somatic selection first requires students to have some understanding on the cell cycle, and how certain types of mutations can lead to cells that escape regulatory control of replication. Once this is covered, students can be asked to think about how tumors might progress. One way to do this is by having them draw visual models that indicate unique mutations among cells. Students can be asked to think through different scenarios. For example, what would the progression of cancer look if an early mutation led to lower fidelity DNA replication compared to a cancer where this did not occur?

Instructors can continue this exercise by providing similar illustrations of tumors that vary in the number of resistant cells, and the level of resistance among those cells. Students can then be asked to predict what might occur over time when chemotherapy is used. This same method can be used to have students to diagram the logic behind adaptive therapy.

Trade-offs

Adaptive therapy is just one of many contexts that instructors can use to teach trade-offs, which are ubiquitous across biology. A strength of focusing on trade-offs when teaching adaptive therapy is that it requires students to apply this principle, not just understand its meaning. In doing so, students can go through different component ideas involved in trade-offs. Students can be asked to work through the impact of the local environment on trade-offs by explaining why the fitness of chemotherapy resistant cells compared to chemotherapy sensitive cells differs based on the presence or absence of chemotherapy. Instructors can have students focus on proximate reasons for trade-offs by having students discuss how known resistance mutations could result in a trade-off in the first place. Students can also learn about the evolutionary impacts of trade-offs, including how it can lead to selection that mitigates their costs. Instructors could propose a hypothetical situation where a compensatory mutation emerges within a resistant cell line, eliminating the cost of the trade-off.

Additional resources:

Readings

1. https://www.wsj.com/articles/a-new-approach-to-cancer-treatment-draws-lessons-from-darwin-11578414469
2. https://moffitt.org/publications/moffitt-momentum/volume-4-issue-2-moffitt-momentum/adapting-for-survival/
3. https://www.wired.com/story/cancer-treatment-darwin-evolution/

Videos

Journal articles

1. Longley, D. B., & Johnston, P. G. (2005). Molecular mechanisms of drug resistance. The Journal of Pathology: A Journal of the Pathological Society of Great Britain and Ireland, 205(2), 275-292.
2. Greaves, M., & Maley, C. C. (2012). Clonal evolution in cancer. Nature, 481(7381), 306.
3. Fisher, R., Pusztai, L., & Swanton, C. (2013). Cancer heterogeneity: implications for targeted therapeutics. British journal of cancer, 108(3), 479-485.
4. Gatenby, R. A., Silva, A. S., Gillies, R. J., & Frieden, B. R. (2009). Adaptive therapy. Cancer research, 69(11), 4894-4903.
5. Willyard, C. (2016). Cancer therapy: an evolved approach. Nature, 532, 166-168.
6. Zhang, J., Cunningham, J. J., Brown, J. S., & Gatenby, R. A. (2017). Integrating evolutionary dynamics into treatment of metastatic castrate-resistant prostate cancer. Nature communications, 8(1), 1-9.