One way a tumor might acquire resistance, however, is treatment. Insofar as any chemotherapy or immunotherapy destroys non-resistant cells but fails to destroy sensitive cells, selection will favor resistant cells, which renders future therapies of the same mechanism ineffective for as long as resistance is evolutionarily favored. This could explain why rotating different treatments that act upon the same pathway fail.
Dr van der Burg noted that “when a doctor gives immunotherapy, the immune system will once again fiercely attack the tumor…if this is not completely successful, new, more resistant clones may appear…they could already have been there (and now become more dominant), or they could develop under the immune system’s attack. Chemotherapy or targeted therapies may act similarly.”
Therapy-induced resistance does not, of course, explain why some tumors are unresponsive to treatment in the first-line setting, though natural selection predicts that resistance will be perpetuated by further treatment. Biomarkers may help oncologists to assign therapies effectively for the first line, but acquired resistance should be seen as a distinct, treatment-induced phenomenon. The best recipes in cancer treatment must therefore be those that rely on distinct combinations that reduce the likelihood of a tumor’s acquiring resistance or suppressing immune activity before treatments like immunotherapies are introduced.
Evolutionary theory and likelihood of immunosuppression should be important factors not only for individualized treatment, but for trial design. A phase 3 trial testing a combination of drugs that act on the same pathway may be guaranteed to fail by logic before it does so in practice, not only making the study ineffective, but a waste of resources and participating patients’ time.
There are, however, far too many possible combinations of treatments and genetic variations to test for the optimal combinations of therapy timing in the clinical trial setting. Dr van der Burg noted that “If you want to test each combination for its effect in a randomized phase 3 trial, this is undoable. What we need to do is to understand the mechanisms of action of the compounds very well and also understand which of the blocks posed by the tumor are the most important/strongest. If so we can make an informed decision.”
Evolutionary theory may, however, function as a litmus test for studies unlikely to succeed: if researchers understand the selective pressures they are placing on tumors with treatments employed, the universe of practical studies is likely to shrink. While this process would still require sifting through a daunting set of variables, it may be possible to program tools like Watson to process tumor genomic data using a function, such as the Price equation, to determine the likelihood of a particular treatment inducing resistance in a particular tumor.
While there are many questions left to answer, it would be ideal if oncologists, drug developers, and evolutionary theorists collaborated to design treatments and treatment combinations that are the most likely to succeed given the laws of natural selection, though evolutionary reasoning is not often integrated into trial design.9 For the present the potential impact of evolutionary logic in oncology is largely speculative, though the empirical evidence noted above should be counted as evidence in favor of its integration.
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