I had better step back a few years. My early research was part of the wave of studies showing that “rapid” or “contemporary" evolution” often occurs in natural populations. Through time, my interest in this phenomenon expanded into “evolutionary applications” – how rapid evolution relates to applied situations, such as natural resource management, agriculture, conservation biology, and medicine. I even wrote two papers about the topic, one titled “Evolutionary biology in biodiversity science, conservation, and policy: a call to action” and one titled “Evolutionary principles and their practical application”. I also enthusiastically joined the editorial board of the new journal Evolutionary Applications that was founded by Louis Bernatchez and Michelle Tseng. I am here interested specifically in medical applications.
Evolutionary principles are now firmly ensconced in medical research and patient treatment, especially in relation to the evolution of bacterial resistance to antibiotics and viral resistance to antivirals. In particular, the use of any new antibiotic is swiftly followed by the evolution of resistance to that antibiotic, which then necessitates the development of new antibiotics. If this weren’t the case, then penicillin might still be the treatment of choice. The same evolution of resistance is also true for drugs designed to treat HIV. For instance, genome sequencing studies have documented the course of mutations that arise and spread to confer resistance to a given treatment. In both contexts, then, the evolution of resistance alters pathogen population dynamics to allow “evolutionary rescue” in the face of a treatment that would otherwise cause its extirpation from the host. In short, the evolution of resistance by human pathogens provides a clear example of eco-evolutionary dynamics.
In each of the above cases, the first part of the eco-evolutionary problem is evolution by the pathogen population within a given host. To be specific, replication by the pathogen in the host is accompanied by occasional errors (mutations), some of which (by chance) show enhanced resistance to whatever treatment is being applied. As a result, the resistant pathogens show reduced mortality relative to the pathogens without the resistance mutation and, as a consequence, resistance spreads over time through the pathogen population. Eventually, the treatment is no longer effective. The second part of the eco-evolutionary problem is that pathogens that have evolved resistance within one host can spread to new hosts, meaning that a new infection in a new host starts from a position where resistance to the preferred treatment is already strong. This, then, is the two-pronged eco-evolutionary problem faced in the treatment of infectious disease – evolution of resistance within hosts and the transmission of that resistance to new hosts.
I have recently had occasion to re-consider this eco-evolutionary medicine problem from the perspective of cancer. Many years earlier, I had heard several talks about evolution and cancer but my renewed interest came from reading two outstanding books: The Emperor of All Maladies and The Philadelphia Chromosome. Both of these books openly discuss cancer as an evolutionary problem. That is, a tumor is a population of cells in which each (or many) of the cells are replicating out of control – usually as the result of a series of mutations that originally occurred in a line of normal cells. Chemotherapy and radiation therapy are designed to kill these aberrant cells but (hopefully) not normal cells. Sometimes these treatments work right away and sometimes they do not. Other times they work initially but the patient eventually relapses. Both of these limitations are the result of evolution by the cancer cells. During replication by the cancer cells, mutations arise that can (by chance) reduce the cell’s susceptibility to the treatment. As a result – and similar to the first prong of the eco-evolutionary problem discussed above for infectious diseases – the cancer cells without these resistant mutations decrease in proportion relative to the cancer cells with the resistant mutations. During this period, the tumors often shrink as the population of non-resistant cells dies and the patient recovers. Eventually, however, the resistant cells have increased in frequency to the point where they cause expansion of the tumor again.
Cancer biology now has the above evolutionary principles firmly in mind. In particular, tumors are often genetically screened to see what mutations predominate and then treatment is based on chemotherapies known to work best against those mutations – this is the so-called “personalized medicine.” In addition, patients that relapse as their cancer evolves to escape the original treatment are often given a planned subsequent treatment that might better target the newly-evolved resistant cells. Eco-evolutionary cancer medicine!
What struck me when thinking about these phenomena is that – in relation to evolution – cancer is both the same and different from infectious diseases. It is the same with respect to the first prong of the eco-evolutionary problem discussed above – evolution within a patient – but is entirely different with respect to the second problem – transmission to a new person. As far as infectious diseases are concerned, the human body is simply a vehicle for replication and spread to other humans. As far as most cancers are concerned, however, their life – and their genetic line – ends with the death of their host. For cancer then, each human is its own independent world – never to interact with other worlds and with a finite life span beyond which propagation will not be possible. As a result, resistance that evolves within one patient will never (with rare exceptions) be transmitted to another person – which by contrast is the defining feature of infectious diseases.
To me, this suggests that evolutionary applications to cancer treatment will ultimately be very different from those to treat infectious diseases. In cancer, we can – in principle – design a series of effective targeted therapies for the common (and eventually less common) mutations. We can then use those treatments from the get-go, monitor new mutations and treat those with additional targeted therapies, and eventually cure the patient. The unique – and hopeful – part is that the same sequence of treatments should work in the next patient with the same starting mutations – because any resistance that evolved in the first patient will not have transmitted to the next. That is, evolutionary history is reset in each new patient. With infectious diseases, however, the new patient might well be starting from the ending point of the old patient – thus making the personalized treatment protocol much more difficult and the design of new therapies a never ending treadmill.