Unnatural Selection: Targeting Cancer With Evolution

Illustrated by Angela Chen

While most people might think of fitness as a measure of athleticism or time spent at the gym, evolution gives the word a much different meaning. In evolutionary theory, the term “fitness” describes the ability of an organism to reproduce and survive in its environment. (1) As time goes on, organisms acquire mutations in their DNA which change their genotype and impact their fitness. This phenomenon of differences in genotype affecting fitness is called natural selection. (1) Since the advent of modern genetics, fitness and natural selection have been used to describe populations of organisms in natural ecosystems, from Darwin’s famous finches to humans. Recently, scientists have begun using these same ideas on smaller ecosystems, like groups of cells in the human body, leading to significant advances in medical research.

A key area of research where evolutionary theory is being applied is cancer. Cancer originates from mutations in the DNA of somatic cells, which are cells that do not play a role in reproduction. (2) In particular, the disease is initiated by certain early “driver” mutations that significantly contribute to cancer progression and proliferation. Each cell can be modeled as a “parent”; when it divides, it passes down its DNA footprint to its offspring. As the disease progresses and more cells divide, differences in fitness result in cells with certain traits surviving, while others die off. This creates a diverse environment, composed of cells with different mutations and gene expression levels. (7

On a broader scale, a diverse tumor environment results in a cancer that is more heterogeneous, with distinct “clones” that share a parent cell. (3) A consequence of tumor heterogeneity is that clones may respond differently to treatments. Multiple studies have shown that heterogeneity, and cancer evolution in general, promote drug resistance in two distinct ways. (2) First, pre-existing clones that are drug-resistant may survive treatment while clones that are drug-susceptible die off. After treatment, the cells in these drug-resistant clones rapidly divide, resulting in a tumor dominated by drug-resistant cells. (2) Alternatively, some cell subpopulations can enter a “persister” state in which cell growth is limited. (4) This is evolutionarily advantageous since many cancer treatment options target cells that quickly divide. (4) After entering this “persister” state, these cells can acquire even more mutations which can result in the formation of multiple drug-resistant clones. (2) As a result, conventional treatments like chemotherapy may actually result in a cancer that is more difficult to treat. 

Current research aims to understand both the specifics of tumor heterogeneity as well as how to take advantage of this phenomenon in treatment. Scientists, including those at Washington University, are developing complex algorithms that statistically infer the clonal structure of tumors from the frequencies of mutations they possess. (5) The general method relies on the idea that low mutational frequencies are more likely to be found in subclones that developed later in time, while higher mutational frequencies are likely more prevalent in clones that contributed to disease initiation. (5) By clustering these mutational frequencies, the algorithm can designate clones and ultimately show the clonal structure of a tumor sample. Furthermore, computational advances have enabled the discovery of the evolutionary history of cancers, such as the when the driver mutations occurred in time. (7)

In more clinical settings, physicians are beginning to evaluate how to use ideas from evolutionary theory to inform therapy. One approach known as adaptive therapy focuses on selectively eliminating a portion of drug-sensitive cells instead of targeting the whole tumor. (6) This allows for the population of drug-sensitive cells to compete with the population of drug-resistant cells and therefore, limit the growth of the drug-resistant cells. The end result is a tumor that is overall more responsive to treatment since it is not dominated by drug-resistant cells, but instead still has a small, stable population of targetable, drug-sensitive cells. (6) An alternative approach targets driver mutations, which are present in the founders of the cancer cell population, and therefore are also in the offspring of these founder cells. Understanding the evolutionary history of a cancer allows for the identification of key driver events in the founder cells of the cancer. Targeting multiple driver mutations eliminates cells in multiple clones and therefore, increases the possibility of eliminating the tumor. (7) By using innovative therapies which exploit the evolutionary history of cancers, groups of cancer cells can be selectively targeted to improve patient survival. 

The application of evolutionary theory to populations of cells has led to major advances in medicine. In the area of cancer, researchers have developed new perspectives on the disease, tools to understand cancer progression, and innovative forms of therapy. Similar ideas can be applied in areas such as immunology, where immune cells must rapidly evolve to successfully combat an infection. The development of novel antibiotics relies on understanding how evolution results in groups of bacteria which respond differently to drugs. (8) Future cancer therapies will surely evolve in order to catch up to the cells they target.

Edited by: Ryan Chang
Illustrated by: Angela Chen




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