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Using the Power of Evolution to Treat Cancer

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Lisa Winter

Guest Author

9 Using the Power of Evolution to Treat Cancer
Itayba, Wikimedia Commons

Life on Earth at every scale is a culmination of evolutionary processes. The same methods learned in evolution for large phenomena, such as speciation events, can be applied to small populations of cells. Cancer is heterogeneous and understanding the differences between individual tumor cells is the key to developing effective, noninvasive therapies that will save lives. Cells that transition from somatic to malignant follow many of the same principles of evolution as do entire genomes, such as natural selection, adaptation, and mutation. This has provided the basis for an emerging field of scientific discovery: evolutionary medicine. 



Natural Selection


Certain phenotypic characteristics of a tumor allow some cells to thrive, where others have failed. In populations of cancerous cells, which are largely heterogeneous, certain lineages prove to be more reproductively fit, and over time, will dominate the tumor population. In cancer, (much like bacteria with antibiotics) certain cells form multidrug resistance to entire families of chemotherapeutic agents. Circumventing this challenge lies in further understanding the mechanisms of tumor cell biology, such as P-glycoprotein which binds and arrests the ATP-cassette, so that they might be exploited to find other avenues to choke off life of the cancer cells.





As environments inevitably change, the ability to adapt is the absolute difference between survival and demise. Microarray-based comparative genomic hybridization was used to compare length of microsattelite DNA length between tumor cells and normal somatic cells. Surprisingly, there was little variation in the microsattelite region, and its pattern was congruent with that of pathogens that replicate asexually. The tumor is more successful, because it can reproduce when it is most advantageous for the tumor cell itself. This gives a new insight to the genesis of cancer, allowing scientists to determine if environmental factors or spontaneous mutation are to blame.



Typical tumor cells have also adapted to circumvent regular somatic functions, such as surviving under hypoxic conditions. Tumor cells need, but rarely get, oxygen to perform glycolysis to make energy. Those cells that are able to perform a modified glycolysis, essentially anaerobic respiration, survive where others may not. 





Mutations are spontaneous and unpredictable. While they serve as an entry point for genetic variations to permeate populations, not all mutations are advantageous, or neutral. Deleterious mutations can turn proto-oncogenes (cells responsible for normal growth) into oncogenes, that cause cancer. This can occur in two capacities: a gain or loss of function. 


Caretaker genes, such as BRCA1/2, proofread DNA and excise any mistakes before they are replicated and have a chance to proliferate. A loss of function leads to unchecked DNA, which can snowball into an increasingly degenerative genome. 



Gatekeeper genes, such as p53, regulate each part of the cell cycle, making sure that the cell is ready to continue on to the next step. If a cell is not ready, it is given time to correct any mistakes, or initiates apoptosis. A loss of function in these genes allow cells to grow uncontrollably, resulting in cancer. Oncogenes, those that are responsible for cell growth, are also negatively affected by mutation, though this does not come through a loss of function. Normally, when a signal comes in to stimulate cell growth, an oncogene such as MYC or RAS sends a signal to the nucleus. However, a gain of function mutation means the gene is constantly being stimulated, and constantly sending signals to the nucleus to grow. If left unchecked, this growth results in cancer.


Personal Genomics



Just as the Human Genome Project allowed for widespread collaboration, geneticists are now taking a similar approach with the cancer genome. A large scale, systematic approach is necessary to ensuring that all facets of the notoriously degenerative genome are accounted for, which gives key insight into the specifics of the somatic mutations. This is no easy task, given the heterogeneity within tumor cells, but the information that is to be gained is invaluable. Not only will the findings give a deeper level of understanding of cancer genetics and promote further studies, but sequencing costs will decrease (as with all technologies that become increasingly commonplace). Further understanding the mechanisms of tumorigenesis on many levels will allow heightened sensitivity and accuracy of screening procedures. 


Surgery is the most common tool when battling cancer in its earliest stages. However, when combatting recurrent or contra lateral breast cancer, there are great numbers of failure in this localized treatment, perhaps because of human genetic variation. Sequencing single nucleotide polymorphisms (SNPs) as well as copy number variation would give an unparalleled level of personalized surgery, which would increase successes. Rather than juxtapose old treatment methods with new research capabilities, the latter is being used to make the former more effective.



Origin of Disease


A key point to understanding how disease will progress is to observe origins, and see how it has changed. Cancer is not a single disease; it is a collective term for several subtypes with distinct characteristic clinical outcomes. Understanding this heterogeneity, which seems baffling at times, is the link to developing highly targeted cancer preventive applications, as well as new treatments and therapies. The two hypotheses currently used to model cancer origins are the Cancer Stem Cell and Clonal Evolution theories. 



The Cancer Stem Cell Model (CSCM) states that a cancer tumor is made up of phenotypically heterogenous stable cells. They are heritable, and the cells rarely have characteristics that have the potential to become tumorigenic. This rarity, however, allows for the tumorigenic cells to be targeted for treatment. However, under treatment with conventional methods, such as chemotherapies, the regular tumor cells are killed, but the stem cell is not eliminated. The potential for relapse of the cancer remains high, and metastasis becomes more threatening. As the stem cells are organized hierarchically, a misstep from an early progenitor cell passes the trait on, stepwise, until the tumor cell is stable. The clearest evidence for this theory is the shown in cancers that are passed through germ line cells.


Clonal Evolution Model (CEM) is the less predictable of the two. While the characteristics of the CSCM are clear and consistent, CEM is less stringent on guidelines for cell behavior. Cancer cells can be either heterogenous or homogenous, based on the phenotype of the founding tumor cells. Similarly to the CSCM, the CEM may be hierarchical, though not always. B cell lymphoblastic leukemia operates in cohesion with this model, and is overwhelmingly used as the disease of choice for further exploring its principles.



Functional Proteomics


Early detection and treatment of disease is the best way to combat cancer. However, many current testing procedures lack the sensitivity to detect cancerous cells at the earliest stages. The blooming field of proteomics, which addresses the action of all protein from a genome, provides a noninvasive way to thoroughly screen even asymptomatic patients. The potential for widespread use of this technique would give every patient the best chance to survival. Additionally, even after the onset of cancer, proteomics can monitor the progression of the disease and use those biomarkers to establish trends. These trends can then be applied to emerging cancers, using any correlation as a starting point for future therapies. 



At its inception, proteomics was a field that existed to identify proteins expressed by the genome. Now, it has graduated into analyzing the function of proteins. These functions illustrate how some things, such as Epidermal Growth Factor Receptor (EGFR), operate under a complex network of multiprotein machinery. This has shown how information is processed under dynamic environments shaped by the cancerous genetic mutations. This could imply that mutations are not entirely random, and may be responding to environmental pressures. Proteomics can also be used to monitor widespread genomic degeneration, which, given any specific patterns, could be an avenue of exploitation during treatment.



Evolution makes sense out of the chaos of biology. This is especially true of cancer biology, which does not, at first glance, seem to follow the stable conventions frequently noted in most biological systems. Genetics has given invaluable insight to the inner workings of all biological structure. Genes, though, are subject to forces of change, such as natural selection, adaptation, and mutation. While still new and not without certain obstacles that need to be overcome, evolutionary medicine is the forefront of biological discovery and, with time, at the heart of trailblazing clinical therapies. As technology continues to progress, treatment of cancer will be more effective and less invasive. Any perceived shortcomings that exist at present should not be a call for critics to discount the potential for great amounts of good to occur. Evolutionary medicine allows for a unique window into a previously unseen world of cancer mechanics that can aid in triumph over disease.


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