Cancer as Evolution — 2008 Summary

Click here to read part 4 in this series.

As 2008 closes, it appears that momentum is picking up for the somatic evolution view of cancer.  Here are three recently published papers of note:

  • The Evolution of Cancer (Goymer, et al, Aug 2008, Nature)
  • Cancer Research Meets Evolutionary Biology (Pepper, et al, in press, 2008 Evolutionary Applications; Santa Fe Institute working paper)
  • Genome Based Cell Population Heterogeneity Promotes Tumorigenicity: The Evolutionary Mechanism of Cancer (Ye, et al, Dec 2008, Journal of Cellular Physiology)

The first paper is a short review of the major work to date, accessible to non-specialists.  The summary suggests: “Cancer cells vary; they compete; the fittest survive. Patrick Goymer reports on how evolutionary biology can be applied to cancer — and what good it might do.”  I will summarize the other two papers below, and then give my view of the implications and where this should be heading.

Cancer Research Meets Evolutionary Biology (my summary)*

  • “Although the role of somatic evolution in cancer is rarely disputed, it has seldom been integrated into biomedical research.”
  • Not all pre-malignant neoplasms progress to cancer. It is therefore important to identify risk factors for progression as early as possible because, in many cancers, early detection and intervention improve survival.”
  • “Because neoplastic progression is a process of somatic evolution, reducing evolutionary rates should decrease cancer incidence.”
    • “One possibility is to reduce the mutation rate via therapeutic reduction in mutagen exposure.”  For example, suppressing inflammation has been show as effective in this regard.
  • “Decades ago, Nowell postulated that the emergence of drug resistance in cancer was driven by somatic evolution, an hypothesis for which there is now substantial empirical support.”
  • “The Darwinian perspective suggests that interventions that ameliorate progression or virulence without directly killing neoplastic cells would delay the emergence of resistance.”
  • When developing drugs, somatic evolution tells us that “…tumor cell toxicity does not invariably imply effective treatment” and “short-term therapeutic response may bear little relationship to the likelihood of effective longer-term treatment.”  In other words, just because you shrink or remove the tumor, doesn’t mean you’ve stopped the cancer.  In fact, “…the longer-term cost may well be an accelerated rate of resistance evolution.”
  • “By targeting the cancer cell products that alter the micro-environment, it is possible to halt or reverse tumor growth without using cytotoxins to directly kill cancer cells.”
  • Somatic evolution suggests that the cancer stem cell theory, while consistent with somatic evolution, is largely irrelevant.  The same phenomena and clinical results can be explained more parsimoniously by somatic evolution, including group selection.
  • “Direct observational studies of human neoplasms have provided insights into how somatic evolution leads to cancer outcomes and to therapeutic resistance.”
  • By acknowledging somatic evolution as the primary mechanism in cancer progression, we are and will continue to make actual clinical progress in preventing, detecting and treating cancer in humans.

Genome Based Cell Population Heterogeneity Promotes Tumorigenicity (my summary)

  • “The impact of genetic variation at the genome level is much more profound than at the gene level, as the higher level of organization often constrains lower levels and displays more stable characteristics than lower levels.”  Genome level in this paper refers to the chromosomal organization of genetic material.  If you substitute “chromosome” wherever you see “genome,” you won’t be too far off in your understanding.
    • When the genome context changes, even when the gene state is the same, it often does not keep the same biological meaning.”
  • Clinical support for focusing on the genome level and for somatic evolution theory is established in experiments and studies using a form of chromosomal imaging called spectral karyotyping (SKY):

  • To a first approximation, genetic variation at the genome level can be measured using SKY to document non-clonal chromosome aberrations (NCCAs).  An example of an NCCA can be seen above involving chromosomes 19 and 2 (simple, non-reciprocal translocation).
  • Although gene-level mutations and molecular pathways are always implicated in cancer progression, nobody has ever been able to find a pattern that is predictable enough to effectively cure cancer.  “Based on the concept of cancer evolution and the realization that cancer is a disease of probability, one can understand why elevated genome diversity will lead to the success of cancer evolution regardless of which molecular pathways or mechanisms are involved.”
  • “Significantly, the only common link to tumorigenicity is increased levels of NCCAs!”
  • Levels of observed NCCAs represent a measure of cell population diversity and “… population diversity provides the necessary pre-condition for cancer evolution to proceed….”
  • “… [the] hidden link between population diversity and tumorigenicity can be easily found in cancer literature.”
  • The genome level corresponds to the evolutionary mechanism while the gene level corresponds to particular molecular mechanisms.  “Thus our current study offers a new direction that uses the degree of [genome level] heterogeneity to effectively monitor tumorigenicity.”
  • “… using a system approach to monitor [genome level] dynamics is not contradictory to studying the function of various cancer genes, similar to not seeing the forest for the trees, these two approaches focus on two levels of genetic organization, and try to address different mechanisms (evolutionary and molecular) of cancer formation.”
  • “… it seems that the complexity of cancer is too high and that just tracing individual pathways will not lead to understanding the nature of cancer due to the highly dynamic (stochastic and less predictable) features of this disease.  It is time to focus more on the system’s behavior and its patterns of evolution rather than mainly focusing on individual pathways alone.”


There now seems ample evidence that the somatic evolution theory of cancer not only parsimoniously describes the disease but also makes falsifiable predictions which are being verified in experimental and clinical settings.  More money and attention should be applied to this area, but it’s hard to turn the aircraft carrier that is the cancer industry quickly enough.  There are some very real and practical near term implications of this work in terms of saving lives.

First, we need to overhaul the methodologies used for detecting cancer.  Instead of focusing on individual (or even collections of) biomarkers, we need to look at the patterns of evolutionary change in cell populations in the living organism.  Currently our best bet is to look for patterns of chromosomal change, in particular overall genomic diversity within the body.  Ultimately we need smart nanotechnology for this.  In the near term we need to push the envelope of computational imaging technology (like SKY) and figure out ways to prophylactically monitor as much of the cells in the body as possible as a matter of course.  Clearly, it makes sense to focus first on individuals with high congenital and environmental risk, and also to focus on parts of the body which are showing evidence of needing monitoring.  At the very least, all biopsies from now on should include SKY analysis (and/or its more sophisticated successors).

The implications for treatment will come as more of a shock to the cancer industry.  I’ve suggested before that somatic evolution contraindicates cytotoxic and non-targeted chemotherapy in many cases.  The good news for the pharmaceuticals is that there is still a role for drug therapy.  But if you take the evolutionary argument to its logical conclusion, even targeted cytotoxic therapies are likely to be thwarted by the cleverness of evolution.  As Pepper, et al suggest above it is possible to halt or reverse tumor growth by non-toxically altering the environment in which cells are proliferating.  Let’s get the drug companies to shift gears here, and let’s think about ways to alter somatic evolution that are less costly and more effective than drug therapy.

Finally, we should be aware of the implications of somatic evolution when it comes to detecting tumors and how we react.  The theory says that somatic evolution is occurring all the time in our bodies, just at an extremely low rate as to be undetectable most of the time.  Furthermore, our bodies have (thanks to macro-evolution) incredibly intricate and redundant mechanisms to keep somatic evolution in check and as benign as possible.  But this suggests that as long as somatic evolution is acting benignly, those defense mechanisms may not be triggered and there may be allowed to evolve (within your body) a plethora of pre-cancerous neoplasms all the time, the vast majority of which remain indolent or are eventually eliminated by the body.  Indeed, as imaging technologies are exploding in their usage, we are detecting these so-called incidentalomas in mass quantities and like never before.  This has (understandably) lead to overreaction based on outdated understanding of cancer: you see a tumor, and even if it’s currently benign you remove it just in case.  This, as a recent Wired Magazine cover article points out, leads to the riddle of early detection:

Some cancers can be too easy to find. About 80 percent of prostate cancers are detected early.  Yet most patients survive at least five years even if untreated.  The problem: deciding whether medical intervention is necessary.

Other cancers are inherently elusive. Pancreatic cancer, for one, betrays almost no symptoms, making diagnosis a matter of pure luck.  Only 3 percent of cases are found in the first, most curable stage.

The money goes where the cancer is. Some malignancies, notably lung cancer, are mostly detected only in late stages.  As a result, that’s where most research is directed.  Shifting those priorities won’t be easy.

And while we have no good solution to this riddle yet, somatic evolution theory does suggest an alternative to burying our heads in the sand and defiantly attempting to excise or poison every neoplasm we detect.  Again it comes back to shaping the evolutionary process through altering the micro-environment.  Instead of letting evolution run amok (or worse, fan the flames), let’s take control of somatic evolution, and maybe even work with it.  After all, the goal isn’t to cure cancer, it’s to stop human death and suffering caused by cancer.

* Full disclosure: I was part of the SFI working group out of which this paper resulted. However, I did not write or edit the paper directly, and the commentary outside of quotes is my personal summary of  the contents of the paper.  All emphasis is mine.

  • Just found out that the author of The Rough Guide to Evolution has a blog and he posted not long ago on the the rise of the somatic evolution meme. I’ve referenced most of same sources (and more) in my Cancer as Evolution thread if you read back.

  • Just recently announced, a new initiative by the NIH/NCI:

    B. Exploring and Understanding Evolution and Evolutionary Theory in Cancer from a Physics Perspective through the incorporation of theories of Darwinian and somatic evolution with experimental approaches from the physical sciences to better define and understand and control cancer at all length scales.
    An interesting theme area that emerged as critically important in the collective view of physicists, engineers, mathematicians, and cancer biologists was the critical importance of evolution and evolutionary theory in understanding all aspects of the origin and behavior of cancer cells at multiple scales. In terms of the physical sciences, cancer should be considered as a complex adaptive system that is most appropriately studied in the context of evolution and evolutionary theory. A major foundational aspect of this focus area will be development of experimental and theoretical models that can support the advancement of an evolutionary construct for understanding, predicting, and controlling cancer. Such a construct would be expected to take advantage of a variety of “omics” data along with appropriate physical measurements for evaluating and testing robust theoretical constructs and ways to measure physical science parameters.

    The amount of funding set-aside for this program is approximately $75M - $105M over a 5-year period.

    Here’s the complete description.

    Hat tip: John Pepper

  • Ben

    Martin Nowak has also done some great work showing how the spatial arrangement of cells can promote or inhibit the somatic evolution of cancer cells. here’s one especially cool paper; see his publications page for others.

  • Ben, it would be great to take a look at some visualizations of the spatial model and/or see simulations. Any chance of getting something and posting it here? It’s not apparently obvious to me how the paper shows the relationship between spatial arrangement and inhibition/promotion of evolution, but I’m sure that has more to do with my own shortcomings as a mathematician :-) Still, to have any real impact on cancer research and medical practice, it’s important to bridge the formal and intuitive arguments.

    Thanks for posting this. I’m a fan of Nowak’s work in general, and would like to see his group spend more time on cancer in the future. Spatial and network evolution is key in my mind.

  • Ben

    Yeah, I could see why it would be difficult to get the underlying point of that paper.

    Here’s the idea: cancer cells have an evolutionary advantage over noncancer cells, in that they reproduce faster. To prevent cancer cells from taking over, the body needs mechanisms to reduce their advantage.

    In the colon, this is accomplished by the spatial structure of the cells and the way they replace each other. They basically form a line with a “germ cell” at one end. (I’m not a biologist so I may be misusing terminology.) Whenever a cell reproduces, it pushes the other cells down the line, and when a cell gets to the end of the line (opposite from the germ cell), it dies. (This model is obviously a conceptual simplification.)

    This structure basically eliminates between-cell competition. If a cancer cell breaks out anywhere in the middle of the line, all the cancer cells will still eventually be pushed to the end of the line and die. Only if the germ cell turns cancerous will cancer take over.

    This paper takes the idea further by investigating which spatial structures (represented as directed graphs) magnify or inhibit competiton and selection.

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