Spencer et al: Modeling Somatic Evolution in Tumorigenesis

S. Spencer, R. Gerety, K. Pienta and S. Forrest. Modeling Somatic Evolution in Tumourigenesis. PLoS Computational Biology, Vol 8, 2, pp 939-947.

Because the paper has been published in an open source journal it means that any reader, regardless of location or affiliation, will be able to download and print it.

I am spending the remaining of this month and most of February in Lyon working with Dr. Benjamin Ribba, from the University Hospital of the University of Lyon. The month will be busy so I am not sure of how much time will be left for posts in this blog but at least on my way here I had time to take a look at the paper mentioned at the beginning.

This paper, together with the one mentioned in a previous post from Anderson et al, tries to create a mathematical framework in which to study the phenotypical view of carcinogenesis presented by Hanahan and Weinberg in their paper. As in Anderson's case, they use a Cellular Automata in which tumour cells occupy the discretised space and can grow and produce angiogenic factors in order to provision themselves with oxygen. The CA is let to evolve the initial population of cells in which mutations might alter the phenotype and acquire any of the six capabilities (ignoring antigrowth signals, production of paracrine growth signals, limitless replicative potential, evasion of apoptosis, angiogenesis and invasion/metastasis). Additionally a tumour cell might acquire genetical instability which significantly increases the mutation rate during mitosis. Cancer is assumed to take place whenever the cells grow over the natural boundaries of the tissue and claim 90% of the total space. This definition of cancer is, at least from my not so extensive experience, quite unconventional since it seems to allow no role to the phenotypes present in the tumour or the the shape of the tumour. At any rate, tumour growth is determined by the ability of the tumour cells of proliferating AND of surviving.

With this not too complicated system, the authors use several simulations to explore different tumourigenetic paths (or as they call them, pathways to cancer). This is how early mutations determine the likelihood of other mutations to appear successfully (the mutant cell has to survive and have other successful offspring) and how some mutations lead to early or later cancer (which I translate as the speed of the tumourigenesis).

So what do they find? They find that the mutator phenotype should play an important role in the case of early onset tumours but not necessarily in others that take more time to develop. They also find that not all the 'pathways to cancer' are equally probable and that is on its own something quite interesting. If in a particular tumour it was possible to see what genes are responsible for particular capabilities in the Hanahan&Weinberg description, then a genetic analysis of representative cells in the tumour could be used to see what further mutations would be the more likely to be successful and maybe design a therapy for it or try to alter the microenvironment to favour other competing mutations.

They also study the heterogeneity of the tumours which is an important feature when designing a therapy. They use a metric based on what it is done in evolutionary biology. The diversity is measured by aligning the different mutational paths of the different cells in the tumour and counting all the ones that have a different path. This seems to me a strange approach given that they treat tumour cells at the phenotypic level. I wonder if it would be better just to count the different phenotypes (defining phenotype in this case as a particular combination of H&W capabilities, regardless of what was the path to reach them)?

To conclude this review: it is clearly a theoretical model aimed to provide qualitative, not quantitative, results. It is probably complicated enough that biomathematicians might not feel very comfortable with it. The results are mainly simulations and it is unlikely that physicians could devise experiments to compare results with the model. Still I have to say that I like it, the quantitative results are interesting enough and the implementation of the word-model is very easy to follow.

The Darwinian perspective, the mutator phenotype and response to stress

Although today I will be writing about a paper recently published in the international journal of Epidemiology and authored by Paolo Vineis and Marianne Berwick, this is not going to be a review in the usual sense. This time I would like to write about the ideas and make no reference to the methodology.

The Vineis and Berwick emphasize the role of population dynamics on cancer progression. The usual view on cancer is that cancer cells grow at a faster rate than normal cells and that is the reason why they end up (if successful) killing the host. Growing populations can be due to this but they can also be the result of other factors (think of longer lifespan). The authors hint that the success of most cancers (with respect to taking over a tissue) lays on the fact that cancer cells have a greater proportion of replicating daughter cells. That makes sense to me. For instance, in a tumour whose cells that are capable of dividing near the tumour growth front (let's call them motile tumour cells) will have an advantage over other non motile but faster proliferating tumour cells in terms of how many of its daughter cells will be in position to proliferative (regardless to the speed at which they can divide).

The authors have also something to say about the highly controversial topic of the mutator phenotype. Quick reminder: the amount of time to pick up all the mutations necessary for a neoplastic cell to become a cancer cell is, according to some researchers, big enough as to be unlikely to happen in our life time. Thus cancer is the consequence of a single mutation that makes the cell more likely to produced mutated offspring. To prove their point they compare tumour cells to the behaviour of E.coli under stress. Under normal circumstances the mutation rate of the E.coli is low but when the going gets tough the mutation rates increases significantly. The speculation is that this is no accident but a feature of the bacterial DNA that in such a way can explore a genetic solution out of the problem. Could tumour cells be attempting something similar?

I find this hypothesis quite interesting and from my limited experience it seems quite novel. It should be interesting to do some experimental work (maybe more than theoretical) to see if there are any molecular mechanisms that might have an effect on the probability of mutation (say, the DNA repair mechanism) that could be held down when there are 'stress' signals in the environment. It could even be that the mechanism is similar to that of the E.coli although since bacteria are far simpler cells than human cells that could be unlikely (not having any experience with molecular biology should make any one be skeptic about statements like this).

Edge of existance

Researchers of the Zoological Society of London have started a new campaign (called EDGE) to promote the protection of certain species that might not be so close to extinction as some other more famous ones (say, whales?) but whose impact on the survival of other species might be significant. The news can be found in BBC or for those of you that can read Spanish in El Pais.

Now, this is probably not something that many people might find relevant to cancer research but I think that there might be a connection. In ecological systems (and here I assume that a tumour is one of those) species depend on other species for their survival. This dependency does not need to come in terms of food webs (a species needs other to prey on) but also in the way that one species can change the environment for the benefit (or not) of the other ones. The idea then would be to identify 'agents' in a tumour whose role in principle might not look so relevant but that might provide support to other more important but less vulnerable targets. Given the current emphasis on the role of the microenvironment in cancer research I would be surprised if there was not already some work pointing in this direction.

Online introduction to computational oncology

I have recently found an interesting introduction to computational oncology on the website of Paul Macklin, a graduate student at UC Irvine.

The website comes complete with the stages of cancer evolution (not the usual 6 capabilities of Hanahan & Weinberg but a version a little bit coarse grained for my taste) and therapies. He also points out some of the challenges that need to be addressed such as explicitly incorporating the tumour microenvironment (it is a well known fact that some tumour cells behave like regular healthy cells if left in a different microenvironment from the one it comes from). Useful for people who might want to understand some of the aim of the papers I mention in my reviews.

Merlo et al: Cancer as an evolutionary and ecological process.

L. Merlo, J. Pepper, B. Reid and C. Maley. Cancer as an evolutionary and ecological process. Nature reviews cancer, Vol 6, pp 924-935, December 2006.

New year and (maybe not so) old traditions: the review of a paper. Nature reviews cancer is one of the world's top scientific journal in terms of impact factor for a reason: they publish very interesting and comprehensive reviews in a field of such importance and as crowded as cancer research. Review papers are comparatively more likely to be cited than the ones about one group's research, review papers in cancer research are normally highly cited since there are so many researchers working in the field. Review papers in a prestigious journal like Nature reviews cancer are thus bound to be cited once and once again and one would expect that only very good scientists would be invited to write for them (I believe that is only by invitation that you get to publish in these journals).

This review covers a topic that is very close to my interests: cancer from an evolutionary and ecological point of view. This view sees a neoplasm, a tumour, as a population of cells with a diversity of inheritable features. This means that evolution will happen and the fitter phenotypes will tend to be more abundant in the tumour population. Questions that might arise are how to alter the mutation rates, clone expansion and how does this expansion happen. Furthermore, given that in many cancers we can find mutations in vast areas of DNA, how do these cells retain enough genetic material to even function? The authors put forward the idea that this could be because most of the human genome is devoted to the development and homeostasis of a multicellular body and thus has no effect on the survival of the single cell in a tumour.

Having a diverse range of individuals whose uniqueness is inheritable is only one of the requirements of evolution. The other one is selection. In a tumour we have two sources of selection. Natural selection is the one that takes place in any tumour when there is scarcity of resources (oxygen, glucose, space). Under these circumstances some phenotypes are bound to be better at surviving and dividing than others. Additionally there is artificial selection which is the result of a therapy applied to a patient with a tumour. Artificial selection changes the fitness landscape, hopefully in such a way as to make survival impossible to every tumour cell. Unfortunately that is somewhat difficult so in many situations is worth altering the fitness landscape in ways that promote the survival of the least aggressive (that is, less likely to be able to invade and metastasise) tumour cells. In any case, altering the fitness landscape in favour of the patient is significantly easier when the tumour did not have much time to evolve.

Evolution in a tumour is not entirely the same as the one in organismal populations. That is to be expected given that tumour evolution lacks something of great importance: time. That is why during chemotherapy, surviving tumour cells are not the ones that develop mechanisms to resist but the ones that due to other reasons (genetic drift for example) already had the capability to resist before the therapy was used. Other differences include the reliance on stem cells for population diversity or that reproduction is asexual (which, incidentally, makes mathematical treatment much easier).

If a tumour resembles an ecosystem we should expect things such as cooperation, competition or parasitism. It seems that you get some of that. Like in an ecosystem individuals compete for the available resources (there is some speculation about that in the paper from Tomlinson reviewed the 10th of October of last year). There are predators if we understand as predation the behaviour of the cells from the immune system when they meet tumour cells. There should be parasitism, mutualism and commensalism (although the authors provide no evidence for that).

I found this a very nice and readable paper. I think it will make a good introduction to any cancer researcher that wants to study the evolutionary aspects of it. My only criticism of a paper that claims to deal with cancer as an evolutionary and ecological process is that the ecological part is significantly weaker than the evolutionary one.

Gatenby and Smallbone: Glycolysis and tumour invasion. Two papers

Why do cancers have high aerobic glycolysis? R Gatenby, R. Gillies. Nature reviews cancer, Vol. 4, November 2004, pp 891-899.

The role of acidity in solid tumour growth and invasion. K. Smallbone, D. Gavaghan, R. Gatenby and P. Maini. Journal of Theoretical Biology 235 (2005) pp 476-484.

Gatenby and Gillies present a review paper in which they explain that the glycolytic metabolism is a requirement for a tumour to progress into a cancer. Cancer cells tend, at least by the time they become invasive, to have an altered glucose metabolism. This metabolism has drawbacks when compared with the conventional glucose metabolism in at least two senses. First of all the glycolytic metabolism is less efficient since it produces 2 ATP (the cell's energy currency) compared with 38 ATP that result from normal metabolism. Second of all, as a by product of this metabolism lactic acid is produced. Lactic acid increases the pH of the microenvironment of the cell and when it reaches a given threshold results in apoptosis or necrosis. As a consequence of this the switch from conventional to glycolytic metabolism does not happen under normal circumstances but under hypoxia, that is, when there is insufficient access to oxygen. In those circumstances the inefficient glycolytic metabolism, which does not need oxygen, represents a significant advantage. Hypoxia is a normal event in a growing tumour since there will always come a point in which the tumour cells are far from blood vessels carrying the needed oxygen. Periodic moments of hypoxia select for cells with the glycolytic metabolism that have adapted to acid environments by, for instance, resistance to apoptosis or by reducing intracellular acidity by pumping it out. The end result of this selection is that a tumour will have a group of cells that, despite having a less efficient metabolism, are capable of harming other cells and also of degrading the ECM (Extra Cellular Matrix, that hold tissue cells together) so the next thing you know is that your tumour cells are capable of invasion and metastasis.

In the second paper, Smallbone and colleagues introduce a mathematical model to study the possibility that tumour invasion and growth could be the result, not of genetic changes, but of changes in the tumour environment. This is, of course, a mathematical formalisation of the hypothesis presented in the other paper. They decided to take multicellular spheroids and produce an ODE model that describes tumour growth and progression as travelling waves: the one for the increased microenvironmental acidity and the second one with the tumour cells invading normal tissue. This is a clearly a quantitative model that, unfortunately, has not yet been validated by in vitro experiments although it looks to me that its design has been done with care so it would be feasible to do so. The model predicts among other things that avascular tumours will have higher acidity than vascular ones (which makes sense since blood vessels can be used to take part of this acidity out of the microenvironment) and that tumour necrosis could potentially be explained without the need to talk about cell starvation or overcrowding but by the acidification of the environment. They make a good case for antiangiogenic therapies since blood vessels can contribute to a decrease of microenvironment acidity that could be sufficient for tumour cells to survive but not for healthy cells that have a lower threshold of acidity resistance. They also suggest a treatment in which the membrane pumps that transport the acidity from within the cell to the outside environment, would be somehow blocked so glycolytic cells would literally poison themselves to death without changing the microenvironment for the healthy tissue cells.

Quote of the week

Maybe this will start a new trend in this blog: quotes. I was recently rereading some old issues of Nature and found this article in the careers section. It is a nice article in which the authors give an alternative view to that of this other article of how long and hard should a PhD student work. According to the original view a researcher should work all the time. According to this other alternative view some scientists have many of the weaknesses commonly associated with humans and thus this approach is likely to be useless.

They cite this anecdote from Ernest Rutherford. It seems that he found a student working late on an evening and asked him if he also worked in the mornings. The student answered that that is what he usually did and Rutherford then asked "But, when do you think?". Some times I think to myself that my best ideas usually come in the most unexpected places and circumstances. Now I think that I never had any good idea while in the office in front of the computer. Since now I am off for Xmas holidays there is a chance I will have time for some good ideas.

Evolution and creationism in Europe

Many people in the European academic community have a condescending view on the lack of scientific knowledge of the American population in general and also of their political class. While probably these concerns are fair, it would be useful to redirect part of it towards our own citizens and politicians here in Europe.

The Nature issue of a couple of weeks ago reports about how creationism is on the increase in Europe (at least the public awareness of what used to be a dormant line of 'thought'). They also have an interview with the leader of a European creationist group (who has a PhD in astrophysics and whose photo in the article seems to be taken by one of his enemies). Nature has also recently published the letter of Maciej Giertych, an MEP and scientist, with a PhD in population genetics, of the Polish Academy of Sciences in which he criticises evolution. The publication of this letter in a journal with the reputation of Nature has generated a considerable amount of controversy as can be seen in the letters send to the editor and published in the last issue.

Anderson et al: Tumor morphology and phenotypic evolution driven by selective pressure from the microenvironment

A. Anderson, A. Weaver, P. Cummings and V. Quaranta. Tumor morphology and phenotypics evolution driven by selective pressure from the microenvironment. Cell 127, 905-915, December 2006.

This is the paper I mentioned in my previous post. It is not that usual to find a mathematical model in a journal like Cell so I hope that this is part of a growing trend.

The paper investigates how the microenvironments helps to drive cancer evolution. To do so they use a hybrid cellular automata model in which cells live in the discrete lattice and the microenvironment (oxygen concentration, extra cellular matrix macromolecule concentration and matrix degrading enzyme) is modeled using continuous variables. The cells are characterised by a number of parameters that determine their behaviour with respect to proliferation, cell-cell adhesion, oxygen consumption, haptotaxis or production of matrix degradation enzymes. Cells follow a life cycle and only proliferate when they reach a certain age. That age depends on the cell's phenotype. During mitosis a cell might alter its phenotype and change the values of proliferation, adhesion, oxygen consumption, etc.

With heterogeneous microenvironment and cell behaviour you get different patterns of tumour growth, some of them favouring agressive invading phenotypes and some of them favouring the coexistance of all sorts of phenotypes. Having the model they described it is possible to study who different microenvironmental factors determine evolution. The results show that harsh environments (little oxygen) select for aggressive phenotypes whereas in milder environments allow for the coexistance of a much bigger range of phenotypes and that these tumours are unlikely to be invasive.

The model is very interesting and the conclusions seem pretty reasonable: Tough microenvironments lead to aggressive tumours. My intuition tells me that on the other hand, heterogeneous populations are more likely to be able to cope with an external aggression which would imply that a less aggressive but more diverse tumour would not respond well to therapies that target any specific kind of behaviour. The main problem with the paper is that the model is fairly complicated for clinical validation.

We are big news!

We are big news. Ok, I am personally not included but it seems that the theorical medicine community starts to be noticed.

I browse a tech-news website, called slashdot, very often. One of the entries today is the following: "Computer simulations of cancer growth". There they talk about research performed by Sandy Anderson (Dundee, part of the Marie Curie Network in which I am involved), Vito Quaranta (Vanderbilt, I talked about him in my post on the Lyon workshop in late September) and colleagues. They have just got a paper published in Cell of which I will talk about it in a later post.

At any rate, it looks impressive than sites such as Slashdot report on mathematical and computational models of cancer research. The audience of Slashdot is reputed to be very competent in matters of IT but I could see that some of them are medically competent too (I mean, enough to convince a computer scientist like myself, not necessarily more than that), even if, as in many news in Slashdot, people tend to concentrate on what they want to say regardless of the news they are supposed to comment on.