Experimental and comparative oncology: zebrafish, dogs, elephants
September 18, 2014 5 Comments
One of the exciting things about mathematical oncology is that thinking about cancer often forces me to leave my comfortable arm-chair and look at some actually data. No matter how much I advocate for the merits of heuristic modeling, when it comes to cancer, data-agnostic models take second stage to data-rich modeling. This close relationship between theory and experiment is of great importance to the health of a discipline, and the MBI Workshop on the Ecology and Evolution of Cancer highlights the health of mathematical oncology: mathematicians are sitting side-by-side with clinicians, biologists with computer scientists, and physicists next to ecologists. This means that the most novel talks for me have been the ones highlighting the great variety of experiments that are being done and how they inform theory.In this post I want to highlight some of these talks, with a particular emphasis on using the study of cancer in non-humans to inform human medicine.
Experimentalists are collecting more and more data from biological systems, and from the theorist’s perspective it can seem that technology is driving this overabundance of data while leaving theory behind. In the third talk of Wednesday, Shannon Mumenthaler offered a contrasting view from the eyes of an experimenalist. She told her personal story of how interaction with theorists has changed the form and technology of her experiments. In particular, her group has shifted from the qualitative and static observations that have dominated biology to the quantitative and dynamic data that theorist demand. For me, the most impressive aspect of Mumenthaler’s work was her ability to film cultures on the level of single cells. This allows them to generate a rich data source that can be used to quantify tumor heterogeneity and monitor phenotypic plasticity. To get you an idea of what such videos look like, here is a similar one made by the National Cancer Institute in 2009:
Hours of this sort of footage can be transformed with image processing software like CellAnimate into a computer friendly format (Georgescu, Wikswo, & Quaranta, 2012), and various properties like motility, replication rate, and interaction patterns can then be analyzed. This can then be used by theorists to calibrate or test their models. In the last talk on Tuesday, Jill Gallaher described just this sort of model, calibrated with data from the Quaranta lab. She used her model to explore the effects of phenotypic heterogeneity for tumor dynamics, and the importance of phenotypic trait inheritance (Gallaher & Anderson, 2013). In particular, she studied the traits of proliferation rate and motility, a more detailed analysis than the game-theoretic approach of the go-grow game (Basanta, Harzikirou, & Deutsch, 2008). Her simulations were also aesthetic appealing; the growing digital tumors in her off-lattice model were beautiful to look at, the fact that they provided a better understanding of real tumors was an added bonus.
I would be very interested in getting my hands on Jill’s code and using it as a test-bed for analytic models. In particular, I think it would be fun to explore how her digital tumors interact with fixed edges, instead of unconstrained grown. It could be a way to check if there is merit to our work on edge-effects (Kaznatcheev, Scott, & Basanta, 2013). Darryl Shibata shares this interest, and it was great that the workshop provided the three of us with an opportunity to brainstorm over lunch.
Mumenthaler’s group does not confine their experiments to isolated in vitro cell cultures. Together with collaborators from Wake Forest University, they’ve also developed a bioengineered liver platform to study cancer at the level of tissues. This is a promising step, but even a tissue does not provide us with all the complexity of a full living organism. In his talk, Richard White presented the zebrafish as an alternative to the mouse model — the typical organismic workhorse of cancer research. He stressed their utility because of the ability to do (1) high-throughput transgenesis, (2) in vivo imagining, (3) unbiased genetic screens, (4) small molecule screening, and (5) scale: one mouse is like 3000 zebra fish! And although it is difficult to compare the specific genome changes between zebrafish (or mice) and humans, at the level of RNA there is a lot of conservation of pathways.
White focused on metastasis of melanoma as an example of an effect that can only be studied at the organismal level. To study this problem, he used the translucent Casper variant of the zebrafish that he engineered as a post-doc (White et al., 2008). This allows him to watch in real time as the original skin tumor sends out metastases through out the fish as it swims around in its environment. Through careful fish-surgery, he can also transplant the same tumor to different parts of the zebrafish to study the effects of microenvironment, or transplant different kinds of tumors to the same location to study the effects of heterogeneity or other tumor-specific factors.
In the process of watching metastasis in the zebrafish, White’s group has noticed regularities in where tumors tend to spread. For instance, there seems to be a lot of spread to the eye area. This reminded me of the work by Scott, Kuhn, & Anderson (2012) on the importance of modeling the vascular system in human metastasis. I wonder if their model can be adapted to the zebrafish and its predictions tested in White’s Caspers. After all, fish have many of the same major organs, just arranged differently from humans.
Of course, searching for similarities between engineered model organisms and humans is not the only way to understand cancer. Sometimes it is useful to take the approach of comparative oncology and look at the similarities and differences between naturally occurring cancers in humans and other animals. Joshua D. Schiffman highlighted this approach in a very entertaining closing talk of Monday. He is a pediatrician, and thus has a strong interest in the genetic basis of cancer — a determining factor in around 1/3 of cancers in children. His particular focus is the Li–Fraumeni syndrome that tends to run in families and draws his attention to family trees.
Dogs are a particularly important comparison for Schiffman, since pure-breeds have well traced family trees and a large number of genetic cancers. In fact, dogs are very cancer prone and are around 11 times more likely than humans to suffer from cancer. Some of these naturally occurring cancers are very similar to their human analogs; for example, chronic myeloid leukimia has the same basis as in humans, except with a different pair of chromosomes undergoing a crossover and resulting in the same BCR-ABL fusion gene. Finally, man’s best friend is fortunate enough to have large studies, and Schiffman highlighted ones for Golden Retrievers and pre-screening brain tumors in Boxers. I was surprised, however, that he didn’t mention the curious case of the canine transmissible venereal tumor — probably because it has no clear analog in humans.
On the opposite end of the cancer spectrum from dogs is the elephant. If cancer was merely the random mutation of cells then you would expect the prevalence of cancer to scale with the number of cells, but the opposite is observed with large animals like elephants and whales having much lower rates of cancer than humans, dogs, or mice. This is know as Peto’s paradox (Peto et al., 1975): at the species level, the incidence of cancer is not correlate with the number of cells in an organism. A leading hypothesis for this in elephants is better p53 and cell repair. After a serendipitous series of events led Schiffman to a supply of elephant blood from the Hogle Zoo, he came to the surprising conclusion that the richer p53 in African elephants did not result in better cell repair, but in more apoptosis. If you are as large as an elephant, then you have some cells to spare, so might as well destroy the malfunctioning ones instead of trying to repair them.
But Schiffman’s main message was not the particulars of dogs or elephants. His final message was that evolution has had millions of years to learn how to deal with cancer. Why don’t we do our best to learn from it? I am hopeful that as we explore these myriad experimental and mathematical models, from single cells to elephants, we will be able to uncover cancer’s secrets.
This is my second post of a series on the MBI Workshop on the Ecology and Evolution of Cancer. The previous post was: Colon cancer, mathematical time travel, and questioning the sequential mutation model.
Basanta, D., Hatzikirou, H., & Deutsch, A. (2008). Studying the emergence of invasiveness in tumours using game theory. The European Physical Journal B, 63 (3): 393-397
Gallaher, J., & Anderson, A.R. (2013). Evolution of intratumoral phenotypic heterogeneity: the role of trait inheritance. Interface Focus, 3 (4) arXiv: 1305.0524v1
Georgescu, W., Wikswo, J. P., & Quaranta, V. (2012). CellAnimation: an open source MATLAB framework for microscopy assays. Bioinformatics, 28(1): 138-139.
Kaznatcheev, A., Scott, J.G., & Basanta, D. (2013). Edge effects in game theoretic dynamics of spatially structured tumours. arXiv: 1307.6914v2.
Peto, R., Roe, F.J.C., Lee, P.N., Levy,L., & Clack, J. (1975). Cancer and ageing in mice and men. British Journal of Cancer, 32(4): 411–426.
Scott, J., Kuhn, P., & Anderson, A. R. (2012). Unifying metastasis—integrating intravasation, circulation and end-organ colonization. Nature Reviews Cancer, 12(7): 445-446.
White, R. M., Sessa, A., Burke, C., Bowman, T., LeBlanc, J., Ceol, C., … & Zon, L. I. (2008). Transparent adult zebrafish as a tool for in vivo transplantation analysis. Cell: Stem cell, 2(2): 183-189.