Edge effects on the invasiveness of solid tumours

MetastasisCareful readers might have noticed that, until last night’s post, the blog was silent for an atypically long 10 days (17 days since I last posted). As usual, the primary culprit is laziness, but this time it is not alone! After my fun visit to the Integrated Mathematical Oncology Department of the Moffit Cancer Research Center, I have been working closely with Jacob Scott and David Basanta to finish up our first joint paper. The last week was particularly busy as we pushed the paper out for submission and posted a draft to the ArXiv.

We look at the effect of spatial structure, in particular a spatial boundary, on the evolutionary dynamics of motility in cancer. For a tumor, one of the key steps in going from a benign to malignant is gaining the ability to spread from one organ to another non-adjacent organ. To achieve this, a cancer cell has to transition from simple proliferative cells (AG) to a motile ones (INV). However, motility usually involves a cost to the organism. We show that spatial structure can lower this cost, and smaller neighborhood size at an edge can promote motile cells at the boundary even when they are absent in the tumour body.

I’ve discussed the motile-proliferate model previously, and Basanta, Hatzikirou, & Deutsch (2008) encode the game for motility in the payoff matrix:

\begin{pmatrix}  \frac{b}{2} + \frac{1}{2}(b - c) & b - c \\  b & \frac{b}{2}  \end{pmatrix}

Whenever b > c, this game is a social dilemma with invasive cells as cooperators. Hence, it is natural to expect that spatial structure will help with invasiveness. This is summarized in the main figure:

mainPlotLabel

in the above, you see the level of viscosity 1/(k - 2) (where k is the number of neighbours a cell has, the inviscid case is k \rightarrow \infty) versus relative cost of motility c/b. The parameter space is divided into three regions with qualitatively different dynamics:

  • Red — population evolves toward all INV,
  • Yellow — population evolves toward a mixed equilibrium of INV and AG,
  • Green — population remains benign with all AG.

The lowest horizontal dotted line marks k = 8 and corresponds to a cell in the middle of a 2D tumor having all of its Moore neighbors. The second horizontal dotted line marks k = 5 and corresponds to a cell at the edge of a 2D tumor, and thus having 3 of its neighbors eliminated by the boundary. The vertical dotted line marks an example of a fixed game c/b = 0.53 where the population in the body of the tumor (k = 8) is benign, but the population at the boundary (k = 5) is malignant with around 8% of the cells invasive.

From the medical perspective, this can add yet another source of sampling bias to tissue biopsies and
suggests that the architectural, not just the molecular, context is important. For instance, consider an idealized fine-needle aspiration biopsy, assuming the standard 0.7 mm needle samples a perfect column around 20 cells in diameter of tumour cells right next to a critical boundary such as a capillary. In our example of c/b = 0.53 this would result in the sample containing only about 0.4\% invasive cells since 19 out of every 20 cells are not at the boundary, and so contain no invasive phenotypes. This is below the detection levels of the state of the art medical practice. However, the critical 1 out of every 20 cells at the boundary, would have a dangerous 8\% invasive cells. Thus, an oncologist performing a diagnostic fine-needle aspiration biopsy would misdiagnose a malignant tumour as benign because she is using a technique that destroys the structure of the tumour and mixes the cells in the critical tumour boundary with the (in this case) irrelevant tumour body.

Biologically, this also offers an alternative to the Warburg shift in tumor progression. An invasive phenotype can be favored without glycolytic metabolism by the simple change in local neighborhood size at the boundary. Our future work will combine this analysis with a game-theoretic model of the Warburg effect, and paint a fuller picture of how acid mediated invasion and edge effects interact. The early results are promising, and I look forward to a continuing collaboration.

Although this is my ninth paper, in many ways it feels like a first. It is the first paper to come from this blog; much of the analysis comes from my July 5th post and relies on the Ohtsuki-Nowak transform that I detailed in October. It is my first experience of an online contact (I met David on Twitter in September, and Jacob in October; for a more detailed overview, see Jacob’s post) growing into one of my closest collaborations (highlighted by Jacob and I sharing first authorship). Finally, it is my first submission to the ArXiv! You should give it a read.

ResearchBlogging.orgBasanta, 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.

Kaznatcheev, Artem, Scott, Jacob G., & Basanta, David (2013). Edge effects in game theoretic dynamics of spatially structured tumours ArXiv arXiv: 1307.6914v1

Advertisements

About Artem Kaznatcheev
From the Department of Computer Science at Oxford University and Department of Translational Hematology & Oncology Research at Cleveland Clinic, I marvel at the world through algorithmic lenses. My mind is drawn to evolutionary dynamics, theoretical computer science, mathematical oncology, computational learning theory, and philosophy of science. Previously I was at the Department of Integrated Mathematical Oncology at Moffitt Cancer Center, and the School of Computer Science and Department of Psychology at McGill University. In a past life, I worried about quantum queries at the Institute for Quantum Computing and Department of Combinatorics & Optimization at University of Waterloo and as a visitor to the Centre for Quantum Technologies at National University of Singapore. Meander with me on Google+ and Twitter.

12 Responses to Edge effects on the invasiveness of solid tumours

  1. Pingback: How teachers help us learn deterministic finite automata | Theory, Evolution, and Games Group

  2. Pingback: Cataloging a year of blogging: applications of evolutionary game theory | Theory, Evolution, and Games Group

  3. Pingback: Approximating spatial structure with the Ohtsuki-Nowak transform | Theory, Evolution, and Games Group

  4. Pingback: Misleading models in mathematical oncology | Theory, Evolution, and Games Group

  5. Pingback: Experimental and comparative oncology: zebrafish, dogs, elephants | Theory, Evolution, and Games Group

  6. Pingback: Ecology of cancer: mimicry, eco-engineers, morphostats, and nutrition | Theory, Evolution, and Games Group

  7. Pingback: Cataloging a year of blogging: cancer and biology | Theory, Evolution, and Games Group

  8. Pingback: Space and stochasticity in evolutionary games | Theory, Evolution, and Games Group

  9. Pingback: Seeing edge effects in tumour histology | Theory, Evolution, and Games Group

  10. Pingback: Making model assumptions clear | CancerEvo

  11. Pingback: Boundaries and evolutionary dynamics in cancer | CancerEvo

  12. Pingback: Spatializing the Go-vs-Grow game with the Ohtsuki-Nowak transform | Theory, Evolution, and Games Group

Leave a Reply

Fill in your details below or click an icon to log in:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out / Change )

Twitter picture

You are commenting using your Twitter account. Log Out / Change )

Facebook photo

You are commenting using your Facebook account. Log Out / Change )

Google+ photo

You are commenting using your Google+ account. Log Out / Change )

Connecting to %s