Jennerex oncolytic virus
Jennerex” virus (green) replicating within
a tumor mass.

Viruses infect cells, and quite often (depending on the type of virus) destroy the cell they’re infecting. Usually having your cells destroyed is a bad thing for the host, because you need those cells. But there are some cells that you don’t want to have, and it might be convenient to have a controlled virus destroy those cells for you.

The obvious example would be cancer cells, and in fact there’s a flourishing research industry that is trying to harness the destructive power of viruses to eliminate tumors. The trick, of course, is to have the virus infect only the cancer cells, not the normal healthy cells you want to keep; and the general approach is to take advantage of some of the common features of viruses and cancers. Cancers have to mutate to overcome some of the same cellular controls that viruses do (both viruses and cancers need to overcome the regulation of uncontrolled genome replication, for example). If you cripple some of these viruses, then, they can’t replicate in normal cells, but can replicate in, and destroy, cancer cells that have mutated the appropriate pathway.

(I talk about oncolytic viruses in more detail here.)

As often happens with these intriguing cancer treatments, oncolytic viruses seem to work sometimes, and not to work sometimes, and it’s not always clear why not. In a paper in PLoS One the other day,1 Wodarz and Komarova attempt to come up with a way of predicting which tumors will and will not respond to oncolytic virus therapy, using a computational approach.

As I think I’ve said before, I’m excited by the concept of computational biology. (I’m not so much talking about bioinformatics as such here, but rather about attempts to model AND PREDICT complex biological processes.) But I’ve been kind of disappointed by some of the process. It’s seemed to me that when simple processes are modeled we haven’t really learned very much new, and when complex processes are modeled the assumptions are often too simplistic to make a reasonable prediction. I don’t think we’re at a point where we can usefully model an immune system, for example.

Wodarz oncolysis model
Growth of cancer in a mouse in the presence of  oncolytic virus 1

However, I do think there are a class of problems in the middle where the approach is more successful, and though I’m not really able to critically assess their results I think over the years Dominik Wodarz has done a good job of identifying these problems. Questions like the emergence of drug resistance in cancer,2 effects on vaccination on HIV,3 and so on seem like the kind of problem where mathematical analysis can actually get a handle on the issues and help guide research to some extent. Again, I don’t feel that I can really judge the results, but I like the approach. What’s more, Wodarz seems to at least consider experimental evidence in his analyses, which isn’t always the case in these computational things.

(I don’t think it’s a coincidence that many of the questions he’s asked are microcosmic versions of ecological issues.  My impression is that population biology has a much longer and more successful history of mathematical analysis than do cell and molecular biology.)

This particular paper leads to a conclusion that, once reached, seems fairly obvious in hindsight, but it’s one I haven’t seen explicitly made before. (I am not in the field, and it may be taken for granted.  That said, one hallmark of a successful prediction is that everyone immediately says they knew it all along.) Briefly, tumor growth rate per se turn out to be relatively unimportant, and growth patterns are important. If the cancerous cells are relatively spread out in the tumor, then an oncolytic virus has a good chance of eliminating the tumor; whereas if the cancer cells are in clumps, the virus is much less effective. This is simply because in the clumpy masses most of the infected cells are contacting already-infected cells, and the only route to reach new targets is from the surface of the clump, so spread is inefficient.

In one group, virus growth is relatively fast because the infected cells are dispersed among the uninfected cells rather than being clustered together. In this case most infected cells contribute to virus spread. In these models, there is a clear viral replication rate threshold beyond which the number of cancer cells drops to levels of the order of one or less, corresponding to extinction in practical terms. … In the other category, infected cells are assumed to be clustered together to some degree in a mass, which might be realistic for solid tumors. In this case, only the infected cells located at the surface of the cluster contribute to virus spread because they are in the vicinity of uninfected cells. … In this scenario, virus therapy is more difficult. 1

  1. Dominik Wodarz, Natalia Komarova (2009). Towards Predictive Computational Models of Oncolytic Virus Therapy: Basis for Experimental Validation and Model Selection PLoS ONE, 4 (1) DOI: 10.1371/journal.pone.0004271[][][]
  2. Drug resistance in cancer: principles of emergence and prevention. Komarova NL, Wodarz D. Proc Natl Acad Sci U S A. 2005 Jul 5;102(27):9714-9.[]
  3. Immunity and protection by live attenuated HIV/SIV vaccines. Wodarz D. Virology. 2008 Sep 1;378(2):299-305.[]