Miles for Moffitt 2016

As every year since I arrived to Tampa, I will be running Miles for Moffitt, the charity race that raises fund for cancer research at Moffitt. I am not in great shape, having injured my right calf 10 days ago, but that is clearly not the point and I will run slowly if that is what it takes. So I hope to see some of you this coming Saturday (14th May) and, if you cannot come but want to help cancer research, here’s the fundraising page for our team IMOffitt as well as my personal one.

Templeton invades the World Science Festival (again)

This will not be a problem with the Pint of Science US events (

Why Evolution Is True

Every year the World Science Festival, organized by physicist Brian Greene and CEO Tracy Day, gets a dollop of cash from Templeton (the sponsors are here), and every year it has a few “Big Ideas” Symposia directly sponsored by Templeton. Most of the ones for this year (program here) look fairly tame, but then there’s this one, with the graphic shown below. The indented material is taken from the Science Festival Announcment.



DATE: Thursday, June 2, 2016
TIME: 8:00 PM-9:30 PM
VENUE: NYU Skirball Center for the Performing Arts
PARTICIPANTS: Brian Greene, Leon Wieseltier, and others

As long ago as the early 19th century, the poet Keats bemoaned the washing away of the world’s beauty and mystery in the wake of natural philosophy’s reductionist insights—its tendency to unweave a rainbow.  Two centuries later, the tentacles of science…

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Counting cancer cells with computer vision for time-lapse microscopy — Theory, Evolution, and Games Group

Here is another blog post by Artem on some joint work with Jacob Scott and Andriy Marusyk. Hear hear!

Some people characterize TheEGG as a computer science blog. And although (theoretical) computer science almost always informs my thought, I feel like it has been a while since I have directly dealt with the programming aspects of computer science here. Today, I want to remedy that. In the process, I will share some Python code […]

via Counting cancer cells with computer vision for time-lapse microscopy — Theory, Evolution, and Games Group

You can play with Adaptive therapies

Some of my previous posts have dealt with the use of adaptive therapies (ATs). These ATs relay on the fact the resistance to chemotherapy involves a cost that susceptible cells (that do not pay that cost) put to better use when chemo is not applied: increased proliferation. Again you can read about ATs here and see Gatenby and colleagues latest experimental evidence for the use of AT here. Of course I have described recently a very simple math model to explore the role of heterogeneity in the efficacy of ATs [here, here and here].

What I forgot to mention is that my friend and colleague (now at the Ronin Institute) Edward Flach played with ATs some time ago. The interesting facts about his model are:

  1. It is online so anybody with a browser can play with it.
  2. You get to decide when treatment is on and when it is off.

So if you want to play doctor and not fear the consequences go ahead and visit Edward’s AT site.

Aiming high

Douglas Lowy and Francis Collins (the director of the NIH) have just published this article in the New England Journal of Medicine where the follow up on the decision by US president Barack Obama to find a cure to cancer and change the impact of the disease on our society.

This initiative is meant to focus efforts on:

  1. Prevention and cancer vaccine development: which makes sense since in many cases (and despite the fact that luck does play a role), preventing cancer before it happens will help more people and will be more affordable than treating an established cancer.
  2. Early cancer detection: Again, finding about your tumor before it becomes aggressive or metastatic can substantially improve the chances of curing it. Having said that we (and others) work on the seemingly opposite idea: is that in certain cancers (prostate or breast for instance) some times it is better to watch and wait than to act in a hurry. What the best course of action might be very dependent on the type of cancer (and tissue!) the patient has.
  3. Cancer Immunotherapy: which seems to be the area where a lot of the cancer research field is going so we should expect some developments there relatively soon.
  4. Genomic analysis of a tumor and surrounding cells. The key here is that we are talking as well about the surrounding healthy cells. If you want to understand cancer and its evolution you need context.
  5. Enhancing data sharing. An area where we are hurting ourselves unnecessarily: More sharing, more caring, more smiles, better treatments.
  6. Oncology Centers of Excellence. Who does not like excellence anyway? There is a risk of course that you may put too many eggs in one massive but fragile basket.
  7. Pediatric cancer. Of course (and luckily) cancer is less prevalent in kids but when it happens it usually has a very different nature than cancer in adults. More needs to be done to understand why and what to do with them.
  8. VP’s exceptional fund CR fund. Designed to go through the red tape: sounds good and I bet there is a lot we can do to simplify things but there are obvious limits: patient safety and privacy.

To that Lowly and Collins reply with a list of targets that will receive considerably funding and focus in 2017:

  1. Cancer vaccines: sure, we had a lot of success with the HPV one and it is thought that about 20% of cancers are the result of a virus.
  2. Early cancer detection.
  3. Single cell genomic analysis: tumors are heterogeneous masses of cells so a better handle of that would be nice but do not forget that a tumor is more than a mass of tumor cells.
  4. Cancer immunotherapy.
  5. Pediatric cancer.
  6. Data sharing.
  7. Exceptional opportunities in CR fund. The NIH is a relatively conservative funding body so this sounds like the usual high risk high reward spiel that I will believe when I see it.

In any case these targets are well aligned with the vision laid out by POTUS. It is a bit of a shame that trying to understand the role of healthy cells near or infiltrated in the tumor is not described as boldly as in the president’s vision. Maybe 2018?

Welcome to Etienne Baratchart

Thanks to funding from the state of Florida’s Bankhead Coley (funding a project I talked about before), our group now includes Dr. Etienne Baratchart. Etienne is joining us from the University of Bordeaux in France where he just finished a PhD under the supervision of Sebastien Benzekry and  Thierry Colin with a project of modelling the pre-metastatic niche.

Etienne will be working with Chenhao Lo and Conor Lynch to study how macrophages and other stromal cells in the bone impact prostate cancer to bone metastases. Stay tuned!

Competition and evolutionary-enlightened treatments: treatment (part 3)

From previous posts we now know that chemo-sensitive and chemo-resistant clones compete even when space is abundant and thus sensitive cells can be used to control the resistant population. We also know that in many, probably most cases, chemotherapy alone will not kill a tumour and that just “giving it all we got” is likely to expedite the emergence of resistant untreatable tumours. So, are Adaptive Therapies (ATs) so much better?

I have read a few slightly different implementations of ATs by the authors (CR 2009, Biol Direct 2010, CR 2012). The easiest definition of an AT involves the use of chemotherapy only as a way to control a population. Thus we will define a population size we want to achieve and some margin around which we can allow some wiggle. So let’s compare three simulations, one with no treatment, one with conventional lets-give-it-all chemo and one with an AT. The AT will aim to control the tumour population around the 100,000 cells mark and cease chemo when the population goes below 90,000. Here’s a simulation that shows how the AT approach works in this implementation:


As it is before, darker colors represent susceptible cells but as the cells display brighter colors (cyan->yellow->red) they become less susceptible.

And this plot compares the change in tumour population for the three different simulations (untreated, conventional chemo and AT):


The results show that not treating the tumor is clearly the worst of the options, that using chemotherapy in a conventional way can quickly select for resistance and that ATs could be a worthwhile alternative. The little insert on the top-left corner shows what happens when we run AT for longer. The Untreated and conventional treatments were run for about half the time that AT was, mostly because I could see what the result would be at the end. The effects of AT clearly do not last for ever and, if you look at the movie, it is easy to see why: every time the treatment is applied the overall phenotypic composition of the tumor shifts slightly towards more resistance.

Of course this has only scratched the surface of the issue. A more systematic and thorough comparison would allow us not only to be more certain about the advantages of ATs over conventional application of chemotherapy but also to say something about the importance of intra tumour heterogeneity in determining this.

Interestingly, mathematical models of AT typically assume resistance to be binary: either the cell is resistant (in which case it will resist any amount of chemo) or is not. I decided to try to see how AT would work on a version of the model where cells are either resistant or susceptible. I think the results suggest that AT would work better under this binary assumption which makes me think that ATs would work even better in treatments where evolution would have a less smooth landscape to operate on.

It is clear to many of us that ATs (and other evolutionary enlightened approaches such as evolutionary double binds) are likely to be more effective in many situations. The NCI has invested some good money to further explore this idea. Even a clinical trial is currently under way at Moffitt to test approaches based on AT in prostate cancer. But as we said on the first post of this series, the trade off is that we would need to consider cancer as a chronic disease and not something that could be cured once and for all. This would seem a reasonable compromise for a lot of patients but if we could find out more about which ones would do better with AT and which ones we can treat in a more definite way , that would be undoubtedly better. I expect that tumour heterogeneity (its origin and the mechanisms that maintain it) and a better understanding of the interactions with the microenvironment will be key for help us determine this. Expect some more thorough analysis discussing these points in a preprint server near you .