New publication: using integrated computational modeling to improve treatment of metastatic prostate cancer

Quite the title for the post, no? We just had a paper accepted in Nature Scientific Reports entitled Predictive computational modeling to define effective treatment strategies for bone metastatic prostate cancer and we thought we would share the news with you. This paper is part of our ongoing work with the Lynch lab at Moffitt to better understand the evolutionary dynamics of prostate cancer metastases in the bone. Continuing on our previous integrated computational platform [blog, description, paper] we decided to investigate a novel treatment which is not typically used in the clinic: the inhibition of the key signaling molecule TGF-Beta. If you know anything about prostate cancer research you will find TGF-Beta familiar. It has been studied to death and not much of clinical use has ever been found. The issue is that although we know a lot about it, cancer biologists have not found a model to integrate all those findings. We decided to use a computational agent-based model to integrate a lot of what we know of the biology of TGF-Beta in bone metastatic prostate cancer.

We took our previous model and worked carefully to make sure all the cell types responded to TGF-Beta to the best of the cancer biology community’s knowledge. We then went back and forth between this computational model and a mouse model in order to make sure that we could recapitulate TGF-Beta inhibition in the context of both: normal bone and cancerous bone. As you can see in the figure, one advantage of the computational model is that resolution of information about each cell type in any point of the tumor at any stage of the progression. This information can be contrasted with the relatively scarce experimental one to asses whether there are disagreements that need to be resolved.

Another advantage of the computational model is that, even if it is relatively complex like this one, it can test hypothesis at a much faster rate, for longer periods of time (and in a much cheaper and humane fashion) than mouse models. This allowed us to explore all kinds of schemes for the application of the TGF-Beta inhibitor. Once something promising is found we can then test those schemes experimentally to see how they fare with the mouse model.

What we found is that pre-application of the inhibitor is a much better option than application once the metastasis has established itself in the bone. What this could mean to a potential patient using a TGF-Beta inhibitor (which as said, is not a clinical option at the moment) is that we could give these treatments after the primary tumor has been identified in the prostate but before metastases have been found. It is important to understand though that this is the result of basic research and that this conclusion will have to remain hypothetical while we find how move our approach from a pre-clinical model to a clinical one. More importantly, what we have shown is that the integration of various sources of experimental data into a purposely made mechanistic mathematical model with the right amount of complexity (not too simple, not too complex) allows us to identify and explore novel treatments in ways (and at speeds) that would not be possible otherwise.

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 (www.pintofscience.us).

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.

SciencevsScientism_800x494-1-757x467

TO UNWEAVE A RAINBOW: SCIENCE AND THE UNDERSTANDING OF HUMAN EXPERIENCE

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?