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 […]
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:
It is online so anybody with a browser can play with it.
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.
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.
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.
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.
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.
Enhancing data sharing. An area where we are hurting ourselves unnecessarily: More sharing, more caring, more smiles, better treatments.
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.
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.
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:
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.
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?