Evolutionary dynamics of cancer in the bone

I don’t know about you, dear reader, but when I was a senior in highschool, I was busy skipping class to play CounterStrike. And I wasn’t even any good at it. Pranav Warman, however, is busy spending his senior year curing cancer. Or at least modeling it. On Friday, David Basanta, Pranav, and I spent much of the evening trying to understand prostate cancer after it has metastasized to the bone. Below, you can see us trying to make sense of some Mathematica calculations.[1]

WorkingBoneCancer

In this post, I want to sketch some of the ideas that we fooled around with. First is a model of healthy bone. Second is an introduction of the tumour into the system. Third, we will consider a model of a simple chemotherapy as treatment. You might notice some similarities to Warman et al. (2015) and my old discussions of the Basanta et al. (2012) model of tumour-stroma interaction. This is not accidental.

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From linear to nonlinear payoffs in the double public goods game

If you recall, dear reader, around this time last year, Robert Vander Velde, David Basanta, Jacob Scott and I got excited about the Archetti (2013,2014) approach to modeling non-linear public goods in cancer. We’ve been working on this intermittently for the last year, but aim to focus now that I have settled in here at Moffitt. This means there will be a lot more cancer posts as I resume thinking careful about mathematical oncology. Although I didn’t update the blog in the summer, it doesn’t mean that nothing was written. The work below is mostly from when I visited Tampa in late July. As are these two blackboards:

DPG_bb_long

In this project, we are combining growth factor production (Archetti, 2013) and acidity (2014) as a pair of anti-correlated public goods. The resulting dynamics cannot be understood by studying just one or the other good. The goal is to explore the richer behaviors that are possible with coupled social dilemmas. At the start of the year — in my first analysis of the double public goods game — as a sanity check I considered the linear public goods f(q) = b_f q and m(p) = b_m p. After a long meeting with Robert a few month ago, I think that these were misleading payoffs to consider. I jotted these notes after the meeting, but forgot to release them on the blog. Instead, you get to enjoy them now while I refresh my memory.

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Cytokine storms during CAR T-cell therapy for lymphoblastic leukemia

For most of the last 70 years or so, treating cancer meant one of three things: surgery, radiation, or chemotherapy. In most cases, some combination of these remains the standard of care. But cancer research does not stand still. More recent developments have included a focus on immunotherapy: using, modifying, or augmenting the patient’s natural immune system to combat cancer. Last week, we pushed the boundaries of this approach forward at the 5th annual Integrated Mathematical Oncology Workshop. Divided into four teams of around 15 people each — mathematicians, biologists, and clinicians — we competed for a $50k start-up grant. This was my 3rd time participating,[1] and this year — under the leadership of Arturo Araujo, Marco Davila, and Sungjune Kim — we worked on chimeric antigen receptor T-cell therapy for acute lymphoblastic leukemia. CARs for ALL.

Team Red busy at work in the collaboratorium

Team Red busy at work in the collaboratorium. Photo by team leader Arturo Araujo.

In this post I will describe the basics of acute lymphoblastic leukemia, CAR T-cell therapy, and one of its main side-effects: cytokine release syndrome. I will also provide a brief sketch of a machine learning approach to and justification for modeling the immune response during therapy. However, the mathematical details will come in future posts. This will serve as a gentle introduction.

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A detailed update on readership for the first 200 posts

It is time — this is the 201st article on TheEGG — to get an update on readership since our 151st post and lament on why academics should blog. I apologize for this navel-gazing post, and it is probably of no interest to you unless you are really excited about blog statistics. I am writing this post largely for future reference and to celebrate this arbitrary milestone.

The of statistics in this article are largely superficial proxies — what does a view even mean? — and only notable because of how easy they are to track. These proxies should never be used to seriously judge academics but I do think they can serve as a useful self-tracking tool. Making your blog’s statistics available publicly can be a useful comparison for other bloggers to get an idea of what sort of readership and posting habits are typical. In keeping with this rough and lighthearted comparison, according to Jeromy Anglim’s order-of-magnitude rules of thumb, in the year since the last update the blog has been popular in terms of RSS subscribers and relatively popular in terms of annual page views.

As before, I’ll start with the public self-metrics of the viewership graph for the last 6 and a half months:

Columns are views per week at TheEGG blog since the end of August, 2014. The vertical lines separate months, and the black line is average views per day for each month. The scale for weeks is on the left, it is different from the scale for daily average, those are labeled at each height.

Columns are views per week at TheEGG blog since the end of August, 2014. The vertical lines separate months, and the black line is average views per day for each month. The scale for weeks is on the left, it is different from the scale for daily average, those are labeled at each height.

If you’d like to know more, dear reader, then keep reading. Otherwise, I will see you on the next post!
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Helicobacter pylori and stem cells in the gastric crypt

IMO2014Group

Last Friday, the 4th Integrated Mathematical Oncology Workshop finished here at Moffitt. The event drew a variety of internal and external participants — you can see a blurry photo of many of them above — and was structured as a competition between four teams specializing in four different domains: Microbiome, Hepatitis C, Human papillomavirus, and Helicobacter pylori. The goal of each team was to build mathematical models of a specific problem in their domain that were well integrated with existing clinical and biological resources, the reward was a start-up grant to the project that seemed most promising to the team of judges. As I mentioned earlier in the week, I was on team H. Pylori — lead by Heiko Enderling with clinical insights from Domenico Coppola and Jose M. Pimiento. To get a feeling for the atmosphere of this workshop, I recommend a video summary of 2013’s workshop made by Parmvir Bahia, David Basanta, and Arturo Araujo:

I want to use this post to summarize some of the modeling that we did for the interaction of H. Pylori and gastric cancer. This is a brief outline — a reminder of sorts — and concentrates only on the parts that I was closely involved in. Unfortunately, this means that I won’t cover all the perspectives that our team offered, nor all the great work that they did. I apologize for the content I omitted. Hopefully, I can convince some other team members to blog about their experience to give a more balanced perspective.

This post also won’t cover all that you might want to know about bacteria and gastric cancer. As we saw earlier, fun questions about H. Pylori span many length and temporal scales and it was difficult to pick one to focus on. Domenico pointed us toward Houghton et al.’s (2004) work on the effect of H. Pylori on stem cell recruitment (for a recent survey, see Bessede et al., 2014), and suggested we aim our modeling at a level where we can discuss stem cells quantitatively. The hope is to use the abundance of stem cells as a new marker for disease progression. In the few days of the workshop, we ended up building and partially integrating two complimentary models; one agent-based and one based purely on ODEs. In the future, we hope to refine and parametrize these models based on patient data from Moffitt for the non-H. Pylori related gastric cancers, and from our partners in Cali, Colombia for H. Pylori related disease.
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Why academics should blog and an update on readership

It’s that time again, TheEGG has passed a milestone — 150 posts under our belt!– and so I feel obliged to reflect on blogging plus update the curious on the readerships statistics.

About a month ago, Nicholas Kristof bemoaned the lack of public intellectuals in the New York Times. Some people responded with defenses of the ‘busy academic’, and others agreement but with a shift of conversation medium to blogs from the more traditional media Kristof was focused on. As a fellow blogger, I can’t help but support this shift, but I also can’t help but notice the conflation of two very different notions: the public intellectual and the public educator.
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Two heads are better than one. How about more?

I like hiking a lot, especially with a few good friends of mine. But when the scenery is wild, or when the weather conditions are harsh, it is not uncommon to lose trail, or  at least – be in doubt whether we are going the right way. In these situations we discuss with each other, consulting as well a map and compass. And even if none of us is certain about the right path we need to take on an overgrown crossroad, we usually manage to reach the mountain hut as we planned.

A common wisdom says that two heads are better than one. In this article we will investigate empirical basis for this claim. Additionally, we will look at frameworks quantifying performance in simple tasks, trying to answer how does ‘doing better’ scale with the number of participants, and their skills.
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Predicting the risk of relapse after stopping imatinib in chronic myeloid leukemia

IMODay1To escape the Montreal cold, I am visiting the Sunshine State this week. I’m in Tampa for Moffitt’s 3rd annual integrated mathematical oncology workshop. The goal of the workshop is to lock clinicians, biologists, and mathematicians in the same room for a week to develop and implement mathematical models focussed on personalizing treatment for a range of different cancers. The event is structured as a competition between four teams of ten to twelve people focused on specific cancer types. I am on Javier Pinilla-Ibarz, Kendra Sweet, and David Basanta‘s team working on chronic myeloid leukemia. We have a nice mix of three clinicians, one theoretical biologist, one machine learning scientist, and five mathematical modelers from different backgrounds. The first day was focused on getting modelers up to speed on the relevant biology and defining a question to tackle over the next three days.
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Stats 101: an update on readership

Sorry, I couldn’t resist the title. This is the hundred and first post on TheEGG blog and I wanted to use the opportunity to update those curious about viewership stats. This is also a way for me to record milestones for the blog and proselytize people to blogging. Read on only if you want to learn about the behind the scenes of this blog.
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Some stats on the first 50 posts

We have just started the 18th month for this blog, and this post is to celebrate this anniversary and the passing of a milestone: this is the 51st post! It is also to share some statistics about the blog, partially because I wish other bloggers would share theirs (as benchmarks or aspirations for people like me that enjoy metrics too much) and so that I can quickly refer to them later.

When I first launched this blog, I had the ambitious goal of having two posts a week, but in the back of my mind kept the more realistic target of creating one post a week. Unfortunately, even the modest goal has not been met with an average of ~2.9 posts a month. Most importantly, it has been hard to keep a regular schedule with large post-less gaps like 2011/10/13 – 2012/01/10, 2012/03/29 – 2012/05/13, 2012/07/23 – 2012/10/11, and the most recent 2012/12/04 – 2013/01/23. How do you keep yourself on a regular blogging schedule? How do you balance blogging with things “that pay the bills” such as school work and research?

Monthly viewership statistics for TheEGG.

Monthly viewership statistics for TheEGG.

Surprisingly enough, it was during one of these gaps that I realized that this blog can actually reach people. In the first 9 months of activity, the blog received a total of 1806 views. In June and July, I started cross-listing more heavily on researchblogging and the viewership jumped up to 996 (June) and 1108 (July). On August 23rd, somebody shared on Reddit my July 23rd Programming Playground post. The share resulted in a flood of 2146 views on that day alone, and brought August up to 4628 views. This is a significant fraction of the 16751 views that the blog received so far.

After this realization, I started promoting the blog on Reddit, although never as successfully as this first share. The self-share that generated the most traction and insightful comments was my critique of Chaitin’s “Proving Darwin”. This was the one post I noticed circulating through twitter, G+, and tumblr. However, most of the interest seemed to stem from the novelty of Chaitin’s book rather than my critique.

Overall, in terms of sites driving traffic here, Reddit leads by a long shot with 5146 views, followed by search engines with 2707, and then a more tightly spaced list: researchblogging (251), Facebook (240), Twitter (245), and scoop.it (137). That being said, I don’t really understand how WordPress collects these views statistics. For instance, if I was to trust researchblogging’s view statistics then they say that they drove 5514 views to TheEGG, with some posts having more clicks from researchblogging than total number of views (from all sources) recorded for those posts by wordpress. Unless 19/20ths of the people get lost between clicking on a link on researchblogging and arriving at WordPress then there is some big discrepancy with how the two sites record views. Can anybody more familiar on web analytics fill me in?

In terms of WordPress viewership, the top 5 posts are:

  1. Programming playground: A whole-cell computational model (2863)
  2. Is Chaitin proving Darwin with metabiology? (2195)
  3. How would Alan Turing develop biology? (1319)
  4. Marcel’s Generating random k-regular graphs (1012), and
  5. Marcel’s Spatial Structure (559)

On the other hand, the researchblogging stats tend to have less spread and correlate more closely with age:

  1. Tom’s Fewer Friends, More Cooperation (485)
  2. How would Alan Turing develop biology? (418)
  3. Can we expand our moral circle towards an empathic civilization? (397)
  4. Bifurcation of cooperation and inviscid ethnocentrism (385)
  5. Is Chaitin proving Darwin with metabiology? (331)

The primary goal of this blog remains as a way to foster collaboration, and as a companion to the EGT reading group that I host. The reading group is resuming next week after a hiatus. If you have suggestions for what to read, please email them to me or leave them as comments. In terms of collaboration, I am happy to say that 22% of the posts on the blog thus far have come from my colleagues and co-authors: Julian Z. Xue (6 posts), Marcel Montrey (3 posts), and Thomas R. Shultz (2 posts). I am very thankful for their contribution and encouragement, and I hope they will continue to participate in this blog. In the coming months, I am also trying to recruit some new faces with four potential new contributors expressing interest. If you would like to write a guest post about evolutionary game theory, mathematical or computational approaches to evolution, or agent-based modeling in general then let me know!

I look forward to the adventure of the next 50 posts with you.