Last week I returned from the 6th annual IMO Workshop at the Moffitt Cancer Center in Tampa, Florida. As I’ve sketched in an earlier post, my team worked on understanding ruxolitinib resistance in chronic myelomonocytic leukemia (CMML). We developed a suite of integrated multi-scale models for uncovering how resistance arises in CMML with no apparent strong selective pressures, no changes in tumour burden, and no genetic changes in the clonal architecture of the tumour. On the morning of Friday, November 11th, we were the final group of five to present. Eric Padron shared the clinical background, Andriy Marusyk set up our paradox of resistance, and I sketched six of our mathematical models, the experiments they define, and how we plan to go forward with the 50k pilot grant that was the prize of this competition. You can look through our whole slide deck. But in this post, I will concentrate on the four models that make up the core of our approach. Three models at the level of cells corresponding to different mechanisms of dark selection, and a model at the level of receptors to justify them. The goal is to show that these models lead to qualitatively different dynamics that are sufficiently different that the models could be distinguished between by experiments with realistic levels of noise. Read more of this post ## Dark selection and ruxolitinib resistance in myeloid neoplasms I am weathering the US election in Tampa, Florida. For this week, I am back at the Moffitt Cancer Center to participate in the 6th annual IMO Workshop. The 2016 theme is one of the biggest challenges to current cancer treatment: therapy resistance. All five teams participating this year are comfortable with the evolutionary view of cancer as a highly heterogeneous disease. And up to four of the teams are ready to embrace and refine a classic model of resistance. The classic model that supposes that: • treatment changes the selective pressure on the treatment-naive tumour. • This shifting pressure creates a proliferative or survival difference between sensitive cancer cells and either an existing or de novo mutant. • The resistant cells then outcompete the sensitive cells and — if further interventions (like drug holidays or new drugs or dosage changes) are not pursued — take over the tumour: returning it to a state dangerous to the patient. Clinically this process of response and relapse is usually characterised by a (usually rapid) decrease in tumour burden, a transient period of low tumour burden, and finally a quick return of the disease. But what if your cancer isn’t very heterogeneous? What if there is no proliferative or survival differences introduced by therapy among the tumour cells? And what if you don’t see the U curve of tumour burden? But resistance still emerges. This year, that is the paradox facing team orange as we look at chronic myelomonocytic leukemia (CMML) and other myeloid neoplasms. CMML is a leukemia that usually occurs in the elderly and is the most frequent myeloproliferative neoplasm (Vardiman et al., 2009). It has a median survival of 30 months, with death coming from progression to AML in 1/3rd of cases and cytopenias in the others. In 2011, the dual JAK1/JAK2 inhibitor ruxolitinib was approved for treatment of the related cancer of myelofibrosis based on its ability to releave the symptoms of the disease. Recently, it has also started to see use for CMML. When treating these cancers with ruxolitinib, Eric Padron — our clinical leader alongside David Basanta and Andriy Marusyk — sees the drastic reduction and then relapse in symptoms (most notably fatigue and spleen size) but none of the microdynamical signs of the classic model of resistance. We see the global properties of resistance, but not the evidence of selection. To make sense of this, our team has to illuminate the mechanism of an undetected — dark — selection. Once we classify this microdynamical mechanism, we can hope to refine existing therapies or design new therapies to adapt to it. ## Evolutionary dynamics of acid and VEGF production in tumours Today was my presentation day at ECMTB/SMB 2016. I spoke in David Basanta’s mini-symposium on the games that cancer cells play and postered during the poster session. The mini-symposium started with a brief intro from David, and had 25 minute talks from Jacob Scott, myself, Alexander Anderson, and John Nagy. David, Jake, Sandy, and John are some of the top mathematical oncologists and really drew a crowd, so I felt privileged at the opportunity to address that crowd. It was also just fun to see lots of familiar faces in the same place. A crowded room by the end of Sandy’s presentation. My talk was focused on two projects. The first part was the advertised “Evolutionary dynamics of acid and VEGF production in tumours” that I’ve been working on with Robert Vander Velde, Jake, and David. The second part — and my poster later in the day — was the additional “(+ measuring games in non-small cell lung cancer)” based on work with Jeffrey Peacock, Andriy Marusyk, and Jake. You can download my slides here (also the poster), but they are probably hard to make sense of without a presentation. I had intended to have a preprint out on this prior to today, but it will follow next week instead. Since there are already many blog posts about the double goods project on TheEGG, in this post I will organize them into a single annotated linkdex. ## Modeling influenza at ECMTB/SMB 2016 This week, I am at the University of Nottingham for the joint meeting of the Society of Mathematical Biology and the European Conference on Mathematical and Theoretical Biology — ECMTB/SMB 2016. It is a huge meeting, with over 800 delegates in attendance, 308 half-hour mini-symposium talks, 264 twenty-minute contributed talks, 190 posters, 7 prize talks, 7 plenary talks, and 1 public lecture. With seventeen to eighteen sessions running in parallel, it is impossible to see more than a tiny fraction of the content. And impossible for me to give you a comprehensive account of the event. However, I did want to share some moments from this week. If you are at ECMTB and want to share some of your highlights for TheEGG then let me know, and we can have you guest post. I did not come to Nottingham alone. Above is a photo of current/recent Moffitteers that made their way to the meeting this year. On the train ride to Nottingham, I needed to hear some success stories of mathematical biology. One of the ones that Dan Nichol volunteered was the SIR-model for controlling the spread of infectious disease. This is a simple system of ODEs with three compartments corresponding to the infection status of individuals in the population: susceptible (S), infectious (I), recovered (R). It is given by the following equations \begin{aligned} \dot{S} & = - \beta I S \\ \dot{I} & = \beta I S - \gamma I \\ \dot{R} & = \gamma I, \end{aligned} where $\beta$ and $\gamma$ are usually taken to be constants dependent on the pathogen, and the total number of individuals $N = S + I + R$ is an invariant of the dynamics. As the replicator dynamics are to evolutionary game theory, the SIR-model is to epidemiology. And it was where Julia Gog opened the conference with her plenary on the challenges of modeling infectious disease. In this post, I will briefly touch on her extensions of the SIR-model and how she used it to look at the 2009 swine flu outbreak in the US. Read more of this post ## 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 a50k 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. 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.

## Abusing numbers and the importance of type checking

What would you say if I told you that I could count to infinity on my hands? Infinity is large, and I have a typical number of fingers. Surely, I must be joking. Well, let me guide you through my process. Since you can’t see me right now, you will have to imagine my hands. When I hold out the thumb on my left hand, that’s one, and when I hold up the thumb and the index finger, that’s two. Actually, we should be more rigorous, since you are imagining my fingers, it actually isn’t one and two, but i and 2i. This is why they call them imaginary numbers.

Let’s continue the process of extending my (imaginary) fingers from the leftmost digits towards the right. When I hold out my whole left hand and the pinky, ring, and middle fingers on my right hand, I have reached 8i.

But this doesn’t look like what I promised. For the final step, we need to remember the geometric interpretation of complex numbers. Multiplying by i is the same thing as rotating counter-clockwise by 90 degrees in the plane. So, let’s rotate our number by 90 degrees and arrive at $\infty$.

I just counted to infinity on my hands.

Of course, I can’t stop at a joke. I need to overanalyze it. There is something for scientists to learn from the error that makes this joke. The disregard for the type of objects and jumping between two different — and usually incompatible — ways of interpreting the same symbol is something that scientists, both modelers and experimentalists, have to worry about it.

If you want an actually funny joke of this type then I recommend the image of a ‘rigorous proof’ above that was tweeted by Moshe Vardi. My writen version was inspired by a variant on this theme mentioned on Reddit by jagr2808.

I will focus this post on the use of types from my experience with stoichiometry in physics. Units in physics allow us to perform sanity checks after long derivations, imagine idealized experiments, and can even suggest refinements of theory. These are all features that evolutionary game theory, and mathematical biology more broadly, could benefit from. And something to keep in mind as clinicians, biologists, and modelers join forces this week during the 5th annual IMO Workshop at the Moffitt Cancer Center.

## Diversity working together: cancer, immune system, and microbiome

After a much needed few weeks of recovery, I’ve found some time to post about our annual IMO workshop held this year on the topic of viruses in cancer. Our group had the challenge of learning about all of the complexities of the human microbiome and its interactions with a cancerous lesion. The human microbiome, in a nutshell, is the ecological community of commensal, symbiotic, and pathogenic microorganisms that live on our inner and outer surfaces including bacteria, fungi, and viruses. The number of cells in the human microbiome is more than 10 times the amount of cells in our bodes (Costello et al., 2009), which means that 2-6 pounds of us is made of, not exactly us, but microorganisms. The microbiome has become a popular topic as of recent, with more than just human-centric studies sparking interest (see links for kittens, seagrass, the University of Chicago’s hospital, and the earth). See the video below for a nice introduction to the microbiome (and the cutest depiction of a colon you will ever see):

The first thing that I learned about the human microbiome is the extreme diversity of the bacterial communities. We have quite unique microbiomes, though they are shared through kissing, similar diets, and among families and pets (Song et al., 2013; Kort et al., 2014)! Further, there are huge discrepancies of the microbial communities that live in our hair, nose, ear, gut and foot (Human Microbiome Project Consortium, 2012). So the challenge to find a project that would address this diverse microbiome and its interaction with cancer in a way that we could test with real data to BOTH answer a clinically-relevant question AND be mathematically modeled in 4 days (what!?) was a little daunting. Good thing we had an epidemiologist and expert in the microbiome (Christine Pierce Campbell), a medical oncologist specializing in head and neck cancers (Jeffery Russell), and an excellent team of biologists, mathematicians, computer scientists, and biophysicists (#teamFecal) ready to rumble.

## Helicobacter pylori and stem cells in the gastric crypt

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.

## From H. pylori to Spanish colonialism: the scales of cancer.

Yesterday was the first day of the 4th Integrated Mathematical Oncology Workshop here at Moffitt. This year, it is run jointly with the Center for Infection Research in Cancer and is thus focused on the interaction of infection disease and cancer. This is a topic that I have not focused much attention on — except for the post on canine transmissible venereal tumor and passing mentions of Human papillomavirus (HPV) — so I am excited for the opportunity to learn. The workshop opened with a half-day focused on getting to know the external visitors, Alexander Anderson’s introduction, and our team assignments. I will be teammates with Heiko Enderling, Domenico Coppola, Jose M. Pimiento, and others. We will be looking at Helicobacter pylori. Go team blue! If you are curious, the more popularly known HPV went to David Basanta’s team, it will be great to compete against my team leader from last year. As you can expect, the friendly trash talking and subtle intimidation has already begun.

To be frank, before yesterday, I’ve only ever heard of H. pylori once and knew nothing of its links to stomach cancer. The story I heard was associated with Barry J. Marshall and J. Robin Warren’s award of the 2005 Nobel Prize in Physiology and Medicine “for their discovery of the bacterium Helicobacter pylori and its role in gastritis and peptic ulcer disease”. In 1984, Marshall was confident in the connection between H. pylori, inflammation, and ulcers, but the common knowledge of the day was that ulcers were caused by things like stress and smoking, not bacteria. The drug companies even happened to have an expensive drug that could manage the associated stomach inflammation, and given the money it was bringing in, nobody was concerned with finding some bacterium that could be cured with cheap antibiotics. Having difficulty convincing his colleagues (apart from Warren), Marshall decided to drink a Petri dish of cultured H. pylori, and within a few days grew sick, developing severe inflammation of the stomach before finally (two weeks after the ingestion) going on antibiotics and curing himself. This dramatic display was sufficient to push for bigger studies that eventually lead to the Nobel prize; I recommend listening to Warren’s podcast with Nobel Prize Talks or his acceptance speech for the whole story.

This is a fascinating tale, but from the modeling perspective, the real excitement of H. pylori and its role in stomach cancer is the multitude of scales that are central to the development of disease. We see important players from the scale of molecules involved in changing stomach acidity, to the single-cell scale of the bacteria and stomach lining, to the changes across the stomach as a whole organ, and the role of the individual patient’s life style and nutrition. These are the usual scales we see when modeling cancer, and dovetail nicely with Anderson’s opening remarks on the centrality of mathematics in helping us bridge the gaps. However, in the case of H. pylori, the scales go beyond the single individual at which Anderson stops and extend to the level of populations of humans in the co-evolution of host and pathogen, and even populations of groups of humans in a speculative connection to a topic familiar to TheEGG readers — the evolution of ethnocentrism. In preparation for the second half of the second day and the intense task of finding a specific question for team blue to focus on, I wanted to give a quick overview of these scales.
When you were born, you probably had 270 bones in your body. Unless you’ve experienced some very drastic traumas, and assuming that you are fully grown, then you probably have 206 bones now. Much like the number and types of internal organs, we can call this question of science solved. Unfortunately, it isn’t always helpful to think of you as made of bones and other organs. For medical purposes, it is often better to think of you as made of cells. It becomes natural to ask how many cells you are made of, and then maybe classify them into cell types. Of course, you wouldn’t expect this number to be as static as the number of bones or organs, as individual cells constantly die and are replaced, but you’d expect the approximate number to be relatively constant. Thus number is surprisingly difficult to measure, and our best current estimate is around $3.72 \times 10^{13}$ (Bianconi et al., 2013).
Both 206 and $3.72 \times 10^{13}$ are just numbers, but to a modeler they suggest a very important distinction over which tools we should use. Suppose that my bones and cells randomly popped in and out of existence without about equal probability (thus keeping the average number constant). In that case I wouldn’t expect to see exactly 206 bones, or exactly 37200000000000 cells; if I do a quick back-of-the-envelope calculation then I’d expect to see somewhere between 191 and 220 bones, and between 37199994000000 and 37200006000000. Unsurprisingly, the variance in the number of bones is only around 29 bones, while the number of cells varies by around 12 million. However, in terms of the percentage, I have 14% variance for the bones and only 0.00003% variance in the cell count. This means that in terms of dynamic models, I would be perfectly happy to model the cell population by their average, since the stochastic fluctuations are irrelevant, but — for the bones — a 14% fluctuation is noticeable so I would need to worry about the individual bones (and we do; we even give them names!) instead of approximating them by an average. The small numbers would be a case of when results can depend heavily on if one picks a discrete or continuous model.