Diversity working together: cancer, immune system, and microbiome
December 12, 2014 6 Comments
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.
We decided with our team’s expertise that we should focus on oropharengeal cancer (OPC), which has been on the rise for HPV+ men, even though HPV- cancers (usually attributable to tobacco and alcohol use) has been declining. Our team figured that there could be some interesting dynamics between the virus, the microbiome, and the virally-induced cancer. We rely on our microbiome to aid in nutrition, recycle materials, resist pathogens, and educate our immune system (Human Microbiome Project Consortium, 2012), but they also cause disease, destroy food sources, and degrade structures (Gonzalez et al., 2012) so we knew that the immune system would be deeply ingrained into our abstracted model. And we wanted to ensure that the focus remained on the microbiome, so we thought about how we could manipulate the microbial communities through the use of probiotics (introduction of new live bacteria), prebiotics (nourishment for “good” bacteria that is already present), and/or antibiotics (kills or slows proliferation of bacteria, which may be specific or generic).
In order to make this clinically-relevant, we asked Dr. Russell what some of the major problems were in treating these cancers. OPC is generally treated with radiation and chemo, and most patients respond well to the treatment. However, around a third will have relapsed locally or regionally at 3 years post-treatment. It is also seen that HPV+ patients respond better to treatment than HPV- patients (probably due to the fact that damage done by the alternate route — alcohol and tobacco — has been more devastating mutationally and to the local tissue environment). So with this, we defined our question: Can we manipulate the microbiome to influence OPC treatment outcomes?
Though there has been a rise in interest, this field is just beginning to crawl. We did find a lot of good information, though more pertaining to colorectal cancers than oropharengeal. But generally, the microbiome must maintain homeostasis. The dysbiosis hypothesis suggests that even a small disruption in homeostasis can cause inflammation, an immune response, and disease (Hajishengallis et al., 2011). Dysbiosis is associated with cancer (Xuan et al., 2014; Sobhani, 2013), and specifically oral dysbiosis is associated with pancreatic and oral cancers (Mager et al., 2005; Michaud, 2013). We also found that the oral microbiomes DO substantially differ between healthy individuals and oral cancer patients (Schmidt et al., 2014). We also defined how the immune system responds to dysbiosis (Round and Mazmanian, 2009).
So to begin with a math model, we decided that a compartment model with the most significant elements would help us answer our question. This included generic cancer cells, immune cells, and a diverse microbiome. Each one of those compartments could be made more complex, but we wanted to make the main focus be on the microbiome. So we defined a set of ordinary differential equations that described the tumor cells, the immune cells, and different phenotypes of the microbiome (categorized by their interactions with the other compartments or their responses or interactions with treatments). From more searching through the published studies, we were able to reasonably quantify our parameters.
Next, we calibrated the model. We decided that before we try to understand the disease state, we should try to understand the homeostatic, normal state without a tumor present. This is an interesting question in itself: how does a diverse population competing for space maintain this symbiotic relationship with the host? We defined the microbiome to be both self-regulatory (the microbiome is sloughed off periodically), and immune-regulated (triggered by dysbiosis and overabundance), so how does this intimate relationship of microbiome components and the immune system produce a stable output? We attacked this from two angles. The system was simple enough to be analyzed, and we could also go straight to the numerical solver. We found analytically that we could get homeostasis as long as the immune response decay rate balances the microbes’ turnover, which is both a product of net proliferation and self-suppression caused by overabundance and lack of diversity. This helped inform the calibration of the parameters to numerically get a stable result, which was established.
Finally, we went straight to the treatment scheme. Standard of care includes chemo weekly with radiation administered daily with weekends off for 6 weeks. We were able to match the average patient response to treatment and show the variant response of microbial components during the treatment. We were able to show that differences in the patient’s microbiome, with all else equal, did have a small but quantifiable effect on patients, and more importantly, following treatment, some patients would continue to respond, while some would relapse. We also found that if the patient had a more beneficial microbiome, they could take breaks or miss some treatments and still have similar results. Breaks are beneficial because they both allow recovery from harsh treatments and improve quality of life.
Though we learned a lot here, we only just started. We didn’t end up winning the pilot grant, but we will end up getting the data that we wanted to spend some of that money on, anyway, so it feels good that through this exercise we were able to make an impact on what data is collected and how it’s quantified. There will be more to come from team microbiome in the future when we can analyze and utilize the information from patient’s oral gargles, blood samples, and tumor imaging to further understand this question.
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