[under construction! In the meantime, see https://scholar.google.com/citations?user=OhPYulMAAAAJ&hl=en ]
Every second, hundreds of thousands of our cells die. Don’t worry, we are made up of about 30 trillion cells, and the cells that die are replaced by others that divide. However, DNA replication is not perfect, and mutations accumulate in continually dividing cell lineages. Some of these mutations alter a cell’s ability to survive and reproduce. Certain lineages of cells may be naturally selected to survive at the expense of others. Over time, the populations of cells in our bodies evolve.
Most of my research revolves around this evolution. How do mutations change the survivability and replicative potential of our own cells? What is this distribution of these mutational effects, i.e., what proportion of mutations decrease or increase cell division? How do these changes eventually result in tumor formation, cancer, and aging? And the myriad of questions that have been coming up along the way.
Below I highlight some of my favorite take-home messages from my research. For a more up-to-date picture I invite visitors to check out the preprints and presentations linked within my CV.
Aging and cancer are two (inevitable) sides of the same coin.
Imagine you have a bicycle, and you hit that bicycle in a random spot with a hammer. What is the relative probability that your hit made the bicycle slower? Faster? No change?
Most hammer hits will just ding the frame and have no appreciable effect on speed. Among those rare hits to components that do influence speed, the majority of these will decrease speed as they destroy crucial components of the bike. We can also imagine rare hits that actually increase speed (maybe you knocked off a brake!), although perhaps at the expense of the rider’s safety.
Above, I’m drawing an analogy to the genome and fitness of our cells—the bike is the genome of our cells, the speed of the bike is cellular fitness, and the hammer hit is a mutation in the genome. As our cells accumulate mutations that decrease their ability to function, we age. As our cells accumulate mutations that increase their ability to divide and survive, tumors form and grow. Over our lifetimes, mutations accumulate in our tissues and drive both of these processes.
We created a mathematical model of an evolving stem cell population within the intestines, and calculated the distribution of accumulated fitness effects (after they occur and then take over the population). By modeling the dynamics of healthy tissue under biologically plausible distributions of mutational “hits”, we demonstrated how our bodies accumulate both deleterious mutations everywhere (gradual aging throughout the body) and also rare mutations of large effect that increase cellular fitness (localized areas of increased cellular fitness, i.e. a tumor).
The distribution of mutational effects and stem cell niche population size govern the rate of aging and tumorigenesis.
In the above example, the distribution of hit effects (the number of hits that will not have an effect, will reduce division, or will increase division) has been estimated for many “model” organisms under experimental evolution observed in the laboratory. This distribution is unknown for the cells within our bodies, and may very well be quite different! Unlike “whole” organisms, the cells without our bodies are far from their highest fitness. They did not evolve under a “survival of the fittest” regime, they evolved under a “survival of the best behaved” regime—meaning they divide at a slow-and-steady pace. This may also mean that they have a lot of room for improvement, in a “survival of the fittest” sense. We tested the implications of our cells evolving under a regime typical of what is measured in the laboratory for whole organisms, or under a regime where they can make larger jumped of improvement. We found that the regime typical of whole organisms best fit actual tumorigenesis curves.
Small stem cell populations permit the accumulation of mutations that lead to aging, while minimizing the probability of accumulating rare mutations that drive cancer.
This work raised the question: Why did evolution select for small stem cell niche population sizes if they permit such extensive mutation accumulation via genetic drift? Especially since much of these mutations are deleterious to cellular growth and contribute to aging. We created a mathematical model of the entire intestines and all subpopulations within the crypts and varied the population size of stem cells that replenish the entire tissue. We found that there was a population size of stem cells that minimizes the probability of accumulating mutations necessary to initiate a tumor—populations with higher or lower numbers of cells had a higher probability of tumorigenesis—and we found that this population size matches those measured within organisms. We showed that multicellular organisms face a trade-off between the rate that they age (via the accumulation of mutations deleterious to cellular fitness) and the rate that they succumb to cancer (via the accumulation of mutations beneficial to cellular fitness), and it seems that the architecture of the intestines was selected to minimize the rate of cancer, at the expense of aging.
Calculating the selection intensity of genetic variants found in tumors informs basic research prioritization, clinical decision making, and predictions of mechanisms that lead to cancer recurrence following targeted therapy.
Up until now, I have wrote about the distribution of mutational effects before selection—all potential effects that happen to cells within tissues before they compete for fixation or go extinct. On the other side of the evolutionary process is everything that we see (or do not see) after selection, i.e. out of all the potential mutations that occur, which ones spread in tumors (which ones ‘drive’ cancer)? Recently, we developed a method to calculate the selection intensity for the substitutions found in tumors. We calculated the selection intensity, or the effect size, of all recurrent mutations detected in 23 different cancer types, spanning over 10,000 tumors that underwent DNA sequencing. This metric ranks the relative importance of the genetic variants driving tumor growth and cancer progression and thus is an extremely important metric to quantify when prioritizing basic research and clinical decision making. This metric, and the parameters we calculate in its derivation, are also useful in predicting what mutations will both occur and drive resistance to chemotherapy—as we show in another study investigating mechanisms of resistance to a novel chemotherapy.