Last month, the International Society for Evolution, Ecology, and Cancer Conference (ISEEC) took place in Hinxton, UK, at the Wellcome Genome Center. It was the second ISEEC I’ve been to, the first being held in Tempe, Arizona in 2017. It’s a unique conference in that it focuses on cancer as an evolving organism, taking into account the environment and conditions inside the body upon which selection acts. The speakers run the gamut from clinicians, physicists, mathematicians, to ecologists - and everything in between. Despite the range of disciplines, everyone who participates is focused on the singular goal of better understanding cancer through the lens of evolution. It is always exciting to see the latest developments in evolutionarily-driven therapies for the disease, and this year was no exception.
There was an undeniable emphasis on mathematical modelling this year across the conference, with even a full section dedicated to it hosted by Trevor Graham. The other sections were cooperation and conflict in multicellularity, cellular competition, cooperation and cancer, evolvability and adaptation, and transmissible cancer. As the theme of this blog is on mathematical oncology, I’ll focus on summarizing the math modelling section. Within this section of the conference, there was a common thread of cooperation and conflict, but spatial organization and environmental constraints were frequently mentioned by the speakers. First up was Diana Fusco, and her talk was titled: “What can microbial colonies teach us about spatial intra-tumor heterogeneity?”
Diana is based at the University of Cambridge, and her work combines experimental microbiology with spatial modelling and statistics. Her talk was specifically on how microbial colonies can be used as analogs for intratumor heterogeneity. Spatial structure limits the ability of populations to grow indefinitely, but in well-mixed, exponentially growing populations, the frequency of mutation alleles follows a power law distribution. Her lab repeated the famous Luria-Delbruck experiment in E. coli (mutations are pre-existing in a population, not generated as a result of selection itself), and then used modern sequencing techniques to show that this law holds true (link to her paper on this topic). But they went a step further, and found that when E. coli are grown as a colony on a petri plate, far more ‘jackpot’ mutation events are produced than when grown in well-mixed environments. Just like winning the genetic lottery, these jackpot events result in massive enrichments of high-frequency clones. Mutants at the resource-rich edge of range expansions can generate whole sectors of clones, a phenomenon also known as gene-surfing. Further, Diana’s lab found similar results in silico by replicating the experiment using an ‘Eden’ simulation (cells on a square lattice that can only reproduce into empty, neighbouring squares) and in 3D models.
Diana’s experiments serve to reinforce the idea of controlling cancer, rather than trying to eradicate every last cell in a tumor. They turned to an engineered strain of S. cerevisiae that stochastically switched fluorescent marker expression at a rate of 1.6 x 10^-3 per cell division. During growth of a yeast colony, her group saw that most mutants actually go extinct - these pockets or ‘bubbles’ of mutant cells become trapped and outcompeted by wild-type cells, effectively becoming sealed off from the leading edge. If these mutants have a growth cost associated with them, it’s unsurprising that they can’t outgrow the wild-type. But when high doses of antibiotics are used, resistant mutants are released from their pockets. Whatever costs are associated with their growth are negligible compared to the wholesale destruction of their wild-type competitors. At intermediate levels of antibiotics, however, the whole population is suppressed without releasing those resistant clones - similar in principle to adaptive therapy for cancer. Diana’s group used microfluidics to see that the clone frequency distributions hold true regardless of dimensions, growth profiles, or even the mode of cell division.