Do genetically closely related organisms under identical, but strong selection pressure converge to a common resistant genotype or will they diverge to different genomic solutions? This question gets at the heart of how rough is the fitness landscape in the local vicinity of two closely related strains under stress. We chose a Growth Advantage in Stationary Phase (GASP) E scherichia coli strain to address this question because the GASP strain has very similar fitness to the wild-type (WT) strain in the absence of metabolic stress but in the presence of metabolic stress continues to divide and does not enter into stationary phase. We find that under strong antibiotic selection pressure by the fluoroquinolone antibiotic ciprofloxacin in a complex ecology that the GASP strain rapidly evolves in under 20 h missense mutation in gyrA only 2 amino acids removed from the WT strain indicating a convergent solution, yet does not evolve the other 3 mutations of the WT strain. Further the GASP strain evolves a prophage e14 excision which completely inhibits biofilm formation in the mutant strain, revealing the hidden complexity of E. coli evolution to antibiotics as a function of selection pressure. We conclude that there is a cryptic roughness to fitness landscapes in the absence of stress.

Preventing relapse is the major challenge to effective therapy in cancer. Within the tumour, stromal (ST) cells play an important role in cancer progression and the emergence of drug resistance. During cancer treatment, the fitness of cancer cells can be enhanced by ST cells because their molecular signalling interaction delays the drug-induced apoptosis of cancer cells. On the other hand, competition among cancer and ST cells for space or resources should not be ignored. We explore the population dynamics of multiple myeloma (MM) versus bone marrow ST cells by using an experimental microecology that we call the death galaxy, with a stable drug gradient and connected microhabitats. Evolutionary game theory is a quantitative way to capture the frequency-dependent nature of interactive populations. Therefore, we use evolutionary game theory to model the populations in the death galaxy with the gradients of pay-offs and successfully predict the future densities of MM and ST cells. We discuss the possible clinical use of such analysis for predicting cancer progression.

The emergence of resistance to chemotherapy by cancer cells, when combined with metastasis, is the primary driver of mortality in cancer and has proven to be refractory to many efforts. Theory and computer modeling suggest that the rate of emergence of resistance is driven by the strong selective pressure of mutagenic chemotherapy and enhanced by the motility of mutant cells in a chemotherapy gradient to areas of higher drug concentration and lower population competition. To test these models, we constructed a synthetic microecology which superposed a mutagenic doxorubicin gradient across a population of motile, metastatic breast cancer cells (MDA-MB-231). We observed the emergence of MDA-MB-231 cancer cells capable of proliferation at 200 nM doxorubicin in this complex microecology. Individual cell tracking showed both movement of the MDA-MB-231 cancer cells toward higher drug concentrations and proliferation of the cells at the highest doxorubicin concentrations within 72 h, showing the importance of both motility and drug gradients in the emergence of resistance.

We use a microfabricated ecology with a doxorubicin gradient and population fragmentation to produce a strong Darwinian selective pressure that drives forward the rapid emergence of doxorubicin resistance in multiple myeloma (MM) cancer cells. RNA sequencing of the resistant cells was used to examine (i) emergence of genes with high de novo substitution densities (i.e., hot genes) and (ii) genes never substituted (i.e., cold genes). The set of cold genes, which were 21% of the genes sequenced, were further winnowed down by examining excess expression levels. Both the most highly substituted genes and the most highly expressed never-substituted genes were biased in age toward the most ancient of genes. This would support the model that cancer represents a revision back to ancient forms of life adapted to high fitness under extreme stress, and suggests that these ancient genes may be targets for cancer therapy.