Complex biological systems often display a randomness paralleled in processes studied in fundamental physics. This simple stochasticity emerges owing to the complexity of the system and underlies a fundamental aspect of biology called phenotypic stochasticity. Ongoing stochastic fluctuations in phenotype at the single-unit level can contribute to two emergent population phenotypes. Phenotypic stochasticity not only generates heterogeneity within a cell population, but also allows reversible transitions back and forth between multiple states. This phenotypic interconversion tends to restore a population to a previous composition after that population has been depleted of specific members. We call this tendency homeostatic heterogeneity. These concepts of dynamic heterogeneity can be applied to populations composed of molecules, cells, individuals, etc. Here we discuss the concept that phenotypic stochasticity both underlies the generation of heterogeneity within a cell population and can be used to control population composition, contributing, in particular, to both the ongoing emergence of drug resistance and an opportunity for depleting drug-resistant cells. Using notions of both 'large' and 'small' numbers of biomolecular components, we rationalize our use of Markov processes to model the generation and eradication of drug-resistant cells. Using these insights, we have developed a graphical tool, called a metronomogram, that we propose will allow us to optimize dosing frequencies and total course durations for clinical benefit.

It is increasingly appreciated that phenotypic stochasticity plays fundamental roles in biological systems at the cellular level and that a variety of mechanisms generates phenotypic interconversion over a broad range of time scales. The ensuing dynamic heterogeneity can be used to understand biological and clinical processes involving diverse phenotypes in different cell populations. The same principles can be applied, not only to populations composed of cells, but also to populations composed of molecules, tissues, and multicellular organisms. Stochastic units generating dynamic heterogeneity can be integrated across various length scales. We propose that a graphical tool we have developed, called a metronomogram, will allow us to identify factors that suitably influence the restoration of homeostatic heterogeneity so as to modulate the consequences of dynamic heterogeneity for desired outcomes.

Cyclooxygenases (COXs) are enzymes that play a vital role in the inflammatory cascade through the generation of prostaglandins. Their over-expression has been implicated in numerous diseases. In particular, over-expression of COX-2 has been shown to be a predictive biomarker for progression of pre-malignant lesions towards invasive cancer in various tissues. This makes the early detection of COX-2 expressing lesions of high clinical relevance. Herein we describe the development of the first self-immolating trigger which targets COXs. We incorporated our trigger design into 2 activatable fluorogenic probes and demonstrated COX-specific activation in vitro. Experimental data revealed probe activation was likely caused by solvent-exposed amino acids on the surface of the COXs. Overall, the probes reported here mark the first step towards developing self-immolating imaging/therapeutic agents targeted to specific COXs.

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.

Cancer cells rapidly evolve drug resistance through somatic evolution and, in order to continue growth in the metastatic phase, violate the organism-wide consensus of regulated growth and beneficial communal interactions. We suggest that there is a fundamental mechanistic connection between the rapid evolution of resistance to chemotherapy in cellular communities within malignant tissues and the rapid evolution of antibiotic resistance in bacterial communities. We propose that this evolution is the result of a programmed and collective stress response performed by interacting cells, and that, given this fundamental connection, studying bacterial communities can provide deeper insights into the dynamics of adaptation and the evolution of cells within tumours.

To investigate the transition from non-cancerous to metastatic from a physical sciences perspective, the Physical Sciences-Oncology Centers (PS-OC) Network performed molecular and biophysical comparative studies of the non-tumorigenic MCF-10A and metastatic MDA-MB-231 breast epithelial cell lines, commonly used as models of cancer metastasis. Experiments were performed in 20 laboratories from 12 PS-OCs. Each laboratory was supplied with identical aliquots and common reagents and culture protocols. Analyses of these measurements revealed dramatic differences in their mechanics, migration, adhesion, oxygen response, and proteomic profiles. Model-based multi-omics approaches identified key differences between these cells' regulatory networks involved in morphology and survival. These results provide a multifaceted description of cellular parameters of two widely used cell lines and demonstrate the value of the PS-OC Network approach for integration of diverse experimental observations to elucidate the phenotypes associated with cancer metastasis.