Lung adenocarcinomas without driver genes converge to common adaptive strategies through diverse genetic, epigenetic, and niche construction evolutionary pathways

Somatic evolution selects cancer cell phenotypes that maximize survival and proliferation in dynamic environments. Although cancer cells are molecularly heterogeneous, we hypothesized convergent adaptive strategies to common host selection forces can be inferred from patterns of epigenetic and genetic evolutionary selection in similar tumors. We systematically investigated gene mutations and expression changes in lung adenocarcinomas with no common driver genes (n = 313). Although 13,461 genes were mutated in at least one sample, only 376 non-synonymous mutations evidenced positive evolutionary selection with conservation of 224 genes, while 1736 and 2430 genes exhibited ≥ two-fold increased and ≥ 50% decreased expression, respectively. Mutations under positive selection are more frequent in genes with significantly altered expression suggesting they often "hardwire" pre-existing epigenetically driven adaptations. Conserved genes averaged 16-fold higher expression in normal lung tissue compared to those with selected mutations demonstrating pathways necessary for both normal cell function and optimal cancer cell fitness. The convergent LUAD phenotype exhibits loss of differentiated functions and cell-cell interactions governing tissue organization. Conservation with increased expression is found in genes associated with cell cycle, DNA repair, p53 pathway, epigenetic modifiers, and glucose metabolism. No canonical driver gene pathways exhibit strong positive selection, but extensive down-regulation of membrane ion channels suggests decreased transmembrane potential may generate persistent proliferative signals. NCD LUADs perform niche construction generating a stiff, immunosuppressive microenvironment through selection of specific collagens and proteases. NCD LUADs evolve to a convergent phenotype through a network of interconnected genetic, epigenetic, and ecological pathways.

Updating the Definition of Cancer

Most definitions of cancer broadly conform to the current NCI definition: "Cancer is a disease in which some of the body's cells grow uncontrollably and spread to other parts of the body." These definitions tend to describe what cancer "looks like" or "does" but do not describe what cancer "is" or "has become." While reflecting past insights, current definitions have not kept pace with the understanding that the cancer cell is itself transformed and evolving. We propose a revised definition of cancer: Cancer is a disease of uncontrolled proliferation by transformed cells subject to evolution by natural selection. We believe this definition captures the essence of the majority of previous and current definitions. To the simplest definition of cancer as a disease of uncontrolled proliferation of cells, our definition adds in the adjective "transformed" to capture the many tumorigenic processes that cancer cells adopt to metastasize. To the concept of uncontrolled proliferation of transformed cells, our proposed definition then adds "subject to evolution by natural selection." The subject to evolution by natural selection modernizes the definition to include the genetic and epigenetic changes that accumulate within a population of cancer cells that lead to the lethal phenotype. Cancer is a disease of uncontrolled proliferation by transformed cells subject to evolution by natural selection.

The contribution of evolvability to the eco-evolutionary dynamics of competing species

Evolvability is the capacity of a population to generate heritable variation that can be acted upon by natural selection. This ability influences the adaptations and fitness of individual organisms. By viewing this capacity as a trait, evolvability is subject to natural selection and thus plays a critical role in eco-evolutionary dynamics. Understanding this role provides insight into how species respond to changes in their environment and how species coexistence can arise and be maintained. Here, we create a G-function model of competing species, each with a different evolvability. We analyze population and strategy (= heritable phenotype) dynamics of the two populations under clade initiation (when species are introduced into a population), evolutionary tracking (constant, small changes in the environment), adaptive radiation (availability of multiple ecological niches), and evolutionary rescue (extreme environmental disturbances). We find that when species are far from an eco-evolutionary equilibrium, faster-evolving species reach higher population sizes, and when species are close to an equilibrium, slower-evolving species are more successful. Frequent, minor environmental changes promote the extinction of species with small population sizes, regardless of their evolvability. When several niches are available for a species to occupy, coexistence is possible, though slower-evolving species perform slightly better than faster-evolving ones due to the well-recognized inherent cost of evolvability. Finally, disrupting the environment at intermediate frequencies can result in coexistence with cyclical population dynamics of species with different rates of evolution.

A mathematical investigation of polyaneuploid cancer cell memory and cross-resistance in state-structured cancer populations

The polyaneuploid cancer cell (PACC) state promotes cancer lethality by contributing to survival in extreme conditions and metastasis. Recent experimental evidence suggests that post-therapy PACC-derived recurrent populations display cross-resistance to classes of therapies with independent mechanisms of action. We hypothesize that this can occur through PACC memory, whereby cancer cells that have undergone a polyaneuploid transition (PAT) reenter the PACC state more quickly or have higher levels of innate resistance. In this paper, we build on our prior mathematical models of the eco-evolutionary dynamics of cells in the 2N+ and PACC states to investigate these two hypotheses. We show that although an increase in innate resistance is more effective at promoting cross-resistance, this trend can also be produced via PACC memory. We also find that resensitization of cells that acquire increased innate resistance through the PAT have a considerable impact on eco-evolutionary dynamics and extinction probabilities. This study, though theoretical in nature, can help inspire future experimentation to tease apart hypotheses surrounding how cross-resistance in structured cancer populations arises.

Escherichia coli survival in response to ciprofloxacin antibiotic stress correlates with increased nucleoid length and effective misfolded protein management

The evolution of antibiotic resistance is a fundamental problem in disease management but is rarely quantified on a single-cell level owing to challenges associated with capturing the spatial and temporal variation across a population. To evaluate cell biological phenotypic responses, we tracked the single-cell dynamics of filamentous bacteria through time in response to ciprofloxacin antibiotic stress. We measured the degree of phenotypic variation in nucleoid length and the accumulation of protein damage under ciprofloxacin antibiotic and quantified the impact on bacterial survival. Increased survival was correlated with increased nucleoid length and the variation in this response was inversely correlated with antibiotic concentration. Survival time was also increased through clearance of misfolded proteins, an unexpected mechanism of stress relief deployed by the filamentous bacteria. Our results reveal a diverse range of survival tactics employed by bacteria in response to ciprofloxacin and suggest potential evolutionary routes to resistance.

Nuclear morphology predicts cell survival to cisplatin chemotherapy

The emergence of chemotherapy resistance drives cancer lethality in cancer patients, with treatment initially reducing overall tumor burden followed by resistant recurrent disease. While molecular mechanisms underlying resistance phenotypes have been explored, less is known about the cell biological characteristics of cancer cells that survive to eventually seed the recurrence. To identify the unique phenotypic characteristics associated with survival upon chemotherapy exposure, we characterized nuclear morphology and function as prostate cancer cells recovered following cisplatin treatment. Cells that survived in the days and weeks after treatment and resisted therapy-induced cell death showed increasing cell size and nuclear size, enabled by continuous endocycling resulting in repeated whole genome doubling. We further found that cells that survive after therapy release were predominantly mononucleated and likely employ more efficient DNA damage repair. Finally, we show that surviving cancer cells exhibit a distinct nucleolar phenotype and increased rRNA levels. These data support a paradigm where soon after therapy release, the treated population mostly contains cells with a high level of widespread and catastrophic DNA damage that leads to apoptosis, while the minority of cells that have successful DDR are more likely to access a pro-survival state. These findings are consistent with accession of the polyaneuploid cancer cell (PACC) state, a recently described mechanism of therapy resistance and tumor recurrence. Our findings demonstrate the fate of cancer cells following cisplatin treatment and define key cell phenotypic characteristics of the PACC state. This work is essential for understanding and, ultimately, targeting cancer resistance and recurrence.

Modeling cancer's ecological and evolutionary dynamics

In this didactic paper, we present a theoretical modeling framework, called the G-function, that integrates both the ecology and evolution of cancer to understand oncogenesis. The G-function has been used in evolutionary ecology, but has not been widely applied to problems in cancer. Here, we build the G-function framework from fundamental Darwinian principles and discuss how cancer can be seen through the lens of ecology, evolution, and game theory. We begin with a simple model of cancer growth and add on components of cancer cell competition and drug resistance. To aid in exploration of eco-evolutionary modeling with this approach, we also present a user-friendly software tool. By the end of this paper, we hope that readers will be able to construct basic G function models and grasp the usefulness of the framework to understand the games cancer plays in a biologically mechanistic fashion.

Model-Free Measurement of Local Entropy Production and Extractable Work in Active Matter

Time-reversal symmetry breaking and entropy production are universal features of nonequilibrium phenomena. Despite its importance in the physics of active and living systems, the entropy production of systems with many degrees of freedom has remained of little practical significance because the high dimensionality of their state space makes it difficult to measure. Here we introduce a local measure of entropy production and a numerical protocol to estimate it. We establish a connection between the entropy production and extractability of work in a given region of the system and show how this quantity depends crucially on the degrees of freedom being tracked. We validate our approach in theory, simulation, and experiments by considering systems of active Brownian particles undergoing motility-induced phase separation, as well as active Brownian particles and E.coli in a rectifying device in which the time-reversal asymmetry of the particle dynamics couples to spatial asymmetry to reveal its effects on a macroscopic scale.

A life history model of the ecological and evolutionary dynamics of polyaneuploid cancer cells

Therapeutic resistance is one of the main reasons for treatment failure in cancer patients. The polyaneuploid cancer cell (PACC) state has been shown to promote resistance by providing a refuge for cancer cells from the effects of therapy and by helping them adapt to a variety of environmental stressors. This state is the result of aneuploid cancer cells undergoing whole genome doubling and skipping mitosis, cytokinesis, or both. In this paper, we create a novel mathematical framework for modeling the eco-evolutionary dynamics of state-structured populations and use this framework to construct a model of cancer populations with an aneuploid and a PACC state. Using in silico simulations, we explore how the PACC state allows cancer cells to (1) survive extreme environmental conditions by exiting the cell cycle after S phase and protecting genomic material and (2) aid in adaptation to environmental stressors by increasing the cancer cell's ability to generate heritable variation (evolvability) through the increase in genomic content that accompanies polyploidization. In doing so, we demonstrate the ability of the PACC state to allow cancer cells to persist under therapy and evolve therapeutic resistance. By eliminating cells in the PACC state through appropriately-timed PACC-targeted therapies, we show how we can prevent the emergence of resistance and promote cancer eradication.

Abstract A001: Modeling cancer’s ecological and evolutionary dynamics

We present a theoretical modeling framework, called the G function, to understand cancer speciation, diversification, and environmental adaptation. The G function integrates both the ecology and evolution of cancer and has been used in evolutionary ecology. However, the G function has not yet been widely applied to problems in cancer. Here, we build the G-function framework from fundamental Darwinian principles and discuss how cancer is inherently an evolutionary game. We begin with a simple model of cancer growth and add on components of cancer cell competition and drug resistance. To aid in exploration of eco-evolutionary modeling with this approach, we also present a user-friendly online tool. We argue that G-functions are useful to understand the games cancer plays in a biologically mechanistic fashion.

Citation Format: Anuraag Bukkuri, Kenneth J. Pienta, Ian Hockett, Robert H. Austin, Emma U. Hammarlund, Sarah R. Amend, Joel S. Brown. Modeling cancer’s ecological and evolutionary dynamics [abstract]. In: Proceedings of the AACR Special Conference on the Evolutionary Dynamics in Carcinogenesis and Response to Therapy; 2022 Mar 14-17. Philadelphia (PA): AACR; Cancer Res 2022;82(10 Suppl):Abstract nr A001.

It doesn't always pay to be fit: success landscapes

Landscapes play an important role in many areas of biology, in which biological lives are deeply entangled. Here we discuss a form of landscape in evolutionary biology which takes into account (1) initial growth rates, (2) mutation rates, (3) resource consumption by organisms, and (4) cyclic changes in the resources with time. The long-term equilibrium number of surviving organisms as a function of these four parameters forms what we call a success landscape, a landscape we would claim is qualitatively different from fitness landscapes which commonly do not include mutations or resource consumption/changes in mapping genomes to the final number of survivors. Although our analysis is purely theoretical, we believe the results have possibly strong connections to how we might treat diseases such as cancer in the future with a deeper understanding of the interplay between resource degradation, mutation, and uncontrolled cell growth.

Protein dynamics implications of the low- and high-temperature denaturation of myoglobin

We reinvestigate a simple model used in the literature concerning the thermodynamic analysis of protein cold denaturation. We derive an exact thermodynamic expression for cold denaturation and give a better approximation than exists in the literature for predicting cold denaturation temperatures in the two-state model. We discuss the "dark-side" implications of this work for previous temperature-dependent protein dynamics experiments and discuss microfluidic experimental technologies, which could explore the thermal stability range of proteins below the bulk freezing point of water.

Emergent Field-Driven Robot Swarm States

We present an ecology-inspired form of active matter consisting of a robot swarm. Each robot moves over a planar dynamic resource environment represented by a large light-emitting diode array in search of maximum light intensity; the robots deplete (dim) locally by their presence the local light intensity and seek maximum light intensity. Their movement is directed along the steepest local light intensity gradient; we call this emergent symmetry breaking motion "field drive." We show there emerge dynamic and spatial transitions similar to gas, crystalline, liquid, glass, and jammed states as a function of robot density, resource consumption rates, and resource recovery rates. Paradoxically the nongas states emerge from smooth, flat resource landscapes, not rough ones, and each state can directly move to a glassy state if the resource recovery rate is slow enough, at any robot density.

Deterministic Lateral Displacement: Challenges and Perspectives

The advent of microfluidics in the 1990s promised a revolution in multiple industries from healthcare to chemical processing. Deterministic lateral displacement (DLD) is a continuous-flow microfluidic particle separation method discovered in 2004 that has been applied successfully and widely to the separation of blood cells, yeast, spores, bacteria, viruses, DNA, droplets, and more. Deterministic lateral displacement is conceptually simple and can deliver consistent performance over a wide range of flow rates and particle concentrations. Despite wide use and in-depth study, DLD has not yet been fully elucidated or optimized, with different approaches to the same problem yielding varying results. We endeavor here to provide up-to-date expert opinion on the state-of-art and current fundamental, practical, and commercial challenges with DLD as well as describe experimental and modeling opportunities. Because these challenges and opportunities arise from constraints on hydrodynamics, fabrication, and operation at the micro- and nanoscale, we expect this Perspective to serve as a guide for the broader micro- and nanofluidic community to identify and to address open questions in the field.

Scaling of deterministic lateral displacement devices to a single column of bumping obstacles

We describe a deterministic lateral displacement (DLD) for particle separation with only a single column of bumping features. The bifurcation of fluid streams at obstacles is not set by the "tilt" of columns with respect to macroscopic current flow, but rather by the fluidic resistances for lateral flow at each obstacle. With one column of 14 bumping features and corresponding inlet/outlet channels, the single-column DLD can separate particles with diameters of 4.8 μm and 9.9 μm at 30 μL min^-1, with an area of only 0.37 mm × 1.5 mm (0.55 mm²). The large-cell output contains over 99% of the 9.9 μm particles and only 0.2% of the 4.8 m particles. The throughput per area of 54 μL min^-1 per mm² represents a 10× increase over previous selective harvesting reports for microfluidic devices in a similar particle size range.

An in vitro tumor swamp model of heterogeneous cellular and chemotherapeutic landscapes

The heterogenous, highly metabolic stressed, poorly irrigated, solid tumor microenvironment - the tumor swamp - is widely recognized to play an important role in cancer progression as well as the development of therapeutic resistance. It is thus important to create realistic in vitro models within the therapeutic pipeline that can recapitulate the fundamental stress features of the tumor swamp. Here we describe a microfluidic system which generates a chemical gradient within connected microenvironments achieved through a static diffusion mechanism rather than active pumping. We show that the gradient can be stably maintained for over a week. Due to the accessibility and simplicity of the experimental platform, the system allows for not only well-controlled continuous studies of the interactions among various cell types at single-cell resolution, but also parallel experimentation for time-resolved downstream cellular assays on the time scale of weeks. This approach enables simple, compact implementation and is compatible with existing 6-well imaging technology for simultaneous experiments. As a proof-of-concept, we report the co-culture of a human bone marrow stromal cell line and a bone-metastatic prostate cancer cell line using the presented device, revealing on the same chip a transition in cancer cell survival as a function of drug concentration on the population level while exhibiting an enrichment of poly-aneuploid cancer cells (PACCs) as an evolutionary consequence of high stress. The device allows for the quantitative study of cancer cell dynamics on a stress landscape by real-time monitoring of various cell types with considerable experimental throughput.

Polyploid giant cancer cells: Unrecognized actuators of tumorigenesis, metastasis, and resistance

Cancer led to the deaths of more than 9 million people worldwide in 2018, and most of these deaths were due to metastatic tumor burden. While in most cases, we still do not know why cancer is lethal, we know that a total tumor burden of 1 kg-equivalent to one trillion cells-is not compatible with life. While localized disease is curable through surgical removal or radiation, once cancer has spread, it is largely incurable. The inability to cure metastatic cancer lies, at least in part, to the fact that cancer is resistant to all known compounds and anticancer drugs. The source of this resistance remains undefined. In fact, the vast majority of metastatic cancers are resistant to all currently available anticancer therapies, including chemotherapy, hormone therapy, immunotherapy, and systemic radiation. Thus, despite decades-even centuries-of research, metastatic cancer remains lethal and incurable. We present historical and contemporary evidence that the key actuators of this process-of tumorigenesis, metastasis, and therapy resistance-are polyploid giant cancer cells.

Abstract 3774: Elevated cancer evolution dynamics: Emergence of polyploid cancer cells in response to multimodal therapy as an adaptive response on both individual and collective levels

Polyploidy has been often associated with poor prognosis and has been described to some extent in histological samples for most types of solid tumors. In cancer biology, it has been proposed to induce the gain of a stem-like phenotype and drive tumor progression by increasing the potential for cellular transformation. Experiments with chemotherapy gradients in our engineered microfluidic device with prostate cancer cell lines exhibited a stiff polyploidy distribution pattern of heterogeneous cell populations across the gradient. This demonstrates the generation of polyploid giant cancer cells (PGCCs) is an emergent response to high doses across the microfluidic drug stress landscape. Furthermore, in these experiments we observed polyploidization events occurring concurrently with the development of drug resistance and delayed relapse by non-polypoidal cell budding off from PGCCs leading to a complete repopulation, even in the high-dose areas of the array. Recent publications suggest polyploidy might play a crucial role in resistance to chemo-, hormone- and radio-therapy across most solid tumors investigated, thus independent of the therapy’s mechanism of action. To confirm these findings, we conducted experiments in conventional cell culture using different chemotherapeutic agents and ionizing radiation in several cell lines and confirmed the development of polyploidy was a common feature among them. Although these data supporting the association of polyploidy with resistance are extensive, the effects of these therapies on tumor heterogeneity and the cancer stem niche remains unknown. Different molecular mechanisms, such as mitotic slippage, aborted cytokinesis, endo-replication or cell fusion can lead to polyploidy. We hypothesize a subset of polyploid cells with stem-like features may be the reservoir of therapeutic resistance in cancer. In this work we focused on the characterization of this subset of cells within the tumor leading to the polyploid-stem phenotype, particularly the subpopulation with potential to reverse polyploidy and repopulate tumor heterogeneity after therapy. In order to establish causality and chronology of these phenomena, as well as quantify the complex population dynamics involved in this process, we engineered several cell lines from various common types of solid tumors with two fluorescence biosensors enabling simultaneous monitoring of SOX2/OCT4 activity and cell cycle status at the single-cell level using high-throughput confocal videomicroscopy. The emergence of polyploid cancer cells in response to multimodal therapy as an adaptive response on both individual and collective levels may be a hallmark of elevated cancer evolution dynamics and constitutes a promising potential target to prevent relapse.

Citation Format: Gonzalo Torga, Ke-Chih Lin, Kayla Myers, Kimberly Smith, Robert H. Austin, Kenneth J. Pienta. Elevated cancer evolution dynamics: Emergence of polyploid cancer cells in response to multimodal therapy as an adaptive response on both individual and collective levels [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 3774.

Gamblers: An Antibiotic-Induced Evolvable Cell Subpopulation Differentiated by Reactive-Oxygen-Induced General Stress Response

Antibiotics can induce mutations that cause antibiotic resistance. Yet, despite their importance, mechanisms of antibiotic-promoted mutagenesis remain elusive. We report that the fluoroquinolone antibiotic ciprofloxacin (cipro) induces mutations by triggering transient differentiation of a mutant-generating cell subpopulation, using reactive oxygen species (ROS). Cipro-induced DNA breaks activate the Escherichia coli SOS DNA-damage response and error-prone DNA polymerases in all cells. However, mutagenesis is limited to a cell subpopulation in which electron transfer together with SOS induce ROS, which activate the sigma-S (σS) general-stress response, which allows mutagenic DNA-break repair. When sorted, this small σS-response-"on" subpopulation produces most antibiotic cross-resistant mutants. A U.S. Food and Drug Administration (FDA)-approved drug prevents σS induction, specifically inhibiting antibiotic-promoted mutagenesis. Further, SOS-inhibited cell division, which causes multi-chromosome cells, promotes mutagenesis. The data support a model in which within-cell chromosome cooperation together with development of a "gambler" cell subpopulation promote resistance evolution without risking most cells.

The role of heterogeneous environment and docetaxel gradient in the emergence of polyploid, mesenchymal and resistant prostate cancer cells

The ability of a population of PC3 prostate epithelial cancer cells to become resistant to docetaxel therapy and progress to a mesenchymal state remains a fundamental problem. The progression towards resistance is difficult to directly study in heterogeneous ecological environments such as tumors. In this work, we use a micro-fabricated "evolution accelerator" environment to create a complex heterogeneous yet controllable in-vitro environment with a spatially-varying drug concentration. With such a structure we observe the rapid emergence of a surprisingly large number of polyploid giant cancer cells (PGCCs) in regions of very high drug concentration, which does not occur in conventional cell culture of uniform concentration. This emergence of PGCCs in a high drug environment is due to migration of diploid epithelial cells from regions of low drug concentration, where they proliferate, to regions of high drug concentration, where they rapidly convert to PGCCs. Such a mechanism can only occur in spatially-varying rather than homogeneous environments. Further, PGCCs exhibit increased expression of the mesenchymal marker ZEB1 in the same high-drug regions where they are formed, suggesting the possible induction of an epithelial to mesenchymal transition (EMT) in these cells. This is consistent with prior work suggesting the PGCC cells are mediators of resistance in response to chemotherapeutic stress. Taken together, this work shows the key role of spatial heterogeneity and the migration of proliferative diploid cells to form PGCCs as a survival strategy for the cancer population, with implications for new therapies.

Abstract B039: Multinucleation precedes the emergence of drug resistance in prostate cancer

Metastatic prostate cancer remains a lethal disease. Taxane chemotherapy used to be restricted to the hormone-resistant metastatic setting in which most patients with castration-resistant prostate cancer (CRPC) develop therapeutic resistance to chemotherapy, achieving limited therapeutic benefit. Recently, two trials demonstrated adjuvant chemohormonal therapy to increase survival in men with high-risk metastatic hormone-sensitive prostate cancer.

Although chemotherapy has been extensively used for decades, how it affects tumor heterogeneity at the molecular level is poorly understood. Experiments with drug gradients in our engineered microfluidic device have shown that docetaxel therapy leads to multinucleation with a stiff distribution pattern of heterogeneous cell populations across the concentration gradient. Furthermore, in these experiments, multinucleation always preceded the development of drug resistance. To confirm these findings, we conducted experiments in conventional cell culture using different chemotherapeutic agents in several prostate cancer cell lines and confirmed multinucleation was a common feature among them. Interestingly, multinucleation occurred to some extent, independently of the mechanism of action of the drug tested.

Aneuploidy, an inherent consequence of multinucleation, has been described as a common characteristic of tumors and proposed to drive tumor progression by increasing the potential for cellular transformation. However, whether this is a cause or a consequence of cancer remains unclear. Aneuploidy can be caused by very diverse molecular mechanisms such as mitotic slippage, endo-replication, or cell fusion. In cancer biology, it has been associated with epithelial-mesenchymal transition as well as with gain of stem-like potential. Recent data in prostate cancer demonstrated the association of multinucleation with chemotherapy, androgen-deprivation therapy, and radiotherapy. Although the data supporting the association of multinucleation with drug resistance are extensive, the effects of these therapies on tumor heterogeneity remain unknown. A better understanding of the resistance mechanisms might reveal new targets for cancer therapy.

In this work, we focus on the characterization of the subset of cells within the tumor population that leads to the multinucleated phenotype, particularly the subpopulation with the potential to reverse polyploidy and repopulate tumor heterogeneity after therapy.

Citation Format: Gonzalo Torga, Ke-Chih Lin, Robert H. Austin, Kenneth J. Pienta. Multinucleation precedes the emergence of drug resistance in prostate cancer [abstract]. In: Proceedings of the AACR Special Conference: Prostate Cancer: Advances in Basic, Translational, and Clinical Research; 2017 Dec 2-5; Orlando, Florida. Philadelphia (PA): AACR; Cancer Res 2018;78(16 Suppl):Abstract nr B039.

A bacterial antibiotic resistance accelerator and applications

The systematic emergence of drug resistance remains a major problem in the treatment of infectious diseases (antibiotics) and cancer (chemotherapy), with possible common fundamental origins linking bacterial antibiotic resistance and emergence of chemotherapy resistance. The common link may be evolution in a complex fitness landscape with connected small population niches. We report a detailed method for observing bacterial adaptive behavior in heterogeneous microfluidic environment designed to mimic the environmental heterogeneity found in natural microbial niches. First, the device is structured with multiple connected micro-chambers that allow the cell population to communicate and organize into smaller populations. Second, bacteria evolve within an antibiotic gradient generated throughout the micro-chambers that creates a wide range of fitness landscapes. High-resolution images of the adaptive response to the antibiotic stress are captured by epifluorescence microscopy at various levels of the bacterial organization for quantitative analysis. Thus, the experimental setup we have developed provides a powerful frame for visualizing evolution at work: bacterial movement, survival and death. It also presents a basis for exploring the rates at which drug resistance arises in bacteria and other biological contexts such as cancer.

Abstract 1977: KIFC1 is a potential target to prevent treatment resistance in prostate cancer

Metastatic prostate cancer remains incurable. Taxane-based chemotherapy used to be restricted to the hormone-resistant metastatic setting. Unfortunately, most patients with castration-resistant prostate cancer (CRPC) rapidly develop resistance to chemotherapy. Recently, two trials demonstrated increased survival in men with high-risk metastatic hormone-sensitive prostate cancer treated with adjuvant chemohormonal therapy. Polyploidy has been demonstrated in most types of tumors to some extent and proposed to drive tumor progression by increasing the potential for cellular transformation. However, whether this is a cause or a consequence of cancer remains unclear. Different molecular mechanism can lead to polyploidy, such as mitotic slippage, endo-replication or cell fusion. In cancer biology, it has been associated with epithelial-mesenchymal transition (EMT), as well as with gain of stem-like potential. Experiments with drug gradients in our engineered microfluidic device have shown docetaxel therapy leads to polyploidy with a stiff distribution pattern of heterogeneous cell populations across the gradient. Moreover, in these experiments we evidenced polyploidization events occurring concurrently with the development of drug resistance. To confirm these findings, we conducted experiments in conventional cell culture using different chemotherapeutic agents in several prostate cancer cell lines and confirmed the development of polyploidy was a common feature among them. Interestingly, this feature occurred at some extent for all drugs, independently of the mechanism of action of the drug tested. Recent publications demonstrated the association of multinucleation with chemotherapy, androgen-deprivation therapy and radiotherapy. Although the data supporting the association of polyploidy with resistance is extensive, the effects of these therapies on tumor heterogeneity remains unknown. Most polyploid cells undergo cell death due to a mitotic catastrophe subsequent to multipolar cell division, however a small percentage of them survive and produce viable progeny of chemotherapy resistant clones via asymmetric cell division by undergoing centrosome clustering mediated by KIFC1. In this work, we focus on the characterization of the subset of cells within the tumor population that leads to the polyploid phenotype, particularly the subpopulation with potential to reverse polyploidy and repopulate tumor heterogeneity after therapy. We hypothesize a subset of polyploid cells with stem-like features may be the reservoir of therapeutic resistance in cancer. Given KIFC1 is required for centrosome clustering and clinical data suggests its association with polyploidy, poor prognosis and previous taxane-therapy in prostate cancer, we studied KIFC1 as a potential target to prevent relapse after therapy.

Citation Format: Gonzalo Torga, Ke-Chih Lin, Bernat Navarro Serer, Cathleen Nguyen, Robert H. Austin, Kenneth J. Pienta. KIFC1 is a potential target to prevent treatment resistance in prostate cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 1977.

On-chip cell labelling and washing by capture and release using microfluidic trap arrays

Flow cytometry analysis requires a large amount of isolated, labelled, and purified cells for accurate results. To address the demand for a large quantity of cells prepared in a timely manner, we describe a novel microfluidic trap structure array for on-chip cell labelling, such as intracellular and extracellular labelling, and subsequent washing and release of cells. Each device contains [Formula: see text] trap structures, which made the preparation of large numbers of cells [Formula: see text] possible. The structure has a streamlined shape, which minimizes clogging of cells in capture and release steps. The trap structure arrays are built and tested using leukocytes, with different load flow speeds, incubation times, and release flow speeds. ∼85% of cells are captured independent of the input flow speed. The release efficiency depends on the incubation time, with over ∼80% of captured cells released for up to 20 min incubation, and on-chip labelling and washing with STYO13 are demonstrated. Qualitative models are developed as guidance for designing the proposed trap structure and to explain the increased performance over previous approaches.

Broken flow symmetry explains the dynamics of small particles in deterministic lateral displacement arrays

Deterministic lateral displacement (DLD) is a technique for size fractionation of particles in continuous flow that has shown great potential for biological applications. Several theoretical models have been proposed, but experimental evidence has demonstrated that a rich class of intermediate migration behavior exists, which is not predicted. We present a unified theoretical framework to infer the path of particles in the whole array on the basis of trajectories in a unit cell. This framework explains many of the unexpected particle trajectories reported and can be used to design arrays for even nanoscale particle fractionation. We performed experiments that verify these predictions and used our model to develop a condenser array that achieves full particle separation with a single fluidic input.

Abstract IA04: Game theory and personalized cancer treatment

The presence of driver mutations and subsequent clonal expansion by Darwinian evolution does not explain dormancy and re-emergence of cancer from a community of cancer and host cells (including stromal and immune cells). Dormancy appears to be a slow-driven, interaction-dominated, threshold system which is poorly prognosed. At the simplest level, we view cancer cells interacting with host cells via complex, non-linear population dynamics, which can lead to very non-intuitive but perhaps deterministic and understandable progression dynamics of cancer. We explore here the dynamics of host-cancer cell populations in the presence of (1) payoffs gradients and (2) perturbation due to cell migration to determine to what extent the time-dependence of the populations can be quantitatively understood in spite of the underlying complexity of the individual agents. The population dynamics presented here provide a model system for the clinic to map the payoffs matrices and suggest new avenues to predict drug dosages.

Citation Format: Robert H. Austin. Game theory and personalized cancer treatment. [abstract]. In: Proceedings of the AACR Special Conference on Engineering and Physical Sciences in Oncology; 2016 Jun 25-28; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2017;77(2 Suppl):Abstract nr IA04.

How to connect time-lapse recorded trajectories of motile microorganisms with dynamical models in continuous time

We provide a tool for data-driven modeling of motility, data being time-lapse recorded trajectories. Several mathematical properties of a model to be found can be gleaned from appropriate model-independent experimental statistics, if one understands how such statistics are distorted by the finite sampling frequency of time-lapse recording, by experimental errors on recorded positions, and by conditional averaging. We give exact analytical expressions for these effects in the simplest possible model for persistent random motion, the Ornstein-Uhlenbeck process. Then we describe those aspects of these effects that are valid for any reasonable model for persistent random motion. Our findings are illustrated with experimental data and Monte Carlo simulations.

Automated leukocyte processing by microfluidic deterministic lateral displacement

We previously developed a Deterministic Lateral Displacement (DLD) microfluidic method in silicon to separate cells of various sizes from blood (Davis et al., Proc Natl Acad Sci 2006;103:14779-14784; Huang et al., Science 2004;304:987-990). Here, we present the reduction-to-practice of this technology with a commercially produced, high precision plastic microfluidic chip-based device designed for automated preparation of human leukocytes (white blood cells; WBCs) for flow cytometry, without centrifugation or manual handling of samples. After a human blood sample was incubated with fluorochrome-conjugated monoclonal antibodies (mAbs), the mixture was input to a DLD microfluidic chip (microchip) where it was driven through a micropost array designed to deflect WBCs via DLD on the basis of cell size from the Input flow stream into a buffer stream, thus separating WBCs and any larger cells from smaller cells and particles and washing them simultaneously. We developed a microfluidic cell processing protocol that recovered 88% (average) of input WBCs and removed 99.985% (average) of Input erythrocytes (red blood cells) and >99% of unbound mAb in 18 min (average). Flow cytometric evaluation of the microchip Product, with no further processing, lysis or centrifugation, revealed excellent forward and side light scattering and fluorescence characteristics of immunolabeled WBCs. These results indicate that cost-effective plastic DLD microchips can speed and automate leukocyte processing for high quality flow cytometry analysis, and suggest their utility for multiple other research and clinical applications involving enrichment or depletion of common or rare cell types from blood or tissue samples. © 2016 International Society for Advancement of Cytometry.

Nanoscale lateral displacement arrays for the separation of exosomes and colloids down to 20 nm

Deterministic lateral displacement (DLD) pillar arrays are an efficient technology to sort, separate and enrich micrometre-scale particles, which include parasites, bacteria, blood cells and circulating tumour cells in blood. However, this technology has not been translated to the true nanoscale, where it could function on biocolloids, such as exosomes. Exosomes, a key target of 'liquid biopsies', are secreted by cells and contain nucleic acid and protein information about their originating tissue. One challenge in the study of exosome biology is to sort exosomes by size and surface markers. We use manufacturable silicon processes to produce nanoscale DLD (nano-DLD) arrays of uniform gap sizes ranging from 25 to 235 nm. We show that at low Péclet (Pe) numbers, at which diffusion and deterministic displacement compete, nano-DLD arrays separate particles between 20 to 110 nm based on size with sharp resolution. Further, we demonstrate the size-based displacement of exosomes, and so open up the potential for on-chip sorting and quantification of these important biocolloids.

Abstract 2418: Microenvironment-on-chip: Development of a microfluidics-based tumor ecosystem

Despite its limited clinical predictive capacity, conventional cell culture remains the most frequently used preclinical model in biomedical research. For many years, microfluidics-based organ-on-chips have been proposed to overcome conventional cell culture limitations but given their inability to provide reliable and reproducible data, their usage remains anecdotal.

Immune, stromal and cancer cells constitute a complex adaptive system in each tumor microenvironment. To study the causes and consequences of tumor heterogeneity in these complex adaptive systems, we have developed a platform to model and investigate tumor microenvironments at primary and metastatic sites in which multiple nuclear-labelled cell types can be grown in a multi-welled, communicating habitat with controlled gradients for temperature, pH, nutrients, and oxygen tension. In conjunction with labelled-nuclei, genetically-engineered cell lines with fluorescence-based biosensors can be generated to detect specific genetic expression changes in response to cell to cell interactions as well as varying environmental conditions over time.

One of the major caveats for microfluidics prototypes is reproducibility. The proposed design is easily adaptable to most of the commercially available incubation microscopy systems, allowing the experiments to be replicated by independent groups in an effort to provide reliable and reproducible data.

The ability to establish dynamic gradients within individual habitats longitudinally over time and space provides the capacity to study the effect of chemokines and drugs on the tumor ecosystem in order to discover key interactions between host cells and cancer cells and to develop improved therapeutic strategies.

In contrast to previous organ-on-chip models, our technology is not limited to a mere observational platform for automated cell counting and tracking but also provides flexibility for downstream capacities such as immunofluorescence, in situ hybridization or single-cell sequencing, as well as retrieval of single cells or specific subpopulations based on the fluorescent reporters and labelled nuclei.

The presented engineered microenvironment allows continued in vitro quantitative studies of the interactions of multiple cell types as well as varying environmental conditions. Here we propose a cancer-on-chip platform that is able to recapitulate key components and interactions to mimic different tumor microenvironments in a comprehensive manner, yet simple enough to provide reliable and reproducible data.

Citation Format: Gonzalo Torga, Ke-Chih Lin, Robert H. Austin, Kenneth J. Pienta. Microenvironment-on-chip: Development of a microfluidics-based tumor ecosystem. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 2418.

Microfluidic chemical processing with on-chip washing by deterministic lateral displacement arrays with separator walls

We describe a microfluidic device for on-chip chemical processing, such as staining, and subsequent washing of cells. The paper introduces "separator walls" to increase the on-chip incubation time and to improve the quality of washing. Cells of interest are concentrated into a treatment stream of chemical reagents at the first separator wall for extended on-chip incubation without causing excess contamination at the output due to diffusion of the unreacted treatment chemicals, and then are directed to the washing stream before final collections. The second separator wall further reduces the output contamination from diffusion to the washing stream. With this approach, we demonstrate on-chip leukocyte staining with Rhodamine 6G and washing. The results suggest that other conventional biological and analytical processes could be replaced by the proposed device.

Ancient hot and cold genes and chemotherapy resistance emergence

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.

Inhibition of clot formation in deterministic lateral displacement arrays for processing large volumes of blood for rare cell capture

Microfluidic deterministic lateral displacement (DLD) arrays have been applied for fractionation and analysis of cells in quantities of ~100 μL of blood, with processing of larger quantities limited by clogging in the chip. In this paper, we (i) demonstrate that this clogging phenomenon is due to conventional platelet-driven clot formation, (ii) identify and inhibit the two dominant biological mechanisms driving this process, and (iii) characterize how further reductions in clot formation can be achieved through higher flow rates and blood dilution. Following from these three advances, we demonstrate processing of 14 mL equivalent volume of undiluted whole blood through a single DLD array in 38 minutes to harvest PC3 cancer cells with ~86% yield. It is possible to fit more than 10 such DLD arrays on a single chip, which would then provide the capability to process well over 100 mL of undiluted whole blood on a single chip in less than one hour.

Concentrating genomic length DNA in a microfabricated array

We demonstrate that a microfabricated bump array can concentrate genomic-length DNA molecules efficiently at continuous, high flow velocities, up to 40 μm/s, if the single-molecule DNA globule has a sufficiently large shear modulus. Increase in the shear modulus is accomplished by compacting the DNA molecules to minimal coil size using polyethylene glycol (PEG) derived depletion forces. We map out the sweet spot, where concentration occurs, as a function of PEG concentration and flow speed using a combination of theoretical analysis and experiment. Purification of DNA from enzymatic reactions for next-generation DNA-sequencing libraries will be an important application of this development.

Resistance to chemotherapy: patient variability and cellular heterogeneity

The issue of resistance to targeted drug therapy is of pressing concern, as it constitutes a major barrier to progress in managing cancer. One important aspect is the role of stochasticity in determining the nature of the patient response. We examine two particular experiments. The first measured the maximal response of melanoma to targeted therapy before the resistance causes the tumor to progress. We analyze the data in the context of a Delbruck-Luria type scheme, wherein the continued growth of preexistent resistant cells are responsible for progression. We show that, aside from a finite fraction of resistant cell-free patients, the maximal response in such a scenario would be quite uniform. To achieve the measured variability, one is necessarily led to assume a wide variation from patient to patient of the sensitive cells' response to the therapy. The second experiment is an in vitro system of multiple myeloma cells. When subject to a spatial gradient of a chemotherapeutic agent, the cells in the middle of the system acquire resistance on a rapid (two-week) timescale. This finding points to the potential important role of cell-to-cell differences, due to differing local environments, in addition to the patient-to-patient differences encountered in the first part. See all articles in this Cancer Research section, "Physics in Cancer Research."

Ratchets in hydrodynamic flow: more than waterwheels

The transport of objects in microfluidic arrays of obstacles is a surprisingly rich area of physics and statistical mechanics. Tom Duke's mastery of these areas had a major impact in the development of biotechnology which uses these ideas at an increasing scale. We first review how biological objects are transported in fluids at low Reynolds numbers, including a discussion of electrophoresis, then concentrate on the separation of objects in asymmetric arrays, sometimes called Brownian ratchets when diffusional symmetry is broken by the structures. We move beyond this to what are called deterministic arrays where non-hydrodynamic forces in asymmetric arrays allow for extraordinary separation, and we look to the future of using these unusual arrays at the nanoscale and at the hundreds of micrometre scale. The emphasis is on how the original ideas of Tom Duke drove this work forward.

Rejoice in the hubris: useful things biologists could do for physicists

Political correctness urges us to state how wonderful it is to work with biologists and how, just as the lion will someday lie down with the lamb, so will interdisciplinary work, where biologists and physicists are mixed together in light, airy buildings designed to force socialization, give rise to wonderful new science. But it has been said that the only drive in human nature stronger than the sex drive is the drive to censor and suppress, and so I claim that it is OK for physicists and biologists to maintain a wary distance from each other, so that neither one censors or suppresses the wild ideas of the other.

You cannot tell a book by looking at the cover: Cryptic complexity in bacterial evolution

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.

Game theory in the death galaxy: interaction of cancer and stromal cells in tumour microenvironment

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.

Bacteria and game theory: the rise and fall of cooperation in spatially heterogeneous environments

One of the predictions of game theory is that cooperative behaviours are vulnerable to exploitation by selfish individuals, but this result seemingly contradicts the survival of cooperation observed in nature. In this review, we will introduce game theoretical concepts that lead to this conclusion and show how the spatial competition dynamics between microorganisms can be used to model the survival and maintenance of cooperation. In particular, we focus on how Escherichia coli bacteria with a growth advantage in stationary phase (GASP) phenotype maintain a proliferative phenotype when faced with overcrowding to gain a fitness advantage over wild-type populations. We review recent experimental approaches studying the growth dynamics of competing GASP and wild-type strains of E. coli inside interconnected microfabricated habitats and use a game theoretical approach to analyse the observed inter-species interactions. We describe how the use of evolutionary game theory and the ideal free distribution accurately models the spatial distribution of cooperative and selfish individuals in spatially heterogeneous environments. Using bacteria as a model system of cooperative and selfish behaviours may lead to a better understanding of the competition dynamics of other organisms-including tumour-host interactions during cancer development and metastasis.

In vitro microbial culture models and their application in drug development

Drug development faces its nemesis in the form of drug resistance. The rate of bacterial resistance to antibiotics, or tumor resistance to chemotherapy decisively depends on the surrounding heterogeneous tissue. However, in vitro drug testing is almost exclusively done in well stirred, homogeneous environments. Recent advancements in microfluidics and microfabrication introduce opportunities to develop in vitro culture models that mimic the complex in vivo tissue environment. In this review, we will first discuss the design principles underlying such models. Then we will demonstrate two types of microfluidic devices that combine stressor gradients, cell motility, large population of competing/cooperative cells and time varying dosage of drugs. By incorporating ideas from how natural selection and evolution move drug resistance forward, we show that drug resistance can occur at much greater rates than in well-stirred environments. Finally, we will discuss the future direction of in vitro microbial culture models and how to extend the lessons learned from microbial systems to eukaryotic cells.

Applications of Microfluidics in Stem Cell Biology

Stem cell research can significantly benefit from recent advances of microfluidics technology. In a rationally designed microfluidics device, analyses of stem cells can be done in a much deeper and wider way than in a conventional tissue culture dish. Miniaturization makes analyses operated in a high-throughput fashion, while controls of fluids help to reconstruct the physiological environments. Through integration with present characterization tools like fluorescent microscope, microfluidics offers a systematic way to study the decision-making process of stem cells, which has attractive medical applications. In this paper, recent progress of microfluidics devices on stem cell research are discussed. The purpose of this review is to highlight some key features of microfluidics for stem cell biologists, as well as provide physicists/engineers an overview of how microfluidics has been and could be used for stem cell research.

Deterministic separation of cancer cells from blood at 10 mL/min

Circulating tumor cells (CTCs) and circulating clusters of cancer and stromal cells have been identified in the blood of patients with malignant cancer and can be used as a diagnostic for disease severity, assess the efficacy of different treatment strategies and possibly determine the eventual location of metastatic invasions for possible treatment. There is thus a critical need to isolate, propagate and characterize viable CTCs and clusters of cancer cells with their associated stroma cells. Here, we present a microfluidic device for mL/min flow rate, continuous-flow capture of viable CTCs from blood using deterministic lateral displacement (DLD) arrays. We show here that a DLD array device can isolate CTCs from blood with capture efficiency greater than 85% CTCs at volumetric flow rates of up to 10 mL/min with no effect on cell viability.

DNA confinement in nanochannels: physics and biological applications

DNA is the central storage molecule of genetic information in the cell, and reading that information is a central problem in biology. While sequencing technology has made enormous advances over the past decade, there is growing interest in platforms that can readout genetic information directly from long single DNA molecules, with the ultimate goal of single-cell, single-genome analysis. Such a capability would obviate the need for ensemble averaging over heterogeneous cellular populations and eliminate uncertainties introduced by cloning and molecular amplification steps (thus enabling direct assessment of the genome in its native state). In this review, we will discuss how the information contained in genomic-length single DNA molecules can be accessed via physical confinement in nanochannels. Due to self-avoidance interactions, DNA molecules will stretch out when confined in nanochannels, creating a linear unscrolling of the genome along the channel for analysis. We will first review the fundamental physics of DNA nanochannel confinement--including the effect of varying ionic strength--and then discuss recent applications of these systems to genomic mapping. Apart from the intense biological interest in extracting linear sequence information from elongated DNA molecules, from a physics view these systems are fascinating as they enable probing of single-molecule conformation in environments with dimensions that intersect key physical length-scales in the 1 nm to 100 µm range.

The Goldilocks principle and antibiotic resistance in bacteria

We have designed and fabricated a microecology to mimic a naturally occurring bacterial culture, which includes the stress gradient, metapopulation, and cellular motility. In this microecology, we show that it is possible to fix the resistance to the mutagenic antibiotic Ciprofloxacin in wild-type Escherichia coli within 10 h. We found the evolution of resistance is further accelerated in microecology if bacteria have already acquired the phenotype of growth advantage at the stationary phase (GASP).

Acceleration of emergence of bacterial antibiotic resistance in connected microenvironments

The emergence of bacterial antibiotic resistance is a growing problem, yet the variables that influence the rate of emergence of resistance are not well understood. In a microfluidic device designed to mimic naturally occurring bacterial niches, resistance of Escherichia coli to the antibiotic ciprofloxacin developed within 10 hours. Resistance emerged with as few as 100 bacteria in the initial inoculation. Whole-genome sequencing of the resistant organisms revealed that four functional single-nucleotide polymorphisms attained fixation. Knowledge about the rapid emergence of antibiotic resistance in the heterogeneous conditions within the mammalian body may be helpful in understanding the emergence of drug resistance during cancer chemotherapy.

Collapse of DNA in ac electric fields

We report that double-stranded DNA collapses in the presence of ac electric fields at frequencies of a few hundred Hertz, and does not stretch as commonly assumed. In particular, we show that confinement-stretched DNA can collapse to about one quarter of its equilibrium length. We propose that this effect is based on finite relaxation times of the counterion cloud, and the subsequent partitioning of the molecule into mutually attractive units. We discuss alternative models of those attractive units.

An analogy between the evolution of drug resistance in bacterial communities and malignant tissues

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.