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Unusually large cancer cells with abnormal nuclei have been documented in the cancer literature since 1858. For more than 100 years, they have been generally disregarded as irreversibly senescent or dying cells, too morphologically misshapen and chromatin too disorganized to be functional. Cell enlargement, accompanied by whole genome doubling or more, is observed across organisms, often associated with mitigation strategies against environmental change, severe stress, or the lack of nutrients. Our comparison of the mechanisms for polyploidization in other organisms and non-transformed tissues suggest that cancer cells draw from a conserved program for their survival, utilizing whole genome doubling and pausing proliferation to survive stress. These polyaneuploid cancer cells (PACCs) are the source of therapeutic resistance, responsible for cancer recurrence and, ultimately, cancer lethality.

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.

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.

Chemoresistance is a major cause of treatment failure in many cancers. However, the life cycle of cancer cells as they respond to and survive environmental and therapeutic stress is understudied. In this study, we utilized a microfluidic device to induce the development of doxorubicin-resistant (DOXR) cells from triple negative breast cancer (TNBC) cells within 11 days by generating gradients of DOX and medium.In vivochemoresistant xenograft models, an unbiased genome-wide transcriptome analysis, and a patient data/tissue analysis all showed that chemoresistance arose from failed epigenetic control of the nuclear protein-1 (NUPR1)/histone deacetylase 11 (HDAC11) axis, and high Nupr1 expression correlated with poor clinical outcomes. These results suggest that the chip can rapidly induce resistant cells that increase tumor heterogeneity and chemoresistance, highlighting the need for further studies on the epigenetic control of the NUPR1/HDAC11 axis in TNBC.

In this study, we characterized nuclear morphology and function as cancer cells underwent recovery following chemotherapeutic treatment to identify the unique characteristics associated with treatment resistance and successful survival. Cells that survived following treatment and resisted therapy-induced cell death were predominantly mononucleated with increased nuclear/cellular size, enabled by continuous endocycling. We found that cells that survive after therapy release likely employ more efficient DNA damage repair and exhibit a distinct nucleolar phenotype - fewer but larger nucleoli – 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 suggest that one way cancer cells can survive systemic therapy is to enter the polyaneuploid cancer cell (PACC) state, a recently-described mechanism of therapy resistance. Cancer cells in this state are physically enlarged, undergo whole-genome doubling resulting in polyaneuploid genomes, and are associated with worse prognosis in cancer patients. The PACC state is accessed when a cancer cell experiences external stress, such as genotoxic chemotherapy; after a period of recovery, cells exit the PACC state and resume proliferation to repopulate the tumor cell pool. Our findings demonstrate the fate of cancer cells following chemotherapy treatment and define key characteristics of the resistant PACC state. This work is essential for understanding and, ultimately, targeting, cancer resistance and recurrence.

We describe an evolutionary game theory model that has been used to predict the population dynamics of interacting cancer and stromal cells. We first consider the mean field case assuming homogeneous and nondiscrete populations. Interacting Particle Systems (IPS) are then presented as a discrete and spatial alternative to the mean field approach. Finally, we discuss cases where IPS gives results different from the mean field approach.

Significance We present a fully realized adaptive resource landscape with diploid three-gene robots presenting interacting roles of population dynamics, mutations, breeding, death, and birth. Although modeling and theory serves as a guide here, the inherent complexity of our robobiology world makes it an experiment in exploring rules of Darwinian natural selection at a level difficult to simulate. We find that the lower the genetic diversity, the lower the survival probability of the robot population. We propose that diploid gene robots can act as avatars of diploid mammalian cells to explore novel programs of administration of drugs.

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.

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.

Cancer led to the deaths of more than 9 million people worldwide in 2018,(1) and most of these deaths are 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 one kilogram – 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 anti-cancer drugs. The source of this resistance remains undefined.(2) In fact, the vast majority of metastatic cancers are resistant to all currently available anti-cancer therapies, including chemotherapy, hormone therapy, immunotherapy, and systemic radiation. Thus, despite decades – even centuries – of research, metastatic cancer remains lethal and incurable.(3) We present historical and contemporary evidence that the key actuators of this process – of tumorigenesis, metastasis, and therapy resistance – are polyploid giant cancer cells.

The paper “Encoding memory in tube diameter hierarchy of living flow network” by Kramar and Alim (1) may be an example of the unbridgeable gulf between physics and biology; I am not sure, but it may be. Kramar and Alim study how the giant unicellular slime mold Physarum polycephalum develops an associative memory by adjusting wall stiffness and removal of the tubing network to encode the location of a nutrient source. The paper is primarily a “how” and “what” physics-based model of how the mold remembers where nutrients are, and somewhat skirts the “why” question, as physicists are apt to do. Let me explain. In physics, the “why” question is forbidden; the “how” and “what” questions are perfectly OK. For physicists, things are what they are; they don’t exist for a reason. Things are different in biology: While the “how” and “what” questions are the primary ones as in physics, the “why” question is tolerted by evolutionary biologists. The tolerance comes from an understanding of evolution and how it has shaped biology. Billions of years ago, amazingly, the first self-reproducing ur-cell appeared, with the remarkable ability to reproduce itself based on an internal set of instructions, presumably some form of double-stranded DNA, or some other stable polymer, and able to store a long chain of information. Importantly, this chain of instructions can be mutated, and progeny can be produced with different instructions, and hence different phenotypes, from the parent. Darwin’s natural selection and subtle mathematics dictated that this ur-cell would then evolve into the millions of different species we see … [↵][1]1Email: austin{at}princeton.edu. [1]: #xref-corresp-1-1

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.

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.

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.

Metastatic cancer is incurable because tumors evolve resistance to all classes of therapy, resulting the in the deaths of 10 million people each year. Therapy resistance is typically attributed to the presence of a cancer stem cell or genetic tumor cell heterogeneity. However, therapeutic resistance can also emerge through a transient cell state: the Polyaneuploid Cancer Cell (PACC) state. The Polyaneuploid transition (PAT) is an evolutionary conserved polyploid program accessed by cancer cells in response to an external stress such as chemotherapy. Once the stress subsides, cells exit the PACC state and repopulate a non-polyploid population that is resistant to multiple classes of therapy. We hypothesize that the PAT facilitates inherited universal therapeutic resistance. We utilized the bacteria E. coli as a model system of the evolutionary conserved polyploid mechanism of resistance. The E. coli strain MG1655 was engineered to express an hupA::GFP reporter to monitor nucleoid DNA or a ibpA::GFP reporter to monitor the aggregation of misfolded proteins. We treated these cultures with ciprofloxacin at near lethal doses for 12 hours and recorded the dynamics with fluorescent time-lapse microscopy. Individual bacteria underwent a transient phase of polyploidization causing an elongated filamentous phenotype that is at least an order of magnitude bigger compared to the unstressed bacteria. During filamentation, we found that the misfolded protein aggregates are shuttled along the cell body to the tips. Eventually, the filaments bud resulting in a population of “typical” length cells. The buds that are produced by the filaments are filled with an excess of misfolded protein aggregate and, surprisingly, are commonly void of DNA. This response is unexpected in a cell that is already resistant due to the high fitness cost. However, if the cell is not resistant, then each misfolded protein may represent an attempt to solve the antibiotic puzzle and to achieve a resistant phenotype. In turn, the ability to efficiently collect and dispose of the misfolded proteins, i.e., the previous failed attempt, may be advantageous to reduce the need for protein degradation and ensure that future protein production is not impaired. Taken together, this finding suggests that antibiotic resistance might be dependent on the ability to endure repeated futile endeavors to explore the phenotypic landscape rather than selecting for individuals that already exist at an evolutionary optimum. As a result, the PAT may act as a buffer by which cancer cells “buy time” to search within the phenotypic landscape and look for a path to resistance. Future work will specifically test this model in cancer cells. If true, then this finding would suggest that therapeutic resistance can be actively delayed by inhibiting the PAT during first line treatment. Citation Format: George Butler, Julia Bos, Robert H. Austin, Sarah R. Amend, Kenneth J. Pienta. The polyaneuploid transition as a hedge against failures in resistance acquisition [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 B022.

Despite major advances in cancer research over the last several decades, advanced cancers largely remain incurable due to the evolution of resistance. Recent experimental evidence suggests that a poly-aneuploid cancer cell (PACC) state may contribute to the emergence of therapeutic resistance by allowing cancer cells to avoid the effects of and adapt to extreme environmental stressors. The PACC state is enabled when aneuploid cells undergo whole genome doubling but subsequently bypass mitosis, cytokinesis, or both. 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 evolvability (ability to generate heritable variation) through the increase in genomic content that accompanies polyploidization. In doing so, we demonstrate the ability of the PACC state to contribute to therapeutic resistance and stress the importance of targeting PACC populations for improved therapeutic outcomes. Citation Format: Anuraag Bukkuri, Kenneth J. Pienta, Robert H. Austin, Emma U. Hammarlund, Sarah R. Amend, Joel S. Brown. Eco-evolutionary dynamics of poly-aneuploid cancer cells: A life history model [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 B015.

Significance We have discovered an emergent mechanism by which Escherichia coli can escape high-stress regions, such as near-lethal concentrations of antibiotics, by forming long motile helical filaments that are poized at the critical shear buckling point: 2 π twist rotations independent of the length of the filament. All filaments, independent of length, have the same twist, indicating that this is a highly evolved response. The helices do not tumble as nonstressed bacteria do but rather, over a period of tens of seconds, reverse direction. The result is that the persistence length of the filaments’ translational motion is much larger than in unstressed normal-size bacteria, giving filaments an extremely large effective diffusion coefficient, allowing the bacteria to escape high existential stress.

We present a phosphorescence lifetime imaging (PLIM) based oxygen sensor to monitor the local O2 level in conventional petri dish-based or microfluidic in vitro cell culture. The sensor film is fabricated by dissolving Pt(II) meso-tetrakis(pentafluorophenyl) porphine (PtTFPP) into a thermal curable poly(perfluoroether) (PFPE). The lifetime response of the sensor to O2 follows a linear Stern-Volmer relationship. Compared with PDMS as polymer matrix, the use of PFPE as the polymer matrix for PtTFPP mitigates both the dye induced cytotoxicity and the biomolecule adsorption problems of polydimethylsiloxane (PDMS), while maintaining cell compatibility. By using the sensor film with PFPE as the polymer matrix, the O2 tension during cell proliferation can be monitored in real time, and the resulting value can be used to calculate the per-cell O2 consumption. We tested the application of the sensor for monitoring the O2 tension during long term cell culture of HeLa cells and MDA-MB-231 cells, and calculated the O2 consumption per cell for MDA-MB-231 cells. Our study demonstrates that the PtTFPP/PFPE sensor can be employed as a general noninvasive lifetime imaging platform for studying the di-oxygen in in vitro tissue models at the cellular level as a function of space and time.

Background The physics of cancer dormancy, the time between initial cancer treatment and re-emergence after a protracted period, is a puzzle. Cancer cells interact 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 and dormancy. Results We explore here the dynamics of host-cancer cell populations in the presence of (1) payoffs gradients and (2) perturbations due to cell migration. Conclusions We determine to what extent the time-dependence of the populations can be quantitively understood in spite of the underlying complexity of the individual agents and model the phenomena of dormancy.

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.

Conventional cell culture remains the most frequently used preclinical model, despite its proven limited ability to predict clinical results in cancer. Microfluidic cancer-on-chip models have been proposed to bridge the gap between the oversimplified conventional 2D cultures and more complicated animal models, which have limited ability to produce reliable and reproducible quantitative results. Here, we present a microfluidic cancer-on-chip model that reproduces key components of a complex tumor microenvironment in a comprehensive manner, yet is simple enough to provide robust quantitative descriptions of cancer dynamics. This microfluidic cancer-on-chip model, the "Evolution Accelerator," breaks down a large population of cancer cells into an interconnected array of tumor microenvironments while generating a heterogeneous chemotherapeutic stress landscape. The progression and the evolutionary dynamics of cancer in response to drug gradient can be monitored for weeks in real time, and numerous downstream experiments can be performed complementary to the time-lapse images taken through the course of the experiments.

In vitro prediction of the probable rapid emergence of resistance to a drug in tumors could act to winnow out potential candidates for further costly development. We have developed a microfluidic device consisting of ∼500 hexagonal microcompartments that provides a complex ecology with wide ranges of drug and nutrient gradients and local populations. This ecology of a fragmented metapopulation induced the drug resistance in stage IV U87 glioblastoma cells to doxorubicin in 7 d. Exome and transcriptome sequencing of the resistant cells identified mutations and differentially expressed genes. Gene ontology and pathway analyses of the genes identified showed that they were functionally relevant to the established mechanisms of doxorubicin action. Specifically, we identified (i) a frame-shift insertion in the filamin-A gene, which regulates the influx and efflux of topoisomerase II poisons; (ii) the overexpression of aldo-keto reductase enzymes, which convert doxorubicin into doxorubicinol; and (iii) activation of NF-κB via alterations in the nucleotide-binding oligomerization domain (NOD)-like receptor signaling pathway from mutations in three genes (CARD6, NSD1, and NLRP13) and the overexpression of inflammatory cytokines. Functional experiments support the in silico analyses and, together, demonstrate the effects of these genetic changes. Our findings suggest that, given the rapid evolution of resistance and the focused response, this technology could act as a rapid screening modality for genetic aberrations leading to resistance to chemotherapy as well as counter selection of drugs unlikely to be successful ultimately.

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.

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.

We tested the in vivo potential of a MHC class I-restricted blocking peptide to sufficiently lower an anti-viral CTL response for preventing virus-induced CTL-mediated autoimmune diabetes (insulin-dependent diabetes mellitus (IDDM)) in vivo without affecting systemic viral clearance. By designing and screening several peptides with high binding affinities to MHC class I H-2Db for best efficiency in blocking killing of target cells by lymphocytic choriomeningitis virus (LCMV) and other viral CTL, we identified the peptide for this study. In vitro, it selectively lowered CTL killing restricted to the Db allele, which correlated directly with the affinity of the respective epitopes. Expression of the blocking peptide in the target cell lowered recognition of all Db-restricted LCMV epitopes. In addition, in vitro expansion of LCMV memory CTL was prevented, resulting in decreased IFN-γ secretion. In vivo, a 2-wk treatment with this peptide lowered the LCMV Db-restricted CTL response by over threefold without affecting viral clearance. However, the CTL reduction by the peptide treatment was sufficient to prevent LCMV-induced IDDM in rat insulin promoter-LCMV-glycoprotein transgenic mice. Following LCMV infection, these mice develop IDDM, which depends on Db-restricted anti-self (viral) CTL. Precursor numbers of splenic LCMV-CTL in peptide-treated mice were reduced, but their cytokine profile was not altered, indicating that the peptide did not induce regulatory cells. Further, non-LCMV-CTL recognizing the blocking peptide secreted IFN-γ and did not protect from IDDM. This study demonstrates that in vivo treatment with a MHC class I blocking peptide can prevent autoimmune disease by directly affecting expansion of autoreactive CTL.

Bacteria under external stress can reveal unexpected emergent phenotypes. We show that the intensely studied bacterium Escherichia coli can transform into long, highly motile helical filaments poized at a torsional buckling criticality when exposed to minimum inhibitory concentrations of several antibiotics. While the highly motile helices are physically either right- or left-handed, the motile helices always rotate with a right-handed angular velocity [Formula: see text] , which points in the same direction as the translational velocity [Formula: see text] of the helix. Furthermore, these helical cells do not swim by a “run and tumble” but rather synchronously flip their spin [Formula: see text] and thus translational velocity—backing up rather than tumbling. By increasing the translational persistence length, these dynamics give rise to an effective diffusion coefficient up to 20 times that of a normal E. coli cell. Finally, we propose an evolutionary mechanism for this phenotype’s emergence whereby the increased effective diffusivity provides a fitness advantage in allowing filamentous cells to more readily escape regions of high external stress.

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.

We have improved our microfluidic cell culture device that generates an in vitro landscape of stress heterogeneity. We now can do continuous observations of different cancer cell lines and carry out downstream analysis of cell phenotype as a function of position on the stress landscape. We use this technology to probe adaption and evolution dynamics in prostate cancer cell metapopulations under a stress landscape of a chemotherapeutic drug (docetaxel). The utility of this approach is highlighted by analysis of heterogenous prostate cancer cell motility changes as a function of position in the stress landscape. Because the technology presented here is easily adapted to a standard epifluorescence microscope it has the potential for broad application in preclinical drug development and assays of likely drug efficacy.

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.

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.

Significance Deterministic lateral displacement (DLD) is a technique for size fractionation of particles in continuous flow that has shown great potential for biological and clinical applications. Several theoretical models have been proposed to explain the trajectories of different-sized particles in relation to the geometry of the pillar array, but experimental evidence has demonstrated that a rich class of intermediate migration behavior exists, which is not predicted by models. In this work, we present a unified theoretical framework to infer the trajectory of particles in the whole array on the basis of trajectories in the unit cell. This framework explains many of the unexpected particle trajectories reported in literature and can be used to design arrays for the fractionation of particles, even at the smallest scales reaching the molecular realm. We also performed experiments that verified our predictions, even at the nanoscales. Using our model as a set of design rules, we developed a condenser structure that achieves full particle separation with a single fluidic input.

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.

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.” Cancer Res; 74(17); 4663–70. ©2014 AACR.

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.

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.

Metastasis, the truly lethal aspect of cancer, occurs when metastatic cancer cells in a tumor break through the basement membrane and penetrate the extracellular matrix. We show that MDA-MB-231 metastatic breast cancer cells cooperatively invade a 3D collagen matrix while following a glucose gradient. The invasion front of the cells is a dynamic one, with different cells assuming the lead on a time scale of 70 h. The front cell leadership is dynamic presumably because of metabolic costs associated with a long-range strain field that precedes the invading cell front, which we have imaged using confocal imaging and marker beads imbedded in the collagen matrix. We suggest this could be a quantitative assay for an invasive phenotype tracking a glucose gradient and show that the invading cells act in a cooperative manner by exchanging leaders in the invading front.