Sabrina L. Spencer

Non-genetic origins of cell-to-cell variability in TRAIL-induced apoptosis

In microorganisms, noise in gene expression gives rise to cell-to-cell variability in protein concentrations. In mammalian cells, protein levels also vary and individual cells differ widely in their responsiveness to uniform physiological stimuli. In the case of apoptosis mediated by TRAIL (tumour necrosis factor (TNF)-related apoptosis-inducing ligand) it is common for some cells in a clonal population to die while others survive—a striking divergence in cell fate. Among cells that die, the time between TRAIL exposure and caspase activation is highly variable. Here we image sister cells expressing reporters of caspase activation and mitochondrial outer membrane permeabilization after exposure to TRAIL. We show that naturally occurring differences in the levels or states of proteins regulating receptor-mediated apoptosis are the primary causes of cell-to-cell variability in the timing and probability of death in human cell lines. Protein state is transmitted from mother to daughter, giving rise to transient heritability in fate, but protein synthesis promotes rapid divergence so that sister cells soon become no more similar to each other than pairs of cells chosen at random. Our results have implications for understanding ‘fractional killing’ of tumour cells after exposure to chemotherapy, and for variability in mammalian signal transduction in general.

Modeling a snap-action, variable-delay switch controlling extrinsic cell death.

When exposed to tumor necrosis factor (TNF) or TNF-related apoptosis-inducing ligand (TRAIL), a closely related death ligand and investigational therapeutic, cells enter a protracted period of variable duration in which only upstream initiator caspases are active. A subsequent and sudden transition marks activation of the downstream effector caspases that rapidly dismantle the cell. Thus, extrinsic apoptosis is controlled by an unusual variable-delay, snap-action switch that enforces an unambiguous choice between life and death. To understand how the extrinsic apoptosis switch functions in quantitative terms, we constructed a mathematical model based on a mass-action representation of known reaction pathways. The model was trained against experimental data obtained by live-cell imaging, flow cytometry, and immunoblotting of cells perturbed by protein depletion and overexpression. The trained model accurately reproduces the behavior of normal and perturbed cells exposed to TRAIL, making it possible to study switching mechanisms in detail. Model analysis shows, and experiments confirm, that the duration of the delay prior to effector caspase activation is determined by initiator caspase-8 activity and the rates of other reactions lying immediately downstream of the TRAIL receptor. Sudden activation of effector caspases is achieved downstream by reactions involved in permeabilization of the mitochondrial membrane and relocalization of proteins such as Smac. We find that the pattern of interactions among Bcl-2 family members, the partitioning of Smac from its binding partner XIAP, and the mechanics of pore assembly are all critical for snap-action control.

Measuring and Modeling Apoptosis in Single Cells

Cell death plays an essential role in the development of tissues and organisms, the etiology of disease, and the responses of cells to therapeutic drugs. Here we review progress made over the last decade in using mathematical models and quantitative, often single-cell, data to study apoptosis. We discuss the delay that follows exposure of cells to prodeath stimuli, control of mitochondrial outer membrane permeabilization, switch-like activation of effector caspases, and variability in the timing and probability of death from one cell to the next. Finally, we discuss challenges facing the fields of biochemical modeling and systems pharmacology.

Non-genetic cell-to-cell variability and the consequences for pharmacology.

Recent advances in single-cell assays have focused attention on the fact that even members of a genetically identical group of cells or organisms in identical environments can exhibit variability in drug sensitivity, cellular response, and phenotype. Underlying much of this variability is stochasticity in gene expression, which can produce unique proteomes even in genetically identical cells. Here we discuss the consequences of non-genetic cell-to-cell variability in the cellular response to drugs and its potential impact for the treatment of human disease.

Modeling Somatic Evolution in Tumorigenesis

Tumorigenesis in humans is thought to be a multistep process where certain mutations confer a selective advantage, allowing lineages derived from the mutated cell to outcompete other cells. Although molecular cell biology has substantially advanced cancer research, our understanding of the evolutionary dynamics that govern tumorigenesis is limited. This paper analyzes the computational implications of cancer progression presented by Hanahan and Weinberg in The Hallmarks of Cancer. We model the complexities of tumor progression as a small set of underlying rules that govern the transformation of normal cells to tumor cells. The rules are implemented in a stochastic multistep model. The model predicts that (i) early-onset cancers proceed through a different sequence of mutation acquisition than late-onset cancers; (ii) tumor heterogeneity varies with acquisition of genetic instability, mutation pathway, and selective pressures during tumorigenesis; (iii) there exists an optimal initial telomere length which lowers cancer incidence and raises time of cancer onset; and (iv) the ability to initiate angiogenesis is an important stage-setting mutation, which is often exploited by other cells. The model offers insight into how the sequence of acquired mutations affects the timing and cellular makeup of the resulting tumor and how the cellular-level population dynamics drive neoplastic evolution.

Cancer research meets evolutionary biology

There is increasing evidence that Darwin’s theory of evolution by natural selection provides insights into the etiology and treatment of cancer. On a microscopic scale, neoplastic cells meet the conditions for evolution by Darwinian selection: cell reproduction with heritable variability that affects cell survival and replication. This suggests that, like other areas of biological and biomedical research, Darwinian theory can provide a general framework for understanding many aspects of cancer, including problems of great clinical importance. With the availability of raw molecular data increasing rapidly, this theory may provide guidance in translating data into understanding and progress. Several conceptual and analytical tools from evolutionary biology can be applied to cancer biology. Two clinical problems may benefit most from the application of Darwinian theory: neoplastic progression and acquired therapeutic resistance. The Darwinian theory of cancer has especially profound implications for drug development, both in terms of explaining past difficulties, and pointing the way toward new approaches. Because cancer involves complex evolutionary processes, research should incorporate both tractable (simplified) experimental systems, and also longitudinal observational studies of the evolutionary dynamics of cancer in laboratory animals and in human patients. Cancer biology will require new tools to control the evolution of neoplastic cells.

Exploring the Contextual Sensitivity of Factors that Determine Cell-to-Cell Variability in Receptor-Mediated Apoptosis

Stochastic fluctuations in gene expression give rise to cell-to-cell variability in protein levels which can potentially cause variability in cellular phenotype. For TRAIL (TNF-related apoptosis-inducing ligand) variability manifests itself as dramatic differences in the time between ligand exposure and the sudden activation of the effector caspases that kill cells. However, the contribution of individual proteins to phenotypic variability has not been explored in detail. In this paper we use feature-based sensitivity analysis as a means to estimate the impact of variation in key apoptosis regulators on variability in the dynamics of cell death. We use Monte Carlo sampling from measured protein concentration distributions in combination with a previously validated ordinary differential equation model of apoptosis to simulate the dynamics of receptor-mediated apoptosis. We find that variation in the concentrations of some proteins matters much more than variation in others and that precisely which proteins matter depends both on the concentrations of other proteins and on whether correlations in protein levels are taken into account. A prediction from simulation that we confirm experimentally is that variability in fate is sensitive to even small increases in the levels of Bcl-2. We also show that sensitivity to Bcl-2 levels is itself sensitive to the levels of interacting proteins. The contextual dependency is implicit in the mathematical formulation of sensitivity, but our data show that it is also important for biologically relevant parameter values. Our work provides a conceptual and practical means to study and understand the impact of cell-to-cell variability in protein expression levels on cell fate using deterministic models and sampling from parameter distributions.

Cells surviving fractional killing by TRAIL exhibit transient but sustainable resistance and inflammatory phenotypes

Cells that survive fractional killing by TRAIL or FasR agonists enter a state of resistance accompanied by inflammatory phenotypes. This state is transient, decaying over the course of several days, but can be sustained by periodic TRAIL treatments. This finding has implications for optimal dosing strategies of extrinsic cell death agents.

Irreversible APCCdh1 Inactivation Underlies the Point of No Return for Cell-Cycle Entry

Proliferating cells must cross a point of no return before they replicate their DNA and divide. This commitment decision plays a fundamental role in cancer and degenerative diseases and has been proposed to be mediated by phosphorylation of retinoblastoma (Rb) protein. Here, we show that inactivation of the anaphase-promoting complex/cyclosome (APC(Cdh1)) has the necessary characteristics to be the point of no return for cell-cycle entry. Our study shows that APC(Cdh1) inactivation is a rapid, bistable switch initiated shortly before the start of DNA replication by cyclin E/Cdk2 and made irreversible by Emi1. Exposure to stress between Rb phosphorylation and APC(Cdh1) inactivation, but not after APC(Cdh1) inactivation, reverted cells to a mitogen-sensitive quiescent state, from which they can later re-enter the cell cycle. Thus, APC(Cdh1) inactivation is the commitment point when cells lose the ability to return to quiescence and decide to progress through the cell cycle.

Basal p21 controls population heterogeneity in cycling and quiescent cell cycle states

Significance Population heterogeneity can make the treatment of tumors more challenging. Whereas a therapeutic agent may be effective against one fraction of a population, it may be less effective against another fraction. Although heterogeneity can be genetic and attributed to mutations, there can also be nongenetic heterogeneity, where a clonal population can harbor distinct subpopulations. Here, we identified a single gene, p21, that was responsible for population heterogeneity in cell cycle activity and explain that this heterogeneity can arise from regulatory relationships of p21 with Cyclin-dependent kinase 2 (CDK2) and E3 ubiquitin ligases. We suggest that, instead of using CDK inhibitors (CKIs) in cancer therapy, CKIs themselves should be targeted. Given concurrently with chemotherapy agents, CKI inhibitors would reduce tumor heterogeneity and thus increase chemotherapy efficacy. Phenotypic heterogeneity within a population of genetically identical cells is emerging as a common theme in multiple biological systems, including human cell biology and cancer. Using live-cell imaging, flow cytometry, and kinetic modeling, we showed that two states—quiescence and cell cycling—can coexist within an isogenic population of human cells and resulted from low basal expression levels of p21, a Cyclin-dependent kinase (CDK) inhibitor (CKI). We attribute the p21-dependent heterogeneity in cell cycle activity to double-negative feedback regulation involving CDK2, p21, and E3 ubiquitin ligases. In support of this mechanism, analysis of cells at a point before cell cycle entry (i.e., before the G1/S transition) revealed a p21–CDK2 axis that determines quiescent and cycling cell states. Our findings suggest a mechanistic role for p21 in generating heterogeneity in both normal tissues and tumors.