Do cancer cells undergo phenotypic switching? The case for imperfect cancer stem cell markers

MicroRNA-222 Regulates Melanoma Plasticity

Melanoma is one of the most aggressive and highly resistant tumors. Cell plasticity in melanoma is one of the main culprits behind its metastatic capabilities. The detailed molecular mechanisms controlling melanoma plasticity are still not completely understood. Here we combine mathematical models of phenotypic switching with experiments on IgR39 human melanoma cells to identify possible key targets to impair phenotypic switching. Our mathematical model shows that a cancer stem cell subpopulation within the tumor prevents phenotypic switching of the other cancer cells. Experiments reveal that hsa-mir-222 is a key factor enabling this process. Our results shed new light on melanoma plasticity, providing a potential target and guidance for therapeutic studies.

Mitochondrial Stress-Mediated Targeting of Quiescent Cancer Stem Cells in Oral Squamous Cell Carcinoma

Introduction

Despite improved therapeutics in oral squamous cell carcinoma (OSCC), tumor cells that are either quiescent and/or endowed with stem cell–like attributes usually survive treatment and recreate tumor load at relapse. Through this study, we aimed strategically to eliminate these stem cell–like cancer cells using a combination drug approach.

Methods

Primary cultures from 15 well–moderately differentiated OSCC were established, and the existence of cancer cells with stem cell–like characteristics using five cancer stem cell (CSC) specific markers — CD44, CD133, CD147, C166, SOX2 and spheroid assay was ascertained. Next, we assessed quiescence in CSCs under normal and growth factor–deprived conditions using Ki67. Among several gene signatures regulating quiescent cellular state, we evaluated the effect of inhibiting Dyrk1b in combination with topoisomerase II and histone deacetylase inhibitors in targeting quiescent CSCs. Multiple drug-effect analysis was carried out with CompuSyn software to determine combination-index values.

Results

We observed that CD44⁺CD133⁺ showed the highest level of SOX2 expression. CSCs showed varying degrees of quiescence, and inhibition of Dyrk1b decreased quiescence and sensitized CSCs to apoptosis. In the drug-combination study, Dyrk1b inhibitor was combined with topoisomerase II and histone deacetylase inhibitors to target quiescent CSCs. In combination, a synergistic effect was seen even at a 16-fold lower dose than IC₅₀. Furthermore, combined treatment decreased glutathione levels and increased ROS and mitochondrial stress, leading to increased DNA damage and cytochrome c in CSCs.

Conclusion

We report marker-based identification of CSC subpopulations and synergy of Dyrk1b inhibitor with topoisomerase II and HDAC inhibitors in primary OSCC. The results provide a new therapeutic strategy to minimize quiescence and target oral CSCs simultaneously.

Head and neck cancer management and cancer stem cells implication

Head and neck squamous cell carcinomas (HNSCCs) arise in the mucosal linings of the upper aerodigestive tract and are heterogeneous in nature. Risk factors for HNSCCs are smoking, excessive alcohol consumption, and the human papilloma virus. Conventional treatments are surgery, radiotherapy, chemotherapy, or a combined modality; however, no international standard mode of therapy exists. In contrast to the conventional model of clonal evolution in tumor development, there is a newly proposed theory based on the activity of cancer stem cells (CSCs) as the model for carcinogenesis. This “CSC hypothesis” may explain the high mortality rate, low response to treatments, and tendency to develop multiple tumors for HNSCC patients. We review current knowledge on HNSCC etiology and treatment, with a focus on CSCs, including their origins, identifications, and effects on therapeutic options.

Immune Curbing of Cancer Stem Cells by CTLs Directed to NANOG

Cancer stem cells (CSCs) have been identified as the source of tumor growth and disease recurrence. Eradication of CSCs is thus essential to achieve durable responses, but CSCs are resistant to current anti-tumor therapies. Novel therapeutic approaches that specifically target CSCs will, therefore, be crucial to improve patient outcome. Immunotherapies, which boost the body's own immune system to eliminate cancerous cells, could be an alternative approach to target CSCs. Vaccines of dendritic cells (DCs) loaded with tumor antigens can evoke highly specific anti-tumor T cell responses. Importantly, DC vaccination also promotes immunological memory formation, paving the way for long-term cancer control. Here, we propose a DC vaccination that specifically targets CSCs. DCs loaded with NANOG peptides, a protein required for maintaining stem cell properties, could evoke a potent anti-tumor immune response against CSCs. We hypothesize that the resulting immunological memory will also control newly formed CSCs, thereby preventing disease recurrence.

Phenotypic Switching Can Speed up Microbial Evolution

Stochastic phenotype switching has been suggested to play a beneficial role in microbial populations by leading to the division of labour among cells, or ensuring that at least some of the population survives an unexpected change in environmental conditions. Here we use a computational model to investigate an alternative possible function of stochastic phenotype switching: as a way to adapt more quickly even in a static environment. We show that when a genetic mutation causes a population to become less fit, switching to an alternative phenotype with higher fitness (growth rate) may give the population enough time to develop compensatory mutations that increase the fitness again. The possibility of switching phenotypes can reduce the time to adaptation by orders of magnitude if the “fitness valley” caused by the deleterious mutation is deep enough. Our work has important implications for the emergence of antibiotic-resistant bacteria. In line with recent experimental findings, we hypothesise that switching to a slower growing — but less sensitive — phenotype helps bacteria to develop resistance by providing alternative, faster evolutionary routes to resistance.

Phenotypic heterogeneity in modeling cancer evolution

The unwelcome evolution of malignancy during cancer progression emerges through a selection process in a complex heterogeneous population structure. In the present work, we investigate evolutionary dynamics in a phenotypically heterogeneous population of stem cells (SCs) and their associated progenitors. The fate of a malignant mutation is determined not only by overall stem cell and non-stem cell growth rates but also differentiation and dedifferentiation rates. We investigate the effect of such a complex population structure on the evolution of malignant mutations. We derive exactly calculated results for the fixation probability of a mutant arising in each of the subpopulations. The exactly calculated results are in almost perfect agreement with the numerical simulations. Moreover, a condition for evolutionary advantage of a mutant cell versus the wild type population is given in the present study. We also show that microenvironment-induced plasticity in invading mutants leads to more aggressive mutants with higher fixation probability. Our model predicts that decreasing polarity between stem and non-stem cells’ turnover would raise the survivability of non-plastic mutants; while it would suppress the development of malignancy for plastic mutants. The derived results are novel and general with potential applications in nature; we discuss our model in the context of colorectal/intestinal cancer (at the epithelium). However, the model clearly needs to be validated through appropriate experimental data. This novel mathematical framework can be applied more generally to a variety of problems concerning selection in heterogeneous populations, in other contexts such as population genetics, and ecology.

Secreted Frizzled-related protein 4 (sFRP4) chemo-sensitizes cancer stem cells derived from human breast, prostate, and ovary tumor cell lines

This study investigated molecular signals essential to sustain cancer stem cells (CSCs) and assessed their activity in the presence of secreted frizzled-related protein 4 (sFRP4) alone or in combination with chemotherapeutic drugs. SFRP4 is a known Wnt antagonist, and is also pro-apoptotic and anti-angiogenic. Additionally, sFRP4 has been demonstrated to confer chemo-sensitization and improve chemotherapeutic efficacy. CSCs were isolated from breast, prostate, and ovary tumor cell lines, and characterized using tumor-specific markers such as CD44⁺/CD24⁻/CD133⁺. The post-transcription data from CSCs that have undergone combinatorial treatment with sFRP4 and chemotherapeutic drugs suggest downregulation of stemness genes and upregulation of pro-apoptotic markers. The post-translational modification of CSCs demonstrated a chemo-sensitization effect of sFRP4 when used in combination with tumor-specific drugs. SFRP4 in combination with doxorubicin/cisplatin reduced the proliferative capacity of the CSC population in vitro. Wnt/β-catenin signaling is important for proliferation and self-renewal of CSCs in association with human tumorigenesis. The silencing of this signaling pathway by the application of sFRP4 suggests potential for improved in vivo chemo-responses.

Rationally Designed 2-in-1 Nanoparticles Can Overcome Adaptive Resistance in Cancer

The development of resistance is the major cause of mortality in cancer. Combination chemotherapy is used clinically to reduce the probability of evolution of resistance. A similar trend toward the use of combinations of drugs is also emerging in the application of cancer nanomedicine. However, should a combination of two drugs be delivered from a single nanoparticle or should they be delivered in two different nanoparticles for maximal efficacy? We explored these questions in the context of adaptive resistance, which emerges as a phenotypic response of cancer cells to chemotherapy. We studied the phenotypic dynamics of breast cancer cells under cytotoxic chemotherapeutic stress and analyzed the data using a phenomenological mathematical model. We demonstrate that cancer cells can develop adaptive resistance by entering into a predetermined transitional trajectory that leads to phenocopies of inherently chemoresistant cancer cells. Disrupting this deterministic program requires a unique combination of inhibitors and cytotoxic agents. Using two such combinations, we demonstrate that a 2-in-1 nanomedicine can induce greater antitumor efficacy by ensuring that the origins of adaptive resistance are terminated by deterministic spatially constrained delivery of both drugs to the target cells. In contrast, a combination of free-form drugs or two nanoparticles, each carrying a single payload, is less effective, arising from a stochastic distribution to cells. These findings suggest that 2-in-1 nanomedicines could emerge as an important strategy for targeting adaptive resistance, resulting in increased antitumor efficacy.

Bidirectional interconversion of stem and non-stem cancer cell populations: A reassessment of theoretical models for tumor heterogeneity

Resolving the origin of intratumor heterogeneity has proven to be one of the central challenges in cancer research during recent years. Two theoretical models explaining the emergence of intratumor heterogeneity have come to dominate cancer biology literature: the clonal evolution model and the hierarchical/cancer stem cell model. Recently, a plastic model that combines elements of both the clonal and the hierarchical model has gained traction. Basically, this model proposes that cancer stem cells engage in bidirectional interconversion with non-stem cells, thereby providing the missing link between the 2 conventional models. Confirming bidirectional interconversion as a hallmark of cancer is a crucial step in understanding tumor heterogeneity and has important therapeutic implications. In this review, current methodologies and theoretical and empirical evidence regarding bidirectional interconversion will be discussed.

The overshoot and phenotypic equilibrium in characterizing cancer dynamics of reversible phenotypic plasticity

The paradigm of phenotypic plasticity indicates reversible relations of different cancer cell phenotypes, which extends the cellular hierarchy proposed by the classical cancer stem cell (CSC) theory. Since it is still questionable if the phenotypic plasticity is a crucial improvement to the hierarchical model or just a minor extension to it, it is worthwhile to explore the dynamic behavior characterizing the reversible phenotypic plasticity. In this study we compare the hierarchical model and the reversible model in predicting the cell-state dynamics observed in biological experiments. Our results show that the hierarchical model shows significant disadvantages over the reversible model in describing both long-term stability (phenotypic equilibrium) and short-term transient dynamics (overshoot) in cancer cell populations. In a very specific case in which the total growth of population due to each cell type is identical, the hierarchical model predicts neither phenotypic equilibrium nor overshoot, whereas the reversible model succeeds in predicting both of them. Even though the performance of the hierarchical model can be improved by relaxing the specific assumption, its prediction to the phenotypic equilibrium strongly depends on a precondition that may be unrealistic in biological experiments. Moreover, it still does not show as rich dynamics as the reversible model in capturing the overshoots of both CSCs and non-CSCs. By comparison, it is more likely for the reversible model to correctly predict the stability of the phenotypic mixture and various types of overshoot behavior.

Overshoot during phenotypic switching of cancer cell populations

The dynamics of tumor cell populations is hotly debated: do populations derive hierarchically from a subpopulation of cancer stem cells (CSCs), or are stochastic transitions that mutate differentiated cancer cells to CSCs important? Here we argue that regulation must also be important. We sort human melanoma cells using three distinct cancer stem cell (CSC) markers - CXCR6, CD271 and ABCG2 - and observe that the fraction of non-CSC-marked cells first overshoots to a higher level and then returns to the level of unsorted cells. This clearly indicates that the CSC population is homeostatically regulated. Combining experimental measurements with theoretical modeling and numerical simulations, we show that the population dynamics of cancer cells is associated with a complex miRNA network regulating the Wnt and PI3K pathways. Hence phenotypic switching is not stochastic, but is tightly regulated by the balance between positive and negative cells in the population. Reducing the fraction of CSCs below a threshold triggers massive phenotypic switching, suggesting that a therapeutic strategy based on CSC eradication is unlikely to succeed.

The phenotypic equilibrium of cancer cells: From average-level stability to path-wise convergence

The phenotypic equilibrium, i.e. heterogeneous population of cancer cells tending to a fixed equilibrium of phenotypic proportions, has received much attention in cancer biology very recently. In the previous literature, some theoretical models were used to predict the experimental phenomena of the phenotypic equilibrium, which were often explained by different concepts of stabilities of the models. Here we present a stochastic multi-phenotype branching model by integrating conventional cellular hierarchy with phenotypic plasticity mechanisms of cancer cells. Based on our model, it is shown that: (i) our model can serve as a framework to unify the previous models for the phenotypic equilibrium, and then harmonizes the different kinds of average-level stabilities proposed in these models; and (ii) path-wise convergence of our model provides a deeper understanding to the phenotypic equilibrium from stochastic point of view. That is, the emergence of the phenotypic equilibrium is rooted in the stochastic nature of (almost) every sample path, the average-level stability just follows from it by averaging stochastic samples.

Targeting proapoptotic protein BAD inhibits survival and self-renewal of cancer stem cells

Emerging evidence suggests that the resistance of cancer stem cells (CSC) to many conventional therapies is one of the major limiting factors of cancer therapy efficacy. Identification of mechanisms responsible for survival and self-renewal of CSC will help design new therapeutic strategies that target and eliminate both differentiated cancer cells and CSC. Here we demonstrated the potential role of proapoptotic protein BAD in the biology of CSC in melanoma, prostate and breast cancers. We enriched CD44(+)/CD24(-) cells (CSC) by tumorosphere formation and purified this population by FACS. Both spheres and CSC exhibited increased potential for proliferation, migration, invasion, sphere formation, anchorage-independent growth, as well as upregulation of several stem cell-associated markers. We showed that the phosphorylation of BAD is essential for the survival of CSC. Conversely, ectopic expression of a phosphorylation-deficient mutant BAD induced apoptosis in CSC. This effect was enhanced by treatment with a BH3-mimetic, ABT-737. Both pharmacological agents that inhibit survival kinases and growth factors that are involved in drug resistance delivered their respective cytotoxic and protective effects by modulating the BAD phosphorylation in CSC. Furthermore, the frequency and self-renewal capacity of CSC was significantly reduced by knocking down the BAD expression. Consistent with our in vitro results, significant phosphorylation of BAD was found in CD44(+) CSC of 83% breast tumor specimens. In addition, we also identified a positive correlation between BAD expression and disease stage in prostate cancer, suggesting a role of BAD in tumor advancement. Our studies unveil the role of BAD in the survival and self-renewal of CSC and propose BAD not only as an attractive target for cancer therapy but also as a marker of tumor progression.

CCAST: a model-based gating strategy to isolate homogeneous subpopulations in a heterogeneous population of single cells

A model-based gating strategy is developed for sorting cells and analyzing populations of single cells. The strategy, named CCAST, for Clustering, Classification and Sorting Tree, identifies a gating strategy for isolating homogeneous subpopulations from a heterogeneous population of single cells using a data-derived decision tree representation that can be applied to cell sorting. Because CCAST does not rely on expert knowledge, it removes human bias and variability when determining the gating strategy. It combines any clustering algorithm with silhouette measures to identify underlying homogeneous subpopulations, then applies recursive partitioning techniques to generate a decision tree that defines the gating strategy. CCAST produces an optimal strategy for cell sorting by automating the selection of gating markers, the corresponding gating thresholds and gating sequence; all of these parameters are typically manually defined. Even though CCAST is optimized for cell sorting, it can be applied for the identification and analysis of homogeneous subpopulations among heterogeneous single cell data. We apply CCAST on single cell data from both breast cancer cell lines and normal human bone marrow. On the SUM159 breast cancer cell line data, CCAST indicates at least five distinct cell states based on two surface markers (CD24 and EPCAM) and provides a gating sorting strategy that produces more homogeneous subpopulations than previously reported. When applied to normal bone marrow data, CCAST reveals an efficient strategy for gating T-cells without prior knowledge of the major T-cell subtypes and the markers that best define them. On the normal bone marrow data, CCAST also reveals two major mature B-cell subtypes, namely CD123+ and CD123- cells, which were not revealed by manual gating but show distinct intracellular signaling responses. More generally, the CCAST framework could be used on other biological and non-biological high dimensional data types that are mixtures of unknown homogeneous subpopulations.

A multi-phenotypic cancer model with cell plasticity

The conventional cancer stem cell (CSC) theory indicates a hierarchy of CSCs and non-stem cancer cells (NSCCs), that is, CSCs can differentiate into NSCCs but not vice versa. However, an alternative paradigm of CSC theory with reversible cell plasticity among cancer cells has received much attention very recently. Here we present a generalized multi-phenotypic cancer model by integrating cell plasticity with the conventional hierarchical structure of cancer cells. We prove that under very weak assumption, the nonlinear dynamics of multi-phenotypic proportions in our model has only one stable steady state and no stable limit cycle. This result theoretically explains the phenotypic equilibrium phenomena reported in various cancer cell lines. Furthermore, according to the transient analysis of our model, it is found that cancer cell plasticity plays an essential role in maintaining the phenotypic diversity in cancer especially during the transient dynamics. Two biological examples with experimental data show that the phenotypic conversions from NCSSs to CSCs greatly contribute to the transient growth of CSCs proportion shortly after the drastic reduction of it. In particular, an interesting overshooting phenomenon of CSCs proportion arises in three-phenotypic example. Our work may pave the way for modeling and analyzing the multi-phenotypic cell population dynamics with cell plasticity.

Dynamics between cancer cell subpopulations reveals a model coordinating with both hierarchical and stochastic concepts

Tumors are often heterogeneous in which tumor cells of different phenotypes have distinct properties. For scientific and clinical interests, it is of fundamental importance to understand their properties and the dynamic variations among different phenotypes, specifically under radio- and/or chemo-therapy. Currently there are two controversial models describing tumor heterogeneity, the cancer stem cell (CSC) model and the stochastic model. To clarify the controversy, we measured probabilities of different division types and transitions of cells via in situ immunofluorescence. Based on the experiment data, we constructed a model that combines the CSC with the stochastic concepts, showing the existence of both distinctive CSC subpopulations and the stochastic transitions from NSCCs to CSCs. The results showed that the dynamic variations between CSCs and non-stem cancer cells (NSCCs) can be simulated with the model. Further studies also showed that the model can be used to describe the dynamics of the two subpopulations after radiation treatment. More importantly, analysis demonstrated that the experimental detectable equilibrium CSC proportion can be achieved only when the stochastic transitions from NSCCs to CSCs occur, indicating that tumor heterogeneity may exist in a model coordinating with both the CSC and the stochastic concepts. The mathematic model based on experimental parameters may contribute to a better understanding of the tumor heterogeneity, and provide references on the dynamics of CSC subpopulation during radiotherapy.

A possible explanation for the variable frequencies of cancer stem cells in tumors

A controversy surrounds the frequency of cancer stem cells (CSCs) in solid tumors. Initial studies indicated that these cells had a frequency ranging from 0.0001 to 0.1% of the total cells. Recent studies have shown that this does not always seem to be the case. Some of these studies have indicated a frequency of [Formula: see text]. In this paper we propose a stochastic model that is able to capture this potential variability in the frequency of CSCs among the various type of tumors. Considerations regarding the heterogeneity of the tumor cells and its consequences are included. Possible effects on conventional treatments in clinical practice are also described. The model results suggest that traditional attempts to combat cancer cells with rapid cycling can be very stimulating for the cancer stem cell populations.

Population dynamics of cancer cells with cell state conversions

Cancer stem cell (CSC) theory suggests a cell-lineage structure in tumor cells in which CSCs are capable of giving rise to the other non-stem cancer cells (NSCCs) but not vice versa. However, an alternative scenario of bidirectional interconversions between CSCs and NSCCs was proposed very recently. Here we present a general population model of cancer cells by integrating conventional cell divisions with direct conversions between different cell states, namely, not only can CSCs differentiate into NSCCs by asymmetric cell division, NSCCs can also dedifferentiate into CSCs by cell state conversion. Our theoretical model is validated when applying the model to recent experimental data. It is also found that the transient increase in CSCs proportion initiated from the purified NSCCs subpopulation cannot be well predicted by the conventional CSC model where the conversion from NSCCs to CSCs is forbidden, implying that the cell state conversion is required especially for the transient dynamics. The theoretical analysis also gives the condition such that our general model can be equivalently reduced into a simple Markov chain with only cell state transitions keeping the same cell proportion dynamics.