Modeling of Cancer Stem Cell State Transitions Predicts Therapeutic Response

Stem Cell Division and Its Critical Role in Mammary Gland Development and Tumorigenesis: Current Progress and Remaining Challenges

The origin of breast cancer (BC) has traditionally been a focus of medical research. It is widely acknowledged that BC originates from immortal mammary stem cells and that these stem cells participate in two division modes: symmetric cell division (SCD) and asymmetrical cell division (ACD). Although both of these modes are key to the process of breast development and their imbalance is closely associated with the onset of BC, the molecular mechanisms underlying these phenomena deserve in-depth exploration. In this review, we first outline the molecular mechanisms governing ACD/SCD and analyze the role of ACD/SCD in various stages of breast development. We describe that the changes in telomerase activity, the role of polar proteins, and the stimulation of ovarian hormones subsequently lead to two distinct consequences: breast development or carcinogenesis. Finally, gene mutations, abnormalities in polar proteins, modulation of signal-transduction pathways, and alterations in the microenvironment disrupt the balance of BC stem cell division modes and cause BC. Important regulatory factors such as mammalian Inscuteable mInsc, Numb, Eya1, PKCα, PKCθ, p53, and IL-6 also play significant roles in regulating pathways of ACD/SCD and may constitute key targets for future research on stem cell division, breast development, and tumor therapy.

A High-Throughput Circular Tumor Cell Sorting Chip with Trapezoidal Cross Section

Circulating tumor cells are typically found in the peripheral blood of patients, offering a crucial pathway for the early diagnosis and prediction of cancer. Traditional methods for early cancer diagnosis are inefficient and inaccurate, making it difficult to isolate tumor cells from a large number of cells. In this paper, a new spiral microfluidic chip with asymmetric cross-section is proposed for rapid, high-throughput, label-free enrichment of CTCs in peripheral blood. A mold of the desired flow channel structure was prepared and inverted to make a trapezoidal cross-section using a micro-nanotechnology process of 3D printing. After a systematic study of how flow rate, channel width, and particle concentration affect the performance of the device, we utilized the device to simulate cell sorting of 6 μm, 15 μm, and 25 μm PS (Polystyrene) particles, and the separation efficiency and separation purity of 25 μm PS particles reached 98.3% and 96.4%. On this basis, we realize the enrichment of a large number of CTCs in diluted whole blood (5 mL). The results show that the separation efficiency of A549 was 88.9% and the separation purity was 96.4% at a high throughput of 1400 μL/min. In conclusion, we believe that the developed method is relevant for efficient recovery from whole blood and beneficial for future automated clinical analysis.

Predicting anti-cancer drug combination responses with a temporal cell state network model

Cancer chemotherapy combines multiple drugs, but predicting the effects of drug combinations on cancer cell proliferation remains challenging, even for simple in vitro systems. We hypothesized that by combining knowledge of single drug dose responses and cell state transition network dynamics, we could predict how a population of cancer cells will respond to drug combinations. We tested this hypothesis here using three targeted inhibitors of different cell cycle states in two different cell lines in vitro. We formulated a Markov model to capture temporal cell state transitions between different cell cycle phases, with single drug data constraining how drug doses affect transition rates. This model was able to predict the landscape of all three different pairwise drug combinations across all dose ranges for both cell lines with no additional data. While further application to different cell lines, more drugs, additional cell state networks, and more complex co-culture or in vivo systems remain, this work demonstrates how currently available or attainable information could be sufficient for prediction of drug combination response for single cell lines in vitro.

Novel size-based design of spiral microfluidic devices with elliptic configurations and trapezoidal cross-section for ultra-fast isolation of circulating tumor cells

Investigation and analysis of circulating tumor cells (CTCs) have been valuable resources for detecting and diagnosing cancer in its early stages. Recently, enumeration and separation of CTCs via microfluidic devices have attracted significant attention due to their low cost and easy setup. In this study, novel microfluidic devices based on size-dependent cell-sorting with a trapezoidal cross-section and elliptic spiral configurations were proposed to reach label-free, ultra-fast CTCs enrichment. Firstly, the possibility and quality of separation in the devices were evaluated via a numerical simulation. Subsequently, these devices were fabricated to investigate the effects of the altering curvature and the trapezoidal cross-section on the isolation of CTCs from the peripheral blood sample at varying flow rates ranging from 0.5 mL/min to 3.5 mL/min. The experimental results indicated that the flow rate of 2.5 mL/min provided the optimal separation efficiency in the proposed devices, which was in fine agreement with the numerical analysis results. In this experiment, the purity values of CTCs between 88% and 90% were achieved, which is an indicator of the high capability of the proposed devices for the isolation and enrichment of CTCs. This strategy is hoped to overcome the limitations of classical affinity-based CTC separation approaches in the future.

Intrinsically Disordered Proteins: Critical Components of the Wetware

Despite the wealth of knowledge gained about intrinsically disordered proteins (IDPs) since their discovery, there are several aspects that remain unexplored and, hence, poorly understood. A living cell is a complex adaptive system that can be described as a wetware─a metaphor used to describe the cell as a computer comprising both hardware and software and attuned to logic gates─capable of "making" decisions. In this focused Review, we discuss how IDPs, as critical components of the wetware, influence cell-fate decisions by wiring protein interaction networks to keep them minimally frustrated. Because IDPs lie between order and chaos, we explore the possibility that they can be modeled as attractors. Further, we discuss how the conformational dynamics of IDPs manifests itself as conformational noise, which can potentially amplify transcriptional noise to stochastically switch cellular phenotypes. Finally, we explore the potential role of IDPs in prebiotic evolution, in forming proteinaceous membrane-less organelles, in the origin of multicellularity, and in protein conformation-based transgenerational inheritance of acquired characteristics. Together, these ideas provide a new conceptual framework to discern how IDPs may perform critical biological functions despite their lack of structure.

Intrinsically disordered proteins: Ensembles at the limits of Anfinsen's dogma

Intrinsically disordered proteins (IDPs) are proteins that lack rigid 3D structure. Hence, they are often misconceived to present a challenge to Anfinsen's dogma. However, IDPs exist as ensembles that sample a quasi-continuum of rapidly interconverting conformations and, as such, may represent proteins at the extreme limit of the Anfinsen postulate. IDPs play important biological roles and are key components of the cellular protein interaction network (PIN). Many IDPs can interconvert between disordered and ordered states as they bind to appropriate partners. Conformational dynamics of IDPs contribute to conformational noise in the cell. Thus, the dysregulation of IDPs contributes to increased noise and "promiscuous" interactions. This leads to PIN rewiring to output an appropriate response underscoring the critical role of IDPs in cellular decision making. Nonetheless, IDPs are not easily tractable experimentally. Furthermore, in the absence of a reference conformation, discerning the energy landscape representation of the weakly funneled IDPs in terms of reaction coordinates is challenging. To understand conformational dynamics in real time and decipher how IDPs recognize multiple binding partners with high specificity, several sophisticated knowledge-based and physics-based in silico sampling techniques have been developed. Here, using specific examples, we highlight recent advances in energy landscape visualization and molecular dynamics simulations to discern conformational dynamics and discuss how the conformational preferences of IDPs modulate their function, especially in phenotypic switching. Finally, we discuss recent progress in identifying small molecules targeting IDPs underscoring the potential therapeutic value of IDPs. Understanding structure and function of IDPs can not only provide new insight on cellular decision making but may also help to refine and extend Anfinsen's structure/function paradigm.

Protein conformational dynamics and phenotypic switching

Intrinsically disordered proteins (IDPs) are proteins that lack rigid 3D structure but exist as conformational ensembles. Because of their structural plasticity, they can interact with multiple partners. The protein interactions between IDPs and their partners form scale-free protein interaction networks (PINs) that facilitate information flow in the cell. Because of their plasticity, IDPs typically occupy hub positions in cellular PINs. Furthermore, their conformational dynamics and propensity for post-translational modifications contribute to "conformational" noise which is distinct from the well-recognized transcriptional noise. Therefore, upregulation of IDPs in response to a specific input, such as stress, contributes to increased noise and, hence, an increase in stochastic, "promiscuous" interactions. These interactions lead to activation of latent pathways or can induce "rewiring" of the PIN to yield an optimal output underscoring the critical role of IDPs in regulating information flow. We have used PAGE4, a highly intrinsically disordered stress-response protein as a paradigm. Employing a variety of experimental and computational techniques, we have elucidated the role of PAGE4 in phenotypic switching of prostate cancer cells at a systems level. These cumulative studies over the past decade provide a conceptual framework to better understand how IDP conformational dynamics and conformational noise might facilitate cellular decision-making.

A review of dynamical systems approaches for the detection of chaotic attractors in cancer networks

Cancers are complex dynamical systems. They remain the leading cause of disease-related pediatric mortality in North America. To overcome this burden, we must decipher the state-space attractor dynamics of gene expression patterns and protein oscillations orchestrated by cancer stemness networks. The review provides an overview of dynamical systems theory to steer cancer research in pattern science. While most of our current tools in network medicine rely on statistical correlation methods, causality inference remains primitively developed. As such, a survey of attractor reconstruction methods and machine algorithms for the detection of causal structures applicable in experimentally derived time series cancer datasets is presented. A toolbox of complex systems approaches are discussed for reconstructing the signaling state space of cancer networks, interpreting causal relationships in their time series gene expression patterns, and assisting clinical decision making in computational oncology. As a proof of concept, the applicability of some algorithms are demonstrated on pediatric brain cancer datasets and the requirement of their time series analysis is highlighted.

A polymer-film inertial microfluidic sorter fabricated by jigsaw puzzle method for precise size-based cell separation

A polymer-film inertial microfluidic jigsaw (PIMJ) sorter with trapezoidal spiral channels using the jigsaw puzzle method was proposed to realize precise and high-throughput rare cell separation. The PIMJ sorter was fabricated by assembling laser-patterned polymer-film layers of different thicknesses. After illustrating the conceptual design and fabrication process, the effects of the cross-sectional dimension, particle size, and operational flow rate on particle focusing were systematically explored under a broad flow rate range. Then, the separation performances of the PIMJ sorter were characterized using the binary particle mixture and the blood samples spiked with four types of tumor cells. The results indicated that the complete separation of the binary particles with a minimum size difference of 2 μm was successfully realized at a high throughput up to 3000 μL/min. A high recovery ratio of 90.57%-94.14% and a high purity of 48.67%-79.05% were achieved for the separation of rare tumor cells from white blood cells (WBCs). Finally, the PIMJ sorter was successfully employed for separating rare circulating tumor cells (CTCs) from the metastatic breast and lung cancer patients with a capture ratio of 7-226 CTCs per 5 mL sample. The results proved the high sensitivity and high reliability of the PIMJ sorter. The PIMJ sorter is expected to be a potential device for precise CTC separation towards the clinical applications.

Oxidized ATM promotes breast cancer stem cell enrichment through energy metabolism reprogram-mediated acetyl-CoA accumulation

Cancer stem cell (CSC) is a challenge in the therapy of triple-negative breast cancer (TNBC). Intratumoral hypoxia is a common feature of solid tumor. Hypoxia may contribute to the maintenance of CSC, resulting in a poor efficacy of traditional treatment and recurrence of TNBC cases. However, the underlying molecular mechanism involved in hypoxia-induced CSC stemness maintenance remains unclear. Here, we report that hypoxia stimulated DNA double-strand breaks independent of ATM kinase activation (called oxidized ATM in this paper) play a crucial role in TNBC mammosphere formation and stemness maintenance by governing a specific energy metabolism reprogramming (EMR). Oxidized ATM up-regulates GLUT1, PKM2, and PDHa expressions to enhance the uptake of glucose and production of pyruvate rather than lactate products, which facilitates glycolytic flux to mitochondrial pyruvate and citrate, thus resulting in accumulation of cytoplasmic acetyl-CoA instead of the tricarboxylic acid (TCA) cycle by regulating ATP-citrate lyase (ACLY) activity. Our findings unravel a novel model of TNBC-CSC glucose metabolism and its functional role in maintenance of hypoxic TNBC-CSC stemness. This work may help us to develop new therapeutic strategies for TNBC treatment.

Modeling heterogeneous tumor growth dynamics and cell-cell interactions at single-cell and cell-population resolution

Cancer is a complex, dynamic disease that despite recent advances remains mostly incurable. Inter- and intratumoral heterogeneity are generally considered major drivers of therapy resistance, metastasis, and treatment failure. Recent advances in high-throughput experimentation have produced a wealth of data on tumor heterogeneity and researchers are increasingly turning to mathematical modeling to aid in the interpretation of these complex datasets. In this mini-review, we discuss three important classes of approaches for modeling cellular dynamics within heterogeneous tumors: agent-based models, population dynamics, and multiscale models. An important new focus, for which we provide an example, is the role of intratumoral cell-cell interactions.

Dynamics of Phenotypic Heterogeneity Associated with EMT and Stemness during Cancer Progression

Genetic and phenotypic heterogeneity contribute to the generation of diverse tumor cell populations, thus enhancing cancer aggressiveness and therapy resistance. Compared to genetic heterogeneity, a consequence of mutational events, phenotypic heterogeneity arises from dynamic, reversible cell state transitions in response to varying intracellular/extracellular signals. Such phenotypic plasticity enables rapid adaptive responses to various stressful conditions and can have a strong impact on cancer progression. Herein, we have reviewed relevant literature on mechanisms associated with dynamic phenotypic changes and cellular plasticity, such as epithelial-mesenchymal transition (EMT) and cancer stemness, which have been reported to facilitate cancer metastasis. We also discuss how non-cell-autonomous mechanisms such as cell-cell communication can lead to an emergent population-level response in tumors. The molecular mechanisms underlying the complexity of tumor systems are crucial for comprehending cancer progression, and may provide new avenues for designing therapeutic strategies.

Intrinsically disordered proteins and phenotypic switching: Implications in cancer

It is now well established that intrinsically disordered proteins (IDPs) that constitute a large part of the proteome across the three kingdoms, play critical roles in several biological processes including phenotypic switching. However, dysregulated expression of IDPs that engage in promiscuous interactions can lead to pathological states. In this chapter, using cancer as a paradigm, we discuss how IDP conformational dynamics and the resultant conformational noise can modulate phenotypic switching. Thus, contrary to the prevailing wisdom that phenotypic switching is highly deterministic (has a genetic underpinning) in cancer, emerging evidence suggests that non-genetic mechanisms, at least in part due to the conformational noise, may also be a confounding factor in phenotypic switching.

AKT2 siRNA delivery with amphiphilic-based polymeric micelles show efficacy against cancer stem cells

Development of RNA interference-based therapies with appropriate therapeutic window remains a challenge for advanced cancers. Because cancer stem cells (CSC) are responsible of sustaining the metastatic spread of the disease to distal organs and the progressive gain of resistance of advanced cancers, new anticancer therapies should be validated specifically for this subpopulation of cells. A new amphihilic-based gene delivery system that combines Pluronic^® F127 micelles with polyplexes spontaneously formed by electrostatic interaction between anionic siRNA and cationic polyethylenimine (PEI) 10K, was designed (PM). Resultant PM gather the requirements for an efficient and safe transport of siRNA in terms of its physicochemical characteristics, internalization capacity, toxicity profile and silencing efficacy. PM were loaded with a siRNA against AKT2, an important oncogene involved in breast cancer tumorigenesis, with a special role in CSC malignancy. Efficacy of siAKT2-PM was validated in CSC isolated from two breast cancer cell lines: MCF-7 and Triple Negative MDA-MB-231 corresponding to an aggressive subtype of breast cancer. In both cases, we observed significant reduction on cell invasion capacity and strong inhibition of mammosphere formation after treatment. These results prompt AKT2 inhibition as a powerful therapeutic target against CSC and pave the way to the appearance of more effective nanomedicine-based gene therapies aimed to prevent CSC-related tumor recurrence.

Extranuclear signaling by sex steroid receptors and clinical implications in breast cancer

Estrogen and progesterone play essential roles in the development and progression of breast cancer. Over 70% of breast cancers express estrogen receptors (ER) and progesterone receptors (PR), emphasizing the need for better understanding of ER and PR signaling. ER and PR are traditionally viewed as transcription factors that directly bind DNA to regulate gene networks. In addition to nuclear signaling, ER and PR mediate hormone-induced, rapid extranuclear signaling at the cell membrane or in the cytoplasm which triggers downstream signaling to regulate rapid or extended cellular responses. Specialized membrane and cytoplasmic proteins may also initiate hormone-induced extranuclear signaling. Rapid extranuclear signaling converges with its nuclear counterpart to amplify ER/PR transcription and specify gene regulatory networks. This review summarizes current understanding and updates on ER and PR extranuclear signaling. Further investigation of ER/PR extranuclear signaling may lead to development of novel targeted therapeutics for breast cancer management.

Characterization of a Dual Rac/Cdc42 Inhibitor MBQ-167 in Metastatic Cancer

The Rho GTPases Rac (Ras-related C3 botulinum toxin substrate) and Cdc42 (cell division control protein 42 homolog) regulate cell functions governing cancer malignancy, including cell polarity, migration, and cell-cycle progression. Accordingly, our recently developed Rac inhibitor EHop-016 (IC₅₀, 1,100 nmol/L) inhibits cancer cell migration and viability and reduces tumor growth, metastasis, and angiogenesis in vivo Herein, we describe MBQ-167, which inhibits Rac and Cdc42 with IC₅₀ values of 103 and 78 nmol/L, respectively, in metastatic breast cancer cells. Consequently, MBQ-167 significantly decreases Rac and Cdc42 downstream effector p21-activated kinase (PAK) signaling and the activity of STAT3, without affecting Rho, MAPK, or Akt activities. MBQ-167 also inhibits breast cancer cell migration, viability, and mammosphere formation. Moreover, MBQ-167 affects cancer cells that have undergone epithelial-to-mesenchymal transition by a loss of cell polarity and inhibition of cell surface actin-based extensions to ultimately result in detachment from the substratum. Prolonged incubation (120 hours) in MBQ-167 decreases metastatic cancer cell viability with a GI₅₀ of approximately 130 nmol/L, without affecting noncancer mammary epithelial cells. The loss in cancer cell viability is due to MBQ-167-mediated G₂-M cell-cycle arrest and subsequent apoptosis, especially of the detached cells. In vivo, MBQ-167 inhibits mammary tumor growth and metastasis in immunocompromised mice by approximately 90%. In conclusion, MBQ-167 is 10× more potent than other currently available Rac/Cdc42 inhibitors and has the potential to be developed as an anticancer drug, as well as a dual inhibitory probe for the study of Rac and Cdc42. Mol Cancer Ther; 16(5); 805-18. ©2017 AACR.

Modeling of Interactions between Cancer Stem Cells and their Microenvironment: Predicting Clinical Response

Mathematical models of cancer stem cells are useful in translational cancer research for facilitating the understanding of tumor growth dynamics and for predicting treatment response and resistance to combined targeted therapies. In this chapter, we describe appealing aspects of different methods used in mathematical oncology and discuss compelling questions in oncology that can be addressed with these modeling techniques. We describe a simplified version of a model of the breast cancer stem cell niche, illustrate the visualization of the model, and apply stochastic simulation to generate full distributions and average trajectories of cell type populations over time. We further discuss the advent of single-cell data in studying cancer stem cell heterogeneity and how these data can be integrated with modeling to advance understanding of the dynamics of invasive and proliferative populations during cancer progression and response to therapy.

Notch-Jagged signalling can give rise to clusters of cells exhibiting a hybrid epithelial/mesenchymal phenotype

Metastasis can involve repeated cycles of epithelial-to-mesenchymal transition (EMT) and its reverse mesenchymal-to-epithelial transition. Cells can also undergo partial transitions to attain a hybrid epithelial/mesenchymal (E/M) phenotype that allows the migration of adhering cells to form a cluster of circulating tumour cells. These clusters can be apoptosis-resistant and possess an increased metastatic propensity as compared to the cells that undergo a complete EMT (mesenchymal cells). Hence, identifying the key players that can regulate the formation and maintenance of such clusters may inform anti-metastasis strategies. Here, we devise a mechanism-based theoretical model that links cell–cell communication via Notch-Delta-Jagged signalling with the regulation of EMT. We demonstrate that while both Notch-Delta and Notch-Jagged signalling can induce EMT in a population of cells, only Jagged-dominated Notch signalling, but not Delta-dominated signalling, can lead to the formation of clusters containing hybrid E/M cells. Our results offer possible mechanistic insights into the role of Jagged in tumour progression, and offer a framework to investigate the effects of other microenvironmental signals during metastasis.

Deciphering Epithelial-Mesenchymal Transition Regulatory Networks in Cancer through Computational Approaches

Epithelial–mesenchymal transition (EMT), the process by which epithelial cells can convert into motile mesenchymal cells, plays an important role in development and wound healing but is also involved in cancer progression. It is increasingly recognized that EMT is a dynamic process involving multiple intermediate or “hybrid” phenotypes rather than an “all-or-none” process. However, the role of EMT in various cancer hallmarks, including metastasis, is debated. Given the complexity of EMT regulation, computational modeling has proven to be an invaluable tool for cancer research, i.e., to resolve apparent conflicts in experimental data and to guide experiments by generating testable hypotheses. In this review, we provide an overview of computational modeling efforts that have been applied to regulation of EMT in the context of cancer progression and its associated tumor characteristics. Moreover, we identify possibilities to bridge different modeling approaches and point out outstanding questions in which computational modeling can contribute to advance our understanding of pathological EMT.

Viscoelasticity Measurements Reveal Rheological Differences Between Stem-like and Non-stem-like Breast Cancer Cells

Defining the characteristics of cancer stem cells (CSC) has become an important subject in cancer research during the past decade. Although molecular surface expression levels have been used for CSC recognition, the clinical and prognostic impacts of these markers have remained a controversial issue. The finding that cancerous cells are considerably more deformable than normal ones provides the motivation for the hypothesis that the mechanical properties can be used as biomarkers to distinguish between stem-like and non-stem-like cancer cells. In this study, using micropipette aspiration (MA) and intracellular particle tracking (IPT) microrheology, measurements of the whole-cell and local viscoelasticity were made on four breast cancer cell lines with different CSC phenotypes based on their surface markers. Stem-like Hs578T and MDA-MB-231 cell lines were found to be the most deformable, while the non-stem-like MDA-MB-468 line was the least deformable. The non-stem-like BT-20 cell line showed an intermediate deformability. The enhanced deformability for stem-like cells was consistent with the observed lower and more dispersed F-actin content for the stem-like cells. Therefore, the cytoskeleton-related differences in the rheological properties of cancer cells can be a potential biomarker for CSC and eventually lead to novel cancer diagnostic and therapeutic methods.

Early mutation bursts in colorectal tumors

Tumor growth is an evolutionary process involving accumulation of mutations, copy number alterations, and cancer stem cell (CSC) division and differentiation. As direct observation of this process is impossible, inference regarding when mutations occur and how stem cells divide is difficult. However, this ancestral information is encoded within the tumor itself, in the form of intratumoral heterogeneity of the tumor cell genomes. Here we present a framework that allows simulation of these processes and estimation of mutation rates at the various stages of tumor development and CSC division patterns for single-gland sequencing data from colorectal tumors. We parameterize the mutation rate and the CSC division pattern, and successfully retrieve their posterior distributions based on DNA sequence level data. Our approach exploits Approximate Bayesian Computation (ABC), a method that is becoming widely-used for problems of ancestral inference.

Predicting cell viability within tissue scaffolds under equiaxial strain: multi-scale finite element model of collagen-cardiomyocytes constructs

Successful tissue engineering and regenerative therapy necessitate having extensive knowledge about mechanical milieu in engineered tissues and the resident cells. In this study, we have merged two powerful analysis tools, namely finite element analysis and stochastic analysis, to understand the mechanical strain within the tissue scaffold and residing cells and to predict the cell viability upon applying mechanical strains. A continuum-based multi-length scale finite element model (FEM) was created to simulate the physiologically relevant equiaxial strain exposure on cell-embedded tissue scaffold and to calculate strain transferred to the tissue scaffold (macro-scale) and residing cells (micro-scale) upon various equiaxial strains. The data from FEM were used to predict cell viability under various equiaxial strain magnitudes using stochastic damage criterion analysis. The model validation was conducted through mechanically straining the cardiomyocyte-encapsulated collagen constructs using a custom-built mechanical loading platform (EQUicycler). FEM quantified the strain gradients over the radial and longitudinal direction of the scaffolds and the cells residing in different areas of interest. With the use of the experimental viability data, stochastic damage criterion, and the average cellular strains obtained from multi-length scale models, cellular viability was predicted and successfully validated. This methodology can provide a great tool to characterize the mechanical stimulation of bioreactors used in tissue engineering applications in providing quantification of mechanical strain and predicting cellular viability variations due to applied mechanical strain.

Non-coding RNA as mediators in microenvironment–breast cancer cell communication

The tumor microenvironment has a critical role in the survival and decision of the cancer cells. These include support by enhanced angiogenesis, and metastasis or adaptation of dormancy. This article discusses methods by which the microenvironment sustains the tumor. This process is important as it will identify avenues of drug targets. Non-coding RNAs (ncRNAs) are evolving as key mediators in the interaction between the cancer cells and the microenvironment. Thus, the question is how to develop methods to effectively block the effects of the ncRNA and/or to introduce them to prevent metastasis, dormancy or to reverse dormancy. We focused on the advantages of using mesenchymal stem cells (MSCs) for RNA delivery. MSCs can be available as "off-the-shelf" cells. Thus far, MSCs are shown to be safe when transplanted across allogeneic barriers. We discussed the various methods by which MSCs can interact with cancer cells to deliver ncRNA or antagomirs. We also include the advances and possible confounds of using these methods. Overall, this review article provides a potential method by which MSCs can be used for effective delivery of nucleic acid to treat cancer.

Evaluating biomarkers to model cancer risk post cosmic ray exposure

Robust predictive models are essential to manage the risk of radiation-induced carcinogenesis. Chronic exposure to cosmic rays in the context of the complex deep space environment may place astronauts at high cancer risk. To estimate this risk, it is critical to understand how radiation-induced cellular stress impacts cell fate decisions and how this in turn alters the risk of carcinogenesis. Exposure to the heavy ion component of cosmic rays triggers a multitude of cellular changes, depending on the rate of exposure, the type of damage incurred and individual susceptibility. Heterogeneity in dose, dose rate, radiation quality, energy and particle flux contribute to the complexity of risk assessment. To unravel the impact of each of these factors, it is critical to identify sensitive biomarkers that can serve as inputs for robust modeling of individual risk of cancer or other long-term health consequences of exposure. Limitations in sensitivity of biomarkers to dose and dose rate, and the complexity of longitudinal monitoring, are some of the factors that increase uncertainties in the output from risk prediction models. Here, we critically evaluate candidate early and late biomarkers of radiation exposure and discuss their usefulness in predicting cell fate decisions. Some of the biomarkers we have reviewed include complex clustered DNA damage, persistent DNA repair foci, reactive oxygen species, chromosome aberrations and inflammation. Other biomarkers discussed, often assayed for at longer points post exposure, include mutations, chromosome aberrations, reactive oxygen species and telomere length changes. We discuss the relationship of biomarkers to different potential cell fates, including proliferation, apoptosis, senescence, and loss of stemness, which can propagate genomic instability and alter tissue composition and the underlying mRNA signatures that contribute to cell fate decisions. Our goal is to highlight factors that are important in choosing biomarkers and to evaluate the potential for biomarkers to inform models of post exposure cancer risk. Because cellular stress response pathways to space radiation and environmental carcinogens share common nodes, biomarker-driven risk models may be broadly applicable for estimating risks for other carcinogens.

Oncofetal Epigenetic Bivalency in Breast Cancer Cells: H3K4 and H3K27 Tri-Methylation as a Biomarker for Phenotypic Plasticity

Alterations in the epigenetic landscape are fundamental drivers of aberrant gene expression that contribute to cancer progression and pathology. Understanding specific modes of epigenetic regulation can be used to identify novel biomarkers or targets for therapeutic intervention to clinically treat solid tumors and leukemias. The bivalent marking of gene promoters by H3K4me3 and H3K27me3 is a primary mechanism to poise genes for expression in pluripotent embryonic stem cells (ESC). In this study we interrogated three well-established mammary cell lines to model epigenetic programming observed among breast cancer subtypes. Evidence is provided for a distinct bivalent signature, activating and repressive histone marks co-residing at the same gene promoter, in the MCF7 (ESR/PGR+) luminal breast cancer cell line. We identified a subset of genes, enriched for developmental pathways that regulate cellular phenotype and signaling, and partially recapitulate the bivalent character observed in ESC. We validated the biological relevance of this "oncofetal epigenetic" signature using data from ESR/PGR+ tumor samples from breast cancer patients. This signature of oncofetal epigenetic control is an informative biomarker and may provide novel therapeutic targets, selective for both recurring and treatment-resistant cancers. J. Cell. Physiol. 231: 2474-2481, 2016. © 2016 Wiley Periodicals, Inc.

[On the physiological roles of fibrinogen and fibrin]

Cancer is now viewed as a stem cell disease. There is still no consensus on the metabolic characteristics of cancer stem cells, with several studies indicating that they are mainly glycolytic and others pointing instead to mitochondrial metabolism as their principal source of energy. Cancer stem cells also seem to adapt their metabolism to microenvironmental changes by conveniently shifting energy production from one pathway to another, or by acquiring intermediate metabolic phenotypes. Determining the role of cancer stem cell metabolism in carcinogenesis has become a major focus in cancer research, and substantial efforts are conducted towards discovering clinical targets.