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

The clinical value of nutritional and inflammatory indicators in predicting pneumonia among patients with intracerebral hemorrhage

Immunosuppression and malnutrition play pivotal roles in the complications of intracerebral hemorrhage (ICH) and are intricately linked to the development of stroke-associated pneumonia (SAP). Inflammatory markers, including NLR (neutrophil-to-lymphocyte ratio), SII (systemic immune inflammation index), SIRI (systemic inflammatory response index), and SIS (systemic inflammation score), along with nutritional indexes such as CONUT (controlling nutritional status) and PNI (prognostic nutritional index), are crucial indicators influencing the inflammatory state following ICH. In this study, our objective was to compare the predictive efficacy of inflammatory and nutritional indices for SAP in ICH patients, aiming to determine and explore their clinical utility in early pneumonia detection. Patients with severe ICH requiring ICU admission were screened from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. The outcomes included the occurrence of SAP and in-hospital death. Receiver operating characteristic (ROC) analysis, multivariate logistic regression, smooth curve analysis, and stratified analysis were employed to investigate the relationship between the CONUT index and the clinical outcomes of patients with severe ICH. A total of 348 patients were enrolled in the study. The incidence of SAP was 21.3%, and the in-hospital mortality rate was 17.0%. Among these indicators, multiple regression analysis revealed that CONUT, PNI, and SIRI were independently associated with SAP. Further ROC curve analysis demonstrated that CONUT (AUC 0.6743, 95% CI 0.6079-0.7408) exhibited the most robust predictive ability for SAP in patients with ICH. Threshold analysis revealed that when CONUT < 6, an increase of 1 point in CONUT was associated with a 1.39 times higher risk of SAP. Similarly, our findings indicate that CONUT has the potential to predict the prognosis of patients with ICH. Among the inflammatory and nutritional markers, CONUT stands out as the most reliable predictor of SAP in patients with ICH. Additionally, it proves to be a valuable indicator for assessing the prognosis of patients with ICH.

Predictive value of inflammatory prognostic index for contrast-induced nephropathy in patients undergoing coronary angiography and/or percutaneous coronary intervention

The purpose of this study was to investigate the relationship between Inflammatory Prognostic Index (IPI) levels and Contrast-Induced Nephropathy (CIN) risk and postoperative clinical outcomes in patients undergoing coronary angiography (CAG) and/or percutaneous coronary intervention (PCI). A total of 3,340 consecutive patients who underwent CAG and/or PCI between May 2017 and December 2022 were enrolled in this study. Based on their baseline IPI levels, patients were categorized into four groups. Clinical characteristics and postoperative outcomes were compared among these groups. In-hospital outcomes focused on CIN risk, repeated revascularization, major bleeding, and major adverse cardiovascular events (MACEs), while the long-term outcome examined the all-cause readmission rate. Quartile analysis found a significant link between IPI levels and CIN risk, notably in the highest quartile (P < 0.001). Even after adjusting for baseline factors, this association remained significant, with an adjusted Odds Ratio (aOR) of 2.33 (95%CI 1.50-3.64; P = 0.001). Notably, baseline IPI level emerged as an independent predictor of severe arrhythmia, with aOR of 0.50 (95%CI 0.35-0.69; P < 0.001), particularly driven by the highest quartile. Furthermore, a significant correlation between IPI and acute myocardial infarction was observed (P < 0.001), which remained significant post-adjustment. For patients undergoing CAG and/or PCI, baseline IPI levels can independently predict clinical prognosis. As a comprehensive inflammation indicator, IPI effectively identifies high-risk patients post-procedure. This study underscores IPI's potential to assist medical professionals in making more precise clinical decisions, ultimately reducing mortality and readmission rates linked to cardiovascular disease (CVD).

Ascites-derived hsa-miR-181a-5p serves as a prognostic marker for gastric cancer-associated malignant ascites

BACKGROUND

Peritoneal carcinomatosis was the main reason leading to gastric cancer (GC)-related death. We aimed to explore the roles of dysregulated microRNAs (miRNAs) and related immune regulation activities in GC-associated malignant ascites.

METHODS

GSE126399 were downloaded from GEO database. Differentially expressed miRNAs in GC ascites samples was firstly screened, and critical miRNAs were further investigated by LASSO (least absolute shrinkage and selection operator) logistic regression and random forest (RF) algorithm. Receiver operating characteristic of critical miRNAs was also constructed. Moreover, functional analysis, immune cell infiltration associated with differentially expressed mRNAs were further analyzed. After selecting key modules by weighted gene co-expression network analysis, mRNAs related with survival performance and transcription factor (TF)-miRNA-mRNA network were constructed.

RESULTS

Hsa-miR-181b-5p was confirmed as critical differentially expressed miRNAs in GC ascites. Then, the tumor samples were divided into high- and low- expression groups divided by mean expression levels of hsa-miR-181b-5p, and subjects with high hsa-miR-181b-5p levels had better survival outcomes. In total, 197 differentially expressed mRNAs associated with hsa-miR-181b-5p levels were obtained, and these mRNAs were mainly enriched in muscle activity and vascular smooth muscle contraction. Hsa-miR-181b-5 was positively related with activated CD4 T cells and negatively related with eosinophil. 17 mRNAs were selected as mRNAs significantly related with prognosis of GC, such as PDK4 and RAMP1. Finally, 75 TF-miRNA-mRNA relationships were obtained, including 15 TFs, hsa-miR-181b-5p, and five mRNAs.

CONCLUSION

Our data suggest that the differentially expressed hsa-miR-181b-5p in ascites samples of GC patients may be a valuable prognostic marker and a potential target for therapeutic intervention, which should be validated in the near future.

Cancer associated fibroblasts serve as an ovarian cancer stem cell niche through noncanonical Wnt5a signaling

Frequent relapse and chemoresistance cause poor outcome in ovarian cancer (OC) and cancer stem cells (CSCs) are important contributors. While most studies focus exclusively on CSCs, the role of the microenvironment in providing optimal conditions to maintain their tumor-initiating potential remains poorly understood. Cancer associated fibroblasts (CAFs) are a major constituent of the OC tumor microenvironment and we show that CAFs and CSCs are enriched following chemotherapy in patient tumors. CAFs significantly increase OC cell resistance to carboplatin. Using heterotypic CAF-OC cocultures and in vivo limiting dilution assay, we confirm that the CAFs act by enriching the CSC population. CAFs increase the symmetric division of CSCs as well as the dedifferentiation of bulk OC cells into CSCs. The effect of CAFs is limited to OC cells in their immediate neighborhood, which can be prevented by inhibiting Wnt. Analysis of single cell RNA-seq data from OC patients reveal Wnt5a as the highest expressed Wnt in CAFs and that certain subpopulations of CAFs express higher levels of Wnt5a. Our findings demonstrate that Wnt5a from CAFs activate a noncanonical Wnt signaling pathway involving the ROR2/PKC/CREB1 axis in the neighboring CSCs. While canonical Wnt signaling is found to be predominant in interactions between cancer cells in patients, non-canonical Wnt pathway is activated by the CAF-OC crosstalk. Treatment with a Wnt5a inhibitor sensitizes tumors to carboplatin in vivo. Together, our results demonstrate a novel mechanism of CSC maintenance by signals from the microenvironmental CAFs, which can be targeted to treat OC chemoresistance and relapse.

Unified tumor growth mechanisms from multimodel inference and dataset integration

Mechanistic models of biological processes can explain observed phenomena and predict responses to a perturbation. A mathematical model is typically constructed using expert knowledge and informal reasoning to generate a mechanistic explanation for a given observation. Although this approach works well for simple systems with abundant data and well-established principles, quantitative biology is often faced with a dearth of both data and knowledge about a process, thus making it challenging to identify and validate all possible mechanistic hypothesis underlying a system behavior. To overcome these limitations, we introduce a Bayesian multimodel inference (Bayes-MMI) methodology, which quantifies how mechanistic hypotheses can explain a given experimental datasets, and concurrently, how each dataset informs a given model hypothesis, thus enabling hypothesis space exploration in the context of available data. We demonstrate this approach to probe standing questions about heterogeneity, lineage plasticity, and cell-cell interactions in tumor growth mechanisms of small cell lung cancer (SCLC). We integrate three datasets that each formulated different explanations for tumor growth mechanisms in SCLC, apply Bayes-MMI and find that the data supports model predictions for tumor evolution promoted by high lineage plasticity, rather than through expanding rare stem-like populations. In addition, the models predict that in the presence of cells associated with the SCLC-N or SCLC-A2 subtypes, the transition from the SCLC-A subtype to the SCLC-Y subtype through an intermediate is decelerated. Together, these predictions provide a testable hypothesis for observed juxtaposed results in SCLC growth and a mechanistic interpretation for tumor treatment resistance.

The Glioblastoma Landscape: Hallmarks of Disease, Therapeutic Resistance, and Treatment Opportunities

Malignant brain tumors are aggressive and difficult to treat. Glioblastoma is the most common and lethal form of primary brain tumor, often found in patients with no genetic predisposition. The median life expectancy for individuals diagnosed with this condition is 6 months to 2 years and there is no known cure. New paradigms in cancer biology implicate a small subset of tumor cells in initiating and sustaining these incurable brain tumors. Here, we discuss the heterogenous nature of glioblastoma and theories behind its capacity for therapy resistance and recurrence. Within the cancer landscape, cancer stem cells are thought to be both tumor initiators and major contributors to tumor heterogeneity and therapy evasion and such cells have been identified in glioblastoma. At the cellular level, disruptions in the delicate balance between differentiation and self-renewal spur transformation and support tumor growth. While rapidly dividing cells are more sensitive to elimination by traditional treatments, glioblastoma stem cells evade these measures through slow division and reversible exit from the cell cycle. At the molecular level, glioblastoma tumor cells exploit several signaling pathways to evade conventional therapies through improved DNA repair mechanisms and a flexible state of senescence. We examine these common evasion techniques while discussing potential molecular approaches to better target these deadly tumors. Equally important, the presented information encourages the idea of augmenting conventional treatments with novel glioblastoma stem cell-directed therapies, as eliminating these harmful progenitors holds great potential to modulate tumor recurrence.

Unraveling non-genetic heterogeneity in cancer with dynamical models and computational tools

Individual cells within an otherwise genetically homogenous population constantly undergo fluctuations in their molecular state, giving rise to non-genetic heterogeneity. Such diversity is being increasingly implicated in cancer therapy resistance and metastasis. Identifying the origins of non-genetic heterogeneity is therefore crucial for making clinical breakthroughs. We discuss with examples how dynamical models and computational tools have provided critical multiscale insights into the nature and consequences of non-genetic heterogeneity in cancer. We demonstrate how mechanistic modeling has been pivotal in establishing key concepts underlying non-genetic diversity at various biological scales, from population dynamics to gene regulatory networks. We discuss advances in single-cell longitudinal profiling techniques to reveal patterns of non-genetic heterogeneity, highlighting the ongoing efforts and challenges in statistical frameworks to robustly interpret such multimodal datasets. Moving forward, we stress the need for data-driven statistical and mechanistically motivated dynamical frameworks to come together to develop predictive cancer models and inform therapeutic strategies.

Hox genes are crucial regulators of periosteal stem cell identity

Periosteal stem and progenitor cells (PSPCs) are major contributors to bone maintenance and repair. Deciphering the molecular mechanisms that regulate their function is crucial for the successful generation and application of future therapeutics. Here, we pinpoint Hox transcription factors as necessary and sufficient for periosteal stem cell function. Hox genes are transcriptionally enriched in periosteal stem cells and their overexpression in more committed progenitors drives reprogramming to a naïve, self-renewing stem cell-like state. Crucially, individual Hox family members are expressed in a location-specific manner and their stem cell-promoting activity is only observed when the Hox gene is matched to the anatomical origin of the PSPC, demonstrating a role for the embryonic Hox code in adult stem cells. Finally, we demonstrate that Hoxa10 overexpression partially restores the age-related decline in fracture repair. Together, our data highlight the importance of Hox genes as key regulators of PSPC identity in skeletal homeostasis and repair.

Targeting Breast Cancer Stem Cells

The potential roles of breast cancer stem cells (BCSCs) in tumor initiation and recurrence have been recognized for many decades. Due to their strong capacity for self-renewal and differentiation, BCSCs are the major reasons for poor clinical outcomes and low therapeutic response. Several hypotheses on the origin of cancer stem cells have been proposed, including critical gene mutations in stem cells, dedifferentiation of somatic cells, and cell plasticity remodeling by epithelial-mesenchymal transition (EMT) and the tumor microenvironment. Moreover, the tumor microenvironment, including cellular components and cytokines, modulates the self-renewal and therapeutic resistance of BCSCs. Small molecules, antibodies, and chimeric antigen receptor (CAR)-T cells targeting BCSCs have been developed, and their applications in combination with conventional therapies are undergoing clinical trials. In this review, we focus on the features of BCSCs, emphasize the major factors and tumor environment that regulate the stemness of BCSCs, and discuss potential BCSC-targeting therapies.

Growth dynamics of breast cancer stem cells: effects of self-feedback and EMT mechanisms

Breast cancer stem cells (BCSCs) with the ability to self-renew and differentiate have been identified in primary breast cancer tissues and cell lines. The BCSCs are often resistant to traditional radiation and/or chemotherapies. Previous studies have also shown that successful therapy must eradicate cancer stem cells. The purpose of this paper is to develop a mathematical model with self-feedback mechanism to illustrate the issues regarding the difficulties of absolutely eliminating a breast cancer. In addition, we introduce the mechanism of the epithelial-mesenchymal transition (EMT) to investigate the influence of EMT on the effects of breast cancer growth and treatment. Results indicate that the EMT mechanism facilitates the growth of breast cancer and makes breast cancer more difficult to be cured. Therefore, targeting the signals involved in EMT can halt tumor progression in breast cancer. Finally, we apply the experimental data to carry out numerical simulations and validate our theoretical conclusions.

Immune evasion by cancer stem cells

Tumor immunity represents a new avenue for cancer therapy. Immune checkpoint inhibitors have successfully improved outcomes in several tumor types. In addition, currently, immune cell-based therapy is also attracting significant attention. However, the clinical efficacy of these treatments requires further improvement. The mechanisms through which cancer cells escape the immune response must be identified and clarified. Cancer stem cells (CSCs) play a central role in multiple aspects of malignant tumors. CSCs can initiate tumors in partially immunocompromised mice, whereas non-CSCs fail to form tumors, suggesting that tumor initiation is a definitive function of CSCs. However, the fact that non-CSCs also initiate tumors in more highly immunocompromised mice suggests that the immune evasion property may be a more fundamental feature of CSCs rather than a tumor-initiating property. In this review, we summarize studies that have elucidated how CSCs evade tumor immunity and create an immunosuppressive milieu with a focus on CSC-specific characteristics and functions. These profound mechanisms provide important clues for the development of novel tumor immunotherapies.

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Cancer stem cells (CSCs) play a central role in multiple aspects of malignant tumors.

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Immune evasion is a fundamental feature of CSCs.

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Immune evasion mechanisms must be precisely clarified to improve tumor immunotherapy.

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CSCs are promising targets for tumor immunotherapy.

Knockdown of E-cadherin induces cancer stem-cell-like phenotype and drug resistance in cervical cancer cells

Cervical cancer is one of the leading causes of mortality amongst women in developing countries, and resistance to therapy is the main reason for treatment failure. Recent advances suggest that cancer stem cells (CSCs) are critically involved in regulating the chemo-resistant behavior of cervical cancer cells. In our study, cells with the CSC phenotype were isolated, and we examined the expression levels of stem cell markers and genes associated with epithelial-mesenchymal transition (EMT) using different assays. However, the cells with the CSC phenotype could not be cultured for further cytotoxicity studies, so we established a model of CSC in cervical cancer cells. We performed siRNA-mediated knockdown of E-cadherin in these cells, and studied them for EMT-associated stem-cell-like properties. We also performed dose-dependent cell viability assays using clinically relevant drugs such as cisplatin, cyclopamine, and GANT58 to analyze the drug resistant behavior of these cancer cells. We found that knockdown of E-cadherin induces EMT in cervical cancer cells, imparting stem-cell like characteristics along with enhanced tumorsphere formation, cell migration, invasiveness, and drug resistance. This is the first study to establish a CSC model in cervical cancer cells by knockdown of E-cadherin, which can be used to develop anti-cancer therapies.

Epigenetic targeting of cancer stem cells by polyphenols (cancer stem cells targeting)

Epigenetic alterations are one of the main factors that disrupt the expression of genes and consequently, they have an important role in the carcinogenicity and the progression of different cancers. Cancer stem cells (CSCs) are accountable for the recurrence, metastasis, and therapeutic failure of cancer. The noticeable and specific pathways in CSCs can be organized by epigenetic mechanisms such as DNA methylation, chromatin remodeling, regulatory RNAs, among others. Since epigenetics modifications can be changed and reversed, it is a possible tool for cancer control and treatment. Epigenetic therapies against CSCs are emerging as a very new strategy with a good future expectation to treat cancer patients. Phenolic compounds are a vast group of substances with anticarcinogenic functions, antiinflammatory, and antioxidative activities. It seems these characteristics are related to neutralizing CSCs development, their microenvironment, and metabolism through epigenetic mechanisms. In the current work, the types of epigenetic changes known in these cells are introduced. In addition, some studies about the use of polyphenols acting through a variety of epigenetic mechanisms to counteract these cells will be reviewed. The reported results seem to indicate that the use of these phenolic compounds may be useful for CSCs defeat.

Clinical and Biological Implications of Cancer Stem Cells in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is a malignant tumor with poor prognosis, and is one of the leading causes of cancer-related deaths worldwide. Recently, the development of therapeutic drugs via novel mechanisms of action, involving molecular-targeted drugs and immune checkpoint inhibitors, has progressed in the field of HCC. However, the recurrence rate remains high, and further improvement of the prognosis of patients with HCC is urgently needed. Cancer stem cells (CSCs) are a promising target for further development of novel anti-cancer drugs because they are reportedly involved in tumor initiation, maintenance, recurrence, and resistance to conventional therapies. Although several studies have already been conducted, the functions and roles of CSCs in the development and progression of tumors remain to be elucidated. In this review article, we will clarify the fundamental knowledge of CSCs necessary for the understanding of CSCs and will outline so-far identified markers specific to liver CSCs and the pathological and therapeutic implications of CSCs in HCC.

Mathematical Modeling of Plasticity and Heterogeneity in EMT

The epithelial-mesenchymal transition (EMT) and the corresponding reverse process, mesenchymal-epithelial transition (MET), are dynamic and reversible cellular programs orchestrated by many changes at both biochemical and morphological levels. A recent surge in identifying the molecular mechanisms underlying EMT/MET has led to the development of various mathematical models that have contributed to our improved understanding of dynamics at single-cell and population levels: (a) multi-stability-how many phenotypes can cells attain during an EMT/MET?, (b) reversibility/irreversibility-what time and/or concentration of an EMT inducer marks the "tipping point" when cells induced to undergo EMT cannot revert?, (c) symmetry in EMT/MET-do cells take the same path when reverting as they took during the induction of EMT?, and (d) non-cell autonomous mechanisms-how does a cell undergoing EMT alter the tendency of its neighbors to undergo EMT? These dynamical traits may facilitate a heterogenous response within a cell population undergoing EMT/MET. Here, we present a few examples of designing different mathematical models that can contribute to decoding EMT/MET dynamics.

Stemness regulation of the adrenal mixed corticomedullary tumorigenesis-a case-control study

Mixed corticomedullary tumor is an adrenal tumor intermixed with cortical and medullary cells. It is extremely rare with unclear tumorigenesis. We reported a 32-year-old female, manifested with typical Cushing’s syndrome and hypertension, to be diagnosed with right huge adrenal mixed corticomedullary tumor (8.8 cm). Right adrenalectomy was done to document the tumor intimately admixed with adrenal cortical adenoma and pheochromocytoma by biochemistry and immunohistochemistry. A case-control study was designed to explore the tumorigenesis of mixed corticomedullary tumor by whole exome sequencing. Expression of the stemness markers was controlled by a tissue array of 80 adrenal tumors. Overall, 1559 identical variants coexisted in parts of adrenal cortical adenoma and pheochromocytoma, which mainly (85.8%) originated from germline mutations. These enriched mutations were engaged in stemness control, coherent with substantial expression of the stemness markers (SOX2, CD44 and OCT4) in both parts. The differential stemness expressions were demonstrated in other adrenal tumors as well. The germline mutations were also enriched in signaling involving cancer proliferation, hypoxia inducible factor-1, focal adhesion and extracellular matrix receptor interaction. Somatic mutations affecting mitogen-activated protein kinase signaling, glycolysis and the citrate cycle were found in some tumor elements. This is the first study to verify the rare mixed corticomedullary tumor by molecular and genetic evidence to link with its phenotype. Germline mutations involving the stemness regulation and cancer proliferative signaling may drive intermixed tumor formation. Somatic mutations related to glycolysis and the citrate cycle may contribute to greater tumor outgrowth.

Cancer stem-like cells with hybrid epithelial/mesenchymal phenotype leading the collective invasion

Collective invasion of cancer cells is the key process of circulating tumor cell (CTC) cluster formation, and greatly contributes to metastasis. Cancer stem-like cells (CSC) have a distinct advantage of motility for metastatic dissemination. To verify the role of CSC in the collective invasion, we performed 3D assays to investigate the collective invasion from cancer cell spheroids. The results demonstrated that CSC can significantly promote both collective and single-cell invasion. Further study showed that CSC prefer to move outside and lead the collective invasion. More interestingly, approximately 60% of the leader CSC in collective invasion co-expressed both epithelial and mesenchymal genes, while only 4% co-expressed in single invasive CSC, indicating that CSC with hybrid epithelial/mesenchymal phenotype play a key role in cancer cell collective invasion.

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.

Rapid induction of pancreatic cancer cells to cancer stem cells via heterochromatin modulation

Mounting evidence supports that CSCs (cancer stem cells) play a vital role in cancer recurrence. Therefore elimination of CSCs is currently considered to be an important therapeutic strategy for complete remission. A major obstacle in CSC research is the obtainment of sufficient numbers of functional CSC populations. Here, we established a method to induce bulk pancreatic cancer cells to CSCs via heterochromatin modulation. Two pancreatic cancer cell lines Panc1 and Bxpc3 were cultured for 4 days in inducing medium (mTeSR containing FBS, B27, MEK inhibitor, GSK3 inhibitor, and VPA), and another 2 days in sphere culture medium (mTeSR supplemented with B27). Then the induced cells were dissociated into single cells and cultured in suspension in sphere culture medium. It was found that the majority of induced cells formed spheres which could grow larger and be passaged serially. Characterization of Panc1 sphere cells demonstrated that the sphere cells expressed increased pancreatic cancer stem cell surface markers and stem cell genes, were more resistant to chemotherapy, and were more tumorigenic in vivo, indicating that the induced sphere cells acquired CSC properties. Thus, the inducing method we developed may be used to obtain a sufficient number of CSCs from cancer cells, and contribute to the research for CSC-targeting therapy.

Metabolic regulation of glioma stem-like cells in the tumor micro-environment

Cancer metabolism has emerged as one of the most interesting old ideas being revisited from a new perspective. In the early 20th century Otto Warburg declared metabolism the prime cause in a disease of many secondary causes, and this statement seems more prescient in view of modern expositions into the true nature of tumor evolution. As the complexity of tumor heterogeneity becomes more clear from a genetic perspective, it is important to consider the inevitably heterogeneous metabolic components of the tumor and the tumor microenvironment. High grade gliomas remain one of the most difficult to treat solid tumors, due in part to the highly vascularized nature of the tumor and the maintenance of more resistant stem-like subpopulations within the tumor. Maintenance of glioma stem cells (GSCs) requires specific alterations within the cells and the greater tumor microenvironment with regards to signaling and metabolism. Specific niches within gliomas help foster the survival of stem-like sub-populations of cells with high tumorigenicity and high metabolic plasticity. Understanding these maintenance pathways and the metabolic dependencies within the niche may highlight potential avenues of addressing tumor resistance and recurrence in glioma patients.

Modeling Cancer Cell Growth Dynamics In vitro in Response to Antimitotic Drug Treatment

Investigating the role of intrinsic cell heterogeneity emerging from variations in cell-cycle parameters and apoptosis is a crucial step toward better informing drug administration. Antimitotic agents, widely used in chemotherapy, target exclusively proliferative cells and commonly induce a prolonged mitotic arrest followed by cell death via apoptosis. In this paper, we developed a physiologically motivated mathematical framework for describing cancer cell growth dynamics that incorporates the intrinsic heterogeneity in the time individual cells spend in the cell-cycle and apoptosis process. More precisely, our model comprises two age-structured partial differential equations for the proliferative and apoptotic cell compartments and one ordinary differential equation for the quiescent compartment. To reflect the intrinsic cell heterogeneity that governs the growth dynamics, proliferative and apoptotic cells are structured in "age," i.e., the amount of time remaining to be spent in each respective compartment. In our model, we considered an antimitotic drug whose effect on the cellular dynamics is to induce mitotic arrest, extending the average cell-cycle length. The prolonged mitotic arrest induced by the drug can trigger apoptosis if the time a cell will spend in the cell cycle is greater than the mitotic arrest threshold. We studied the drug's effect on the long-term cancer cell growth dynamics using different durations of prolonged mitotic arrest induced by the drug. Our numerical simulations suggest that at confluence and in the absence of the drug, quiescence is the long-term asymptotic behavior emerging from the cancer cell growth dynamics. This pattern is maintained in the presence of small increases in the average cell-cycle length. However, intermediate increases in cell-cycle length markedly decrease the total number of cells and can drive the cancer population to extinction. Intriguingly, a large "switch-on/switch-off" increase in the average cell-cycle length maintains an active cell population in the long term, with oscillating numbers of proliferative cells and a relatively constant quiescent cell number.

CellTrans: An R Package to Quantify Stochastic Cell State Transitions

Many normal and cancerous cell lines exhibit a stable composition of cells in distinct states which can, e.g., be defined on the basis of cell surface markers. There is evidence that such an equilibrium is associated with stochastic transitions between distinct states. Quantifying these transitions has the potential to better understand cell lineage compositions. We introduce CellTrans, an R package to quantify stochastic cell state transitions from cell state proportion data from fluorescence-activated cell sorting and flow cytometry experiments. The R package is based on a mathematical model in which cell state alterations occur due to stochastic transitions between distinct cell states whose rates only depend on the current state of a cell. CellTrans is an automated tool for estimating the underlying transition probabilities from appropriately prepared data. We point out potential analytical challenges in the quantification of these cell transitions and explain how CellTrans handles them. The applicability of CellTrans is demonstrated on publicly available data on the evolution of cell state compositions in cancer cell lines. We show that CellTrans can be used to (1) infer the transition probabilities between different cell states, (2) predict cell line compositions at a certain time, (3) predict equilibrium cell state compositions, and (4) estimate the time needed to reach this equilibrium. We provide an implementation of CellTrans in R, freely available via GitHub (https://github.com/tbuder/CellTrans).

Novel Small Molecule Inhibitors of Cancer Stem Cell Signaling Pathways

The main aim of oncologists worldwide is to understand and then intervene in the primary tumor initiation and propagation mechanisms. This is essential to allow targeted elimination of cancer cells without altering normal mitotic cells. Currently, there are two main rival theories describing the process of tumorigenesis. According to the Stochastic Model, potentially any cell, once defunct, is capable of initiating carcinogenesis. Alternatively the Cancer Stem Cell (CSC) Model posits that only a small fraction of undifferentiated tumor cells are capable of triggering carcinogenesis. Like healthy stem cells, CSCs are also characterized by a capacity for self-renewal and the ability to generate differentiated progeny, possibly mediating treatment resistance, thus leading to tumor recurrence and metastasis. Moreover, molecular signaling profiles are similar between CSCs and normal stem cells, including Wnt, Notch and Hedgehog pathways. Therefore, development of novel chemotherapeutic agents and proteins (e.g., enzymes and antibodies) specifically targeting CSCs are attractive pharmaceutical candidates. This article describes small molecule inhibitors of stem cell pathways Wnt, Notch and Hedgehog, and their recent chemotherapy clinical trials.

Coupling the modules of EMT and stemness: A tunable 'stemness window' model

Metastasis of carcinoma involves migration of tumor cells to distant organs and initiate secondary tumors. Migration requires a complete or partial Epithelial-to-Mesenchymal Transition (EMT), and tumor-initiation requires cells possessing stemness. Epithelial cells (E) undergoing a complete EMT to become mesenchymal (M) have been suggested to be more likely to possess stemness. However, recent studies suggest that stemness can also be associated with cells undergoing a partial EMT (hybrid E/M phenotype). Therefore, the correlation between EMT and stemness remains elusive. Here, using a theoretical framework that couples the core EMT and stemness modules (miR-200/ZEB and LIN28/let-7), we demonstrate that the positioning of ‘stemness window’ on the ‘EMT axis’ need not be universal; rather it can be fine-tuned. Particularly, we present OVOL as an example of a modulating factor that, due to its coupling with miR-200/ZEB/LIN28/let-7 circuit, fine-tunes the EMT-stemness interplay. Coupling OVOL can inhibit the stemness likelihood of M and elevate that of the hybrid E/M (partial EMT) phenotype, thereby pulling the ‘stemness window’ away from the M end of ‘EMT axis’. Our results unify various apparently contradictory experimental findings regarding the interconnection between EMT and stemness, corroborate the emerging notion that partial EMT associates with stemness, and offer new testable predictions.

Deterministic and Stochastic Study for a Microscopic Angiogenesis Model: Applications to the Lewis Lung Carcinoma

Angiogenesis modelling is an important tool to understand the underlying mechanisms yielding tumour growth. Nevertheless, there is usually a gap between models and experimental data. We propose a model based on the intrinsic microscopic reactions defining the angiogenesis process to link experimental data with previous macroscopic models. The microscopic characterisation can describe the macroscopic behaviour of the tumour, which stability analysis reveals a set of predicted tumour states involving different morphologies. Additionally, the microscopic description also gives a framework to study the intrinsic stochasticity of the reactive system through the resulting Langevin equation. To follow the goal of the paper, we use available experimental information on the Lewis lung carcinoma to infer meaningful parameters for the model that are able to describe the different stages of the tumour growth. Finally we explore the predictive capabilities of the fitted model by showing that fluctuations are determinant for the survival of the tumour during the first week and that available treatments can give raise to new stable tumour dormant states with a reduced vascular network.

Identifying an ovarian cancer cell hierarchy regulated by bone morphogenetic protein 2

Whether human cancer follows a hierarchical or stochastic model of differentiation is controversial. Furthermore, the factors that regulate cancer stem-like cell (CSC) differentiation potential are largely unknown. We used a novel microfluidic single-cell culture method to directly observe the differentiation capacity of four heterogeneous ovarian cancer cell populations defined by the expression of the CSC markers aldehyde dehydrogenase (ALDH) and CD133. We evaluated 3,692 progeny from 2,833 cells. We found that only ALDH(+)CD133(+) cells could generate all four ALDH(+/-)CD133(+/-) cell populations and identified a clear branched differentiation hierarchy. We also observed a single putative stochastic event. Within the hierarchy of cells, bone morphologenetic protein 2 (BMP2) is preferentially expressed in ALDH(-)CD133(-) cells. BMP2 promotes ALDH(+)CD133(+) cell expansion while suppressing the proliferation of ALDH(-)CD133(-) cells. As such, BMP2 suppressed bulk cancer cell growth in vitro but increased tumor initiation rates, tumor growth, and chemotherapy resistance in vivo whereas BMP2 knockdown reduced CSC numbers, in vivo growth, and chemoresistance. These data suggest a hierarchical differentiation pattern in which BMP2 acts as a feedback mechanism promoting ovarian CSC expansion and suppressing progenitor proliferation. These results explain why BMP2 suppresses growth in vitro and promotes growth in vivo. Together, our results support BMP2 as a therapeutic target in ovarian cancer.

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.

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.

Cancer cell metabolism as new targets for novel designed therapies

Metabolic processes are altered in cancer cells, which obtain advantages from this metabolic reprogramming in terms of energy production and synthesis of biomolecules that sustain their uncontrolled proliferation. Due to the conceptual progresses in the last decade, metabolic reprogramming was recently included as one of the new hallmarks of cancer. The advent of high-throughput technologies to amass an abundance of omic data, together with the development of new computational methods that allow the integration and analysis of omic data by using genome-scale reconstructions of human metabolism, have increased and accelerated the discovery and development of anticancer drugs and tumor-specific metabolic biomarkers. Here we review and discuss the latest advances in the context of metabolic reprogramming and the future in cancer research.

Pathophysiology of ocular surface squamous neoplasia

The incidence of ocular surface squamous neoplasia (OSSN) is strongly associated with solar ultraviolet (UV) radiation, HIV and human papilloma virus (HPV). Africa has the highest incidence rates in the world. Most lesions occur at the limbus within the interpalpebral fissure particularly the nasal sector. The nasal limbus receives the highest intensity of sunlight. Limbal epithelial crypts are concentrated nasally and contain niches of limbal epithelial stem cells in the basal layer. It is possible that these are the progenitor cells in OSSN. OSSN arises in the basal epithelial cells spreading towards the surface which resembles the movement of corneo-limbal stem cell progeny before it later invades through the basement membrane below. UV radiation damages DNA producing pyrimidine dimers in the DNA chain. Specific CC → TT base pair dimer transformations of the p53 tumour-suppressor gene occur in OSSN allowing cells with damaged DNA past the G1-S cell cycle checkpoint. UV radiation also causes local and systemic photoimmunosuppression and reactivates latent viruses such as HPV. The E7 proteins of HPV promote proliferation of infected epithelial cells via the retinoblastoma gene while E6 proteins prevent the p53 tumour suppressor gene from effecting cell-cycle arrest of DNA-damaged and infected cells. Immunosuppression from UV radiation, HIV and vitamin A deficiency impairs tumour immune surveillance allowing survival of aberrant cells. Tumour growth and metastases are enhanced by; telomerase reactivation which increases the number of cell divisions a cell can undergo; vascular endothelial growth factor for angiogenesis and matrix metalloproteinases (MMPs) that destroy the intercellular matrix between cells. Despite these potential triggers, the disease is usually unilateral. It is unclear how HPV reaches the conjunctiva.

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.

Stochasticity in plant cellular growth and patterning

Plants, along with other multicellular organisms, have evolved specialized regulatory mechanisms to achieve proper tissue growth and morphogenesis. During development, growing tissues generate specialized cell types and complex patterns necessary for establishing the function of the organ. Tissue growth is a tightly regulated process that yields highly reproducible outcomes. Nevertheless, the underlying cellular and molecular behaviors are often stochastic. Thus, how does stochasticity, together with strict genetic regulation, give rise to reproducible tissue development? This review draws examples from plants as well as other systems to explore stochasticity in plant cell division, growth, and patterning. We conclude that stochasticity is often needed to create small differences between identical cells, which are amplified and stabilized by genetic and mechanical feedback loops to begin cell differentiation. These first few differentiating cells initiate traditional patterning mechanisms to ensure regular development.