Systems biology of cancer: entropy, disorder, and selection-driven evolution to independence, invasion and "swarm intelligence"

Unveiling the Dynamics behind Glioblastoma Multiforme Single-Cell Data Heterogeneity

Glioblastoma Multiforme is a brain tumor distinguished by its aggressiveness. We suggested that this aggressiveness leads single-cell RNA-sequence data (scRNA-seq) to span a representative portion of the cancer attractors domain. This conjecture allowed us to interpret the scRNA-seq heterogeneity as reflecting a representative trajectory within the attractor's domain. We considered factors such as genomic instability to characterize the cancer dynamics through stochastic fixed points. The fixed points were derived from centroids obtained through various clustering methods to verify our method sensitivity. This methodological foundation is based upon sample and time average equivalence, assigning an interpretative value to the data cluster centroids and supporting parameters estimation. We used stochastic simulations to reproduce the dynamics, and our results showed an alignment between experimental and simulated dataset centroids. We also computed the Waddington landscape, which provided a visual framework for validating the centroids and standard deviations as characterizations of cancer attractors. Additionally, we examined the stability and transitions between attractors and revealed a potential interplay between subtypes. These transitions might be related to cancer recurrence and progression, connecting the molecular mechanisms of cancer heterogeneity with statistical properties of gene expression dynamics. Our work advances the modeling of gene expression dynamics and paves the way for personalized therapeutic interventions.

Omics data integration suggests a potential idiopathic Parkinson's disease signature

The vast majority of Parkinson's disease cases are idiopathic. Unclear etiology and multifactorial nature complicate the comprehension of disease pathogenesis. Identification of early transcriptomic and metabolic alterations consistent across different idiopathic Parkinson's disease (IPD) patients might reveal the potential basis of increased dopaminergic neuron vulnerability and primary disease mechanisms. In this study, we combine systems biology and data integration approaches to identify differences in transcriptomic and metabolic signatures between IPD patient and healthy individual-derived midbrain neural precursor cells. Characterization of gene expression and metabolic modeling reveal pyruvate, several amino acid and lipid metabolism as the most dysregulated metabolic pathways in IPD neural precursors. Furthermore, we show that IPD neural precursors endure mitochondrial metabolism impairment and a reduced total NAD pool. Accordingly, we show that treatment with NAD precursors increases ATP yield hence demonstrating a potential to rescue early IPD-associated metabolic changes.

Delineating intra-tumoral heterogeneity and tumor evolution in breast cancer using precision-based approaches

The burden of breast cancer continues to increase worldwide as it remains the most diagnosed tumor in females and the second leading cause of cancer-related deaths. Breast cancer is a heterogeneous disease characterized by different subtypes which are driven by aberrations in key genes such as BRCA1 and BRCA2, and hormone receptors. However, even within each subtype, heterogeneity that is driven by underlying evolutionary mechanisms is suggested to underlie poor response to therapy, variance in disease progression, recurrence, and relapse. Intratumoral heterogeneity highlights that the evolvability of tumor cells depends on interactions with cells of the tumor microenvironment. The complexity of the tumor microenvironment is being unraveled by recent advances in screening technologies such as high throughput sequencing; however, there remain challenges that impede the practical use of these approaches, considering the underlying biology of the tumor microenvironment and the impact of selective pressures on the evolvability of tumor cells. In this review, we will highlight the advances made thus far in defining the molecular heterogeneity in breast cancer and the implications thereof in diagnosis, the design and application of targeted therapies for improved clinical outcomes. We describe the different precision-based approaches to diagnosis and treatment and their prospects. We further propose that effective cancer diagnosis and treatment are dependent on unpacking the tumor microenvironment and its role in driving intratumoral heterogeneity. Underwriting such heterogeneity are Darwinian concepts of natural selection that we suggest need to be taken into account to ensure evolutionarily informed therapeutic decisions.

Effects of Carbon Nanomaterials and Aloe vera on Melanomas-Where Are We? Recent Updates

Melanoma is an aggressive skin cancer that affects approximately 140,000 people worldwide each year, with a high fatality rate. Available treatment modalities show limited efficacy in more severe cases. Hence, the search for new treatment modalities, including immunotherapies, for curing, mitigating, and/or preventing cancer is important and urgently needed. Carbon nanoparticles associated with some plant materials, such as Aloe vera, have shown appealing antineoplastic activity, derived mainly from the compounds aloin, aloe-emodin, barbaloin acemannan, and octapeptide, thus representing new possibilities as antitumor agents. This systematic review aims to arouse interest and present the possibilities of using Aloe vera combined with carbon-based nanomaterials as an antineoplastic agent in the treatment and prevention of melanoma. Limitations and advances in melanoma treatment using functionalized carbon nanomaterials are discussed here. Moreover, this review provides the basis for further studies designed to fully explore the potential of carbon nanomaterials associated with Aloe vera in the treatment of various cancers, with a focus on melanoma.

The Applicability of the Poincaré Plot in the Analysis of Variability of Reaction Time during Serial Testing

(1) Background: This study aims to put into evince the relationship between the variability of the reaction time (RT) during repeated testing, expressed through indicators extracted by the Poincaré plot method, and the age of the participants, their self-reported health (SRH), and level of perceived anxiety. (2) Methods: The study was performed using computerized RT testing software. An observational cross-sectional study was performed on a group of 120 subjects (mean age 42.33 ± 21.12 years), sex ratio men to women 1.14:1. Data were processed through descriptive and inferential statistics. The Poincaré plot method was applied in the analysis of the RT series of data, by calculating the indicators SD₁, SD₂, SD₁/SD₂, and area of the fitting ellipse (AFE) (3) Results: We provided evidence of the excellent reliability of the web-based RT serial testing (Cronbach’s Alpha 0.991) with this sample group. Our results showed that age is an important predictor for mean values of RT, while SD₁, SD₂, and AFE indicators are for SRH (p < 0.01). (4) Conclusions: the variability of RT, expressed by the Poincaré plot indicators, reflects the health status rather than the aging of the subjects and is barely influenced by their level of anxiety.

Concepts and Applications of Information Theory to Immuno-Oncology

Recent successes of immune-modulating therapies for cancer have stimulated research on information flow within the immune system and, in turn, clinical applications of concepts from information theory. Through information theory, one can describe and formalize, in a mathematically rigorous fashion, the function of interconnected components of the immune system in health and disease. Specifically, using concepts including entropy, mutual information, and channel capacity, one can quantify the storage, transmission, encoding, and flow of information within and between cellular components of the immune system on multiple temporal and spatial scales. To understand, at the quantitative level, immune signaling function and dysfunction in cancer, we present a methodology-oriented review of information-theoretic treatment of biochemical signal transduction and transmission coupled with mathematical modeling.

eIF4E S209 phosphorylation licenses myc- and stress-driven oncogenesis

To better understand a role of eIF4E S209 in oncogenic translation, we generated EIF4E^S209A/+ heterozygous knockin (4EKI) HCT 116 human colorectal cancer (CRC) cells. 4EKI had little impact on total eIF4E levels, cap binding or global translation, but markedly reduced HCT 116 cell growth in spheroids and mice, and CRC organoid growth. 4EKI strongly inhibited Myc and ATF4 translation, the integrated stress response (ISR)-dependent glutamine metabolic signature, AKT activation and proliferation in vivo. 4EKI inhibited polyposis in Apc^Min/+ mice by suppressing Myc protein and AKT activation. Furthermore, p-eIF4E was highly elevated in CRC precursor lesions in mouse and human. p-eIF4E cooperated with mutant KRAS to promote Myc and ISR-dependent glutamine addiction in various CRC cell lines, characterized by increased cell death, transcriptomic heterogeneity and immune suppression upon deprivation. These findings demonstrate a critical role of eIF4E S209-dependent translation in Myc and stress-driven oncogenesis and as a potential therapeutic vulnerability.

Identification of miRNA Master Regulators in Breast Cancer

Breast cancer is the neoplasm with the highest number of deaths in women. Although the molecular mechanisms associated with the development of this tumor have been widely described, metastatic disease has a high mortality rate. In recent years, several studies show that microRNAs or miRNAs regulate complex processes in different biological systems including cancer. In the present work, we describe a group of 61 miRNAs consistently over-expressed in breast cancer (BC) samples that regulate the breast cancer transcriptome. By means of data mining from TCGA, miRNA and mRNA sequencing data corresponding to 1091 BC patients and 110 normal adjacent tissues were downloaded and a miRNA-mRNA network was inferred. Calculations of their oncogenic activity demonstrated that they were involved in the regulation of classical cancer pathways such as cell cycle, PI3K-AKT, DNA repair, and k-Ras signaling. Using univariate and multivariate analysis, we found that five of these miRNAs could be used as biomarkers for the prognosis of overall survival. Furthermore, we confirmed the over-expression of two of them in 56 locally advanced BC samples obtained from the histopathological archive of the National Cancer Institute of Mexico, showing concordance with our previous bioinformatic analysis.

ATP-Dependent Diffusion Entropy and Homogeneity in Living Cells

Intracellular dynamics is highly complex, and includes diffusion of poly-dispersed objects in a non-homogeneous, out-of-equilibrium medium. Assuming non-equilibrium steady-state, we developed a framework that relates non-equilibrium fluctuations to diffusion, and generalized entropy in cells. We employed imaging of live Jurkat T cells, and showed that active cells have higher diffusion parameters (K_α and α) and entropy relative to the same cells after ATP depletion. K_α and α were related in ATP-depleted cells while this relation was not apparent in untreated cells, probably due to non-equilibrium applied work. Next we evaluated the effect of intracellular diffusion and entropy on the cell content homogeneity, which was displayed by the extent of its liquid–liquid phase separation (LLPS). Correlations between intracellular diffusion parameters, entropy and cell homogeneity could be demonstrated only in active cells while these correlations disappeared after ATP depletion. We conclude that non-equilibrium contributions to diffusivity and entropy by ATP-dependent mechanical work allow cells to control their content homogeneity and LLPS state. Such understanding may enable better intervention in extreme LLPS conditions associated with various cell malignancies and degenerative diseases.

Next Generation Networks: Featuring the Potential Role of Emerging Applications in Translational Oncology

Nowadays, networks are pervasively used as examples of models suitable to mathematically represent and visualize the complexity of systems associated with many diseases, including cancer. In the cancer context, the concept of network entropy has guided many studies focused on comparing equilibrium to disequilibrium (i.e., perturbed) conditions. Since these conditions reflect both structural and dynamic properties of network interaction maps, the derived topological characterizations offer precious support to conduct cancer inference. Recent innovative directions have emerged in network medicine addressing especially experimental omics approaches integrated with a variety of other data, from molecular to clinical and also electronic records, bioimaging etc. This work considers a few theoretically relevant concepts likely to impact the future of applications in personalized/precision/translational oncology. The focus goes to specific properties of networks that are still not commonly utilized or studied in the oncological domain, and they are: controllability, synchronization and symmetry. The examples here provided take inspiration from the consideration of metastatic processes, especially their progression through stages and their hallmark characteristics. Casting these processes into computational frameworks and identifying network states with specific modular configurations may be extremely useful to interpret or even understand dysregulation patterns underlying cancer, and associated events (onset, progression) and disease phenotypes.

Biomaterial Scaffolds Recruit an Aggressive Population of Metastatic Tumor Cells In Vivo

For most cancers, metastasis is the point at which clinical treatment shifts from curative intent to extending survival. Biomaterial implants acting as a synthetic premetastatic niche recruit metastatic cancer cells and provide a survival advantage, and their use as a diagnostic platform requires assessing their relevance to disease progression. Here, we showed that scaffold-captured tumor cells (SCAF) were 30 times more metastatic to the lung than primary tumor (PT) cells, similar to cells derived from lung micrometastases (LUNG). SCAF cells were more aggressive in vitro, demonstrated higher levels of migration, invasion, and mammosphere formation, and had a greater proportion of cancer stem cells than PT. SCAF cells were highly enriched for gene expression signatures associated with metastasis and had associated genomic structural changes, including globally enhanced entropy. Collectively, our findings demonstrate that SCAF cells are distinct from PT and more closely resemble LUNG, indicating that tumor cells retrieved from scaffolds are reflective of cells at metastatic sites. SIGNIFICANCE: These findings suggest that metastatic tumor cells captured by a biomaterial scaffold may serve as a diagnostic for molecular staging of metastasis.Graphical Abstract: http://cancerres.aacrjournals.org/content/canres/79/8/2042/F1.large.jpg.

Cancer heterogeneity is not compatible with one unique cancer cell metabolic map

The Warburg effect and its accompanying metabolic features (anaplerosis, cataplerosis) are presented in textbooks and reviews as a hallmark (general characteristic): the metabolic map of cancer. On the other hand, research articles on specific tumors since a few years emphasize various biological features of different cancers, different cells in a cancer and the dynamic heterogeneity of these cells. We have analysed the research literature of the subject and show the generality of a dynamic, evolving biological and metabolic, spatial and temporal heterogeneity of individual cancers. We conclude that there is no one metabolic map of cancer but several and describe the two extremes of a panel from the hypoxic to the normoxic state. The implications for the significance of general 'omic' studies, and on therapeutic conclusions drawn from them and for the diagnostic use of fractional biopsies is discussed.

Mammographic texture and risk of breast cancer by tumor type and estrogen receptor status

Background

Several studies have shown that mammographic texture features are associated with breast cancer risk independent of the contribution of breast density. Thus, texture features may provide novel information for risk stratification. We examined the association of a set of established texture features with breast cancer risk by tumor type and estrogen receptor (ER) status, accounting for breast density.

Methods

This study combines five case–control studies including 1171 breast cancer cases and 1659 controls matched for age, date of mammogram, and study. Mammographic breast density and 46 breast texture features, including first- and second-order features, Fourier transform, and fractal dimension analysis, were evaluated from digitized film-screen mammograms. Logistic regression models evaluated each normalized feature with breast cancer after adjustment for age, body mass index, first-degree family history, percent density, and study.

Results

Of the mammographic features analyzed, fractal dimension and second-order statistics features were significantly associated (p < 0.05) with breast cancer. Fractal dimensions for the thresholds equal to 10% and 15% (FD_TH10 and FD_TH15) were associated with an increased risk of breast cancer while thresholds from 60% to 85% (FD_TH60 to FD_TH85) were associated with a decreased risk. Increasing the FD_TH75 and Energy feature values were associated with a decreased risk of breast cancer while increasing Entropy was associated with a decreased risk of breast cancer. For example, 1 standard deviation increase of FD_TH75 was associated with a 13% reduced risk of breast cancer (odds ratio = 0.87, 95% confidence interval 0.79–0.95). Overall, the direction of associations between features and ductal carcinoma in situ (DCIS) and invasive cancer, and estrogen receptor positive and negative cancer were similar.

Conclusion

Mammographic features derived from film-screen mammograms are associated with breast cancer risk independent of percent mammographic density. Some texture features also demonstrated associations for specific tumor types. For future work, we plan to assess risk prediction combining mammographic density and features assessed on digital images.

Electronic supplementary material

The online version of this article (doi:10.1186/s13058-016-0778-1) contains supplementary material, which is available to authorized users.

Origin of Cancer: An Information, Energy, and Matter Disease

Cells are open, highly ordered systems that are far away from equilibrium. For this reason, the first function of any cell is to prevent the permanent threat of disintegration that is described by thermodynamic laws and to preserve highly ordered cell characteristics such as structures, the cell cycle, or metabolism. In this context, three basic categories play a central role: energy, information, and matter. Each of these three categories is equally important to the cell and they are reciprocally dependent. We therefore suggest that energy loss (e.g., through impaired mitochondria) or disturbance of information (e.g., through mutations or aneuploidy) or changes in the composition or distribution of matter (e.g., through micro-environmental changes or toxic agents) can irreversibly disturb molecular mechanisms, leading to increased local entropy of cellular functions and structures. In terms of physics, changes to these normally highly ordered reaction probabilities lead to a state that is irreversibly biologically imbalanced, but that is thermodynamically more stable. This primary change—independent of the initiator—now provokes and drives a complex interplay between the availability of energy, the composition, and distribution of matter and increasing information disturbance that is dependent upon reactions that try to overcome or stabilize this intracellular, irreversible disorder described by entropy. Because a return to the original ordered state is not possible for thermodynamic reasons, the cells either die or else they persist in a metastable state. In the latter case, they enter into a self-driven adaptive and evolutionary process that generates a progression of disordered cells and that results in a broad spectrum of progeny with different characteristics. Possibly, 1 day, one of these cells will show an autonomous and aggressive behavior—it will be a cancer cell.

The rich phase structure of a mutator model

We propose a modification of the Crow-Kimura and Eigen models of biological molecular evolution to include a mutator gene that causes both an increase in the mutation rate and a change in the fitness landscape. This mutator effect relates to a wide range of biomedical problems. There are three possible phases: mutator phase, mixed phase and non-selective phase. We calculate the phase structure, the mean fitness and the fraction of the mutator allele in the population, which can be applied to describe cancer development and RNA viruses. We find that depending on the genome length, either the normal or the mutator allele dominates in the mixed phase. We analytically solve the model for a general fitness function. We conclude that the random fitness landscape is an appropriate choice for describing the observed mutator phenomenon in the case of a small fraction of mutators. It is shown that the increase in the mutation rates in the regular and the mutator parts of the genome should be set independently; only some combinations of these increases can push the complex biomedical system to the non-selective phase, potentially related to the eradication of tumors.

Transcriptomic Heterogeneity in Cancer as a Consequence of Dysregulation of the Gene-Gene Interaction Network

Many pathways are dysregulated in cancer. Dysregulation of the regulatory network results in less control of transcript levels in the cell. Hence, dysregulation is reflected in the heterogeneity of the transcriptome: the more dysregulated the pathway, the more the transcriptomic heterogeneity. We identify four scenarios for a transcriptomic heterogeneity increase (i.e., pathway dysregulation) in cancer: (1) activation of a molecular switch, (2) a structural change in a regulator, (3) a temporal change in a regulator, and (4) weakening of gene–gene interactions. These mechanisms are statistically motivated, explored in silico, and their plausibility to occur in vivo illustrated by means of oncogenomics data of breast cancer studies.

Isolation of circulating tumor cells by dielectrophoresis

Dielectrophoresis (DEP) is an electrokinetic method that allows intrinsic dielectric properties of suspended cells to be exploited for discrimination and separation. It has emerged as a promising method for isolating circulation tumor cells (CTCs) from blood. DEP-isolation of CTCs is independent of cell surface markers. Furthermore, isolated CTCs are viable and can be maintained in culture, suggesting that DEP methods should be more generally applicable than antibody-based approaches. The aim of this article is to review and synthesize for both oncologists and biomedical engineers interested in CTC isolation the pertinent characteristics of DEP and CTCs. The aim is to promote an understanding of the factors involved in realizing DEP-based instruments having both sufficient discrimination and throughput to allow routine analysis of CTCs in clinical practice. The article brings together: (a) the principles of DEP; (b) the biological basis for the dielectric differences between CTCs and blood cells; (c) why such differences are expected to be present for all types of tumors; and (d) instrumentation requirements to process 10 mL blood specimens in less than 1 h to enable routine clinical analysis. The force equilibrium method of dielectrophoretic field-flow fractionation (DEP-FFF) is shown to offer higher discrimination and throughput than earlier DEP trapping methods and to be applicable to clinical studies.

Cancer stem cells, a fuzzy evolving concept: a cell population or a cell property?

The cancer stem cells (CSC) hypothesis represents a pathological extrapolation of the physiological concept of embryonic and somatic stem cells. In its initial definition, it encompassed the hypothesis of a qualitatively distinct population of immortal cancer cells originating from somatic stem cells, which generate in xenotransplants by a deterministic irreversible process, the hierarchy of more differentiated finite lifespan derived cells, which constitute, themselves, the bulk of the cancer. These CSC would express specific biomarkers and gene expressions related to chemo- and radioresistance, stemness, epithelial–mesenchymal transition, etc.

No convincing congruence of several of these properties in one cell population has been demonstrated. The concept has greatly evolved with time and with different authors (“the plasticity of cancer stem cells”), leading to a minimal definition of cells generating a hierarchy of derived cells. In this article these concepts are analyzed. It is proposed that stemness is a property, more or less reversible, a hallmark of some cells at some time in a cancer cell population, as immortality, dormancy, chemo- or radioresistance, epithelial–mesenchymal transition etc. These phenotypic properties represent the result of independent, linked, or more or less congruent, genetic, epigenetic, or signaling programs.