Thermodynamic energetics underlying genomic instability and whole-genome doubling in cancer.

Genomic instability contributes to tumorigenesis through the amplification and deletion of cancer driver genes. DNA copy number (CN) profiling of ensembles of tumors allows a thermodynamic analysis of the profile for each tumor. The free energy of the distribution of CNs is found to be a monotonically increasing function of the average chromosomal ploidy. The dependence is universal across several cancer types. Surprisal analysis distinguishes two main known subgroups: tumors with cells that have or have not undergone whole-genome duplication (WGD). The analysis uncovers that CN states having a narrower distribution are energetically more favorable toward the WGD transition. Surprisal analysis also determines the deviations from a fully stable-state distribution. These deviations reflect constraints imposed by tumor fitness selection pressures. The results point to CN changes that are more common in high-ploidy tumors and thus support altered selection pressures upon WGD.

Intracellular redox potential is correlated with miRNA expression in MCF7 cells under hypoxic conditions.

Hypoxia is a ubiquitous feature of cancers, encouraging glycolytic metabolism, proliferation, and resistance to therapy. Nonetheless, hypoxia is a poorly defined term with confounding features described in the literature. Redox biology provides an important link between the external cellular microenvironment and the cell's response to changing oxygen pressures. In this paper, we demonstrate a correlation between intracellular redox potential (measured using optical nanosensors) and the concentrations of microRNAs (miRNAs) involved in the cell's response to changes in oxygen pressure. The correlations were established using surprisal analysis (an approach derived from thermodynamics and information theory). We found that measured redox potential changes reflect changes in the free energy computed by surprisal analysis of miRNAs. Furthermore, surprisal analysis identified groups of miRNAs, functionally related to changes in proliferation and metastatic potential that played the most significant role in the cell's response to changing oxygen pressure.

Personalized disease signatures through information-theoretic compaction of big cancer data.

Every individual cancer develops and grows in its own specific way, giving rise to a recognized need for the development of personalized cancer diagnostics. This suggested that the identification of patient-specific oncogene markers would be an effective diagnostics approach. However, tumors that are classified as similar according to the expression levels of certain oncogenes can eventually demonstrate divergent responses to treatment. This implies that the information gained from the identification of tumor-specific biomarkers is still not sufficient. We present a method to quantitatively transform heterogeneous big cancer data to patient-specific transcription networks. These networks characterize the unbalanced molecular processes that deviate the tissue from the normal state. We study a number of datasets spanning five different cancer types, aiming to capture the extensive interpatient heterogeneity that exists within a specific cancer type as well as between cancers of different origins. We show that a relatively small number of altered molecular processes suffices to accurately characterize over 500 tumors, showing extreme compaction of the data. Every patient is characterized by a small specific subset of unbalanced processes. We validate the result by verifying that the processes identified characterize other cancer patients as well. We show that different patients may display similar oncogene expression levels, albeit carrying biologically distinct tumors that harbor different sets of unbalanced molecular processes. Thus, tumors may be inaccurately classified and addressed as similar. These findings highlight the need to expand the notion of tumor-specific oncogenic biomarkers to patient-specific, comprehensive transcriptional networks for improved patient-tailored diagnostics.

Intercellular signaling through secreted proteins induces free-energy gradient-directed cell movement.

Controlling cell migration is important in tissue engineering and medicine. Cell motility depends on factors such as nutrient concentration gradients and soluble factor signaling. In particular, cell-cell signaling can depend on cell-cell separation distance and can influence cellular arrangements in bulk cultures. Here, we seek a physical-based approach, which identifies a potential governed by cell-cell signaling that induces a directed cell-cell motion. A single-cell barcode chip (SCBC) was used to experimentally interrogate secreted proteins in hundreds of isolated glioblastoma brain cancer cell pairs and to monitor their relative motions over time. We used these trajectories to identify a range of cell-cell separation distances where the signaling was most stable. We then used a thermodynamics-motivated analysis of secreted protein levels to characterize free-energy changes for different cell-cell distances. We show that glioblastoma cell-cell movement can be described as Brownian motion biased by cell-cell potential. To demonstrate that the free-energy potential as determined by the signaling is the driver of motion, we inhibited two proteins most involved in maintaining the free-energy gradient. Following inhibition, cell pairs showed an essentially random Brownian motion, similar to the case for untreated, isolated single cells.

A Thermodynamic-Based Interpretation of Protein Expression Heterogeneity in Different Glioblastoma Multiforme Tumors Identifies Tumor-Specific Unbalanced Processes.

We describe a thermodynamic-motivated, information theoretic analysis of proteomic data collected from a series of 8 glioblastoma multiforme (GBM) tumors. GBMs are considered here as prototypes of heterogeneous cancers. That heterogeneity is viewed here as manifesting in different unbalanced biological processes that are associated with thermodynamic-like constraints. The analysis yields a molecular description of a stable steady state that is common across all tumors. It also resolves molecular descriptions of unbalanced processes that are shared by several tumors, such as hyperactivated phosphoprotein signaling networks. Further, it resolves unbalanced processes that provide unique classifiers of tumor subgroups. The results of the theoretical interpretation are compared against those of statistical multivariate methods and are shown to provide a superior level of resolution for identifying unbalanced processes in GBM tumors. The identification of specific constraints for each GBM tumor suggests tumor-specific combination therapies that may reverse this imbalance.

Statistical thermodynamics of transcription profiles in normal development and tumorigeneses in cohorts of patients.

Experimental biology is providing the distribution of numerous different biological molecules inside cells and in body fluids of patients. Statistical methods of analysis have very successfully examined these rather large databases. We seek to use a thermodynamic analysis to provide a physical understanding and quantitative characterization of human cancers and other pathologies within a molecule-centered approach. The key technical development is the introduction of a Lagrangian. By imposing constraints the minimal value of the Lagrangian defines a thermodynamically stable state of the cellular system. The minimization also allows using experimental data measured at a number of different conditions to evaluate the steady-state distribution of biomolecules such as messenger RNAs. Thereby the number of effectively accessible quantum states of biomolecules is determined from the experimentally measured expression levels. With the increased resolution provided by the minimization of the Lagrangian one can differentiate between normal and diseased patients and further between disease subtypes. Each such refinement corresponds to imposing an additional constraint of biological origin. The constraints are the unbalanced ongoing biological processes in the system. MicroRNA expression level data for control and diseased lung cancer patients are analyzed as an example.

Computational surprisal analysis speeds-up genomic characterization of cancer processes.

Surprisal analysis is increasingly being applied for the examination of transcription levels in cellular processes, towards revealing inner network structures and predicting response. But to achieve its full potential, surprisal analysis should be integrated into a wider range computational tool. The purposes of this paper are to combine surprisal analysis with other important computation procedures, such as easy manipulation of the analysis results--e.g. to choose desirable result sub-sets for further inspection--, retrieval and comparison with relevant datasets from public databases, and flexible graphical displays for heuristic thinking. The whole set of computation procedures integrated into a single practical tool is what we call Computational Surprisal Analysis. This combined kind of analysis should facilitate significantly quantitative understanding of different cellular processes for researchers, including applications in proteomics and metabolomics. Beyond that, our vision is that Computational Surprisal Analysis has the potential to reach the status of a routine method of analysis for practitioners. The resolving power of Computational Surprisal Analysis is here demonstrated by its application to a variety of cellular cancer process transcription datasets, ours and from the literature. The results provide a compact biological picture of the thermodynamic significance of the leading gene expression phenotypes in every stage of the disease. For each transcript we characterize both its inherent steady state weight, its correlation with the other transcripts and its variation due to the disease. We present a dedicated website to facilitate the analysis for researchers and practitioners.

Surprisal analysis characterizes the free energy time course of cancer cells undergoing epithelial-to-mesenchymal transition.

The epithelial-to-mesenchymal transition (EMT) initiates the invasive and metastatic behavior of many epithelial cancers. Mechanisms underlying EMT are not fully known. Surprisal analysis of mRNA time course data from lung and pancreatic cancer cells stimulated to undergo TGF-ß1-induced EMT identifies two phenotypes. Examination of the time course for these phenotypes reveals that EMT reprogramming is a multistep process characterized by initiation, maturation, and stabilization stages that correlate with changes in cell metabolism. Surprisal analysis characterizes the free energy time course of the expression levels throughout the transition in terms of two state variables. The landscape of the free energy changes during the EMT for the lung cancer cells shows a stable intermediate state. Existing data suggest this is the previously proposed maturation stage. Using a single-cell ATP assay, we demonstrate that the TGF-ß1-induced EMT for lung cancer cells, particularly during the maturation stage, coincides with a metabolic shift resulting in increased cytosolic ATP levels. Surprisal analysis also characterizes the absolute expression levels of the mRNAs and thereby examines the homeostasis of the transcription system during EMT.

Glioblastoma cellular architectures are predicted through the characterization of two-cell interactions.

To understand how pairwise cellular interactions influence cellular architectures, we measured the levels of functional proteins associated with EGF receptor (EGFR) signaling in pairs of U87EGFR variant III oncogene receptor cells (U87EGFRvIII) at varying cell separations. Using a thermodynamics-derived approach we analyzed the cell-separation dependence of the signaling stability, and identified that the stable steady state of EGFR signaling exists when two U87EGFRvIII cells are separated by 80-100 µm. This distance range was verified as the characteristic intercellular separation within bulk cell cultures. EGFR protein network signaling coordination for the U87EGFRvIII system was lowest at the stable state and most similar to isolated cell signaling. Measurements of cultures of less tumorigenic U87PTEN cells were then used to correctly predict that stable EGFR signaling occurs for those cells at smaller cell-cell separations. The intimate relationship between functional protein levels and cellular architectures explains the scattered nature of U87EGFRvIII cells relative to U87PTEN cells in glioblastoma multiforme tumors.

miRNA and mRNA cancer signatures determined by analysis of expression levels in large cohorts of patients.

Toward identifying a cancer-specific gene signature we applied surprisal analysis to the RNAs expression behavior for a large cohort of breast, lung, ovarian, and prostate carcinoma patients. We characterize the cancer phenotypic state as a shared response of a set of mRNA or microRNAs (miRNAs) in cancer patients versus noncancer controls. The resulting signature is robust with respect to individual patient variability and distinguishes with high fidelity between cancer and noncancer patients. The mRNAs and miRNAs that are implicated in the signature are correlated and are known to contribute to the regulation of cancer-signaling pathways. The miRNA and mRNA networks are common to the noncancer and cancer patients, but the disease modulates the strength of the connectivities. Furthermore, we experimentally assessed the cancer-specific signatures as possible therapeutic targets. Specifically we restructured a single dominant connectivity in the cancer-specific gene network in vitro. We find a deflection from the cancer phenotype, significantly reducing cancer cell proliferation and altering cancer cellular physiology. Our approach is grounded in thermodynamics augmented by information theory. The thermodynamic reasoning is demonstrated to ensure that the derived signature is bias-free and shows that the most significant redistribution of free energy occurs in programming a system between the noncancer and cancer states. This paper introduces a platform that can elucidate miRNA and mRNA behavior on a systems level and provides a comprehensive systematic view of both the energetics of the expression levels of RNAs and of their changes during tumorigenicity.

Hypoxia induces a phase transition within a kinase signaling network in cancer cells.

Hypoxia is a near-universal feature of cancer, promoting glycolysis, cellular proliferation, and angiogenesis. The molecular mechanisms of hypoxic signaling have been intensively studied, but the impact of changes in oxygen partial pressure (pO2) on the state of signaling networks is less clear. In a glioblastoma multiforme (GBM) cancer cell model, we examined the response of signaling networks to targeted pathway inhibition between 21% and 1% pO2. We used a microchip technology that facilitates quantification of a panel of functional proteins from statistical numbers of single cells. We find that near 1.5% pO2, the signaling network associated with mammalian target of rapamycin (mTOR) complex 1 (mTORC1)--a critical component of hypoxic signaling and a compelling cancer drug target--is deregulated in a manner such that it will be unresponsive to mTOR kinase inhibitors near 1.5% pO2, but will respond at higher or lower pO2 values. These predictions were validated through experiments on bulk GBM cell line cultures and on neurosphere cultures of a human-origin GBM xenograft tumor. We attempt to understand this behavior through the use of a quantitative version of Le Chatelier's principle, as well as through a steady-state kinetic model of protein interactions, both of which indicate that hypoxia can influence mTORC1 signaling as a switch. The Le Chatelier approach also indicates that this switch may be thought of as a type of phase transition. Our analysis indicates that certain biologically complex cell behaviors may be understood using fundamental, thermodynamics-motivated principles.

On a fundamental structure of gene networks in living cells.

Computers are organized into hardware and software. Using a theoretical approach to identify patterns in gene expression in a variety of species, organs, and cell types, we found that biological systems similarly are comprised of a relatively unchanging hardware-like gene pattern. Orthogonal patterns of software-like transcripts vary greatly, even among tumors of the same type from different individuals. Two distinguishable classes could be identified within the hardware-like component: those transcripts that are highly expressed and stable and an adaptable subset with lower expression that respond to external stimuli. Importantly, we demonstrate that this structure is conserved across organisms. Deletions of transcripts from the highly stable core are predicted to result in cell mortality. The approach provides a conceptual thermodynamic-like framework for the analysis of gene-expression levels and networks and their variations in diseased cells.

Protein signaling networks from single cell fluctuations and information theory profiling.

Protein signaling networks among cells play critical roles in a host of pathophysiological processes, from inflammation to tumorigenesis. We report on an approach that integrates microfluidic cell handling, in situ protein secretion profiling, and information theory to determine an extracellular protein-signaling network and the role of perturbations. We assayed 12 proteins secreted from human macrophages that were subjected to lipopolysaccharide challenge, which emulates the macrophage-based innate immune responses against Gram-negative bacteria. We characterize the fluctuations in protein secretion of single cells, and of small cell colonies (n = 2, 3,···), as a function of colony size. Measuring the fluctuations permits a validation of the conditions required for the application of a quantitative version of the Le Chatelier's principle, as derived using information theory. This principle provides a quantitative prediction of the role of perturbations and allows a characterization of a protein-protein interaction network.

Convergence of logic of cellular regulation in different premalignant cells by an information theoretic approach.

BACKGROUND: Surprisal analysis is a thermodynamic-like molecular level approach that identifies biological constraints that prevents the entropy from reaching its maximum. To examine the significance of altered gene expression levels in tumorigenesis we apply surprisal analysis to the WI-38 model through its precancerous states. The constraints identified by the analysis are transcription patterns underlying the process of transformation. Each pattern highlights the role of a group of genes that act coherently to define a transformed phenotype. RESULTS: We identify a major transcription pattern that represents a contraction of signaling networks accompanied by induction of cellular proliferation and protein metabolism, which is essential for full transformation. In addition, a more minor, "tumor signature" transcription pattern completes the transformation process. The variation with time of the importance of each transcription pattern is determined. Midway through the transformation, at the stage when cells switch from slow to fast growth rate, the major transcription pattern undergoes a total inversion of its weight while the more minor pattern does not contribute before that stage. CONCLUSIONS: A similar network reorganization occurs in two very different cellular transformation models: WI-38 and the cervical cancer HF1 models. Our results suggest that despite differences in a list of transcripts expressed in different cancer models the rationale of the network reorganization remains essentially the same.

All-DNA finite-state automata with finite memory.

Biomolecular logic devices can be applied for sensing and nano-medicine. We built three DNA tweezers that are activated by the inputs H(+)/OH(-); ; nucleic acid linker/complementary antilinker to yield a 16-states finite-state automaton. The outputs of the automata are the configuration of the respective tweezers (opened or closed) determined by observing fluorescence from a fluorophore/quencher pair at the end of the arms of the tweezers. The system exhibits a memory because each current state and output depend not only on the source configuration but also on past states and inputs.

Information-theoretic analysis of phenotype changes in early stages of carcinogenesis.

Cancer is a multistep process characterized by altered signal transduction, cell growth, and metabolism. To identify such processes in early carcinogenesis we use an information theoretic approach to characterize gene expression quantified as mRNA levels in primary keratinocytes (K) and human papillomavirus 16 (HPV16)-transformed keratinocytes (HF1 cells) from early (E) and late (L) passages and from benzo(a)pyrene-treated (BP) L cells. Our starting point is that biological signaling processes are subjected to the same quantitative laws as inanimate, nonequilibrium chemical systems. Environmental and genomic constraints thereby limit the maximal thermodynamic entropy that the biological system can reach. The procedure uncovers the changes in gene expression patterns in different networks and defines the significance of each altered network in the establishment of a particular phenotype. The development of transformed HF1 cells is shown to be represented by one major transcription pattern that is important at all times. Two minor transcription patterns are also identified, one that contributes at early times and a distinguishably different pattern that contributes at later times. All three transcription patterns defined by our analysis were validated by gene expression values and biochemical means. The major transcription pattern includes reduced transcripts participating in the apoptotic network and enhanced transcripts participating in cell cycle, glycolysis, and oxidative phosphorylation. The two minor patterns identify genes that are mainly involved in lipid or carbohydrate metabolism.

Maximal entropy inference of oncogenicity from phosphorylation signaling.

Point mutations in the phosphorylation domain of the Bcr-Abl fusion oncogene give rise to drug resistance in chronic myelogenous leukemia patients. These mutations alter kinase-mediated signaling function and phenotypic outcome. An information theoretic analysis of the correlation of phosphoproteomic profiling and transformation potency of the oncogene in different mutants is presented. The theory seeks to predict the leukemic transformation potency from the observed signaling by constructing a distribution of maximal entropy of site-specific phosphorylation events. The theory is developed with special reference to systems biology where high throughput measurements are typical. We seek sets of phosphorylation events most contributory to predicting the phenotype by determining the constraints on the signaling system. The relevance of a constraint is measured by how much it reduces the value of the entropy from its global maximum, where all events are equally likely. Application to experimental phospho-proteomics data for kinase inhibitor-resistant mutants shows that there is one dominant constraint and that other constraints are not relevant to a similar extent. This single constraint accounts for much of the correlation of phosphorylation events with the oncogenic potency and thereby usefully predicts the trends in the phenotypic output. An additional constraint possibly accounts for biological fine structure.

Mechanical simulation of the pressure and the relaxation to thermal equilibrium of a hot and dense rare gas cluster.

A cold atomic cluster can be very rapidly heated and compressed by a hypersonic impact at a hard surface. The impact can be simulated by computing a classical trajectory for the motion of the atoms. By suddenly confining the hot and dense cluster within a rigid container, it is possible to monitor the time evolution of the force acting on the faces of the container. It is found that the pressure computed this way very rapidly decays to a time-independent value. After a somewhat longer time, this value reproduces the value for the pressure computed as the sum of the kinetic and internal pressures. This agreement is expected for a system in equilibrium. These observations support the conclusion that there is a fast relaxation to thermal equilibrium in these essentially hard-sphere systems. The deviation from equilibrium is primarily due to the propagation of shock waves within the cluster. The equilibrium pressure can reach up to the megabar range.

Dissociation kinetics of peptide ions.

The dissociation of peptide ions has been found to have ultrafast components that in many ways are uniquely different from typical unimolecular kinetics. As such, some peptide reactions provide new channels, which do not conform to statistical models of reaction kinetics. When the dissociation rates are in the 100 fs range, they are in a time scale where statistical methods do not yet apply, although molecules that have not yet dissociated will later in time undergo statistical redistribution of their excess energy, which, however, may not lead to noticeable reactivity within the experimental time frames for large peptides and hence are simply dissipative. This work is meant to reconcile the long time statistical results of Lifshitz et al. (2003) with the work of Schlag et al. (1995/6) that suggests an alternate parallel and much faster time scale for dissociation. It is argued that the two sets of results and interpretations augment one another and in fact open up a most interesting new field of peptide kinetics in addition to the unimolecular behavior, which becomes de facto arrested by the shear size of the molecule being unable to find a transition state on any reasonable time scale.

An electronic time scale in chemistry.

Ultrafast, subfemtosecond charge migration in small peptides is discussed on the basis of computational studies and compared with the selective bond dissociation after ionization as observed by Schlag and Weinkauf. The reported relaxation could be probed in real time if the removal of an electron could be achieved on the attosecond time scale. Then the mean field seen by an electron would be changing rapidly enough to initiate the migration. Tyrosine-terminated tetrapeptides have a particularly fast charge migration where in <1 fs the charge arrives at the other end. A femtosecond pulse can be used to observe the somewhat slower relaxation induced by correlation between electrons of different spins. A slower relaxation also is indicated when removing a deeper-lying valence electron. When a chromophoric amino acid is at one end of the peptide, the charge can migrate all along the peptide backbone up to the N end, but site-selective ionization is probably easier to detect for tryptophan than for tyrosine.