synergy: a Python library for calculating, analyzing and visualizing drug combination synergy.

SUMMARY: Combinations of multiple pharmacological agents can achieve a substantial benefit over treatment with single agents alone. Combinations that achieve 'more than the sum of their parts' are called synergistic. There have been many proposed frameworks to understand and quantify drug combination synergy with different assumptions and domains of applicability. We introduce here synergy, a Python library that (i) implements a broad array of popular synergy models, (ii) provides tools for evaluating confidence intervals and conducting power analysis and (iii) provides standardized tools to analyze and visualize drug combinations and their synergies and antagonisms. AVAILABILITY AND IMPLEMENTATION: synergy is available on all operating systems for Python >=3.5. It is freely available from https://pypi.org/project/synergy, and its source code is available at https://github.com/djwooten/synergy. This software is released under the GNU General Public License, version 3.0 or later. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Mathematical modeling of the Candida albicans yeast to hyphal transition reveals novel control strategies.

Candida albicans, an opportunistic fungal pathogen, is a significant cause of human infections, particularly in immunocompromised individuals. Phenotypic plasticity between two morphological phenotypes, yeast and hyphae, is a key mechanism by which C. albicans can thrive in many microenvironments and cause disease in the host. Understanding the decision points and key driver genes controlling this important transition and how these genes respond to different environmental signals is critical to understanding how C. albicans causes infections in the host. Here we build and analyze a Boolean dynamical model of the C. albicans yeast to hyphal transition, integrating multiple environmental factors and regulatory mechanisms. We validate the model by a systematic comparison to prior experiments, which led to agreement in 17 out of 22 cases. The discrepancies motivate alternative hypotheses that are testable by follow-up experiments. Analysis of this model revealed two time-constrained windows of opportunity that must be met for the complete transition from the yeast to hyphal phenotype, as well as control strategies that can robustly prevent this transition. We experimentally validate two of these control predictions in C. albicans strains lacking the transcription factor UME6 and the histone deacetylase HDA1, respectively. This model will serve as a strong base from which to develop a systems biology understanding of C. albicans morphogenesis.

Systems-level network modeling of Small Cell Lung Cancer subtypes identifies master regulators and destabilizers.

Adopting a systems approach, we devise a general workflow to define actionable subtypes in human cancers. Applied to small cell lung cancer (SCLC), the workflow identifies four subtypes based on global gene expression patterns and ontologies. Three correspond to known subtypes (SCLC-A, SCLC-N, and SCLC-Y), while the fourth is a previously undescribed ASCL1+ neuroendocrine variant (NEv2, or SCLC-A2). Tumor deconvolution with subtype gene signatures shows that all of the subtypes are detectable in varying proportions in human and mouse tumors. To understand how multiple stable subtypes can arise within a tumor, we infer a network of transcription factors and develop BooleaBayes, a minimally-constrained Boolean rule-fitting approach. In silico perturbations of the network identify master regulators and destabilizers of its attractors. Specific to NEv2, BooleaBayes predicts ELF3 and NR0B1 as master regulators of the subtype, and TCF3 as a master destabilizer. Since the four subtypes exhibit differential drug sensitivity, with NEv2 consistently least sensitive, these findings may lead to actionable therapeutic strategies that consider SCLC intratumoral heterogeneity. Our systems-level approach should generalize to other cancer types.

Mathematical models of cell phenotype regulation and reprogramming: Make cancer cells sensitive again!

A cell's phenotype is the observable actualization of complex interactions between its genome, epigenome, and local environment. While traditional views in cancer have held that cellular and tumor phenotypes are largely functions of genomic instability, increasing attention has recently been given to epigenetic and microenvironmental influences. Such non-genetic factors allow cancer cells to experience intrinsic diversity and plasticity, and at the tumor level can result in phenotypic heterogeneity and treatment evasion. In 2006, Takahashi and Yamanaka exploited the epigenome's plasticity by "reprogramming" differentiated cells into a pluripotent state by inducing expression of a cocktail of four transcription factors. Recent advances in cancer biology have shown not only that cellular reprogramming is possible for malignant cells, but it may provide a foundation for future therapies. Nevertheless, cell reprogramming experiments are frequently plagued by low efficiency, activation of aberrant transcriptional programs, instability, and often rely on expertise gathered from systems which may not translate directly to cancer. Here, we review a theoretical framework tracing back to Waddington's epigenetic landscape which may be used to derive quantitative and qualitative understanding of cellular reprogramming. Implications for tumor heterogeneity, evolution and adaptation are discussed in the context of designing new treatments to re-sensitize recalcitrant tumors. This article is part of a Special Issue entitled: Evolutionary principles - heterogeneity in cancer?, edited by Dr. Robert A. Gatenby.

Selenium Induces Pancreatic Cancer Cell Death Alone and in Combination with Gemcitabine.

Survival rate for pancreatic cancer remains poor and newer treatments are urgently required. Selenium, an essential trace element, offers protection against several cancer types and has not been explored much against pancreatic cancer specifically in combination with known chemotherapeutic agents. The present study was designed to investigate selenium and Gemcitabine at varying doses alone and in combination in established pancreatic cancer cell lines growing in 2D as well as 3D platforms. Comparison of multi-dimensional synergy of combinations' (MuSyc) model and highest single agent (HSA) model provided quantitative insights into how much better the combination performed than either compound tested alone in a 2D versus 3D growth of pancreatic cancer cell lines. The outcomes of the study further showed promise in combining selenium and Gemcitabine when evaluated for apoptosis, proliferation, and ENT1 protein expression, specifically in BxPC-3 pancreatic cancer cells in vitro.

Data-Driven Math Model of FLT3-ITD Acute Myeloid Leukemia Reveals Potential Therapeutic Targets.

FLT3-mutant acute myeloid leukemia (AML) is an aggressive form of leukemia with poor prognosis. Treatment with FLT3 inhibitors frequently produces a clinical response, but the disease nevertheless often recurs. Recent studies have revealed system-wide gene expression changes in FLT3-mutant AML cell lines in response to drug treatment. Here we sought a systems-level understanding of how these cells mediate these drug-induced changes. Using RNAseq data from AML cells with an internal tandem duplication FLT3 mutation (FLT3-ITD) under six drug treatment conditions including quizartinib and dexamethasone, we identified seven distinct gene programs representing diverse biological processes involved in AML drug-induced changes. Based on the literature knowledge about genes from these modules, along with public gene regulatory network databases, we constructed a network of FLT3-ITD AML. Applying the BooleaBayes algorithm to this network and the RNAseq data, we created a probabilistic, data-driven dynamical model of acquired resistance to these drugs. Analysis of this model reveals several interventions that may disrupt targeted parts of the system-wide drug response. We anticipate co-targeting these points may result in synergistic treatments that can overcome resistance and prevent eventual recurrence.

MuSyC is a consensus framework that unifies multi-drug synergy metrics for combinatorial drug discovery.

Drug combination discovery depends on reliable synergy metrics but no consensus exists on the correct synergy criterion to characterize combined interactions. The fragmented state of the field confounds analysis, impedes reproducibility, and delays clinical translation of potential combination treatments. Here we present a mass-action based formalism to quantify synergy. With this formalism, we clarify the relationship between the dominant drug synergy principles, and present a mapping of commonly used frameworks onto a unified synergy landscape. From this, we show how biases emerge due to intrinsic assumptions which hinder their broad applicability and impact the interpretation of synergy in discovery efforts. Specifically, we describe how traditional metrics mask consequential synergistic interactions, and contain biases dependent on the Hill-slope and maximal effect of single-drugs. We show how these biases systematically impact synergy classification in large combination screens, potentially misleading discovery efforts. Thus the proposed formalism can provide a consistent, unbiased interpretation of drug synergy, and accelerate the translatability of synergy studies.

Fibrosis improvement in patients with HCV treated with direct-acting antivirals.

BACKGROUND AND AIM: More prospective studies are needed to characterize fibrosis improvement in patients with hepatitis C virus (HCV) who are treated with direct-acting antivirals (DAAs). The aims of this study were to assess changes in elastography scores from baseline to 1-year follow-up in patients with HCV, to identify factors that were independently associated with improvement in fibrosis staging in patients who receive treatment, and to identify factors that were independently associated with no improvement in fibrosis staging among patients who achieved sustained virologic responses (SVR). METHODS: Ultrasound elastography and laboratory tests were performed and collected at baseline and at 1-year follow-up for patients who received HCV treatment and for those who did not receive treatment (n = 240). Binomial logistic regression was used to examine factors that were independently associated with improvement in fibrosis staging. RESULTS: In patients who achieved SVR, the mean fibrosis score decreased significantly (-1.3) from 7.4 (2.3) before treatment to 6.1 (2.0) after treatment (P = 0.00). In multivariate analysis of patients who received treatment, higher pre-treatment fibrosis stages [odds ratio (OR) = 13.02, P < 0.00] were positively associated with improvement in fibrosis staging at 1-year follow-up. Higher BMI (OR = 0.93, P < 0.05) was negatively associated with improvement in fibrosis staging. DISCUSSION: This study supports the growing body of literature that suggests fibrosis regression is achievable in a significant number of patients who achieve SVR with all-oral DAA regimens. Equally important, fibrosis regression is more likely to occur in patients with advanced stages of fibrosis and less likely in patients who are obese.

Charting the Fragmented Landscape of Drug Synergy.

Even as the clinical impact of drug combinations continues to accelerate, no consensus on how to quantify drug synergy has emerged. Rather, surveying the landscape of drug synergy reveals the persistence of historical fissures regarding the appropriate domains of conflicting synergy models - fissures impacting all aspects of combination therapy discovery and deployment. Herein we chronicle the impact of these divisions on: (i) the design, interpretation, and reproducibility of high-throughput combination screens; (ii) the performance of algorithms to predict synergistic mixtures; and (iii) the search for higher-order synergistic interactions. Further progress in each of these subfields hinges on reaching a consensus regarding the long-standing rifts in the field.

Comparison of n-type Gd(2)O(3) and Gd-doped HfO(2).

Gd(2)O(3) and Gd-doped HfO(2) films were deposited on p-type silicon substrates in a reducing atmosphere. Gd 4f photoexcitation peaks at roughly 7 and 5 eV below the valence band maximum have been identified using the resonant photoemission of Gd(2)O(3) and Gd-doped HfO(2) films, respectively. In the case of Gd(2)O(3), strong hybridization with the O 2p band is demonstrated, and there is evidence that the Gd 4f weighted band exhibits dispersion in the bulk band structure. The rectifying (diode-like) properties of Gd-doped HfO(2)-silicon and Gd(2)O(3)-silicon heterojunctions are demonstrated.

Ventricular-Subventricular Zone Contact by Glioblastoma is Not Associated with Molecular Signatures in Bulk Tumor Data.

Whether patients with glioblastoma that contacts the ventricular-subventricular zone stem cell niche (VSVZ + GBM) have a distinct survival profile from VSVZ - GBM patients independent of other known predictors or molecular profiles is unclear. Using multivariate Cox analysis to adjust survival for widely-accepted predictors, hazard ratios (HRs) for overall (OS) and progression free (PFS) survival between VSVZ + GBM and VSVZ - GBM patients were calculated in 170 single-institution patients and 254 patients included in both The Cancer Genome (TCGA) and Imaging (TCIA) atlases. An adjusted, multivariable analysis revealed that VSVZ contact was independently associated with decreased survival in both datasets. TCGA molecular data analyses revealed that VSVZ contact by GBM was independent of mutational, DNA methylation, gene expression, and protein expression signatures in the bulk tumor. Therefore, while survival of GBM patients is independently stratified by VSVZ contact, with VSVZ + GBM patients displaying a poor prognosis, the VSVZ + GBMs do not possess a distinct molecular signature at the bulk sample level. Focused examination of the interplay between the VSVZ microenvironment and subsets of GBM cells proximal to this region is warranted.

Quantifying Drug Combination Synergy along Potency and Efficacy Axes.

Two goals motivate treating diseases with drug combinations: reduce off-target toxicity by minimizing doses (synergistic potency) and improve outcomes by escalating effect (synergistic efficacy). Established drug synergy frameworks obscure such distinction, failing to harness the potential of modern chemical libraries. We therefore developed multi-dimensional synergy of combinations (MuSyC), a formalism based on a generalized, multi-dimensional Hill equation, which decouples synergistic potency and efficacy. In mutant-EGFR-driven lung cancer, MuSyC reveals that combining a mutant-EGFR inhibitor with inhibitors of other kinases may result only in synergistic potency, whereas synergistic efficacy can be achieved by co-targeting mutant-EGFR and epigenetic regulation or microtubule polymerization. In mutant-BRAF melanoma, MuSyC determines whether a molecular correlate of BRAFi insensitivity alters a BRAF inhibitor's potency, efficacy, or both. These findings showcase MuSyC's potential to transform the enterprise of drug-combination screens by precisely guiding translation of combinations toward dose reduction, improved efficacy, or both.

Novel Hybrid Phenotype Revealed in Small Cell Lung Cancer by a Transcription Factor Network Model That Can Explain Tumor Heterogeneity.

Small cell lung cancer (SCLC) is a devastating disease due to its propensity for early invasion and refractory relapse after initial treatment response. Although these aggressive traits have been associated with phenotypic heterogeneity, our understanding of this association remains incomplete. To fill this knowledge gap, we inferred a set of 33 transcription factors (TF) associated with gene signatures of the known neuroendocrine/epithelial (NE) and non-neuroendocrine/mesenchymal-like (ML) SCLC phenotypes. The topology of this SCLC TF network was derived from prior knowledge and was simulated using Boolean modeling. These simulations predicted that the network settles into attractors, or TF expression patterns, that correlate with NE or ML phenotypes, suggesting that TF network dynamics underlie the emergence of heterogeneous SCLC phenotypes. However, several cell lines and patient tumor specimens failed to correlate with either the NE or ML attractors. By flow cytometry, single cells within these cell lines simultaneously expressed surface markers of both NE and ML differentiation, confirming the existence of a "hybrid" phenotype. Upon exposure to standard-of-care cytotoxic drugs or epigenetic modifiers, NE and ML cell populations converged toward the hybrid state, suggesting possible escape from treatment. Our findings indicate that SCLC phenotypic heterogeneity can be specified dynamically by attractor states of a master regulatory TF network. Thus, SCLC heterogeneity may be best understood as states within an epigenetic landscape. Understanding phenotypic transitions within this landscape may provide insights to clinical applications. Cancer Res; 77(5); 1063-74. ©2016 AACR.

Control of T lymphocyte morphology by the GTPase Rho.

BACKGROUND: Rho family GTPase regulation of the actin cytoskeleton governs a variety of cell responses. In this report, we have analyzed the role of the GTPase Rho in maintenance of the T lymphocyte actin cytoskeleton. RESULTS: Inactivation of the GTPase Rho in the human T lymphocytic cell line HPB-ALL does not inhibit constitutively high adhesion to the integrin beta1 substrate fibronectin. It did however result in the aberrant extension of finger-like dendritic processes on the substrates VCAM-1, Fn, and mAb specific to beta1 integrins. Time-lapse video microscopy demonstrated that C3 induced extensions were primarily the result of an altered pseudopod elongation rather than retraction. Once the stellate pseudopodia extended, none retracted, and cells became completely immobile. Filipodial structures were absent and the dendritic-like processes in C3 treated cells were rich in filamentous actin. Immunolocalization of RhoA in untreated HPB-ALL cells spreading on fibronectin demonstrated a diffuse staining pattern within the pseudopodia. In C3 treated cells, clusters of RhoA were pronounced and localized within the altered extensions. CONCLUSIONS: GTPase Rho is actively involved in the regulation of T lymphocyte morphology and motility.

The Electronic Structure and Secondary Pyroelectric Properties of Lithium Tetraborate.

We review the pyroelectric properties and electronic structure of Li2B4O7(110) and Li2B4O7(100) surfaces. There is evidence for a pyroelectric current along the [110] direction of stoichiometric Li2B4O7 so that the pyroelectric coefficient is nonzero but roughly 10³ smaller than along the [001] direction of spontaneous polarization. Abrupt decreases in the pyroelectric coefficient along the [110] direction can be correlated with anomalies in the elastic stiffness contributing to the concept that the pyroelectric coefficient is not simply a vector but has qualities of a tensor, as expected. The time dependent surface photovoltaic charging suggests that surface charging is dependent on crystal orientation and doping, as well as temperature.

Trace elements in lesser scaup (Aythya affinis) from the Mississippi flyway.

Previous research reported that concentrations of selenium in the livers of 88-95% of lesser scaup from locations in Lake Erie, Lake St. Clair, and Lake Michigan, USA were either elevated (10-33 microg/g dry weight [dw]) or in the potentially harmful range (> 33 microg/g dw). In order to determine the geographic extent of these high selenium concentrations, we collected lesser scaup in Louisiana, Arkansas, Illinois, Minnesota, Wisconsin, and Manitoba and analyzed the livers for 19 trace elements. We found that all trace element concentrations, except for selenium, generally were low. Arsenic, which usually is not detected in liver samples, was detected in Louisiana and may be related to past agricultural usages. Chromium, which also is not usually detected, was only present in lesser scaup from Arkansas and may be related to fertilizer applications. Cadmium and mercury concentrations did not differ among locations and concentrations were low. Selenium concentrations in Arkansas (geometric mean = 4.2 microg/g dw) were significantly lower than those in Louisiana (10.7 microg/g dw), Illinois (10.5 microg/g dw), and Minnesota (8.0 microg/g dw); concentrations in Wisconsin and Manitoba were intermediate (6.6 and 6.5 microg/g dw). About 25% of lesser scaup livers contained elevated selenium concentrations; however, none were in the harmful range. We concluded that selenium concentrations in lesser scaup in the Mississippi Flyway are elevated in some individuals, but not to the extent that has been documented in the industrial portions of the Great Lakes.