Data-driven structural analysis of small cell lung cancer transcription factor network suggests potential subtype regulators and transition pathways.

Small cell lung cancer (SCLC) is an aggressive disease and challenging to treat due to its mixture of transcriptional subtypes and subtype transitions. Transcription factor (TF) networks have been the focus of studies to identify SCLC subtype regulators via systems approaches. Yet, their structures, which can provide clues on subtype drivers and transitions, are barely investigated. Here, we analyze the structure of an SCLC TF network by using graph theory concepts and identify its structurally important components responsible for complex signal processing, called hubs. We show that the hubs of the network are regulators of different SCLC subtypes by analyzing first the unbiased network structure and then integrating RNA-seq data as weights assigned to each interaction. Data-driven analysis emphasizes MYC as a hub, consistent with recent reports. Furthermore, we hypothesize that the pathways connecting functionally distinct hubs may control subtype transitions and test this hypothesis via network simulations on a candidate pathway and observe subtype transition. Overall, structural analyses of complex networks can identify their functionally important components and pathways driving the network dynamics. Such analyses can be an initial step for generating hypotheses and can guide the discovery of target pathways whose perturbation may change the network dynamics phenotypically.

Integrated Multi-omics Analysis of Early Lung Adenocarcinoma Links Tumor Biological Features with Predicted Indolence or Aggressiveness.

Lung adenocarcinoma (LUAD) is a heterogeneous group of tumors associated with different survival rates, even when detected at an early stage. Here, we aim to investigate the biological determinants of early LUAD indolence or aggressiveness using radiomics as a surrogate of behavior. We present a set of 92 patients with LUAD with data collected across different methodologies. Patients were risk-stratified using the CT-based Score Indicative of Lung cancer Aggression (SILA) tool (0 = least aggressive, 1 = most aggressive). We grouped the patients as indolent (x ≤ 0.4, n = 14), intermediate (0.4 > x ≤ 0.6, n = 27), and aggressive (0.6 > x ≤ 1, n = 52). Using Cytometry by time of flight (CyTOF), we identified subpopulations with high HLA-DR expression that were associated with indolent behavior. In the RNA sequencing (RNA-seq) dataset, pathways related to immune response were associated with indolent behavior, while pathways associated with cell cycle and proliferation were associated with aggressive behavior. We extracted quantitative radiomics features from the CT scans of the patients. Integrating these datasets, we identified four feature signatures and four patient clusters that were associated with survival. Using single-cell RNA-seq, we found that indolent tumors had significantly more T cells and less B cells than aggressive tumors, and that the latter had a higher abundance of regulatory T cells and Th cells. In conclusion, we were able to uncover a correspondence between radiomics and tumor biology, which could improve the discrimination between indolent and aggressive LUAD tumors, enhance our knowledge in the biology of these tumors, and offer novel and personalized avenues for intervention. Significance: This study provides a comprehensive profiling of LUAD indolence and aggressiveness at the biological bulk and single-cell levels, as well as at the clinical and radiomics levels. This hypothesis generating study uncovers several potential future research avenues. It also highlights the importance and power of data integration to improve our systemic understanding of LUAD and to help reduce the gap between basic science research and clinical practice.

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.

Data-driven structural analysis of Small Cell Lung Cancer transcription factor network suggests potential subtype regulators and transition pathways.

Small Cell Lung Cancer (SCLC) is an aggressive disease and challenging to treat due to its mixture of transcriptional subtypes and subtype transitions. Transcription factor (TF) networks have been the focus of studies to identify SCLC subtype regulators via systems approaches. Yet, their structures, which can provide clues on subtype drivers and transitions, are barely investigated. Here, we analyze the structure of an SCLC TF network by using graph theory concepts and identify its structurally important components responsible for complex signal processing, called hubs. We show that the hubs of the network are regulators of different SCLC subtypes by analyzing first the unbiased network structure and then integrating RNA-seq data as weights assigned to each interaction. Data-driven analysis emphasizes MYC as a hub, consistent with recent reports. Furthermore, we hypothesize that the pathways connecting functionally distinct hubs may control subtype transitions and test this hypothesis via network simulations on a candidate pathway and observe subtype transition. Overall, structural analyses of complex networks can identify their functionally important components and pathways driving the network dynamics. Such analyses can be an initial step for generating hypotheses and can guide the discovery of target pathways whose perturbation may change the network dynamics phenotypically.

A biochemical necroptosis model explains cell-type-specific responses to cell death cues.

Necroptosis is a form of regulated cell death associated with degenerative disorders, autoimmune and inflammatory diseases, and cancer. To better understand the biochemical mechanisms regulating necroptosis, we constructed a detailed computational model of tumor necrosis factor-induced necroptosis based on known molecular interactions from the literature. Intracellular protein levels, used as model inputs, were quantified using label-free mass spectrometry, and the model was calibrated using Bayesian parameter inference to experimental protein time course data from a well-established necroptosis-executing cell line. The calibrated model reproduced the dynamics of phosphorylated mixed lineage kinase domain-like protein, an established necroptosis reporter. A subsequent dynamical systems analysis identified four distinct modes of necroptosis signal execution, distinguished by rate constant values and the roles of the RIP1 deubiquitinating enzymes A20 and CYLD. In one case, A20 and CYLD both contribute to RIP1 deubiquitination, in another RIP1 deubiquitination is driven exclusively by CYLD, and in two modes either A20 or CYLD acts as the driver with the other enzyme, counterintuitively, inhibiting necroptosis. We also performed sensitivity analyses of initial protein concentrations and rate constants to identify potential targets for modulating necroptosis sensitivity within each mode. We conclude by associating numerous contrasting and, in some cases, counterintuitive experimental results reported in the literature with one or more of the model-predicted modes of necroptosis execution. In all, we demonstrate that a consensus pathway model of tumor necrosis factor-induced necroptosis can provide insights into unresolved controversies regarding the molecular mechanisms driving necroptosis execution in numerous cell types under different experimental conditions.

Selective Inhibition of JAK1 Primes STAT5-Driven Human Leukemia Cells for ATRA-Induced Differentiation.

BACKGROUND: All-trans retinoic acid (ATRA), a derivate of vitamin A, has been successfully used as a therapy to induce differentiation in M3 acute promyelocytic leukemia (APML), and has led to marked improvement in outcomes. Previously, attempts to use ATRA in non-APML in the clinic, however, have been underwhelming, likely due to persistent signaling through other oncogenic drivers. Dysregulated JAK/STAT signaling is known to drive several hematologic malignancies, and targeting JAK1 and JAK2 with the JAK1/JAK2 inhibitor ruxolitinib has led to improvement in survival in primary myelofibrosis and alleviation of vasomotor symptoms and splenomegaly in polycythemia vera and myelofibrosis. OBJECTIVE: While dose-dependent anemia and thrombocytopenia limit the use of JAK2 inhibition, selectively targeting JAK1 has been explored as a means to suppress inflammation and STAT-associated pathologies related to neoplastogenesis. The objective of this study is to employ JAK1 inhibition (JAK1i) in the presence of ATRA as a potential therapy in non-M3 acute myeloid leukemia (AML). METHODS: Efficacy of JAK1i using INCB52793 was assessed by changes in cell cycle and apoptosis in treated AML cell lines. Transcriptomic and proteomic analysis evaluated effects of JAK1i. Synergy between JAK1i+ ATRA was assessed in cell lines in vitro while efficacy in vivo was assessed by tumor reduction in MV-4-11 cell line-derived xenografts. RESULTS: Here we describe novel synergistic activity between JAK1i inhibition and ATRA in non-M3 leukemia. Transcriptomic and proteomic analysis confirmed structural and functional changes related to maturation while in vivo combinatory studies revealed significant decreases in leukemic expansion. CONCLUSIONS: JAK1i+ ATRA lead to decreases in cell cycle followed by myeloid differentiation and cell death in human leukemias. These findings highlight potential uses of ATRA-based differentiation therapy of non-M3 human leukemia.

Unsupervised logic-based mechanism inference for network-driven biological processes.

Modern analytical techniques enable researchers to collect data about cellular states, before and after perturbations. These states can be characterized using analytical techniques, but the inference of regulatory interactions that explain and predict changes in these states remains a challenge. Here we present a generalizable, unsupervised approach to generate parameter-free, logic-based models of cellular processes, described by multiple discrete states. Our algorithm employs a Hamming-distance based approach to formulate, test, and identify optimized logic rules that link two states. Our approach comprises two steps. First, a model with no prior knowledge except for the mapping between initial and attractor states is built. We then employ biological constraints to improve model fidelity. Our algorithm automatically recovers the relevant dynamics for the explored models and recapitulates key aspects of the biochemical species concentration dynamics in the original model. We present the advantages and limitations of our work and discuss how our approach could be used to infer logic-based mechanisms of signaling, gene-regulatory, or other input-output processes describable by the Boolean formalism.

Thunor: visualization and analysis of high-throughput dose-response datasets.

High-throughput cell proliferation assays to quantify drug-response are becoming increasingly common and powerful with the emergence of improved automation and multi-time point analysis methods. However, pipelines for analysis of these datasets that provide reproducible, efficient, and interactive visualization and interpretation are sorely lacking. To address this need, we introduce Thunor, an open-source software platform to manage, analyze, and visualize large, dose-dependent cell proliferation datasets. Thunor supports both end-point and time-based proliferation assays as input. It provides a simple, user-friendly interface with interactive plots and publication-quality images of cell proliferation time courses, dose-response curves, and derived dose-response metrics, e.g. IC50, including across datasets or grouped by tags. Tags are categorical labels for cell lines and drugs, used for aggregation, visualization and statistical analysis, e.g. cell line mutation or drug class/target pathway. A graphical plate map tool is included to facilitate plate annotation with cell lines, drugs and concentrations upon data upload. Datasets can be shared with other users via point-and-click access control. We demonstrate the utility of Thunor to examine and gain insight from two large drug response datasets: a large, publicly available cell viability database and an in-house, high-throughput proliferation rate dataset. Thunor is available from www.thunor.net.

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.

Activated Oncogenic Pathway Modifies Iron Network in Breast Epithelial Cells: A Dynamic Modeling Perspective.

Dysregulation of iron metabolism in cancer is well documented and it has been suggested that there is interdependence between excess iron and increased cancer incidence and progression. In an effort to better understand the linkages between iron metabolism and breast cancer, a predictive mathematical model of an expanded iron homeostasis pathway was constructed that includes species involved in iron utilization, oxidative stress response and oncogenic pathways. The model leads to three predictions. The first is that overexpression of iron regulatory protein 2 (IRP2) recapitulates many aspects of the alterations in free iron and iron-related proteins in cancer cells without affecting the oxidative stress response or the oncogenic pathways included in the model. This prediction was validated by experimentation. The second prediction is that iron-related proteins are dramatically affected by mitochondrial ferritin overexpression. This prediction was validated by results in the pertinent literature not used for model construction. The third prediction is that oncogenic Ras pathways contribute to altered iron homeostasis in cancer cells. This prediction was validated by a combination of simulation experiments of Ras overexpression and catalase knockout in conjunction with the literature. The model successfully captures key aspects of iron metabolism in breast cancer cells and provides a framework upon which more detailed models can be built.

An unbiased metric of antiproliferative drug effect in vitro.

In vitro cell proliferation assays are widely used in pharmacology, molecular biology, and drug discovery. Using theoretical modeling and experimentation, we show that current metrics of antiproliferative small molecule effect suffer from time-dependent bias, leading to inaccurate assessments of parameters such as drug potency and efficacy. We propose the drug-induced proliferation (DIP) rate, the slope of the line on a plot of cell population doublings versus time, as an alternative, time-independent metric.

Competition and allostery govern substrate selectivity of cyclooxygenase-2.

Cyclooxygenase-2 (COX-2) oxygenates arachidonic acid (AA) and its ester analog, 2-arachidonoylglycerol (2-AG), to prostaglandins (PGs) and prostaglandin glyceryl esters (PG-Gs), respectively. Although the efficiency of oxygenation of these substrates by COX-2 in vitro is similar, cellular biosynthesis of PGs far exceeds that of PG-Gs. Evidence that the COX enzymes are functional heterodimers suggests that competitive interaction of AA and 2-AG at the allosteric site of COX-2 might result in differential regulation of the oxygenation of the two substrates when both are present. Modulation of AA levels in RAW264.7 macrophages uncovered an inverse correlation between cellular AA levels and PG-G biosynthesis. In vitro kinetic analysis using purified protein demonstrated that the inhibition of 2-AG oxygenation by high concentrations of AA far exceeded the inhibition of AA oxygenation by high concentrations of 2-AG. An unbiased systems-based mechanistic model of the kinetic data revealed that binding of AA or 2-AG at the allosteric site of COX-2 results in a decreased catalytic efficiency of the enzyme toward 2-AG, whereas 2-AG binding at the allosteric site increases COX-2's efficiency toward AA. The results suggest that substrates interact with COX-2 via multiple potential complexes involving binding to both the catalytic and allosteric sites. Competition between AA and 2-AG for these sites, combined with differential allosteric modulation, gives rise to a complex interplay between the substrates, leading to preferential oxygenation of AA.

Programming biological models in Python using PySB.

Mathematical equations are fundamental to modeling biological networks, but as networks get large and revisions frequent, it becomes difficult to manage equations directly or to combine previously developed models. Multiple simultaneous efforts to create graphical standards, rule-based languages, and integrated software workbenches aim to simplify biological modeling but none fully meets the need for transparent, extensible, and reusable models. In this paper we describe PySB, an approach in which models are not only created using programs, they are programs. PySB draws on programmatic modeling concepts from little b and ProMot, the rule-based languages BioNetGen and Kappa and the growing library of Python numerical tools. Central to PySB is a library of macros encoding familiar biochemical actions such as binding, catalysis, and polymerization, making it possible to use a high-level, action-oriented vocabulary to construct detailed models. As Python programs, PySB models leverage tools and practices from the open-source software community, substantially advancing our ability to distribute and manage the work of testing biochemical hypotheses. We illustrate these ideas using new and previously published models of apoptosis.

Arco aórtico derecho circunflejo retroesofágico con ligamento arterioso izquierdo: una variedad infrecuente de anillo vascular completo / Circumflex retroesophageal right aortic arch with left arterial ligament: an infrequent variety of complete vascular ring

Los anillos vasculares pertenecen a un grupo de anomalías congénitas de los arcos aórticos en los que la tráquea y el esófago, o ambos, están completamente rodeados por estructuras vasculares. Con frecuencia pueden causar obstrucción y, en consecuencia, alteración de la deglución y dificultad respiratoria, por lo cual deben incluirse en el diagnóstico diferencial de obstrucción de la vía aérea superior. El diagnóstico temprano y la liberación quirúrgica oportuna de la obstrucción de la vía aérea y del esófago, o ambos, pueden mejorar los síntomas en la mayoría de casos. Esta afección debe sospecharse y evaluarse en lactantes o niños pequeños con síntomas respiratorios recurrentes como tos crónica, estridor y sibilancias o, lo que es menos común, con síntomas relacionados con alteración de la deglución. A continuación se ilustra el caso de un niño de seis años con problemas de deglución crónicos y desnutrición a quién se le realizó un diagnóstico incidental del arco aórtico derecho circunflejo retroesofágico con ligamento arterioso izquierdo corregido mediante cirugía.

Vascular rings are a group of congenital anomalies of the aortic arches in which the trachea and esophagus, or both, are completely surrounded by vascular structures. Often, they can cause obstruction and consequently impaired swallowing and respiratory distress, so they must be included in the differential diagnosis of obstruction of the upper airway. Early diagnosis and timely surgical release of obstruction of the airway and esophagus, or both, may improve symptoms in the majority of cases. This condition must be suspected and evaluated in infants or young children with recurrent respiratory symptoms such as chronic cough, stridor, wheezing or, less commonly, with symptoms related to impaired swallowing. We illustrate the case of a six-year-old child with chronic swallowing problems and malnutrition who underwent an incidental diagnosis of circumflex retroesophageal right aortic arch with left ligamentum arteriosum corrected by surgery.

Transmembrane peptide-induced lipid sorting and mechanism of Lalpha-to-inverted phase transition using coarse-grain molecular dynamics.

Molecular dynamics results are presented for a coarse-grain model of 1,2-di-n-alkanoyl-sn-glycero-3-phosphocholine, water, and a capped cylindrical model of a transmembrane peptide. We first demonstrate that different alkanoyl-length lipids are miscible in the liquid-disordered lamellar (Lalpha) phase. The transmembrane peptide is constructed of hydrophobic sites with hydrophilic caps. The hydrophobic length of the peptide is smaller than the hydrophobic thickness of a bilayer consisting of an equal mixture of long and short alkanoyl tail lipids. When incorporated into the membrane, a meniscus forms in the vicinity of the peptide and the surrounding area is enriched in the short lipid. The meniscus region draws water into it. In the regions that are depleted of water, the bilayers can fuse. The lipid headgroups then rearrange to solvate the newly formed water pores, resulting in an inverted phase. This mechanism appears to be a viable pathway for the experimentally observed Lalpha-to-inverse hexagonal (HII) peptide-induced phase transition.