Concepts of multi-level dynamical modelling: understanding mechanisms of squamous cell carcinoma development in Fanconi anemia.

Fanconi anemia (FA) is a rare disease (incidence of 1:300,000) primarily based on the inheritance of pathogenic variants in genes of the FA/BRCA (breast cancer) pathway. These variants ultimately reduce the functionality of different proteins involved in the repair of DNA interstrand crosslinks and DNA double-strand breaks. At birth, individuals with FA might present with typical malformations, particularly radial axis and renal malformations, as well as other physical abnormalities like skin pigmentation anomalies. During the first decade of life, FA mostly causes bone marrow failure due to reduced capacity and loss of the hematopoietic stem and progenitor cells. This often makes hematopoietic stem cell transplantation necessary, but this therapy increases the already intrinsic risk of developing squamous cell carcinoma (SCC) in early adult age. Due to the underlying genetic defect in FA, classical chemo-radiation-based treatment protocols cannot be applied. Therefore, detecting and treating the multi-step tumorigenesis process of SCC in an early stage, or even its progenitors, is the best option for prolonging the life of adult FA individuals. However, the small number of FA individuals makes classical evidence-based medicine approaches based on results from randomized clinical trials impossible. As an alternative, we introduce here the concept of multi-level dynamical modelling using large, longitudinally collected genome, proteome- and transcriptome-wide data sets from a small number of FA individuals. This mechanistic modelling approach is based on the "hallmarks of cancer in FA", which we derive from our unique database of the clinical history of over 750 FA individuals. Multi-omic data from healthy and diseased tissue samples of FA individuals are to be used for training constituent models of a multi-level tumorigenesis model, which will then be used to make experimentally testable predictions. In this way, mechanistic models facilitate not only a descriptive but also a functional understanding of SCC in FA. This approach will provide the basis for detecting signatures of SCCs at early stages and their precursors so they can be efficiently treated or even prevented, leading to a better prognosis and quality of life for the FA individual.

Alpha-tocopherylquinone-mediated activation of the Aryl Hydrocarbon Receptor regulates the production of inflammation-inducing cytokines and ameliorates intestinal inflammation.

This study investigated the role of Alpha-tocopherylquinone (TQ) in regulating the intestinal immune system and the underlying mechanisms. In the experimental dextran sodium sulfate and T cell-mediated colitis models, TQ significantly reduced the mRNA levels of interleukin (IL)-6, IL-1ß, IL-17A, IL-23, and tumor necrosis factor (TNF)-α and the abundance of proinflammatory macrophages, T helper (Th)17 cells, and ILC3s in the colons of wild-type mice. TQ also prevented lipopolysaccharide (LPS)-induced activation of NFκB and signal transducer and activator of transcription (Stat)-3 pathways in the human macrophage U937 cells. Pharmacological inhibition or CRISPR-Cas-9-mediated knockout of Aryl hydrocarbon Receptor (AhR) prevented the anti-inflammatory effects of TQ in the LPS-treated U937 cells. Furthermore, TQ reduced the mRNA levels of the LPS-induced pro-inflammatory cytokines in the WT but not Ahr-/- mice splenocytes. TQ also reduced IL-6R protein levels and IL-6-induced Stat-3 activation in Jurkat cells and in vitro differentiation of Th17 cells from wild-type but not Ahr-/- mice naive T cells. Additionally, TQ prevented the pro-inflammatory effects of LPS on macrophages and stimulation of T cells in human PBMCs and significantly reduced the abundance of tumor necrosis factor-α, IL-1ß, and IL-6hi inflammatory macrophages and Th17 cells in surgically resected Crohn's disease (CD) tissue. Our study shows that TQ is a naturally occurring, non-toxic, and effective immune modulator that activates AhR and suppresses the Stat-3-NFκB signaling.

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.

Creating a Surgical Biobank: The Hershey Medical Center Experience.

BACKGROUND: Tissue harvesting at the time of surgery offers surgeons and scientists a unique opportunity to discover and better understand disease pathophysiology. Tissue biobanking presents challenges in patient consents, specimen collection, preparation, and storage, but the potential for scientific discovery justifies the effort. Although the number of tissue biobanks is increasing worldwide, information regarding necessary infrastructure, process flow, and management of expected obstacles is lacking. OBJECTIVE: To provide a framework and motivation for clinician scientists intending to start an intestinal tissue biobank under their direction. DATA SOURCES: The Carlino Family Inflammatory Bowel and Colorectal Diseases Biobank is housed at the Milton S. Hershey Medical Center. STUDY SELECTION: Review. INTERVENTION: Implementation of a surgical tissue biobank at a large tertiary care institution. MAIN OUTCOME MEASURES: Assess critical challenges and obstacles over the years as well as keys to the success of the program. RESULTS: Over 2 decades, the institutional biobank grew from an IBD biobank to one which now incorporates thousands of surgical specimens representing numerous colorectal diseases. This was done through a process of refinement focusing on patient recruitment and an efficient consenting and specimen management process. The biobank's success is further insured by institutional, external, and philanthropic support; scientific collaborations; and sharing of biological specimens with other groups of dedicated researchers. LIMITATIONS: This is a single-center experience in collecting surgically resected colorectal specimens. CONCLUSIONS: Surgical specimen biobanks are essential in studying disease cause using genomics, transcriptomics, and proteomic technologies. Therefore, surgeons, clinicians, and scientists should build biobanks at their institutions to promote further scientific discovery and improve specimen diversity.

Involvement of Epithelial-Mesenchymal Transition Genes in Small Cell Lung Cancer Phenotypic Plasticity.

Small cell lung cancer (SCLC) is an aggressive cancer recalcitrant to treatment, arising predominantly from epithelial pulmonary neuroendocrine (NE) cells. Intratumor heterogeneity plays critical roles in SCLC disease progression, metastasis, and treatment resistance. At least five transcriptional SCLC NE and non-NE cell subtypes were recently defined by gene expression signatures. Transition from NE to non-NE cell states and cooperation between subtypes within a tumor likely contribute to SCLC progression by mechanisms of adaptation to perturbations. Therefore, gene regulatory programs distinguishing SCLC subtypes or promoting transitions are of great interest. Here, we systematically analyze the relationship between SCLC NE/non-NE transition and epithelial to mesenchymal transition (EMT)-a well-studied cellular process contributing to cancer invasiveness and resistance-using multiple transcriptome datasets from SCLC mouse tumor models, human cancer cell lines, and tumor samples. The NE SCLC-A2 subtype maps to the epithelial state. In contrast, SCLC-A and SCLC-N (NE) map to a partial mesenchymal state (M1) that is distinct from the non-NE, partial mesenchymal state (M2). The correspondence between SCLC subtypes and the EMT program paves the way for further work to understand gene regulatory mechanisms of SCLC tumor plasticity with applicability to other cancer types.

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.

Processes in DNA damage response from a whole-cell multi-omics perspective.

Technological advances have made it feasible to collect multi-condition multi-omic time courses of cellular response to perturbation, but the complexity of these datasets impedes discovery due to challenges in data management, analysis, visualization, and interpretation. Here, we report a whole-cell mechanistic analysis of HL-60 cellular response to bendamustine. We integrate both enrichment and network analysis to show the progression of DNA damage and programmed cell death over time in molecular, pathway, and process-level detail using an interactive analysis framework for multi-omics data. Our framework, Mechanism of Action Generator Involving Network analysis (MAGINE), automates network construction and enrichment analysis across multiple samples and platforms, which can be integrated into our annotated gene-set network to combine the strengths of networks and ontology-driven analysis. Taken together, our work demonstrates how multi-omics integration can be used to explore signaling processes at various resolutions and demonstrates multi-pathway involvement beyond the canonical bendamustine mechanism.

Real-time luminescence enables continuous drug-response analysis in adherent and suspension cell lines.

The drug-induced proliferation (DIP) rate is a metric of in vitro drug response that avoids inherent biases in commonly used metrics such as 72 h viability. However, DIP rate measurements rely on direct cell counting over time, a laborious task that is subject to numerous challenges, including the need to fluorescently label cells and automatically segment nuclei. Moreover, it is incredibly difficult to directly count cells and accurately measure DIP rates for cell populations in suspension. As an alternative, we use real-time luminescence measurements derived from the cellular activity of NAD(P)H oxidoreductase to efficiently estimate drug response in both adherent and suspension cell populations to a panel of known anticancer agents. For the adherent cell lines, we collect both luminescence reads and direct cell counts over time simultaneously to assess their congruency. Our results demonstrate that the proposed approach significantly speeds up data collection, avoids the need for cellular labels and image segmentation, and opens the door to significant advances in high-throughput screening of anticancer drugs.

COLQ and ARHGAP15 are Associated with Diverticular Disease and are Expressed in the Colon.

BACKGROUND: Diverticular disease is a common but poorly understood disease of the gastrointestinal tract. Recent studies have identified several single nucleotide polymorphisms (SNPs) that are associated with diverticular disease. MATERIALS AND METHODS: The genotypes of three SNPs (rs4662344 in ARHGAP15, rs7609897 in COLQ, and rs67153654 in FAM155A) were identified by Taqman assay in 204 patients with diverticular disease. Clinical characteristics were obtained from the medical record to study association with genotype. To evaluate gene expression in colon tissue, qPCR was performed on 24 patients with diverticulitis, and COLQ was localized using immunohistochemistry. RESULTS: The ARHGAP15 and COLQ SNPs were significantly associated with both diverticular disease and specifically diverticulitis, while the FAM155A was not associated with either. No association was found with clinical disease characteristics. Heterozygous genotypes at the ARHGAP15 SNP was associated with lower ARHGAP15 expression in colon tissues. COLQ protein localized to the myenteric plexus in the colon. CONCLUSIONS: This study confirmed association of the ARHGAP15 and COLQ SNPs with diverticular disease in our patients but could not confirm FAM155A SNP association. Neither of these SNPs appeared to associate with more severe disease, but genotype at the ARHGAP15 SNP did impact expression of ARHGAP15 in the colon. Additionally, this study is the first to localize COLQ in the colon. Its presence in the myenteric nervous system suggests COLQ SNP variants may contribute to diverticular disease by altering motility.

An in vitro model of tumor heterogeneity resolves genetic, epigenetic, and stochastic sources of cell state variability.

Tumor heterogeneity is a primary cause of treatment failure and acquired resistance in cancer patients. Even in cancers driven by a single mutated oncogene, variability in response to targeted therapies is well known. The existence of additional genomic alterations among tumor cells can only partially explain this variability. As such, nongenetic factors are increasingly seen as critical contributors to tumor relapse and acquired resistance in cancer. Here, we show that both genetic and nongenetic factors contribute to targeted drug response variability in an experimental model of tumor heterogeneity. We observe significant variability to epidermal growth factor receptor (EGFR) inhibition among and within multiple versions and clonal sublines of PC9, a commonly used EGFR mutant nonsmall cell lung cancer (NSCLC) cell line. We resolve genetic, epigenetic, and stochastic components of this variability using a theoretical framework in which distinct genetic states give rise to multiple epigenetic "basins of attraction," across which cells can transition driven by stochastic noise. Using mutational impact analysis, single-cell differential gene expression, and correlations among Gene Ontology (GO) terms to connect genomics to transcriptomics, we establish a baseline for genetic differences driving drug response variability among PC9 cell line versions. Applying the same approach to clonal sublines, we conclude that drug response variability in all but one of the sublines is due to epigenetic differences; in the other, it is due to genetic alterations. Finally, using a clonal drug response assay together with stochastic simulations, we attribute subclonal drug response variability within sublines to stochastic cell fate decisions and confirm that one subline likely contains genetic resistance mutations that emerged in the absence of drug treatment.

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.

Clinical and Genetic Factors Impact Time to Surgical Recurrence After Ileocolectomy for Crohn's Disease.

OBJECTIVE: The aim of this study was to evaluate factors associated with time to surgical recurrence after Crohn's ileocolectomy. SUMMARY BACKGROUND DATA: The most common surgery performed for Crohn's disease is ileocolectomy. Identifying patients at high risk for surgical recurrence may assist with medical and surgical decision-making. METHODS: Data were obtained from 409 patients with Crohn's disease (CD) who had undergone ≥1 ileocolectomies at Penn State Hershey Medical Center. Six single-nucleotide polymorphisms (SNPs) associated with CD were evaluated in these patients: rs2076756, rs2066844, and rs2066845 in NOD2, rs4958847 and rs13361189 in IRGM, and rs2241880 in ATG16L1. Genotype and clinical factors were analyzed to determine associations with time to recurrent ileocolectomy. A subgroup analysis was performed on 241 patients naïve to biologics before initial ileocolectomy to assess the effect of biologic therapy on time to recurrent surgery. RESULTS: There were 286 patients who underwent a single ileocolectomy, whereas 123 required multiple ileocolectomies. Ileocolonic involvement [hazard ratio (HR) 1.90, 95% confidence interval (CI) 1.21-3.00, P = 0.006] and rs2066844 in NOD2 (HR 1.8, 95% CI 1.17-2.77, P = 0.007) were associated with decreased time to surgical recurrence by multivariate analysis. In patients naïve to preoperative biologics, the initiation of postoperative biologics was associated with a 40% decreased incidence of surgical recurrence (HR 0.60, CI 0.39-0.93, P = 0.02) over time. CONCLUSIONS: Ileocolonic distribution of disease and the rs2066844 SNP in NOD2 are associated with shorter time to recurrent ileocolectomy. The initiation of postoperative biologics in naïve patients was associated with a reduced incidence of recurrence over time.

Novel Therapies for Relapsed or Refractory Diffuse Large B-Cell Lymphoma.

The most common type of non-Hodgkin lymphoma in adults is diffuse large B-cell (DLBCL). There is a historical unmet need for more effective therapies in the 2nd and 3rd line setting. Emerging immunochemotherapies have shown activity in small studies of heavily pre-treated patients with prolonged remissions achieved in some patients. Anti-CD19 CAR (chimeric antigen receptor) T cells are potentially curative in the 3rd line and beyond setting and are under investigation in earlier lines of therapy. Antibody-drug conjugates (ADC's) such as polatuzumab vedotin targeting the pan-B-cell marker CD79b has proven effectiveness in multiply-relapsed DLBCL patients. Tafasitamab (MOR208) is an anti-CD19 monoclonal antibody producing prolonged remissions when combined with Lenalidomide (LEN) in patients who were not candidates for salvage chemotherapy or autologous stem cell transplant. Selinexor, an oral, small-molecule selective inhibitor of XPO1-mediated nuclear export (SINE), demonstrated prolonged activity against heavily-pretreated DLBCL without cumulative toxicity and is being investigated as part of an oral, chemotherapy-free regimen for relapsed aggressive lymphoma. This article reviews current strategies and novel therapies for relapsed/refractory DLBCL.

Transcriptomic analysis of ileal tissue from Crohn's disease patients identifies extracellular matrix genes that distinguish individuals by age at diagnosis.

Crohn's disease (CD) is a debilitating gastrointestinal (GI) disorder that can impact the entirety of the GI tract. While substantial progress has been made in the medical management of CD, it remains incurable, frequently relapses, and is a significant financial and medical burden. The pathophysiology of CD is not well understood, but it is thought to arise in genetically susceptible individuals upon an environmental insult. Further elucidation of the disease etiology promises to expose additional therapeutic avenues, with the hope of reducing the burden of CD. One approach to understanding disease pathophysiology is to identify clinically relevant molecular disease subsets by using transcriptomics. In this report, we use hierarchical clustering of the ileal transcriptomes of 34 patients and identify two CD subsets. Clinically, these clusters differed in the age of the patients at CD diagnosis, suggesting that age of onset affects disease pathophysiology. The clusters were segregated by three major gene ontology categories: developmental processes, ion homeostasis, and the immune response. Of the genes constituting the immune system category, expression of extracellular matrix-associated genes, COL4A1, S100A9, ADAMTS2, SERPINE1, and FCN1, exhibits the strongest correlation with an individual's age at CD diagnosis. Together these findings demonstrate that transcriptional profiling is a powerful approach to subclassify CD patients.

Association Between Pesticide Use and Incidence of Diffuse Large B-Cell Lymphoma.

BACKGROUND/AIM: Exposure to pesticides has been reportedly associated with several types of cancer. MATERIALS AND METHODS: In this study, we used data from The United States Geological Survey (USGS), United States Census, and the Surveillance, Epidemiology, and End Results (SEER) database to analyze the association between the area density of specific agricultural pesticides and the county level annual incidence of diffuse large B-cell lymphoma (DLBCL). RESULTS: Incidence of DLBCL was significantly associated with an area density of 14 of the pesticides reported by USGS. CONCLUSION: This highlights the need for further investigation into the safety of the use of these pesticides. The importance of this study comes not only from the significant association it shows between pesticides and the incidence of cancer, but also from the fact that it included all compounds reported to USGS as being used in agriculture. This helps in prioritizing pesticides for further evaluation.

Systems biology markup language (SBML) level 3 package: multistate, multicomponent and multicompartment species, version 1, release 2.

Rule-based modeling is an approach that permits constructing reaction networks based on the specification of rules for molecular interactions and transformations. These rules can encompass details such as the interacting sub-molecular domains and the states and binding status of the involved components. Conceptually, fine-grained spatial information such as locations can also be provided. Through "wildcards" representing component states, entire families of molecule complexes sharing certain properties can be specified as patterns. This can significantly simplify the definition of models involving species with multiple components, multiple states, and multiple compartments. The systems biology markup language (SBML) Level 3 Multi Package Version 1 extends the SBML Level 3 Version 1 core with the "type" concept in the Species and Compartment classes. Therefore, reaction rules may contain species that can be patterns and exist in multiple locations. Multiple software tools such as Simmune and BioNetGen support this standard that thus also becomes a medium for exchanging rule-based models. This document provides the specification for Release 2 of Version 1 of the SBML Level 3 Multi package. No design changes have been made to the description of models between Release 1 and Release 2; changes are restricted to the correction of errata and the addition of clarifications.

Enhanced Recovery after Abdominoplasty Using Perisurgical Nutritional Supplementation.

Nutritional supplements are common in other surgical specialties but not widely used in the plastic surgery setting. This study compares the surgical outcomes of patients using our standard ERAS protocol involving arnica and bromelain with an updated ERAS protocol using perisurgical nutritional supplementation using a staged administration of nutraceuticals. METHODS: In total, 300 female abdominoplasty patients were randomly provided with perioperative supplementation consisting of arnica and bromelain, or a 3-stage nutraceutical regimen containing arginine, citrulline, glutamine, bromelain, and vitamin C. Narcotic use in recovery and post-operative drainage were measured, and both groups completed a self-assessment of bruising coloration, days to independent activity, perceived pain, and documented the quantity of narcotic and non-prescription pain killers they took over a 14-day recovery period. RESULTS: There were 130 patients in the nutraceutical group and 80 in the arnica and bromelain group; patients were excluded due to non-compliance or due to incomplete data. Patients taking the nutraceutical regimen reported a shorter duration of pain and had a 41% reduction in narcotic use in recovery and experienced 48% less post-operative drainage. Home use of narcotic pain killers decreased by 25%. There also was a trend toward decreased and earlier clearing/maturation of bruising as well as return to daily activates without assistance. Patients also reported an increase in satisfaction with their surgical experience. CONCLUSIONS: This study demonstrated that perioperative supplementation with nitric oxide precursors, antioxidants, and proteolytic enzymes in a staged fashion can positively affect post-operative outcomes and is an adjunct to enhanced surgical recovery protocols.

Elevated Colonic Mucin Expression Correlates with Extended Time to Surgery for Ulcerative Colitis Patients.

BACKGROUND AND AIMS: Both genetic and environmental factors contribute to the development and persistence of ulcerative colitis (UC). As supported by differential responses to therapy, multiple subclasses of disease likely comprise UC. We reasoned that profiling the colonic transcriptomes may offer one approach to molecular subtype UC. METHODS: We conducted RNA-sequencing (RNA-seq) on full-thickness colonic tissues from 26 UC patients undergoing colectomy. Hierarchal clustering from transcriptomic data identified disease subsets. Subsets were characterized using differential gene expression analysis, cell type deconvolution, and network analysis. RESULTS: We identified two UC subsets that were distinguished by 957 differentially expressed genes. Cluster 1 was enriched in genes associated with intestinal epithelial cell (IEC) differentiation, while cluster 2 was enriched in genes associated with epithelial-to-mesenchymal transition (EMT) and inflammatory responses. Cluster 1 was associated with an extended time from diagnosis to colectomy [hazard ratio = 0.45 (95% CI: 0.14-0.88); p=0.03]. Of cluster 1 genes, elevated MUC5B, MUC4, and MUC2 expression displayed the strongest correlation with increased time to surgery [hazard ratio = 0.37 (95% CI: 0.11-0.61); p=0.0044]. CONCLUSIONS: Our transcriptome analysis indicates that UC can be sub-classified into at least two molecular signatures. We found that elevated mucin gene expression correlated with prolonged time to colectomy following diagnosis. This work identified MUC5B, MUC4, and MUC2 as potential prognostic indicators of disease severity, as reflected in time to surgery after diagnosis.

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.