We examined the microRNA (miRNA) expression profile of 40 prostatectomy specimens from stage T2a/b, early relapse and nonrelapse cancer patients, to better understand the relationship between miRNA dysregulation and prostate oncogenesis. Paired analysis was carried out with microdissected, malignant and non-involved areas of each specimen, using high-throughput liquidphase hybridization (mirMASA) reactions and 114 miRNA probes. Five miRNAs (miR-23b, -100, -145, -221 and -222) were significantly downregulated in malignant tissues, according to significance analysis of microarrays and paired t-test with Bonferroni correction. Lowered expression of miR-23b, -145, -221 and -222 in malignant tissues was validated by quantitative reverse transcription (qRT)-PCR analyses. Ectopic expression of these miRNAs significantly reduced LNCaP cancer cell growth, suggesting growth modulatory roles for these miRNAs. Patient subset analysis showed that those with post-surgery elevation of prostatespecific antigen (chemical relapse) displayed a distinct expression profile of 16 miRNAs, as compared with patients with nonrelapse disease. A trend of increased expression (440%) of miR-135b and miR-194 was observed by qRT-PCR confirmatory analysis of 11 patients from each clinical subset. These findings indicate that an altered miRNA expression signature accompanied the prostate oncogenic process. Additional, aberrant miRNA expression features may reflect a tendency for early disease relapse. Growth inhibition through the reconstitution of miRNAs is potentially applicable for experimental therapy of prostate cancer, pending molecular validation of targeted genes.

BackgroundBetter understanding and prediction of PD progression could improve disease management and clinical trial design. We aimed to use longitudinal clinical, molecular, and genetic data to develop predictive models, compare potential biomarkers, and identify novel predictors for motor progression in PD. We also sought to assess the use of these models in the design of treatment trials in PD.MethodsA Bayesian multivariate predictive inference platform was applied to data from the Parkinson’s Progression Markers Initiative (PPMI) study (NCT01141023). We used genetic data and baseline molecular and clinical variables from PD patients and healthy controls to construct an ensemble of models to predict the annualised rate of the Movement Disorder Society-Unified Parkinson’s Disease Rating Scale parts II and III combined. We tested our overall explanatory power, as assessed by the coefficient of determination (R2), and replicated novel findings in an independent clinical cohort of PD patients from the Longitudinal and Biomarker Study in PD (LABS-PD; NCT00605163). The potential utility of these models for clinical trial design was quantified by comparing simulated randomized placebo-controlled trials within the out-of sample LABS-PD cohort.FindingsA total of 117 controls and 312 PD cases were available for analysis. Our model ensemble exhibited strong performance in-cohort (5-fold cross-validated R2=41%, 95% CI: 35% – 47%) and significant, though reduced, performance out-of-cohort (R2=9%, 95% CI: 4% – 16%). Individual predictive features identified from PPMI data were confirmed in the LABS-PD cohort of 317 PD patients. These included significant replication of higher baseline motor score, male sex, and increased age, as well as a novel PD-specific epistatic interaction all indicative of faster motor progression. Genetic variation was the most useful predictive marker of motor progression (2.9%, 95%CI: 1.5–4.3%). CSF biomarkers at baseline showed a more modest (0.3%; 95%CI: 0.1–0.5%), but still significant effect on motor progression prediction. The simulations (n=5000) showed that incorporating the predicted rates of motor progression into the final models of treatment effect reduced the variability in the study outcome allowing significant differences to be detected at sample sizes up to 20% smaller than in naïve trials.InterpretationOur model ensemble confirmed established and identified novel predictors of PD motor progression. Improving existing prognostic models through machine learning approaches should benefit trial design and evaluation, as well as clinical disease monitoring and treatment.FundingMichael J. Fox Foundation for Parkinson’s Research and National Institute of Neurological Disorders and Stroke (1P20NS092529-01).

Molecular networks governing responses to targeted therapies in cancer cells are complex dynamic systems that demonstrate non-intuitive behaviors. We applied a novel computational strategy to infer probabilistic causal relationships between network components based on gene expression. We constructed model comprised of an ensemble of networks using multi-dimensional data from cell line models of cell cycle arrest caused by inhibition of MEK1/2. Through simulation of reverse-engineered Bayesian network model, we generated predictions of G1-S transition. The model identified known components of the cell cycle machinery, such as CCND1, CCNE2, and CDC25A, as well as revealed novel regulators of G1-S transition, IER2, TRIB1, TRIM27. Experimental validation of model predictions confirmed 10 out 12 predicted genes to have a role in G1-S progression. Further analysis showed that TRIB1 regulated the cyclin D1 promoter via NFκB and AP-1 sites and sensitized cells to TNF-Related Apoptosis-Inducing Ligand (TRAIL)-induced apoptosis. In clinical specimens of breast cancer, TRIB1 levels correlated with expression of NFκB and its target genes (IL8, CSF2), and TRIB1 copy number and expression were predictive of clinical outcome. Together our results establish a critical role of TRIB1 in cell cycle and survival that is mediated via the modulation of NFkB signaling.

The current dogma of G 1 cell-cycle progression relies on growth factor-induced increase of cyclin D:Cdk4/6 complex activity to partially inactivate pRb by phosphorylation and to sequester p27 Kip1 triggering activation of cyclin E:Cdk2 complexes that further inactivate pRb. pRb oscillates between an active, hypophosphorylated form associated with E2F transcription factors in early G 1 phase and an inactive, hyperphosphorylated form in late G 1 , S and G 2 /M phases. However, under constant growth factor stimulation, cells show constitutively active cyclin D:Cdk4/6 throughout the cell cycle and thereby exclude cyclin D:Cdk4/6 inactivation of pRb. To address this paradox, we developed a mathematical model of G 1 progression using physiological expression and activity profiles from synchronized cells exposed to constant growth factors and included a metabolically responsive, activating modifier of cyclin E:Cdk2. Our mathematical model accurately simulates G 1 progression, recapitulates observations from targeted gene deletion studies and serves as a foundation for development of therapeutics targeting G 1 cell-cycle progression.

An important challenge facing the field is how better to translate in vitro discoveries to the clinic. Computational systems biology approaches that use omic data to predict biology along with novel experimental systems that better represent human in vivo biology will prove useful in bridging this gap. Although still early, the potential application of systems biology and the future evolution of the field will significantly affect understanding of cancer disease mechanisms and the ability to devise effective therapeutics.

Tumor necrosis factor α (TNF-α) is a key regulator of inflammation and rheumatoid arthritis (RA). TNF-α blocker therapies can be very effective for a substantial number of patients, but fail to work in one third of patients who show no or minimal response. It is therefore necessary to discover new molecular intervention points involved in TNF-α blocker treatment of rheumatoid arthritis patients. We describe a data analysis strategy for predicting gene expression measures that are critical for rheumatoid arthritis using a combination of comprehensive genotyping, whole blood gene expression profiles and the component clinical measures of the arthritis Disease Activity Score 28 (DAS28) score. Two separate network ensembles, each comprised of 1024 networks, were built from molecular measures from subjects before and 14 weeks after treatment with TNF-α blocker. The network ensemble built from pre-treated data captures TNF-α dependent mechanistic information, while the ensemble built from data collected under TNF-α blocker treatment captures TNF-α independent mechanisms. In silico simulations of targeted, personalized perturbations of gene expression measures from both network ensembles identify transcripts in three broad categories. Firstly, 22 transcripts are identified to have new roles in modulating the DAS28 score; secondly, there are 6 transcripts that could be alternative targets to TNF-α blocker therapies, including CD86 - a component of the signaling axis targeted by Abatacept (CTLA4-Ig), and finally, 59 transcripts that are predicted to modulate the count of tender or swollen joints but not sufficiently enough to have a significant impact on DAS28.

Using the Diagrammatic Cell Language trade mark, Gene Network Sciences (GNS) has created a network model of interconnected signal transduction pathways and gene expression networks that control human cell proliferation and apoptosis. It includes receptor activation and mitogenic signaling, initiation of cell cycle, and passage of checkpoints and apoptosis. Time-course experiments measuring mRNA abundance and protein activity are conducted on Caco-2 and HCT 116 colon cell lines. These data were used to constrain unknown regulatory interactions and kinetic parameters via sensitivity analysis and parameter optimization methods contained in the DigitalCell computer simulation platform. FACS, RNA knockdown, cell growth, and apoptosis data are also used to constrain the model and to identify unknown pathways, and cross talk between known pathways will also be discussed. Using the cell simulation, GNS tested the efficacy of various drug targets and performed validation experiments to test computer simulation predictions. The simulation is a powerful tool that can in principle incorporate patient-specific data on the DNA, RNA, and protein levels for assessing efficacy of therapeutics in specific patient populations and can greatly impact success of a given therapeutic strategy.

Introduction: To identify predictors of hypoglycemia and five other clinical and economic outcomes among treated patients with type 2 diabetes (T2D) using machine learning and structured data from a large, geographically diverse administrative claims database. Methods: A retrospective cohort study design was applied to Optum Clinformatics claims data indexed on first antidiabetic prescription date. A hypothesis-free, Bayesian machine learning analytics platform (GNS Healthcare REFS TM : Reverse Engineering and Forward Simulation) was used to build ensembles of generalized linear models to predict six outcomes defined in patients' 1-year post-index claims history, including hypoglycemia, antidiabetic class persistence, glycated hemoglobin (HbA1c) target Enhanced Digital Features To view enhanced digital features for this article go to https://doi.org/10.6084/ m9.figshare.11527902.