Alexander Statnikov

The Notch/Hes1 Pathway Sustains NF-κB Activation through CYLD Repression in T Cell Leukemia

It was previously shown that the NF-κB pathway is downstream of oncogenic Notch1 in T cell acute lymphoblastic leukemia (T-ALL). Here, we visualize Notch-induced NF-κB activation using both human T-ALL cell lines and animal models. We demonstrate that Hes1, a canonical Notch target and transcriptional repressor, is responsible for sustaining IKK activation in T-ALL. Hes1 exerts its effects by repressing the deubiquitinase CYLD, a negative IKK complex regulator. CYLD expression was found to be significantly suppressed in primary T-ALL. Finally, we demonstrate that IKK inhibition is a promising option for the targeted therapy of T-ALL as specific suppression of IKK expression and function affected both the survival of human T-ALL cells and the maintenance of the disease in vivo.

Regression of atherosclerosis is characterized by broad changes in the plaque macrophage transcriptome.

We have developed a mouse model of atherosclerotic plaque regression in which an atherosclerotic aortic arch from a hyperlipidemic donor is transplanted into a normolipidemic recipient, resulting in rapid elimination of cholesterol and monocyte-derived macrophage cells (CD68+) from transplanted vessel walls. To gain a comprehensive view of the differences in gene expression patterns in macrophages associated with regressing compared with progressing atherosclerotic plaque, we compared mRNA expression patterns in CD68+ macrophages extracted from plaque in aortic aches transplanted into normolipidemic or into hyperlipidemic recipients. In CD68+ cells from regressing plaque we observed that genes associated with the contractile apparatus responsible for cellular movement (e.g. actin and myosin) were up-regulated whereas genes related to cell adhesion (e.g. cadherins, vinculin) were down-regulated. In addition, CD68+ cells from regressing plaque were characterized by enhanced expression of genes associated with an anti-inflammatory M2 macrophage phenotype, including arginase I, CD163 and the C-lectin receptor. Our analysis suggests that in regressing plaque CD68+ cells preferentially express genes that reduce cellular adhesion, enhance cellular motility, and overall act to suppress inflammation.

A comprehensive evaluation of multicategory classification methods for microbiomic data

BackgroundRecent advances in next-generation DNA sequencing enable rapid high-throughput quantitation of microbial community composition in human samples, opening up a new field of microbiomics. One of the promises of this field is linking abundances of microbial taxa to phenotypic and physiological states, which can inform development of new diagnostic, personalized medicine, and forensic modalities. Prior research has demonstrated the feasibility of applying machine learning methods to perform body site and subject classification with microbiomic data. However, it is currently unknown which classifiers perform best among the many available alternatives for classification with microbiomic data.ResultsIn this work, we performed a systematic comparison of 18 major classification methods, 5 feature selection methods, and 2 accuracy metrics using 8 datasets spanning 1,802 human samples and various classification tasks: body site and subject classification and diagnosis.ConclusionsWe found that random forests, support vector machines, kernel ridge regression, and Bayesian logistic regression with Laplace priors are the most effective machine learning techniques for performing accurate classification from these microbiomic data.

Quantitative forecasting of PTSD from early trauma responses: A Machine Learning application

There is broad interest in predicting the clinical course of mental disorders from early, multimodal clinical and biological information. Current computational models, however, constitute a significant barrier to realizing this goal. The early identification of trauma survivors at risk of post-traumatic stress disorder (PTSD) is plausible given the disorders salient onset and the abundance of putative biological and clinical risk indicators. This work evaluates the ability of Machine Learning (ML) forecasting approaches to identify and integrate a panel of unique predictive characteristics and determine their accuracy in forecasting non-remitting PTSD from information collected within 10 days of a traumatic event. Data on event characteristics, emergency department observations, and early symptoms were collected in 957 trauma survivors, followed for fifteen months. An ML feature selection algorithm identified a set of predictors that rendered all others redundant. Support Vector Machines (SVMs) as well as other ML classification algorithms were used to evaluate the forecasting accuracy of i) ML selected features, ii) all available features without selection, and iii) Acute Stress Disorder (ASD) symptoms alone. SVM also compared the prediction of a) PTSD diagnostic status at 15 months to b) posterior probability of membership in an empirically derived non-remitting PTSD symptom trajectory. Results are expressed as mean Area Under Receiver Operating Characteristics Curve (AUC). The feature selection algorithm identified 16 predictors, present in ≥ 95% cross-validation trials. The accuracy of predicting non-remitting PTSD from that set (AUC = .77) did not differ from predicting from all available information (AUC = .78). Predicting from ASD symptoms was not better then chance (AUC = .60). The prediction of PTSD status was less accurate than that of membership in a non-remitting trajectory (AUC = .71). ML methods may fill a critical gap in forecasting PTSD. The ability to identify and integrate unique risk indicators makes this a promising approach for developing algorithms that infer probabilistic risk of chronic posttraumatic stress psychopathology based on complex sources of biological, psychological, and social information.

Microbiomic signatures of psoriasis: Feasibility and methodology comparison

Psoriasis is a common chronic inflammatory disease of the skin. We sought to use bacterial community abundance data to assess the feasibility of developing multivariate molecular signatures for differentiation of cutaneous psoriatic lesions, clinically unaffected contralateral skin from psoriatic patients, and similar cutaneous loci in matched healthy control subjects. Using 16S rRNA high-throughput DNA sequencing, we assayed the cutaneous microbiome for 51 such matched specimen triplets including subjects of both genders, different age groups, ethnicities and multiple body sites. None of the subjects had recently received relevant treatments or antibiotics. We found that molecular signatures for the diagnosis of psoriasis result in significant accuracy ranging from 0.75 to 0.89 AUC, depending on the classification task. We also found a significant effect of DNA sequencing and downstream analysis protocols on the accuracy of molecular signatures. Our results demonstrate that it is feasible to develop accurate molecular signatures for the diagnosis of psoriasis from microbiomic data.

Assessing quality and completeness of human transcriptional regulatory pathways on a genome-wide scale

BackgroundPathway databases are becoming increasingly important and almost omnipresent in most types of biological and translational research. However, little is known about the quality and completeness of pathways stored in these databases. The present study conducts a comprehensive assessment of transcriptional regulatory pathways in humans for seven well-studied transcription factors: MYC, NOTCH1, BCL6, TP53, AR, STAT1, and RELA. The employed benchmarking methodology first involves integrating genome-wide binding with functional gene expression data to derive direct targets of transcription factors. Then the lists of experimentally obtained direct targets are compared with relevant lists of transcriptional targets from 10 commonly used pathway databases.ResultsThe results of this study show that for the majority of pathway databases, the overlap between experimentally obtained target genes and targets reported in transcriptional regulatory pathway databases is surprisingly small and often is not statistically significant. The only exception is MetaCore pathway database which yields statistically significant intersection with experimental results in 84% cases. Additionally, we suggest that the lists of experimentally derived direct targets obtained in this study can be used to reveal new biological insight in transcriptional regulation and suggest novel putative therapeutic targets in cancer.ConclusionsOur study opens a debate on validity of using many popular pathway databases to obtain transcriptional regulatory targets. We conclude that the choice of pathway databases should be informed by solid scientific evidence and rigorous empirical evaluation.ReviewersThis article was reviewed by Prof. Wing Hung Wong, Dr. Thiago Motta Venancio (nominated by Dr. L Aravind), and Prof. Geoff J McLachlan.

Analysis and Computational Dissection of Molecular Signature Multiplicity

Molecular signatures are computational or mathematical models created to diagnose disease and other phenotypes and to predict clinical outcomes and response to treatment. It is widely recognized that molecular signatures constitute one of the most important translational and basic science developments enabled by recent high-throughput molecular assays. A perplexing phenomenon that characterizes high-throughput data analysis is the ubiquitous multiplicity of molecular signatures. Multiplicity is a special form of data analysis instability in which different analysis methods used on the same data, or different samples from the same population lead to different but apparently maximally predictive signatures. This phenomenon has far-reaching implications for biological discovery and development of next generation patient diagnostics and personalized treatments. Currently the causes and interpretation of signature multiplicity are unknown, and several, often contradictory, conjectures have been made to explain it. We present a formal characterization of signature multiplicity and a new efficient algorithm that offers theoretical guarantees for extracting the set of maximally predictive and non-redundant signatures independent of distribution. The new algorithm identifies exactly the set of optimal signatures in controlled experiments and yields signatures with significantly better predictivity and reproducibility than previous algorithms in human microarray gene expression datasets. Our results shed light on the causes of signature multiplicity, provide computational tools for studying it empirically and introduce a framework for in silico bioequivalence of this important new class of diagnostic and personalized medicine modalities.

Bridging a translational gap: using machine learning to improve the prediction of PTSD

BackgroundPredicting Posttraumatic Stress Disorder (PTSD) is a pre-requisite for targeted prevention. Current research has identified group-level risk-indicators, many of which (e.g., head trauma, receiving opiates) concern but a subset of survivors. Identifying interchangeable sets of risk indicators may increase the efficiency of early risk assessment. The study goal is to use supervised machine learning (ML) to uncover interchangeable, maximally predictive combinations of early risk indicators.MethodsData variables (features) reflecting event characteristics, emergency department (ED) records and early symptoms were collected in 957 trauma survivors within ten days of ED admission, and used to predict PTSD symptom trajectories during the following fifteen months. A Target Information Equivalence Algorithm (TIE*) identified all minimal sets of features (Markov Boundaries; MBs) that maximized the prediction of a non-remitting PTSD symptom trajectory when integrated in a support vector machine (SVM). The predictive accuracy of each set of predictors was evaluated in a repeated 10-fold cross-validation and expressed as average area under the Receiver Operating Characteristics curve (AUC) for all validation trials.ResultsThe average number of MBs per cross validation was 800. MBs’ mean AUC was 0.75 (95% range: 0.67-0.80). The average number of features per MB was 18 (range: 12–32) with 13 features present in over 75% of the sets.ConclusionsOur findings support the hypothesized existence of multiple and interchangeable sets of risk indicators that equally and exhaustively predict non-remitting PTSD. ML’s ability to increase prediction versatility is a promising step towards developing algorithmic, knowledge-based, personalized prediction of post-traumatic psychopathology.

A comprehensive empirical comparison of modern supervised classification and feature selection methods for text categorization

An important aspect to performing text categorization is selecting appropriate supervised classification and feature selection methods. A comprehensive benchmark is needed to inform best practices in this broad application field. Previous benchmarks have evaluated performance for a few supervised classification and feature selection methods and limited ways to optimize them. The present work updates prior benchmarks by increasing the number of classifiers and feature selection methods order of magnitude, including adding recently developed, state‐of‐the‐art methods. Specifically, this study used 229 text categorization data sets/tasks, and evaluated 28 classification methods (both well‐established and proprietary/commercial) and 19 feature selection methods according to 4 classification performance metrics. We report several key findings that will be helpful in establishing best methodological practices for text categorization.

Low-grade inflammation in symptomatic knee osteoarthritis: prognostic value of inflammatory plasma lipids and peripheral blood leukocyte biomarkers.

Inflammatory mediators, such as prostaglandin E2 (PGE2) and interleukin‐1β (IL‐1β), are produced by osteoarthritic (OA) joint tissue, where they may contribute to disease pathogenesis. We undertook the present study to examine whether inflammation, evidenced in plasma and peripheral blood leukocytes (PBLs), reflects the presence, progression, or specific symptoms of symptomatic knee OA.