Stable Feature Selection for Biomarker Discovery

Feature selection techniques have been used as the workhorse in biomarker discovery applications for a long time. Surprisingly, the stability of feature selection with respect to sampling variations has long been under-considered. It is only until recently that this issue has received more and more attention. In this article, we review existing stable feature selection methods for biomarker discovery using a generic hierarchal framework. We have two objectives: (1) providing an overview on this new yet fast growing topic for a convenient reference; (2) categorizing existing methods under an expandable framework for future research and development

DNA methylation-associated colonic mucosal immune and defense responses in treatment-naïve pediatric ulcerative colitis

Inflammatory bowel diseases (IBD) are emerging globally, indicating that environmental factors may be important in their pathogenesis. Colonic mucosal epigenetic changes, such as DNA methylation, can occur in response to the environment and have been implicated in IBD pathology. However, mucosal DNA methylation has not been examined in treatment-naïve patients. We studied DNA methylation in untreated, left sided colonic biopsy specimens using the Infinium HumanMethylation450 BeadChip array. We analyzed 22 control (C) patients, 15 untreated Crohn’s disease (CD) patients, and 9 untreated ulcerative colitis (UC) patients from two cohorts. Samples obtained at the time of clinical remission from two of the treatment-naïve UC patients were also included into the analysis. UC-specific gene expression was interrogated in a subset of adjacent samples (5 C and 5 UC) using the Affymetrix GeneChip PrimeView Human Gene Expression Arrays. Only treatment-naïve UC separated from control. One-hundred-and-twenty genes with significant expression change in UC (> 2-fold, P < 0.05) were associated with differentially methylated regions (DMRs). Epigenetically associated gene expression changes (including gene expression changes in the IFITM1, ITGB2, S100A9, SLPI, SAA1, and STAT3 genes) were linked to colonic mucosal immune and defense responses. These findings underscore the relationship between epigenetic changes and inflammation in pediatric treatment-naïve UC and may have potential etiologic, diagnostic, and therapeutic relevance for IBD

NELFE-Dependent MYC Signature Identifies a Unique Cancer Subtype in Hepatocellular Carcinoma.

The MYC oncogene is dysregulated in approximately 30% of liver cancer. In an effort to exploit MYC as a therapeutic target, including in hepatocellular carcinoma (HCC), strategies have been developed on the basis of MYC amplification or gene translocation. Due to the failure of these strategies to provide accurate diagnostics and prognostic value, we have developed a Negative Elongation Factor E (NELFE)-Dependent MYC Target (NDMT) gene signature. This signature, which consists of genes regulated by MYC and NELFE, an RNA binding protein that enhances MYC-induced hepatocarcinogenesis, is predictive of NELFE/MYC-driven tumors that would otherwise not be identified by gene amplification or translocation alone. We demonstrate the utility of the NDMT gene signature to predict a unique subtype of HCC, which is associated with a poor prognosis in three independent cohorts encompassing diverse etiologies, demographics, and viral status. The application of gene signatures, such as the NDMT signature, offers patients access to personalized risk assessments, which may be utilized to direct future care

Machine Learning and Integrative Analysis of Biomedical Big Data.

Recent developments in high-throughput technologies have accelerated the accumulation of massive amounts of omics data from multiple sources: genome, epigenome, transcriptome, proteome, metabolome, etc. Traditionally, data from each source (e.g., genome) is analyzed in isolation using statistical and machine learning (ML) methods. Integrative analysis of multi-omics and clinical data is key to new biomedical discoveries and advancements in precision medicine. However, data integration poses new computational challenges as well as exacerbates the ones associated with single-omics studies. Specialized computational approaches are required to effectively and efficiently perform integrative analysis of biomedical data acquired from diverse modalities. In this review, we discuss state-of-the-art ML-based approaches for tackling five specific computational challenges associated with integrative analysis: curse of dimensionality, data heterogeneity, missing data, class imbalance and scalability issues

Dynamical robustness of biological networks with hierarchical distribution of time scales

We propose the concepts of distributed robustness and r-robustness, well adapted to functional genetics. Then we discuss the robustness of the relaxation time using a chemical reaction description of genetic and signalling networks. First, we obtain the following result for linear networks: for large multiscale systems with hierarchical distribution of time scales the variance of the inverse relaxation time (as well as the variance of the stationary rate) is much lower than the variance of the separate constants. Moreover, it can tend to 0 faster than 1/n, where n is the number of reactions. We argue that similar phenomena are valid in the nonlinear case as well. As a numerical illustration we use a model of signalling network that can be applied to important transcription factors such as NFkB

An efficient platform for astrocyte differentiation from human induced pluripotent stem cells

Summary: Growing evidence implicates the importance of glia, particularly astrocytes, in neurological and psychiatric diseases. Here, we describe a rapid and robust method for the differentiation of highly pure populations of replicative astrocytes from human induced pluripotent stem cells (hiPSCs), via a neural progenitor cell (NPC) intermediate. We evaluated this protocol across 42 NPC lines (derived from 30 individuals). Transcriptomic analysis demonstrated that hiPSC-astrocytes from four individuals are highly similar to primary human fetal astrocytes and characteristic of a non-reactive state. hiPSC-astrocytes respond to inflammatory stimulants, display phagocytic capacity, and enhance microglial phagocytosis. hiPSC-astrocytes also possess spontaneous calcium transient activity. Our protocol is a reproducible, straightforward (single medium), and rapid (<30 days) method to generate populations of hiPSC-astrocytes that can be used for neuron-astrocyte and microglia-astrocyte co-cultures for the study of neuropsychiatric disorders. : Brennand, Goate, and colleagues report a rapid and robust method for the differentiation of highly pure populations of replicative astrocytes from human induced pluripotent stem cells (hiPSCs) via a neural progenitor cell (NPC) intermediate. hiPSC-astrocytes resemble primary human fetal astrocytes, have a transcriptional signature consistent with a non-reactive state, respond to inflammatory stimulants, and enhance microglial phagocytosis. Keywords: human induced pluripotent stem cell, iPSC, astrocyt

Typing tumors using pathways selected by somatic evolution.

Many recent efforts to analyze cancer genomes involve aggregation of mutations within reference maps of molecular pathways and protein networks. Here, we find these pathway studies are impeded by molecular interactions that are functionally irrelevant to cancer or the patient's tumor type, as these interactions diminish the contrast of driver pathways relative to individual frequently mutated genes. This problem can be addressed by creating stringent tumor-specific networks of biophysical protein interactions, identified by signatures of epistatic selection during tumor evolution. Using such an evolutionarily selected pathway (ESP) map, we analyze the major cancer genome atlases to derive a hierarchical classification of tumor subtypes linked to characteristic mutated pathways. These pathways are clinically prognostic and predictive, including the TP53-AXIN-ARHGEF17 combination in liver and CYLC2-STK11-STK11IP in lung cancer, which we validate in independent cohorts. This ESP framework substantially improves the definition of cancer pathways and subtypes from tumor genome data

The Interaction of Genetic Background and Mutational Effects in Regulation of Mouse Craniofacial Shape.

Inbred genetic background significantly influences the expression of phenotypes associated with known genetic perturbations and can underlie variation in disease severity between individuals with the same mutation. However, the effect of epistatic interactions on the development of complex traits, such as craniofacial morphology, is poorly understood. Here, we investigated the effect of three inbred backgrounds (129X1/SvJ, C57BL/6J, and FVB/NJ) on the expression of craniofacial dysmorphology in mice (Mus musculus) with loss of function in three members of the Sprouty family of growth factor negative regulators (Spry1, Spry2, or Spry4) in order to explore the impact of epistatic interactions on skull morphology. We found that the interaction of inbred background and the Sprouty genotype explains as much craniofacial shape variation as the Sprouty genotype alone. The most severely affected genotypes display a relatively short and wide skull, a rounded cranial vault, and a more highly angled inferior profile. Our results suggest that the FVB background is more resilient to Sprouty loss of function than either C57 or 129, and that Spry4 loss is generally less severe than loss of Spry1 or Spry2 While the specific modifier genes responsible for these significant background effects remain unknown, our results highlight the value of intercrossing mice of multiple inbred backgrounds to identify the genes and developmental interactions that modulate the severity of craniofacial dysmorphology. Our quantitative results represent an important first step toward elucidating genetic interactions underlying variation in robustness to known genetic perturbations in mice