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A traceable biomarker is a member of a disease’s molecular pathway. A disease may be associated with several molecular pathways. Each different combination of these molecular pathways, to which detected traceable biomarkers belong, may serve as an indicative of the elicitation of the disease at a different time frame in the future. Based on this notion, we introduce a novel methodology for personalizing an individual’s degree of future susceptibility to a specific disease. We implemented the methodology in a working system called Susceptibility Degree to a Disease Predictor (SDDP). For a specific disease d, let S be the set of molecular pathways, to which traceable biomarkers detected from most patients of d belong. For the same disease d, let S′ be the set of molecular pathways, to which traceable biomarkers detected from a certain individual belong. SDDP is able to infer the subset S′′ ⊆{S-S′} of undetected molecular pathways for the individual. Thus, SDDP can infer undetected molecular pathways of a disease for an individual based on few molecular pathways detected from the individual. SDDP can also help in inferring the combination of molecular pathways in the set {S′+S′′}, whose traceable biomarkers collectively is an indicative of the disease. SDDP is composed of the following four components: information extractor, interrelationship between molecular pathways modeler, logic inferencer, and risk indicator. The information extractor takes advantage of the exponential increase of biomedical literature to automatically extract the common traceable biomarkers for a specific disease. The interrelationship between molecular pathways modeler models the hierarchical interrelationships between the molecular pathways of the traceable biomarkers. The logic inferencer transforms the hierarchical interrelationships between the molecular pathways into rule-based specifications. It employs the specification rules and the inference rules for predicate logic to infer as many as possible undetected molecular pathways of a disease for an individual. The risk indicator outputs a risk indicator value that reflects the individual’s degree of future susceptibility to the disease. We evaluated SDDP by comparing it experimentally with other methods. Results revealed marked improvement.

Recently, we described a new animal model of CNS primitive neuroectodermal tumors (CNS-PNET), which was generated by orthotopic transplantation of human Radial Glial (RG) cells into NOD-SCID mice’s brain sub-ventricular zone. In the current study we conducted comprehensive RNA-Seq analyses to gain insights on the mechanisms underlying tumorigenesis in this mouse model of CNS-PNET. Here we show that the RNA-Seq profiles derived from these tumors cluster with those reported for patients’ PNETs. Moreover, we found that (i) stabilization of HIF-1α and HIF-2α, which are involved in mediation of the hypoxic responses in the majority of cell types, (ii) up-regulation of MYCC, a key onco-protein whose dysregulation occurs in ~70% of human tumors, and (iii) accumulation of stabilized p53, which is commonly altered in human cancers, constitute hallmarks of our tumor model, and might represent the basis for CNS-PNET tumorigenesis in this model. We discuss the possibility that these three events might be interconnected. These results indicate that our model may prove invaluable to uncover the molecular events leading to MYCC and TP53 alterations, which would be of broader interest considering their relevance to many human malignancies. Lastly, this mouse model might prove useful for drug screening targeting MYCC and related members of its protein interaction network.

The Protein Kinase A (PKA) and Wnt signaling cascades are fundamental pathways involved in cellular development and maintenance. In the osteoblast lineage, these pathways have been demonstrated functionally to be essential for the production of mineralized bone. Evidence for PKA-Wnt crosstalk has been reported both during tumorigenesis and during organogenesis, and the nature of the interaction is thought to rely on tissue and cell context. In this manuscript, we analyzed bone tumors arising from mice with activated PKA caused by mutation of the PKA regulatory subunit Prkar1a. In primary cells from these tumors, we observed relocalization of β-catenin to intranuclear punctuate structures, which were identified as PML bodies. Cellular redistribution of β-catenin could be recapitulated by pharmacologic activation of PKA. Using 3T3-E1 pre-osteoblasts as a model system, we found that PKA phosphorylation sites on β-catenin were required for nuclear re-localization. Further, β-catenin's transport to the nucleus was accompanied by an increase in canonical Wnt-dependent transcription, which also required the PKA sites. PKA-Wnt crosstalk in the cells was bi-directional, including enhanced interactions between β-catenin and the cAMP-responsive element binding protein (CREB) and transcriptional crosstalk between the Wnt and PKA signaling pathways. Increases in canonical Wnt/β-catenin signaling were associated with a decrease in the activity of the non-canonical Wnt/Ror2 pathway, which has been shown to antagonize canonical Wnt signaling. Taken together, this study provides a new understanding of the complex regulation of the subcellular distribution of β-catenin and its differential protein-protein interaction that can be modulated by PKA signaling.

The ecotropic virus integration site 1 (EVI1) transcription factor is associated with human myeloid malignancy of poor prognosis and is overexpressed in 8-10% of adult AML and strikingly up to 27% of pediatric MLL-rearranged leukemias. For the first time, we report comprehensive genomewide EVI1 binding and whole transcriptome gene deregulation in leukemic cells using a combination of ChIP-Seq and RNA-Seq expression profiling. We found disruption of terminal myeloid differentiation and cell cycle regulation to be prominent in EVI-induced leukemogenesis. Specifically, we identified EVI1 directly binds to and downregulates the master myeloid differentiation gene Cebpe and several of its downstream gene targets critical for terminal myeloid differentiation. We also found EVI1 binds to and downregulates Serpinb2 as well as numerous genes involved in the Jak-Stat signaling pathway. Finally, we identified decreased expression of several ATP-dependent P2X purinoreceptors genes involved in apoptosis mechanisms. These findings provide a foundation for future study of potential therapeutic gene targets for EVI1-induced leukemia.

Most of the position weight matrix (PWM) based bioinformatics methods developed to predict transcription factor binding sites (TFBS) assume each nucleotide in the sequence motif contributes independently to the interaction between protein and DNA sequence, usually producing high false positive predictions. The increasing availability of TF enrichment profiles from recent ChIP-Seq methodology facilitates the investigation of dependent structure and accurate prediction of TFBSs. We develop a novel Tree-based PWM (TPWM) approach to accurately model the interaction between TF and its binding site. The whole tree-structured PWM could be considered as a mixture of different conditional-PWMs. We propose a discriminative approach, called TPD (TPWM based Discriminative Approach), to construct the TPWM from the ChIP-Seq data with a pre-existing PWM. To achieve the maximum discriminative power between the positive and negative datasets, the cutoff value is determined based on the Matthew Correlation Coefficient (MCC). The resulting TPWMs are evaluated with respect to accuracy on extensive synthetic datasets. We then apply our TPWM discriminative approach on several real ChIP-Seq datasets to refine the current TFBS models stored in the TRANSFAC database. Experiments on both the simulated and real ChIP-Seq data show that the proposed method starting from existing PWM has consistently better performance than existing tools in detecting the TFBSs. The improved accuracy is the result of modelling the complete dependent structure of the motifs and better prediction of true positive rate. The findings could lead to better understanding of the mechanisms of TF-DNA interactions.

Lung cancer is the leading cause of cancer related mortality worldwide, with non-small cell lung cancer (NSCLC) as the most prevalent form. Despite advances in treatment options including minimally invasive surgery, CT-guided radiation, novel chemotherapeutic regimens, and targeted therapeutics, prognosis remains dismal. Therefore, further molecular analysis of NSCLC is necessary to identify novel molecular targets that impact prognosis and the design of new-targeted therapies. In recent years, tumor “activated/reprogrammed” stromal cells that promote carcinogenesis have emerged as potential therapeutic targets. However, the contribution of stromal cells to NSCLC is poorly understood. Here, we show increased numbers of bone marrow (BM)-derived hematopoietic cells in the tumor parenchyma of NSCLC patients compared with matched adjacent non-neoplastic lung tissue. By sorting specific cellular fractions from lung cancer patients, we compared the transcriptomes of intratumoral myeloid compartments within the tumor bed with their counterparts within adjacent non-neoplastic tissue from NSCLC patients. The RNA sequencing of specific myeloid compartments (immature monocytic myeloid cells and polymorphonuclear neutrophils) identified differentially regulated genes and mRNA isoforms, which were inconspicuous in whole tumor analysis. Genes encoding secreted factors, including osteopontin (OPN), chemokine (C-C motif) ligand 7 (CCL7) and thrombospondin 1 (TSP1) were identified, which enhanced tumorigenic properties of lung cancer cells indicative of their potential as targets for therapy. This study demonstrates that analysis of homogeneous stromal populations isolated directly from fresh clinical specimens can detect important stromal genes of therapeutic value.