Profiling Secreted miRNA Biomarkers of Chemical-Induced Neurodegeneration in Human iPSC-Derived Neurons.

Elucidation of predictive fluidic biochemical markers to detect and monitor chemical-induced neurodegeneration has been a major challenge due to a lack of understanding of molecular mechanisms driving altered neuronal morphology and function, as well as poor sensitivity in methods to quantify low-level biomarkers in bodily fluids. Here, we evaluated 5 neurotoxicants (acetaminophen [negative control], bisindolylmaleimide-1, colchicine, doxorubicin, paclitaxel, and rotenone) in human-induced pluripotent stem cell-derived neurons to profile secreted microRNAs (miRNAs) at early and late stages of decline in neuronal cell morphology and viability. Based on evaluation of these morphological (neurite outgrowth parameters) and viability (adenosine triphosphate) changes, 2 concentrations of each chemical were selected for analysis in a human 754 miRNA panel: a low concentration with no/minimal effect on cell viability but a significant decrease in neurite outgrowth, and a high concentration with a significant decrease in both endpoints. A total of 39 miRNAs demonstrated significant changes (fold-change ≥ 1.5 or ≤ 0.67, p value < .01) with at least 1 exposure. Further analyses of targets modulated by these miRNAs revealed 38 key messenger RNA targets with roles in neurological dysfunctions, and identified transforming growth factor-beta (TGF-ß) signaling as a commonly enriched pathway. Of the 39 miRNAs, 5 miRNAs, 3 downregulated (miR-20a, miR-30b, and miR-30d) and 3 upregulated (miR-1243 and miR-1305), correlated well with morphological changes induced by multiple neurotoxicants and were notable based on their relationship to various neurodegenerative conditions and/or key pathways, such as TGF-ß signaling. These datasets reveal miRNA candidates that warrant further evaluation as potential safety biomarkers of chemical-induced neurodegeneration.

Development of a Drug-Response Modeling Framework to Identify Cell Line Derived Translational Biomarkers That Can Predict Treatment Outcome to Erlotinib or Sorafenib.

Development of drug responsive biomarkers from pre-clinical data is a critical step in drug discovery, as it enables patient stratification in clinical trial design. Such translational biomarkers can be validated in early clinical trial phases and utilized as a patient inclusion parameter in later stage trials. Here we present a study on building accurate and selective drug sensitivity models for Erlotinib or Sorafenib from pre-clinical in vitro data, followed by validation of individual models on corresponding treatment arms from patient data generated in the BATTLE clinical trial. A Partial Least Squares Regression (PLSR) based modeling framework was designed and implemented, using a special splitting strategy and canonical pathways to capture robust information for model building. Erlotinib and Sorafenib predictive models could be used to identify a sub-group of patients that respond better to the corresponding treatment, and these models are specific to the corresponding drugs. The model derived signature genes reflect each drug's known mechanism of action. Also, the models predict each drug's potential cancer indications consistent with clinical trial results from a selection of globally normalized GEO expression datasets.

Drug target prediction and repositioning using an integrated network-based approach.

The discovery of novel drug targets is a significant challenge in drug development. Although the human genome comprises approximately 30,000 genes, proteins encoded by fewer than 400 are used as drug targets in the treatment of diseases. Therefore, novel drug targets are extremely valuable as the source for first in class drugs. On the other hand, many of the currently known drug targets are functionally pleiotropic and involved in multiple pathologies. Several of them are exploited for treating multiple diseases, which highlights the need for methods to reliably reposition drug targets to new indications. Network-based methods have been successfully applied to prioritize novel disease-associated genes. In recent years, several such algorithms have been developed, some focusing on local network properties only, and others taking the complete network topology into account. Common to all approaches is the understanding that novel disease-associated candidates are in close overall proximity to known disease genes. However, the relevance of these methods to the prediction of novel drug targets has not yet been assessed. Here, we present a network-based approach for the prediction of drug targets for a given disease. The method allows both repositioning drug targets known for other diseases to the given disease and the prediction of unexploited drug targets which are not used for treatment of any disease. Our approach takes as input a disease gene expression signature and a high-quality interaction network and outputs a prioritized list of drug targets. We demonstrate the high performance of our method and highlight the usefulness of the predictions in three case studies. We present novel drug targets for scleroderma and different types of cancer with their underlying biological processes. Furthermore, we demonstrate the ability of our method to identify non-suspected repositioning candidates using diabetes type 1 as an example.

Functional synergies yet distinct modulators affected by genetic alterations in common human cancers.

An important general concern in cancer research is how diverse genetic alterations and regulatory pathways can produce common signaling outcomes. In this study, we report the construction of cancer models that combine unique regulation and common signaling. We compared and functionally analyzed sets of genetic alterations, including somatic sequence mutations and copy number changes, in breast, colon, and pancreatic cancer and glioblastoma that had been determined previously by global exon sequencing and SNP (single nucleotide polymorphism) array analyses in multiple patients. The genes affected by the different types of alterations were mostly unique in each cancer type, affected different pathways, and were connected with different transcription factors, ligands, and receptors. In our model, we show that distinct amplifications, deletions, and sequence alterations in each cancer resulted in common signaling pathways and transcription regulation. In functional clustering, the impact of the type of alteration was more pronounced than the impact of the kind of cancer. Several pathways such as TGF-ß/SMAD signaling and PI3K (phosphoinositide 3-kinase) signaling were defined as synergistic (affected by different alterations in all four cancer types). Despite large differences at the genetic level, all data sets interacted with a common group of 65 "universal cancer genes" (UCG) comprising a concise network focused on proliferation/apoptosis balance and angiogenesis. Using unique nodal regulators ("overconnected" genes), UCGs, and synergistic pathways, the cancer models that we built could combine common signaling with unique regulation. Our findings provide a novel integrated perspective on the complex signaling and regulatory networks that underlie common human cancers.