While synthetic lethality (SL) holds promise in developing effective cancer therapies, SL candidates found via experimental screens often have limited translational value. Here we present a data-driven approach, ISLE (identification of clinically relevant synthetic lethality), that mines TCGA cohort to identify the most likely clinically relevant SL interactions (cSLi) from a given candidate set of lab-screened SLi. We first validate ISLE via a benchmark of large-scale drug response screens and by predicting drug efficacy in mouse xenograft models. We then experimentally test a select set of predicted cSLi via new screening experiments, validating their predicted context-specific sensitivity in hypoxic vs normoxic conditions and demonstrating cSLi’s utility in predicting synergistic drug combinations. We show that cSLi can successfully predict patients’ drug treatment response and provide patient stratification signatures. ISLE thus complements existing actionable mutation-based methods for precision cancer therapy, offering an opportunity to expand its scope to the whole genome.

Postgenomics data are produced in large volumes by life sciences and clinical applications of novel omics diagnostics and therapeutics for precision medicine. To move from “data-to-knowledge-to-innovation,” a crucial missing step in the current era is, however, our limited understanding of biological and clinical contexts associated with data. Prominent among the emerging remedies to this challenge are the gene set enrichment tools. This study reports on GeneAnalytics™ (), a comprehensive and easy-to-apply gene set analysis tool for rapid contextualization of expression patterns and functional signatures embedded in the postgenomics Big Data domains, such as Next Generation Sequencing (NGS), RNAseq, and microarray experiments. GeneAnalytics' differentiating features include in-depth evidence-based scoring algorithms, an intuitive user interface and proprietary unified data. GeneAnalytics employs the LifeMap Science's GeneCards suite, including the GeneCards®—the human gene database; the MalaCards—the human diseases database; and the PathCards—the biological pathways database. Expression-based analysis in GeneAnalytics relies on the LifeMap Discovery®—the embryonic development and stem cells database, which includes manually curated expression data for normal and diseased tissues, enabling advanced matching algorithm for gene–tissue association. This assists in evaluating differentiation protocols and discovering biomarkers for tissues and cells. Results are directly linked to gene, disease, or cell “cards” in the GeneCards suite. Future developments aim to enhance the GeneAnalytics algorithm as well as visualizations, employing varied graphical display items. Such attributes make GeneAnalytics a broadly applicable postgenomics data analyses and interpretation tool for translation of data to knowledge-based innovation in various Big Data fields such as precision medicine, ecogenomics, nutrigenomics, pharmacogenomics, vaccinomics, and others yet to emerge on the postgenomics horizon.

In the human fetal kidney (HFK) self-renewing stem cells residing in the metanephric mesenchyme (MM)/blastema are induced to form all cell types of the nephron till 34th week of gestation. Definition of useful markers is crucial for the identification of HFK stem cells. Because wilms' tumor, a pediatric renal cancer, initiates from retention of renal stem cells, we hypothesized that surface antigens previously up-regulated in microarrays of both HFK and blastema-enriched stem-like wilms' tumor xenografts (NCAM, ACVRIIB, DLK1/PREF, GPR39, FZD7, FZD2, NTRK2) are likely to be relevant markers. Comprehensive profiling of these putative and of additional stem cell markers (CD34, CD133, c-Kit, CD90, CD105, CD24) in mid-gestation HFK was performed using immunostaining and FACS in conjunction with EpCAM, an epithelial surface marker that is absent from the MM and increases along nephron differentiation and hence can be separated into negative, dim or bright fractions. No marker was specifically localized to the MM. Nevertheless, FZD7 and NTRK2 were preferentially localized to the MM and emerging tubules (<10% of HFK cells) and were mostly present within the EpCAMneg and EpCAMdim fractions, indicating putative stem/progenitor markers. In contrast, single markers such as CD24 and CD133 as well as double-positive CD24+CD133+ cells comprise >50% of HFK cells and predominantly co-express EpCAMbright, indicating they are mostly markers of differentiation. Furthermore, localization of NCAM exclusively in the MM and in its nephron progenitor derivatives but also in stroma and the expression pattern of significantly elevated renal stem/progenitor genes Six2, Wt1, Cited1, and Sall1 in NCAM+EpCAM- and to a lesser extent in NCAM+EpCAM+ fractions confirmed regional identity of cells and assisted us in pinpointing the presence of subpopulations that are putative MM-derived progenitor cells (NCAM+EpCAM+FZD7+), MM stem cells (NCAM+EpCAM-FZD7+) or both (NCAM+FZD7+). These results and concepts provide a framework for developing cell selection strategies for human renal cell-based therapies.

Cell therapies aim to repair the mechanisms underlying disease initiation and progression, achieved through trophic effect or by cell replacement. Multiple cell types can be utilized in such therapies, including stem, progenitor or primary cells. This review covers the current state of cell therapies designed for the prominent disorders, including cardiovascular, neurological (Parkinson's disease, amyotrophic lateral sclerosis, stroke, spinal cord injury), autoimmune (Type 1 diabetes, multiple sclerosis, Crohn's disease), ophthalmologic, renal, liver and skeletal (osteoarthritis) diseases. Various cell therapies have reached advanced clinical trial phases with potential marketing approvals in the near future, many of which are based on mesenchymal stem cells. Advances in pluripotent stem cell research hold great promise for regenerative medicine. The information presented in this review is based on the analysis of the cell therapy collection detailed in LifeMap Discovery(®) (LifeMap Sciences Inc., USA) the database of embryonic development, stem cell research and regenerative medicine.

LifeMap Discovery™ provides investigators with an integrated database of embryonic development, stem cell biology and regenerative medicine. The hand-curated reconstruction of cell ontology with stem cell biology; including molecular, cellular, anatomical and disease-related information, provides efficient and easy-to-use, searchable research tools. The database collates in vivo and in vitro gene expression and guides translation from in vitro data to the clinical utility, and thus can be utilized as a powerful tool for research and discovery in stem cell biology, developmental biology, disease mechanisms and therapeutic discovery. LifeMap Discovery is freely available to academic nonprofit institutions at http://discovery.lifemapsc.com