Time series modeling of cell cycle exit identifies Brd4 dependent regulation of cerebellar neurogenesis

Cerebellar neuronal progenitors undergo a series of divisions before irreversibly exiting the cell cycle and differentiating into neurons. Dysfunction of this process underlies many neurological diseases including ataxia and the most common pediatric brain tumor, medulloblastoma. To better define the pathways controlling the most abundant neuronal cells in the mammalian cerebellum, cerebellar granule cell progenitors (GCPs), we performed RNA-sequencing of GCPs exiting the cell cycle. Time-series modeling of GCP cell cycle exit identified downregulation of activity of the epigenetic reader protein Brd4. Brd4 binding to the Gli1 locus is controlled by Casein Kinase 1δ (CK1 δ)-dependent phosphorylation during GCP proliferation, and decreases during GCP cell cycle exit. Importantly, conditional deletion of Brd4 in vivo in the developing cerebellum induces cerebellar morphological deficits and ataxia. These studies define an essential role for Brd4 in cerebellar granule cell neurogenesis and are critical for designing clinical trials utilizing Brd4 inhibitors in neurological indications

Ontological representation, integration, and analysis of LINCS cell line cells and their cellular responses

Abstract Background Aiming to understand cellular responses to different perturbations, the NIH Common Fund Library of Integrated Network-based Cellular Signatures (LINCS) program involves many institutes and laboratories working on over a thousand cell lines. The community-based Cell Line Ontology (CLO) is selected as the default ontology for LINCS cell line representation and integration. Results CLO has consistently represented all 1097 LINCS cell lines and included information extracted from the LINCS Data Portal and ChEMBL. Using MCF 10A cell line cells as an example, we demonstrated how to ontologically model LINCS cellular signatures such as their non-tumorigenic epithelial cell type, three-dimensional growth, latrunculin-A-induced actin depolymerization and apoptosis, and cell line transfection. A CLO subset view of LINCS cell lines, named LINCS-CLOview, was generated to support systematic LINCS cell line analysis and queries. In summary, LINCS cell lines are currently associated with 43 cell types, 131 tissues and organs, and 121 cancer types. The LINCS-CLO view information can be queried using SPARQL scripts. Conclusions CLO was used to support ontological representation, integration, and analysis of over a thousand LINCS cell line cells and their cellular responses.https://deepblue.lib.umich.edu/bitstream/2027.42/140390/1/12859_2017_Article_1981.pd

Drug and disease signature integration identifies synergistic combinations in glioblastoma

Dataset for the paper "Drug and disease signature integration identifies synergistic combinations in glioblastoma

Data-Driven Identification of Patient-Specific Drug Combinations for the Treatment of Glioblastoma

Glioblastoma is the most common malignant primary adult brain tumor with a standard of care consisting of maximal surgical resection followed by radiotherapy and adjuvant temozolomide (TMZ) chemotherapy. However, despite medical advances in the field, recurrence is almost universal. As with most cancers, heterogeneity and adoptive reprogramming upon therapy, which often leads to resistance, represent huge barriers to clinical care. Novel targeted therapies, which are the foundation of precision medicine, are therefore urgently required. During the last decade, several large research consortia have generated unprecedented amounts of data to characterize and model complex biological systems and disease, in particular, cancer. Consequently, ‘big data’ approaches are now emerging in biomedical research. However, significant data science challenges still exist to fully leverage these resources. In this work, several informatics and computational solutions were developed and implemented to integrate, analyze and model data generated in the Library of Integrated Network-based Cellular Signatures (LINCS) Consortium. This work formed the foundation for the development of a general approach to prioritize and rank glioblastoma combination therapies. In addition, the data science work contributed to several tools enabling the community in data-driven research projects. Towards the development of glioblastoma therapies, transcriptional data from patient samples were used to generate personalized gene co-expression networks. The networks were further characterized and validated using available protein-protein interaction and biochemical data. Following these initial results to identify prospective personalized therapies, the glioblastoma data was then integrated with the LINCS L1000 transcriptional perturbation response library. Analyzing the L1000 data across many cell lines and small molecules, LINCS compounds could be clustered into distinct pharmacological drug classes based on the transcriptional changes they induce and the pathways they modulate. The integrative data analytics approaches were facilitated by the rich metadata and curated LINCS compound target annotations. These results led to the hypothesis that compounds affecting orthogonal transcriptional pathways, or distinct co-expression networks, would synergize in reducing proliferation of glioblastoma cells. The top compound combination was selected and tested in in vitro and in vivo assays validating this approach. In summary, foundational data science work and novel computational biology algorithms enabled the integration and modeling of data from several distinct sources providing a novel platform for identifying therapeutic combinations in glioblastoma and other cancers

Identifying Glioblastoma Gene Networks Based on Hypergeometric Test Analysis

Patient specific therapy is emerging as an important possibility for many cancer patients. However, to identify such therapies it is essential to determine the genomic and transcriptional alterations present in one tumor relative to control samples. This presents a challenge since use of a single sample precludes many standard statistical analysis techniques. We reasoned that one means of addressing this issue is by comparing transcriptional changes in one tumor with those observed in a large cohort of patients analyzed by The Cancer Genome Atlas (TCGA). To test this directly, we devised a bioinformatics pipeline to identify differentially expressed genes in tumors resected from patients suffering from the most common malignant adult brain tumor, glioblastoma (GBM). We performed RNA sequencing on tumors from individual GBM patients and filtered the results through the TCGA database in order to identify possible gene networks that are overrepresented in GBM samples relative to controls. Importantly, we demonstrate that hypergeometric-based analysis of gene pairs identifies gene networks that validate experimentally. These studies identify a putative workflow for uncovering differentially expressed patient specific genes and gene networks for GBM and other cancers

Machine and deep learning approaches for cancer drug repurposing

Knowledge of the underpinnings of cancer initiation, progression and metastasis has increased exponentially in recent years. Advanced “omics” coupled with machine learning and artificial intelligence (deep learning) methods have helped elucidate targets and pathways critical to those processes that may be amenable to pharmacologic modulation. However, the current anti-cancer therapeutic armamentarium continues to lag behind. As the cost of developing a new drug remains prohibitively expensive, repurposing of existing approved and investigational drugs is sought after given known safety profiles and reduction in the cost barrier. Notably, successes in oncologic drug repurposing have been infrequent. Computational in-silico strategies have been developed to aid in modeling biological processes to find new disease-relevant targets and discovering novel drug-target and drug-phenotype associations. Machine and deep learning methods have especially enabled leaps in those successes. This review will discuss these methods as they pertain to cancer biology as well as immunomodulation for drug repurposing opportunities in oncologic diseases

The Clinical Kinase Index: A Method to Prioritize Understudied Kinases as Drug Targets for the Treatment of Cancer

The approval of the first kinase inhibitor, Gleevec, ushered in a paradigm shift for oncological treatment—the use of genomic data for targeted, efficacious therapies. Since then, over 48 additional small-molecule kinase inhibitors have been approved, solidifying the case for kinases as a highly druggable and attractive target class. Despite the role deregulated kinase activity plays in cancer, only 8% of the kinome has been effectively “drugged.” Moreover, 24% of the 634 human kinases are understudied. We have developed a comprehensive scoring system that utilizes differential gene expression, pathological parameters, overall survival, and mutational hotspot analysis to rank and prioritize clinically relevant kinases across 17 solid tumor cancers from The Cancer Genome Atlas. We have developed the clinical kinase index (CKI) app (http://cki.ccs.miami.edu) to facilitate interactive analysis of all kinases in each cancer. Collectively, we report that understudied kinases have potential clinical value as biomarkers or drug targets that warrant further study. [Display omitted] CKI is a novel method to prioritize dark kinases as cancer drug targetsExpression of understudied kinases in tumors is prognostic of poor outcomesDark kinases are likely clinically relevant cancer targetsCancer cell dependency correlates with tumor pathology and survival Essegian et. al conduct a kinome-wide pan-cancer analysis and provide an intuitive interface to prioritize understudied kinases as prospective novel cancer drug targets. While many of the approved kinase drug targets rank high in each cancer cohort, several understudied kinases also appear to be clinically relevant and warrant further exploration

The Clinical Kinase Index: Prioritizing Understudied Kinases as Targets for the Treatment of Cancer

Abstract The approval of the first kinase inhibitor, Gleevec, in 2001, ushered in a paradigm shift for oncological treatment—the use of genomic data for targeted, efficacious therapies. Since then, over 48 additional small molecule kinase inhibitors have been approved, solidifying the case for kinases as a highly druggable and attractive target class. Despite the established role deregulated kinase activity plays in cancer, only 8% of the entire kinome has been effectively “drugged”. Moreover, a quarter of the 634 human kinases are vastly understudied. We have developed a comprehensive scoring system which utilizes differential gene expression, clinical and pathological parameters, overall survival and mutational hotspot analysis to rank and prioritize clinically-relevant kinase targets across 17 solid tumor cancers from The Cancer Genome Atlas (TCGA). Collectively, we report that dark kinases have potential clinical value as biomarkers or as new drug targets that warrant further study