The BioMart community portal: an innovative alternative to large, centralized data repositories.

The BioMart Community Portal (www.biomart.org) is a community-driven effort to provide a unified interface to biomedical databases that are distributed worldwide. The portal provides access to numerous database projects supported by 30 scientific organizations. It includes over 800 different biological datasets spanning genomics, proteomics, model organisms, cancer data, ontology information and more. All resources available through the portal are independently administered and funded by their host organizations. The BioMart data federation technology provides a unified interface to all the available data. The latest version of the portal comes with many new databases that have been created by our ever-growing community. It also comes with better support and extensibility for data analysis and visualization tools. A new addition to our toolbox, the enrichment analysis tool is now accessible through graphical and web service interface. The BioMart community portal averages over one million requests per day. Building on this level of service and the wealth of information that has become available, the BioMart Community Portal has introduced a new, more scalable and cheaper alternative to the large data stores maintained by specialized organizations.

Whole-genome sequencing identifies recurrent mutations in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is one of the most deadly cancers worldwide and has no effective treatment, yet the molecular basis of hepatocarcinogenesis remains largely unknown. Here we report findings from a whole-genome sequencing (WGS) study of 88 matched HCC tumor/normal pairs, 81 of which are Hepatitis B virus (HBV) positive, seeking to identify genetically altered genes and pathways implicated in HBV-associated HCC. We find beta-catenin to be the most frequently mutated oncogene (15.9%) and TP53 the most frequently mutated tumor suppressor (35.2%). The Wnt/beta-catenin and JAK/STAT pathways, altered in 62.5% and 45.5% of cases, respectively, are likely to act as two major oncogenic drivers in HCC. This study also identifies several prevalent and potentially actionable mutations, including activating mutations of Janus kinase 1 (JAK1), in 9.1% of patients and provides a path toward therapeutic intervention of the disease.

Dose-dependent effects of small-molecule antagonists on the genomic landscape of androgen receptor binding.

BACKGROUND: The androgen receptor plays a critical role throughout the progression of prostate cancer and is an important drug target for this disease. While chromatin immunoprecipitation coupled with massively parallel sequencing (ChIP-Seq) is becoming an essential tool for studying transcription and chromatin modification factors, it has rarely been employed in the context of drug discovery. RESULTS: Here we report changes in the genome-wide AR binding landscape due to dose-dependent inhibition by drug-like small molecules using ChIP-Seq. Integration of sequence analysis, transcriptome profiling, cell viability assays and xenograft tumor growth inhibition studies enabled us to establish a direct cistrome-activity relationship for two novel potent AR antagonists. By selectively occupying the strongest binding sites, AR signaling remains active even when androgen levels are low, as is characteristic of first-line androgen ablation therapy. Coupled cistrome and transcriptome profiling upon small molecule antagonism led to the identification of a core set of AR direct effector genes that are most likely to mediate the activities of targeted agents: unbiased pathway mapping revealed that AR is a key modulator of steroid metabolism by forming a tightly controlled feedback loop with other nuclear receptor family members and this oncogenic effect can be relieved by antagonist treatment. Furthermore, we found that AR also has an extensive role in negative gene regulation, with estrogen (related) receptor likely mediating its function as a transcriptional repressor. CONCLUSIONS: Our study provides a global and dynamic view of AR's regulatory program upon antagonism, which may serve as a molecular basis for deciphering and developing AR therapeutics.

Assessment of a 3D neural spheroid model to detect pharmaceutical-induced neurotoxicity.

Drug-induced neurotoxicity is a leading cause of safety-related attrition for therapeutics in clinical trials, often driven by poor predictivity of preclinical in vitro and in vivo models of neurotoxicity. Over a dozen different iPSC-derived 3D spheroids have been described in recent years, but their ability to predict neurotoxicity in patients has not been evaluated nor compared with the predictive power of nonclinical species. To assess the predictive capabilities of human iPSC-derived neural spheroids (microBrains), we used 84 structurally diverse pharmaceuticals with robust clinical and pre-clinical datasets with varying degrees of seizurogenic and neurodegenerative liability. Drug-induced changes in neural viability and phenotypic calcium bursts were assessed using 7 endpoints based on calcium oscillation profiles and cel-lular ATP levels. These endpoints, normalized by therapeutic exposure, were used to build logistic regression models to establish endpoint cutoffs and evaluate probability for clinical neurotoxicity. The neurotoxicity score calculated from the logistic regression model could distinguish neurotoxic from non-neurotoxic clinical molecules with a specificity as high as 93.33% and a sensitivity of 53.49%, demonstrating a very low false positive rate for the prediction of seizures, convulsions, and neurodegeneration. In contrast, nonclinical species showed a higher sensitivity (75%) but much lower specificity (30.4%). The neural spheroids demonstrated higher likelihood ratio positive and inverse likelihood ratio neg-ative values compared with nonclinical safety studies. This assay has the potential to be used as a predictive assay to detect neurotoxicity in early drug discovery, aiding in the early identification of compounds that eventually may fail due to neurotoxicity.

Lipid Nanoparticle-Mediated Delivery of Anti-miR-17 Family Oligonucleotide Suppresses Hepatocellular Carcinoma Growth.

Hepatocellular carcinoma (HCC) is one of the most common human malignancies with poor prognosis and urgent unmet medical need. Aberrant expression of multiple members of the miR-17 family are frequently observed in HCC, and their overexpression promotes tumorigenic properties of HCC cells. However, whether pharmacologic inhibition of the miR-17 family inhibits HCC growth remains unknown. In this study, we validated that the miR-17 family was upregulated in a subset of HCC tumors and cell lines and its inhibition by a tough decoy inhibitor suppressed the growth of Hep3B and HepG2 cells, which overexpress the miR-17 family. Furthermore, inhibition of the miR-17 family led to a global derepression of direct targets of the family in all three HCC cell lines tested. Pathway analysis of the deregulated genes indicated that the genes associated with TGFß signaling pathway were highly enriched in Hep3B and HepG2 cells. A miR-17 family target gene signature was established and used to identify RL01-17(5), a lipid nanoparticle encapsulating a potent anti-miR-17 family oligonucleotide. To address whether pharmacologic modulation of the miR-17 family can inhibit HCC growth, RL01-17(5) was systemically administrated to orthotopic Hep3B xenografts. Suppression of Hep3B tumor growth in vivo was observed and tumor growth inhibition correlated with induction of miR-17 family target genes. Together, this study provides proof-of-concept for targeting the miR-17 family in HCC therapy. Mol Cancer Ther; 16(5); 905-13. ©2017 AACR.

Targeting small cell lung cancer harboring PIK3CA mutation with a selective oral PI3K inhibitor PF-4989216.

PURPOSE: Constitutive activation of phosphoinositide 3-kinase (PI3K) occurs frequently in many human tumors via either gene mutation in the p110α catalytic subunit of PI3K or functional loss of tumor suppressor PTEN. Patients with small-cell lung cancer (SCLC) have very poor prognosis and survival rates such that an effective targeted therapy is in strong demand for these patients. In this study, we characterized the highly selective oral PI3K inhibitor, PF-4989216, in preclinical SCLC models to investigate whether targeting the PI3K pathway is an effective targeted therapy option for SCLCs that harbor a PIK3CA mutation. EXPERIMENTAL DESIGN: A panel of SCLC cell lines with PIK3CA mutation or PTEN loss were treated with PF-4989216 in several in vitro assays, including PI3K pathway signaling, cell viability, apoptosis, cell-cycle progression, and cell transformation. SCLC cell lines that were sensitive in vitro to PF-4989216 were further evaluated by in vivo animal studies to determine the pharmacokinetic/pharmacodynamic relationship and tumor growth inhibition (TGI) by PF-4989216 treatment. RESULTS: PF-4989216 inhibited PI3K downstream signaling and subsequently led to apoptosis induction, and inhibition in cell viability, transformation, and xenograft tumor growth in SCLCs harboring PIK3CA mutation. In SCLCs with PTEN loss, PF-4989216 also inhibited PI3K signaling but did not induce BCL2-interacting mediator (BIM)-mediated apoptosis nor was there any effect on cell viability or transformation. These results implicate differential tumorigenesis and apoptosis mechanisms in SCLCs harboring PIK3CA mutation versus PTEN loss. CONCLUSIONS: Our results suggest that PF-4989216 is a potential cancer drug candidate for patients with SCLC with PIK3CA mutation but not PTEN loss.

Test of four colon cancer risk-scores in formalin fixed paraffin embedded microarray gene expression data.

BACKGROUND: Prognosis prediction for resected primary colon cancer is based on the T-stage Node Metastasis (TNM) staging system. We investigated if four well-documented gene expression risk scores can improve patient stratification. METHODS: Microarray-based versions of risk-scores were applied to a large independent cohort of 688 stage II/III tumors from the PETACC-3 trial. Prognostic value for relapse-free survival (RFS), survival after relapse (SAR), and overall survival (OS) was assessed by regression analysis. To assess improvement over a reference, prognostic model was assessed with the area under curve (AUC) of receiver operating characteristic (ROC) curves. All statistical tests were two-sided, except the AUC increase. RESULTS: All four risk scores (RSs) showed a statistically significant association (single-test, P < .0167) with OS or RFS in univariate models, but with HRs below 1.38 per interquartile range. Three scores were predictors of shorter RFS, one of shorter SAR. Each RS could only marginally improve an RFS or OS model with the known factors T-stage, N-stage, and microsatellite instability (MSI) status (AUC gains < 0.025 units). The pairwise interscore discordance was never high (maximal Spearman correlation = 0.563) A combined score showed a trend to higher prognostic value and higher AUC increase for OS (HR = 1.74, 95% confidence interval [CI] = 1.44 to 2.10, P < .001, AUC from 0.6918 to 0.7321) and RFS (HR = 1.56, 95% CI = 1.33 to 1.84, P < .001, AUC from 0.6723 to 0.6945) than any single score. CONCLUSIONS: The four tested gene expression-based risk scores provide prognostic information but contribute only marginally to improving models based on established risk factors. A combination of the risk scores might provide more robust information. Predictors of RFS and SAR might need to be different.

Identification of a poor-prognosis BRAF-mutant-like population of patients with colon cancer.

PURPOSE: Our purpose was development and assessment of a BRAF-mutant gene expression signature for colon cancer (CC) and the study of its prognostic implications. MATERIALS AND METHODS: A set of 668 stage II and III CC samples from the PETACC-3 (Pan-European Trails in Alimentary Tract Cancers) clinical trial were used to assess differential gene expression between c.1799T>A (p.V600E) BRAF mutant and non-BRAF, non-KRAS mutant cancers (double wild type) and to construct a gene expression-based classifier for detecting BRAF mutant samples with high sensitivity. The classifier was validated in independent data sets, and survival rates were compared between classifier positive and negative tumors. RESULTS: A 64 gene-based classifier was developed with 96% sensitivity and 86% specificity for detecting BRAF mutant tumors in PETACC-3 and independent samples. A subpopulation of BRAF wild-type patients (30% of KRAS mutants, 13% of double wild type) showed a gene expression pattern and had poor overall survival and survival after relapse, similar to those observed in BRAF-mutant patients. Thus they form a distinct prognostic subgroup within their mutation class. CONCLUSION: A characteristic pattern of gene expression is associated with and accurately predicts BRAF mutation status and, in addition, identifies a population of BRAF mutated-like KRAS mutants and double wild-type patients with similarly poor prognosis. This suggests a common biology between these tumors and provides a novel classification tool for cancers, adding prognostic and biologic information that is not captured by the mutation status alone. These results may guide therapeutic strategies for this patient segment and may help in population stratification for clinical trials.