Challenges to Using Big Data in Cancer.

Big data in healthcare can enable unprecedented understanding of diseases and their treatment, particularly in oncology. These data may include electronic health records, medical imaging, genomic sequencing, payor records, and data from pharmaceutical research, wearables, and medical devices. The ability to combine datasets and use data across many analyses is critical to the successful use of big data and is a concern for those who generate and use the data. Interoperability and data quality continue to be major challenges when working with different healthcare datasets. Mapping terminology across datasets, missing and incorrect data, and varying data structures make combining data an onerous and largely manual undertaking. Data privacy is another concern addressed by the Health Insurance Portability and Accountability Act, the Common Rule, and the General Data Protection Regulation. The use of big data is now included in the planning and activities of the FDA and the European Medicines Agency. The willingness of organizations to share data in a precompetitive fashion, agreements on data quality standards, and institution of universal and practical tenets on data privacy will be crucial to fully realizing the potential for big data in medicine.

A forward selection algorithm to identify mutually exclusive alterations in cancer studies.

Mutual exclusivity analyses provide an effective tool to identify driver genes from passenger genes for cancer studies. Various algorithms have been developed for the detection of mutual exclusivity, but controlling false positive and improving accuracy remain challenging. We propose a forward selection algorithm for identification of mutually exclusive gene sets (FSME) in this paper. The method includes an initial search of seed pair of mutually exclusive (ME) genes and subsequently including more genes into the current ME set. Simulations demonstrated that, compared to recently published approaches (i.e., CoMEt, WExT, and MEGSA), FSME could provide higher precision or recall rate to identify ME gene sets, and had superior control of false positive rates. With application to TCGA real data sets for AML, BRCA, and GBM, we confirmed that FSME can be utilized to discover cancer driver genes.

A systematic assessment of mitochondrial function identified novel signatures for drug-induced mitochondrial disruption in cells.

Mitochondrial perturbation has been recognized as a contributing factor to various drug-induced organ toxicities. To address this issue, we developed a high-throughput flow cytometry-based mitochondrial signaling assay to systematically investigate mitochondrial/cellular parameters known to be directly impacted by mitochondrial dysfunction: mitochondrial membrane potential (MMP), mitochondrial reactive oxygen species (ROS), intracellular reduced glutathione (GSH) level, and cell viability. Modulation of these parameters by a training set of compounds, comprised of established mitochondrial poisons and 60 marketed drugs (30 nM to 1mM), was tested in HL-60 cells (a human pro-myelocytic leukemia cell line) cultured in either glucose-supplemented (GSM) or glucose-free (containing galactose/glutamine; GFM) RPMI-1640 media. Post-hoc bio-informatic analyses of IC50 or EC50 values for all parameters tested revealed that MMP depolarization in HL-60 cells cultured in GSM was the most reliable parameter for determining mitochondrial dysfunction in these cells. Disruptors of mitochondrial function depolarized MMP at concentrations lower than those that caused loss of cell viability, especially in cells cultured in GSM; cellular GSH levels correlated more closely to loss of viability in vitro. Some mitochondrial respiratory chain inhibitors increased mitochondrial ROS generation; however, measuring an increase in ROS alone was not sufficient to identify mitochondrial disruptors. Furthermore, hierarchical cluster analysis of all measured parameters provided confirmation that MMP depletion, without loss of cell viability, was the key signature for identifying mitochondrial disruptors. Subsequent classification of compounds based on ratios of IC50s of cell viability:MMP determined that this parameter is the most critical indicator of mitochondrial health in cells and provides a powerful tool to predict whether novel small molecule entities possess this liability.

A multifactorial approach to hepatobiliary transporter assessment enables improved therapeutic compound development.

The bile salt export pump (BSEP) is expressed at the canalicular domain of hepatocytes, where it serves as the primary route of elimination for monovalent bile acids (BAs) into the bile canaliculi. The most compelling evidence linking dysfunction in BA transport with liver injury in humans is found with carriers of mutations that render BSEP nonfunctional. Based on mounting evidence, there appears to be a strong association between drug-induced BSEP interference and liver injury in humans; however, causality has not been established. For this reason, drug-induced BSEP interference is best considered a susceptibility factor for liver injury as other host- or drug-related properties may contribute to the development of hepatotoxicity. To better understand the association between BSEP interference and liver injury in humans, over 600 marketed or withdrawn drugs were evaluated in BSEP expressing membrane vesicles. The example of a compound that failed during phase 1 human trials is also described, AMG 009. AMG 009 showed evidence of liver injury in humans that was not predicted by preclinical safety studies, and BSEP inhibition was implicated. For 109 of the drugs with some effect on in vitro BSEP function, clinical use, associations with hepatotoxicity, pharmacokinetic data, and other information were annotated. A steady state concentration (C(ss)) for each of these annotated drugs was estimated, and a ratio between this value and measured IC50 potency values were calculated in an attempt to relate exposure to in vitro potencies. When factoring for exposure, 95% of the annotated compounds with a C(ss)/BSEP IC50 ratio ≥ 0.1 were associated with some form of liver injury. We then investigated the relationship between clinical evidence of liver injury and effects to multidrug resistance-associated proteins (MRPs) believed to play a role in BA homeostasis. The effect of 600+ drugs on MRP2, MRP3, and MRP4 function was also evaluated in membrane vesicle assays. Drugs with a C(ss)/BSEP IC50 ratio ≥ 0.1 and a C(ss)/MRP IC50 ratio ≥ 0.1 had almost a 100% correlation with some evidence of liver injury in humans. These data suggest that integration of exposure data, and knowledge of an effect to not only BSEP but also one or more of the MRPs, is a useful tool for informing the potential for liver injury due to altered BA transport.

Membrane vesicle ABC transporter assays for drug safety assessment.

The use of plasma membrane vesicles that overexpress the bile salt export pump (BSEP) or multidrug resistance-associated protein 2, 3, or 4 (MRP2-4) with an in vitro vacuum filtration system offers a rapid and reliable means for screening drug candidates for their effects on transporter function in hepatocytes and thus their potential for causing drug-induced liver injury (DILI). Comparison of transporter activity in the presence and absence of ATP allows for determination of a specific assay window for each transporter. This window is used to determine the degree to which each test compound inhibits transporter activity. This assay battery is helpful for prioritizing and rank-ordering compounds within a chemical series with respect to each other and in the context of known inhibitors of transporter activity and/or liver injury. This model can be used to influence the drug development process at an early stage and provide rapid feedback regarding the selection of compounds for advancement to in vivo safety evaluations. A detailed protocol for the high-throughput assessment of ABC transporter function is provided, including specific recommendations for curve-fitting to help ensure consistent results.

Assessment of repeated microarray experiments using mixed tissue RNA reference samples.

Genome-scale gene expression technologies are increasingly being applied for biological research as a whole and toxicological screening in particular. In order to monitor data quality and process drift, we adopted the use of two rat-tissue mixtures (brain, liver, kidney, and testis) previously introduced as RNA reference samples. These samples were processed every time a microarray experiment was hybridized, thereby verifying the comparability of the resulting expression data for cross-study comparison. This study presents the analysis of 21 technical replicates of these two mixed-tissue samples using Affymetrix RAE230_2 GeneChip over a period of 12 months. The results show that detection sensitivity, measured by the number of present and absent sequences, is robust, and data correlation, indicated by scatter plots, varies little over time. Receiver operating characteristic (ROC) curves show the sensitivity and specificity of the current measurements are consistent with arrays previously classified as well performing. Overall, this paper shows that the inclusion of standard samples during microarray labeling and hybridization experiments is useful to benchmark the performance of microarray experiments over time and allows discovery of any process drift that, if it occurs, may confound the comparison of these datasets.

An overview of toxicogenomics.

Toxicogenomics is a rapidly developing discipline that promises to aid scientists in understanding the molecular and cellular effects of chemicals in biological systems. This field encompasses global assessment of biological effects using technologies such as DNA microarrays or high throughput NMR and protein expression analysis. This review provides an overview of advancing multiple approaches (genomic, proteomic, metabonomic) that may extend our understanding of toxicology and highlights the importance of coupling such approaches with classical toxicity studies.