An enriched approach to combining high-dimensional genomic and low-dimensional phenotypic data.

We describe an approach for combining and analyzing high-dimensional genomic and low-dimensional phenotypic data. The approach leverages a scheme of weights applied to the variables instead of observations and, hence, permits incorporation of the information provided by the low dimensional data source. It can also be incorporated into commonly used downstream techniques, such as random forest or penalized regression. Finally, the simulated lupus studies involving genetic and clinical data are used to illustrate the overall idea and show that the proposed enriched penalized method can select significant genetic variables while keeping several important clinical variables in the final model.

Genomic alterations in plasma DNA from patients with metastasized prostate cancer receiving abiraterone or enzalutamide.

In patients with metastatic castrate-resistant prostate cancer (mCRPC), circulating tumor DNA (ctDNA) analysis offers novel opportunities for the development of non-invasive biomarkers informative of treatment response with novel agents targeting the androgen-receptor (AR) pathway, such as abiraterone or enzalutamide. However, the relationship between ctDNA abundance, detectable somatic genomic alterations and clinical progression of mCRPC remains unexplored. Our study aimed to investigate changes in plasma DNA during disease progression and their associations with clinical variables in mCRPC patients. We analyzed ctDNA in two cohorts including 94 plasma samples from 25 treatment courses (23 patients) and 334 plasma samples from 125 patients, respectively. We conducted whole-genome sequencing (plasma-Seq) for genome-wide profiling of somatic copy number alterations and targeted sequencing of 31 prostate cancer-associated genes. The combination of plasma-Seq with targeted AR analyses identified prostate cancer-related genomic alterations in 16 of 25 (64%) treatment courses in the first cohort, in which we demonstrated that AR amplification does not always correlate with poor abiraterone and enzalutamide therapy outcome. As we observed a wide variability of ctDNA levels, we evaluated ctDNA levels and their association with clinical parameters and included the second, larger cohort for these analyses. Employing altogether 428 longitudinal plasma samples from 148 patients, we identified the presence of bone metastases, increased lactate dehydrogenase and prostate-specific antigen (PSA) as having the strongest association with high ctDNA levels. In summary, ctDNA alterations are observable in the majority of patients with mCRPC and may eventually be useful to guide clinical decision-making in this setting.

Group-based variant calling leveraging next-generation supercomputing for large-scale whole-genome sequencing studies.

MOTIVATION: Next-generation sequencing (NGS) technologies have become much more efficient, allowing whole human genomes to be sequenced faster and cheaper than ever before. However, processing the raw sequence reads associated with NGS technologies requires care and sophistication in order to draw compelling inferences about phenotypic consequences of variation in human genomes. It has been shown that different approaches to variant calling from NGS data can lead to different conclusions. Ensuring appropriate accuracy and quality in variant calling can come at a computational cost. RESULTS: We describe our experience implementing and evaluating a group-based approach to calling variants on large numbers of whole human genomes. We explore the influence of many factors that may impact the accuracy and efficiency of group-based variant calling, including group size, the biogeographical backgrounds of the individuals who have been sequenced, and the computing environment used. We make efficient use of the Gordon supercomputer cluster at the San Diego Supercomputer Center by incorporating job-packing and parallelization considerations into our workflow while calling variants on 437 whole human genomes generated as part of large association study. CONCLUSIONS: We ultimately find that our workflow resulted in high-quality variant calls in a computationally efficient manner. We argue that studies like ours should motivate further investigations combining hardware-oriented advances in computing systems with algorithmic developments to tackle emerging 'big data' problems in biomedical research brought on by the expansion of NGS technologies.

Leveraging Machine Learning to Facilitate Individual Case Causality Assessment of Adverse Drug Reactions.

INTRODUCTION: Causality assessment of individual case safety reports (ICSRs) is an important step in pharmacovigilance case-level review and aims to establish a position on whether a patient's exposure to a drug is causally related to the patient experiencing an untoward adverse event. There are many different approaches for case causality adjudication, including the use of expert opinions and algorithmic frameworks; however, a great deal of variability exists between assessment methods, products, therapeutic classes, individual physicians, change of process and conventions over time, and other factors. OBJECTIVE: The objective of this study was to develop a machine learning-based model that can predict the likelihood of a causal association of an observed drug-reaction combination in an ICSR. METHODS: In this study, we used a set of annotated solicited ICSRs (50K cases) from a company post-marketing database. These data were enriched with novel supplementary features from external and internal data sources that aim to capture facets such as temporal plausibility, scientific validity, and confoundedness that have been shown to contribute to causality adjudication. Using these features, we constructed a Bayesian network (BN) model to predict drug-event pair causality assessment. BN topology was driven by an internally developed ICSR causality decision support tool. Performance of the model was evaluated through examination of sensitivity, positive predictive value (PPV), and the area under the receiver operating characteristic curve (AUC) on an independent set of data from a temporally adjacent interval (20K cases). No external validation was performed because of a lack of publicly available ICSRs with causality assessments for drug-event pairs. RESULTS: The model demonstrated high performance in predicting the causality assessment of drug-event pairs compared with clinical judgment using global introspection (AUC 0.924; 95% confidence interval [CI] 0.922-0.927). The sensitivity of the model was 0.900 (95% CI 0.896-0.904), and the PPV of the model was 0.778 (95% CI 0.773-0.783). CONCLUSION: These results show that robust probabilistic modeling of ICSR causality is feasible, and the approach used in the development of the model can serve as a framework for such causality assessments, leading to improvements in safety decision making.

Blood-based gene expression signature associated with metastatic castrate-resistant prostate cancer patient response to abiraterone plus prednisone or enzalutamide.

BACKGROUND: Precision medicine approaches for managing patients with metastatic castrate-resistant prostate cancer (mCRPC) are lacking. Non-invasive approaches for molecular monitoring of disease are urgently needed, especially for patients suffering from bone metastases for whom tissue biopsy is challenging. Here we utilized baseline blood samples to identify mCRPC patients most likely to benefit from abiraterone plus prednisone (AAP) or enzalutamide. METHODS: Baseline blood samples were collected for circulating tumor cell (CTC) enumeration and qPCR-based gene expression analysis from 51 men with mCRPC beginning treatment with abiraterone or enzalutamide. RESULTS: Of 51 patients (median age 68 years [51-82]), 22 received AAP (abiraterone 1000 mg/day plus prednisone 10 mg/day) and 29 received enzalutamide (160 mg/day). The cohort was randomly divided into training (n = 37) and test (n = 14) sets. Baseline clinical variables (Gleason score, PSA, testosterone, and hemoglobin), CTC count, and qPCR-based gene expression data for 141 genes/isoforms in CTC-enriched blood were analyzed with respect to overall survival (OS). Genes with expression most associated with OS included MSLN, ARG2, FGF8, KLK3, ESRP2, NPR3, CCND1, and WNT5A. Using a Cox-elastic net model for our test set, the 8-gene expression signature had a c-index of 0.87 (95% CI [0.80, 0.94]) and was more strongly associated with OS than clinical variables or CTC count alone, or a combination of the three variables. For patients with a low-risk vs. high-risk gene expression signature, median OS was not reached vs. 18 months, respectively (HR 5.32 [1.91-14.80], p = 0.001). For the subset of 41 patients for whom progression-free survival (PFS) data was available, the median PFS for patients with a low-risk vs high-risk gene expression signature was 20 vs. 5 months, respectively (HR 2.95 [1.46-5.98], p = 0.003). CONCLUSIONS: If validated in a larger prospective study, this test may predict patients most likely to benefit from second-generation antiandrogen therapy.

ABC gene-ranking for prediction of drug-induced cholestasis in rats.

As legacy toxicogenomics databases have become available, improved data mining approaches are now key to extracting and visualizing subtle relationships between toxicants and gene expression. In the present study, a novel "aggregating bundles of clusters" (ABC) procedure was applied to separate cholestatic from non-cholestatic drugs and model toxicants in the Johnson & Johnson (Janssen) rat liver toxicogenomics database [3]. Drug-induced cholestasis is an important issue, particularly when a new compound enters the market with this liability, with standard preclinical models often mispredicting this toxicity. Three well-characterized cholestasis-responsive genes (Cyp7a1, Mrp3 and Bsep) were chosen from a previous in-house Janssen gene expression signature; these three genes show differing, non-redundant responses across the 90+ paradigm compounds in our database. Using the ABC procedure, extraneous contributions were minimized in comparisons of compound gene responses. All genes were assigned weights proportional to their correlations with Cyp7a1, Mrp3 and Bsep, and a resampling technique was used to derive a stable measure of compound similarity. The compounds that were known to be associated with rat cholestasis generally had small values of this measure relative to each other but also had large values of this measure relative to non-cholestatic compounds. Visualization of the data with the ABC-derived signature showed a very tight, essentially identically behaving cluster of robust human cholestatic drugs and experimental cholestatic toxicants (ethinyl estradiol, LPS, ANIT and methylene dianiline, disulfiram, naltrexone, methapyrilene, phenacetin, alpha-methyl dopa, flutamide, the NSAIDs--indomethacin, flurbiprofen, diclofenac, flufenamic acid, sulindac, and nimesulide, butylated hydroxytoluene, piperonyl butoxide, and bromobenzene), some slightly less active compounds (3'-acetamidofluorene, amsacrine, hydralazine, tannic acid), some drugs that behaved very differently, and were distinct from both non-cholestatic and cholestatic drugs (ketoconazole, dipyridamole, cyproheptadine and aniline), and many postulated human cholestatic drugs that in rat showed no evidence of cholestasis (chlorpromazine, erythromycin, niacin, captopril, dapsone, rifampicin, glibenclamide, simvastatin, furosemide, tamoxifen, and sulfamethoxazole). Most of these latter drugs were noted previously by other groups as showing cholestasis only in humans. The results of this work suggest that the ABC procedure and similar statistical approaches can be instrumental in combining data to compare toxicants across toxicogenomics databases, extract similarities among responses and reduce unexplained data varation.

Integrative genomic deconvolution of rheumatoid arthritis GWAS loci into gene and cell type associations.

BACKGROUND: Although genome-wide association studies (GWAS) have identified over 100 genetic loci associated with rheumatoid arthritis (RA), our ability to translate these results into disease understanding and novel therapeutics is limited. Most RA GWAS loci reside outside of protein-coding regions and likely affect distal transcriptional enhancers. Furthermore, GWAS do not identify the cell types where the associated causal gene functions. Thus, mapping the transcriptional regulatory roles of GWAS hits and the relevant cell types will lead to better understanding of RA pathogenesis. RESULTS: We combine the whole-genome sequences and blood transcription profiles of 377 RA patients and identify over 6000 unique genes with expression quantitative trait loci (eQTLs). We demonstrate the quality of the identified eQTLs through comparison to non-RA individuals. We integrate the eQTLs with immune cell epigenome maps, RA GWAS risk loci, and adjustment for linkage disequilibrium to propose target genes of immune cell enhancers that overlap RA risk loci. We examine 20 immune cell epigenomes and perform a focused analysis on primary monocytes, B cells, and T cells. CONCLUSIONS: We highlight cell-specific gene associations with relevance to RA pathogenesis including the identification of FCGR2B in B cells as possessing both intragenic and enhancer regulatory GWAS hits. We show that our RA patient cohort derived eQTL network is more informative for studying RA than that from a healthy cohort. While not experimentally validated here, the reported eQTLs and cell type-specific RA risk associations can prioritize future experiments with the goal of elucidating the regulatory mechanisms behind genetic risk associations.

Direct Imaging of Hippocampal Epileptiform Calcium Motifs Following Kainic Acid Administration in Freely Behaving Mice.

Prolonged exposure to abnormally high calcium concentrations is thought to be a core mechanism underlying hippocampal damage in epileptic patients; however, no prior study has characterized calcium activity during seizures in the live, intact hippocampus. We have directly investigated this possibility by combining whole-brain electroencephalographic (EEG) measurements with microendoscopic calcium imaging of pyramidal cells in the CA1 hippocampal region of freely behaving mice treated with the pro-convulsant kainic acid (KA). We observed that KA administration led to systematic patterns of epileptiform calcium activity: a series of large-scale, intensifying flashes of increased calcium fluorescence concurrent with a cluster of low-amplitude EEG waveforms. This was accompanied by a steady increase in cellular calcium levels (>5 fold increase relative to the baseline), followed by an intense spreading calcium wave characterized by a 218% increase in global mean intensity of calcium fluorescence (n = 8, range [114-349%], p < 10(-4); t-test). The wave had no consistent EEG phenotype and occurred before the onset of motor convulsions. Similar changes in calcium activity were also observed in animals treated with 2 different proconvulsant agents, N-methyl-D-aspartate (NMDA) and pentylenetetrazol (PTZ), suggesting the measured changes in calcium dynamics are a signature of seizure activity rather than a KA-specific pathology. Additionally, despite reducing the behavioral severity of KA-induced seizures, the anticonvulsant drug valproate (VA, 300 mg/kg) did not modify the observed abnormalities in calcium dynamics. These results confirm the presence of pathological calcium activity preceding convulsive motor seizures and support calcium as a candidate signaling molecule in a pathway connecting seizures to subsequent cellular damage. Integrating in vivo calcium imaging with traditional assessment of seizures could potentially increase translatability of pharmacological intervention, leading to novel drug screening paradigms and therapeutics designed to target and abolish abnormal patterns of both electrical and calcium excitation.

Rare variant collapsing in conjunction with mean log p-value and gradient boosting approaches applied to Genetic Analysis Workshop 17 data.

In addition to methods that can identify common variants associated with susceptibility to common diseases, there has been increasing interest in approaches that can identify rare genetic variants. We use the simulated data provided to the participants of Genetic Analysis Workshop 17 (GAW17) to identify both rare and common single-nucleotide polymorphisms and pathways associated with disease status. We apply a rare variant collapsing approach and the usual association tests for common variants to identify candidates for further analysis using pathway-based and tree-based ensemble approaches. We use the mean log p-value approach to identify a top set of pathways and compare it to those used in simulation of GAW17 dataset. We conclude that the mean log p-value approach is able to identify those pathways in the top list and also related pathways. We also use the stochastic gradient boosting approach for the selected subset of single-nucleotide polymorphisms. When compared the result of this tree-based method with the list of single-nucleotide polymorphisms used in dataset simulation, in addition to correct SNPs we observe number of false positives.