Landscape of circRNAs Across 11 Organs and 4 Ages in Fischer 344 Rats.

Circular RNAs (circRNAs) are a class of endogenous noncoding RNAs with a covalently closed loop. Aside from their recognized regulatory functions (e.g., sponging microRNAs to reduce their activity, and altering parental gene transcription by competing with the canonical splicing of pre-mRNA), expression of circRNAs is abundant, diverse, and conservative across species, rendering them as potential biomarker candidates. Consequently, the landscape of circRNAs has been studied for several species. Although the rat is one of the most important animal models for drug safety and toxicological research, few attempts have been made to understand the landscape of rat circRNAs. One noticeable challenge in analyzing circRNAs with next-generation sequencing (NGS) data is to find ways to use rapidly advancing bioinformatics approaches to improve accuracy while also reducing the number of resulting false positives that occur in circRNA identification with these new methods. Here, we applied two of the most advanced circRNA bioinformatics pipelines to provide a landscape of circRNAs in rats by analyzing an RNA-seq data set for 11 organs (adrenal gland, brain, heart, kidney, liver, lung, muscle, spleen, thymus, and testis or uterus) from Fischer 344 rats of both sexes in four age groups (juvenile, adolescence, adult, and aged). The circRNAs displayed organ-specific patterns and sex differences in most organs. Lowest numbers of circRNAs were seen in the liver and muscle, while highest numbers of circRNAs occurred in the brain, which correlated to gene expression patterns seen across those organs. Concordance of circRNAs between males and females was approximately 50% in nonsex organs, implying that some caution needs to be exercised when selecting specific circRNAs as biomarkers for both sexes. The number of common circRNAs between sexes increased with age for most organs except heart, spleen, and thymus. A dramatic drop in the number of circRNAs in kidney, thymus, and testis was observed in aged rats, suggesting that the regulatory function of circRNAs is age dependent. From the 1595 circRNAs identified with high confidence, only 6 appeared in all 9 of the nonsex organs in both sexes and four age groups. Forty-one and 48 circRNAs were identified in more than 5 nonsex organs in males and females, respectively, while close to 280 circRNAs were found in an organ for more than 2 age groups in both sexes. This study offers an overview of rat circRNAs, which contributes to the effort of identifying circRNAs as potential biomarkers for safety and risk assessment.

Impact of Sequencing Depth and Library Preparation on Toxicological Interpretation of RNA-Seq Data in a "Three-Sample" Scenario.

While RNA-sequencing (RNA-seq) has emerged as a standard approach in toxicogenomics, its full potential in gaining underlying toxicological mechanisms is still not clear when only three biological replicates are used. This "three-sample" study design is common in toxicological research, particularly in animal studies during preclinical drug development. Sequencing depth (the total number of reads in an experiment) and library preparation are critical to the resolution and integrity of RNA-seq data and biological interpretation. We used aflatoxin b1 (AFB1), a model toxicant, to investigate the effect of sequencing depth and library preparation in RNA-seq on toxicological interpretation in the "three-sample" scenario. We also compared different gene profiling platforms (RNA-seq, TempO-seq, microarray, and qPCR) using identical liver samples. Well-established mechanisms of AFB1 toxicity served as ground truth for our comparative analyses. We found that a minimum of 20 million reads was sufficient to elicit key toxicity functions and pathways underlying AFB1-induced liver toxicity using three replicates and that identification of differentially expressed genes was positively associated with sequencing depth to a certain extent. Further, our results showed that RNA-seq revealed toxicological insights from pathway enrichment with overall higher statistical power and overlap ratio, compared with TempO-seq and microarray. Moreover, library preparation using the same methods was important to reproducing the toxicological interpretation.

Discovering functional modules by topic modeling RNA-Seq based toxicogenomic data.

Toxicogenomics (TGx) endeavors to elucidate the underlying molecular mechanisms through exploring gene expression profiles in response to toxic substances. Recently, RNA-Seq is increasingly regarded as a more powerful alternative to microarrays in TGx studies. However, realizing RNA-Seq's full potential requires novel approaches to extracting information from the complex TGx data. Considering read counts as the number of times a word occurs in a document, gene expression profiles from RNA-Seq are analogous to a word by document matrix used in text mining. Topic modeling aiming at to discover the latent structures in text corpora would be helpful to explore RNA-Seq based TGx data. In this study, topic modeling was applied on a typical RNA-Seq based TGx data set to discover hidden functional modules. The RNA-Seq based gene expression profiles were transformed into "documents", on which latent Dirichlet allocation (LDA) was used to build a topic model. We found samples treated by the compounds with the same modes of actions (MoAs) could be clustered based on topic similarities. The topic most relevant to each cluster was identified as a "marker" topic, which was interpreted by gene enrichment analysis with MoAs then confirmed by compound and pathways associations mined from literature. To further validate the "marker" topics, we tested topic transferability from RNA-Seq to microarrays. The RNA-Seq based gene expression profile of a topic specifically associated with peroxisome proliferator-activated receptors (PPAR) signaling pathway was used to query samples with similar expression profiles in two different microarray data sets, yielding accuracy of about 85%. This proof-of-concept study demonstrates the applicability of topic modeling to discover functional modules in RNA-Seq data and suggests a valuable computational tool for leveraging information within TGx data in RNA-Seq era.

Towards accurate indel calling for oncopanel sequencing through an international pipeline competition at precisionFDA.

Accurately calling indels with next-generation sequencing (NGS) data is critical for clinical application. The precisionFDA team collaborated with the U.S. Food and Drug Administration's (FDA's) National Center for Toxicological Research (NCTR) and successfully completed the NCTR Indel Calling from Oncopanel Sequencing Data Challenge, to evaluate the performance of indel calling pipelines. Top performers were selected based on precision, recall, and F1-score. The performance of many other pipelines was close to the top performers, which produced a top cluster of performers. The performance was significantly higher in high confidence regions and coding regions, and significantly lower in low complexity regions. Oncopanel capture and other issues may have occurred that affected the recall rate. Indels with higher variant allele frequency (VAF) may generally be called with higher confidence. Many of the indel calling pipelines had good performance. Some of them performed generally well across all three oncopanels, while others were better for a specific oncopanel. The performance of indel calling can further be improved by restricting the calls within high confidence intervals (HCIs) and coding regions, and by excluding low complexity regions (LCR) regions. Certain VAF cut-offs could be applied according to the applications.

Extend the benchmarking indel set by manual review using the individual cell line sequencing data from the Sequencing Quality Control 2 (SEQC2) project.

Accurate indel calling plays an important role in precision medicine. A benchmarking indel set is essential for thoroughly evaluating the indel calling performance of bioinformatics pipelines. A reference sample with a set of known-positive variants was developed in the FDA-led Sequencing Quality Control Phase 2 (SEQC2) project, but the known indels in the known-positive set were limited. This project sought to provide an enriched set of known indels that would be more translationally relevant by focusing on additional cancer related regions. A thorough manual review process completed by 42 reviewers, two advisors, and a judging panel of three researchers significantly enriched the known indel set by an additional 516 indels. The extended benchmarking indel set has a large range of variant allele frequencies (VAFs), with 87% of them having a VAF below 20% in reference Sample A. The reference Sample A and the indel set can be used for comprehensive benchmarking of indel calling across a wider range of VAF values in the lower range. Indel length was also variable, but the majority were under 10 base pairs (bps). Most of the indels were within coding regions, with the remainder in the gene regulatory regions. Although high confidence can be derived from the robust study design and meticulous human review, this extensive indel set has not undergone orthogonal validation. The extended benchmarking indel set, along with the indels in the previously published known-positive set, was the truth set used to benchmark indel calling pipelines in a community challenge hosted on the precisionFDA platform. This benchmarking indel set and reference samples can be utilized for a comprehensive evaluation of indel calling pipelines. Additionally, the insights and solutions obtained during the manual review process can aid in improving the performance of these pipelines.

Combined gene expression and protein interaction analysis of dynamic modularity in glioma prognosis.

Because of the variety of factors affecting glioma prognosis, prediction of patient survival is particularly difficult. Protein-protein interaction (PPI) networks have been considered with regard to how their spatial characteristics relate to glioma. However, the dynamic nature of PPIs in vivo makes them temporally and spatially complex events. Integration of prognosis-specific co-expression information adds further dynamic features to these networks. Although some biomarkers for glioma prognosis have been identified, none is sufficient for accurate prediction of either prognosis or improved survival. We have established co-expressed protein-interaction networks that integrate protein-protein interactions with glioma gene-expression profiles related to different survival times. Biomarkers related to glioma prognosis were identified by comparative analysis of the dynamic features of the glioma prognosis network, particularly subnetworks. Four significantly differently expressed genes (SDEGs) are upregulated and ten SDEGs downregulated as lifetime is extended. In addition, 97 enhanced differently co-expressed protein interactions (DCPIs) and 99 weakened DCPIs were associated with glioma patient lifetime extension. We propose a method for estimating glioma prognosis on the basis of the construction of a dynamic modular network. We have used this method to identify dynamic genes and interactions related to glioma prognosis. Among these, enhanced MYC expression was related to lifetime extension, as were interactions between E2F1 and RB1 and between EGFR and p38. This method is a novel means of studying the molecular mechanisms determining prognosis in glioma.

Ultra-deep multi-oncopanel sequencing of benchmarking samples with a wide range of variant allele frequencies.

The lack of suitable reference genomic material to enable a transparent cross-lab study of oncopanels inspired the SEQC2 Oncopanel Sequencing Working Group to develop four reference samples, sequenced with eight oncopanels at independent test laboratories with ultra-deep sequencing depth. This rich, publicly available dataset enabled performance assessment of the clinical applicability of oncopanels. In addition, this dataset present sample opportunities for developing specific and robust bioinformatics pipelines and fine-tuning parameters for more accurate variant calling, investigating ideal sequencing depth for variant calling of a given minimum VAF and variant type, and also recommending best use cases for Unique Molecular Identifier (UMI) technology.

Ultra-deep sequencing data from a liquid biopsy proficiency study demonstrating analytic validity.

Recently we reported the accuracy and reproducibility of circulating tumor DNA (ctDNA) assays using a unique set of reference materials, associated analytical framework, and suggested best practices. With the rapid adoption of ctDNA sequencing in precision oncology, it is critical to understand the analytical validity and technical limitations of this cutting-edge and medical-practice-changing technology. The SEQC2 Oncopanel Sequencing Working Group has developed a multi-site, cross-platform study design for evaluating the analytical performance of five industry-leading ctDNA assays. The study used tailor-made reference samples at various levels of input material to assess ctDNA sequencing across 12 participating clinical and research facilities. The generated dataset encompasses multiple key variables, including a broad range of mutation frequencies, sequencing coverage depth, DNA input quantity, etc. It is the most comprehensive public-facing dataset of its kind and provides valuable insights into ultra-deep ctDNA sequencing technology. Eventually the clinical utility of ctDNA assays is required and our proficiency study and corresponding dataset are needed steps towards this goal.

Deep oncopanel sequencing reveals within block position-dependent quality degradation in FFPE processed samples.

BACKGROUND: Clinical laboratories routinely use formalin-fixed paraffin-embedded (FFPE) tissue or cell block cytology samples in oncology panel sequencing to identify mutations that can predict patient response to targeted therapy. To understand the technical error due to FFPE processing, a robustly characterized diploid cell line was used to create FFPE samples with four different pre-tissue processing formalin fixation times. A total of 96 FFPE sections were then distributed to different laboratories for targeted sequencing analysis by four oncopanels, and variants resulting from technical error were identified. RESULTS: Tissue sections that fail more frequently show low cellularity, lower than recommended library preparation DNA input, or target sequencing depth. Importantly, sections from block surfaces are more likely to show FFPE-specific errors, akin to "edge effects" seen in histology, while the inner samples display no quality degradation related to fixation time. CONCLUSIONS: To assure reliable results, we recommend avoiding the block surface portion and restricting mutation detection to genomic regions of high confidence.

DOSim: an R package for similarity between diseases based on Disease Ontology.

BACKGROUND: The construction of the Disease Ontology (DO) has helped promote the investigation of diseases and disease risk factors. DO enables researchers to analyse disease similarity by adopting semantic similarity measures, and has expanded our understanding of the relationships between different diseases and to classify them. Simultaneously, similarities between genes can also be analysed by their associations with similar diseases. As a result, disease heterogeneity is better understood and insights into the molecular pathogenesis of similar diseases have been gained. However, bioinformatics tools that provide easy and straight forward ways to use DO to study disease and gene similarity simultaneously are required. RESULTS: We have developed an R-based software package (DOSim) to compute the similarity between diseases and to measure the similarity between human genes in terms of diseases. DOSim incorporates a DO-based enrichment analysis function that can be used to explore the disease feature of an independent gene set. A multilayered enrichment analysis (GO and KEGG annotation) annotation function that helps users explore the biological meaning implied in a newly detected gene module is also part of the DOSim package. We used the disease similarity application to demonstrate the relationship between 128 different DO cancer terms. The hierarchical clustering of these 128 different cancers showed modular characteristics. In another case study, we used the gene similarity application on 361 obesity-related genes. The results revealed the complex pathogenesis of obesity. In addition, the gene module detection and gene module multilayered annotation functions in DOSim when applied on these 361 obesity-related genes helped extend our understanding of the complex pathogenesis of obesity risk phenotypes and the heterogeneity of obesity-related diseases. CONCLUSIONS: DOSim can be used to detect disease-driven gene modules, and to annotate the modules for functions and pathways. The DOSim package can also be used to visualise DO structure. DOSim can reflect the modular characteristic of disease related genes and promote our understanding of the complex pathogenesis of diseases. DOSim is available on the Comprehensive R Archive Network (CRAN) or http://bioinfo.hrbmu.edu.cn/dosim.

Disease embryo development network reveals the relationship between disease genes and embryo development genes.

A basic problem for contemporary biology and medicine is exploring the correlation between human disease and underlying cellular mechanisms. For a long time, several efforts were made to reveal the similarity between embryo development and disease process, but few from the system level. In this article, we used the human protein-protein interactions (PPIs), disease genes with their classifications and embryo development genes and reconstructed a human disease-embryo development network to investigate the relationship between disease genes and embryo development genes. We found that disease genes and embryo development genes are prone to connect with each other. Furthermore, diseases can be categorized into three groups according to the closeness with embryo development in gene overlapping, interacting pattern in PPI network and co-regulated by microRNAs or transcription factors. Embryo development high-related disease genes show their closeness with embryo development at least in three biological levels. But it is not for embryo development medium-related disease genes and embryo development low-related disease genes. We also found that embryo development high-related disease genes are more central than other disease genes in the human PPI network. In addition, the results show that embryo development high-related disease genes tend to be essential genes compared with other diseases' genes. This network-based approach could provide evidence for the intricate correlation between disease process and embryo development, and help to uncover potential mechanisms of human complex diseases.

SubpathwayMiner: a software package for flexible identification of pathways.

With the development of high-throughput experimental techniques such as microarray, mass spectrometry and large-scale mutagenesis, there is an increasing need to automatically annotate gene sets and identify the involved pathways. Although many pathway analysis tools are developed, new tools are still needed to meet the requirements for flexible or advanced analysis purpose. Here, we developed an R-based software package (SubpathwayMiner) for flexible pathway identification. SubpathwayMiner facilitates sub-pathway identification of metabolic pathways by using pathway structure information. Additionally, SubpathwayMiner also provides more flexibility in annotating gene sets and identifying the involved pathways (entire pathways and sub-pathways): (i) SubpathwayMiner is able to provide the most up-to-date pathway analysis results for users; (ii) SubpathwayMiner supports multiple species ( approximately 100 eukaryotes, 714 bacteria and 52 Archaea) and different gene identifiers (Entrez Gene IDs, NCBI-gi IDs, UniProt IDs, PDB IDs, etc.) in the KEGG GENE database; (iii) the system is quite efficient in cooperating with other R-based tools in biology. SubpathwayMiner is freely available at http://cran.r-project.org/web/packages/SubpathwayMiner/.

Assessment of Clonal Expansion Using CarcSeq Measurement of Lung Cancer Driver Mutations and Correlation With Mouse Strain- and Sex-Related Incidence of Spontaneous Lung Neoplasia.

Quantification of variation in levels of spontaneously occurring cancer driver mutations (CDMs) was developed to assess clonal expansion and predict future risk of neoplasm development. Specifically, an error-corrected next-generation sequencing method, CarcSeq, and a mouse CarcSeq panel (analogous to human and rat panels) were developed and used to quantify low-frequency mutations in a panel of amplicons enriched in hotspot CDMs. Mutations in a subset of panel amplicons, Braf, Egfr, Kras, Stk11, and Tp53, were related to incidence of lung neoplasms at 2 years. This was achieved by correlating median absolute deviation (MAD) from the overall median mutant fraction (MF) measured in the lung DNA of 16-week-old male and female, B6C3F1 and CD-1 mice (10 mice/sex/strain) with percentages of spontaneous alveolar/bronchioloalveolar adenomas and carcinomas reported in bioassay control groups. A total of 1586 mouse lung mutants with MFs >1 × 10-4 were recovered. The ratio of nonsynonymous to synonymous mutations was used to assess the proportion of recovered mutations conferring a positive selective advantage. The greatest ratio was observed in what is considered the most lung tumor-sensitive model examined, male B6C3F1 mice. Of the recurrent, nonsynonymous mouse mutations recovered, 55.5% have been reported in human tumors, with many located in or around the mouse equivalent of human cancer hotspot codons. MAD for the same subset of amplicons measured in normal human lung DNA samples showed a correlation of moderate strength and borderline significance with age (a cancer risk factor), as well as age-related cumulative lung cancer risk, suggesting MAD may inform species extrapolation.

CarcSeq Measurement of Rat Mammary Cancer Driver Mutations and Relation to Spontaneous Mammary Neoplasia.

The ability to deduce carcinogenic potential from subchronic, repeat dose rodent studies would constitute a major advance in chemical safety assessment and drug development. This study investigated an error-corrected NGS method (CarcSeq) for quantifying cancer driver mutations (CDMs) and deriving a metric of clonal expansion predictive of future neoplastic potential. CarcSeq was designed to interrogate subsets of amplicons encompassing hotspot CDMs applicable to a variety of cancers. Previously, normal human breast DNA was analyzed by CarcSeq and metrics based on mammary-specific CDMs were correlated with tissue donor age, a surrogate of breast cancer risk. Here we report development of parallel methodologies for rat. The utility of the rat CarcSeq method for predicting neoplastic potential was investigated by analyzing mammary tissue of 16-week-old untreated rats with known differences in spontaneous mammary neoplasia (Fischer 344, Wistar Han, and Sprague Dawley). Hundreds of mutants with mutant fractions ≥ 10-4 were quantified in each strain, most were recurrent mutations, and 42.5% of the nonsynonymous mutations have human homologs. Mutants in the mammary-specific target of the most tumor-sensitive strain (Sprague Dawley) showed the greatest nonsynonymous/synonymous mutation ratio, indicative of positive selection consistent with clonal expansion. For the mammary-specific target (Hras, Pik3ca, and Tp53 amplicons), median absolute deviation correlated with percentages of rats that develop spontaneous mammary neoplasia at 104 weeks (Pearson r = 1.0000, 1-tailed p = .0010). Therefore, this study produced evidence CarcSeq analysis of spontaneously occurring CDMs can be used to derive an early metric of clonal expansion relatable to long-term neoplastic outcome.

Identification of Translational microRNA Biomarker Candidates for Ketoconazole-Induced Liver Injury Using Next-Generation Sequencing.

Drug-induced liver injury (DILI) is a leading cause of acute liver failure. Reliable and translational biomarkers are needed for early detection of DILI. microRNAs (miRNAs) have received wide attention as a novel class of potential DILI biomarkers. However, it is unclear how DILI drugs other than acetaminophen may influence miRNA expression or which miRNAs could serve as useful biomarkers in humans. We selected ketoconazole (KCZ), a classic hepatotoxin, to study miRNA biomarkers for DILI as a proof of concept for a workflow that integrated in vivo, in vitro, and bioinformatics analyses. We examined hepatic miRNA expression in KCZ-treated rats at multiple doses and durations using miRNA-sequencing and correlated our results with conventional DILI biomarkers such as liver histology. Significant dysregulation of rno-miR-34a-5p, rno-miR-331-3p, rno-miR-15b-3p, and rno-miR-676 was associated with cytoplasmic vacuolization, a phenotype in rat livers with KCZ-induced injury, which preceded the elevation of serum liver transaminases (ALT and AST). Between rats and humans, miR-34a-5p, miR-331-3p, and miR-15b-3p were evolutionarily conserved with identical sequences, whereas miR-676 showed 73% sequence similarity. Using quantitative PCR, we found that the levels of hsa-miR-34a-5p, hsa-miR-331-3p, and hsa-miR-15b-3p were significantly elevated in the culture media of HepaRG cells treated with 100 µM KCZ (a concentration that induced cytotoxicity). Additionally, we computationally characterized the miRNA candidates for their gene targeting, target functions, and miRNA/target evolutionary conservation. In conclusion, we identified miR-34a-5p, miR-331-3p, and miR-15b-3p as translational biomarker candidates for early detection of KCZ-induced liver injury with a workflow applicable to computational toxicology studies.

Cross-oncopanel study reveals high sensitivity and accuracy with overall analytical performance depending on genomic regions.

BACKGROUND: Targeted sequencing using oncopanels requires comprehensive assessments of accuracy and detection sensitivity to ensure analytical validity. By employing reference materials characterized by the U.S. Food and Drug Administration-led SEquence Quality Control project phase2 (SEQC2) effort, we perform a cross-platform multi-lab evaluation of eight Pan-Cancer panels to assess best practices for oncopanel sequencing. RESULTS: All panels demonstrate high sensitivity across targeted high-confidence coding regions and variant types for the variants previously verified to have variant allele frequency (VAF) in the 5-20% range. Sensitivity is reduced by utilizing VAF thresholds due to inherent variability in VAF measurements. Enforcing a VAF threshold for reporting has a positive impact on reducing false positive calls. Importantly, the false positive rate is found to be significantly higher outside the high-confidence coding regions, resulting in lower reproducibility. Thus, region restriction and VAF thresholds lead to low relative technical variability in estimating promising biomarkers and tumor mutational burden. CONCLUSION: This comprehensive study provides actionable guidelines for oncopanel sequencing and clear evidence that supports a simplified approach to assess the analytical performance of oncopanels. It will facilitate the rapid implementation, validation, and quality control of oncopanels in clinical use.

A verified genomic reference sample for assessing performance of cancer panels detecting small variants of low allele frequency.

BACKGROUND: Oncopanel genomic testing, which identifies important somatic variants, is increasingly common in medical practice and especially in clinical trials. Currently, there is a paucity of reliable genomic reference samples having a suitably large number of pre-identified variants for properly assessing oncopanel assay analytical quality and performance. The FDA-led Sequencing and Quality Control Phase 2 (SEQC2) consortium analyze ten diverse cancer cell lines individually and their pool, termed Sample A, to develop a reference sample with suitably large numbers of coding positions with known (variant) positives and negatives for properly evaluating oncopanel analytical performance. RESULTS: In reference Sample A, we identify more than 40,000 variants down to 1% allele frequency with more than 25,000 variants having less than 20% allele frequency with 1653 variants in COSMIC-related genes. This is 5-100× more than existing commercially available samples. We also identify an unprecedented number of negative positions in coding regions, allowing statistical rigor in assessing limit-of-detection, sensitivity, and precision. Over 300 loci are randomly selected and independently verified via droplet digital PCR with 100% concordance. Agilent normal reference Sample B can be admixed with Sample A to create new samples with a similar number of known variants at much lower allele frequency than what exists in Sample A natively, including known variants having allele frequency of 0.02%, a range suitable for assessing liquid biopsy panels. CONCLUSION: These new reference samples and their admixtures provide superior capability for performing oncopanel quality control, analytical accuracy, and validation for small to large oncopanels and liquid biopsy assays.

Evaluating the analytical validity of circulating tumor DNA sequencing assays for precision oncology.

Circulating tumor DNA (ctDNA) sequencing is being rapidly adopted in precision oncology, but the accuracy, sensitivity and reproducibility of ctDNA assays is poorly understood. Here we report the findings of a multi-site, cross-platform evaluation of the analytical performance of five industry-leading ctDNA assays. We evaluated each stage of the ctDNA sequencing workflow with simulations, synthetic DNA spike-in experiments and proficiency testing on standardized, cell-line-derived reference samples. Above 0.5% variant allele frequency, ctDNA mutations were detected with high sensitivity, precision and reproducibility by all five assays, whereas, below this limit, detection became unreliable and varied widely between assays, especially when input material was limited. Missed mutations (false negatives) were more common than erroneous candidates (false positives), indicating that the reliable sampling of rare ctDNA fragments is the key challenge for ctDNA assays. This comprehensive evaluation of the analytical performance of ctDNA assays serves to inform best practice guidelines and provides a resource for precision oncology.

Application of microRNA profiling to understand sevoflurane-induced adverse effects on developing monkey brain.

We have described that prolonged sevoflurane exposure at a clinically-relevant concentration of 2.5 % caused neuronal cell death in the developing monkey brain. Postnatal day 5 or 6 rhesus monkeys (n = 3) were exposed to 2.5 % sevoflurane for 8 h. Monkeys kept at environmental conditions in the procedure room served as controls (n = 3). Brain tissues were harvested four hours after sevoflurane exposure for histological analysis, and RNA or protein extraction. MicroRNA (miRNA) profiling on the frontal cortex of monkey brains was performed using next-generation sequencing. 417 miRNAs were identified in the frontal cortex, where most neuronal cell death was observed. 7 miRNAs were differentially expressed in frontal cortex after sevoflurane exposure. Five of these were expressed at significantly lower levels than controls and the other two miRNAs were expressed significantly higher. These differentially expressed miRNAs (DEMs) were then loaded to the Ingenuity Pathway Analysis database for pathway analysis, in which targeting information was available for 5 DEMs. The 5 DEMs target 2,919 mRNAs which are involved in pathways that contribute to various cellular functions. Of note, 78 genes that are related to axon guidance signaling were targeted, suggesting that development of the neural circuit may be affected after sevoflurane exposure. Such changes may have long-term effects on brain development and function. These findings are supplementary to our previous observations and provide more evidence for better understanding the adverse effects of sevoflurane on the developing brain after an 8 -h exposure.

Quantification of cancer driver mutations in human breast and lung DNA using targeted, error-corrected CarcSeq.

There is a need for scientifically-sound, practical approaches to improve carcinogenicity testing. Advances in DNA sequencing technology and knowledge of events underlying cancer development have created an opportunity for progress in this area. The long-term goal of this work is to develop variation in cancer driver mutation (CDM) levels as a metric of clonal expansion of cells carrying CDMs because these important early events could inform carcinogenicity testing. The first step toward this goal was to develop and validate an error-corrected next-generation sequencing method to analyze panels of hotspot cancer driver mutations (hCDMs). The "CarcSeq" method that was developed uses unique molecular identifier sequences to construct single-strand consensus sequences for error correction. CarcSeq was used for mutational analysis of 13 amplicons encompassing >20 hotspot CDMs in normal breast, normal lung, ductal carcinomas, and lung adenocarcinomas. The approach was validated by detecting expected differences related to tissue type (normal vs. tumor and breast vs. lung) and mutation spectra. CarcSeq mutant fractions (MFs) correlated strongly with previously obtained ACB-PCR mutant fraction (MF) measurements from the same samples. A reconstruction experiment, in conjunction with other analyses, showed CarcSeq accurately quantifies MFs ≥10-4 . CarcSeq MF measurements were correlated with tissue donor age and breast cancer risk. CarcSeq MF measurements were correlated with deviation from median MFs analyzed to assess clonal expansion. Thus, CarcSeq is a promising approach to advance cancer risk assessment and carcinogenicity testing practices. Paradigms that should be investigated to advance this strategy for carcinogenicity testing are proposed.