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The article by Nygaard and others (2016) proposes that applying batch correction approaches to microarray data from studies with unbalanced designs may inadvertently exaggerate the differences observed. In seeking to illustrate their point, Nygaard and others (2016) utilized a dataset (GSE61901) from a study we published (Towfic and others, 2014) and showed that one analysis pipeline utilizing the traditional approach to batch correction (ComBat) yielded over 1000 differentially expressed probesets, while an alternative approach proposed by Nygaard and others (2016). (utilizing batch as a fixed effect and averaging technical replicates) recovered 11 differentially expressed probesets. [ABSTRACT FROM AUTHOR]

Next-generation sequencing (NGS) has revolutionized genomic research by enabling high-throughput, cost-effective genome and transcriptome sequencing accelerating personalized medicine for complex diseases, including cancer. Whole genome/transcriptome sequencing (WGS/WTS) provides comprehensive insights, while targeted sequencing is more cost-effective and sensitive. In comparison to short-read sequencing, which still dominates the field due to high speed and cost-effectiveness, long-read sequencing can overcome alignment limitations and better discriminate similar sequences from alternative transcripts or repetitive regions. Hybrid sequencing combines the best strengths of different technologies for a more comprehensive view of genomic/transcriptomic variations. Understanding each technology's strengths and limitations is critical for translating cutting-edge technologies into clinical applications. In this study, we sequenced DNA and RNA libraries of reference samples using various targeted DNA and RNA panels and the whole transcriptome on both short-read and long-read platforms. This study design enables a comprehensive analysis of sequencing technologies, targeting protocols, and library preparation methods. Our expanded profiling landscape establishes a reference point for assessing current sequencing technologies, facilitating informed decision-making in genomic research and precision medicine., (© 2024. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.)

Text summarization is crucial in scientific research, drug discovery and development, regulatory review, and more. This task demands domain expertise, language proficiency, semantic prowess, and conceptual skill. The recent advent of large language models (LLMs), such as ChatGPT, offers unprecedented opportunities to automate this process. We compared ChatGPT-generated summaries with those produced by human experts using FDA drug labeling documents. The labeling contains summaries of key labeling sections, making them an ideal human benchmark to evaluate ChatGPT's summarization capabilities. Analyzing >14000 summaries, we observed that ChatGPT-generated summaries closely resembled those generated by human experts. Importantly, ChatGPT exhibited even greater similarity when summarizing drug safety information. These findings highlight ChatGPT's potential to accelerate work in critical areas, including drug safety., (Published by Elsevier Ltd.)

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., (© 2024. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.)

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., (© 2024. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.)

Background: The cancer genome is commonly altered with thousands of structural rearrangements including insertions, deletions, translocation, inversions, duplications, and copy number variations. Thus, structural variant (SV) characterization plays a paramount role in cancer target identification, oncology diagnostics, and personalized medicine. As part of the SEQC2 Consortium effort, the present study established and evaluated a consensus SV call set using a breast cancer reference cell line and matched normal control derived from the same donor, which were used in our companion benchmarking studies as reference samples., Results: We systematically investigated somatic SVs in the reference cancer cell line by comparing to a matched normal cell line using multiple NGS platforms including Illumina short-read, 10X Genomics linked reads, PacBio long reads, Oxford Nanopore long reads, and high-throughput chromosome conformation capture (Hi-C). We established a consensus SV call set of a total of 1788 SVs including 717 deletions, 230 duplications, 551 insertions, 133 inversions, 146 translocations, and 11 breakends for the reference cancer cell line. To independently evaluate and cross-validate the accuracy of our consensus SV call set, we used orthogonal methods including PCR-based validation, Affymetrix arrays, Bionano optical mapping, and identification of fusion genes detected from RNA-seq. We evaluated the strengths and weaknesses of each NGS technology for SV determination, and our findings provide an actionable guide to improve cancer genome SV detection sensitivity and accuracy., Conclusions: A high-confidence consensus SV call set was established for the reference cancer cell line. A large subset of the variants identified was validated by multiple orthogonal methods., (© 2022. The Author(s).)

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., (© 2022. The Author(s).)

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., (© 2022. This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply.)

Background: Reproducible detection of inherited variants with whole genome sequencing (WGS) is vital for the implementation of precision medicine and is a complicated process in which each step affects variant call quality. Systematically assessing reproducibility of inherited variants with WGS and impact of each step in the process is needed for understanding and improving quality of inherited variants from WGS., Results: To dissect the impact of factors involved in detection of inherited variants with WGS, we sequence triplicates of eight DNA samples representing two populations on three short-read sequencing platforms using three library kits in six labs and call variants with 56 combinations of aligners and callers. We find that bioinformatics pipelines (callers and aligners) have a larger impact on variant reproducibility than WGS platform or library preparation. Single-nucleotide variants (SNVs), particularly outside difficult-to-map regions, are more reproducible than small insertions and deletions (indels), which are least reproducible when > 5 bp. Increasing sequencing coverage improves indel reproducibility but has limited impact on SNVs above 30×., Conclusions: Our findings highlight sources of variability in variant detection and the need for improvement of bioinformatics pipelines in the era of precision medicine with WGS., (© 2021. The Author(s).)

Cancer is the second leading cause of mortality worldwide despite tremendous advances in treatment. The promise of precision oncology depends on accurate characterization of tumor mutations and subsequent therapy selection. The lack of tumor reference samples along with the associated next generation sequencing (NGS) technical assessments has hindered the development of NGS assays and the realization of benefits for precision oncology. The summarized results and recommendations of several seminal SEQC2 studies along with a vision of the changing landscape of precision oncology and anticipated next steps by the SEQC2 consortium are reported. Importantly, these studies utilized a new robust reference sample material which was developed and constructed to support multiple DNA and RNA-based NGS assay studies. These studies focused on a wide variety of precision oncology assay scenarios and provided guidelines for standardized analyses and best practice recommendations. The evolving landscape of precision oncology requires insights into critical factors supporting the sensitivity and reproducibility of clinical NGS assays for continued improvement in patient outcomes. Persistent development of robust reference materials, quantitative performance metrics, and actionable data analysis recommendations are needed. This series of SEQC2 studies serve to advance NGS-based assays for precision oncology and support regulatory science endeavors.

The primary objective of the FDA-led Sequencing and Quality Control Phase 2 (SEQC2) project is to develop standard analysis protocols and quality control metrics for use in DNA testing to enhance scientific research and precision medicine. This study reports a targeted next-generation sequencing (NGS) method that will enable more accurate detection of actionable mutations in circulating tumor DNA (ctDNA) clinical specimens. To accomplish this, a synthetic internal standard spike-in was designed for each actionable mutation target, suitable for use in NGS following hybrid capture enrichment and unique molecular index (UMI) or non-UMI library preparation. When mixed with contrived ctDNA reference samples, internal standards enabled calculation of technical error rate, limit of blank, and limit of detection for each variant at each nucleotide position in each sample. True-positive mutations with variant allele fraction too low for detection by current practice were detected with this method, thereby increasing sensitivity., Competing Interests: J.C.W. has 5%–10% equity interest in and serves as a consultant to AccuGenomics, Inc. Technology relevant to this manuscript was developed and patented by J.C.W., E.L.C., and T.B.M. and is licensed to AccuGenomics, Inc. These relationships do not alter our adherence to all policies on sharing data and materials. The views presented in this article do not necessarily reflect the current or future opinion or policy of the US FDA. Any mention of commercial products is for clarification and not intended as an endorsement., (© 2021 The Authors.)

The particularly interdisciplinary nature of human microbiome research makes the organization and reporting of results spanning epidemiology, biology, bioinformatics, translational medicine and statistics a challenge. Commonly used reporting guidelines for observational or genetic epidemiology studies lack key features specific to microbiome studies. Therefore, a multidisciplinary group of microbiome epidemiology researchers adapted guidelines for observational and genetic studies to culture-independent human microbiome studies, and also developed new reporting elements for laboratory, bioinformatics and statistical analyses tailored to microbiome studies. The resulting tool, called 'Strengthening The Organization and Reporting of Microbiome Studies' (STORMS), is composed of a 17-item checklist organized into six sections that correspond to the typical sections of a scientific publication, presented as an editable table for inclusion in supplementary materials. The STORMS checklist provides guidance for concise and complete reporting of microbiome studies that will facilitate manuscript preparation, peer review, and reader comprehension of publications and comparative analysis of published results., (© 2021. The Author(s), under exclusive licence to Springer Nature America, Inc.)

Reproducibility is essential to open science, as there is limited relevance for findings that can not be reproduced by independent research groups, regardless of its validity. It is therefore crucial for scientists to describe their experiments in sufficient detail so they can be reproduced, scrutinized, challenged, and built upon. However, the intrinsic complexity and continuous growth of biomedical data makes it increasingly difficult to process, analyze, and share with the community in a FAIR (findable, accessible, interoperable, and reusable) manner. To overcome these issues, we created a cloud-based platform called ORCESTRA ( orcestra.ca ), which provides a flexible framework for the reproducible processing of multimodal biomedical data. It enables processing of clinical, genomic and perturbation profiles of cancer samples through automated processing pipelines that are user-customizable. ORCESTRA creates integrated and fully documented data objects with persistent identifiers (DOI) and manages multiple dataset versions, which can be shared for future studies., (© 2021. The Author(s).)

Serious harmful effects have been reported for thiophenols, which are widely used industrial materials. To date, little information is available on whether such chemicals can elicit endocrine-related detrimental effects. Herein the potential binding affinity and underlying mechanism of action between human transthyretin (hTTR) and seven halogenated-thiophenols were examined experimentally and computationally. Experimental results indicated that the halogenated-thiophenols, except for pentafluorothiophenol, were powerful hTTR binders. The differentiated hTTR binding affinity of halogenated-thiophenols and halogenated-phenols were observed. The hTTR binding affinity of mono- and di-halo-thiophenols was higher than that of corresponding phenols; while the opposite relationship was observed for tri- and penta-halo-thiophenols and phenols. Our results also confirmed that the binding interactions were influenced by the degree of ligand dissociation. Molecular modeling results implied that the dominant noncovalent interactions in the molecular recognition processes between hTTR and halogenated-thiophenols were ionic pair, hydrogen bonds and hydrophobic interactions. Finally, a model with acceptable predictive ability was developed, which can be used to computationally predict the potential hTTR binding affinity of other halogenated-thiophenols and phenols. Taken together, our results highlighted that more research is needed to determine their potential endocrine-related harmful effects and appropriate management actions should be taken to promote their sustainable use., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

The lack of samples for generating standardized DNA datasets for setting up a sequencing pipeline or benchmarking the performance of different algorithms limits the implementation and uptake of cancer genomics. Here, we describe reference call sets obtained from paired tumor-normal genomic DNA (gDNA) samples derived from a breast cancer cell line-which is highly heterogeneous, with an aneuploid genome, and enriched in somatic alterations-and a matched lymphoblastoid cell line. We partially validated both somatic mutations and germline variants in these call sets via whole-exome sequencing (WES) with different sequencing platforms and targeted sequencing with >2,000-fold coverage, spanning 82% of genomic regions with high confidence. Although the gDNA reference samples are not representative of primary cancer cells from a clinical sample, when setting up a sequencing pipeline, they not only minimize potential biases from technologies, assays and informatics but also provide a unique resource for benchmarking 'tumor-only' or 'matched tumor-normal' analyses., (© 2021. This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply.)

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., (© 2021. This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply.)

Clinical applications of precision oncology require accurate tests that can distinguish true cancer-specific mutations from errors introduced at each step of next-generation sequencing (NGS). To date, no bulk sequencing study has addressed the effects of cross-site reproducibility, nor the biological, technical and computational factors that influence variant identification. Here we report a systematic interrogation of somatic mutations in paired tumor-normal cell lines to identify factors affecting detection reproducibility and accuracy at six different centers. Using whole-genome sequencing (WGS) and whole-exome sequencing (WES), we evaluated the reproducibility of different sample types with varying input amount and tumor purity, and multiple library construction protocols, followed by processing with nine bioinformatics pipelines. We found that read coverage and callers affected both WGS and WES reproducibility, but WES performance was influenced by insert fragment size, genomic copy content and the global imbalance score (GIV; G > T/C > A). Finally, taking into account library preparation protocol, tumor content, read coverage and bioinformatics processes concomitantly, we recommend actionable practices to improve the reproducibility and accuracy of NGS experiments for cancer mutation detection., (© 2021. This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply.)

Background: The concurrent growth of large-scale oncology data alongside the computational methods with which to analyze and model it has created a promising environment for revolutionizing cancer diagnosis, treatment, prevention, and drug discovery. Computational methods applied to large datasets have accelerated the drug discovery process by reducing bottlenecks and widening the search space beyond what is experimentally tractable. As the research community gains understanding of the myriad genetic underpinnings of cancer via sequencing, imaging, screens, and more that are ingested, transformed, and modeled by top open-source machine learning and artificial intelligence tools readily available, the next big drug candidate might seem merely an "Enter" key away. Of course, the reality is more convoluted, but still promising., Scope of Review: We present methods to approach the process of building an AI model, with strong emphasis on the aspects of model development we believe to be crucial to success but that are not commonly discussed: diligence in posing questions, identifying suitable datasets and curating them, and collaborating closely with biology and oncology experts while designing and evaluating the model. Digital pathology, Electronic Health Records, and other data types outside of high-throughput molecular data are reviewed well by others and outside of the scope of this review. This review emphasizes the importance of considering the limitations of the datasets, computational methods, and our minds when designing AI models. For example, datasets can be biased towards areas of research interest, funding, and particular patient populations. Neural networks may learn representations and correlations within the data that are grounded not in biological phenomena, but statistical anomalies erroneously extracted from the training data. Researchers may mis-interpret or over-interpret the output, or design and evaluate the training process such that the resultant model generalizes poorly. Fortunately, awareness of the strengths and limitations of applying data analytics and AI to drug discovery enables us to leverage them carefully and insightfully while maximizing their utility. These applications when performed in close collaboration with domain experts, together with continuous critical evaluation, generation of new data to minimize known blind spots as they are found, and rigorous experimental validation, increases the success rate of the study. We will discuss applications including AI-assisted target identification, drug repurposing, patient stratification, and gene prioritization., Major Conclusions: Data analytics and AI have demonstrated capabilities to revolutionize cancer research, prevention, and treatment by maximizing our understanding and use of the expanding panoply of experimental data. However, to separate promise from true utility, computational tools must be carefully designed, critically evaluated, and constantly improved. Once that is achieved, a human-computer hybrid discovery process will outperform one driven by each alone., General Significance: This review highlights the challenges and promise of synergizing predictive AI models with human expertise towards greater understanding of cancer., (Copyright © 2021 The Authors. Published by Elsevier B.V. All rights reserved.)

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.

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.

Huntington disease (HD) is a hereditary neurodegenerative disorder caused by mutant huntingtin (mHTT). Phosphorylation at serine-421 (pS421) of mHTT has been shown to be neuroprotective in cellular and rodent models. However, the genetic context of these models differs from that of HD patients. Here we employed human pluripotent stem cells (hiPSCs), which express endogenous full-length mHTT. Using genome editing, we generated isogenic hiPSC lines in which the S421 site in mHTT has been mutated into a phospho-mimetic aspartic acid (S421D) or phospho-resistant alanine (S421A). We observed that S421D, rather than S421A, confers neuroprotection in hiPSC-derived neural cells. Although we observed no effect of S421D on mHTT clearance or axonal transport, two aspects previously reported to be impacted by phosphorylation of mHTT at S421, our analysis revealed modulation of several aspects of mitochondrial form and function. These include mitochondrial surface area, volume, and counts, as well as improved mitochondrial membrane potential and oxidative phosphorylation. Our study validates the protective role of pS421 on mHTT and highlights a facet of the relationship between mHTT and mitochondrial changes in the context of human physiology with potential relevance to the pathogenesis of HD.

Endocrine-disrupting chemicals (EDCs) can interact with nuclear receptors, including estrogen receptor α (ERα) and androgen receptor (AR), to affect the normal endocrine system function, causing severe symptoms. Limited studies queried the EDC mechanisms, focusing on limited chemicals or a set of structurally similar compounds. It remained uncertain how hundreds of diverse EDCs could bind to ERα and AR and cause distinct functional consequences. Here, we employed a series of computational methodologies to investigate the structural features of EDCs that bind to and activate ERα and AR based on more than 4000 compounds. We used molecular docking and molecular dynamics simulations to elucidate the functional consequences and validated structure-function correlations experimentally using a time-resolved fluorescence resonance energy-transfer assay. We found that EDCs share three levels of key fragments. Primary (20 for ERα and 18 for AR) and secondary fragments (38 for ERα and 29 for AR) are responsible for the binding to receptors, and tertiary fragments determine the activity type (agonist, antagonist, or mixed). In summary, our study provides a general mechanism for the EDC function. Discovering the three levels of key fragments may drive fast screening and evaluation of potential EDCs from large sets of commercially used synthetic compounds.

Background: The recent global pandemic has placed a high priority on identifying drugs to prevent or lessen clinical infection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), caused by Coronavirus disease-2019 (COVID-19)., Methods: We applied two computational approaches to identify potential therapeutics. First, we sought to identify existing FDA approved drugs that could block coronaviruses from entering cells by binding to ACE2 or TMPRSS2 using a high-throughput AI-based binding affinity prediction platform. Second, we sought to identify FDA approved drugs that could attenuate the gene expression patterns induced by coronaviruses, using our Disease Cancelling Technology (DCT) platform., Results: Top results for ACE2 binding iincluded several ACE inhibitors, a beta-lactam antibiotic, two antiviral agents (Fosamprenavir and Emricasan) and glutathione. The platform also assessed specificity for ACE2 over ACE1, important for avoiding counterregulatory effects. Further studies are needed to weigh the benefit of blocking virus entry against potential counterregulatory effects and possible protective effects of ACE2. However, the data herein suggest readily available drugs that warrant experimental evaluation to assess potential benefit. DCT was run on an animal model of SARS-CoV, and ranked compounds by their ability to induce gene expression signals that counteract disease-associated signals. Top hits included Vitamin E, ruxolitinib, and glutamine. Glutathione and its precursor glutamine were highly ranked by two independent methods, suggesting both warrant further investigation for potential benefit against SARS-CoV-2., Conclusions: While these findings are not yet ready for clinical translation, this report highlights the potential use of two bioinformatics technologies to rapidly discover existing therapeutic agents that warrant further investigation for established and emerging disease processes.

Background: Although substantial concerns about the inflammatory effects of engineered nanomaterial (ENM) have been raised, experimentally assessing toxicity of various ENMs is challenging and time-consuming. Alternatively, quantitative structure-activity relationship (QSAR) models have been employed to assess nanosafety. However, no previous attempt has been made to predict the inflammatory potential of ENMs., Objectives: By employing metal oxide nanoparticles (MeONPs) as a model ENM, we aimed to develop QSAR models for prediction of the inflammatory potential by their physicochemical properties., Methods: We built a comprehensive data set of 30 MeONPs to screen a proinflammatory cytokine interleukin (IL)-1 beta ( IL- 1 β ) release in THP-1 cell line. The in vitro hazard ranking was validated in mouse lungs by oropharyngeal instillation of six randomly selected MeONPs. We established QSAR models for prediction of MeONP-induced inflammatory potential via machine learning. The models were further validated against seven new MeONPs. Density functional theory (DFT) computations were exploited to decipher the key mechanisms driving inflammatory responses of MeONPs., Results: Seventeen out of 30 MeONPs induced excess IL- 1 β production in THP-1 cells. In vivo disease outcomes were highly relevant to the in vitro data. QSAR models were developed for inflammatory potential, with predictive accuracy (ACC) exceeding 90%. The models were further validated experimentally against seven independent MeONPs ( ACC = 86 % ). DFT computations and experimental results further revealed the underlying mechanisms: MeONPs with metal electronegativity lower than 1.55 and positive ζ -potential were more likely to cause lysosomal damage and inflammation., Conclusions: IL- 1 β released in THP-1 cells can be an index to rank the inflammatory potential of MeONPs. QSAR models based on IL- 1 β were able to predict the inflammatory potential of MeONPs. Our approach overcame the challenge of time- and labor-consuming biological experiments and allowed for computational assessment of MeONP inflammatory potential by characterization of their physicochemical properties. https://doi.org/10.1289/EHP6508.

Background: Reference genome selection is a prerequisite for successful analysis of next generation sequencing (NGS) data. Current practice employs one of the two most recent human reference genome versions: HG19 or HG38. To date, the impact of genome version on SNV identification has not been rigorously assessed., Methods: We conducted analysis comparing the SNVs identified based on HG19 vs HG38, leveraging whole genome sequencing (WGS) data from the genome-in-a-bottle (GIAB) project. First, SNVs were called using 26 different bioinformatics pipelines with either HG19 or HG38. Next, two tools were used to convert the called SNVs between HG19 and HG38. Lastly we calculated conversion rates, analyzed discordant rates between SNVs called with HG19 or HG38, and characterized the discordant SNVs., Results: The conversion rates from HG38 to HG19 (average 95%) were lower than the conversion rates from HG19 to HG38 (average 99%). The conversion rates varied slightly among the various calling pipelines. Around 1.5% SNVs were discordantly converted between HG19 or HG38. The conversions from HG38 to HG19 had more SNVs which failed conversion and more discordant SNVs than the opposite conversion (HG19 to HG38). Most of the discordant SNVs had low read depth, were low confidence SNVs as defined by GIAB, and/or were predominated by G/C alleles (52% observed versus 42% expected)., Conclusion: A significant number of SNVs could not be converted between HG19 and HG38. Based on careful review of our comparisons, we recommend HG38 (the newer version) for NGS SNV analysis. To summarize, our findings suggest caution when translating identified SNVs between different versions of the human reference genome.

The tremendous expansion of data analytics and public and private big datasets presents an important opportunity for pre-clinical drug discovery and development. In the field of life sciences, the growth of genetic, genomic, transcriptomic and proteomic data is partly driven by a rapid decline in experimental costs as biotechnology improves throughput, scalability, and speed. Yet far too many researchers tend to underestimate the challenges and consequences involving data integrity and quality standards. Given the effect of data integrity on scientific interpretation, these issues have significant implications during preclinical drug development. We describe standardized approaches for maximizing the utility of publicly available or privately generated biological data and address some of the common pitfalls. We also discuss the increasing interest to integrate and interpret cross-platform data. Principles outlined here should serve as a useful broad guide for existing analytical practices and pipelines and as a tool for developing additional insights into therapeutics using big data., (Copyright © 2018 Elsevier Inc. All rights reserved.)

Background: Huntington Disease (HD) is an incurable autosomal dominant neurodegenerative disorder driven by an expansion repeat giving rise to the mutant huntingtin protein (mHtt), which is known to disrupt a multitude of transcriptional pathways. Pridopidine, a small molecule in development for treatment of HD, has been shown to improve motor symptoms in HD patients. In HD animal models, pridopidine exerts neuroprotective effects and improves behavioral and motor functions. Pridopidine binds primarily to the sigma-1 receptor, (IC50 ~ 100 nM), which mediates its neuroprotective properties, such as rescue of spine density and aberrant calcium signaling in HD neuronal cultures. Pridopidine enhances brain-derived neurotrophic factor (BDNF) secretion, which is blocked by putative sigma-1 receptor antagonist NE-100, and was shown to upregulate transcription of genes in the BDNF, glucocorticoid receptor (GR), and dopamine D1 receptor (D1R) pathways in the rat striatum. The impact of different doses of pridopidine on gene expression and transcript splicing in HD across relevant brain regions was explored, utilizing the YAC128 HD mouse model, which carries the entire human mHtt gene containing 128 CAG repeats., Methods: RNAseq was analyzed from striatum, cortex, and hippocampus of wild-type and YAC128 mice treated with vehicle, 10 mg/kg or 30 mg/kg pridopidine from the presymptomatic stage (1.5 months of age) until 11.5 months of age in which mice exhibit progressive disease phenotypes., Results: The most pronounced transcriptional effect of pridopidine at both doses was observed in the striatum with minimal effects in other regions. In addition, for the first time pridopidine was found to have a dose-dependent impact on alternative exon and junction usage, a regulatory mechanism known to be impaired in HD. In the striatum of YAC128 HD mice, pridopidine treatment initiation prior to symptomatic manifestation rescues the impaired expression of the BDNF, GR, D1R and cAMP pathways., Conclusions: Pridopidine has broad effects on restoring transcriptomic disturbances in the striatum, particularly involving synaptic transmission and activating neuroprotective pathways that are disturbed in HD. Benefits of treatment initiation at early disease stages track with trends observed in the clinic.

When a small molecule binds to the androgen receptor (AR), a conformational change can occur which impacts subsequent binding of co-regulator proteins and DNA. In order to accurately study this mechanism, the scientific community needs a crystal structure of the Wild type AR (WT-AR) ligand binding domain, bound with antagonist. To address this open need, we leveraged molecular docking and molecular dynamics (MD) simulations to construct a structure of the WT-AR ligand binding domain bound with antagonist bicalutamide. The structure of mutant AR (Mut-AR) bound with this same antagonist informed this study. After molecular docking analysis pinpointed the suitable binding orientation of a ligand in AR, the model was further optimized through 1 μs of MD simulations. Using this approach, three molecular systems were studied: (1) WT-AR bound with agonist R1881, (2) WT-AR bound with antagonist bicalutamide, and (3) Mut-AR bound with bicalutamide. Our structures were very similar to the experimentally determined structures of both WT-AR with R1881 and Mut-AR with bicalutamide, demonstrating the trustworthiness of this approach. In our model, when WT-AR is bound with bicalutamide, Val716/Lys720/Gln733, or Met734/Gln738/Glu897 move and thus disturb the positive and negative charge clumps of the AF2 site. This disruption of the AF2 site is key for understanding the impact of antagonist binding on subsequent co-regulator binding. In conclusion, the antagonist induced structural changes in WT-AR detailed in this study will enable further AR research and will facilitate AR targeting drug discovery.

Endocrine disrupting chemicals (EDCs) mimic natural hormones and disrupt endocrine function. Humans and wildlife are exposed to EDCs might alter endocrine functions through various mechanisms and lead to an adverse effects. Hence, EDCs identification is important to protect the ecosystem and to promote the public health. Leveraging in-vitro and in-vivo experiments to identify potential EDCs is time consuming and expensive. Hence, quantitative structure-activity relationship is applied to screen the potential EDCs. Here, we summarize the predictive models developed using various algorithms to forecast the binding activity of chemicals to the estrogen and androgen receptors, alpha-fetoprotein, and sex hormone binding globulin.

Pridopidine is currently under clinical development for Huntington disease (HD), with on-going studies to better characterize its therapeutic benefit and mode of action. Pridopidine was administered either prior to the appearance of disease phenotypes or in advanced stages of disease in the YAC128 mouse model of HD. In the early treatment cohort, animals received 0, 10, or 30 mg/kg pridopidine for a period of 10.5 months. In the late treatment cohort, animals were treated for 8 weeks with 0 mg/kg or an escalating dose of pridopidine (10 to 30 mg/kg over 3 weeks). Early treatment improved motor coordination and reduced anxiety- and depressive-like phenotypes in YAC128 mice, but it did not rescue striatal and corpus callosum atrophy. Late treatment, conversely, only improved depressive-like symptoms. RNA-seq analysis revealed that early pridopidine treatment reversed striatal transcriptional deficits, upregulating disease-specific genes that are known to be downregulated during HD, a finding that is experimentally confirmed herein. This suggests that pridopidine exerts beneficial effects at the transcriptional level. Taken together, our findings support continued clinical development of pridopidine for HD, particularly in the early stages of disease, and provide valuable insight into the potential therapeutic mode of action of pridopidine.

Background: Copaxone is an efficacious and safe therapy that has demonstrated clinical benefit for over two decades in patients with relapsing forms of multiple sclerosis (MS). On an individual level, patients show variability in their response to Copaxone, with some achieving significantly higher response levels. The involvement of genes (e.g., HLA-DRB1*1501) with high inter-individual variability in Copaxone's mechanism of action (MoA) suggests the potential contribution of genetics to treatment response. This study aimed to identify genetic variants associated with Copaxone response in patient cohorts from late-phase clinical trials., Methods: Single nucleotide polymorphisms (SNPs) associated with high and low levels of response to Copaxone were identified using genome-wide SNP data in a discovery cohort of 580 patients from two phase III clinical trials of Copaxone. Multivariable Bayesian modeling on the resulting SNPs in an expanded discovery cohort with 1171 patients identified a multi-SNP signature of Copaxone response. This signature was examined in 941 Copaxone-treated MS patients from seven independent late-phase trials of Copaxone and assessed for specificity to Copaxone in 310 Avonex-treated and 311 placebo-treated patients, also from late-phase trials., Results: A four-SNP signature consisting of rs80191572 (in UVRAG), rs28724893 (in HLA-DQB2), rs1789084 (in MBP), and rs139890339 (in ZAK(CDCA7)) was identified as significantly associated with Copaxone response. Copaxone-treated signature-positive patients had a greater reduction in annualized relapse rate (ARR) compared to signature-negative patients in both discovery and independent cohorts, an effect not observed in Avonex-treated patients. Additionally, signature-positive placebo-treated cohorts did not show a reduction in ARR, demonstrating the predictive as opposed to prognostic nature of the signature. A 10% subset of patients, delineated by the signature, showed marked improvements across multiple clinical parameters, including ARR, MRI measures, and higher proportion with no evidence of disease activity (NEDA)., Conclusions: This study is the largest pharmacogenetic study in MS reported to date. Gene regions underlying the four-SNP signature have been linked with pathways associated with either Copaxone's MoA or the pathophysiology of MS. The pronounced association of the four-SNP signature with clinical improvements in a ~10% subset of the MS patient population demonstrates the complex interplay of immune mechanisms and the individualized nature of response to Copaxone.

The tri-nucleotide repeat expansion underlying Huntington disease (HD) results in corticostriatal synaptic dysfunction and subsequent neurodegeneration of striatal medium spiny neurons (MSNs). HD is a devastating autosomal dominant disease with no disease-modifying treatments. Pridopidine, a postulated "dopamine stabilizer", has been shown to improve motor symptoms in clinical trials of HD. However, the target(s) and mechanism of action of pridopidine remain to be fully elucidated. As binding studies identified sigma-1 receptor (S1R) as a high-affinity receptor for pridopidine, we evaluated the relevance of S1R as a therapeutic target of pridopidine in HD. S1R is an endoplasmic reticulum - (ER) resident transmembrane protein and is regulated by ER calcium homeostasis, which is perturbed in HD. Consistent with ER calcium dysregulation, we observed striatal upregulation of S1R in aged YAC128 transgenic HD mice and HD patients. We previously demonstrated that dendritic MSN spines are lost in aged corticostriatal co-cultures from YAC128 mice. We report here that pridopidine and the chemically similar S1R agonist 3-PPP prevent MSN spine loss in aging YAC128 co-cultures. Spine protection was blocked by neuronal deletion of S1R. Pridopidine treatment suppressed supranormal ER Ca 2+ release, restored ER calcium levels and reduced excessive store-operated calcium (SOC) entry in spines, which may account for its synaptoprotective effects. Normalization of ER Ca 2+ levels by pridopidine was prevented by S1R deletion. To evaluate long-term effects of pridopidine, we analyzed expression profiles of calcium signaling genes. Pridopidine elevated striatal expression of calbindin and homer1a, whereas their striatal expression was reduced in aged Q175KI and YAC128 HD mouse models compared to WT. Pridopidine and 3-PPP are proposed to prevent calcium dysregulation and synaptic loss in a YAC128 corticostriatal co-culture model of HD. The actions of pridopidine were mediated by S1R and led to normalization of ER Ca 2+ release, ER Ca 2+ levels and spine SOC entry in YAC128 MSNs. This is a new potential mechanism of action for pridopidine, highlighting S1R as a potential target for HD therapy. Upregulation of striatal proteins that regulate calcium, including calbindin and homer1a, upon chronic therapy with pridopidine, may further contribute to long-term beneficial effects of pridopidine in HD., Competing Interests: No non-financial conflicts of interest exist for any of the authors., (Copyright © 2016. Published by Elsevier Inc.)

Therapeutic drugs that block DNA repair, including poly(ADP-ribose) polymerase (PARP) inhibitors, fail due to lack of tumor-selectivity. When PARP inhibitors and β-lapachone are combined, synergistic antitumor activity results from sustained NAD(P)H levels that refuel NQO1-dependent futile redox drug recycling. Significant oxygen-consumption-rate/reactive oxygen species cause dramatic DNA lesion increases that are not repaired due to PARP inhibition. In NQO1 + cancers, such as non-small-cell lung, pancreatic, and breast cancers, cell death mechanism switches from PARP1 hyperactivation-mediated programmed necrosis with β-lapachone monotherapy to synergistic tumor-selective, caspase-dependent apoptosis with PARP inhibitors and β-lapachone. Synergistic antitumor efficacy and prolonged survival were noted in human orthotopic pancreatic and non-small-cell lung xenograft models, expanding use and efficacy of PARP inhibitors for human cancer therapy., (Published by Elsevier Inc.)

Pridopidine has demonstrated improvement in Huntington Disease (HD) motor symptoms as measured by secondary endpoints in clinical trials. Originally described as a dopamine stabilizer, this mechanism is insufficient to explain the clinical and preclinical effects of pridopidine. This study therefore explored pridopidine's potential mechanisms of action. The effect of pridopidine versus sham treatment on genome-wide expression profiling in the rat striatum was analysed and compared to the pathological expression profile in Q175 knock-in (Q175 KI) vs Q25 WT mouse models. A broad, unbiased pathway analysis was conducted, followed by testing the enrichment of relevant pathways. Pridopidine upregulated the BDNF pathway (P = 1.73E-10), and its effect on BDNF secretion was sigma 1 receptor (S1R) dependent. Many of the same genes were independently found to be downregulated in Q175 KI mice compared to WT (5.2e-7 < P < 0.04). In addition, pridopidine treatment upregulated the glucocorticoid receptor (GR) response, D1R-associated genes and the AKT/PI3K pathway (P = 1E-10, P = 0.001, P = 0.004, respectively). Pridopidine upregulates expression of BDNF, D1R, GR and AKT/PI3K pathways, known to promote neuronal plasticity and survival, as well as reported to demonstrate therapeutic benefit in HD animal models. Activation of S1R, necessary for its effect on the BDNF pathway, represents a core component of the mode of action of pridopidine. Since the newly identified pathways are downregulated in neurodegenerative diseases, including HD, these findings suggest that pridopidine may exert neuroprotective effects beyond its role in alleviating some symptoms of HD., (© The Author 2016. Published by Oxford University Press.)

Unlabelled: Aberrant expression of microRNAs (miRNA) with oncogenic capacities (oncomiRs) has been described for several different malignancies. The first identified oncomiR, miR-17-92, is frequently overexpressed in a variety of cancers and its targets include the tumor suppressor PTEN. The transcription factor c-Myc (MYC) plays a central role in proliferative control and is rapidly upregulated upon mitogenic stimulation. Expression of c-Myc is frequently deregulated in tumors, facilitating proliferation and inhibiting terminal differentiation. The c-Myc-regulated network comprises a large number of transcripts, including those encoding miRNAs. Here, prostaglandin E2 (PGE2) exposure rapidly upregulates the expression of the MYC gene followed by the elevation of miR-17-92 levels, which in turn suppresses PTEN expression, thus enhancing apoptosis resistance in non-small cell lung cancer (NSCLC) cells. Knockdown of MYC expression or the miR-17-92 cluster effectively reverses this outcome. Similarly, miR-17-92 levels are significantly elevated in NSCLC cells ectopically expressing COX-2. Importantly, circulating miR-17-92 was elevated in the blood of patients with lung cancer as compared with subjects at risk for developing lung cancer. Furthermore, in patients treated with celecoxib, miR-17-92 levels were significantly reduced. These data demonstrate that PGE2, abundantly produced by NSCLC and inflammatory cells in the tumor microenvironment, is able to stimulate cell proliferation and promote resistance to pharmacologically induced apoptosis in a c-Myc and miR-17-92-dependent manner., Implications: This study describes a novel mechanism, involving c-Myc and miR-17-92, which integrates cell proliferation and apoptosis resistance., (©2014 AACR.)

Adult age-specific colorectal cancer incidence rates increase exponentially from maturity, reach a maximum, then decline in extreme old age. Armitage and Doll (1) postulated that the exponential increase resulted from "n" mutations occurring throughout adult life in normal "cells at risk" that initiated the growth of a preneoplastic colony in which subsequent "m" mutations promoted one of the preneoplastic "cells at risk" to form a lethal neoplasia. We have reported cytologic evidence that these "cells at risk" are fetal/juvenile organogenic, then preneoplastic metakaryotic stem cells. Metakaryotic cells display stem-like behaviors of both symmetric and asymmetric nuclear divisions and peculiarities such as bell shaped nuclei and amitotic nuclear fission that distinguish them from embryonic, eukaryotic stem cells. Analyses of mutant colony sizes and numbers in adult lung epithelia supported the inferences that the metakaryotic organogenic stem cells are constitutively mutator/hypermutable and that their contributions to cancer initiation are limited to the fetal/juvenile period. We have amended the two-stage model of Armitage and Doll and incorporated these several inferences in a computer program CancerFit v.5.0. We compared the expectations of the amended model to adult (15-104 years) age-specific colon cancer rates for European-American males born 1890-99 and observed remarkable concordance. When estimates of normal colonic fetal/juvenile APC and OAT gene mutation rates (∼2-5 × 10(-5) per stem cell doubling) and preneoplastic colonic gene loss rates (∼8 × 10(-3)) were applied, the model was in accordance only for the values of n = 2 and m = 4 or 5.