Generic Protocols for the Analytical Validation of Next-Generation Sequencing-Based ctDNA Assays: A Joint Consensus Recommendation of the BloodPAC's Analytical Variables Working Group.

Liquid biopsy, particularly the analysis of circulating tumor DNA (ctDNA), has demonstrated considerable promise for numerous clinical intended uses. Successful validation and commercialization of novel ctDNA tests have the potential to improve the outcomes of patients with cancer. The goal of the Blood Profiling Atlas Consortium (BloodPAC) is to accelerate the development and validation of liquid biopsy assays that will be introduced into the clinic. To accomplish this goal, the BloodPAC conducts research in the following areas: Data Collection and Analysis within the BloodPAC Data Commons; Preanalytical Variables; Analytical Variables; Patient Context Variables; and Reimbursement. In this document, the BloodPAC's Analytical Variables Working Group (AV WG) attempts to define a set of generic analytical validation protocols tailored for ctDNA-based Next-Generation Sequencing (NGS) assays. Analytical validation of ctDNA assays poses several unique challenges that primarily arise from the fact that very few tumor-derived DNA molecules may be present in circulation relative to the amount of nontumor-derived cell-free DNA (cfDNA). These challenges include the exquisite level of sensitivity and specificity needed to detect ctDNA, the potential for false negatives in detecting these rare molecules, and the increased reliance on contrived samples to attain sufficient ctDNA for analytical validation. By addressing these unique challenges, the BloodPAC hopes to expedite sponsors' presubmission discussions with the Food and Drug Administration (FDA) with the protocols presented herein. By sharing best practices with the broader community, this work may also save the time and capacity of FDA reviewers through increased efficiency.

Evidence of an epigenetic origin for high-risk 1q21 copy number aberrations in multiple myeloma.

Multiple myeloma is a B-cell malignancy stratified in part by cytogenetic abnormalities, including the high-risk copy number aberrations (CNAs) of +1q21 and 17p(-). To investigate the relationship between 1q21 CNAs and DNA hypomethylation of the 1q12 pericentromeric heterochromatin, we treated in vitro peripheral blood cultures of 5 patients with balanced constitutional rearrangements of 1q12 and 5 controls with the hypomethylating agent 5-azacytidine. Using G-banding, fluorescence in situ hybridization, and spectral karyotyping, we identified structural aberrations and copy number gains of 1q21 in the treated cells similar to those found in patients with cytogenetically defined high-risk disease. Aberrations included 1q12 triradials, amplifications of regions juxtaposed to 1q12, and jumping translocations 1q12. Strikingly, all 5 patients with constitutional 1q12 rearrangements showed amplifications on the derivative chromosomes distal to the inverted or translocated 1q12 region, including MYCN in 1 case. At the same time, no amplification of the 1q21 region was found when the 1q12 region was inverted or absent. These findings provide evidence that the hypomethylation of the 1q12 region can potentially amplify any genomic region juxtaposed to it and mimic CNAs found in the bone marrow of patients with high-risk disease.

Jumping translocations of 1q12 in multiple myeloma: a novel mechanism for deletion of 17p in cytogenetically defined high-risk disease.

Multiple myeloma (MM) is a B-cell malignancy driven in part by increasing copy number alterations (CNAs) during disease progression. Prognostically significant CNAs accumulate during clonal evolution and include gains of 1q21 and deletions of 17p, among others. Unfortunately, the mechanisms underlying the accumulation of CNAs and resulting subclonal heterogeneity in high-risk MM are poorly understood. To investigate the impact of jumping translocations of 1q12 (JT1q12) on receptor chromosomes (RCs) and subsequent clonal evolution, we analyzed specimens from 86 patients selected for unbalanced 1q12 aberrations by G-banding. Utilizing spectral karyotyping and locus-specific fluorescence in situ hybridization, we identified 10 patients with unexpected focal amplifications of an RC that subsequently translocated as part of a sequential JT1q12 to one or more additional RCs. Four patients exhibited amplification and translocation of 8q24 (MYC), 3 showed amplification of 16q11, and 1 each displayed amplification of 18q21.3 (BCL2), 18q23, or 4p16 (FGFR3). Unexpectedly, in 6 of 14 patients with the combination of the t(4;14) and deletion of 17p, we identified the loss of 17p as resulting from a JT1q12. Here, we provide evidence that the JT1q12 is a mechanism for the simultaneous gain of 1q21 and deletion of 17p in cytogenetically defined high-risk disease.

Enhancing cancer clonality analysis with integrative genomics.

INTRODUCTION: It is understood that cancer is a clonal disease initiated by a single cell, and that metastasis, which is the spread of cancer from the primary site, is also initiated by a single cell. The seemingly natural capability of cancer to adapt dynamically in a Darwinian manner is a primary reason for therapeutic failures. Survival advantages may be induced by cancer therapies and also occur as a result of inherent cell and microenvironmental factors. The selected "more fit" clones outmatch their competition and then become dominant in the tumor via propagation of progeny. This clonal expansion leads to relapse, therapeutic resistance and eventually death. The goal of this study is to develop and demonstrate a more detailed clonality approach by utilizing integrative genomics. METHODS: Patient tumor samples were profiled by Whole Exome Sequencing (WES) and RNA-seq on an Illumina HiSeq 2500 and methylation profiling was performed on the Illumina Infinium 450K array. STAR and the Haplotype Caller were used for RNA-seq processing. Custom approaches were used for the integration of the multi-omic datasets. RESULTS: Reported are major enhancements to CloneViz, which now provides capabilities enabling a formal tumor multi-dimensional clonality analysis by integrating: i) DNA mutations, ii) RNA expressed mutations, and iii) DNA methylation data. RNA and DNA methylation integration were not previously possible, by CloneViz (previous version) or any other clonality method to date. This new approach, named iCloneViz (integrated CloneViz) employs visualization and quantitative methods, revealing an integrative genomic mutational dissection and traceability (DNA, RNA, epigenetics) thru the different layers of molecular structures. CONCLUSION: The iCloneViz approach can be used for analysis of clonal evolution and mutational dynamics of multi-omic data sets. Revealing tumor clonal complexity in an integrative and quantitative manner facilitates improved mutational characterization, understanding, and therapeutic assignments.

Revealing the inherent heterogeneity of human malignancies by variant consensus strategies coupled with cancer clonal analysis.

Tumors are heterogeneous in composition. They are composed of cancer cells proper, along with stromal elements that collectively form a microenvironment, all of which are necessary to nurture the malignant process. In addition, many of the stromal cells are modified to support the unique needs of the malignant state. Tumors are composed of a variety of clones or subpopulations of cancer cells, which may differ in karyotype, growth rate, expression of cell surface markers, sensitivity to therapeutics, etc. New tools and methods to provide an improved understanding of tumor clonal architecture are needed to guide therapy.

Leveraging the new with the old: providing a framework for the integration of historic microarray studies with next generation sequencing.

Next Generation Sequencing (NGS) methods are rapidly providing remarkable advances in our ability to study the molecular profiles of human cancers. However, the scientific discovery offered by NGS also includes challenges concerning the interpretation of large and non-trivial experimental results. This task is potentially further complicated when a multitude of molecular profiling modalities are available, with the goal of a more integrative and comprehensive analysis of the cancer biology.

Towards the integration, annotation and association of historical microarray experiments with RNA-seq.

BACKGROUND: Transcriptome analysis by microarrays has produced important advances in biomedicine. For instance in multiple myeloma (MM), microarray approaches led to the development of an effective disease subtyping via cluster assignment, and a 70 gene risk score. Both enabled an improved molecular understanding of MM, and have provided prognostic information for the purposes of clinical management. Many researchers are now transitioning to Next Generation Sequencing (NGS) approaches and RNA-seq in particular, due to its discovery-based nature, improved sensitivity, and dynamic range. Additionally, RNA-seq allows for the analysis of gene isoforms, splice variants, and novel gene fusions. Given the voluminous amounts of historical microarray data, there is now a need to associate and integrate microarray and RNA-seq data via advanced bioinformatic approaches. METHODS: Custom software was developed following a model-view-controller (MVC) approach to integrate Affymetrix probe set-IDs, and gene annotation information from a variety of sources. The tool/approach employs an assortment of strategies to integrate, cross reference, and associate microarray and RNA-seq datasets. RESULTS: Output from a variety of transcriptome reconstruction and quantitation tools (e.g., Cufflinks) can be directly integrated, and/or associated with Affymetrix probe set data, as well as necessary gene identifiers and/or symbols from a diversity of sources. Strategies are employed to maximize the annotation and cross referencing process. Custom gene sets (e.g., MM 70 risk score (GEP-70)) can be specified, and the tool can be directly assimilated into an RNA-seq pipeline. CONCLUSION: A novel bioinformatic approach to aid in the facilitation of both annotation and association of historic microarray data, in conjunction with richer RNA-seq data, is now assisting with the study of MM cancer biology.

Assessments of tumor mutational burden estimation by targeted panel sequencing: A comprehensive simulation analysis.

Tumor mutational burden (TMB), when at a high level, is an emerging indicative factor of sensitivity to immune checkpoint inhibitors. Previous studies have shown that the more affordable and accurate targeted panels can be used to measure TMB as a substitute for whole exome sequencing (WES). However, additional processes, such as hotspot mutations exclusion and TMB adjustment, are usually required to deal with the effect of the limited panel sizes. A comprehensive investigation of the effective factors is needed for accurate TMB estimation by targeted panels. In this study, we quantitatively evaluated the variances of TMB values calculated by WES and targeted panels using 10,000 simulated targeted panels with panel sizes ranging from 0.2 to 3.1 million bases. With The Cancer Genome Atlas (TCGA) cancer samples and mutation profiles, we fixed regressions on WES-TMBs and panel-TMBs to assess the performance of a given targeted panel. Panel size was found as one of the major effective factors of TMB estimation. Meanwhile, by investigating the well-performing small panels that reported TMB values similar to those of WES, we demonstrated the evidence of the cancer type-specific impacts of genes on TMB estimation and identified high-impact gene sets for different cancer types based on the TCGA data. This study revealed the quantitative correlations between TMB variance and panel size, and the potential impacts of individual genes on TMB estimation. Our results suggested that for cancer patients diagnosed using targeted panels, it would be highly beneficial to have the capability to directly measure TMB from the targeted sequencing data. This would greatly assist in making decisions regarding the use of immunotherapies.

Contrived Materials and a Data Set for the Evaluation of Liquid Biopsy Tests: A Blood Profiling Atlas in Cancer (BLOODPAC) Community Study.

The Blood Profiling Atlas in Cancer (BLOODPAC) Consortium is a collaborative effort involving stakeholders from the public, industry, academia, and regulatory agencies focused on developing shared best practices on liquid biopsy. This report describes the results from the JFDI (Just Freaking Do It) study, a BLOODPAC initiative to develop standards on the use of contrived materials mimicking cell-free circulating tumor DNA, to comparatively evaluate clinical laboratory testing procedures. Nine independent laboratories tested the concordance, sensitivity, and specificity of commercially available contrived materials with known variant-allele frequencies (VAFs) ranging from 0.1% to 5.0%. Each participating laboratory utilized its own proprietary evaluation procedures. The results demonstrated high levels of concordance and sensitivity at VAFs of >0.1%, but reduced concordance and sensitivity at a VAF of 0.1%; these findings were similar to those from previous studies, suggesting that commercially available contrived materials can support the evaluation of testing procedures across multiple technologies. Such materials may enable more objective comparisons of results on materials formulated in-house at each center in multicenter trials. A unique goal of the collaborative effort was to develop a data resource, the BLOODPAC Data Commons, now available to the liquid-biopsy community for further study. This resource can be used to support independent evaluations of results, data extension through data integration and new studies, and retrospective evaluation of data collection.

Proteomic profiling of H-Ras-G12V induced hypertrophic cardiomyopathy in transgenic mice using comparative LC-MS analysis of thin fresh-frozen tissue sections.

Determination of disease-relevant proteomic profiles from limited tissue specimens, such as pathological biopsies and tissues from small model organisms, remains an analytical challenge and a much needed clinical goal. In this study, a transgenic mouse disease model of cardiac-specific H-Ras-G12V induced hypertrophic cardiomyopathy provided a system to explore the potential of using mass spectrometry (MS)-based proteomics to obtain a disease-relevant molecular profile from amount-limited specimens that are routinely used in pathological diagnosis. Our method employs a two-stage methanol-assisted solubilization to digest lysates prepared from 8-µm-thick fresh-frozen histological tissue sections of diseased/experimental and normal/control hearts. Coupling this approach with a nanoflow reversed-phase liquid chromatography (LC) and a hybrid linear ion trap/Fourier transform-ion cyclotron resonance MS resulted in the identification of 704 and 752 proteins in hypertrophic and wild-type (control) myocardium, respectively. The disease driving H-Ras protein along with vimentin were unambiguously identified by LC-MS in hypertrophic myocardium and cross-validated by immunohistochemistry and western blotting. The pathway analysis involving proteins identified by MS showed strong association of proteomic data with cardiovascular disease. More importantly, the MS identification and subsequent cross-validation of Wnt3a and ß-catenin, in conjunction with IHC identification of phosphorylated GSK-3ß and nuclear localization of ß-catenin, provided evidence of Wnt/ß-catenin canonical pathway activation secondary to Ras activation in the course of pathogenic myocardial hypertrophic transformation. Our method yields results indicating that the described proteomic approach permits molecular discovery and assessment of differentially expressed proteins regulating H-Ras induced hypertrophic cardiomyopathy. Selected proteins and pathways can be further investigated using immunohistochemical techniques applied to serial tissue sections of similar or different origin.

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.

Microdissection Methods Utilizing Single-Cell Subtype Analysis and the Impact on Precision Medicine.

Improving the utilization of tumor tissue from diagnostic biopsies is an unmet medical need. This is especially relevant today in the rapidly evolving precision oncology field where tumor genotyping is often essential for the indication of many advanced and targeted therapies. National Comprehensive Cancer Network (NCCN) guidelines now mandate molecular testing for clinically actionable targets in certain malignancies. Utilizing advanced stage lung cancer as an example, an improved genotyping approach for solid tumors is possible. The strategy involves optimization of the microdissection process and analysis of a large number of identical target cells from formalin-fixed paraffin-embedded (FFPE) specimens sharing similar characteristics, in other words, single-cell subtype analysis. The shared characteristics can include immunostaining status, cell phenotype, and/or spatial location within a histological section. Synergy between microdissection and droplet digital PCR (ddPCR) enhances the molecular analysis. We demonstrate here a methodology that illustrates genotyping of a solid tumor from a small tissue biopsy sample in a time- and cost-efficient manner, using immunostain targeting as an example.

An advanced molecular medicine case report of a rare human tumor using genomics, pathomics, and radiomics.

Background: Pulmonary Sclerosing Pneumocytoma (PSP) is a rare tumor of the lung with a low malignant potential that primarily affects females. Initial studies of PSP focused primarily on analyzing features uncovered using conventional X-ray or CT imaging. In recent years, because of the widespread use of next-generation sequencing (NGS), the study of PSP at the molecular-level has emerged. Methods: Analytical approaches involving genomics, radiomics, and pathomics were performed. Genomics studies involved both DNA and RNA analyses. DNA analyses included the patient's tumor and germline tissues and involved targeted panel sequencing and copy number analyses. RNA analyses included tumor and adjacent normal tissues and involved studies covering expressed mutations, differential gene expression, gene fusions and molecular pathways. Radiomics approaches were utilized on clinical imaging studies and pathomics techniques were applied to tumor whole slide images. Results: A comprehensive molecular profiling endeavor involving over 50 genomic analyses corresponding to 16 sequencing datasets of this rare neoplasm of the lung were generated along with detailed radiomic and pathomic analyses to reveal insights into the etiology and molecular behavior of the patient's tumor. Driving mutations (AKT1) and compromised tumor suppression pathways (TP53) were revealed. To ensure the accuracy and reproducibility of this study, a software infrastructure and methodology known as NPARS, which encapsulates NGS and associated data, open-source software libraries and tools including versions, and reporting features for large and complex genomic studies was used. Conclusion: Moving beyond descriptive analyses towards more functional understandings of tumor etiology, behavior, and improved therapeutic predictability requires a spectrum of quantitative molecular medicine approaches and integrations. To-date this is the most comprehensive study of a patient with PSP, which is a rare tumor of the lung. Detailed radiomic, pathomic and genomic molecular profiling approaches were performed to reveal insights regarding the etiology and molecular behavior. In the event of recurrence, a rational therapy plan is proposed based on the uncovered molecular findings.

Effect of Antigen Retrieval on Genomic DNA From Immunodissected Samples.

Immunohistochemical (IHC) staining is an established technique for visualizing proteins in tissue sections for research studies and clinical applications. IHC is increasingly used as a targeting strategy for procurement of labeled cells via tissue microdissection, including immunodissection, computer-aided laser dissection (CALD), expression microdissection (xMD), and other techniques. The initial antigen retrieval (AR) process increases epitope availability and improves staining characteristics; however, the procedure can damage DNA. To better understand the effects of AR on DNA quality and quantity in immunodissected samples, both clinical specimens (KRAS gene mutation positive cases) and model system samples (lung cancer patient-derived xenograft tissue) were subjected to commonly employed AR methods (heat induced epitope retrieval [HIER], protease digestion) and the effects on DNA were assessed by Qubit, fragment analysis, quantitative PCR, digital droplet PCR (ddPCR), library preparation, and targeted sequencing. The data showed that HIER resulted in optimal IHC staining characteristics, but induced significant damage to DNA, producing extensive fragmentation and decreased overall yields. However, neither of the AR methods combined with IHC prevented ddPCR amplification of small amplicons and gene mutations were successfully identified from immunodissected clinical samples. The results indicate for the first time that DNA recovered from immunostained slides after standard AR and IHC processing can be successfully employed for genomic mutation analysis via ddPCR and next-generation sequencing (NGS) short-read methods.

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.

FDA-led consortium studies advance quality control of targeted next generation sequencing assays for precision oncology.

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.

NPARS-A Novel Approach to Address Accuracy and Reproducibility in Genomic Data Science.

Background: Accuracy and reproducibility are vital in science and presents a significant challenge in the emerging discipline of data science, especially when the data are scientifically complex and massive in size. Further complicating matters, in the field of genomic-based science high-throughput sequencing technologies generate considerable amounts of data that needs to be stored, manipulated, and analyzed using a plethora of software tools. Researchers are rarely able to reproduce published genomic studies. Results: Presented is a novel approach which facilitates accuracy and reproducibility for large genomic research data sets. All data needed is loaded into a portable local database, which serves as an interface for well-known software frameworks. These include python-based Jupyter Notebooks and the use of RStudio projects and R markdown. All software is encapsulated using Docker containers and managed by Git, simplifying software configuration management. Conclusion: Accuracy and reproducibility in science is of a paramount importance. For the biomedical sciences, advances in high throughput technologies, molecular biology and quantitative methods are providing unprecedented insights into disease mechanisms. With these insights come the associated challenge of scientific data that is complex and massive in size. This makes collaboration, verification, validation, and reproducibility of findings difficult. To address these challenges the NGS post-pipeline accuracy and reproducibility system (NPARS) was developed. NPARS is a robust software infrastructure and methodology that can encapsulate data, code, and reporting for large genomic studies. This paper demonstrates the successful use of NPARS on large and complex genomic data sets across different computational platforms.

BloodPAC Data Commons for Liquid Biopsy Data.

PURPOSE: The Blood Profiling Atlas in Cancer (BloodPAC) Data Commons (BPDC) is being developed and is operated by the public-private BloodPAC Consortium to support the liquid biopsy community. It is an interoperable data commons with the ultimate aim of serving as a recognized source of valid scientific evidence for liquid biopsy assays for industry, academia, and standards and regulatory stakeholders. METHODS: The BPDC is implemented using the open source Gen3 data commons platform (https://gen3.org). In particular, the BPDC Data Exploration Portal, BPDC Data Submission Portal, the BPDC Workspace Hub, and the BloodPAC application programming interface (API) were all automatically generated from the BloodPAC Data Model using the Gen3 data commons platform. BPDC uses Gen3's implementation of the data commons framework services so that it can interoperate through secure, compliant APIs with other data commons using data commons framework service, such as National Cancer Institute's Cancer Research Data Commons. RESULTS: The BPDC contains 57 studies and projects spanning more than 4,100 cases. This amounts to 5,700 aliquots (blood plasma, serum, or a contrived sample) that have been subjected to a liquid biopsy assay, quantified, and then contributed by members of the BloodPAC Consortium. In all, there are more than 31,000 files in the commons as of December 2020. We describe the BPDC, the data it manages, the process that the BloodPAC Consortium used to develop it, and some of the applications that have been developed using its API. CONCLUSION: The BPDC has been the data platform used by BloodPAC during the past 4 years to manage the data for the consortium and to provide workspaces for its working groups.

Exogenous phosphatidic acid reduces acetaminophen-induced liver injury in mice by activating hepatic interleukin-6 signaling through inter-organ crosstalk.

We previously demonstrated that endogenous phosphatidic acid (PA) promotes liver regeneration after acetaminophen (APAP) hepatotoxicity. Here, we hypothesized that exogenous PA is also beneficial. To test that, we treated mice with a toxic APAP dose at 0 h, followed by PA or vehicle (Veh) post-treatment. We then collected blood and liver at 6, 24, and 52 h. Post-treatment with PA 2 h after APAP protected against liver injury at 6 h, and the combination of PA and N-acetyl-l-cysteine (NAC) reduced injury more than NAC alone. Interestingly, PA did not affect canonical mechanisms of APAP toxicity. Instead, transcriptomics revealed that PA activated interleukin-6 (IL-6) signaling in the liver. Consistent with that, serum IL-6 and hepatic signal transducer and activator of transcription 3 (Stat3) phosphorylation increased in PA-treated mice. Furthermore, PA failed to protect against APAP in IL-6-deficient animals. Interestingly, IL-6 expression increased 18-fold in adipose tissue after PA, indicating that adipose is a source of PA-induced circulating IL-6. Surprisingly, however, exogenous PA did not alter regeneration, despite the importance of endogenous PA in liver repair, possibly due to its short half-life. These data demonstrate that exogenous PA is also beneficial in APAP toxicity and reinforce the protective effects of IL-6 in this model.

Advancing NGS quality control to enable measurement of actionable mutations in circulating tumor DNA.

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.