Informatics Pipeline Research Articles

Informatics and Artificial Intelligence-Guided Assessment of the Regulatory and Translational Research Landscape of First-in-Class Oncology Drugs in the United States, 2018-2022.

Cancer drug development remains a critical but challenging process that affects millions of patients and their families. Using biomedical informatics and artificial intelligence (AI) approaches, we assessed the regulatory and translational research landscape defining successful first-in-class drugs for patients with cancer. This is a retrospective observational study of all novel first-in-class drugs approved by the US Food and Drug Administration (FDA) from 2018 to 2022, stratified by cancer versus noncancer drugs. A biomedical informatics pipeline leveraging interoperability standards and ChatGPT performed integration and analysis of public databases provided by the FDA, National Institutes of Health, and WHO. Between 2018 and 2022, the FDA approved a total of 247 novel drugs, of which 107 (43.3%) were first-in-class drugs involving a new biologic target. Of these first-in-class drugs, 30 (28%) treatments were indicated for patients with cancer, including 19 (63.3%) for solid tumors and the remaining 11 (36.7%) for hematologic cancers. A median of 68 publications of basic, clinical, and other relevant translational science preceded successful FDA approval of first-in-class cancer drugs, with oncology-related treatments involving fewer median years of target-based research than therapies not related to cancer (33 v 43 years; P < .05). Overall, 94.4% of first-in-class drugs had at least 25 years of target-related research papers, while 85.5% of first-in-class drugs had at least 10 years of translational research publications. Novel first-in-class cancer treatments are defined by diverse clinical indications, personalized molecular targets, dependence on expedited regulatory pathways, and translational research metrics reflecting this complex landscape. Biomedical informatics and AI provide scalable, data-driven ways to assess and even address important challenges in the drug development pipeline.

The updated mouse universal genotyping array bioinformatic pipeline improves genetic QC in laboratory mice.

The MiniMUGA genotyping array is a popular tool for genetic quality control of laboratory mice and genotyping samples from most experimental crosses involving laboratory strains, particularly for reduced complexity crosses. The content of the production version of the MiniMUGA array is fixed; however, there is the opportunity to improve the array's performance and the associated report's usefulness by leveraging thousands of samples genotyped since the initial description of MiniMUGA. Here, we report our efforts to update and improve marker annotation, increase the number and the reliability of the consensus genotypes for classical inbred strains and substrains, and increase the number of constructs reliably detected with MiniMUGA. In addition, we have implemented key changes in the informatics pipeline to identify and quantify the contribution of specific genetic backgrounds to the makeup of a given sample, remove arbitrary thresholds, include the Y Chromosome and mitochondrial genome in the ideogram, and improve robust detection of the presence of commercially available substrains based on diagnostic alleles. Finally, we have updated the layout of the report to simplify the interpretation and completeness of the analysis and added a section summarizing the ideogram in table format. These changes will be of general interest to the mouse research community and will be instrumental in our goal of improving the rigor and reproducibility of mouse-based biomedical research.

High-Resolution Mass Spectrometry for Human Exposomics: Expanding Chemical Space Coverage.

In the modern "omics" era, measurement of the human exposome is a critical missing link between genetic drivers and disease outcomes. High-resolution mass spectrometry (HRMS), routinely used in proteomics and metabolomics, has emerged as a leading technology to broadly profile chemical exposure agents and related biomolecules for accurate mass measurement, high sensitivity, rapid data acquisition, and increased resolution of chemical space. Non-targeted approaches are increasingly accessible, supporting a shift from conventional hypothesis-driven, quantitation-centric targeted analyses toward data-driven, hypothesis-generating chemical exposome-wide profiling. However, HRMS-based exposomics encounters unique challenges. New analytical and computational infrastructures are needed to expand the analysis coverage through streamlined, scalable, and harmonized workflows and data pipelines that permit longitudinal chemical exposome tracking, retrospective validation, and multi-omics integration for meaningful health-oriented inferences. In this article, we survey the literature on state-of-the-art HRMS-based technologies, review current analytical workflows and informatic pipelines, and provide an up-to-date reference on exposomic approaches for chemists, toxicologists, epidemiologists, care providers, and stakeholders in health sciences and medicine. We propose efforts to benchmark fit-for-purpose platforms for expanding coverage of chemical space, including gas/liquid chromatography-HRMS (GC-HRMS and LC-HRMS), and discuss opportunities, challenges, and strategies to advance the burgeoning field of the exposome.

The human infertility single-cell testis atlas (HISTA): an interactive molecular scRNA-Seq reference of the human testis.

Single-cell RNA-seq (scRNA-Seq) has been widely adopted to study gene expression of the human testis. Several datasets of scRNA-Seq from human testis have been generated from different groups processed with different informatics pipelines. An integrated atlas of scRNA-Seq expression constructed from multiple donors, developmental ages, and fertility states would be widely useful for the testis research community. To describe the generation and use of the human infertility single-cell testis atlas (HISTA), an interactive web tool for understanding human spermatogenesis through scRNA-Seq analysis. We obtained scRNA-Seq datasets derived from 12 donors, including healthy adult controls, juveniles, and several infertility cases, and reprocessed these data using methods to remove batch effects. Using Shiny, an open-source environment for data visualization, we created numerous interactive tools for exploring the data, some of which support simple statistical hypothesis testing. We used the resulting HISTA browser and its underlying data to demonstrate HISTA's value for testis researchers. A primary application of HISTA is to search by a single gene or a set of genes; thus, we present various analyses that quantify and visualize gene expression across the testis cells and pathology. HISTA also contains machine-learning-derived gene modules ("components") that capture the entire transcriptional landscape of the testis tissue. We show how the use of these components can simplify the highly complex data in HISTA and assist with the interpretation of genes with unknown functions. Finally, we demonstrate the diverse ways HISTA can be used for new data analysis, including hypothesis testing. HISTA is a research environment that can help scientists organize and understand the high-dimensional transcriptional landscape of the human testis. HISTA has already contributed to published testis research and can be updated as needed with input from the research community or downloaded and modified for individual needs.

Genetic and multi-omic risk assessment of Alzheimer's disease implicates core associated biological domains.

Alzheimer's disease (AD) is the predominant dementia globally, with heterogeneous presentation and penetrance of clinical symptoms, variable presence of mixed pathologies, potential disease subtypes, and numerous associated endophenotypes. Beyond the difficulty of designing treatments that address the core pathological characteristics of the disease, therapeutic development is challenged by the uncertainty of which endophenotypic areas and specific targets implicated by those endophenotypes to prioritize for further translational research. However, publicly funded consortia driving large-scale open science efforts have produced multiple omic analyses that address both disease risk relevance and biological process involvement of genes across the genome. Here we report the development of an informatic pipeline that draws from genetic association studies, predicted variant impact, and linkage with dementia associated phenotypes to create a genetic risk score. This is paired with a multi-omic risk score utilizing extensive sets of both transcriptomic and proteomic studies to identify system-level changes in expression associated with AD. These two elements combined constitute our target risk score that ranks AD risk genome-wide. The ranked genes are organized into endophenotypic space through the development of 19 biological domains associated with AD in the described genetics and genomics studies and accompanying literature. The biological domains are constructed from exhaustive Gene Ontology (GO) term compilations, allowing automated assignment of genes into objectively defined disease-associated biology. This rank-and-organize approach, performed genome-wide, allows the characterization of aggregations of AD risk across biological domains. The top AD-risk-associated biological domains are Synapse, Immune Response, Lipid Metabolism, Mitochondrial Metabolism, Structural Stabilization, and Proteostasis, with slightly lower levels of risk enrichment present within the other 13 biological domains. This provides an objective methodology to localize risk within specific biological endophenotypes and drill down into the most significantly associated sets of GO terms and annotated genes for potential therapeutic targets.

Introducing pre-trained transformers for high entropy alloy informatics

High-Entropy Alloys (HEAs) offer a wealth of compositional possibilities, presenting both immense opportunities and challenges in identifying the best candidates for specific applications. This study introduces a new informatics pipeline for HEAs, utilizing Large Language Model architectures and pre-trained transformers inspired by Natural Language Processing techniques. This work demonstrates that transformers pre-trained on theoretical unlabelled thermodynamic data can be fine-tuned with labelled experimental HEA data to significantly improve predictive accuracy while negating the need for extensive experimentally driven labelled datasets.

Development of a novel streamlined workflow (AACRE) and database (inCREDBle) for genomic analysis of carbapenem-resistant Enterobacterales.

In response to the threat of increasing antimicrobial resistance, we must increase the amount of available high-quality genomic data gathered on antibiotic-resistant bacteria. To this end, we developed an integrated pipeline for high-throughput long-read sequencing, assembly, annotation and analysis of bacterial isolates and used it to generate a large genomic data set of carbapenemase-producing Enterobacterales (CPE) isolates collected in Spain. The set of 461 isolates were sequenced with a combination of both Illumina and Oxford Nanopore Technologies (ONT) DNA sequencing technologies in order to provide genomic context for chromosomal loci and, most importantly, structural resolution of plasmids, important determinants for transmission of antimicrobial resistance. We developed an informatics pipeline called Assembly and Annotation of Carbapenem-Resistant Enterobacteriaceae (AACRE) for the full assembly and annotation of the bacterial genomes and their complement of plasmids. To explore the resulting genomic data set, we developed a new database called inCREDBle that not only stores the genomic data, but provides unique ways to filter and compare data, enabling comparative genomic analyses at the level of chromosomes, plasmids and individual genes. We identified a new sequence type, ST5000, and discovered a genomic locus unique to ST15 that may be linked to its increased spread in the population. In addition to our major objective of generating a large regional data set, we took the opportunity to compare the effects of sample quality and sequencing methods, including R9 versus R10 nanopore chemistry, on genome assembly and annotation quality. We conclude that converting short-read and hybrid microbial sequencing and assembly workflows to the latest nanopore chemistry will further reduce processing time and cost, truly enabling the routine monitoring of resistance transmission patterns at the resolution of complete chromosomes and plasmids.

Network based approach to identify interactions between Type 2 diabetes and cancer comorbidities

High blood sugar and insulin insensitivity causes the lifelong chronic metabolic disease called Type 2 diabetes (T2D) which has a higher chance of developing different malignancies. T2D with comorbidities like Cancers can make normal medications for those disorders more difficult. There may be a significant correlation between comorbidities and have an impact on one another's health. These associations may be due to a number of direct and indirect mechanisms. Such molecular mechanisms that underpin T2D and cancer are yet unknown. However, the large volumes of data available on these diseases allowed us to use analytical tools for uncovering their interrelated pathways. Here, we tried to present a system for investigating potential comorbidity relationships between T2D and Cancer disease by looking at the molecular processes involved, analyzing a huge number of freely accessible transcriptomic datasets of various disorders using bioinformatics. Using semantic similarity and gene set enrichment analysis, we created an informatics pipeline that evaluates and integrates Gene Ontology (GO), expression of genes, and biological process data. We discovered genes that are common in T2D and Cancer along with molecular pathways and GOs. We compared the top 200 Differentially Expressed Genes (DEGs) from each selected T2D and cancer dataset and found the most significant common genes. Among all the common genes 13 genes were found most frequent. We also found 4 common GO terms: GO:0000003, GO:0000122, GO:0000165, and GO:0000278 among all the common GO terms between T2d and different cancers. Using these genes and GO term semantic similarity, we calculated the distance between these two diseases. The semantic similarity results of our study showed a higher association of Liver Cancer (LiC), Breast Cancer (BreC), Colorectal Cancer (CC), and Bladder Cancer (BlaC) with T2D. Furthermore we found KIF4A, NUSAP1, CENPF, CCNB1, TOP2A, CCNB2, RRM2, HMMR, NDC80, NCAPG, and IGFBP5 common hub proteins among different cancers correlated to T2D. AGE-RAGE signaling pathway in diabetic complications, Osteoclast differentiation, TNF signaling pathway, IL-17 signaling pathway, p53 signaling pathway, MAPK signaling pathway, Human T-cell leukemia virus 1 infection, and Non-alcoholic fatty liver disease are the 8 most significant pathways found among 18 common pathways between T2D and selected cancers. As a result of our technique, we now know more about disease pathways that are critical between T2D and cancer.

Evaluation of the Thermo Fisher Oncomine Myeloid MRD DNA Assay on the Ion Torrent Platform

Introduction Malignant myeloid diseases are a group of blood cancers that arise from genetic changes in hematopoietic cells in the bone marrow or peripheral blood. The presence of measurable residual disease (MRD), or detection of abnormal cells in the body following completion of therapy, is associated with a higher risk of disease relapse, and decreased overall survival (Schuurhuis 2018). Next-generation sequencing (NGS) can be leveraged as a high-sensitivity, minimally invasive method for detection of MRD. The Ion Torrent Oncomine Myeloid MRD Assay (Myeloid MRD) is a targeted NGS assay designed for the detection of single nucleotide variants (SNVs) and insertions/deletions in a curated selection of clinically relevant myeloid disease-related genes at low limits of detection. Assay performance was evaluated using normal and diseased clinical blood and bone marrow samples and NGS reference materials. Methods The Myeloid MRD DNA panel targets hotspot regions across 33 genes and uses Ion AmpliSeq HD technology with unique molecular tagging to suppress sequencing artifacts and improve analytical sensitivity. Briefly, DNA was extracted from whole blood or bone marrow samples and 120 ng of nucleic acid was used as input for the assay. The DNA was divided between 2 polymerase chain reactions (PCR), where it served as the template for target amplifications, before combining reaction products by sample. Library preparation followed the standard Ion AmpliSeq HD protocol, where amplicons were partially enzymatically digested, and a second round of amplification was performed using barcoded primers. Following bead purification, the final library was measured by quantitative PCR (qPCR) to inform final library loading onto the sequencer. Six libraries were pooled and sequenced on an Ion 550 chip using the Ion GeneStudio S5 System. Primary and secondary analyses were performed using the Torrent Suite software, and results were exported to the Ion Reporter software for variant calling. From the variant calling results, a probit model was utilized to determine the variant allele frequency (VAF) at which the assay achieved 90% power to call an expected variant, defining the limit of detection (LoD). Other parameters evaluated included analytical sensitivity, analytical specificity, positive predictive value (PPV), accuracy, and precision. Results Overall, consistent, high quality on-target reads were observed across 22 sequencing runs with a median Ion Sphere Particle (ISP) loading of 90%, usable sequence of 59%, and over 130 million total reads per chip. When evaluating individual samples according to the vendor-recommended number of mapped reads (&amp;gt; 10 million), 98.3% of samples tested passed quality control (QC). Additional sample QC metrics established during assay validation included thresholds of &amp;gt; 80% reads on-target, &amp;gt; 80% uniformity of base coverage, and a mean read length of at least 80 base pairs. The estimated LoD for the assay was 0.1% VAF for small variants (SNVs, insertions/deletions).Above the LoD, the assay demonstrated 96.03% sensitivity, 99.89% accuracy of base-calling, 100% specificity, and 98.83% PPV.Additionally,a comparison was performed usingmatched whole blood and bone marrow samples collected from patients with acute myeloid leukemia (AML) to evaluate the concordance of variant calling. It was determined that the same variants were identified across both sample types, although at higher allele frequency in bone marrow compared to whole blood. Conclusion This evaluation of the Myeloid MRD assay demonstrates that it is a precise and accurate assay with sensitivity that is acceptable for MRD analysis. Notably, variant detection was concordant between whole blood and bone marrow sample types collected from AML patients, with bone marrow providing improved sensitivity for low-frequency variant detection. The user-friendly workflow and integrated informatics pipeline offer opportunities for streamlined testing and interpretation of results. The Ion Torrent Myeloid MRD DNA assay is a powerful new tool for MRD detection across a growing myeloid disease target landscape.

User-Centered Evaluation and Design Recommendations for an Internal Medicine Resident Competency Assessment Dashboard.

Clinical Competency Committee (CCC) members employ varied approaches to the review process. This makes the design of a competency assessment dashboard that fits the needs of all members difficult. This work details a user-centered evaluation of a dashboard currently utilized by the Internal Medicine Clinical Competency Committee (IM CCC) at the University of Cincinnati College of Medicine and generated design recommendations. Eleven members of the IM CCC participated in semistructured interviews with the research team. These interviews were recorded and transcribed for analysis. The three design research methods used in this study included process mapping (workflow diagrams), affinity diagramming, and a ranking experiment. Through affinity diagramming, the research team identified and organized opportunities for improvement about the current system expressed by study participants. These areas include a time-consuming preprocessing step, lack of integration of data from multiple sources, and different workflows for each step in the review process. Finally, the research team categorized nine dashboard components based on rankings provided by the participants. We successfully conducted user-centered evaluation of an IM CCC dashboard and generated four recommendations. Programs should integrate quantitative and qualitative feedback, create multiple views to display these data based on user roles, work with designers to create a usable, interpretable dashboard, and develop a strong informatics pipeline to manage the system. To our knowledge, this type of user-centered evaluation has rarely been attempted in the medical education domain. Therefore, this study provides best practices for other residency programs to evaluate current competency assessment tools and to develop new ones.

PolyBERT: a chemical language model to enable fully machine-driven ultrafast polymer informatics

Polymers are a vital part of everyday life. Their chemical universe is so large that it presents unprecedented opportunities as well as significant challenges to identify suitable application-specific candidates. We present a complete end-to-end machine-driven polymer informatics pipeline that can search this space for suitable candidates at unprecedented speed and accuracy. This pipeline includes a polymer chemical fingerprinting capability called polyBERT (inspired by Natural Language Processing concepts), and a multitask learning approach that maps the polyBERT fingerprints to a host of properties. polyBERT is a chemical linguist that treats the chemical structure of polymers as a chemical language. The present approach outstrips the best presently available concepts for polymer property prediction based on handcrafted fingerprint schemes in speed by two orders of magnitude while preserving accuracy, thus making it a strong candidate for deployment in scalable architectures including cloud infrastructures.

Assessing variants of uncertain significance implicated in hearing loss using a comprehensive deafness proteome

Hearing loss is the leading sensory deficit, affecting ~ 5% of the population. It exhibits remarkable heterogeneity across 223 genes with 6328 pathogenic missense variants, making deafness-specific expertise a prerequisite for ascribing phenotypic consequences to genetic variants. Deafness-implicated variants are curated in the Deafness Variation Database (DVD) after classification by a genetic hearing loss expert panel and thorough informatics pipeline. However, seventy percent of the 128,167 missense variants in the DVD are “variants of uncertain significance” (VUS) due to insufficient evidence for classification. Here, we use the deep learning protein prediction algorithm, AlphaFold2, to curate structures for all DVD genes. We refine these structures with global optimization and the AMOEBA force field and use DDGun3D to predict folding free energy differences (∆∆GFold) for all DVD missense variants. We find that 5772 VUSs have a large, destabilizing ∆∆GFold that is consistent with pathogenic variants. When also filtered for CADD scores (> 25.7), we determine 3456 VUSs are likely pathogenic at a probability of 99.0%. Of the 224 genes in the DVD, 166 genes (74%) exhibit one or more missense variants predicted to cause a pathogenic change in protein folding stability. The VUSs prioritized here affect 119 patients (~ 3% of cases) sequenced by the OtoSCOPE targeted panel. Approximately half of these patients previously received an inconclusive report, and reclassification of these VUSs as pathogenic provides a new genetic diagnosis for six patients.

Abstract 5547: Evaluation of two clinically focused targeted NGS systems for liquid biopsy testing shows a high level of concordance in resulting actionable mutations

Abstract Introduction: The use of blood-based molecular diagnostics is increasingly becoming routine in clinical oncology practice. The purpose of this study was to evaluate variant resulting concordance for the four major somatic variant classes using two clinical next-generation sequencing (NGS) systems for conducting highly sensitive blood-based analyses. All donor specimens were de-identified remnants from patients previously diagnosed with advanced NSCLC. We utilized two independent panels, systems and bioinformatic pipelines that are focused on clinical testing for key actionable variants. The fifty-gene panel Oncomine Precision Assay (OPA) GX was performed on the Genexus Integrated Sequencer and the 52 gene GeneStrat NGS (GSNGS) test was run as a reference on the Ion GeneStudio S5 PRIME system. The four major classes of mutations evaluated included Single Nucleotide Variants (SNV), Insertions and Deletions (INDEL), copy number amplifications (CNAs) and gene fusions. Methods: For this study, de-identified reference GSNGS cell-free nucleic acid (cfNA) remnant specimens (n=33 variants) that passed all validated QC bioinformatics thresholds were blinded and tested on the Genexus. DNA concentration was measured by fluorometry, and specimens were diluted to an input of 6.6 ng to 53.4 ng for library preparation. The Genexus system is pre-programmed to process specimens to result generation. Variant calls for all mutation classes were conducted using the on-board bioinformatics pipelines, and results were compared to those generated by the GeneStrat NGS Test. We included specimens harboring somatic variant mutations in KRAS, NRAS, BRAF, EGFR, ERBB2, and FGFR; CNAs; EML4-ALK fusions; and MET exon 14 skipping. Results: All samples passed the OPA (Genexus) bioinformatic quality control criteria and mapped reads were highly consistent, demonstrating accuracy of calls between both the Ion Torrent platforms, informatic pipelines and panels. We observed 100% concordance in variant calls for SNVs, INDELs and fusions between the two platforms. There was one CNA variant observed on the S5 that was not called by the Genexus workflow, yielding overall concordance of 97% (32 of 33). Of note, 44 additional somatic variants (SNV and INDELs) and 1 additional fusion were detected using the Genexus workflow. This was due to the lower threshold for variant calling on this system. Conclusion: The high concordance of the independent workflows for the detection of nucleic acid variants in circulation demonstrates the capability of both systems to be used for testing of clinical specimens. Specifically, actionable variants in the four major mutation classes were successfully detected in the reference and test specimen set. As a part of clinical validation, orthogonal testing of variants will need to be conducted. Citation Format: Sarah Kreston, Claire Gould, Kylie Blair, Leisa Jackson, Janice Riley, Gary A. Pestano. Evaluation of two clinically focused targeted NGS systems for liquid biopsy testing shows a high level of concordance in resulting actionable mutations. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5547.

Abstract 156: Establishing An Informatics Framework For Longitudinal Cerebrovascular And Neurological Disease Research

Background: Although developments in computational big data methods have enhanced the ability to meaningfully extract information from hospital EMRs, there are inherent gaps in understanding care utilization and long-term patient outcomes outside of in-patient milieu, particularly across hospital systems. Methods: Utilizing registry data from a 7-hospital stroke certified healthcare system serving a large metropolitan, cohorts were identified for Medicare-insured patients with primary discharge diagnoses of acute ischemic stroke (AIS), intracerebral hemorrhage (ICH), or cerebral amyloid angiopathy (CAA). In collaboration with a CMS Qualified Entity which houses health care utilization data for over 80% of the state population (including 100% of Medicare Fee-for-Service), patient records were securely matched and linked. Socio-demographic and enrollment characteristics for matched cohorts are reported. Results: Medical records and claims data were matched for 98.4% of patients (n=6,531) admitted between 05/2016 and 12/2020 (n=5,789 AIS; 696 ICH; 46 CAA). Across all matched patients, the median (IQR) age at hospital admission was 76.0 (69.0-84.0) (54% female; 56% non-Hispanic White; 24% non-Hispanic Black; 13% Hispanic/Latino; median [IQR] Area Deprivation Index: 4 [2-6]). Age-based standard enrollment represented 86% of patients, with the remainder enrolled due to disability or health complications. Median (IQR) number of total years for patients with Part AB and D coverage was 6.9 (5.0-7.0) and 5.4 (2.9-7.0) years, respectively. At the time of reporting, all-cause death rates among Medicare enrollees who sought care are: 33% (AIS), 55% (ICH), and 41% (CAA). Conclusion: In the absence of systematically captured long-term outcomes data, health informatic pipelines that create linkages across multimodal sources provide a valuable framework for enabling longitudinal research on patients throughout and after stroke hospitalizations.

Diagnostic signature for heart failure with preserved ejection fraction (HFpEF): a machine learning approach using multi-modality electronic health record data

BackgroundHeart failure with preserved ejection fraction (HFpEF) is thought to be highly prevalent yet remains underdiagnosed. Evidence-based treatments are available that increase quality of life and decrease hospitalization. We sought to develop a data-driven diagnostic model to predict from electronic health records (EHR) the likelihood of HFpEF among patients with unexplained dyspnea and preserved left ventricular EF.Methods and resultsThe derivation cohort comprised patients with dyspnea and echocardiography results. Structured and unstructured data were extracted using an automated informatics pipeline. Patients were retrospectively diagnosed as HFpEF (cases), non-HF (control cohort I), or HF with reduced EF (HFrEF; control cohort II). The ability of clinical parameters and investigations to discriminate cases from controls was evaluated by extreme gradient boosting. A likelihood scoring system was developed and validated in a separate test cohort. The derivation cohort included 1585 consecutive patients: 133 cases of HFpEF (9%), 194 non-HF cases (Control cohort I) and 1258 HFrEF cases (Control cohort II). Two HFpEF diagnostic signatures were derived, comprising symptoms, diagnoses and investigation results. A final prediction model was generated based on the averaged likelihood scores from these two models. In a validation cohort consisting of 269 consecutive patients [with 66 HFpEF cases (24.5%)], the diagnostic power of detecting HFpEF had an AUROC of 90% (P < 0.001) and average precision of 74%.ConclusionThis diagnostic signature enables discrimination of HFpEF from non-cardiac dyspnea or HFrEF from EHR and can assist in the diagnostic evaluation in patients with unexplained dyspnea. This approach will enable identification of HFpEF patients who may then benefit from new evidence-based therapies.

Immune-Related Gene Signatures to Predict the Effectiveness of Chemoimmunotherapy in Triple-Negative Breast Cancer Using Exploratory Subgroup Discovery

Triple-negative breast cancer (TNBC) is an aggressive subtype of breast cancer with limited therapeutic options. Although immunotherapy has shown potential in TNBC patients, clinical studies have only demonstrated a modest response. Therefore, the exploration of immunotherapy in combination with chemotherapy is warranted. In this project we identified immune-related gene signatures for TNBC patients that may explain differences in patients' outcomes after anti-PD-L1+chemotherapy treatment. First, we ran the exploratory subgroup discovery algorithm on the TNBC dataset comprised of 422 patients across 24 studies. Secondly, we narrowed down the search to twelve homogenous subgroups based on tumor mutational burden (TMB, low or high), relapse status (disease-free or recurred), tumor cellularity (high, low and moderate), menopausal status (pre- or post) and tumor stage (I, II and III). For each subgroup we identified a union of the top 10% of genotypic patterns. Furthermore, we employed a multinomial regression model to predict significant genotypic patterns that would be linked to partial remission after anti-PD-L1+chemotherapy treatment. Finally, we uncovered distinct immune cell populations (T-cells, B-cells, Myeloid, NK-cells) for TNBC patients with various treatment outcomes. CD4-Tn-LEF1 and CD4-CXCL13 T-cells were linked to partial remission on anti-PD-L1+chemotherapy treatment. Our informatics pipeline may help to select better responders to chemoimmunotherapy, as well as pinpoint the underlying mechanisms of drug resistance in TNBC patients at single-cell resolution.

Large-Scale Evaluation of a Rapid Fully Automated Analysis Platform to Detect and Refute Outbreaks Based on MRSA Genome Comparisons.

Genomic epidemiology of methicillin-resistant Staphylococcus aureus (MRSA) could transform outbreak investigations, but its clinical introduction is hampered by the lack of automated data analysis tools to rapidly and accurately define transmission based on sequence relatedness. We aimed to evaluate a fully automated bioinformatics system for MRSA genome analysis versus a bespoke researcher-led manual informatics pipeline. We analyzed 781 MRSA genomes from 777 consecutive patients identified over a 9-month period in a clinical microbiology laboratory in the United Kingdom. Outputs were bacterial species identification, detection of mec genes, assignment to sequence types (STs), identification of pairwise relatedness using a definition of ≤25 single nucleotide polymorphisms (SNPs) apart, and use of genetic relatedness to identify clusters. There was full concordance between the two analysis methods for species identification, detection of mec genes, and ST assignment. A total of 3,311 isolate pairs ≤25 SNPs apart were identified by at least one method. These had a median (range) SNP difference between the two methods of 1.2 SNPs (0 to 22 SNPs), with most isolate pairs (87%) varying by ≤2 SNPs. This similarity increased when the research pipeline was modified to use a clonal-complex-specific reference (median 0 SNP difference, 91% varying by ≤2 SNPs). Both pipelines clustered 338 isolates/334 patients into 66 unique clusters based on genetic relatedness. We conclude that the automated transmission detection tool worked at least as well as a researcher-led manual analysis and indicates how such tools could support the rapid use of MRSA genomic epidemiology in infection control practice. IMPORTANCE It has been clearly established that genome sequencing of MRSA improves the accuracy of health care-associated outbreak investigations, including the confirmation and exclusion of outbreaks and identification of patients involved. This could lead to more targeted infection control actions but its use in clinical practice is prevented by several barriers, one of which is the availability of genome analysis tools that do not depend on specialist knowledge to analyze or interpret the results. We evaluated a prototype of a fully automated bioinformatics system for MRSA genome analysis versus a bespoke researcher-led manual informatics pipeline, using genomes from 777 patients over a period of 9 months. The performance was at least equivalent to the researcher-led manual genomic analysis. This indicates the feasibility of automated analysis and represents one more step toward the routine use of pathogen sequencing in infection prevention and control practice.

P249 Predictors of adverse outcome in sarcoidosis complicated by chronic pulmonary aspergillosis

Poster session 2, September 22, 2022, 12:30 PM - 1:30 PM IntroductionAspergillosis complicating pulmonary sarcoidosis is associated with high mortality.1 The specific prognostic impact of fibrocavitary sarcoid disease, however, remains poorly understood. A better understanding of the factors that determine adverse outcomes in such patients may improve the management of both the underlying sarcoid disease and associated secondary fungal infection.MethodsWe implemented a clinical informatics pipeline to data-mine the hospital's clinical data warehouse and identify patients for inclusion in this study. Cases of pulmonary sarcoidosis with elevated Aspergillus IgG (>40 mgA/l) presenting between January 2009 and March 2021 were retrospectively identified. Controls (sarcoidosis with normal Aspergillus IgG titer) were case matched by baseline percentage-predicted gas transfer factor (TLco; ±5% variance). CPA cases were identified by using ISHAM criteria for diagnosing CPA, that is Aspergillus IgG >40 mgA/l plus keyword search for cavities or fungal ball in the CT reports based on search terms for aspergilloma, cavity, intra-cavitary, and mycetoma. Computed tomography (CT), baseline lung function, and survival data were analyzed.ResultsAmong 179 cases (high Aspergillus IgG) and 477 controls (normal Aspergillus IgG), no inter-group difference was evident in the median age at presentation [48 (IQR 40-58) vs 50 (IQR 42-59)] or gender (proportion female: 45.5% vs 51.1%). Amongst the cases, 80/179 (45%) had fibrocavitary changes, compared with 14/477 (2.9%) of the controls (P <.001). Radiologically-evident aspergilloma was present in 80% (64/80) of the cases with fibrocavitary sarcoidosis. Evidence of fibrocavitary destruction was associated with higher overall mortality (60% vs 9.1% in the non-fibrocavitary subgroup; <.0001), and poorer median survival (Fig. 1). The median age at death was lower where there was fibrocavitary disease, and even lower with high Aspergillus IgG, and this was statistically significant. These cases also had poorer lung function compared to non-fibrocavitary disease (Fig. 2): mean %-predicted forced vital capacity (FVC) 71.3% vs 91.4% (P <.0001), and in the controls: 69.9% vs 86.0% (P <.01). A similar trend was observed in %-predicted TLco amongst the fibrocavitary cases: 45.8% vs 64.2% non-fibrocavitary (P <.0001) and in the fibrocavitary controls: 39.7% vs 59.0% non-fibrocavitary (P <.0001). Comparing only those with fibrocavitation, neither the percentage-predicted FVC (71.3% vs 69.9% predicted; P = .82) nor the percentage-predicted TLco (45.7% and 39.7% predicted; P = .16) differed between cases and controls.ConclusionsFibrocavitary sarcoidosis is associated with worse lung function and poorer median survival. In this group, elevated Aspergillus IgG highlights a greater incidence of aspergilloma.ImplicationsFibrotic transformation of pulmonary sarcoidosis heightens symptom burden, predisposes to chronic Aspergillus infection, and is prognostically important particularly when there is supervening fibrocavitary lung destruction. Sensitive stratification of such patients for the long-term outcomes may help identify particular individuals for earlier and more focused therapeutic intervention.SourcesUzunhan et al., Chronic pulmonary aspergillosis complicating sarcoidosis, European Respiratory Journal 2017 49: 1 602 396; DOI: 10.1183/13993003.02396-2016

A risk variant for Barrett's esophagus and esophageal adenocarcinoma at chr8p23.1 affects enhancer activity and implicates multiple gene targets.

Nineteen genetic susceptibility loci for esophageal adenocarcinoma (EAC) and its precursor Barrett's esophagus (BE) have been identified through genome-wide association studies (GWAS). Clinical translation of such discoveries, however, has been hindered by the slow pace of discovery of functional/causal variants and gene targets at these loci. We previously developed a systematic informatics pipeline to prioritize candidate functional variants using functional potential scores, applied the pipeline to select high-scoring BE/EAC risk loci and validated a functional variant at chr19p13.11 (rs10423674). Here, we selected two additional prioritized loci for experimental interrogation: chr3p13/rs1522552 and chr8p23.1/rs55896564. Candidate enhancer regions encompassing these variants were evaluated using luciferase reporter assays in two EAC cell lines. One of the two regions tested exhibited allele-specific enhancer activity - 8p23.1/rs55896564. CRISPR-mediated deletion of the putative enhancer in EAC cell lines correlated with reduced expression of three candidate gene targets: B lymphocyte kinase (BLK), nei like DNA glycosylase 2 (NEIL2) and cathepsin B (CTSB). Expression quantitative trait locus (eQTL) mapping in normal esophagus and stomach revealed strong associations between the BE/EAC risk allele at rs55896564 (G) and lower expression of CTSB, a protease gene implicated in epithelial wound repair. These results further support the utility of functional potential scores for GWAS variant prioritization, and provide the first experimental evidence of a functional variant and risk enhancer at the 8p23.1 GWAS locus. Identification of CTSB, BLK and NEIL2 as candidate gene targets suggests that altered expression of these genes may underlie the genetic risk association at 8p23.1 with BE/EAC.

The HDAC9-associated risk locus promotes coronary artery disease by governing TWIST1.

Genome wide association studies (GWAS) have identified thousands of single nucleotide polymorphisms (SNPs) associated with the risk of common disorders. However, since the large majority of these risk SNPs reside outside gene-coding regions, GWAS generally provide no information about causal mechanisms regarding the specific gene(s) that are affected or the tissue(s) in which these candidate gene(s) exert their effect. The 'gold standard' method for understanding causal genes and their mechanisms of action are laborious basic science studies often involving sophisticated knockin or knockout mouse lines, however, these types of studies are impractical as a high-throughput means to understand the many risk variants that cause complex diseases like coronary artery disease (CAD). As a solution, we developed a streamlined, data-driven informatics pipeline to gain mechanistic insights on complex genetic loci. The pipeline begins by understanding the SNPs in a given locus in terms of their relative location and linkage disequilibrium relationships, and then identifies nearby expression quantitative trait loci (eQTLs) to determine their relative independence and the likely tissues that mediate their disease-causal effects. The pipeline then seeks to understand associations with other disease-relevant genes, disease sub-phenotypes, potential causality (Mendelian randomization), and the regulatory and functional involvement of these genes in gene regulatory co-expression networks (GRNs). Here, we applied this pipeline to understand a cluster of SNPs associated with CAD within and immediately adjacent to the gene encoding HDAC9. Our pipeline demonstrated, and validated, that this locus is causal for CAD by modulation of TWIST1 expression levels in the arterial wall, and by also governing a GRN related to metabolic function in skeletal muscle. Our results reconciled numerous prior studies, and also provided clear evidence that this locus does not govern HDAC9 expression, structure or function. This pipeline should be considered as a powerful and efficient way to understand GWAS risk loci in a manner that better reflects the highly complex nature of genetic risk associated with common disorders.