Vaccine Effectiveness Against Long COVID in Children

OBJECTIVES

Vaccination reduces the risk of acute coronavirus disease 2019 (COVID-19) in children, but it is less clear whether it protects against long COVID. We estimated vaccine effectiveness (VE) against long COVID in children aged 5 to 17 years.

METHODS

This retrospective cohort study used data from 17 health systems in the RECOVER PCORnet electronic health record program for visits after vaccine availability. We examined both probable (symptom-based) and diagnosed long COVID after vaccination.

RESULTS

The vaccination rate was 67% in the cohort of 1 037 936 children. The incidence of probable long COVID was 4.5% among patients with COVID-19, whereas diagnosed long COVID was 0.8%. Adjusted vaccine effectiveness within 12 months was 35.4% (95 CI 24.5-44.7) against probable long COVID and 41.7% (15.0-60.0) against diagnosed long COVID. VE was higher for adolescents (50.3% [36.6-61.0]) than children aged 5 to 11 (23.8% [4.9-39.0]). VE was higher at 6 months (61.4% [51.0-69.6]) but decreased to 10.6% (-26.8% to 37.0%) at 18-months.

CONCLUSIONS

This large retrospective study shows moderate protective effect of severe acute respiratory coronavirus 2 vaccination against long COVID. The effect is stronger in adolescents, who have higher risk of long COVID, and wanes over time. Understanding VE mechanism against long COVID requires more study, including electronic health record sources and prospective data.

Understanding Factors that Influence Prognosis and Response to Therapy in Clear Cell Renal Cell Carcinoma

In this review, we highlight and contextualize emerging morphologic prognostic and predictive factors in renal cell carcinoma. We focus on clear cell renal cell carcinoma (ccRCC), the most common histologic subtype. Our understanding of the molecular characterization of ccRCC has dramatically improved in the last decade. Herein, we highlight how these discoveries have laid the foundation for new approaches to prognosis and therapeutic decision-making for patients with ccRCC. We explore the clinical relevance of common mutations, established gene expression signatures, intratumoral heterogeneity, sarcomatoid/rhabdoid morphology and PD-L1 expression, and discuss their impact on predicting response to therapy.

Association between SARS-CoV-2 infection and select symptoms and conditions 31 to 150 days after testing among children and adults

BACKGROUND

An increasing number of studies have described new and persistent symptoms and conditions as potential post-acute sequelae of SARS-CoV-2 infection (PASC). However, it remains unclear whether certain symptoms or conditions occur more frequently among persons with SARS-CoV-2 infection compared with those never infected with SARS-CoV-2. We compared the occurrence of specific COVID-associated symptoms and conditions as potential PASC 31- to 150-day following a SARS-CoV-2 test among adults and children with positive and negative test results.

METHODS

We conducted a retrospective cohort study using electronic health record (EHR) data from 43 PCORnet sites participating in a national COVID-19 surveillance program. This study included 3,091,580 adults (316,249 SARS-CoV-2 positive; 2,775,331 negative) and 675,643 children (62,131 positive; 613,512 negative) who had a SARS-CoV-2 laboratory test during March 1, 2020-May 31, 2021 documented in their EHR. We used logistic regression to calculate the odds of having a symptom and Cox models to calculate the risk of having a newly diagnosed condition associated with a SARS-CoV-2 positive test.

RESULTS

After adjustment for baseline covariates, hospitalized adults and children with a positive test had increased odds of being diagnosed with ≥ 1 symptom (adults: adjusted odds ratio[aOR], 1.17[95% CI, 1.11-1.23]; children: aOR, 1.18[95% CI, 1.08-1.28]) or shortness of breath (adults: aOR, 1.50[95% CI, 1.38-1.63]; children: aOR, 1.40[95% CI, 1.15-1.70]) 31-150 days following a SARS-CoV-2 test compared with hospitalized individuals with a negative test. Hospitalized adults with a positive test also had increased odds of being diagnosed with ≥ 3 symptoms or fatigue compared with those testing negative. The risks of being newly diagnosed with type 1 or type 2 diabetes (adjusted hazard ratio[aHR], 1.25[95% CI, 1.17-1.33]), hematologic disorders (aHR, 1.19[95% CI, 1.11-1.28]), or respiratory disease (aHR, 1.44[95% CI, 1.30-1.60]) were higher among hospitalized adults with a positive test compared with those with a negative test. Non-hospitalized adults with a positive test also had higher odds or increased risk of being diagnosed with certain symptoms or conditions.

CONCLUSIONS

Patients with SARS-CoV-2 infection, especially those who were hospitalized, were at higher risk of being diagnosed with certain symptoms and conditions after acute infection.

Coordinating virus research: The Virus Infectious Disease Ontology

The COVID-19 pandemic prompted immense work on the investigation of the SARS-CoV-2 virus. Rapid, accurate, and consistent interpretation of generated data is thereby of fundamental concern. Ontologies-structured, controlled, vocabularies-are designed to support consistency of interpretation, and thereby to prevent the development of data silos. This paper describes how ontologies are serving this purpose in the COVID-19 research domain, by following principles of the Open Biological and Biomedical Ontology (OBO) Foundry and by reusing existing ontologies such as the Infectious Disease Ontology (IDO) Core, which provides terminological content common to investigations of all infectious diseases. We report here on the development of an IDO extension, the Virus Infectious Disease Ontology (VIDO), a reference ontology covering viral infectious diseases. We motivate term and definition choices, showcase reuse of terms from existing OBO ontologies, illustrate how ontological decisions were motivated by relevant life science research, and connect VIDO to the Coronavirus Infectious Disease Ontology (CIDO). We next use terms from these ontologies to annotate selections from life science research on SARS-CoV-2, highlighting how ontologies employing a common upper-level vocabulary may be seamlessly interwoven. Finally, we outline future work, including bacteria and fungus infectious disease reference ontologies currently under development, then cite uses of VIDO and CIDO in host-pathogen data analytics, electronic health record annotation, and ontology conflict-resolution projects.

Vaccine Effectiveness Against Long COVID in Children: A Report from the RECOVER EHR Cohort

Objective

Vaccination reduces the risk of acute COVID-19 in children, but it is less clear whether it protects against long COVID. We estimated vaccine effectiveness (VE) against long COVID in children aged 5-17 years.

Methods

This retrospective cohort study used data from 17 health systems in the RECOVER PCORnet electronic health record (EHR) Program for visits between vaccine availability, and October 29, 2022. Conditional logistic regression was used to estimate VE against long COVID with matching on age group (5-11, 12-17) and time period and adjustment for sex, ethnicity, health system, comorbidity burden, and pre-exposure health care utilization. We examined both probable (symptom-based) and diagnosed long COVID in the year following vaccination.

Results

The vaccination rate was 56% in the cohort of 1,037,936 children. The incidence of probable long COVID was 4.5% among patients with COVID-19, while diagnosed long COVID was 0.7%. Adjusted vaccine effectiveness within 12 months was 35.4% (95 CI 24.5 - 44.5) against probable long COVID and 41.7% (15.0 - 60.0) against diagnosed long COVID. VE was higher for adolescents 50.3% [36.3 - 61.0]) than children aged 5-11 (23.8% [4.9 - 39.0]). VE was higher at 6 months (61.4% [51.0 - 69.6]) but decreased to 10.6% (-26.8 - 37.0%) at 18-months.

Discussion

This large retrospective study shows a moderate protective effect of SARS-CoV-2 vaccination against long COVID. The effect is stronger in adolescents, who have higher risk of long COVID, and wanes over time. Understanding VE mechanism against long COVID requires more study, including EHR sources and prospective data.

Article Summary

Vaccination against COVID-19 has a protective effect against long COVID in children and adolescents. The effect wanes over time but remains significant at 12 months.

What’s Known on This Subject

Vaccines reduce the risk and severity of COVID-19 in children. There is evidence for reduced long COVID risk in adults who are vaccinated, but little information about similar effects for children and adolescents, who have distinct forms of long COVID.

What This Study Adds

Using electronic health records from US health systems, we examined large cohorts of vaccinated and unvaccinated patients <18 years old and show that vaccination against COVID-19 is associated with reduced risk of long COVID for at least 12 months.

Contributors’ Statement

Drs. Hanieh Razzaghi and Charles Bailey conceptualized and designed the study, supervised analyses, drafted the initial manuscript, and critically reviewed and revised the manuscript.Drs. Christopher Forrest and Yong Chen designed the study and critically reviewed and revised the manuscript.Ms. Kathryn Hirabayashi, Ms. Andrea Allen, and Dr. Qiong Wu conducted analyses, and critically reviewed and revised the manuscript.Drs. Suchitra Rao, H Timothy Bunnell, Elizabeth A. Chrischilles, Lindsay G. Cowell, Mollie R. Cummins, David A. Hanauer, Benjamin D. Horne, Carol R. Horowitz, Ravi Jhaveri, Susan Kim, Aaron Mishkin, Jennifer A. Muszynski, Susanna Nagie, Nathan M. Pajor, Anuradha Paranjape, Hayden T. Schwenk, Marion R. Sills, Yacob G. Tedla, David A. Williams, and Ms. Miranda Higginbotham critically reviewed and revised the manuscript.All authors approved the final manuscript as submitted and agree to be accountable for all aspects of the work.

Authorship statement

Authorship has been determined according to ICMJE recommendations.

Factors Associated With Receipt of Molecular Testing and its Impact on Time to Initial Systemic Therapy in Metastatic Non-Small Cell Lung Cancer

BACKGROUND

Despite recommendations for molecular testing irrespective of patient characteristics, differences exist in receipt of molecular testing for oncogenic drivers amongst metastatic non-small cell lung cancer (mNSCLC) patients. Exploration into these differences and their effects on treatment is needed to identify opportunities for improvement.

PATIENTS AND METHODS

We conducted a retrospective cohort study of adult patients diagnosed with mNSCLC between 2011 and 2018 using PCORnet's Rapid Cycle Research Project dataset (n = 3600). Log-binomial, Cox proportional hazards (PH), and time-varying Cox regression models were used to ascertain whether molecular testing was received, and time from diagnosis to molecular testing and/or initial systemic treatment in the context of patient age, sex, race/ethnicity, and multiple comorbidities status.

RESULTS

The majority of patients in this cohort were ≤ 65 years of age (median [25th, 75th]: 64 [57, 71]), male (54.3%), non-Hispanic white individuals (81.6%), with > 2 comorbidities in addition to mNSCLC (54.1%). About half the cohort received molecular testing (49.9%). Patients who received molecular testing had a 59% higher probability of initial systemic treatment than patients who were yet to receive testing. Multiple comorbidity status was positively associated with receipt of molecular testing (RR, 1.27; 95% CI 1.08, 1.49).

CONCLUSION

Receipt of molecular testing in academic centers was associated with earlier initiation of systemic treatment. This finding underscores the need to increase molecular testing rates amongst mNSCLC patients during a clinically relevant period. Further studies to validate these findings in community centers are warranted.

AIRR community curation and standardised representation for immunoglobulin and T cell receptor germline sets

Analysis of an individual's immunoglobulin or T cell receptor gene repertoire can provide important insights into immune function. High-quality analysis of adaptive immune receptor repertoire sequencing data depends upon accurate and relatively complete germline sets, but current sets are known to be incomplete. Established processes for the review and systematic naming of receptor germline genes and alleles require specific evidence and data types, but the discovery landscape is rapidly changing. To exploit the potential of emerging data, and to provide the field with improved state-of-the-art germline sets, an intermediate approach is needed that will allow the rapid publication of consolidated sets derived from these emerging sources. These sets must use a consistent naming scheme and allow refinement and consolidation into genes as new information emerges. Name changes should be minimised, but, where changes occur, the naming history of a sequence must be traceable. Here we outline the current issues and opportunities for the curation of germline IG/TR genes and present a forward-looking data model for building out more robust germline sets that can dovetail with current established processes. We describe interoperability standards for germline sets, and an approach to transparency based on principles of findability, accessibility, interoperability, and reusability.

Therapeutic Vaccination for HPV-Mediated Cancers

Purpose of Review

The goal of this narrative review is to educate clinicians regarding the foundational concepts, efficacy, and future directions of therapeutic vaccines for human papillomavirus (HPV)-mediated cancers.

Recent Findings

Therapeutic HPV vaccines deliver tumor antigens to stimulate an immune response to eliminate tumor cells. Vaccine antigen delivery platforms are diverse and include DNA, RNA, peptides, proteins, viral vectors, microbial vectors, and antigen-presenting cells. Randomized, controlled trials have demonstrated that therapeutic HPV vaccines are efficacious in patients with cervical intraepithelial neoplasia. In patients with HPV-mediated malignancies, evidence of efficacy is limited. However, numerous ongoing studies evaluating updated therapeutic HPV vaccines in combination with immune checkpoint inhibition and other therapies exhibit significant promise.

Summary

Therapeutic vaccines for HPV-mediated malignancies retain a strong biological rationale, despite their limited efficacy to date. Investigators anticipate they will be most effectively used in combination with other regimens, such as immune checkpoint inhibition.

Data Sharing and Reuse: A Method by the AIRR Community

High-throughput sequencing of adaptive immune receptor repertoires (AIRR, i.e., IG and TR ) has revolutionized the ability to study the adaptive immune response via large-scale experiments. Since 2009, AIRR sequencing (AIRR-seq) has been widely applied to survey the immune state of individuals (see "The AIRR Community Guide to Repertoire Analysis" chapter for details). One of the goals of the AIRR Community is to make the resulting AIRR-seq data FAIR (Findable, Accessible, Interoperable, and Reusable) (Wilkinson et al. Sci Data 3:1-9, 2016), with a primary goal of making it easy for the research community to reuse AIRR-seq data (Breden et al. Front Immunol 8:1418, 2017; Scott and Breden. Curr Opin Syst Biol 24:71-77, 2020). The basis for this is the MiAIRR data standard (Rubelt et al. Nat Immunol 18:1274-1278, 2017). For long-term preservation, it is recommended that researchers store their sequence read data in an INSDC repository. At the same time, the AIRR Community has established the AIRR Data Commons (Christley et al. Front Big Data 3:22, 2020), a distributed set of AIRR-compliant repositories that store the critically important annotated AIRR-seq data based on the MiAIRR standard, making the data findable, interoperable, and, because the data are annotated, more valuable in its reuse. Here, we build on the other AIRR Community chapters and illustrate how these principles and standards can be incorporated into AIRR-seq data analysis workflows. We discuss the importance of careful curation of metadata to ensure reproducibility and facilitate data sharing and reuse, and we illustrate how data can be shared via the AIRR Data Commons.

Prevalence of Select New Symptoms and Conditions Among Persons Aged Younger Than 20 Years and 20 Years or Older at 31 to 150 Days After Testing Positive or Negative for SARS-CoV-2

IMPORTANCE

New symptoms and conditions can develop following SARS-CoV-2 infection. Whether they occur more frequently among persons with SARS-CoV-2 infection compared with those without is unclear.

OBJECTIVE

To compare the prevalence of new diagnoses of select symptoms and conditions between 31 and 150 days after testing among persons who tested positive vs negative for SARS-CoV-2.

DESIGN, SETTING, AND PARTICIPANTS

This cohort study analyzed aggregated electronic health record data from 40 health care systems, including 338 024 persons younger than 20 years and 1 790 886 persons aged 20 years or older who were tested for SARS-CoV-2 during March to December 2020 and who had medical encounters between 31 and 150 days after testing.

MAIN OUTCOMES AND MEASURES

International Statistical Classification of Diseases, Tenth Revision, Clinical Modification codes were used to capture new symptoms and conditions that were recorded 31 to 150 days after a SARS-CoV-2 test but absent in the 18 months to 7 days prior to testing. The prevalence of new symptoms and conditions was compared between persons with positive and negative SARS-CoV-2 tests stratified by age (20 years or older and young than 20 years) and care setting (nonhospitalized, hospitalized, or hospitalized and ventilated).

RESULTS

A total of 168 701 persons aged 20 years or older and 26 665 younger than 20 years tested positive for SARS-CoV-2, and 1 622 185 persons aged 20 years or older and 311 359 younger than 20 years tested negative. Shortness of breath was more common among persons with a positive vs negative test result among hospitalized patients (≥20 years: prevalence ratio [PR], 1.89 [99% CI, 1.79-2.01]; <20 years: PR, 1.72 [99% CI, 1.17-2.51]). Shortness of breath was also more common among nonhospitalized patients aged 20 years or older with a positive vs negative test result (PR, 1.09 [99% CI, 1.05-1.13]). Among hospitalized persons aged 20 years or older, the prevalence of new fatigue (PR, 1.35 [99% CI, 1.27-1.44]) and type 2 diabetes (PR, 2.03 [99% CI, 1.87-2.19]) was higher among those with a positive vs a negative test result. Among hospitalized persons younger than 20 years, the prevalence of type 2 diabetes (PR, 2.14 [99% CI, 1.13-4.06]) was higher among those with a positive vs a negative test result; however, the prevalence difference was less than 1%.

CONCLUSIONS AND RELEVANCE

In this cohort study, among persons hospitalized after a positive SARS-CoV-2 test result, diagnoses of certain symptoms and conditions were higher than among those with a negative test result. Health care professionals should be aware of symptoms and conditions that may develop after SARS-CoV-2 infection, particularly among those hospitalized after diagnosis.

Immune Repertoire Analysis on High-Performance Computing Using VDJServer V1: A Method by the AIRR Community

AIRR-seq data sets are usually large and require specialized analysis methods and software tools. A typical Illumina MiSeq sequencing run generates 20-30 million 2 × 300 bp paired-end sequence reads, which roughly corresponds to 15 GB of sequence data to be processed. Other platforms like NextSeq, which is useful in projects where the full V gene is not needed, create about 400 million 2 × 150 bp paired-end reads. Because of the size of the data sets, the analysis can be computationally expensive, particularly the early analysis steps like preprocessing and gene annotation that process the majority of the sequence data. A standard desktop PC may take 3-5 days of constant processing for a single MiSeq run, so dedicated high-performance computational resources may be required.VDJServer provides free access to high-performance computing (HPC) at the Texas Advanced Computing Center (TACC) through a graphical user interface (Christley et al. Front Immunol 9:976, 2018). VDJServer is a cloud-based analysis portal for immune repertoire sequence data that provides access to a suite of tools for a complete analysis workflow, including modules for preprocessing and quality control of sequence reads, V(D)J gene assignment, repertoire characterization, and repertoire comparison. Furthermore, VDJServer has parallelized execution for tools such as IgBLAST, so more compute resources are utilized as the size of the input data grows. Analysis that takes days on a desktop PC might take only a few hours on VDJServer. VDJServer is a free, publicly available, and open-source licensed resource. Here, we describe the workflow for performing immune repertoire analysis on VDJServer's high-performance computing.

Burden of Renal Cysts Imaging: A Survey of Patients among the Greater Plains Collaborative

PURPOSE

Renal cysts are a frequent incidental finding on cross-sectional radiographic imaging. While most cysts are indolent, individuals with such cysts are frequently monitored for interval growth and potential malignant transformation, which is ultimately rare. In this study, we aimed to assess patients' values and preferences (believes and attitudes) about renal cysts.

METHODS

We deployed a cross-sectional survey to a random sample of patients with a diagnosis of renal cysts who were identified by billing code and self-identification. We collected data about demographics, insurance status, family history and overall health, and characteristics of patients with renal cysts. We performed a binary regression analysis (adjusted for age, gender, family history of cancer and kidney disease, and treatment plan for renal cysts) to determine anxiety predictors in patients with renal cysts.

RESULTS

We included 301 respondents in whom billing code and self-identification corresponded; of these, 138 had renal cysts and 163 did not. In an adjusted regression analysis, there was a suggestion that a clear management plan (OR = 0.49, 95% CI [0.22-1.11]) (p value 0.08) may be associated with less anxiety and a family history of renal disease may be associated with more anxiety (OR = 1.94 [0.76-4.94]) (p value 0.17). Family history of cancer also did not significantly predict anxiety (OR = 0.54 [0.24-1.19]) (p value 0.13). All these results were not statistically significant and had wide confidence intervals of the effect estimates make the results imprecise.

CONCLUSION

Findings of this pilot study suggest a clear management plan for the renal cyst(s) management may be associated with a lower level of anxiety, thereby by emphasizing the importance of good communication, patient engagement and evidence-based guidance. More definitive, adequately powered studies are needed to evaluate this finding further. In addition, further studies exploring differences in imaging practices, patient symptomatology and patient engagement by different provider types would be insightful. Ultimately, tools to improve shared decision-making are needed to provide more patient-centered care.

The Infectious Disease Ontology in the age of COVID-19

BACKGROUND

Effective response to public health emergencies, such as we are now experiencing with COVID-19, requires data sharing across multiple disciplines and data systems. Ontologies offer a powerful data sharing tool, and this holds especially for those ontologies built on the design principles of the Open Biomedical Ontologies Foundry. These principles are exemplified by the Infectious Disease Ontology (IDO), a suite of interoperable ontology modules aiming to provide coverage of all aspects of the infectious disease domain. At its center is IDO Core, a disease- and pathogen-neutral ontology covering just those types of entities and relations that are relevant to infectious diseases generally. IDO Core is extended by disease and pathogen-specific ontology modules.

RESULTS

To assist the integration and analysis of COVID-19 data, and viral infectious disease data more generally, we have recently developed three new IDO extensions: IDO Virus (VIDO); the Coronavirus Infectious Disease Ontology (CIDO); and an extension of CIDO focusing on COVID-19 (IDO-COVID-19). Reflecting the fact that viruses lack cellular parts, we have introduced into IDO Core the term acellular structure to cover viruses and other acellular entities studied by virologists. We now distinguish between infectious agents - organisms with an infectious disposition - and infectious structures - acellular structures with an infectious disposition. This in turn has led to various updates and refinements of IDO Core's content. We believe that our work on VIDO, CIDO, and IDO-COVID-19 can serve as a model for yielding greater conformance with ontology building best practices.

CONCLUSIONS

IDO provides a simple recipe for building new pathogen-specific ontologies in a way that allows data about novel diseases to be easily compared, along multiple dimensions, with data represented by existing disease ontologies. The IDO strategy, moreover, supports ontology coordination, providing a powerful method of data integration and sharing that allows physicians, researchers, and public health organizations to respond rapidly and efficiently to current and future public health crises.

T Cell Receptor Repertoires Acquired via Routine Pap Testing May Help Refine Cervical Cancer and Precancer Risk Estimates

Cervical cancer is the fourth most common cancer and fourth leading cause of cancer death among women worldwide. In low Human Development Index settings, it ranks second. Screening and surveillance involve the cytology-based Papanicolaou (Pap) test and testing for high-risk human papillomavirus (hrHPV). The Pap test has low sensitivity to detect precursor lesions, while a single hrHPV test cannot distinguish a persistent infection from one that the immune system will naturally clear. Furthermore, among women who are hrHPV-positive and progress to high-grade cervical lesions, testing cannot identify the ~20% who would progress to cancer if not treated. Thus, reliable detection and treatment of cancers and precancers requires routine screening followed by frequent surveillance among those with past abnormal or positive results. The consequence is overtreatment, with its associated risks and complications, in screened populations and an increased risk of cancer in under-screened populations. Methods to improve cervical cancer risk assessment, particularly assays to predict regression of precursor lesions or clearance of hrHPV infection, would benefit both populations. Here we show that women who have lower risk results on follow-up testing relative to index testing have evidence of enhanced T cell clonal expansion in the index cervical cytology sample compared to women who persist with higher risk results from index to follow-up. We further show that a machine learning classifier based on the index sample T cells predicts this transition to lower risk with 95% accuracy (19/20) by leave-one-out cross-validation. Using T cell receptor deep sequencing and machine learning, we identified a biophysicochemical motif in the complementarity-determining region 3 of T cell receptor β chains whose presence predicts this transition. While these results must still be tested on an independent cohort in a prospective study, they suggest that this approach could improve cervical cancer screening by helping distinguish women likely to spontaneously regress from those at elevated risk of progression to cancer. The advancement of such a strategy could reduce surveillance frequency and overtreatment in screened populations and improve the delivery of screening to under-screened populations.

Blood-based biomarkers of human papillomavirus-associated cancers: A systematic review and meta-analysis

BACKGROUND

Despite the significant societal burden of human papillomavirus (HPV)-associated cancers, clinical screening interventions for HPV-associated noncervical cancers are not available. Blood-based biomarkers may help close this gap in care.

METHODS

Five databases were searched, 5687 articles were identified, and 3631 unique candidate titles and abstracts were independently reviewed by 2 authors; 702 articles underwent a full-text review. Eligibility criteria included the assessment of a blood-based biomarker within a cohort or case-control study.

RESULTS

One hundred thirty-seven studies were included. Among all biomarkers assessed, HPV-16 E seropositivity and circulating HPV DNA were most significantly correlated with HPV-associated cancers in comparison with cancer-free controls. In most scenarios, HPV-16 E6 seropositivity varied nonsignificantly according to tumor type, specimen collection timing, and anatomic site (crude odds ratio [cOR] for p16+ or HPV+ oropharyngeal cancer [OPC], 133.10; 95% confidence interval [CI], 59.40-298.21; cOR for HPV-unspecified OPC, 25.41; 95% CI, 8.71-74.06; cOR for prediagnostic HPV-unspecified OPC, 59.00; 95% CI, 15.39-226.25; cOR for HPV-unspecified cervical cancer, 12.05; 95% CI, 3.23-44.97; cOR for HPV-unspecified anal cancer, 73.60; 95% CI, 19.68-275.33; cOR for HPV-unspecified penile cancer, 16.25; 95% CI, 2.83-93.48). Circulating HPV-16 DNA was a valid biomarker for cervical cancer (cOR, 15.72; 95% CI, 3.41-72.57). In 3 cervical cancer case-control studies, cases exhibited unique microRNA expression profiles in comparison with controls. Other assessed biomarker candidates were not valid.

CONCLUSIONS

HPV-16 E6 antibodies and circulating HPV-16 DNA are the most robustly analyzed and most promising blood-based biomarkers for HPV-associated cancers to date. Comparative validity analyses are warranted. Variations in tumor type-specific, high-risk HPV DNA prevalence according to anatomic site and world region highlight the need for biomarkers targeting more high-risk HPV types. Further investigation of blood-based microRNA expression profiling appears indicated.

TCRMatch: Predicting T-Cell Receptor Specificity Based on Sequence Similarity to Previously Characterized Receptors

The adaptive immune system in vertebrates has evolved to recognize non-self antigens, such as proteins expressed by infectious agents and mutated cancer cells. T cells play an important role in antigen recognition by expressing a diverse repertoire of antigen-specific receptors, which bind epitopes to mount targeted immune responses. Recent advances in high-throughput sequencing have enabled the routine generation of T-cell receptor (TCR) repertoire data. Identifying the specific epitopes targeted by different TCRs in these data would be valuable. To accomplish that, we took advantage of the ever-increasing number of TCRs with known epitope specificity curated in the Immune Epitope Database (IEDB) since 2004. We compared seven metrics of sequence similarity to determine their power to predict if two TCRs have the same epitope specificity. We found that a comprehensive k-mer matching approach produced the best results, which we have implemented into TCRMatch, an openly accessible tool (http://tools.iedb.org/tcrmatch/) that takes TCR β-chain CDR3 sequences as an input, identifies TCRs with a match in the IEDB, and reports the specificity of each match. We anticipate that this tool will provide new insights into T cell responses captured in receptor repertoire and single cell sequencing experiments and will facilitate the development of new strategies for monitoring and treatment of infectious, allergic, and autoimmune diseases, as well as cancer.

The ADC API: A Web API for the Programmatic Query of the AIRR Data Commons

The Adaptive Immune Receptor Repertoire (AIRR) Community is a research-driven group that is establishing a clear set of community-accepted data and metadata standards; standards-based reference implementation tools; and policies and practices for infrastructure to support the deposit, curation, storage, and use of high-throughput sequencing data from B-cell and T-cell receptor repertoires (AIRR-seq data). The AIRR Data Commons is a distributed system of data repositories that utilizes a common data model, a common query language, and common interoperability formats for storage, query, and downloading of AIRR-seq data. Here is described the principal technical standards for the AIRR Data Commons consisting of the AIRR Data Model for repertoires and rearrangements, the AIRR Data Commons (ADC) API for programmatic query of data repositories, a reference implementation for ADC API services, and tools for querying and validating data repositories that support the ADC API. AIRR-seq data repositories can become part of the AIRR Data Commons by implementing the data model and API. The AIRR Data Commons allows AIRR-seq data to be reused for novel analyses and empowers researchers to discover new biological insights about the adaptive immune system.

Developing and validating an approach for diagnosing and prognosticating cancer from biochemical motifs in T-cell receptors

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Background: Immune repertoire deep sequencing allows profiling T-cell populations and enables novel approaches to diagnose and prognosticate cancer by identifying T-cell receptor sequence patterns associated with clinical phenotypes and outcomes. Methods: Our goal is to develop a method to diagnose and prognosticate cancer using sequenced T-cell receptors. To determine how to profile the specificity of a T-cell receptor, we analyze 3D X-ray crystallographic structures of T-cell receptors bound to antigen. We observe a contiguous strip typically 4 amino acid residues in length from the complimentary determining region 3 (CDR3) lying in direct contact with the antigen. Based on this observation, we extract 4 residue long snippets from every receptor’s CDR3 and represent each snippet using biochemical features encoded by its amino acid sequence. The biochemical features are combined with information about the abundance of the snippet or the receptor and scored using a machine learning based approach. Each predictive model is fitted and validated under the requirement that at least one positively labelled snippet appears per tumor and no positively labelled snippets appear in healthy tissue. Results: Using a patient-holdout cross-validation, we fit predictive models to distinguish: 1. colorectal tumors from healthy tissue matched controls with 93% accuracy, 2. breast tumors from healthy tissue matched controls with 94% accuracy, 3. ovarian tumors from non-cancer patient ovarian tissue with 95% accuracy (80% accuracy on a blinded follow-up cohort) 4. and regression of preneoplastic cervical lesions over 1 year in advance with 96% accuracy. Conclusions: Immune repertoires can be used to diagnose and prognosticate cancer.

Developing & Validating an Approach for Diagnosing and Prognosticating Cancer from Biochemical Motifs in T-cell Receptors

Immune repertoire deep sequencing allows profiling T-cell populations and enables novel approaches to diagnose and prognosticate diseases by identifying T-cell receptor sequence patterns associated with clinical phenotypes and outcomes. Our study objective is to develop a method to diagnose and prognosticate cancer using T-cell receptors sequenced from tissue biopsies. To determine how to profile the specificity of a T-cell receptor, we analyze 3D X-ray crystallographic structures of T-cell receptors bound to antigen. We observe a contiguous strip typically 4 amino acid residues in length from the complimentary determining region 3 (CDR3) lying in direct contact with the antigen. Based on this observation, we extract 4 residue long snippets from every receptor’s CDR3 and represent each snippet using biochemical features encoded by its amino acid sequence. The biochemical features are combined with information about the abundance of the snippet or the receptor and scored using a logistic regression model. Each logistic regression model is fitted and validated under the requirement that at least one positively labelled snippet appears per tumor and no positively labelled snippets appear in healthy tissue. Using a patient-holdout cross-validation, we fit logistic regression models to distinguish colorectal tumors from healthy tissue matched controls with 93% accuracy, breast tumors from healthy tissue matched controls with 94% accuracy, ovarian tumors from non-cancer patient ovarian tissue with 95% accuracy (80% accuracy on a blinded follow-up cohort), and regression of preneoplastic cervical lesions over 1 year in advance with 96% accuracy. In conclusion, immune repertoires can be used to diagnose and prognosticate disease.

Biophysicochemical motifs in T cell receptor sequences as a potential biomarker for high-grade serous ovarian carcinoma

We previously showed, in a pilot study with publicly available data, that T cell receptor (TCR) repertoires from tumor infiltrating lymphocytes (TILs) could be distinguished from adjacent healthy tissue repertoires by the presence of TCRs bearing specific, biophysicochemical motifs in their antigen binding regions. We hypothesized that such motifs might allow development of a novel approach to cancer detection. The motifs were cancer specific and achieved high classification accuracy: we found distinct motifs for breast versus colorectal cancer-associated repertoires, and the colorectal cancer motif achieved 93% accuracy, while the breast cancer motif achieved 94% accuracy. In the current study, we sought to determine whether such motifs exist for ovarian cancer, a cancer type for which detection methods are urgently needed. We made two significant advances over the prior work. First, the prior study used patient-matched TILs and healthy repertoires, collecting healthy tissue adjacent to the tumors. The current study collected TILs from patients with high-grade serous ovarian carcinoma (HGSOC) and healthy ovary repertoires from cancer-free women undergoing hysterectomy/salpingo-oophorectomy for benign disease. Thus, the classification task is distinguishing women with cancer from women without cancer. Second, in the prior study, classification accuracy was measured by patient-hold-out cross-validation on the training data. In the current study, classification accuracy was additionally assessed on an independent cohort not used during model development to establish the generalizability of the motif to unseen data. Classification accuracy was 95% by patient-hold-out cross-validation on the training set and 80% when the model was applied to the blinded test set. The results on the blinded test set demonstrate a biophysicochemical TCR motif found overwhelmingly in women with HGSOC but rarely in women with healthy ovaries, strengthening the proposal that cancer detection approaches might benefit from incorporation of TCR motif-based biomarkers. Furthermore, these results call for studies on large cohorts to establish higher classification accuracies, as well as for studies in other cancer types.

The Diagnostic, Prognostic, and Therapeutic Potential of Adaptive Immune Receptor Repertoire Profiling in Cancer

Lymphocytes play a critical role in antitumor immune responses. They are directly targeted by some therapies, and the composition and spatial organization of intratumor T-cell populations is prognostic in some cancer types. A better understanding of lymphocyte population dynamics over the course of disease and in response to therapy is urgently needed to guide therapy decisions and to develop new therapy targets. Deep sequencing of the repertoire of antigen receptor-encoding genes expressed in a lymphocyte population has become a widely used approach for profiling the population's immune status. Lymphocyte antigen receptor repertoire deep sequencing data can be used to assess the clonal richness and diversity of lymphocyte populations; to track clone members over time, between tissues, and across lymphocyte subsets; to detect clonal expansion; and to detect the recruitment of new clones into a tissue. Repertoire sequencing is thus a critical complement to other methods of lymphocyte and immune profiling in cancer. This review describes the current state of knowledge based on repertoire sequencing studies conducted on human cancer patients, with a focus on studies of the T-cell receptor beta chain locus. The review then outlines important questions left unanswered and suggests future directions for the field.

iReceptor: A platform for querying and analyzing antibody/B-cell and T-cell receptor repertoire data across federated repositories

Next-generation sequencing allows the characterization of the adaptive immune receptor repertoire (AIRR) in exquisite detail. These large-scale AIRR-seq data sets have rapidly become critical to vaccine development, understanding the immune response in autoimmune and infectious disease, and monitoring novel therapeutics against cancer. However, at present there is no easy way to compare these AIRR-seq data sets across studies and institutions. The ability to combine and compare information for different disease conditions will greatly enhance the value of AIRR-seq data for improving biomedical research and patient care. The iReceptor Data Integration Platform (gateway.ireceptor.org) provides one implementation of the AIRR Data Commons envisioned by the AIRR Community (airr-community.org), an initiative that is developing protocols to facilitate sharing and comparing AIRR-seq data. The iReceptor Scientific Gateway links distributed (federated) AIRR-seq repositories, allowing sequence searches or metadata queries across multiple studies at multiple institutions, returning sets of sequences fulfilling specific criteria. We present a review of the development of iReceptor, and how it fits in with the general trend toward sharing genomic and health data, and the development of standards for describing and reporting AIRR-seq data. Researchers interested in integrating their repositories of AIRR-seq data into the iReceptor Platform are invited to contact support@ireceptor.org.

Biophysicochemical Motifs in T-cell Receptor Sequences Distinguish Repertoires from Tumor-Infiltrating Lymphocyte and Adjacent Healthy Tissue

Immune repertoire deep sequencing allows comprehensive characterization of antigen receptor-encoding genes in a lymphocyte population. We hypothesized that this method could enable a novel approach to diagnose disease by identifying antigen receptor sequence patterns associated with clinical phenotypes. In this study, we developed statistical classifiers of T-cell receptor (TCR) repertoires that distinguish tumor tissue from patient-matched healthy tissue of the same organ. The basis of both classifiers was a biophysicochemical motif in the complementarity determining region 3 (CDR3) of TCRβ chains. To develop each classifier, we extracted 4-mers from every TCRβ CDR3 and represented each 4-mer using biophysicochemical features of its amino acid sequence combined with quantification of 4-mer (or receptor) abundance. This representation was scored using a logistic regression model. Unlike typical logistic regression, the classifier is fitted and validated under the requirement that at least 1 positively labeled 4-mer appears in every tumor repertoire and no positively labeled 4-mers appear in healthy tissue repertoires. We applied our method to publicly available data in which tumor and adjacent healthy tissue were collected from each patient. Using a patient-holdout cross-validation, our method achieved classification accuracy of 93% and 94% for colorectal and breast cancer, respectively. The parameter values for each classifier revealed distinct biophysicochemical properties for tumor-associated 4-mers within each cancer type. We propose that such motifs might be used to develop novel immune-based cancer screening assays. SIGNIFICANCE: This study presents a novel computational approach to identify T-cell repertoire differences between normal and tumor tissue.See related commentary by Zoete and Coukos, p. 1299.

VDJServer: A Cloud-Based Analysis Portal and Data Commons for Immune Repertoire Sequences and Rearrangements

Background

Recent technological advances in immune repertoire sequencing have created tremendous potential for advancing our understanding of adaptive immune response dynamics in various states of health and disease. Immune repertoire sequencing produces large, highly complex data sets, however, which require specialized methods and software tools for their effective analysis and interpretation.

Results

VDJServer is a cloud-based analysis portal for immune repertoire sequence data that provide access to a suite of tools for a complete analysis workflow, including modules for preprocessing and quality control of sequence reads, V(D)J gene segment assignment, repertoire characterization, and repertoire comparison. VDJServer also provides sophisticated visualizations for exploratory analysis. It is accessible through a standard web browser via a graphical user interface designed for use by immunologists, clinicians, and bioinformatics researchers. VDJServer provides a data commons for public sharing of repertoire sequencing data, as well as private sharing of data between users. We describe the main functionality and architecture of VDJServer and demonstrate its capabilities with use cases from cancer immunology and autoimmunity.

Conclusion

VDJServer provides a complete analysis suite for human and mouse T-cell and B-cell receptor repertoire sequencing data. The combination of its user-friendly interface and high-performance computing allows large immune repertoire sequencing projects to be analyzed with no programming or software installation required. VDJServer is a web-accessible cloud platform that provides access through a graphical user interface to a data management infrastructure, a collection of analysis tools covering all steps in an analysis, and an infrastructure for sharing data along with workflows, results, and computational provenance. VDJServer is a free, publicly available, and open-source licensed resource.

Reproducibility and Reuse of Adaptive Immune Receptor Repertoire Data

High-throughput sequencing (HTS) of immunoglobulin (B-cell receptor, antibody) and T-cell receptor repertoires has increased dramatically since the technique was introduced in 2009 (1–3). This experimental approach explores the maturation of the adaptive immune system and its response to antigens, pathogens, and disease conditions in exquisite detail. It holds significant promise for diagnostic and therapy-guiding applications. New technology often spreads rapidly, sometimes more rapidly than the understanding of how to make the products of that technology reliable, reproducible, or usable by others. As complex technologies have developed, scientific communities have come together to adopt common standards, protocols, and policies for generating and sharing data sets, such as the MIAME protocols developed for microarray experiments. The Adaptive Immune Receptor Repertoire (AIRR) Community formed in 2015 to address similar issues for HTS data of immune repertoires. The purpose of this perspective is to provide an overview of the AIRR Community’s founding principles and present the progress that the AIRR Community has made in developing standards of practice and data sharing protocols. Finally, and most important, we invite all interested parties to join this effort to facilitate sharing and use of these powerful data sets (join@airr-community.org).

VDJPipe: a pipelined tool for pre-processing immune repertoire sequencing data

Background

Pre-processing of high-throughput sequencing data for immune repertoire profiling is essential to insure high quality input for downstream analysis. VDJPipe is a flexible, high-performance tool that can perform multiple pre-processing tasks with just a single pass over the data files.

Results

Processing tasks provided by VDJPipe include base composition statistics calculation, read quality statistics calculation, quality filtering, homopolymer filtering, length and nucleotide filtering, paired-read merging, barcode demultiplexing, 5′ and 3′ PCR primer matching, and duplicate reads collapsing. VDJPipe utilizes a pipeline approach whereby multiple processing steps are performed in a sequential workflow, with the output of each step passed as input to the next step automatically. The workflow is flexible enough to handle the complex barcoding schemes used in many immunosequencing experiments. Because VDJPipe is designed for computational efficiency, we evaluated this by comparing execution times with those of pRESTO, a widely-used pre-processing tool for immune repertoire sequencing data. We found that VDJPipe requires <10% of the run time required by pRESTO.

Conclusions

VDJPipe is a high-performance tool that is optimized for pre-processing large immune repertoire sequencing data sets.

Statistical classifiers for diagnosing disease from immune repertoires: a case study using multiple sclerosis

Background

Deep sequencing of lymphocyte receptor repertoires has made it possible to comprehensively profile the clonal composition of lymphocyte populations. This opens the door for novel approaches to diagnose and prognosticate diseases with a driving immune component by identifying repertoire sequence patterns associated with clinical phenotypes. Indeed, recent studies support the feasibility of this, demonstrating an association between repertoire-level summary statistics (e.g., diversity) and patient outcomes for several diseases. In our own prior work, we have shown that six codons in VH4-containing genes in B cells from the cerebrospinal fluid of patients with relapsing remitting multiple sclerosis (RRMS) have higher replacement mutation frequencies than observed in healthy controls or patients with other neurological diseases. However, prior methods to date have been limited to focusing on repertoire-level summary statistics, ignoring the vast amounts of information in the millions of individual immune receptors comprising a repertoire. We have developed a novel method that addresses this limitation by using innovative approaches for accommodating the extraordinary sequence diversity of immune receptors and widely used machine learning approaches. We applied our method to RRMS, an autoimmune disease that is notoriously difficult to diagnose.

Results

We use the biochemical features encoded by the complementarity determining region 3 of each B cell receptor heavy chain in every patient repertoire as input to a detector function, which is fit to give the correct diagnosis for each patient using maximum likelihood optimization methods. The resulting statistical classifier assigns patients to one of two diagnosis categories, RRMS or other neurological disease, with 87% accuracy by leave-one-out cross-validation on training data (N = 23) and 72% accuracy on unused data from a separate study (N = 102).

Conclusions

Our method is the first to apply statistical learning to immune repertoires to aid disease diagnosis, learning repertoire-level labels from the set of individual immune repertoire sequences. This method produced a repertoire-based statistical classifier for diagnosing RRMS that provides a high degree of diagnostic capability, rivaling the accuracy of diagnosis by a clinical expert. Additionally, this method points to a diagnostic biochemical motif in the antibodies of RRMS patients, which may offer insight into the disease process.

Electronic supplementary material

The online version of this article (10.1186/s12859-017-1814-6) contains supplementary material, which is available to authorized users.

VDJServer – a web-accessible analysis portal for immune repertoire sequencing analysis

VDJServer is a comprehensive, web-accessible system for analysis of immune repertoire sequencing data. VDJServer provides a complete analysis workflow from pre-processing of sequence reads, to V(D)J assignment, to repertoire characterization and comparison. Recent enhancements in VDJServer include: --Automatic parallelization of analysis tools to handle very large data sets running on a high-performance supercomputer--Import and export subject and sample metadata. User-defined sample groups allows for sophisticated group analysis and comparison.--Extensive analysis functionality such as gene segment usage, CDR3 patterns, clonality, diversity measures, somatic mutation patterns, B cell lineage trees, and quantification of selection pressure. Analysis is performed for both samples and sample groups.--Interactive charting of analysis data provides exploratory visualization for ad-hoc comparison of samples and sample groups. Charts can be downloaded as image files for use in presentations and publications. All analysis data can be downloaded in standard TSV format for use with external tools.--Novel process workflow metadata that is automatically captured by VDJServer. Hiding the complexities of command line tools and their parameters, yet providing complete transparency of the analysis workflow for reproducibility.

VDJServer allows users to upload antigen receptor repertoire sequences and execute a customizable workflow for all steps in the analysis. Data and analysis results can be privately shared with other users for collaborative projects. VDJServer is funded by the NIAID and is freely available.

Reproducibility and Reuse of Adaptive Immune Receptor Repertoire Data

High-throughput sequencing (HTS) of immunoglobulin (B-cell receptor, antibody) and T-cell receptor repertoires has increased dramatically since the technique was introduced in 2009 (1-3). This experimental approach explores the maturation of the adaptive immune system and its response to antigens, pathogens, and disease conditions in exquisite detail. It holds significant promise for diagnostic and therapy-guiding applications. New technology often spreads rapidly, sometimes more rapidly than the understanding of how to make the products of that technology reliable, reproducible, or usable by others. As complex technologies have developed, scientific communities have come together to adopt common standards, protocols, and policies for generating and sharing data sets, such as the MIAME protocols developed for microarray experiments. The Adaptive Immune Receptor Repertoire (AIRR) Community formed in 2015 to address similar issues for HTS data of immune repertoires. The purpose of this perspective is to provide an overview of the AIRR Community's founding principles and present the progress that the AIRR Community has made in developing standards of practice and data sharing protocols. Finally, and most important, we invite all interested parties to join this effort to facilitate sharing and use of these powerful data sets (join@airr-community.org).

Peripheral VH4+ plasmablasts demonstrate autoreactive B cell expansion toward brain antigens in early multiple sclerosis patients

Plasmablasts are a highly differentiated, antibody secreting B cell subset whose prevalence correlates with disease activity in Multiple Sclerosis (MS). For most patients experiencing partial transverse myelitis (PTM), plasmablasts are elevated in the blood at the first clinical presentation of disease (known as a clinically isolated syndrome or CIS). In this study we found that many of these peripheral plasmablasts are autoreactive and recognize primarily gray matter targets in brain tissue. These plasmablasts express antibodies that over-utilize immunoglobulin heavy chain V-region subgroup 4 (VH4) genes, and the highly mutated VH4+ plasmablast antibodies recognize intracellular antigens of neurons and astrocytes. Most of the autoreactive, highly mutated VH4+ plasmablast antibodies recognize only a portion of cortical neurons, indicating that the response may be specific to neuronal subgroups or layers. Furthermore, CIS-PTM patients with this plasmablast response also exhibit modest reactivity toward neuroantigens in the plasma IgG antibody pool. Taken together, these data indicate that expanded VH4+ peripheral plasmablasts in early MS patients recognize brain gray matter antigens. Peripheral plasmablasts may be participating in the autoimmune response associated with MS, and provide an interesting avenue for investigating the expansion of autoreactive B cells at the time of the first documented clinical event.

VDJML: a file format with tools for capturing the results of inferring immune receptor rearrangements

Background

The genes that produce antibodies and the immune receptors expressed on lymphocytes are not germline encoded; rather, they are somatically generated in each developing lymphocyte by a process called V(D)J recombination, which assembles specific, independent gene segments into mature composite genes. The full set of composite genes in an individual at a single point in time is referred to as the immune repertoire. V(D)J recombination is the distinguishing feature of adaptive immunity and enables effective immune responses against an essentially infinite array of antigens. Characterization of immune repertoires is critical in both basic research and clinical contexts. Recent technological advances in repertoire profiling via high-throughput sequencing have resulted in an explosion of research activity in the field. This has been accompanied by a proliferation of software tools for analysis of repertoire sequencing data. Despite the widespread use of immune repertoire profiling and analysis software, there is currently no standardized format for output files from V(D)J analysis. Researchers utilize software such as IgBLAST and IMGT/High V-QUEST to perform V(D)J analysis and infer the structure of germline rearrangements. However, each of these software tools produces results in a different file format, and can annotate the same result using different labels. These differences make it challenging for users to perform additional downstream analyses.

Results

To help address this problem, we propose a standardized file format for representing V(D)J analysis results. The proposed format, VDJML, provides a common standardized format for different V(D)J analysis applications to facilitate downstream processing of the results in an application-agnostic manner. The VDJML file format specification is accompanied by a support library, written in C++ and Python, for reading and writing the VDJML file format.

Conclusions

The VDJML suite will allow users to streamline their V(D)J analysis and facilitate the sharing of scientific knowledge within the community. The VDJML suite and documentation are available from https://vdjserver.org/vdjml/. We welcome participation from the community in developing the file format standard, as well as code contributions.

VDJPipe: a pre-processing pipeline for immune repertoire sequencing data

Pre-processing of high-throughput sequencing data for immune repertoire profiling is essential to insure high quality input for downstream analysis. VDJPipe is a flexible, high-performance tool that can perform multiple pre-processing tasks with just a single pass of the data. These tasks include base composition statistics, read quality statistics, numerous quality filters, homopolymer filtering, length and nucleotide filtering, barcode demultiplexing, 5′ and 3′ PCR primer matching, and filtering of duplicate reads. VDJPipe utilizes a “pipeline” approach whereby multiple processing steps are described in a sequential workflow, with the output of each step passed as input to the next step. The workflow is flexible enough to handle the complex barcoding schemes used in many immunosequencing experiments. VDJPipe is an integral part of the VDJServer (http://www.vdjserver.org) web resource for performing immune repertoire analysis and can be accessed via the VDJServer Software page.

VDJML – tools for capturing the results of inferring immune receptor rearrangements

Despite the widespread use of immune repertoire profiling, there is currently no standardized format for output files from VDJ analysis. Researchers utilize software such as IgBlast and IMGT/High V-Quest to perform VDJ analysis and infer germline rearrangements. Each of these software tools produces results in a different file format, and can identify the same result using different labels. These differences make it challenging for users to perform additional analysis using the output file from one software to the next. We have addressed this problem by developing a standardized file format for representing results. The purpose of VDJML is to provide a common standardized format for different VDJ analysis applications and to facilitate downstream processing of the results in an application-agnostic manner. The VDJML file format is accompanied by a suite of analysis tools, which are accessible via command line and written in C++ and python. The VDJML suite will allow users to streamline their VDJ analysis and facilitate the sharing of scientific knowledge within the community. The VDJML suite and documentation are available from https://www.vdjserver.org/software. We welcome participation from others in developing the file format standard, as well as code contributions.

The auto-reactive profile of B cells from early MS patients

My lab’s recent work has led to the discovery that antibodies expressed by memory B cells in the cerebrospinal fluid (CSF) of patients with Multiple Sclerosis recognize neurons and astrocytes in the brain. Genetic analysis further revealed that only certain heavy chain genes accommodate binding to these brain targets, and that memory B cells from control cohorts rarely express these antibody genes. In fact, in a recent clinical trial, we demonstrated that the prevalence of these antibody genes expressed by memory B cells in the CSF support identification of patients who either have MS or will develop MS in the future with 89–94% accuracy. This Antibody Gene Signature (AGS) is a novel biomarker that heralds the advent of antibody genetics as a means to support categorization of patients early in their disease course. Our goal for this project was to determine whether antibodies expressed by activated memory B cells that have initiated antibody production (called “plasmablasts”) in the peripheral blood of MS patients also recognize neurons and astrocytes. We cloned and expressed more than 50 antibodies expressed by peripheral plasmablasts of early MS patients and control cohorts. We found that antibodies expressed by peripheral plasmablasts from patients who develop MS bind to neurons and astrocytes in the brain. Those plasmablasts that did not express antibody heavy chains associated with the AGS did not bind to brain targets. Interestingly, only those patients in our cohort who have an elevated frequency of peripheral plasmablasts continue to accumulate brain MRI activity. It is possible that brain-reactive peripheral plasmablasts contribute to the brain damage associated with MS.

Individual heritable differences result in unique cell lymphocyte receptor repertoires of naïve and antigen-experienced cells

The adaptive immune system's capability to protect the body requires a highly diverse lymphocyte antigen receptor repertoire. However, the influence of individual genetic and epigenetic differences on these repertoires is not typically measured. By leveraging the unique characteristics of B, CD4⁺ T and CD8⁺ T-lymphocyte subsets from monozygotic twins, we quantify the impact of heritable factors on both the V(D)J recombination process and on thymic selection. We show that the resulting biases in both V(D)J usage and N/P addition lengths, which are found in naïve and antigen experienced cells, contribute to significant variation in the CDR3 region. Moreover, we show that the relative usage of V and J gene segments is chromosomally biased, with ∼1.5 times as many rearrangements originating from a single chromosome. These data refine our understanding of the heritable mechanisms affecting the repertoire, and show that biases are evident on a chromosome-wide level.

The diversity of antigen receptor specificities is largely generated by random recombination of its segments. Here the authors show, by genetic comparison of monozygotic twin lymphocyte subsets, that individual genetic and epigenetic biases also contribute to the shape of the B and T cell repertoires.

MSPrecise: A molecular diagnostic test for multiple sclerosis using next generation sequencing

BACKGROUND

We have previously demonstrated that cerebrospinal fluid-derived B cells from early relapsing-remitting multiple sclerosis (RRMS) patients that express a VH4 gene accumulate specific replacement mutations. These mutations can be quantified as a score that identifies such patients as having or likely to convert to RRMS. Furthermore, we showed that next generation sequencing is an efficient method for obtaining the sequencing information required by this mutation scoring tool, originally developed using the less clinically viable single-cell Sanger sequencing.

OBJECTIVE

To determine the accuracy of MSPrecise, the diagnostic test that identifies the presence of the RRMS-enriched mutation pattern from patient cerebrospinal fluid B cells.

METHODS

Cerebrospinal fluid cell pellets were obtained from RRMS and other neurological disease (OND) patient cohorts. VH4 gene segments were amplified, sequenced by next generation sequencing and analyzed for mutation score.

RESULTS

The diagnostic test showed a sensitivity of 75% on the RRMS cohort and a specificity of 88% on the OND cohort. The accuracy of the test in identifying RRMS patients or patients that will develop RRMS is 84%.

CONCLUSION

MSPrecise exhibits good performance in identifying patients with RRMS irrespective of time with RRMS.

owlcpp: a C++ library for working with OWL ontologies

Background

The increasing use of ontologies highlights the need for a library for working with ontologies that is efficient, accessible from various programming languages, and compatible with common computational platforms.

Results

We developed owlcpp, a library for storing and searching RDF triples, parsing RDF/XML documents, converting triples into OWL axioms, and reasoning. The library is written in ISO-compliant C++ to facilitate efficiency, portability, and accessibility from other programming languages. Internally, owlcpp uses the Raptor RDF Syntax library for parsing RDF/XML and the FaCT++ library for reasoning. The current version of owlcpp is supported under Linux, OSX, and Windows platforms and provides an API for Python.

Conclusions

The results of our evaluation show that, compared to other commonly used libraries, owlcpp is significantly more efficient in terms of memory usage and searching RDF triple stores. owlcpp performs strict parsing and detects errors ignored by other libraries, thus reducing the possibility of incorrect semantic interpretation of ontologies. owlcpp is available at http://owl-cpp.sf.net/ under the Boost Software License, Version 1.0.

Electronic supplementary material

The online version of this article (doi:10.1186/s13326-015-0035-z) contains supplementary material, which is available to authorized users.

RAG Represents a Widespread Threat to the Lymphocyte Genome

The RAG1 endonuclease, together with its cofactor RAG2, is essential for V(D)J recombination but is a potent threat to genome stability. The sources of RAG1 mis-targeting and the mechanisms that have evolved to suppress it are poorly understood. Here, we report that RAG1 associates with chromatin at thousands of active promoters and enhancers in the genome of developing lymphocytes. The mouse and human genomes appear to have responded by reducing the abundance of "cryptic" recombination signals near RAG1 binding sites. This depletion operates specifically on the RSS heptamer, whereas nonamers are enriched at RAG1 binding sites. Reversing this RAG-driven depletion of cleavage sites by insertion of strong recombination signals creates an ectopic hub of RAG-mediated V(D)J recombination and chromosomal translocations. Our findings delineate rules governing RAG binding in the genome, identify areas at risk of RAG-mediated damage, and highlight the evolutionary struggle to accommodate programmed DNA damage in developing lymphocytes.

The antibody genetics of multiple sclerosis: comparing next-generation sequencing to sanger sequencing

We previously identified a distinct mutation pattern in the antibody genes of B cells isolated from cerebrospinal fluid (CSF) that can identify patients who have relapsing-remitting multiple sclerosis (RRMS) and patients with clinically isolated syndromes who will convert to RRMS. This antibody gene signature (AGS) was developed using Sanger sequencing of single B cells. While potentially helpful to patients, Sanger sequencing is not an assay that can be practically deployed in clinical settings. In order to provide AGS evaluations to patients as part of their diagnostic workup, we developed protocols to generate AGS scores using next-generation DNA sequencing (NGS) on CSF-derived cell pellets without the need to isolate single cells. This approach has the potential to increase the coverage of the B-cell population being analyzed, reduce the time needed to generate AGS scores, and may improve the overall performance of the AGS approach as a diagnostic test in the future. However, no investigations have focused on whether NGS-based repertoires will properly reflect antibody gene frequencies and somatic hypermutation patterns defined by Sanger sequencing. To address this issue, we isolated paired CSF samples from eight patients who either had MS or were at risk to develop MS. Here, we present data that antibody gene frequencies and somatic hypermutation patterns are similar in Sanger and NGS-based antibody repertoires from these paired CSF samples. In addition, AGS scores derived from the NGS database correctly identified the patients who initially had or subsequently converted to RRMS, with precision similar to that of the Sanger sequencing approach. Further investigation of the utility of the AGS in predicting conversion to MS using NGS-derived antibody repertoires in a larger cohort of patients is warranted.

A genome-wide association study of variants associated with acquisition of Staphylococcus aureus bacteremia in a healthcare setting

Background

Humans vary in their susceptibility to acquiring Staphylococcus aureus infection, and research suggests that there is a genetic basis for this variability. Several recent genome-wide association studies (GWAS) have identified variants that may affect susceptibility to infectious diseases, demonstrating the potential value of GWAS in this arena.

Methods

We conducted a GWAS to identify common variants associated with acquisition of S. aureus bacteremia (SAB) resulting from healthcare contact. We performed a logistic regression analysis to compare patients with healthcare contact who developed SAB (361 cases) to patients with healthcare contact in the same hospital who did not develop SAB (699 controls), testing 542,410 SNPs and adjusting for age (by decade), sex, and 6 significant principal components from our EIGENSTRAT analysis. Additionally, we evaluated the joint effect of the host and pathogen genomes in association with severity of SAB infection via logistic regression, including an interaction of host SNP with bacterial genotype, and adjusting for age (by decade), sex, the 6 significant principal components, and dialysis status. Bonferroni corrections were applied in both analyses to control for multiple comparisons.

Results

Ours is the first study that has attempted to evaluate the entire human genome for variants potentially involved in the acquisition or severity of SAB. Although this study identified no common variant of large effect size to have genome-wide significance for association with either the risk of acquiring SAB or severity of SAB, the variant (rs2043436) most significantly associated with severity of infection is located in a biologically plausible candidate gene (CDON, a member of the immunoglobulin family) and may warrant further study.

Conclusions

The genetic architecture underlying SAB is likely to be complex. Future investigations using larger samples, narrowed phenotypes, and advances in both genotyping and analytical methodologies will be important tools for identifying causative variants for this common and serious cause of healthcare-associated infection.

Design and evaluation of a bacterial clinical infectious diseases ontology

With antimicrobial resistance increasing worldwide, there is a great need to use automated antimicrobial decision support systems (ADSSs) to lower antimicrobial resistance rates by promoting appropriate antimicrobial use. However, they are infrequently used mostly because of their poor interoperability with different health information technologies. Ontologies can augment portable ADSSs by providing an explicit knowledge representation for biomedical entities and their relationships, helping to standardize and integrate heterogeneous data resources. We developed a bacterial clinical infectious diseases ontology (BCIDO) using Protégé-OWL. BCIDO defines a controlled terminology for clinical infectious diseases along with domain knowledge commonly used in hospital settings for clinical infectious disease treatment decision-making. BCIDO has 599 classes and 2355 object properties. Terms were imported from or mapped to Systematized Nomenclature of Medicine, Unified Medical Language System, RxNorm and National Center for Bitechnology Information Organismal Classification where possible. Domain expert evaluation using the "laddering" technique, ontology visualization, and clinical notes and scenarios, confirmed the correctness and potential usefulness of BCIDO.

CD19-targeted T cells rapidly induce molecular remissions in adults with chemotherapy-refractory acute lymphoblastic leukemia

Adults with relapsed B cell acute lymphoblastic leukemia (B-ALL) have a dismal prognosis. Only those patients able to achieve a second remission with no minimal residual disease (MRD) have a hope for long-term survival in the context of a subsequent allogeneic hematopoietic stem cell transplantation (allo-HSCT). We have treated five relapsed B-ALL subjects with autologous T cells expressing a CD19-specific CD28/CD3ζ second-generation dual-signaling chimeric antigen receptor (CAR) termed 19-28z. All patients with persistent morphological disease or MRD(+) disease upon T cell infusion demonstrated rapid tumor eradication and achieved MRD(-) complete remissions as assessed by deep sequencing polymerase chain reaction. Therapy was well tolerated, although significant cytokine elevations, specifically observed in those patients with morphologic evidence of disease at the time of treatment, required lymphotoxic steroid therapy to ameliorate cytokine-mediated toxicities. Indeed, cytokine elevations directly correlated to tumor burden at the time of CAR-modified T cell infusions. Tumor cells from one patient with relapsed disease after CAR-modified T cell therapy, who was ineligible for additional allo-HSCT or T cell therapy, exhibited persistent expression of CD19 and sensitivity to autologous 19-28z T cell-mediated cytotoxicity, which suggests potential clinical benefit of additional CAR-modified T cell infusions. These results demonstrate the marked antitumor efficacy of 19-28z CAR-modified T cells in patients with relapsed/refractory B-ALL and the reliability of this therapy to induce profound molecular remissions, forming a highly effective bridge to potentially curative therapy with subsequent allo-HSCT.

Logical development of the cell ontology

Background

The Cell Ontology (CL) is an ontology for the representation of in vivo cell types. As biological ontologies such as the CL grow in complexity, they become increasingly difficult to use and maintain. By making the information in the ontology computable, we can use automated reasoners to detect errors and assist with classification. Here we report on the generation of computable definitions for the hematopoietic cell types in the CL.

Results

Computable definitions for over 340 CL classes have been created using a genus-differentia approach. These define cell types according to multiple axes of classification such as the protein complexes found on the surface of a cell type, the biological processes participated in by a cell type, or the phenotypic characteristics associated with a cell type. We employed automated reasoners to verify the ontology and to reveal mistakes in manual curation. The implementation of this process exposed areas in the ontology where new cell type classes were needed to accommodate species-specific expression of cellular markers. Our use of reasoners also inferred new relationships within the CL, and between the CL and the contributing ontologies. This restructured ontology can be used to identify immune cells by flow cytometry, supports sophisticated biological queries involving cells, and helps generate new hypotheses about cell function based on similarities to other cell types.

Conclusion

Use of computable definitions enhances the development of the CL and supports the interoperability of OBO ontologies.

Memory B cells from a subset of treatment-naïve relapsing-remitting multiple sclerosis patients elicit CD4(+) T-cell proliferation and IFN-γ production in response to myelin basic protein and myelin oligodendrocyte glycoprotein

Recent evidence suggests that B- and T-cell interactions may be paramount in relapsing-remitting MS (RRMS) disease pathogenesis. We hypothesized that memory B-cell pools from RRMS patients may specifically harbor a subset of potent neuro-APC that support neuro-Ag reactive T-cell proliferation and cytokine secretion. To test this hypothesis, we compared CD80 and HLA-DR expression, IL-10 and lymphotoxin-α secretion, neuro-Ag binding capacity, and neuro-Ag presentation by memory B cells from RRMS patients to naïve B cells from RRMS patients and to memory and naïve B cells from healthy donors (HD). We identified memory B cells from some RRMS patients that elicited CD4(+) T-cell proliferation and IFN-γ secretion in response to myelin basic protein and myelin oligodendrocyte glycoprotein. Notwithstanding the fact that the phenotypic parameters that promote efficient Ag presentation were observed to be similar between RRMS and HD memory B cells, a corresponding capability to elicit CD4(+) T-cell proliferation in response to myelin basic protein and myelin oligodendrocyte glycoprotein was not observed in HD memory B cells. Our results demonstrate for the first time that the memory B-cell pool in RRMS harbors neuro-Ag specific B cells that can activate T cells.

Two genes on A/J chromosome 18 are associated with susceptibility to Staphylococcus aureus infection by combined microarray and QTL analyses

Although it has recently been shown that A/J mice are highly susceptible to Staphylococcus aureus sepsis as compared to C57BL/6J, the specific genes responsible for this differential phenotype are unknown. Using chromosome substitution strains (CSS), we found that loci on chromosomes 8, 11, and 18 influence susceptibility to S. aureus sepsis in A/J mice. We then used two candidate gene selection strategies to identify genes on these three chromosomes associated with S. aureus susceptibility, and targeted genes identified by both gene selection strategies. First, we used whole genome transcription profiling to identify 191 (56 on chr. 8, 100 on chr. 11, and 35 on chr. 18) genes on our three chromosomes of interest that are differentially expressed between S. aureus-infected A/J and C57BL/6J. Second, we identified two significant quantitative trait loci (QTL) for survival post-infection on chr. 18 using N₂ backcross mice (F₁ [C18A]×C57BL/6J). Ten genes on chr. 18 (March3, Cep120, Chmp1b, Dcp2, Dtwd2, Isoc1, Lman1, Spire1, Tnfaip8, and Seh1l) mapped to the two significant QTL regions and were also identified by the expression array selection strategy. Using real-time PCR, 6 of these 10 genes (Chmp1b, Dtwd2, Isoc1, Lman1, Tnfaip8, and Seh1l) showed significantly different expression levels between S. aureus-infected A/J and C57BL/6J. For two (Tnfaip8 and Seh1l) of these 6 genes, siRNA-mediated knockdown of gene expression in S. aureus–challenged RAW264.7 macrophages induced significant changes in the cytokine response (IL-1 β and GM-CSF) compared to negative controls. These cytokine response changes were consistent with those seen in S. aureus-challenged peritoneal macrophages from CSS 18 mice (which contain A/J chromosome 18 but are otherwise C57BL/6J), but not C57BL/6J mice. These findings suggest that two genes, Tnfaip8 and Seh1l, may contribute to susceptibility to S. aureus in A/J mice, and represent promising candidates for human genetic susceptibility studies.

Staphylococcus aureus has a wide spectrum of human infection, ranging from asymptomatic nasal carriage to overwhelming sepsis and death. Mouse models offer an attractive strategy for investigating complex diseases such as S. aureus infections. A/J mice are highly susceptible to S. aureus infection compared with C57BL/6J mice. We showed that genes on chromosomes 8, 11, and 18 in A/J are responsible for susceptibility to S. aureus by using chromosome substitution strains (CSS). From the ∼4200 genes on these three chromosomes, we identified 191 which were differentially expressed between A/J and C57BL/6J when challenged with S. aureus. Next, we identified two significant QTLs on chromosome 18 that are associated with susceptibility to S. aureus infection in N₂ backcross mice. Ten genes (March3, Cep120, Chmp1b, Dcp2, Dtwd2, Isoc1, Lman1, Spire1, Tnfaip8, and Seh1l) mapped to the two significant QTLs and were differentially expressed between A/J and C57BL/6J. One gene on each QTL, Tnfaip8 and Seh1l, affected expression of cytokines in mouse macrophages exposed to S. aureus. These cytokine response patterns were consistent with those seen in S. aureus-challenged peritoneal macrophages from CSS 18, but not C57BL/6J. Tnfaip8 and Seh1l are strong candidates for genes influencing susceptibility to S. aureus of A/J mice.

Towards an ontological representation of resistance: the case of MRSA

This paper addresses a family of issues surrounding the biological phenomenon of resistance and its representation in realist ontologies. The treatments of resistance terms in various existing ontologies are examined and found to be either overly narrow, internally inconsistent, or otherwise problematic. We propose a more coherent characterization of resistance in terms of what we shall call blocking dispositions, which are collections of mutually coordinated dispositions which are of such a sort that they cannot undergo simultaneous realization within a single bearer. A definition of 'protective resistance' is proposed for use in the Infectious Disease Ontology (IDO) and we show how this definition can be used to characterize the antibiotic resistance in Methicillin-Resistant Staphylococcus aureus (MRSA). The ontological relations between entities in our MRSA case study are used alongside a series of logical inference rules to illustrate logical reasoning about resistance. A description logic representation of blocking dispositions is also provided. We demonstrate that our characterization of resistance is sufficiently general to cover two other cases of resistance in the infectious disease domain involving HIV and malaria.