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BackgroundRecent 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.ResultsVDJServer 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.ConclusionVDJServer 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.

BackgroundPre-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.ResultsProcessing 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

BackgroundDeep 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.ResultsWe 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).ConclusionsOur 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.

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.

BackgroundThe 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.ResultsTo 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.ConclusionsThe 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.

In April 2023, the National Cancer Institute offered a roadmap for cancer research to achieve Cancer Moonshot goals. To reach these goals requires making progress for all cancers, not just those that are most common. Achieving progress against rare cancers, as well as common cancers, requires involvement of large clinical research networks. In 2020, the Patient‐Centered Outcomes Research Institute (PCORI) launched an initiative on Conducting Rare Disease Research using PCORnet, the National Patient‐Centered Clinical Research Network. The purpose of this commentary is to introduce the broader community of cancer researchers to the PCORnet NET‐PRO study (comparing the effects of different treatment approaches for neuroendocrine tumors on patient‐reported outcomes) thereby demonstrating how researchers can use the PCORnet infrastructure to conduct large‐scale patient‐centered studies of rare cancers. Achieving progress against rare cancers requires involvement of large clinical research networks. This commentary showcases the NET‐PRO study (comparing the effects of different treatment approaches for neuroendocrine tumors on patient‐reported outcomes) to demonstrate how cancer researchers can conduct large‐scale patient‐centered studies of rare cancers using PCORnet. [ABSTRACT FROM AUTHOR]

BackgroundWe 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.ObjectiveTo determine the accuracy of MSPrecise, the diagnostic test that identifies the presence of the RRMS-enriched mutation pattern from patient cerebrospinal fluid B cells.MethodsCerebrospinal 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.ResultsThe 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%.ConclusionMSPrecise exhibits good performance in identifying patients with RRMS irrespective of time with RRMS.

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.

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. [ABSTRACT FROM AUTHOR]

Modern data-driven research has the power to promote novel biomedical discoveries through secondary analyses of raw data. Therefore, it is important to ensure data-driven research with great reproducibility and robustness for promoting a precise and accurate secondary analysis of the immunogenomics data. In scientific research, rigorous conduct in designing and conducting experiments is needed, specifically in scientific writing and reporting results. It is also crucial to make raw data available, discoverable, and well described or annotated in order to promote future re-analysis of the data. In order to assess the data availability of published T cell receptor (TCR) repertoire data, we examined 11,918 TCR-Seq samples corresponding to 134 TCR-Seq studies ranging from 2006 to 2022. Among the 134 studies, only 38.1% had publicly available raw TCR-Seq data shared in public repositories. We also found a statistically significant association between the presence of data availability statements and the increase in raw data availability (p = 0.014). Yet, 46.8% of studies with data availability statements failed to share the raw TCRSeq data. There is a pressing need for the biomedical community to increase awareness of the importance of promoting raw data availability in scientific research and take immediate action to improve its raw data availability enabling cost-effective secondary analysis of existing immunogenomics data by the larger scientific community. [ABSTRACT FROM AUTHOR]