BioReader: a text mining tool for performing classification of biomedical literature

Abstract Background Scientific data and research results are being published at an unprecedented rate. Many database curators and researchers utilize data and information from the primary literature to populate databases, form hypotheses, or as the basis for analyses or validation of results. These efforts largely rely on manual literature surveys for collection of these data, and while querying the vast amounts of literature using keywords is enabled by repositories such as PubMed, filtering relevant articles from such query results can be a non-trivial and highly time consuming task. Results We here present a tool that enables users to perform classification of scientific literature by text mining-based classification of article abstracts. BioReader (Biomedical Research Article Distiller) is trained by uploading article corpora for two training categories - e.g. one positive and one negative for content of interest - as well as one corpus of abstracts to be classified and/or a search string to query PubMed for articles. The corpora are submitted as lists of PubMed IDs and the abstracts are automatically downloaded from PubMed, preprocessed, and the unclassified corpus is classified using the best performing classification algorithm out of ten implemented algorithms. Conclusion BioReader supports data and information collection by implementing text mining-based classification of primary biomedical literature in a web interface, thus enabling curators and researchers to take advantage of the vast amounts of data and information in the published literature. BioReader outperforms existing tools with similar functionalities and expands the features used for mining literature in database curation efforts. The tool is freely available as a web service at http://www.cbs.dtu.dk/services/BioReade

Antibody response following the third and fourth SARS-CoV-2 vaccine dose in individuals with common variable immunodeficiency

BackgroundThe antibody response after vaccination is impaired in common variable immunodeficiency (CVID).ObjectiveWe aimed to study the spike receptor-binding domain IgG antibody (anti-S-RBD) levels during a four-dose SARS-CoV-2 vaccination strategy and after monoclonal antibody (mAB) treatment in CVID. Moreover, we assessed the anti-S-RBD levels in immunoglobulin replacement therapy (IgRT) products.MethodsIn an observational study, we examined anti-S-RBD levels after the second, third, and fourth dose of mRNA SARS-CoV-2 vaccines. Moreover, we measured anti-S-RBD after treatment with mAB. Finally, anti-S-RBD was assessed in common IgRT products. Antibody non-responders (anti-S-RBD < 7.1) were compared by McNemar’s test and anti-S-RBD levels were compared with paired and non-paired Wilcoxon signed rank tests as well as Kruskal–Wallis tests.ResultsAmong 33 individuals with CVID, anti-S-RBD levels increased after the third vaccine dose (165 BAU/ml [95% confidence interval: 85; 2280 BAU/ml], p = 0.006) and tended to increase after the fourth dose (193 BAU/ml, [−22; 569 BAU/ml], p = 0.080) compared to the previous dose. With increasing number of vaccinations, the proportion of patients who seroconverted (anti-S-RBD ≥ 7.1) increased non-significantly. mAB treatment resulted in a large increase in anti-S-RBD and a higher median level than gained after the fourth dose of vaccine (p = 0.009). IgRT products had varying concentrations of anti-S-RBD (p < 0.001), but none of the products seemed to affect the overall antibody levels (p = 0.460).ConclusionMultiple SARS-CoV-2 vaccine doses in CVID seem to provide additional protection, as antibody levels increased after the third and fourth vaccine dose. However, anti-S-RBD levels from mAB outperform the levels mounted after vaccination.Clinical ImplicationsBoosting with SARS-CoV-2 vaccines seems to improve the antibody response in CVID patients.Capsule summaryThe third and possibly also the fourth dose of mRNA SARS-CoV-2 vaccine in CVID improve the antibody response as well as stimulate seroconversion in most non-responders

Target isoforms are an overlooked challenge and opportunity in chimeric antigen receptor cell therapy

The development of novel chimeric antigen receptor (CAR) cell therapies is rapidly growing, with 299 new agents being reported and 109 new clinical trials initiated so far this year. One critical lesson from approved CD19-specific CAR therapies is that target isoform switching has been shown to cause tumour relapse, but little is known about the isoforms of CAR targets in solid cancers. Here we assess the protein isoform landscape and identify both the challenges and opportunities protein isoform switching present as CAR therapy is applied to solid cancers

Harnessing the Immune System to Fight Multiple Myeloma

Multiple myeloma (MM) is a heterogeneous plasma cell malignancy differing substantially in clinical behavior, prognosis, and response to treatment. With the advent of novel therapies, many patients achieve long-lasting remissions, but some experience aggressive and treatment refractory relapses. So far, MM is considered incurable. Myeloma pathogenesis can broadly be explained by two interacting mechanisms, intraclonal evolution of cancer cells and development of an immunosuppressive tumor microenvironment. Failures in isotype class switching and somatic hypermutations result in the neoplastic transformation typical of MM and other B cell malignancies. Interestingly, although genetic alterations occur and evolve over time, they are also present in premalignant stages, which never progress to MM, suggesting that genetic mutations are necessary but not sufficient for myeloma transformation. Changes in composition and function of the immune cells are associated with loss of effective immune surveillance, which might represent another mechanism driving malignant transformation. During the last decade, the traditional view on myeloma treatment has changed dramatically. It is increasingly evident that treatment strategies solely based on targeting intrinsic properties of myeloma cells are insufficient. Lately, approaches that redirect the cells of the otherwise suppressed immune system to take control over myeloma have emerged. Evidence of utility of this principle was initially established by the observation of the graft-versus-myeloma effect in allogeneic stem cell-transplanted patients. A variety of new strategies to harness both innate and antigen-specific immunity against MM have recently been developed and intensively tested in clinical trials. This review aims to give readers a basic understanding of how the immune system can be engaged to treat MM, to summarize the main immunotherapeutic modalities, their current role in clinical care, and future prospects

cyCombine allows for robust integration of single-cell cytometry datasets within and across technologies

Combining single-cell cytometry datasets increases the analytical flexibility and the statistical power of data analyses. However, in many cases the full potential of co-analyses is not reached due to technical variance between data from different experimental batches. Here, we present cyCombine, a method to robustly integrate cytometry data from different batches, experiments, or even different experimental techniques, such as CITE-seq, flow cytometry, and mass cytometry. We demonstrate that cyCombine maintains the biological variance and the structure of the data, while minimizing the technical variance between datasets. cyCombine does not require technical replicates across datasets, and computation time scales linearly with the number of cells, allowing for integration of massive datasets. Robust, accurate, and scalable integration of cytometry data enables integration of multiple datasets for primary data analyses and the validation of results using public datasets

cyCombine allows for robust integration of single-cell cytometry datasets within and across technologies.

Combining single-cell cytometry datasets increases the analytical flexibility and the statistical power of data analyses. However, in many cases the full potential of co-analyses is not reached due to technical variance between data from different experimental batches. Here, we present cyCombine, a method to robustly integrate cytometry data from different batches, experiments, or even different experimental techniques, such as CITE-seq, flow cytometry, and mass cytometry. We demonstrate that cyCombine maintains the biological variance and the structure of the data, while minimizing the technical variance between datasets. cyCombine does not require technical replicates across datasets, and computation time scales linearly with the number of cells, allowing for integration of massive datasets. Robust, accurate, and scalable integration of cytometry data enables integration of multiple datasets for primary data analyses and the validation of results using public datasets