Identification of a neutrophil-specific PIK3R1 mutation facilitates targeted treatment in a patient with Sweet syndrome.

BackgroundAcute febrile neutrophilic dermatosis (Sweet syndrome) is a potentially fatal multiorgan inflammatory disease characterized by fever, leukocytosis, and a rash with a neutrophilic infiltrate. The disease pathophysiology remains elusive, and current dogma suggests that Sweet syndrome is a process of reactivity to an unknown antigen. Corticosteroids and steroid-sparing agents remain frontline therapies, but refractory cases pose a clinical challenge.MethodsA 51-year-old woman with multiorgan Sweet syndrome developed serious corticosteroid-related side effects and was refractory to steroid-sparing agents. Blood counts, liver enzymes, and skin histopathology supported the diagnosis. Whole-genome sequencing, transcriptomic profiling, and cellular assays of the patient's skin and neutrophils were performed.ResultsWe identified elevated IL-1 signaling in lesional Sweet syndrome skin caused by a PIK3R1 gain-of-function mutation specifically found in neutrophils. This mutation increased neutrophil migration toward IL-1ß and neutrophil respiratory burst. Targeted treatment of the patient with an IL-1 receptor 1 antagonist resulted in a dramatic therapeutic response and enabled a tapering off of corticosteroids.ConclusionDysregulated PI3K/AKT signaling is the first signaling pathway linked to Sweet syndrome and suggests that this syndrome may be caused by acquired mutations that modulate neutrophil function. Moreover, integration of molecular data across multiple levels identified a distinct subtype within a heterogeneous disease that resulted in a rational and successful clinical intervention. Future patients will benefit from efforts to identify potential mutations. The ability to directly interrogate the diseased skin allows this method to be generalizable to other inflammatory diseases and demonstrates a potential personalized medicine approach for patients with clinically challenging disease.Funding SourcesBerstein Foundation, NIH, Veterans Affairs (VA) Administration, Moseley Foundation, and H.T. Leung Foundation.

NERO: a biomedical named-entity (recognition) ontology with a large, annotated corpus reveals meaningful associations through text embedding.

Machine reading (MR) is essential for unlocking valuable knowledge contained in millions of existing biomedical documents. Over the last two decades1,2, the most dramatic advances in MR have followed in the wake of critical corpus development3. Large, well-annotated corpora have been associated with punctuated advances in MR methodology and automated knowledge extraction systems in the same way that ImageNet4 was fundamental for developing machine vision techniques. This study contributes six components to an advanced, named entity analysis tool for biomedicine: (a) a new, Named Entity Recognition Ontology (NERO) developed specifically for describing textual entities in biomedical texts, which accounts for diverse levels of ambiguity, bridging the scientific sublanguages of molecular biology, genetics, biochemistry, and medicine; (b) detailed guidelines for human experts annotating hundreds of named entity classes; (c) pictographs for all named entities, to simplify the burden of annotation for curators; (d) an original, annotated corpus comprising 35,865 sentences, which encapsulate 190,679 named entities and 43,438 events connecting two or more entities; (e) validated, off-the-shelf, named entity recognition (NER) automated extraction, and; (f) embedding models that demonstrate the promise of biomedical associations embedded within this corpus.