The major therapeutic goal for immune thrombocytopenia (ITP) is to restore normal platelet counts using drugs to promote platelet production or by interfering with mechanisms responsible for platelet destruction. 80% of patients possess anti-integrin αIIbβ3 (GPIIbIIIa) IgG autoantibodies causing platelet opsonization and phagocytosis. The spleen is considered the primary site of autoantibody production by autoreactive B cells and platelet destruction. The immediate failure in ~50% of patients to recover a normal platelet count after anti-CD20 Rituximab-mediated B cell depletion and splenectomy suggest that autoreactive, rituximab-resistant, IgG-secreting B cells (IgG-SC) reside in other anatomical compartments. We analyzed >3,300 single IgG-SC from spleen, bone marrow and/or blood of 27 patients with ITP revealing high inter-individual variability in affinity for GPIIbIIIa with variations over 3 logs. IgG-SC dissemination and range of affinities were however similar per patient. Longitudinal analysis of autoreactive IgG-SC upon treatment with anti-CD38 mAb daratumumab demonstrated variable outcomes, from complete remission to failure with persistence of high-affinity anti-GPIIbIIIa IgG-SC in the bone marrow. This study demonstrates the existence and dissemination of high-affinity autoreactive plasma cells in multiple anatomical compartments of patients with ITP that may cause the failure of current therapies.

We use online search data to predict car sales in the German and UK automobile industries. Search data subsume several distinct search motives, which are not separately observable. We develop a model linking search motives to observable search data and sales. The model shows that predictions of sales relying on observable search data as a proxy for prepurchase search will be biased. We show how to remove the biases and estimate the effect of prepurchase search on sales. To assist identification of this effect, we use the introduction of scrappage subsidies for cars in 2008/2009 as a quasi-natural experiment. We also show that online search data are (i) highly persistent over time, (ii) potentially subject to permanent shocks, and (iii) correlated across products, but to different extent. We address these challenges to estimation and inference by using recent econometric methods for large N, large T panels.

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