Adaptive NK cells are characterized by profound alterations in multiple signaling molecules, transcription factors, and epigenetic modifications compared with canonical NK cells. Although their existence is associated with prior exposure to human cytomegalovirus (HCMV), key questions regarding their regulation and function remain. A large proportion of adaptive NK cells express the activating receptor CD94/NKG2C, binding to human leukocyte antigen E (HLA-E), that presents a limited set of peptides. We show that adaptive NK cells discriminate differences between HLA-E-peptide complexes with exquisite specificity. Prolonged exposure to an environment displaying the HLA-E peptide ligand VMAPRTLFL, derived from the leader sequence of HLA-G, enriched adaptive NK cells with low FcεRγ expression, upregulated CD25 expression, increased proliferative activity, and resulted in elevated antibody-dependent cellular cytotoxicity and IFN-γ responses compared with other HLA-E peptide complexes. Our study demonstrates that recognition of alterations in the HLA-E ligandome via an activating receptor can influence heterologous effector mechanisms and proliferation in adaptive NK cells.

In the era of precision medicine, novel designs are developed to deal with flexible clinical trials that incorporate many treatment strategies for multiple diseases in one trial setting. This situation often leads to small sample sizes in disease‐treatment combinations and has fostered the discussion about the benefits of borrowing of external or historical information for decision‐making in these trials. Several methods have been proposed that dynamically discount the amount of information borrowed from historical data based on the conformity between historical and current data. Specifically, Bayesian methods have been recommended and numerous investigations have been performed to characterize the properties of the various borrowing mechanisms with respect to the gain to be expected in the trials. However, there is common understanding that the risk of type I error inflation exists when information is borrowed and many simulation studies are carried out to quantify this effect. To add transparency to the debate, we show that if prior information is conditioned upon and a uniformly most powerful test exists, strict control of type I error implies that no power gain is possible under any mechanism of incorporation of prior information, including dynamic borrowing. The basis of the argument is to consider the test decision function as a function of the current data even when external information is included. We exemplify this finding in the case of a pediatric arm appended to an adult trial and dichotomous outcome for various methods of dynamic borrowing from adult information to the pediatric arm. In conclusion, if use of relevant external data is desired, the requirement of strict type I error control has to be replaced by more appropriate metrics.

Supplementary data are available at Bioinformatics online.