Natural killer (NK) cell therapies, primarily based on chimeric antigen receptor NK cells (CAR‐NK), have been developed and applied clinically for therapeutic treatment of patients with mid‐to‐late‐stage tumors. However, NK cell therapy has limited efficacy due to insufficient antigen expression on the tumor cell surface. Here, a universal “illuminate tumor homogenization antigen properties” (ITHAP) strategy to achieve stable and controlled antigen expression on the surface of tumor cells using nanomedicine, thus significantly enhancing the immune recognizability of tumor cells, is described. The ITHAP strategy is used to generate bio‐liposomes (Pt@PL‐IgG) composed of intermingled platelet membranes and liposomes with NK‐activatable target antigen (IgG antibodies) and cisplatin pre‐drug. It is demonstrated that Pt@PL‐IgG successfully targets tumor cells using the autonomous drive of platelet membranes and achieves IgG implantation on tumor cells by utilizing membrane fusion properties. Moreover, it is shown that the Pt‐DNA complex combined with NK cell‐induced pyroptosis causes substantial interferon (IFN) secretion, thus providing a synthase‐stimulator of interferon genes (STING)‐IFN‐mediated positive immune microenvironment to further potentiate NK therapy. These results show that anchoring cancer cells with NK‐activatable target antigens is a promising translational strategy for addressing therapeutic challenges in tumor heterogeneity.
Detecting genetic variants with low effect sizes using a moderate sample size is difficult, hindering downstream efforts to learn pathology and estimating heritability. In this work, by utilizing informative weights learned from training genetically predicted gene expression models, we formed an alternative approach to estimate the polygenic term in a linear mixed model (LMM). Our LMM estimates the genetic background by incorporating their relevance to gene expression. Our protocol, expression-directed linear mixed model (edLMM), enables the discovery of subtle signals of low-effect variants using moderate sample size. By applying edLMM to cohorts of around 5,000 individuals with either binary (WTCCC) or quantitative (NFBC1966) traits, we demonstrated its power gain at the low-effect end of the genetic etiology spectrum. In aggregate, the additional low-effect variants detected by edLMM substantially improved estimation of missing heritability. edLMM moves precision medicine forward by accurately detecting the contribution of low-effect genetic variants to human diseases.
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