No abstract

CAR T cell approaches to effectively target AML and T-ALL without off-tumor effects on healthy myeloid or T cell compartments respectively are an unmet medical need. NKG2D-ligands are a promising target given their absence on healthy cells and surface expression in a wide range of malignancies. NKG2D-ligand expression has been reported in a substantial group of patients with AML along with evidence for prognostic significance. However, reports regarding the prevalence and density of NKG2D-ligand expression in AML vary and detailed studies to define whether low level expression is sufficient to trigger NKG2D-ligand directed CART cell responses are lacking. NKG2D ligand expression in T-ALL has not previously been interrogated. Here we report that NKG2D-ligands are expressed in T-ALL cell lines and primary T-ALL. We confirm that NKG2D-ligands are frequently surface expressed in primary AML, albeit at relatively low levels. Utilizing CAR T cells incorporating the natural immune receptor NKG2D as the antigen binding domain, we demonstrate striking in vitro activity of CAR T cells targeting NKG2D-ligands against AML and T-ALL cell lines and show that even low-level ligand expression in primary AML targets results in robust NKG2D-CAR activity. We found that NKG2D-ligand expression can be selectively enhanced in low-expressing AML cell lines and primary AML blasts via pharmacologic HDAC inhibition. Such pharmacologic NKG2D-ligand induction results in enhanced NKG2D-CAR anti-leukemic activity without affecting healthy PBMC, thereby providing rationale for the combination of HDAC-inhibitors with NKG2D-CAR T cell therapy as a potential strategy to achieve clinical NKG2D-CAR T cell efficacy in AML.

Drug repositioning is the process of finding new therapeutic uses for existing, approved drugs-a process thathas value when considering the exorbitant costs of novel drug development. Several computational strategies exist as a way to predict these alternative applications. In this study, we used datasets on: (1) human biological drug targets and (2) disease-associated genes and, based on a direct functional interaction between them, searched for potential opportunities for drug repositioning. From the set of 1125 unique drug targets and their 88 490 interactions with disease-associated genes, 30 drug targets were analyzed and (3) discussed in detail for the purpose of this article. The current indications of the drugs thattarget them were validated through the interactions, and new opportunities for repositioning were predicted. Among the set of drugs for potential repositioning werebenzodiazepines for the treatment of autism spectrum disorders; nortriptyline for the treatment of melanoma, glioma and other cancers; and vitamin B6 in prevention of spontaneous abortions and cleft palate birth defects. Special emphasis was also placed on those new potential indications that pertained to orphan diseases-these are diseases whose rarity means that development of novel treatment is not financially viable. This computational drug repositioning approach uses existing information on drugs and drug targets, and insights into the genetic basis of disease, as a means to systematically generate the most probable new uses for the drugs on offer, and in this way harness their true therapeutic power.

RNA-seq analysis involves multiple steps, from processing raw sequencing data to identifying, organizing, annotating, and reporting differentially expressed genes. bcbio is an open source, community-maintained framework providing automated and scalable RNA-seq methods for identifying gene abundance counts. We have developed bcbioRNASeq, a Bioconductor package that provides ready-to-render templates, objects and wrapper functions to post-process bcbio RNA sequencing output data. bcbioRNASeq helps automate the generation of high-level RNA-seq reports, facilitating the quality control analyses, identification of differentially expressed genes and functional enrichment analyses. Keywords

RNA-seq analysis involves multiple steps from processing raw sequencing data to identifying, organizing, annotating, and reporting differentially expressed genes. bcbio is an open source, community-maintained framework providing automated and scalable RNA-seq methods for identifying gene abundance counts. We have developed bcbioRNASeq, a Bioconductor package that provides ready-to-render templates and wrapper functions to post-process bcbio output data. bcbioRNASeq automates the generation of high-level RNA-seq reports, including identification of differentially expressed genes, functional enrichment analysis and quality control analysis.