Pharmacologic Activation of STING in the Bladder Induces Potent Antitumor Immunity in Non-Muscle Invasive Murine Bladder Cancer.

Stimulator of interferon genes (STING) is an innate immune receptor activated by natural or synthetic agonists to elicit antitumoral immune response via type I IFNs and other inflammatory cytokines. Bacillus Calmette-Guerin (BCG) is the standard of care as intravesical therapy for patients with high-risk non-muscle invasive bladder cancer (NMIBC). There are limited options available for patients with NMIBC who developed BCG unresponsiveness. In this study, we characterized in vitro and in vivo antitumor effects of E7766, a macrocyle-bridged STING agonist, via intravesical instillation in two syngeneic orthotopic murine NMIBC tumor models resistant to therapeutic doses of BCG and anti-PD-1 agents. E7766 bound to recombinant STING protein with a Kd value of 40 nmol/L and induced IFNß expression in primary human peripheral blood mononuclear cells harboring any of seven major STING genotypes with EC50 values of 0.15 to 0.79 µmol/L. Intravesical E7766 was efficacious in both NMIBC models with induction of effective immunologic memory in the treated animals. Pharmacologic activation of the STING pathway in the bladder resulted in IFN pathway activation, infiltration of T cells and natural killer (NK) cells, dendritic cell activation, and antigen presentation in bladder epithelium, leading to the antitumor activity and immunity. In addition, measurements of the pharmacodynamic markers, Ifnß1 and CXCL10, in bladder, urine, and plasma, and of STING pathway intactness in cancer cells, supported this mode of action. Taken together, our studies reveal an antitumor immune effect of pharmacologic activation of the STING pathway in bladder epithelium and thus provide a rationale for subsequent clinical studies in patients with NMIBC.

E7766, a Macrocycle-Bridged Stimulator of Interferon Genes (STING) Agonist with Potent Pan-Genotypic Activity.

A strategy for creating potent and pan-genotypic stimulator of interferon genes (STING) agonists is described. Locking a bioactive U-shaped conformation of cyclic dinucleotides by introducing a transannular macrocyclic bridge between the nucleic acid bases leads to a topologically novel macrocycle-bridged STING agonist (MBSA). In addition to substantially enhanced potency, the newly designed MBSAs, exemplified by clinical candidate E7766, exhibit broad pan-genotypic activity in all major human STING variants. E7766 is shown to have potent antitumor activity with long lasting immune memory response in a mouse liver metastatic tumor model. Two complementary stereoselective synthetic routes to E7766 are also described.

Phenome-wide association studies across large population cohorts support drug target validation.

Phenome-wide association studies (PheWAS) have been proposed as a possible aid in drug development through elucidating mechanisms of action, identifying alternative indications, or predicting adverse drug events (ADEs). Here, we select 25 single nucleotide polymorphisms (SNPs) linked through genome-wide association studies (GWAS) to 19 candidate drug targets for common disease indications. We interrogate these SNPs by PheWAS in four large cohorts with extensive health information (23andMe, UK Biobank, FINRISK, CHOP) for association with 1683 binary endpoints in up to 697,815 individuals and conduct meta-analyses for 145 mapped disease endpoints. Our analyses replicate 75% of known GWAS associations (P < 0.05) and identify nine study-wide significant novel associations (of 71 with FDR < 0.1). We describe associations that may predict ADEs, e.g., acne, high cholesterol, gout, and gallstones with rs738409 (p.I148M) in PNPLA3 and asthma with rs1990760 (p.T946A) in IFIH1. Our results demonstrate PheWAS as a powerful addition to the toolkit for drug discovery.

Causal Network Models for Predicting Compound Targets and Driving Pathways in Cancer.

Gene-expression data are often used to infer pathways regulating transcriptional responses. For example, differentially expressed genes (DEGs) induced by compound treatment can help characterize hits from phenotypic screens, either by correlation with known drug signatures or by pathway enrichment. Pathway enrichment is, however, typically computed with DEGs rather than "upstream" nodes that are potentially causal of "downstream" changes. Here, we present graph-based models to predict causal targets from compound-microarray data. We test several approaches to traversing network topology, and show that a consensus minimum-rank score (SigNet) beat individual methods and could highly rank compound targets among all network nodes. In addition, larger, less canonical networks outperformed linear canonical interactions. Importantly, pathway enrichment using causal nodes rather than DEGs recovers relevant pathways more often. To further validate our approach, we used integrated data sets from the Cancer Genome Atlas to identify driving pathways in triple-negative breast cancer. Critical pathways were uncovered, including the epidermal growth factor receptor 2-phosphatidylinositide 3-kinase-AKT-MAPK growth pathway andATR-p53-BRCA DNA damage pathway, in addition to unexpected pathways, such as TGF-WNT cytoskeleton remodeling, IL12-induced interferon gamma production, and TNFR-IAP (inhibitor of apoptosis) apoptosis; the latter was validated by pooled small hairpin RNA profiling in cancer cells. Overall, our approach can bridge transcriptional profiles to compound targets and driving pathways in cancer.

The multidimensional perturbation value: a single metric to measure similarity and activity of treatments in high-throughput multidimensional screens.

Screens using high-throughput, information-rich technologies such as microarrays, high-content screening (HCS), and next-generation sequencing (NGS) have become increasingly widespread. Compared with single-readout assays, these methods produce a more comprehensive picture of the effects of screened treatments. However, interpreting such multidimensional readouts is challenging. Univariate statistics such as t-tests and Z-factors cannot easily be applied to multidimensional profiles, leaving no obvious way to answer common screening questions such as "Is treatment X active in this assay?" and "Is treatment X different from (or equivalent to) treatment Y?" We have developed a simple, straightforward metric, the multidimensional perturbation value (mp-value), which can be used to answer these questions. Here, we demonstrate application of the mp-value to three data sets: a multiplexed gene expression screen of compounds and genomic reagents, a microarray-based gene expression screen of compounds, and an HCS compound screen. In all data sets, active treatments were successfully identified using the mp-value, and simulations and follow-up analyses supported the mp-value's statistical and biological validity. We believe the mp-value represents a promising way to simplify the analysis of multidimensional data while taking full advantage of its richness.

Determination of minimal transcriptional signatures of compounds for target prediction.

The identification of molecular target and mechanism of action of compounds is a key hurdle in drug discovery. Multiplexed techniques for bead-based expression profiling allow the measurement of transcriptional signatures of compound-treated cells in high-throughput mode. Such profiles can be used to gain insight into compounds' mode of action and the protein targets they are modulating. Through the proxy of target prediction from such gene signatures we explored important aspects of the use of transcriptional profiles to capture biological variability of perturbed cellular assays. We found that signatures derived from expression data and signatures derived from biological interaction networks performed equally well, and we showed that gene signatures can be optimised using a genetic algorithm. Gene signatures of approximately 128 genes seemed to be most generic, capturing a maximum of the perturbation inflicted on cells through compound treatment. Moreover, we found evidence for oxidative phosphorylation to be one of the most general ways to capture compound perturbation.

Genomewide analysis of inherited variation associated with phosphorylation of PI3K/AKT/mTOR signaling proteins.

While there exists a wealth of information about genetic influences on gene expression, less is known about how inherited variation influences the expression and post-translational modifications of proteins, especially those involved in intracellular signaling. The PI3K/AKT/mTOR signaling pathway contains several such proteins that have been implicated in a number of diseases, including a variety of cancers and some psychiatric disorders. To assess whether the activation of this pathway is influenced by genetic factors, we measured phosphorylated and total levels of three key proteins in the pathway (AKT1, p70S6K, 4E-BP1) by ELISA in 122 lymphoblastoid cell lines from 14 families. Interestingly, the phenotypes with the highest proportion of genetic influence were the ratios of phosphorylated to total protein for two of the pathway members: AKT1 and p70S6K. Genomewide linkage analysis suggested several loci of interest for these phenotypes, including a linkage peak for the AKT1 phenotype that contained the AKT1 gene on chromosome 14. Linkage peaks for the phosphorylated:total protein ratios of AKT1 and p70S6K also overlapped on chromosome 3. We selected and genotyped candidate genes from under the linkage peaks, and several statistically significant associations were found. One polymorphism in HSP90AA1 was associated with the ratio of phosphorylated to total AKT1, and polymorphisms in RAF1 and GRM7 were associated with the ratio of phosphorylated to total p70S6K. These findings, representing the first genomewide search for variants influencing human protein phosphorylation, provide useful information about the PI3K/AKT/mTOR pathway and serve as a valuable proof of concept for studies integrating human genomics and proteomics.

CANDID: a flexible method for prioritizing candidate genes for complex human traits.

Genomewide studies and localized candidate gene approaches have become everyday study designs for identifying polymorphisms in genes that influence complex human traits. Yet, in general, the number of significant findings and the need to focus on smaller regions require a prioritization of genes for further study. Some candidate gene identification algorithms have been proposed in recent years to attempt to streamline this prioritization, but many suffer from limitations imposed by the source data or are difficult to use and understand. CANDID is a prioritization algorithm designed to produce impartial, accurate rankings of candidate genes that influence complex human traits. CANDID can use information from publications, protein domain descriptions, cross-species conservation measures, gene expression profiles and protein-protein interactions in its analysis. Additionally, users may supplement these data sources with results from linkage, association and other studies. CANDID was tested on well-known complex trait genes using data from the Online Mendelian Inheritance in Man database. Additionally, CANDID was evaluated in a modeled gene discovery environment, where it ranked genes whose trait associations were published after CANDID's databases were compiled. In all settings, CANDID exhibited high sensitivity and specificity, indicating an improvement upon previously published algorithms. Its accuracy and ease of use make CANDID a highly useful tool in study design and analysis for complex human traits.

Novel polymorphisms and lack of mutations in the ACD gene in patients with ACTH resistance syndromes.

OBJECTIVE: ACTH resistance is a feature of several human syndromes with known genetic causes, including familial glucocorticoid deficiency (types 1 and 2) and triple A syndrome. However, many patients with ACTH resistance lack an identifiable genetic aetiology. The human homolog of the Acd gene, mutated in a mouse model of adrenal insufficiency, was sequenced in 25 patients with a clinical diagnosis of familial glucocorticoid deficiency or triple A syndrome. DESIGN: A 3.4 kilobase genomic fragment containing the entire ACD gene was analysed for mutations in all 25 patients. SETTING: Samples were obtained by three investigators from different institutions. PATIENTS: The primary cohort consisted of 25 unrelated patients, primarily of European or Middle Eastern descent, with a clinical diagnosis of either familial glucocorticoid deficiency (FGD) or triple A syndrome. Patients lacked mutations in other genes known to cause ACTH resistance, including AAAS for patients diagnosed with triple A syndrome and MC2R and MRAP for patients diagnosed with familial glucocorticoid deficiency. Thirty-five additional patients with adrenal disease phenotypes were added to form an expanded cohort of 60 patients. MEASUREMENTS: Identification of DNA sequence changes in the ACD gene in the primary cohort and analysis of putative ACD haplotypes in the expanded cohort. RESULTS: No disease-causing mutations were found, but several novel single nucleotide polymorphisms (SNPs) and two putative haplotypes were identified. The overall frequency of SNPs in ACD is low compared to other gene families. CONCLUSIONS: No mutations were identified in ACD in this collection of patients with ACTH resistance phenotypes. However, the newly identified SNPs in ACD should be more closely examined for possible links to disease.

Tpp1/Acd maintains genomic stability through a complex role in telomere protection.

Telomeres serve to protect the ends of chromosomes, and failure to maintain telomeres can lead to dramatic genomic instability. Human TPP1 was identified as a protein which interacts with components of a telomere cap complex, but does not directly bind to telomeric DNA. While biochemical interactions indicate a function in telomere biology, much remains to be learned regarding the roles of TPP1 in vivo. We previously reported the positional cloning of the gene responsible for the adrenocortical dysplasia (acd) mouse phenotype, which revealed a mutation in the mouse homologue encoding TPP1. We find that cells from homozygous acd mice harbor chromosomes fused at telomere sequences, demonstrating a role in telomere protection in vivo. Surprisingly, our studies also reveal fusions and radial structures lacking internal telomere sequences, which are not anticipated from a simple deficiency in telomere protection. Employing spectral karyotyping and telomere FISH in a combined approach, we have uncovered a striking pattern; fusions with telomeric sequences involve nonhomologous chromosomes while those lacking telomeric sequences involve homologues. Together, these studies show that Tpp1/Acd plays a vital role in telomere protection, but likely has additional functions yet to be defined.

IMAGe association and congenital adrenal hypoplasia: no disease-causing mutations found in the ACD gene.

The spontaneous mouse mutant adrenocortical dysplasia (acd) is characterized by defects in the adrenals, kidneys, and gonads of adult mutant mice and by caudal dysgenesis and vertebral segmentation defects in acd embryos. This association of defects mirrors those identified in patients with known or suspected abnormalities in adrenocortical development, including adrenal hypoplasia congenita and IMAGe association. The identification of the Acd gene in mice has prompted the study of its human homolog ACD, which has recently been shown to be a regulator of telomere length. Sequencing of ACD in 15 patients revealed no coding mutations, but three novel SNPs were identified.

Urogenital and caudal dysgenesis in adrenocortical dysplasia (acd) mice is caused by a splicing mutation in a novel telomeric regulator.

Adrenocortical dysplasia (acd) is a spontaneous autosomal recessive mouse mutant with developmental defects in organs derived from the urogenital ridge. In surviving adult mutants, adrenocortical dysplasia and hypofunction are predominant features. Adults are infertile due to lack of mature germ cells, and 50% develop hydronephrosis due to ureteral hyperplasia. We report the identification of a splice donor mutation in a novel gene, which is the mouse ortholog of a newly discovered telomeric regulator. This gene (Acd) has recently been characterized as a novel component of the TRF1 protein complex that controls telomere elongation by telomerase. Characterization of Acd transcripts in mutant animals reveals two abnormal transcripts, consistent with a splicing defect. Expression of a wild-type Acd transgene in acd mutants rescues the observed phenotype. Most mutants die within 1-2 days of life on the original genetic background. Analysis of these mutant embryos reveals variable, yet striking defects in caudal specification, limb patterning and axial skeleton formation. In the tail bud, reduced expression of Wnt3a and Dll1 correlates with phenotypic severity of caudal regression. In the limbs, expression of Fgf8 is expanded in the dorsal-ventral axis of the apical ectodermal ridge and shortened in the anterior-posterior axis, consistent with the observed loss of anterior digits in older embryos. The axial skeleton of mutant embryos shows abnormal vertebral fusions in cervical, lumbar and caudal regions. This is the first report to show that a telomeric regulator is required for proper urogenital ridge differentiation, axial skeleton specification and limb patterning in mice.