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US Food and Drug Administration (FDA)‐approved diagnostic assays play an increasingly common role in managing patients to prolong lifespan while also enhancing quality of life. Diagnostic assays can be essential for the safe and effective use of therapeutics (companion diagnostic), or may inform on improving the benefit/risk ratio without restricting drug access (complementary diagnostic). This tutorial reviews strategic considerations for drug and assay development resulting in FDA‐approved companion or complementary diagnostic status.

Rheumatoid arthritis (RA) is an immune-mediated inflammatory disease affecting the joints. A heterogeneous response to available therapies demonstrates the need to identify those patients likely to benefit from a particular therapy. Our objective was to identify genetic factors associated with response to tocilizumab, a humanized monoclonal antibody targeting the interleukin (IL)-6 receptor, recently approved for treating RA. We report the first genome-wide association study on the response to tocilizumab in 1683 subjects with RA from six clinical studies. Putative associations were identified with eight loci, previously unrecognized as linked to the IL-6 pathway or associated with RA risk. This study suggests that it is unlikely that a major genetic determinant of response exists, and it illustrates the complexity of performing genome-wide association scans in clinical trials.

Growth hormone is used to increase height in short children who are not deficient in growth hormone, but its efficacy varies largely across individuals. The genetic factors responsible for this variation are entirely unknown. In two cohorts of short children treated with growth hormone, we found that an isoform of the growth hormone receptor gene that lacks exon 3 (d3-GHR) was associated with 1.7 to 2 times more growth acceleration induced by growth hormone than the full-length isoform ( P < 0.0001). In transfection experiments, the transduction of growth hormone signaling through d3-GHR homo- or heterodimers was ∼30% higher than through full-length GHR homodimers ( P < 0.0001). One-half of Europeans are hetero- or homozygous with respect to the allele encoding the d3-GHR isoform, which is dominant over the full-length isoform. These observations suggest that the polymorphism in exon 3 of GHR is important in growth hormone pharmacogenetics.

A map of 191 single-nucleotide polymorphism (SNPs) was built across a 5-Mb segment from chromosome 13q34 that has been genetically linked to schizophrenia. DNA from 213 schizophrenic patients and 241 normal individuals from Canada were genotyped with this marker set. Two 1,400- and 65-kb regions contained markers associated with the disease. Two markers from the 65-kb region were also found to be associated to schizophrenia in a Russian sample. Two overlapping genes G72 and G30 transcribed in brain were experimentally annotated in this 65-kb region. Transfection experiments point to the existence of a 153-aa protein coded by the G72 gene. This protein is rapidly evolving in primates, is localized to endoplasmic reticulum/Golgi in transfected cells, is able to form multimers and specifically binds to carbohydrates. Yeast two-hybrid experiments with the G72 protein identified the enzyme D-amino acid oxidase (DAAO) as an interacting partner. DAAO is expressed in human brain where it oxidizes D-serine, a potent activator of N-methyl-D-aspartate type glutamate receptor. The interaction between G72 and DAAO was confirmed in vitro and resulted in activation of DAAO. Four SNP markers from DAAO were found to be associated with schizophrenia in the Canadian samples. Logistic regression revealed genetic interaction between associated SNPs in vicinity of two genes. The association of both DAAO and a new gene G72 from 13q34 with schizophrenia together with activation of DAAO activity by a G72 protein product points to the involvement of this N-methyl-D-aspartate receptor regulation pathway in schizophrenia.

Biomarker strategies are increasingly being applied in drug development to address the challenges posed by heterogeneity in the underlying mechanisms of disease processes and inter-patient variability in drug responses. With the aim of enhancing understanding of the regulatory significance of such biomarker data by regulators and sponsors, the US FDA initiated a programme in 2004 to allow sponsors to submit exploratory data voluntarily, without immediate regulatory impact. This article discusses a selection of case studies from the first 5 years of this programme, highlighting lessons learned. Heterogeneity in the underlying mechanisms of disease processes and inter-patient variability in drug responses are major challenges in drug development. To address these challenges, biomarker strategies based on a range of platforms, such as microarray gene-expression technologies, are increasingly being applied to elucidate these sources of variability and thereby potentially increase drug development success rates. With the aim of enhancing understanding of the regulatory significance of such biomarker data by regulators and sponsors, the US Food and Drug Administration initiated a programme in 2004 to allow sponsors to submit exploratory genomic data voluntarily, without immediate regulatory impact. In this article, a selection of case studies from the first 5 years of this programme — which is now known as the voluntary exploratory data submission programme, and also involves collaboration with the European Medicines Agency — are discussed, and general lessons are highlighted.

To detect the role of a candidate gene for a trait in a sample of individuals, we may test SNP haplotype or diplotype effects. For a limited sample size, many haplotype or diplotype categories may contain few individuals. This involves a power decrease when testing the association between the trait and the haplotypes or diplotypes as these categories provide little additional information while increasing the degrees of freedom. The present paper proposes a new strategy to group rare categories based on a measure of similarity between haplotypes or diplotypes and compares it to two other possible strategies to deal with rare categories: a SNP selection strategy based on haplotype diversity, and a grouping strategy that pools all rare categories into a single baseline group. This comparison is performed by means of simulation under four scenarios. We show that this new strategy shows the largest increase in power irrespective of the model underlying the candidate gene in the studied trait. This strategy therefore provides a powerful alternative to currently used methods to reduce the number of rare categories.

Transcription at enhancers is a widespread phenomenon which produces so-called enhancer RNA (eRNA) and occurs in an activity-dependent manner. However, the role of eRNA and its utility in exploring disease-associated changes in enhancer function, and the downstream coding transcripts that they regulate, is not well established. We used transcriptomic and epigenomic data to interrogate the relationship of eRNA transcription to disease status and how genetic variants alter enhancer transcriptional activity in the human brain. We combined RNA-seq data from 537 postmortem brain samples from the CommonMind Consortium with cap analysis of gene expression and enhancer identification, using the assay for transposase-accessible chromatin followed by sequencing (ATACseq). We find 118 differentially transcribed eRNAs in schizophrenia and identify schizophrenia-associated gene/eRNA co-expression modules. Perturbations of a key module are associated with the polygenic risk scores. Furthermore, we identify genetic variants affecting expression of 927 enhancers, which we refer to as enhancer expression quantitative loci or eeQTLs. Enhancer expression patterns are consistent across studies, including differentially expressed eRNAs and eeQTLs. Combining eeQTLs with a genome-wide association study of schizophrenia identifies a genetic variant that alters enhancer function and expression of its target gene, GOLPH3L. Our novel approach to analyzing enhancer transcription is adaptable to other large-scale, non-poly-A depleted, RNA-seq studies.

TNFα inhibitor therapy has greatly improved the treatment of patients with rheumatoid arthritis, however at least 30% do not respond. We aimed to investigate insertions and deletions (INDELS) associated with response to TNFα inhibitors in patients with rheumatoid arthritis (RA). In the DANBIO Registry we identified 237 TNFα inhibitor naïve patients with RA (81% women; median age 56 years; disease duration 6 years) who initiated treatment with infliximab (n=160), adalimumab (n=56) or etanercept (n=21) between 1999 and 2008 according to national treatment guidelines. Clinical response was assessed at week 26 using EULAR response criteria. Based on literature, we selected 213 INDELS potentially related to RA and treatment response using the GeneVa® (Compugen) in silico database of 350,000 genetic variations in the human genome. Genomic segments were amplified by polymerase chain reaction (PCR), and genotyped by Sanger sequencing or fragment analysis. We tested the association between genotypes and EULAR good response versus no response, and EULAR good response versus moderate/no response using Fisher's exact test. At baseline the median DAS28 was 5.1. At week 26, 68 (29%) patients were EULAR good responders, while 81 (34%) and 88 (37%) patients were moderate and non-responders, respectively. A 19 base pair insertion within the CD6 gene was associated with EULAR good response vs. no response (OR=4.43, 95% CI: 1.99-10.09, p=7.211×10(-5)) and with EULAR good response vs. moderate/no response (OR=4.54, 95% CI: 2.29-8.99, p=3.336×10(-6)). A microsatellite within the syntaxin binding protein 6 (STXBP6) was associated with EULAR good response vs. no response (OR=4.01, 95% CI: 1.92-8.49, p=5.067×10(-5)). Genetic variations within CD6 and STXBP6 may influence response to TNFα inhibitors in patients with RA.