An adaptive design for updating the threshold value of a continuous biomarker.

Potential predictive biomarkers are often measured on a continuous scale, but in practice, a threshold value to divide the patient population into biomarker 'positive' and 'negative' is desirable. Early phase clinical trials are increasingly using biomarkers for patient selection, but at this stage, it is likely that little will be known about the relationship between the biomarker and the treatment outcome. We describe a single-arm trial design with adaptive enrichment, which can increase power to demonstrate efficacy within a patient subpopulation, the parameters of which are also estimated. Our design enables us to learn about the biomarker and optimally adjust the threshold during the study, using a combination of generalised linear modelling and Bayesian prediction. At the final analysis, a binomial exact test is carried out, allowing the hypothesis that 'no population subset exists in which the novel treatment has a desirable response rate' to be tested. Through extensive simulations, we are able to show increased power over fixed threshold methods in many situations without increasing the type-I error rate. We also show that estimates of the threshold, which defines the population subset, are unbiased and often more precise than those from fixed threshold studies. We provide an example of the method applied (retrospectively) to publically available data from a study of the use of tamoxifen after mastectomy by the German Breast Study Group, where progesterone receptor is the biomarker of interest. © 2016 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.

High content screening of patient-derived cell lines highlights the potential of non-standard chemotherapeutic agents for the treatment of glioblastoma.

BACKGROUND: Glioblastoma (GBM) is the most common primary brain malignancy in adults, yet survival outcomes remain poor. First line treatment is well established, however disease invariably recurs and improving prognosis is challenging. With the aim of personalizing therapy at recurrence, we have established a high content screening (HCS) platform to analyze the sensitivity profile of seven patient-derived cancer stem cell lines to 83 FDA-approved chemotherapy drugs, with and without irradiation. METHODS: Seven cancer stem cell lines were derived from patients with GBM and, along with the established cell line U87-MG, each patient-derived line was cultured in tandem in serum-free conditions as adherent monolayers and three-dimensional neurospheres. Chemotherapeutics were screened at multiple concentrations and cells double-stained to observe their effect on both cell death and proliferation. Sensitivity was classified using high-throughput algorithmic image analysis. RESULTS: Cell line specific drug responses were observed across the seven patient-derived cell lines. Few agents were seen to have radio-sensitizing effects, yet some drug classes showed a marked difference in efficacy between monolayers and neurospheres. In vivo validation of six drugs suggested that cell death readout in a three-dimensional culture scenario is a more physiologically relevant screening model and could be used effectively to assess the chemosensitivity of patient-derived GBM lines. CONCLUSION: The study puts forward a number of non-standard chemotherapeutics that could be useful in the treatment of recurrent GBM, namely mitoxantrone, bortezomib and actinomycin D, whilst demonstrating the potential of HCS to be used for personalized treatment based on the chemosensitivity profile of patient tumor cells.

Use of a Canine Model of Atopic Dermatitis to Investigate the Efficacy of a CCR4 Antagonist in Allergen-Induced Skin Inflammation in a Randomized Study.

Atopic dermatitis (AD) is an inflammatory skin disease characterized by infiltration of skin homing lymphocytes into the dermis. Most of these lymphocytes express the chemokine receptor CCR4, and the frequency of blood CCR4(+) lymphocytes correlates with AD disease severity. Canine AD is a pruritic inflammatory condition that shows many features of the human disease, including CCR4 overexpression. Therefore, we tested a potent selective CCR4 antagonist in an allergen challenge model of canine AD, both clinically and histologically, to investigate whether this chemokine pathway plays a role in the inflammatory response. Using a four-period randomized cross-over study design, 14 beagles were challenged with allergen and clinically monitored. Biopsy samples were taken before and after allergen challenge. A clear reduction of clinical scores was observed with oral prednisolone (P < 0.0001) but not for the CCR4 inhibitor. A subset of the dogs (5/13) showed partial inhibition (30-49%) of the clinical signs with CCR4 inhibitor treatment, and this finding was supported by the results of histopathologic analysis of skin biopsy samples. This partial response is consistent with redundancy in chemokine pathways and highlights the need for therapies blocking multiple pathways. This study shows the utility of this canine model of AD for testing new therapeutic agents.

Single dose of the antivascular agent, ZD6126 (N-acetylcolchinol-O-phosphate), reduces perfusion for at least 96 hours in the GH3 prolactinoma rat tumor model.

Tumor vasculature is an attractive therapeutic target as it differs structurally from normal vasculature, and the destruction of a single vessel can lead to the death of many tumor cells. The effects of antivascular drugs are frequently short term, with regrowth beginning less than 24 hours posttreatment. This study investigated the duration of the response to the vascular targeting agent, ZD6126, of the GH3 prolactinoma, in which efficacy and dose-response have previously been demonstrated. GH3 prolactinomas were grown in the flanks of eight Wistar Furth rats. All animals were treated with 50 mg/kg ZD6126. The tumors were examined with dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) 24 hours pretreatment and posttreatment, and at a single time between 48 and 96 hours posttreatment. No evidence of recovery of perfusion was observed even at the longest (96-hour) time point. Involvement of a statistician at the project planning stage and the use of DCE-MRI, which permits noninvasive quantitation of parameters related to blood flow in intact animals, allowed this highly significant result to be obtained using only eight rats.

Use of factorial designs to optimize animal experiments and reduce animal use.

Optimization of experiments, such as those used in drug discovery, can lead to useful savings of scientific resources. Factors such as sex, strain, and age of the animals and protocol-specific factors such as timing and methods of administering treatments can have an important influence on the response of animals to experimental treatments. Factorial experimental designs can be used to explore which factors and what levels of these factors will maximize the difference between a vehicle control and a known positive control treatment. This information can then be used to design more efficient experiments, either by reducing the numbers of animals used or by increasing the sensitivity so that smaller biological effects can be detected. A factorial experimental design approach is more effective and efficient than the older approach of varying one factor at a time. Two examples of real factorial experiments reveal how using this approach can potentially lead to a reduction in animal use and savings in financial and scientific resources without loss of scientific validity.

In search of preclinical robustness.

Systematic engagement of statisticians in preclinical research could help address the weaknesses that are undermining the likelihood of subsequent success in drug discovery and development.

Proteomic biomarkers for acute interstitial lung disease in gefitinib-treated Japanese lung cancer patients.

Interstitial lung disease (ILD) events have been reported in Japanese non-small-cell lung cancer (NSCLC) patients receiving EGFR tyrosine kinase inhibitors. We investigated proteomic biomarkers for mechanistic insights and improved prediction of ILD. Blood plasma was collected from 43 gefitinib-treated NSCLC patients developing acute ILD (confirmed by blinded diagnostic review) and 123 randomly selected controls in a nested case-control study within a pharmacoepidemiological cohort study in Japan. We generated ∼7 million tandem mass spectrometry (MS/MS) measurements with extensive quality control and validation, producing one of the largest proteomic lung cancer datasets to date, incorporating rigorous study design, phenotype definition, and evaluation of sample processing. After alignment, scaling, and measurement batch adjustment, we identified 41 peptide peaks representing 29 proteins best predicting ILD. Multivariate peptide, protein, and pathway modeling achieved ILD prediction comparable to previously identified clinical variables; combining the two provided some improvement. The acute phase response pathway was strongly represented (17 of 29 proteins, p = 1.0×10(-25)), suggesting a key role with potential utility as a marker for increased risk of acute ILD events. Validation by Western blotting showed correlation for identified proteins, confirming that robust results can be generated from an MS/MS platform implementing strict quality control.

Personalized medicine and proteomics: lessons from non-small cell lung cancer.

Personalized medicine allows the selection of treatments best suited to an individual patient and disease phenotype. To implement personalized medicine, effective tests predictive of response to treatment or susceptibility to adverse events are needed, and to develop a personalized medicine test, both high quality samples and reliable data are required. We review key features of state-of-the-art proteomic profiling and introduce further analytic developments to build a proteomic toolkit for use in personalized medicine approaches. The combination of novel analytical approaches in proteomic data generation, alignment and comparison permit translation of identified biomarkers into practical assays. We further propose an expanded statistical analysis to understand the sources of variability between individuals in terms of both protein expression and clinical variables and utilize this understanding in a predictive test.

Development and disadvantage: implications for the early years and beyond.

Links have long been made between literacy and economic development, and recent governments in the UK have put great emphasis on the teaching of literacy to raise educational standards. There is substantial evidence to show that spoken and written language share some processes in common and that the development of literacy is supported by the development of spoken language. Anecdotal evidence from early years practitioners suggests that many children coming into early years education, particularly those from disadvantaged socioeconomic backgrounds, may not have the spoken language skills needed to develop reading and writing. This preliminary study aimed to investigate the extent to which the spoken language skills of children reared in poverty are depressed in comparison with the general population, and in comparison with their general cognitive abilities. Two tests were administered to children in their first term at nursery school to measure their spoken language skills and cognitive abilities: CELF-PUK and BAS II. More than half of the children were found to be language-delayed, although girls' receptive language abilities were significantly better than those of boys. Participants' language skills were also significantly depressed in comparison with their cognitive abilities. Government initiatives to raise awareness of spoken language in the early years are discussed, and implications for the future role of speech and language therapists working in the pre-school sector are considered.