Neurodevelopmental status and adaptive behavior of pediatric patients with mucopolysaccharidosis II: a longitudinal observational study.

BACKGROUND: Mucopolysaccharidosis (MPS) II is a rare, X-linked lysosomal storage disease. Approximately two-thirds of patients have central nervous system involvement with some demonstrating progressive cognitive impairment (neuronopathic disease). The natural history of cognitive and adaptive function in patients with MPS II is not well-defined. This 2-year, prospective, observational study evaluated the neurodevelopmental trajectories of boys with MPS II aged ≥ 2 years and < 18 years. RESULTS: Overall, 55 patients were enrolled. At baseline, mean (standard deviation [SD]) age was 5.60 (3.32) years; all patients were receiving intravenous idursulfase. Cognitive and adaptive function were assessed using the Differential Ability Scales, Second Edition (DAS-II) General Conceptual Ability (GCA) and the Vineland Adaptive Behavior Scales, Second Edition (VABS-II) Adaptive Behavior Composite (ABC) scores, respectively. Baseline mean (SD) DAS-II GCA and VABS-II ABC scores were 78.4 (19.11) and 83.7 (14.22), respectively, indicating low cognitive function and moderately low adaptive behavior. Over 24 months, modest deteriorations in mean (SD) scores were observed for DAS-II GCA (-3.8 [12.7]) and VABS-II ABC (-2.0 [8.07]). Changes in DAS-II GCA scores varied considerably, and data suggested the existence of four potential patient subgroups: (1) patients with marked early impairment and rapid subsequent decline, (2) patients with marked early impairment then stabilization, (3) patients with mild early impairment then stabilization, and (4) patients without impairment who remained stable. Subgroup analyses revealed numerically greater DAS-II GCA score reductions from baseline in patients aged < 7 years at baseline (vs. those aged ≥ 7 years) and in patients with DAS-II GCA scores ≤ 70 at baseline (vs. those with scores > 70); between-group differences were nonsignificant. No clear subgroups or patterns were identified for individual changes in VABS-II ABC scores. In total, 49 patients (89.1%) reported ≥ 1 adverse event (AE) and nine patients (16.4%) reported serious AEs. CONCLUSIONS: Some patients with MPS II had rapid declines in cognitive ability, whereas others remained relatively stable after an initial decline. These insights provide a basis for more detailed analyses of different patient subgroups, which may enhance the definition and understanding of factors that influence cognitive and adaptive function in MPS II. TRIAL REGISTRATION: ClinicalTrials.gov, NCT01822184. Registered retrospectively: April 2, 2013.

Cognitive and adaptive behaviors associated with disease severity and genotype in patients with mucopolysaccharidosis II.

BACKGROUND: Mucopolysaccharidosis II (MPS II) is a rare, X-linked lysosomal storage disease caused by pathogenic variants of the iduronate-2-sulfatase gene (IDS) and is characterized by a highly variable disease spectrum. MPS II severity is difficult to predict based on IDS variants alone; while some genotypes are associated with specific phenotypes, the disease course of most genotypes remains unknown. This study aims to refine the genotype-phenotype categorization by combining information from the scientific literature with data from two clinical studies in MPS II. METHODS: Genotype, cognitive, and behavioral data from 88 patients in two clinical studies (NCT01822184, NCT02055118) in MPS II were analyzed post hoc in combination with published information on IDS variants from the biomedical literature through a semi-automated multi-stage review process. The Differential Ability Scales, second edition (DAS-II) and the Vineland Adaptive Behavior Scales™, second edition (VABS-II) were used to measure cognitive function and adaptive behavior. RESULTS: The most common category of IDS variant was missense (47/88, 53.4% of total variants). The mean (standard deviation [SD]) baseline DAS-II General Conceptual Ability (GCA) and VABS-II Adaptive Behavior Composite (ABC) scores were 74.0 (16.4) and 82.6 (14.7), respectively. All identified IDS complete deletions/large rearrangements (n = 7) and large deletions (n = 1) were associated with a published 'severe' or 'predicted severe' progressive neuronopathic phenotype, characterized by central nervous system involvement. In categories comprising more than one participant, mean baseline DAS-II GCA scores (SD) were lowest among individuals with complete deletions/large rearrangements 64.0 (9.1, n = 4) and highest among those with splice site variants 83.8 (14.2, n = 4). Mean baseline VABS-II ABC scores (SD) were lowest among patients with unclassifiable variants 79.3 (4.9, n = 3) and highest among those with a splice site variant 87.2 (16.1, n = 5), in variant categories with more than one participant. CONCLUSIONS: Most patients in the studies had an MPS II phenotype categorized as 'severe' or 'predicted severe' according to classifications, as reported in the literature. Patients with IDS complete deletion/large rearrangement variants had lower mean DAS-II GCA scores than those with other variants, as well as low VABS-II ABC, confirming an association with the early progressive 'severe' (neuronopathic) disease. These data provide a starting point to improve the classification of MPS II phenotypes and the characterization of the genotype-phenotype relationship.

Mapping of UK Biobank clinical codes: Challenges and possible solutions.

OBJECTIVE: The UK Biobank provides a rich collection of longitudinal clinical data coming from different healthcare providers and sources in England, Wales, and Scotland. Although extremely valuable and available to a wide research community, the heterogeneous dataset contains inconsistent medical terminology that is either aligned to several ontologies within the same category or unprocessed. To make these data useful to a research community, data cleaning, curation, and standardization are needed. Significant efforts to perform data reformatting, mapping to any selected ontologies (such as SNOMED-CT) and harmonization are required from any data user to integrate UK Biobank hospital inpatient and self-reported data, data from various registers with primary care (GP) data. The integrated clinical data would provide a more comprehensive picture of one's medical history. MATERIALS AND METHODS: We evaluated several approaches to map GP clinical Read codes to International Classification of Diseases (ICD) and Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) terminologies. The results were compared, mapping inconsistencies were flagged, a quality category was assigned to each mapping to evaluate overall mapping quality. RESULTS: We propose a curation and data integration pipeline for harmonizing diagnosis. We also report challenges identified in mapping Read codes from UK Biobank GP tables to ICD and SNOMED CT. DISCUSSION AND CONCLUSION: Some of the challenges-the lack of precise one-to-one mapping between ontologies or the need for additional ontology to fully map terms-are general reflecting trade-offs to be made at different steps. Other challenges are due to automatic mapping and can be overcome by leveraging existing mappings, supplemented with automated and manual curation.

Continuous Monitoring Using a Wearable Device Detects Activity-Induced Heart Rate Changes After Administration of Amphetamine.

Wearable digital devices offer potential advantages over traditional methods for the collection of health-related information, including continuous collection of dense data while study subjects are ambulatory or in remote settings. We assessed the utility of collecting continuous actigraphy and cardiac monitoring by deploying two US Food and Drug Administration (FDA) 510(k)-cleared devices in a phase I clinical trial of a novel compound, which included the use of an amphetamine challenge. The Phillips Actiwatch Spectrum Pro (Actiwatch) was used to assess mobility and sleep. The Preventice BodyGuardian (BodyGuardian) was used for monitoring heart rate (HR) and respiratory rate (RR), via single-lead electrocardiogram (ECG) recordings, together with physical activity. We measured data collection rates, compared device readouts with conventional measures, and monitored changes in HR measures during the amphetamine challenge. Completeness of data collection was good for the Actiwatch (96%) and lower for the BodyGuardian (80%). A good correlation was observed between device and in-clinic measures for HR (r = 0.99; P < 0.001), but was poor for RR (r = 0.39; P = 0.004). Manual reviews of selected ECG strips corresponding to HR measures below, within, and above the normal range were consistent with BodyGuardian measurements. The BodyGuardian device detected clear HR responses after amphetamine administration while subjects were physically active, whereas conventional measures collected at predefined timepoints while subjects were resting and supine did not. Wearable digital technology shows promise for monitoring human subjects for physiologic changes and pharmacologic responses, although fit-for-purpose evaluation and validation continues to be important prior to the wider deployment of these devices.

Evaluation of Wearable Digital Devices in a Phase I Clinical Trial.

We assessed the performance of two US Food and Drug Administration (FDA) 510(k)-cleared wearable digital devices and the operational feasibility of deploying them to augment data collection in a 10-day residential phase I clinical trial. The Phillips Actiwatch Spectrum Pro (Actiwatch) was used to assess mobility and sleep, and the Vitalconnect HealthPatch MD (HealthPatch) was used for monitoring heart rate (HR), respiratory rate (RR), and surface skin temperature (ST). We measured data collection rates, compared device readouts with anticipated readings and conventional in-clinic measures, investigated data limitations, and assessed user acceptability. Six of nine study participants consented; completeness of data collection was adequate (> 90% for four of six subjects). A good correlation was observed between the HealthPatch device derived and in-clinic measures for HR (Pearson r = 0.71; P = 2.2e-16) but this was poor for RR (r = 0.08; P = 0.44) and ST (r = 0.14; P = 0.14). Manual review of electrocardiogram strips recorded during reported episodes of tachycardia > 180 beats/min showed that these were artefacts. The HealthPatch was judged to be not fit-for-purpose because of artefacts and the need for time-consuming manual review. The Actiwatch device was suitable for monitoring mobility, collecting derived sleep data, and facilitating the interpretation of vital sign data. These results suggest the need for fit-for-purpose evaluation of wearable devices prior to their deployment in drug development studies.

Development of a Drug-Response Modeling Framework to Identify Cell Line Derived Translational Biomarkers That Can Predict Treatment Outcome to Erlotinib or Sorafenib.

Development of drug responsive biomarkers from pre-clinical data is a critical step in drug discovery, as it enables patient stratification in clinical trial design. Such translational biomarkers can be validated in early clinical trial phases and utilized as a patient inclusion parameter in later stage trials. Here we present a study on building accurate and selective drug sensitivity models for Erlotinib or Sorafenib from pre-clinical in vitro data, followed by validation of individual models on corresponding treatment arms from patient data generated in the BATTLE clinical trial. A Partial Least Squares Regression (PLSR) based modeling framework was designed and implemented, using a special splitting strategy and canonical pathways to capture robust information for model building. Erlotinib and Sorafenib predictive models could be used to identify a sub-group of patients that respond better to the corresponding treatment, and these models are specific to the corresponding drugs. The model derived signature genes reflect each drug's known mechanism of action. Also, the models predict each drug's potential cancer indications consistent with clinical trial results from a selection of globally normalized GEO expression datasets.

A predictive phosphorylation signature of lung cancer.

BACKGROUND: Aberrant activation of signaling pathways drives many of the fundamental biological processes that accompany tumor initiation and progression. Inappropriate phosphorylation of intermediates in these signaling pathways are a frequently observed molecular lesion that accompanies the undesirable activation or repression of pro- and anti-oncogenic pathways. Therefore, methods which directly query signaling pathway activation via phosphorylation assays in individual cancer biopsies are expected to provide important insights into the molecular "logic" that distinguishes cancer and normal tissue on one hand, and enables personalized intervention strategies on the other. RESULTS: We first document the largest available set of tyrosine phosphorylation sites that are, individually, differentially phosphorylated in lung cancer, thus providing an immediate set of drug targets. Next, we develop a novel computational methodology to identify pathways whose phosphorylation activity is strongly correlated with the lung cancer phenotype. Finally, we demonstrate the feasibility of classifying lung cancers based on multi-variate phosphorylation signatures. CONCLUSIONS: Highly predictive and biologically transparent phosphorylation signatures of lung cancer provide evidence for the existence of a robust set of phosphorylation mechanisms (captured by the signatures) present in the majority of lung cancers, and that reliably distinguish each lung cancer from normal. This approach should improve our understanding of cancer and help guide its treatment, since the phosphorylation signatures highlight proteins and pathways whose phosphorylation should be inhibited in order to prevent unregulated proliferation.