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

Altered Expression of Biodegradative Threonine Dehydratase in Mutant Strains of Escherichia Coli K12.

Although it is well established that synthesis of the biodegradative threonine dehydratase of Escherichia coli requires anaerobic conditions and a medium rich in amino acids but lacking glucose, the precise nature of the inducer(s) or repressor(s) is still unclear. This dissertation is a report of research directed at elucidating the regulation of synthesis of threonine dehydratase by a genetic and biochemical characterization of mutants that are affected in induction of the enzyme. Because conditions selective for mutants that lack threonine dehydratase are not known, a direct enzyme assay was used to isolate mutant strains. Variants of E. coli that produce minimal levels of the enzyme (70% cotransduction) to malB. When cultivated anaerobically in TYE (an amino acid-rich medium) the revertant grew more rapidly, and to a greater cell density than the mutant. However, the addition of 10 mM NO(,3) to the TYE medium improved the growth of MB201 relative to MB202. More significantly, threonine dehydratase was induced by the mutant strain grown in the presence of nitrate and the enzyme produced was identical, by several enzymological and immunological criteria, to that of the revertant and wild type strains (cultivated in the absence of nitrate), implying that the structural gene for the dehydratase in the mutant is intact. The addition of 5 mM nitrite or 40 mM fumarate to the TYE medium also promoted enzyme synthesis in the tdcI('-) strain, suggesting that induction is facilitated by the presence of an exogenous electron acceptor. D-serine deaminase and tryptophanase levels were similar in strains MB201 and MB202, indicating that the mutation in MB201 did not influence all catabolic enzymes. In addition, the levels of fumarate reductase and cytochrome c(,552) were comparable in the mutant and revertant, suggesting that the mutated gene does not generally affect proteins induced under anaerobic conditions. Strain MB201, which exhibited a reduced growth rate in TYE anaerobically, also grew slower (than the revertant, MB202) in aerated glucose minimal medium and did not grow in this medium anaerobically. However, the mutant was able to grow on fructose anaerobically, suggesting that it lacked phosphoglucose isomerase activity--a conclusion confirmed by direct assay of the enzyme. When a malB derivative of MB201 was transduced to malB('+) using bacteriophage P1 grown on MB202, tdcI and pgi (a locus at 91 min which encodes phosphoglucose isomerase) cosegregated in 50/50 transductants. Thus it appears likely that tdcI is identical with pgi; however, in the context of other strain backgrounds, a pgi mutation did not prevent induction of threonine dehydratase. Although the mechanism by which nitrate and the pgi locus affect the induction of threonine dehydratase in strain MB201 is not clear, it is possible that a mutation in pgi results in the accumulation of a metabolite which represses the enzyme, and nitrate allows removal of the putative repressor.Ph.D.MicrobiologyUniversity of Michiganhttp://deepblue.lib.umich.edu/bitstream/2027.42/158311/1/8116298.pd

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

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

ObjectiveThe 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 methodsWe 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.ResultsWe 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 conclusionSome 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

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

<div><p>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.</p></div

Survival analysis on biomarker identified treatment sensitive/resistant sub-groups.

<p>A. Using the Erlotinib model to stratify Erlotinib treated patients; B. Using Sorafenib model to stratify Sorafenib treated patients; C. Using Erlotinib model to stratify Sorafenib treated patients; and D. Using Sorafenib model to stratify Erlotinib treated patients.</p

PLSR modeling workflow applied on 183 cancer cell lines on OncoPanel.

<p>(A). Flow chart on the model building and testing steps. (B). A specially designed splitting strategy divides the training dataset into random training, random validation and balance validation subsets. (C). Representative example of random validation and balance validation. Red points were top performing models on 1000 random splits on this balanced split, based on both AUC and correlation measures. (D). AUC and correlation cutoff selection for the core PLSR model.</p

PLSR models performance in predicting Erlotinib-treated patient survival in the BATTLE trial.

<p>A. Erlotinib model predicting Erlotinib treated patients; B. Sorafenib model predicting Sorafenib treated patients; C. Erlotinib model predicting Sorafenib treated patients; and D. Sorafenib model predicting Erlotinib treated patients. TP: true positive; FP: false positive; TN: true negative; FN: false negative; PPV: positive predictive value.</p