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CONTEXT - - ERBB2 (erb-b2 receptor tyrosine kinase 2 or HER2) is currently the only biomarker established for selection of a specific therapy for patients with advanced gastroesophageal adenocarcinoma (GEA). However, there are no comprehensive guidelines for the assessment of HER2 in patients with GEA.
OBJECTIVES - - To establish an evidence-based guideline for HER2 testing in patients with GEA, to formalize the algorithms for methods to improve the accuracy of HER2 testing while addressing which patients and tumor specimens are appropriate, and to provide guidance on clinical decision making.
DESIGN - - The College of American Pathologists, American Society for Clinical Pathology, and American Society of Clinical Oncology convened an expert panel to conduct a systematic review of the literature to develop an evidence-based guideline with recommendations for optimal HER2 testing in patients with GEA.
RESULTS - - The panel is proposing 11 recommendations with strong agreement from the open-comment participants.
RECOMMENDATIONS - - The panel recommends that tumor specimen(s) from all patients with advanced GEA, who are candidates for HER2-targeted therapy, should be assessed for HER2 status before the initiation of HER2-targeted therapy. Clinicians should offer combination chemotherapy and a HER2-targeted agent as initial therapy for all patients with HER2-positive advanced GEA. For pathologists, guidance is provided for morphologic selection of neoplastic tissue, testing algorithms, scoring methods, interpretation and reporting of results, and laboratory quality assurance.
CONCLUSIONS - - This guideline provides specific recommendations for assessment of HER2 in patients with advanced GEA while addressing pertinent technical issues and clinical implications of the results.
OBJECTIVE - Drug-drug interactions (DDIs) are an important consideration in both drug development and clinical application, especially for co-administered medications. While it is necessary to identify all possible DDIs during clinical trials, DDIs are frequently reported after the drugs are approved for clinical use, and they are a common cause of adverse drug reactions (ADR) and increasing healthcare costs. Computational prediction may assist in identifying potential DDIs during clinical trials.
METHODS - Here we propose a heterogeneous network-assisted inference (HNAI) framework to assist with the prediction of DDIs. First, we constructed a comprehensive DDI network that contained 6946 unique DDI pairs connecting 721 approved drugs based on DrugBank data. Next, we calculated drug-drug pair similarities using four features: phenotypic similarity based on a comprehensive drug-ADR network, therapeutic similarity based on the drug Anatomical Therapeutic Chemical classification system, chemical structural similarity from SMILES data, and genomic similarity based on a large drug-target interaction network built using the DrugBank and Therapeutic Target Database. Finally, we applied five predictive models in the HNAI framework: naive Bayes, decision tree, k-nearest neighbor, logistic regression, and support vector machine, respectively.
RESULTS - The area under the receiver operating characteristic curve of the HNAI models is 0.67 as evaluated using fivefold cross-validation. Using antipsychotic drugs as an example, several HNAI-predicted DDIs that involve weight gain and cytochrome P450 inhibition were supported by literature resources.
CONCLUSIONS - Through machine learning-based integration of drug phenotypic, therapeutic, structural, and genomic similarities, we demonstrated that HNAI is promising for uncovering DDIs in drug development and postmarketing surveillance.
Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.
With the rapidly increasing availability of High-Throughput Screening (HTS) data in the public domain, such as the PubChem database, methods for ligand-based computer-aided drug discovery (LB-CADD) have the potential to accelerate and reduce the cost of probe development and drug discovery efforts in academia. We assemble nine data sets from realistic HTS campaigns representing major families of drug target proteins for benchmarking LB-CADD methods. Each data set is public domain through PubChem and carefully collated through confirmation screens validating active compounds. These data sets provide the foundation for benchmarking a new cheminformatics framework BCL::ChemInfo, which is freely available for non-commercial use. Quantitative structure activity relationship (QSAR) models are built using Artificial Neural Networks (ANNs), Support Vector Machines (SVMs), Decision Trees (DTs), and Kohonen networks (KNs). Problem-specific descriptor optimization protocols are assessed including Sequential Feature Forward Selection (SFFS) and various information content measures. Measures of predictive power and confidence are evaluated through cross-validation, and a consensus prediction scheme is tested that combines orthogonal machine learning algorithms into a single predictor. Enrichments ranging from 15 to 101 for a TPR cutoff of 25% are observed.
OBJECTIVE - This report describes the development of a practice pathway for the identification, evaluation, and management of insomnia in children and adolescents who have autism spectrum disorders (ASDs).
METHODS - The Sleep Committee of the Autism Treatment Network (ATN) developed a practice pathway, based on expert consensus, to capture best practices for an overarching approach to insomnia by a general pediatrician, primary care provider, or autism medical specialist, including identification, evaluation, and management. A field test at 4 ATN sites was used to evaluate the pathway. In addition, a systematic literature review and grading of evidence provided data regarding treatments of insomnia in children who have neurodevelopmental disabilities.
RESULTS - The literature review revealed that current treatments for insomnia in children who have ASD show promise for behavioral/educational interventions and melatonin trials. However, there is a paucity of evidence, supporting the need for additional research. Consensus among the ATN sleep medicine committee experts included: (1) all children who have ASD should be screened for insomnia; (2) screening should be done for potential contributing factors, including other medical problems; (3) the need for therapeutic intervention should be determined; (4) therapeutic interventions should begin with parent education in the use of behavioral approaches as a first-line approach; (5) pharmacologic therapy may be indicated in certain situations; and (6) there should be follow-up after any intervention to evaluate effectiveness and tolerance of the therapy. Field testing of the practice pathway by autism medical specialists allowed for refinement of the practice pathway.
CONCLUSIONS - The insomnia practice pathway may help health care providers to identify and manage insomnia symptoms in children and adolescents who have ASD. It may also provide a framework to evaluate the impact of contributing factors on insomnia and to test the effectiveness of nonpharmacologic and pharmacologic treatment strategies for the nighttime symptoms and daytime functioning and quality of life in ASD.
Eosinophilic esophagitis (EoE) is an increasingly recognized clinical entity. The optimal initial treatment strategy in adults with EoE remains controversial. The aim of this study was to employ a decision analysis model to determine the less costly option between the two most commonly employed treatment strategies in EoE. We constructed a model for an index case of a patient with biopsy-proven EoE who continues to be symptomatic despite proton-pump inhibitor therapy. The following treatment strategies were included: (i) swallowed fluticasone inhaler (followed by esophagogastroduodenoscopy [EGD] with dilation if ineffective); and (ii) EGD with dilation (followed by swallowed fluticasone inhaler if ineffective). The time horizon was 1 year. The model focused on cost analysis of initial treatment strategies. The perspective of the healthcare payer was used. Sensitivity analyses were performed to assess the robustness of the model. For every patient whose symptoms improved or resolved with the strategy of fluticasone first followed by EGD, if necessary, it cost an average of $1078. Similarly, it cost an average of $1171 per patient if EGD with dilation was employed first. Sensitivity analyses indicated that initial treatment with fluticasone was the less costly strategy to improve dysphagia symptoms as long as the effectiveness of fluticasone remains at or above 0.62. Swallowed fluticasone inhaler (followed by EGD with dilation if necessary) is the more economical initial strategy when compared with EGD with dilation first.
© 2012 Copyright the Authors. Journal compilation © 2012, Wiley Periodicals, Inc. and the International Society for Diseases of the Esophagus.
Recognition and identification of abbreviations is an important, challenging task in clinical natural language processing (NLP). A comprehensive lexical resource comprised of all common, useful clinical abbreviations would have great applicability. The authors present a corpus-based method to create a lexical resource of clinical abbreviations using machine-learning (ML) methods, and tested its ability to automatically detect abbreviations from hospital discharge summaries. Domain experts manually annotated abbreviations in seventy discharge summaries, which were randomly broken into a training set (40 documents) and a test set (30 documents). We implemented and evaluated several ML algorithms using the training set and a list of pre-defined features. The subsequent evaluation using the test set showed that the Random Forest classifier had the highest F-measure of 94.8% (precision 98.8% and recall of 91.2%). When a voting scheme was used to combine output from various ML classifiers, the system achieved the highest F-measure of 95.7%.
PURPOSE - Decision analysis techniques can compare management strategies when there are insufficient data from clinical studies to guide decision making. We compared the outcomes of decision analyses and subsequent clinical studies in the infectious disease literature to assess the validity of the conclusions of the decision analyses.
METHODS - A search strategy to identify decision analyses in infectious disease topics published from 1990 to 2005 was developed and performed using PubMed. Abstracts of all identified articles were reviewed, and infectious disease-related decision analyses were retained. Subsequent clinical trials and observational studies that corresponded to these decision analyses were identified using prespecified search strategies. Clinical studies were considered a match for the decision analysis if they assessed the same patient population, intervention, and outcome. Agreement or disagreement between the conclusions of the decision analysis and clinical study were determined by author review.
RESULTS - The initial PubMed search yielded 318 references. Forty decision analyses pertaining to 29 infectious disease topics were identified. Of the 40, 16 (40%) from 13 infectious disease topics had matching clinical studies. In 12 of 16 (75%), conclusions of at least 1 clinical study agreed with those of the decision analysis. Three of the 4 decision analyses in which conclusions disagreed were from the same topic (management of febrile children).
CONCLUSIONS - There was substantial agreement between the conclusions of decision analyses and clinical studies in infectious diseases, supporting the validity of decision analysis and its utility in guiding management decisions.
Vaccinia virus is reactogenic in a significant number of vaccinees, with the most common adverse events being fever, lymphadenopathy, and rash. Although the inoculation is given in the skin, these adverse events suggest a robust systemic inflammatory response. To elucidate the cytokine response signature of systemic adverse events, we used a protein microarray technique to precisely quantitate 108 serum cytokines and chemokines in vaccine recipients before and 1 week after primary immunization with Aventis Pasteur smallpox vaccine. We studied 74 individuals after vaccination, of whom 22 experienced a systemic adverse event and 52 did not. The soluble factors most associated with adverse events were selected on the basis of voting among a committee of machine-learning methods and statistical procedures, and the selected cytokines were used to build a final decision-tree model. On the basis of changes in protein expression, we identified 6 cytokines that accurately discriminate between individuals on the basis of adverse event status: granulocyte colony-stimulating factor, stem cell factor, monokine induced by interferon-gamma (CXCL9), intercellular adhesion molecule-1, eotaxin, and tissue inhibitor of metalloproteinases-2. This cytokine signature is characteristic of particular inflammatory response pathways and suggests that the secretion of cytokines by fibroblasts plays a central role in systemic adverse events.
Human papillomavirus (HPV) is associated with a subset of head and neck squamous cell carcinoma (HNSCC). Between 15% and 35% of HNSCCs harbor HPV DNA. Demographic and exposure differences between HPV-positive (HPV+) and negative (HPV-) HNSCCs suggest that HPV+ tumors may constitute a subclass with different biology, whereas clinical differences have also been observed. Gene expression profiles of HPV+ and HPV- tumors were compared with further exploration of the biological effect of HPV in HNSCC. Thirty-six HNSCC tumors were analyzed using Affymetrix Human 133U Plus 2.0 GeneChip and for HPV by PCR and real-time PCR. Eight of 36 (22%) tumors were positive for HPV subtype 16. Statistical analysis using Significance Analysis of Microarrays based on HPV status as a supervising variable resulted in a list of 91 genes that were differentially expressed with statistical significance. Results for a subset of these genes were verified by real-time PCR. Genes highly expressed in HPV+ samples included cell cycle regulators (p16(INK4A), p18, and CDC7) and transcription factors (TAF7L, RFC4, RPA2, and TFDP2). The microarray data were also investigated by mapping genes by chromosomal location (DIGMAP). A large number of genes on chromosome 3q24-qter had high levels of expression in HPV+ tumors. Further investigation of differentially expressed genes may reveal the unique pathways in HPV+ tumors that may explain the different natural history and biological properties of these tumors. These properties may be exploited as a target of novel therapeutic agents in HNSCC treatment.