Richard Mushlin

LifeLines: Using Visualization to Enhance Navigation and Analysis of Patient Records

LifeLines provide a general visualization environment for personal histories. We explore its use for clinical patient records. A Java user interface is described, which presents a one-screen overview of a computerized patient record using timelines. Problems, diagnoses, test results or medications can be represented as dots or horizontal lines. Zooming provides more details; line color and thickness illustrate relationships or significance. The visual display acts as a giant menu, giving direct access to the data.

Comprehensive behavioral and molecular characterization of a new knock-in mouse model of Huntington's disease: zQ175.

Huntington’s disease (HD) is an autosomal dominant neurodegenerative disorder characterized by motor, cognitive and psychiatric manifestations. Since the mutation responsible for the disease was identified as an unstable expansion of CAG repeats in the gene encoding the huntingtin protein in 1993, numerous mouse models of HD have been generated to study disease pathogenesis and evaluate potential therapeutic approaches. Of these, knock-in models best mimic the human condition from a genetic perspective since they express the mutation in the appropriate genetic and protein context. Behaviorally, however, while some abnormal phenotypes have been detected in knock-in mouse models, a model with an earlier and more robust phenotype than the existing models is required. We describe here for the first time a new mouse line, the zQ175 knock-in mouse, derived from a spontaneous expansion of the CAG copy number in our CAG 140 knock-in colony [1]. Given the inverse relationship typically observed between age of HD onset and length of CAG repeat, since this new mouse line carries a significantly higher CAG repeat length it was expected to be more significantly impaired than the parent line. Using a battery of behavioral tests we evaluated both heterozygous and homozygous zQ175 mice. Homozygous mice showed motor and grip strength abnormalities with an early onset (8 and 4 weeks of age, respectively), which were followed by deficits in rotarod and climbing activity at 30 weeks of age and by cognitive deficits at around 1 year of age. Of particular interest for translational work, we also found clear behavioral deficits in heterozygous mice from around 4.5 months of age, especially in the dark phase of the diurnal cycle. Decreased body weight was observed in both heterozygotes and homozygotes, along with significantly reduced survival in the homozygotes. In addition, we detected an early and significant decrease of striatal gene markers from 12 weeks of age. These data suggest that the zQ175 knock-in line could be a suitable model for the evaluation of therapeutic approaches and early events in the pathogenesis of HD.

Genomic variation in myeloma: design, content, and initial application of the Bank On A Cure SNP Panel to detect associations with progression-free survival

BackgroundWe have engaged in an international program designated the Bank On A Cure, which has established DNA banks from multiple cooperative and institutional clinical trials, and a platform for examining the association of genetic variations with disease risk and outcomes in multiple myeloma.We describe the development and content of a novel custom SNP panel that contains 3404 SNPs in 983 genes, representing cellular functions and pathways that may influence disease severity at diagnosis, toxicity, progression or other treatment outcomes. A systematic search of national databases was used to identify non-synonymous coding SNPs and SNPs within transcriptional regulatory regions. To explore SNP associations with PFS we compared SNP profiles of short term (less than 1 year, n = 70) versus long term progression-free survivors (greater than 3 years, n = 73) in two phase III clinical trials.ResultsQuality controls were established, demonstrating an accurate and robust screening panel for genetic variations, and some initial racial comparisons of allelic variation were done. A variety of analytical approaches, including machine learning tools for data mining and recursive partitioning analyses, demonstrated predictive value of the SNP panel in survival. While the entire SNP panel showed genotype predictive association with PFS, some SNP subsets were identified within drug response, cellular signaling and cell cycle genes.ConclusionA targeted gene approach was undertaken to develop an SNP panel that can test for associations with clinical outcomes in myeloma. The initial analysis provided some predictive power, demonstrating that genetic variations in the myeloma patient population may influence PFS.

Visualizing medical records with LifeLines

DESCRIPTION Computerized medical records pose tremendous problems to system developers, yet all the efforts to solve those problems will succeed only if appropriate attention is also given to user interface and information design [I 1. Long lists to scroll, clumsy searches, endless menus and lengthy dialogs lead to user frustration and rejection. We designed a general visualization technique for personal histories called LifeLines and are currently exploring its use for medical patient records.

Increased Body Weight of the BAC HD Transgenic Mouse Model of Huntington's Disease Accounts for Some but Not All of the Observed HD-like Motor Deficits.

The genome of the Bacterial Artificial Chromosome (BAC) transgenic mouse model of Huntington’s Disease (BAC HD) contains the 170 kb human HTT locus modified by the addition of exon 1 with 97 mixed CAA-CAG repeats. BAC HD mice present robust behavioral deficits in both the open field and the accelerating rotarod tests, two standard behavioral assays of motor function. BAC HD mice, however, also typically present significantly increased body weights relative to wildtype littermate controls (WT) which potentially confounds the interpretation of any motor deficits associated directly with the effects of mutant huntingtin. In order to evaluate this possible confound of body weight, we directly compared the performance of BAC HD and WT female mice under food restricted versus free feeding conditions in both the open field and rotarod tasks to test the hypothesis that some of the motor deficits observed in this HTT-transgenic mouse line results solely from increased body weight. Our results suggest that the rotarod deficit exhibited by BAC HD mice is modulated by both body weight and non-body weight factors resulting from overexpression of full length mutant Htt. When body weights of WT and BAC HD transgenic mice were normalized using restricted feeding, the deficits exhibited by BAC HD mice on the rotarod task were less marked, but were still significant. Since the rotarod deficit between WT and BAC HD mice is attenuated when body weight is normalized by food restriction, utilization of this task in BAC HD mice during pre-clinical evaluation must be powered accordingly and results carefully considered as therapeutic benefit can result from decreased overall body weight and or motoric improvement that may not be related to body mass. Furthermore, after controlling for body weight differences, the hypoactive phenotype displayed by ad libitum fed BAC HD mice in the open field assay was not observed in the BAC HD mice undergoing food restriction. These findings suggest that assessment of spontaneous locomotor activity, as measured in the open field test, may not be the appropriate behavioral endpoint to evaluate the BAC HD mouse during preclinical evaluation since it appears that the apparent hypoactive phenotype in this model is driven primarily by body weight differences.

Genetic deletion of transglutaminase 2 does not rescue the phenotypic deficits observed in R6/2 and zQ175 mouse models of Huntington's disease.

Huntingtons disease (HD) is an autosomal dominant, progressive neurodegenerative disorder caused by expansion of CAG repeats in the huntingtin gene. Tissue transglutaminase 2 (TG2), a multi-functional enzyme, was found to be increased both in HD patients and in mouse models of the disease. Furthermore, beneficial effects have been reported from the genetic ablation of TG2 in R6/2 and R6/1 mouse lines. To further evaluate the validity of this target for the treatment of HD, we examined the effects of TG2 deletion in two genetic mouse models of HD: R6/2 CAG 240 and zQ175 knock in (KI). Contrary to previous reports, under rigorous experimental conditions we found that TG2 ablation had no effect on either motor or cognitive deficits, or on the weight loss. In addition, under optimal husbandry conditions, TG2 ablation did not extend R6/2 lifespan. Moreover, TG2 deletion did not change the huntingtin aggregate load in cortex or striatum and did not decrease the brain atrophy observed in either mouse line. Finally, no amelioration of the dysregulation of striatal and cortical gene markers was detected. We conclude that TG2 is not a valid therapeutic target for the treatment of HD.

A graph-theoretical approach for pattern discovery in epidemiological research

In this paper we describe a graph-theoretical approach for pattern discovery that is especially useful in epidemiological research when applied to case-control studies involving categorical features such as genotypes and exposures. Whereas existing approaches are limited to exploring relationships among two or three factors, or deal with thousands of genes but are unable to isolate interactions among individual genes, we focus on interactions among tens of genes. We present a pattern discovery algorithm that finds associations among multiple factors, such as genetic and environmental factors, and groups of individuals (cases and controls) in a clinical survey. To validate our approach and to demonstrate its effectiveness, we applied it to a selection of synthetic data sets that were devised to emulate the situations encountered in epidemiological studies involving common diseases with suspected associations involving multiple factors that could include inherited genotypes, somatic genotypes, demographic characteristics, or exposures. The results of this experiment show that it is possible to identify the effects of multiple factors in moderate-size surveys (involving hundreds of individuals) even when the number of factors is greater than three.

Clique-Finding for Heterogeneity and Multidimensionality in Biomarker Epidemiology Research: The CHAMBER Algorithm

Background Commonly-occurring disease etiology may involve complex combinations of genes and exposures resulting in etiologic heterogeneity. We present a computational algorithm that employs clique-finding for heterogeneity and multidimensionality in biomedical and epidemiological research (the “CHAMBER” algorithm). Methodology/Principal Findings This algorithm uses graph-building to (1) identify genetic variants that influence disease risk and (2) predict individuals at risk for disease based on inherited genotype. We use a set-covering algorithm to identify optimal cliques and a Boolean function that identifies etiologically heterogeneous groups of individuals. We evaluated this approach using simulated case-control genotype-disease associations involving two- and four-gene patterns. The CHAMBER algorithm correctly identified these simulated etiologies. We also used two population-based case-control studies of breast and endometrial cancer in African American and Caucasian women considering data on genotypes involved in steroid hormone metabolism. We identified novel patterns in both cancer sites that involved genes that sulfate or glucuronidate estrogens or catecholestrogens. These associations were consistent with the hypothesized biological functions of these genes. We also identified cliques representing the joint effect of multiple candidate genes in all groups, suggesting the existence of biologically plausible combinations of hormone metabolism genes in both breast and endometrial cancer in both races. Conclusions The CHAMBER algorithm may have utility in exploring the multifactorial etiology and etiologic heterogeneity in complex disease.