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Translational Case Histories. Harvard Medical School Center for Biomedical Informatics i2b2 National Center for Biomedical Computing Isaac S. Kohane, MD, PhD John Glaser, PhD Susanne Churchill, PhD. First signal: 1 year after Celecoxib 8 months after Rofecoxib.

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Translational case histories l.jpg

Translational Case Histories

Harvard Medical School

Center for Biomedical Informatics

i2b2 National Center for Biomedical Computing

Isaac S. Kohane, MD, PhD

John Glaser, PhD

Susanne Churchill, PhD


Slide3 l.jpg

First signal:

  • 1 year after Celecoxib

  • 8 months after Rofecoxib


Slide4 l.jpg


Effect on patient age l.jpg
Effect on patient age 0.1 to 0.9)

  • Negative association between mean age at MI and prescription volume

  • Spearman correlation -0.67, P<0.05


I2b2 test relnet project l.jpg
I2B2: Test RelNet Project 0.1 to 0.9)

  • Correlate available GEO expression data for GPL96 platform containing expressions for more than 22K human genes

  • Number of gene pairs for this gene chip: ~ 250 Million

  • Multi-threaded application to run on the high-performance Cluster environment from HP

  • Bottleneck: the back-end Database

  • Current, fine-tuned version of the application takes about 2-3 months to complete one data set calculation


Obesity l.jpg
Obesity 0.1 to 0.9)


Recurrent themes l.jpg
Recurrent Themes 0.1 to 0.9)

  • Access to large numbers of phenotyped specimens

  • Inadequacy of informatics at the cutting edge

    • Inadequacy of software solutions alone

  • A persistent multidisciplinary requirement


Overall remission rate with citalopram 32 9 l.jpg
Overall Remission Rate with Citalopram = 32.9% 0.1 to 0.9)

QIDS: Quick Inventory of Depressive Symptoms, self report

N = 943/2876

No depression

Mildsymptoms

Moderatesymptoms

Severesymptoms

Very severe symptoms

Percent (%)

Last QIDS-SR Score

Trivedi MH, et al. Am J Psychiatry 2006;163:28-40.


Slide12 l.jpg
Aims: 0.1 to 0.9)

  • Identify a cohort of patients with TRD, and a matched cohort with SSRI-responsive MDD.

    • Data-mining tools

    • Natural language processing

  • Conduct the first genomewide association study of TRD.


Slide14 l.jpg

  • Scan computerized medical records (DataMart) 0.1 to 0.9)

    • ICD9 RA x 3 plus one of:

      • CCP or RF

      • Erosions on x-ray

      • DMARD treatment

  • Crimson “discarded” blood samples (cases and controls)

  • CCP on all samples (and bank serum)

  • DNA on all samples for genetic studies

adds >95% specificity

www.i2b2.org/disease/arthritis.html


Slide16 l.jpg

Association in population samples 0.1 to 0.9)

Affecteds

Controls

SNP frequency in cases compared to controls

Positive controls: MHC, PTPN22, STAT4, TRAF1-C5, TNFAIP3


Slide17 l.jpg

…...a 0.1 to 0.9)cgt…ggaatac…...

…...acgt…ggaatac…..

Allele ‘A’

NspI

NspI

NspI

NspI

…...acgt…ggattac…..

......acgt…ggattac……

Allele ‘B’

NspI

NspI

NspI

NspI

_ B

_ _

‘B’ methylated

both unmethylated

‘B’ expressed

‘A’ expressed

both expressed

MSRE digested

Allele ‘A’

Allele ‘B’

A B

A _

control

(no digestion)

‘A’ methylated


Methodology dev l.jpg
Methodology Dev 0.1 to 0.9)



Gene network enrichment analysis l.jpg
Gene Network Enrichment Analysis 0.1 to 0.9)

Microarray data

Protein protein interaction network

Molecular Function

Biological Process


Diabetes genome anatomy project mouse models of insulin resistance insulin deficiency and obesity l.jpg
Diabetes Genome Anatomy Project: 0.1 to 0.9)Mouse Models of Insulin Resistance, Insulin Deficiency and Obesity

  • Knockouts

    • Insulin receptor

    • Insulin receptor substrates

    • Leptin

    • PGC1A

  • Environmental

    • High fat diets

    • Drug treatments (Streptozotocin)

Tissues

67 Conditions Total


Three functional sets are consistently over represented in disease models l.jpg
Three Functional Sets Are Consistently Over-represented In Disease Models

  • Insulin signaling, interleukins, and nuclear receptors.

  • Insulin signaling is consistent with the given disease models. Was not identified using standard techniques.

  • Interleukins and nuclear receptors consistent with the inflammation and disordered metabolism associated with type 2 diabetes.

Insulin signaling

NuclearReceptors

Nuclear receptors: 31 of 67.

Interleukins: 38 of 67.

Insulin signaling: 45 of 67.

Interleukins




Thank you l.jpg

Thank you Disease Models