# Presentation: 258122

## From the Data to the Integrome.  Instrumenting the Healthcare System for Translational Research

- Isaac S. Kohane

- Answer #2

## Airways Disease Research at Brigham and Women’s Hospital

- Costa Rica 10 EP

*Linkage*
*Fine Mapping*
*Candidate Genes*
*Pharmacogenetics*

## Who?Health Care Utilization(Hospitalization, ED Visits)

**+**

## Clinical Informatics vs. Bioinformatics

## Bioinformatics: A House Divided

-  bioinformatics "is a tool, not a discipline, and tools have a way of getting absorbed into science." -L. Stein

## The paper that Launched at 100,000 chips: Alizadeh 2000

- Alizadeh et al.

## Example: Shipp 2002

## Example: Rosenwald

## Example: PPAR Pro12Ala and diabetes

**0.1**

**0.2**

**0.3**

**0.4**

**0.5**

**0.6**

**0.7**

**0.8**

**0.9**

**1**

**1.1**

**1.2**

- Estimated risk
-  (Ala allele)

**1.3**

**2.0**

- Deeb et al.

- Mancini et al.

- Ringel et al.

- Meirhaeghe et al.

- Clement et al.

- Hara et al.

- Altshuler et al.

- Hegele et al.

- Oh et al.

- Douglas et al.

- All studies

- Lei et al.

- Hasstedt et al.

**1.4**

**1.5**

**1.6**

**1.7**

**1.8**

**1.9**

- Sample size

- Ala is protective

- Mori et al.

- Overall *P* value = 2 x 10-7
- Odds ratio = 0.79 (0.72-0.86)

- Courtesy J. Hirschhorn

## Triangulating Human Disease

- Physiology

-  Genetics

- Environment

- Behavioral
- Social
- Milieu

## 30+ large-scale measurements or experimental modalities available

**Nearly-comprehensive measured data specific to an experimental context**
- DNA sequencing: includes map of sequence tagged sites; over 600 bacterial, 600 eukaryotic, and 1500 virus species underway or completed.
- DNA genetic distance: Recombination maps have been published for human, mouse, and other species.
- DNA polymorphisms: microsatellite repeats, single nucleotide polymorphisms, and haplotypes
- RNA sequencing: Expressed sequence tags, cDNA sequencing, alternative splicing
- RNA absolute expression: absolute expression measurements theoretically comparable across genes; over 690 experiments with over 11,500 microarrays publicly available
- RNA relative expression: relative expression measurements theoretically comparable across genes
- Protein identification: two-dimensional gel electrophoresis, tandem mass spectrometry
- Protein absolute quantitation: tagging each gene, then Western blotting, or microarray
- Protein relative quantitation: using isotope-coded affinity tags
- Protein activity: change in state, or across activities to determine gene function
- Protein relative activity: interaction with small molecule
- Protein interactions with DNA: chromatin immunoprecipitation of labeled proteins binding to promoter regions, binding to CpG microarrays, oligonucleotide microarrays, or tiling path microarrays
- Protein interactions with proteins: Yeast two-hybrid or mass spectrometry of complexes
- Protein interactions with small molecules: effect on protein interaction with small molecules

- Protein interactions with carbohydrates
- Protein localization: genome wide HA-tagging or GFP-tagged fusion proteins
- Protein modification: tyrosine phosphorylation using Western blots 110 or mass spectrometry
- Metabolites: HPLC or NMR
- Multi-phenotype characterization: after effects of RNAi
**Nearly-comprehensive computed data specific to an experimental context**
- DNA quantitative trait loci: genome regions statistically associated with quantitative traits
- DNA predicted transcription factor binding sites
- RNA co-expression: determined using correlation coefficients
**Nearly-comprehensive applied contexts**
- Gene disruption: targeted or random insertional or chemical
- Gene double disruption: mutations with deletions
- Gene activation: random
- RNA transient knockdown using RNAi or siRNA
- Multiple species
- Tissue microarray
- Presentations of a diagnosis
- Sampling across microscopic field of view, or spatial position
- Extracellular environment: in the presence of an array of carbohydrates, in the presence of pharmacological agents, in the presence of multiple ligands
- Sampling across time: lifespan, or circadian

## The Integrome: The space of inferences from all possible combinations of large-scale modalities in biology

**Nearly-comprehensive measured data specific to an experimental context**
- DNA sequencing
- DNA genetic distance
- DNA polymorphisms
- RNA sequencing
- RNA absolute expression
- RNA relative expression
- Protein identification
- Protein absolute quantitation
- Protein relative quantitation
- Protein activity
- Protein relative activity
- Protein interactions with DNA
- Protein interactions with proteins
- Protein interactions with small molecules
- Protein interactions with carbohydrates
- Protein localization
- Protein modification spectrometry
- Metabolites
- Multi-phenotype characterization
- Nearly-comprehensive computed data specific to an experimental context
- DNA quantitative trait loci
- DNA predicted transcription factor binding sites
- RNA co-expression
- Nearly-comprehensive applied contexts
- Gene disruption
- Gene double disruption
- Gene activation
- RNA transient knockdown
- Multiple species
- Tissue microarray
- Presentations of a diagnosis
- Sampling across microscopic field of view, or spatial position
- Extracellular environment: carbohydrates, pharmacological agents, multiple ligands
- Sampling across time: lifespan, or circadian

- Nearly-comprehensive measured data specific to an experimental context
- DNA sequencing
- DNA genetic distance
- DNA polymorphisms
- RNA sequencing
- RNA absolute expression
- RNA relative expression
- Protein identification
- Protein absolute quantitation
- Protein relative quantitation
- Protein activity
- Protein relative activity
- Protein interactions with DNA
- Protein interactions with proteins
- Protein interactions with small molecules
- Protein interactions with carbohydrates
- Protein localization
- Protein modification spectrometry
- Metabolites
- Multi-phenotype characterization
- Nearly-comprehensive computed data specific to an experimental context
- DNA quantitative trait loci
- DNA predicted transcription factor binding sites
- RNA co-expression
- Nearly-comprehensive applied contexts
- Gene disruption
- Gene double disruption
- Gene activation
- RNA transient knockdown
- Multiple species
- Tissue microarray
- Presentations of a diagnosis
- Sampling across microscopic field of view, or spatial position
- Extracellular environment: carbohydrates, pharmacological agents, multiple ligands
- Sampling across time: lifespan, or circadian

**The Integrome:**** The space of inferences from all possible combinations of large-scale modalities in biology**

**Notes:**
But digital biology is still more than just high-throughput science. To me, the aspect of digital biology that interests me most is in integrative biology, or combining results of genome-scale results synergistically to create new knowledge and testable hypothesis. These genome-scale results can come from integrating across comprehensive gene and protein measurements, genetic and polymorphism scans, clinical and phenotypic measurements, comprehensive mutagenesis and knockout studies, and even the medical literature. The hypothesis is that combinations of multiple experimental findings may lead to new hypotheses that would not have been thought out with individual experiments.

## Hypothesis

- A multimodal approach to medicine is necessary
- To have the most specific and robust diagnoses and therapies
- To overcome weak signals and underpowered studies within a single data modality.
- To see beyond the accumulated, increasingly arbitrary organ and organizational boundaries.
- Biomedical informatics provides the language for this operation.
- For the NIH this means a greater emphasis on “lightly used” data.

## One Very Old Biomarker

**1410 original articles on PSA screening for cancer**
**179 review articles**
**With 1000’s of gene products, how do we systematically address this problem?**

## True names: the goal of biomarkers

## Substrate: Gene Expression Omnibus

- Currently has 72,566 samples contained in 975 series measured using 3136 platforms
- Gaining 2,500 samples per month
- 1200 curated data sets have been established
- Within each GDS, samples are directly comparable
- Most can be downloaded

## Substrate: The Unified Medical Language System

- Label concepts using the Unified Medical Language System
- Largest unification of over 130 biomedical vocabularies
- Contains over one million inter-related concepts
- 20 million links between concepts
- Covers human and model organisms
- Scales from molecular, to physiological, to pathological

## Courtesy Atul Butte

- Concepts
- CUI
- Synonyms and abbreviations
- Source vocabulary and term
- Defined relations: not all
- Statistical relations: not all, 4K+ for diabetes mellitus

**Notes:**
The GEO series description annotation was the most information rich, as it provided unique concepts (Supplementary Table 1 online). This was
likely because GEO series descriptions are often dissimilar to each other, compared to sample descriptions, which are often repeated. As
expected, the concepts mapping to the most annotations are cells and

**Notes:**
Our first method to heighten stringency was to consider only those gene-concept relations where a second relation was present between the same concept and an orthologous gene

## Gene-Driven Nosology

**Notes:**
This resulted in relations between 444 genes and 46 concepts, which were graphically visualized as a phenome-genome network (Fig. 3a). Four of the top five connected concepts were related to muscle, such as Skeletal Muscle, with 128 genes (38 human, 40 mouse and 50
rat) showing significantly increased expression level in data sets annotated with these concepts
and 40 decreasing genes (6 human, 18 mouse and 16 rat) with decreased expression level Many of these genes are known to be uniquely expressed in muscle, including TNNI1, MYBPC1, Mybph and Pgam2; Pnliprp1, Chad and Fgb are expressed in a few tissues
including muscle. Human PDLIM3 shows the greatest differential expression between the eight data sets annotated with the concept Muscle Cells (average normalized expression level 0.968) and the 34 data sets measuring
this transcript without such an annotation (expression level 0.340), a highly significant difference by t-test with P < 1.2 × 10−16, with Q < 0.0002 across 100 permutations of mappings between data sets and the annotated concept (Fig. 3c). A similar pattern is seen for mouse Pdlim3 Fig. 3d, P < 6 × 10−8, Q = 0.0025). Pdlim3 is thought to play a role in skeletal muscle development16. Mouse Pdlim3 is most highly expressed
in skeletal muscle, compared to 21 other tissues with EST count data in
the NCBI UniGene9, and 60 other tissues in the GNF SymAtlas panel of
microarray expression measurements by Su et al.

## Biomarkers as Continuum

## Instance of multiplicity of biomarkers

- 11 genes related to aging concept
-  G6PDH (with p < 10-6)
- Individuals with G6PDH deficiency have been noted to have reduced mortality from cardiovascular disease and have increased longevity
-  BDNF
- has been previously shown to have a significant drop in expression in human skin fibroblasts with aging.** **
- TNNT1, SYNJ1, TADA2L, SLC7A2, MGAT2 and KCTD2 also associated with aging.

## Biological validation or prediction for shh pathway

*Zhao et al. PNAS 2002*

**Notes:**
Fig 3. Examples of in situ hybridization of the expression of identified genes in the postnatal cerebellum.  BM28 (D, E), cdc20 (G, H), RanBP1 (J, K), CKS2 (M, N) revealed specific EGL expression in sagittal sections of postnatal 1 (PN1) and 7 (PN7) cerebellum (4X magnification), similar to the known expression pattern of cyclin D1 (A, B) as a positive control. Higher magnifications (40X) showed expression patterns of cyclin D1 (C) in EGLa, BM28 (F) in EGLa, cdc20 (I) in both EGLa and b,  RanBP1 (L) in EGLa, and CKS2 (O) mainly in EGLa.  DP1 (P, Q, R) represented the tested genes which showed no expression in the developing cerebellum. 

**Notes:**
What can we see at 

**Notes:**
So, what is the relevance of this to hums:  Classical and desmblasic up and down upregulated genes; I can be argued that the desmoplastic are a little more heterogenoues

## Does this pattern generalize to other tissues?

**Notes:**
Is this generalizable to lung development and lung cancer

## Is this phenomenon, merely reporting proliferation?

- Or, does it reflect the tissue-specific developmental program?

**Notes:**
Is this effect tissue specific?

## What about a macro view?

## Lung Cancer

## In-class as well as between class

- Survival time (month)

**B**

**C**

- Genomic PC1

- Genomic PC1

- Survival probability (%)

- Genomic PC3

- Genomic PC3

**Higher PC1; N=51**

**Lower PC1; N=13**

- Liu, H et al., 2006

**Stage I ADs  (64)**

**p = 0.0169**

**All ADs (125)**

**Stage I ADs (64)**

** ****(month of survival for A & B)**

## Array type / human vs mouse difference is significant

- outliers?

## Genes significant across datasets?(method 1: number of datasets in which it is significant)

- 41195  FLJ20296 0610039N19 all-trans-13,14-dihydroretinol saturase
- 1707      KPNB1     Kpnb1  nnn (importin) beta 1
- 2255       SDHB      Sdhb  succinate dehydrogenase complex, subunit B, iron sulfur (Ip)
- 11401    MRPL34    Mrpl34  mitochondrial ribosomal protein L34
- 20480      GPX3      Gpx3  glutathione peroxidase 3 (plasma)
- 37369       PAM       Pam  peptidylglycine alpha-amidating monooxygenase
- 375        NEU1      Neu1  sialidase 1 (lysosomal sialidase)
- 862       ACTN3     Actn3  actinin, alpha 3
- 1413      CPT1A     Cpt1a  carnitine palmitoyltransferase 1A (liver)
- 2189       RFX1      Rfx1  regulatory factor X, 1 (influences HLA class II expression)
- 2484      TSTA3     Tsta3  tissue specific transplantation antigen P35B
- 2525     UQCRC1    Uqcrc1  ubiquinol-cytochrome c reductase core protein I
- 3425      DDX3X     Ddx3x  DEAD (Asp-Glu-Ala-Asp) box polypeptide 3, X-linked
- 4792      DCTN6     Dctn6  dynactin 6
- 5665    TRAPPC4   Trappc4  trafficking protein particle complex 4
- 7573    TGFB1I4   Tgfb1i4  transforming growth factor beta 1 induced transcript 4
- 24923    HNRPAB    Hnrpab  heterogeneous nuclear ribonucleoprotein A/B
- 31043     IFRD1     Ifrd1  interferon-related developmental regulator 1
- 36144      SNX3      Snx3  sorting nexin 3
- 37356     GSTM2     Gstm3  glutathione S-transferase M2 (muscle)
- 38123      TBX2      Tbx2  T-box 2
- 40849      TXN2      Txn2  thioredoxin 2
- 40932    NDUFA8    Ndufa8  NADH dehydrogenase (ubiquinone) 1 alpha subcomplex, 8, 19kDa
- 41384 GABARAPL1 Gabarapl1  GABA(A) receptor-associated protein like 1
- 48323       SCD      Scd1  stearoyl-CoA desaturase (delta-9-desaturase)
- 49589      TNXB      Tnxb  tenascin XB
- 57029              Ifitm3  similar to Interferon-induced transmembrane protein 3

- Significant in > 8 of 16

- 7/16

- 6/16

- “significant” -  in any of the comparisons within each dataset, adjusted for multiple testing

## Patients and cell lines are analyzed as cases
Clinical parameters, laboratory tests, RNA expression, and susceptibility to anti-cancer agents are all example features of those cases

- Clinical parameters, laboratory tests, RNA expression, and susceptibility to anti-cancer agents are all example features of those cases

- Construction of Relevance Networks 1

**Lab Test 1**

- 134

**Lab**
**Test 2**

- 3.7

- 4.5

**Clinical**
**Param 1**

- 105

- 99

**RNA Expr**
**J02923**

**Susceptibility****to Anti-cancer**
**Agent 169517**

- 8.1

**Patient, Cell Line, Time, etc.**

- 138

- 0.7

- 7.4

- 2.1

- 3.3

- 132

- 5.3

- 102

## For all pairs of features, we take overlapping values over the cases and make a scatter plot of values

- Construction of Relevance Networks 2

**Susceptibility****to Anti-cancer**
**Agent 169517**

**Lab Test 2**

**Lab Test 1**

- 134

**Lab**
**Test 2**

- 3.7

- 4.5

**Clinical**
**Param 1**

- 105

- 99

**RNA Expr**
**J02923**

**Susceptibility****to Anti-cancer**
**Agent 169517**

- 8.1

- Patient, Cell Line, Time, etc.

- 138

- 0.7

- 7.4

- 2.1

- 3.3

- 132

- 5.3

- 102

## Construction of Relevance Networks 3

- Perform a pairwise comparison between all features
- For each scatter plot, we fit a linear model and stored
- Correlation coefficient r
- Every feature is completely connected to every other feature by a linear model of varying quality

**Lab Test 2**

**169517**

**r = 0.65**

**Susceptibility****to Anti-cancer**
**Agent 169517**

**Lab Test 2**

## r^2 = r2 * r / abs(r)
Choose a threshold r^2  to split the network
Drop links with r^2 under threshold

Breaks the completelyconnected network intoislands where connectionsare stronger than threshold
Islands are what we call “relevance networks”
Display graphically, with thick lines representing strongest links

- Choose a threshold r^2  to split the network
- Drop links with r^2 under threshold
- Breaks the completelyconnected network intoislands where connectionsare stronger than threshold
- Islands are what we call “relevance networks”
- Display graphically, with thick lines representing strongest links

- Construction of Relevance Networks 4

**Lab Test 1**

**Lab Test 2**

- r^2

**Clinical **
**Param 1**

**Expression of J02923**

**Clinical Param 2**

**Susceptibility to 169517**

- r^2

- r^2

- r^2

- r^2

## Networks in yeast expression: The ribosome

- Several known functional networks identified in yeast expression data
- Largest network links 143 genes
- 102 are the various components of the large and small ribosomal subunits
- 8 are translation initiation factors

## The New Pharmacology

- RNA expression in NCI 60 cell lines was determined using Affymetrix HU6000 arrays
- 5,223 known genes
- 1,193 expressed sequence tags
- The RNA expression data set and Anti-cancer susceptibility data set were merged, using the 60 cell lines the two tables had in common

## Genes and Anti-Cancer Agents

- Threshold r^2 was 0.8
- 202 networks
- 834 features out of 11,692 (7.1%)
- 1,222 links out of 68,345,586 (.0018%)
- Only one link between a gene and anti-cancer agent

## Functional Similarity Networks

- Melanoma-associated antigens 2, 3 and 12
- Caldesmon 1 and alternative splicing products 3 and 4
- Two sequences from MHC class I (D32129 and X12432)
- 3088 (chlorambucil), 344007 (piperazine alkylator), 34462 (uracil nitrogen mustard), 48034 (aziridine), 6396 (thio-tepa), and 9706 (triethylenemelamine) are all alkylating agents
- 243928 (ethanesulfonamide derivative) and 249992 (amsacrine) are both active against topoisomerase II

## Genes and Anti-Cancer Agents

- Elevated levels of J02923 (lymphocyte cytosolic protein-1, LCP1, L-plastin, pp65)  is associated with increased sensitivity to 624044
- Agent 624044 is 4-Thiazolidinecarboxylic acid, 3-[[6-[2-oxo-2-(phenylthio)ethyl]-3-cyclohexen-1-yl]acetyl]-2 thioxo-, methyl ester, [1R-[1a(R*),6a]]- (9CI))
- LCP1 is an actin-binding proteininvolved in leukocyte adhesion
- A role for LCP1 in tumorogenicityhas been previously postulated
- Low level expression of LCP1 is thought to occur in most human cancer cell lines
- Other thiazolidine carboxylic acid derivatives are known to inhibit tumor cell growth

*Butte et al. PNAS 2000*

## The Bibliome Quagmire

## Autism Genomic Bibliome

## Bibliome Workbench

**Problem: Library-Publisher contracts**

## Slide 53

## The single gene case

- Single genomic test that is 99.9% sensitivity (true positive rate) and 99.9% specificity (true negative rate) for a rare treatable disease X
- Works well in patient group that is suspected of disease (high prior)
- In the general population (disease 1 in 100,000)
- In 10,000,000 patients
- 100 true positives, 10,000 false positives
- Less than 1 in 1000 positives are true.
- Who would ever do such a thing?

## The economic case
The comprehensiveness case
Using a much much better test
sensitivity of 100% and a specificity of 99.99%
Only 10 in 100,000 false positives
A chip with 10,000 independent tests

- The comprehensiveness case
- Using a much much better test
- sensitivity of 100% and a specificity of 99.99%
- Only 10 in 100,000 false positives
- A chip with 10,000 independent tests

- Impanelling the Incidentalome

- Answer #2

**Notes:**
That’s the vision behind the NCI funded Shared Pathology Informatics Network
We are leveraging the two compulsions of pathologists: to store specimens and to taxonommize them

We created a network that wrks very much like napster so that every center is autonomous and maintains it own infrastrucrte and yet allows queries which arelady go frm coast to cost.

## User

- Query
- Composer

- HTTP

- Peer
- SPIN
- Node

- Node
- Tools

- Pathology

- UPDATE

- Peer
- SPIN
- Node

- Peer
- SPIN
- Node

- Peer
- SPIN
- Node

- Peer
- SPIN
- Node

- Peer
- SPIN
- Node

- Peer
- SPIN
- Node

- Peer
- SPIN
- Node

- Peer
- SPIN
- Node

- Peer
- SPIN
- Node

- Peer
- SPIN
- Node

- Clinical

**Children’s**
**Hospital**

**MGH**

**Beth Israel**

**HCNR**

**UCLA(1)**

**Pittsburgh**

**UCLA(2)**

**Indiana**

## Design of Clinical Research Chart

- CRC DB

- HL7  MSH|^/&|736401.....
-          PID|102|3231285.....

- Text files

- XML  <Patient1>
-              <image>.....

- database

- Courtesy: Chueh/Murphy

## Airways Disease Research at Brigham and Women’s Hospital

- Costa Rica 10 EP

*Linkage*
*Fine Mapping*
*Candidate Genes*
*Pharmacogenetics*

## Who?Health Care Utilization(Hospitalization, ED Visits)

**+**

## Challenge

- Multiplicity of tools
- Some local favorites, some universal favorites
- Little guidance on how or when to use them for clinical research
- Even less on how to use them together as an ensemble, process to perform clinical research

## The Hive Workflow framework

- Two API’s
- Interprocess
- Managed UI
- Sessions
- Security

## Workflow1

## Workflow2 (package data)

## Workflow: Method dependent on data

## Workflow: Cells with services and managed UI: GenopiaIframe

## Challenge

- Tissue pathology is revealed in the tissues affected
- Health individuals do not readily give up most body tissues
- This reduces apparent utility of molecular markers
- Can we measure molecular signatures in expression and protein in major diseases

## Diabetes Mellitus

- Mitochondrial phenotype of DM well established in fat and muscle and liver
- Current mainstay of DM, glycohemoglobin does not distinguish several physiological states
- Can we measure the mitochondrial phenotype and response to therapy using only WBC?

## Diabetes-Peripheral Signature

## Huntington's Disease

- Dominant neurodegenerative disorder caused by expansion of a CAG trinucleotide repeat in the HD gene
- Correlation but wide variance in clinical course with CAG repeat length
- Goal: Define characteristics of the trigger mechanisms and target pathways correlated with the increasing polyglutamine tract length
- Structural biology approach
- WBC expression approach

## Pathways with decreasing activity as CAG length increases

- 28,372 genes – hgu133 Plus 2.0

## Challenge

- Early predecessors to PubMed, such as PaperChase, showed that doctors could and would perform searches and boolean operations to get the right publications.
- We do not have the equivalent facility for homology search, pathway intersection with expression, conservation against TFBS etc.

## Pathway

## Gene-centric

## Combination Operations

## Challenge

- The 72 names of glucose and other synonymy lack of synchrony
- Where do we get formal rigor to delineate relationships?
- How do we use this formal rigor>

## Protégé

## Mapping program

## Exploring an Integrated Multitude

- Integrating various data sources
- Diagnosis – ICD9, LMR, NLP
- Medications – Inpatient cost codes, LMR, NLP
- Smoking – ICD9, health maintenance, LMR, NLP
- Across millions (2.5M) of patients
- Where data may be sparse
- PREVIOUSLY: Researchers did not know what they **COULD** ask, let alone **HOW**

## Slide 84

## Slide 85

## Slide 86

## Challenge

- How to perform unsupervised learning in  systems that contains heterogenous data
- Discrete and continuous
- Asynchronous
- Sparse
- Codified and codified through parsing
- Biased by utilization

## Initial approaches

- “Low hanging fruit” first
- Remove “spiking” behavior through entropic filters
- Aggregate over related categories
- Use sampling frequencies based on the highest sampling frequency for a data type
- Renormalize laboratory tests that have shifting baselines.

## One Approach: Relevance Networks

## Challenge

- Most phenotypic data in modern electronic healthcare systems is in the form of narrative text
- Texts are idiosyncratic, jargon filled, and vary in style by institution

## Approach

- Both statistical and hand-crafted pattern matching
- Implemented as modules in the widely used GATE framework.
- GATE itself is being wrapped in the Hive
- Scrubbing to maintain anonymity
- Validation of codification by sampling strategies with experts.

## NLP (and comedy) is not pretty

## But it works

- 96,000 asthma patients identified out of 2.5M PHS patients
- Stratified by severity, pharmaco-responsiveness and exposures
- Now with cases and controls (from extrema) reconsented  and biomaterials obtained for genome-wide scans ++

## And we want to recruit more colleagues to the challenge

- 16 groups have already responded to a de-identification and smoking challenge.

## Challenge

- Order *n**2* IRB process with *n* institutions
- See it propagate during updates
- Prospective vs just-in-time- consent

## Aggregate-able and delegated approval

**Researcher enters through web browser**

**HMS eCommons:**
**credentialing of **
**researcher**

**Compose Web Query**

- HMS
- Admin
- Supernode

- HMS IRB approval for entire  network

***Institutional***
***Firewall***

**BWH**
**&**
**MGH**

**CHMC**

**BIDMC**

**Different HIPAA-covered entities**

**  ****Partners    IRB Approval**

**Caregroup IRB Approval**

**  ****CHMC      IRB Approval**

- Institutional IRB approval to become a node

## Challenge

- Multidiscplinary teams are subject to persistent centrifugal forces
- Utilities and incentives are not well aligned in current funding and reward ecosystem
- And yet, cohesion is necessary precondition for success of i2b2

## Solution to the Sociological Challenge I

## Solution II

- Promotion paths
- (even within current system)
- Clarity on intellectual contributions and authorship
- Funding that structurally incents
- E.g i2b2 DBP’s
- Transparency in IP, data sharing.

## Solution III

## Thank you

- _[And](http://www.chip.org/~zak)_ thanks to Chuck Friedman, Valerie Florance, Valentina Di Francesco