***Adoption of caBIG^TM^ Tools: Application to Acute Myelogenous
Leukemia (AML) and Neuro-Oncology***

Data Access Plan

Rakesh Nagarajan, M.D., Ph.D.

660 S. Euclid Avenue, Campus Box 8118

Saint Louis, MO 63110

PHONE : 314-362-8859

FAX: 314-747-7999

EMAIL: <rakesh@wustl.edu>

Mark Watson, M.D., Ph.D.

660 S. Euclid Avenue, Campus Box 8118

Saint Louis, MO 63110

PHONE : 314-454-7919

FAX: 314-454-5208

EMAIL: <watsonm@wustl.edu>

**Background**

Nucleic acid microarrays (e.g. expression, comparative genomic
hybridization \[aCGH\], and SNP profiling) are a powerful technology
that is heavily utilized to unravel the molecular genetic basis of
complex diseases such as tumorigenesis. At Washington University School
of Medicine (WU) and the Alvin J. Siteman Cancer Center (SCC), these
technologies have been leveraged to conduct almost 1,200 microarray
expression experiments alone. These include data for over 11,500 chips
derived from human and model organism (e.g. mouse, rat, zebrafish, and
drosophila) tumor specimens from virtually every organ site. In
addition, the cancer community at WU/SCC has conducted many experiments
employing aCGH and SNP chips from NimbleGen and Affymetrix respectively.
This rapid influx of large scale data sets has resulted in several major
needs by our cancer research community. These include:

1)  A centralized, standards-compliant repository for microarray data.
    This would not only enable users to:

    a.  Access their data remotely.

    b.  Facilitate re-analysis of data in other contexts (e.g. normal
        prostate tissues that were originally profiled against tumor
        specimens may be compared to normal specimens from other organ
        sites to identify tissue-specific markers).

    c.  Facilitate meta-analyses. Repositories such as OncoMine
        (<http://www.oncomine.org>) have been used to conduct such
        meta-analyses successfully across many profiling experiments
        performed against the same tumor type (e.g. prostate tumor data
        sets generated by different research groups).

    d.  Facilitate data sharing between collaborators prior to
        publication.

    e.  Make their data available to the entire research community in a
        MIAME-compliant format after publication.

2)  A standards-based biospecimen repository that contains quality
    assurance/quality control (QA/QC) data as well as associated
    pathology and clinical annotations that is **ALSO** semantically
    interoperable with the microarray repository described in \#1 above.

3)  Tools to analyze and visualize this microarray and biospecimen
    information in the context of genome annotation. While independent
    tools exist to analyze gene expression data alone (e.g. commercial
    tools such as SpotFire DecisionSite and Ingenuity Pathways as well
    as our own Function Express client) at WU/SCC, there are no tools
    which facilitate the entire analysis workflow (e.g. combine
    microarray data with biospecimen and clinicopathology information,
    filter, analyze, and visualize in the context of genome annotation).
    Furthermore, even basic user-friendly (i.e. for a bench scientist or
    translational researcher) tools to analyze SNP data do not exist to
    date.

**End Users**

The Alvin J. Siteman Cancer Center established the Bioinformatics Core
in 2000 to support the informatics needs of the cancer research
community. The Bioinformatics Core serves over 500 investigators across
\~200 independent laboratories spread across the WU campuses. While our
goal is to serve the needs described above to this entire community, we
will engage two specific research groups that are engaged in complex
translational research projects across three tumor types. These include:

1)  Dr. Timothy Ley (Genomics of Acute Myelogenous Leukemia Program
    Project Grant \[GAML-PPG\])- Dr. Ley and his collaborators are
    integrating comprehensive clinical information, pathology and
    cytogenetics data, and profiling data sets (expression, aCGH, SNP,
    proteomics, and mutation profiling) to determine the molecular
    mechanisms underlying AML.

2)  Dr. David Gutmann (Neuro-Oncology Research Group \[NORG\])- Dr.
    Gutmann and his collaborators are studying a wide range of
    peripheral and central nervous system tumors and are correlating
    molecular markers with clinical observations such as outcome and
    survival. The major tumor types being studied are juvenile pilocytic
    astrocytomas (JPAs) and malignant peripheral nerve sheath tumors
    (MPNSTs).

Details of each research group's data sets to date (more data is being
continuously generated) are provided in the following table:

  **Tumor Type**      **Patients**   **Samples**   **Expression Arrays**   **NimbleGen aCGH**   **SNP Arrays**
  ------------------- -------------- ------------- ----------------------- -------------------- ----------------
  AML                 564            4,490         483                     235                  100
  JPA                 93             174           110                     10                   20
  MPNST               38             38            38                      8                    38
  Oligodendroglioma   7              7             7                        0                   0 

**Proposal**

Our proposal to support the informatics needs of the GAML-PPG and the
NORG is composed of the following five elements (See Figure 1):

1)  [caArray Adoption]{.underline} - caArray will be utilized to manage
    and annotate microarray expression data initially. When caArray
    begins to support SNP and aCGH chips, these data sets will be
    managed in caArray as well. Our current metadata model will be
    mapped onto the caArray model, and gaps (e.g. attributes in caArray
    that are missing in our data model) will be identified. These gaps
    will be filled by data curators within the SCC Bioinformatics Core
    working closely with end users within the GAML-PPG and NORG groups.
    While this task is manually intensive, we firmly believe it is a
    crucial step for sharing of data with an appropriate level of
    accompanying metadata. After mapping and data curation, we will
    import both the data (raw and processed) and metadata. Note that
    this process will eventually enable us to utilize caArray for ALL of
    our microarray data management needs (i.e. for all projects)
    although, only the GAML-PPG and NORG projects will be specifically
    supported within the scope of this proposal. Another benefit of this
    adoption is that for ALL **future** microarray experiments,

> ![](media/image1.png){width="5.1875in" height="3.375in"}
>
> caArray will be utilized by the SCC Bioinformatics Core for data
> management, dissemination, and publication.

2)  [caTissue Adoption]{.underline}- All PPG- and NORG-related
    biospecimen information is currently stored in the SCC Tissue
    Procurement Facility's (TPF) caTissue Core. We will migrate from
    caTissue Core to caTissue Suite when a production release is
    available.

3)  [Analytical Services]{.underline}- To analyze microarray expression
    data, we will adopt the services exposed by GenePattern (Consensus
    Clustering and Comparative Marker Selection) and geWorkBench
    (Hierarchical, k-means, and SOM clustering). Note that as part of
    the caBench-to-Bedside (caB2B) project, we are already integrating
    such services. As part of this proposal, we anticipate the need to
    install some of these services locally both for performance reasons
    as well as proprietary issues (i.e. investigators may not be
    comfortable having their ***pre-publication*** data sent across the
    internet for analysis at another organization or institution and the
    service providers may not be will/able to accommodate our analytical
    needs). Furthermore, we will adopt BioConductor's DNAcopy for
    analysis of aCGH and SNP genotyping data. Finally, we will
    **develop** novel analytical services by wrapping other existing
    BioConductor/R packages and Affymetrix software for similar copy
    number analyses. These include PLASQ, GLAD, aCGH, RLMM, and
    Affymetrix Power Tools (DM and BRLMM algorithms). Note that as part
    of the caB2B project, we have already created an analytical service
    for identifying coexpressed genes (Template Matching Algorithm),
    thus demonstrating our competency in developing caGrid analytical
    services.

4)  [Workflow Use Cases and Requirements]{.underline}- Microarray
    analyses are commonly composed of serial (acquire, filter,
    normalize, cluster), parallel (cluster using several algorithms),
    and iterative (perform iterations of data randomization and
    clustering to determine family-wise error rate \[i.e. to correct for
    multiple comparisons\]) steps. Performing such steps manually is
    very time consuming (See Figure 2). Such steps may be choreographed
    as one or more workflows to facilitate such analyses. In fact,
    caGrid has developed a workflow service that is based on Business
    Process Execution Language (BPEL). However, the current interface is
    too cumbersome and is not end user friendly (e.g. a BPEL document is
    required as input). Therefore, working with our end users in the
    GAML-PPG and NORG groups, we will define the use cases and
    requirements for visually describing workflows by examining the
    range of analyses that are performed by these groups (See Figure 3).
    These will be given to the caB2B/caGrid development team for design
    and implementation.

> []{.underline}![](media/image2.emf)
>
> [\
> ]{.underline}![](media/image3.emf)

5)  [Integration]{.underline}- caB2B will be utilized to acquire
    microarray and biospecimen data from caArray and caTissue Core (and
    later caTissue Suite) and will then be used to analyze and visualize
    these data sets. Using the workflow interface built as a result of
    \#4 above, users may perform complex analyses that utilize
    computational services as well as annotation services. For example,
    "find all amplified and deleted genes as well as those genes whose
    expression is perturbed in AML/JPA in samples with poor outcome but
    NOT in those with good outcome, and show me these genes in the
    context of a literature-based gene network." This would require:

    a.  caArray for acquisition of expression/aCGH/SNP data

    b.  caTissue Core/Suite for acquisition of outcomes information
        associated with biospecimens used for the array analysis.

    c.  analytical tools capable of finding genes with perturbed
        expression (Comparative Marker Selection) and identifying copy
        number changes (BioConductor/R/Affymetrix tools)

    d.  caFE for literature-based gene network information

> **Data Sharing**
>
> Biospecimen and microarray data that are part of this project will be
> shared with other institutions and organizations through the
> deployment of caTissue Core/caTissue Suite and caArray instances,
> respectively, on publicly available servers. We currently manage and
> run \>50 servers within the SCC Bioinformatics Core where the vast
> majority of servers are publicly accessible. In fact, we are one of
> the few proponents within the caBIG^TM^ community of a SINGLE caTissue
> database and application server instance architecture where a single
> database behind the institutional firewall is connected to by a server
> running the web application within a de-militarized zone (DMZ). This
> server in the DMZ is publicly accessible and runs the caTissue
> instance using secure socket layer (SSL). For data sets where
> protected health information (PHI) is not involved (e.g. microarray
> data), our servers (database and application) our fully accessible
> through the Wide Area Network (WAN) as well. Examples include
> <http://bioinformatics.wustl.edu> (production instance),
> <http://geneconnect.wustl.edu> (production instance), and
> <http://catisuecore.wustl.edu> (our caTissue demo site). All of our
> production servers have been available \>95% of the time since the
> inception of the SCC Bioinformatics Core.
>
> Data sets that are anticipated to be shared include:

-   Microarray-based gene expression data- Raw (e.g. Affymetrix DAT,
    CEL, and DTT files) and processed files (e.g. Affymetrix MAS 5.0
    analysis results)

-   Microarray-based whole genome genotyping (SNP) data- Raw (e.g.
    Affymetrix DAT, CEL, and DTT files) and processed files (e.g.
    Affymetrix GTYPE/CNAT/CNAG analysis results)

-   aCGH data- Processed files (e.g. NimbleGen processed results)

-   Higher order analytical results from caB2B (e.g.
    clustering/classification results and cross-tool genomic segments
    that are amplified/deleted, or have loss of heterozygosity)

-   De-identified, non-PHI biospecimen and related patient information
    (e.g. tissue site, clinical diagnosis, pathological status, and
    associated annotation information as they become available through
    caTissue Suite)

> The following regulatory and proprietary issues are anticipated:

-   IRB approval to share de-identified biospecimen information- To
    > facilitate this step, Mark Watson, the director of our TPF, is
    > completing a revised document for IRB review and approval. This
    > [document](http://cabigcvs.nci.nih.gov/viewcvs/viewcvs.cgi/caties_adp_washu/IRB%20Requirements%20Packet/07Revised%2099-0573.pdf)
    > (click to view) has been authored and made available as part of
    > our caTissue adoption project. This will allow the TPF to use
    > caTissue Core and in the future caTissue Suite for its biospecimen
    > informatics needs. It will also enable this data to reside on a
    > networked server that is accessible from any WAN IP using SSL. If
    > the IRB denies our request for a single instance of caTissue, we
    > will deploy a de-identified instance outside of the institutional
    > firewall. This feature will be available within caTissue Suite
    > v1.1 which will be available in March 2008. For the purposes of
    > this project, if the IRB denies our request for a single instance
    > of caTissue, we will deploy a de-identified instance and migrate
    > biospecimen data relevant to the GAML-PPG and NORG groups.

-   There are no anticipated regulatory (i.e. HIPAA) issues with
    > microarray expression and aCGH data sets. For genome-wide SNP
    > profiling data sets, it has been proposed that a complete SNP
    > profile may constitute PHI. This issue is under active debate
    > nationally. Thus, this issue needs to be addressed by the DSIC
    > workspace for resolution. In addition, our local IRB will be
    > notified of these issues, and they will need to set policies
    > regarding such data. For example, Dr. Tim Ley has recently
    > modified the IRB document for the GAML-PPG to include explicit
    > consent to share genome-wide sequence information.

-   There are no proprietary issues with regard to published microarray
    > (expression, SNP, and aCGH) data sets. Some of the data sets
    > referenced above have been already published, but are not easily
    > accessible or well annotated using current systems. These will be
    > made more accessible through publicly available caArray and
    > caTissue Core/Suite servers as stated above. Note that the SAME
    > servers will be used by both Washington University investigators
    > (both members and non-members of the GAML-PPG and NORG) and
    > investigators from the broader caBIG^TM^ and biomedical research
    > communities.

> Data shared through the publicly available caArray and caTissue
> Core/Suite servers will be available indefinitely as these will ALSO
> be the same servers through which all future data sharing of such data
> sets will occur for the entire SCC Bioinformatics Core. Thus, as
> stated above, it is our intention to leverage and test the GAML-PPG
> and NORG use cases for data sharing and to apply the same process for
> ALL future microarray and biospecimen data sets generated by both
> cancer and non-cancer groups. For example, beyond the GAML-PPG and
> NORG specific data sets, our institution has well over 1,800
> microarray data sets from human tumor biospecimens that will be
> represented in both caTissue Core / Suite and caArray. Many of these
> data sets have been previously published or have little or none of the
> proprietary constraints discussed above. Thus, specific work using
> GAML-PPG and NORG data sets will pave the way for the sharing of even
> larger cohorts of combined biospecimen and genomic data.