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Specific Aims
Identification and characterization of the genes and cellular aberrations responsible for the onset and
progression of malignancies arising in childhood are major goals of pediatric cancer research. Recent
advances in genomic and proteomic research has made available many large data sets relevant to these
goals. However, sufficient resources for systematically and comprehensively collecting, managing, and
integrating these data are currently lacking for molecular biology in general, and for cancer-specific
applications in particular. To address these issues, we previously created a novel computational procedure
that effectively catalogues position- and functionally-based genomic information (eGenome), and which
distributes this information via an interactive, user-friendly Internet site to researchers involved in
identifying human disease loci. Here, we propose to create a parallel resource that focuses upon cancerspecific cellular data, to which we would initially target pediatric malignancies. The proposed resource
(provisionally named cGenome), would create a collective knowledgebase directly linking positional
genomic, functional genomic, and proteomic information with available pediatric cancer-oriented genotypic
and phenotypic observations.
Specific Aim 1: Compile an evidence set of pediatric clinico-molecular observations. We will
identify all sizable Internet- or database-accessible data sets pertaining to the molecular biology of human
neoplasia. This will include curated lists of genes, loci, and proteins implicated in neoplasia; collections of
cancer-specific chromosomal rearrangements and/or gene mutations; transcriptional and translational
profiles; whole-genome analyses of malignant cells; and cancer research literature. From this information,
we will compile a comprehensive set of evidence expressions, each of which link a tumor phenotypic
observation to a molecular observation in a specific pediatric malignancy. This expression set will be
consolidated into a non-redundant subset and standardized by use of a modular and scalable molecular
ontology. Furthermore, we will link proof of each observation directly to the fraction of the cancer literature
which supports the observation.
Specific Aim 2: Integrate this evidence set with existing molecular and clinical data. Each evidence
expression will have a molecular, tumor type, and proof component. We will link molecular components to
basic cellular data sets within eGenome corresponding to each component, such as the gene involved, site
of chromosomal rearrangement, or protein affected. In addition, we will integrate cancer-specific variation,
transcriptional, proteomic, and functional molecular data sets that will link directly to applicable individual
evidence events. We will link the tumor component to trial/protocol, epidemiological, diagnosis,
presentation, and therapy-based clinical information corresponding to the malignant subtype and, whenever
possible, directly to the specific observation. This information will be integrated using a relational database
management system, and relevant data sets will be parsed and imported into the database to build a
comprehensive knowledgebase.
Specific Aim 3: Analyze and annotate the knowledgebase to improve accuracy and integrity.
Initially, we will track molecular-clinical evidence events that directly conflict with each other, such as:
BCR is involved in “t(9;22)(q34;q11)” or “t(9;12)(q34;q11)”. These conflicts will be automatically
determined and annotated in the web resource’s display of this information, and will also serve as an
internal quality control mechanism to assure proper data integration and standardization. Similarly, data
that appears to be incorrect, such as an aberrant reporting of a commonly occurring cytogenetic
rearrangement, will be identified and annotated. Subsequently, we will identify and report evidence events
that appear to be biologically incompatible, such as a gene that is reported to be deleted and over-expressed
within the same malignancy by different reference sources. We will also incorporate a number of quality
control procedures to track and maintain the cGenome database holdings.
Specific Aim 4: Create a web resource for public dissemination of the compiled information. We
will create a freely accessible Internet website to serve as a front end for the cGenome relational database. A
variety of search and output options will be provided. Structurally- (e.g. chromosomal position, gene name,
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DNA sequence), functionally- (e.g. tissue, type of malignancy, protein function), and clinically- (e.g.
malignant subtype, supporting study) directed searching mechanisms will be provided. Query result pages
will be dynamically generated and will contain positional, functional, and descriptive data of the gene,
transcript, protein, pathway, malignancy, clinical features, or literature citation of interest. Individual pages
will also contain element-specific hyperlinks to supplementary data at many external websites. Supportive
web content, such as background information, definitions/help, site navigation, and methodological details
will also be included. The website will act as an Internet portal for entry into molecular pediatric cancer
information.
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Hypothesis
Two significant gaps currently hinder cancer research: a gap between data generation capabilities and
data managment/analysis capabilities; and a second gap between the generic molecular, cancer-specific
molecular, and translational/clinical cancer information universes. We hypothesize that a systematic effort to
unite and deliver these information sets will empower cancer researchers, providing increased efficiency as
well as an opportunity for higher order analyses of malignancy.
Background
Molecular biology and cancer. The human genome project (HGP) is dramatically accelerating both the
pace and philosophy in which disease-related research is performed (1). Technical advances have shifted the
major molecular research bottleneck from data generation to data processing, heightening the importance of
computational approaches (2-7). This transformation has tremendous potential, including the possibility of
systems-based understanding of disease, but it also poses complex challenges. As genomics and proteomics
are leading this paradigm shift, research related to cancer as a molecular disease will be greatly impacted.
A substantial number of genomic and proteomic abnormalities playing causative roles in neoplasia have
been identified (8-11), and these successes are anticipated to translate to the clinic. Most of these successes
have been facilitated by an obvious molecular signature. Even with such clues, elucidation of these loci
usually requires substantial experimentation (12-14). However, most malignancy-locus relationships have
not yet been identified (15). Moreover, the majority of these relationships likely manifest as post-genomic
aberrations, such as dysregulated transcriptional or translational levels, which will complicate their
identification (16-18).
Only a few recently implicated molecular aberrations in neoplasia have yet employed computational
approaches as the primary means for discovery (19-25). Despite the availability of substantial genomic and
functional genomic data, these resources have not yet accelerated cancer locus identification; nor have they
shifted the approach in which these loci are usually identified to computation-centric methods. The amount
of available human molecular information is staggering, including a draft sequence of the genome,
determination of most functional cellular elements, identification of the vast majority of DNA-based
variation, and elucidated representative structures for most protein families (1,26-38). As the HGP continues
to generate large-scale genomic, functional genomic, and proteomic data sets, cancer researchers are finding
themselves awash in the means, but not the ends, in which to identify important molecular dysregulations.
We now have sufficient data in which to accelerate malignant disease research, but the tools in which
to manage and utilize this information are still lacking (11). Supplying these needs is of critical
importance for basic, translational, and clinical cancer researchers in order to create a “bench-to-bedside”
information pipeline.
The cancer knowledge universe. The quality and quantity of both basic and cancer-specific molecular
data already publicly available makes computationally-focused approaches to identifying molecular
aberrations in cancer feasible, but data sources are currently widely dispersed and poorly integrated, severely
hampering effective mining strategies. Some of the basic molecular genomic and proteomic data is
consolidated at several large distribution centers (33,39,40). These and other resources are invaluable as
entry points into genomic and functional genomic information. However, the approach taken by these
groups has largely been representational rather than comprehensive. In addition, many large data sets exist as
repositories rather than as curated sets. This has led to confusion and inefficiency among end-users of this
information.
A significant number of sizable cancer genomic and functional genomic data sets have been recently
generated and are available publicly, including lists of genes known to be involved in neoplasia,
chromosomal rearrangement compilations, whole-genome analyses of malignant cells, tumor expression
profiles, and locus-specific mutation databases (Appendix Table 1). Unfortunately, this substantial and
collectively comprehensive array of information is not yet well coordinated with either basic genomic or
clinical cancer data. There have been only sporadic efforts to compile together a wide range of cancer
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molecular data, most notably the Infobiogen Oncology Atlas, Cancer Genome Anatomy Project, and Human
Transcriptome resources (33,41-54). However, these data sets are not well integrated with each other, and
they rarely cross the generic molecular–cancer molecular and cancer molecular–cancer translational/clinical
information boundaries.
Conversely, clinical cancer information is comprehensive and well integrated within its own universe.
For example, the NCI’s CancerNet provides a wide-ranging resource for cancer professionals (55).
Contained within CancerNet are CANCERLIT and PDQ, which together provide access to the scientific
cancer literature, expert-drawn summaries covering various oncology topics, directories of physicians, a
clinical trials database, and epidemiological data (56-58). Other independent online resources provide
additional compilations of information useful to clinical oncologists (Appendix Table 1). However, while
this approximates a seamless clinical cancer knowledgebase, there is virtually no interconnectivity with
molecular information. We know of no group that is seeking to systematically assimilate molecular and
clinical cancer information in a comprehensive manner. This lack of integration requires researchers
themselves to connect data from various sources. Data mining that crosses sub-disciplinary boundaries, such
as determining “known drugs targeting genes within the region of chromosome 1p36.3 deleted in
neuroblastomas”, is extremely difficult.
Preliminary results. Our laboratory has created a process which collects available human genomic
information and integrates it into a single, comprehensive catalog (59,60). The aim is to deliver this data,
from as many sources as possible, to biomedical researchers involved in disease gene identification without
requiring bioinformatics and/or genomics expertise to successfully utilize these data. We first created a pilot
Internet resource (CompView) for human chromosome 1 in August, 1999 (61). This resource localized
genes, polymorphisms, and other genomic elements to precise chromosomal positions. We then integrated
additional genomic and functional genomic features relative to this localization framework, including SNPs,
DNA clones, cytogenetic anchors, and transcript clusters. The resulting integrated data set yielded a
genomic catalog with resolutions, order confidences, and element populations superior to previously
available resources (62). The CompView data is accessible through a web resource which has been widely
used and acknowledged by chromosome 1 researchers (61-66). CompView has aided candidate disease gene
searches for numerous groups, including localization of tumor suppressor loci for meningioma and
neuroblastoma (67,68).
A subsequent project (eGenome) seeks to integrate available human genomic and functional genomic
data by building upon the CompView procedure. The eGenome methodology: 1) creates integrated
foundations of objective genomic data representing each human chromosome; 2) increases depth to the
chromosomal foundations by adding supplemental structural genomic data sets; and 3) layers subjective
functional genomic and proteomic data onto the structural foundations. A large collection of DNA
sequence-defined elements was localized within the genome with four separate techniques: radiation hybrid
(RH), genetic linkage, cytogenetic, and DNA sequence localization procedures (1,27-29,69,70). From this
structural framework, integration of additional data sets was easily achieved. eGenome currently integrates
2.7 million genomic elements: 51,903 RH-based localizations, 12,461 genetic linkage-based localizations,
14,706 cytogenetic localizations, 51,334 DNA sequence-based localizations, 36,402 genes/EST cluster
representations, 116,608 large-insert DNA clones, and 2.5 million SNPs, altogether tracking 3.6 million
names and aliases. The resulting knowledgebase tracks both localizations and nomenclature in a systematic
and comprehensive manner. Because genomic elements usually have multiple independently determined
genomic localizations, this provides powerful quality control. Identified discrepancies are annotated as such
in the eGenome database and collectively analyzed for recurrent patterns indicating specific data source and
data analysis inaccuracies. Collection of a centralized genomics knowledgebase has also aided in precise
nomenclature management, allowing users to immediately collect non-redundant sets of genomic data. The
eGenome database structure is shown in Appendix Figure 1.
To disseminate eGenome to the public, we developed an Internet site providing graphical, ideographic-,
and text-based query options for data perusal. For text-based query results, users can perform a simple text
search using gene names or database IDs, define a region with two flanking markers, select a cytogenetic
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band in an ideogram, or choose a cytogenetic band or range from a list. Query result options include
definition of a defined region in a customizable Java applet. Query results display pertinent information
about the element of interest, including cytogenetic, sequence, RH, and/or genetic linkage positions;
transcript clusters; aliases; large-insert clones containing the element; and linked SNPs. Direct hyperlinks
are provided to element-specific data in external databases (e.g. GenBank records). Additional tools
incorporate analysis utilities. eGenome query and results interface examples are included in Appendix
Figure 2. Overall, the website incorporates or directly links to 50 external databases, thus creating elementcustomized data portals to a wide network of genomic, sequence, and functional data (62,64). The
eGenome website launched publicly in January, 2002 (61). Compilation of these data sets has allowed us to
study the human genome on a chromosome-wide basis, including cytogenetic band patterns on
chromosome 1 and sequence-chromosome breakage comparisons for chromosome 22 (62,71). The
eGenome project places our laboratory in a position to efficiently add disease-specific components to
this basic genomic knowledgebase.
eGenome creates a solid foundation of integrated genomic and, increasingly, functional genomic data.
We have designed a conceptual framework for appending cancer molecular data to this foundation, using a
single, unified database schema (Figure 1), which we provisionally call cGenome. This framework is a
conceptual model for how cellular, translational, and clinical information can be related together. The
central components are the categories Evidence and Malignancy. This relationship links disease
information to cellular information, and the links are provided by specific instances of evidence tying an
observed clinical phenotype to its corresponding observed molecular dysregulation event(s). The schema
has been designed to encompass all malignancy information; however, this proposal targets pediatric
neoplasia as a test case.
Genome
Literature
Clinical
Figure 1. Proposed skeletal database
structure for cGenome. Arrows depict
data flow direction. This structure is
discussed in greater detail later in the
proposal.
Using several publicly available
Genomic
curated cancer-specific data sets, we
have compiled lists of 4,759 cancervariations
associated loci and 466 established
Translational
gross chromosomal abnormalities
Transcriptome
for 84 malignant subtypes. These
Proteome
two lists have been cross-referenced
to each other through shared gene
symbols and also to their matching
Pathways
eGenome records. For example, the
t(9;22)(q34;q11) observed in AML,
ANLL, and CML is related to the genes ABL and BCR, to literature references characterizing the
abnormality in each leukemic subtype, and to base genomic data in eGenome (e.g. sequence position,
polymorphisms, and mRNA sequence). While this is a relatively preliminary and non-systematic procedure
for relating cancer genotypes and phenotypes, it has served as an instrumental first step in establishing
rigorous data handling and integration procedures.
Significance
Reductionist-based approaches to cancer research have been historically successful in providing an
exceedingly rich base of molecular and clinical observations. Optimal use of this knowledge requires novel
interdisciplinary and systems-based methods to understand malignancy in its entirety, which in turn
requires sufficient resources to organize, express, and deliver relevant information directly into the research
laboratory. The proposed project aims to take the first critical steps toward this eventual goal. An integrated
information resource providing instantaneous, comprehensive, and facile data-gathering functionality to
pediatric cancer researchers would be a dramatic improvement over inefficient "web-surfing" techniques
Evidence:
Variation
Transcription
Translation
Function
Malignancy
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often used for extracting public data. The cGenome project would give users an entry point into a
boundary-free universe of cancer knowledge spanning all facets of neoplasia. It would also provide
opportunities for discovering new or previously unrecognized associations between molecular and clinical
observations. For the current project, we are utilizing pediatric malignancy for the formulation of a working
prototype, which will be designed to be easily scalable for encompassing all malignant subtypes. This
project should help shorten the pediatric cancer gene discovery process, which could subsequently improve
our understanding and intervention of childhood neoplasia. In time, interconnectivity with eGenome and
other, similar research knowledgebases could establish a computationally-based information network
spanning all cancer-related fields in biomedicine.
Proposed Methods
Specific Aim 1: Compile an evidence set of observed pediatric clinico-molecular observations.
Approach. Aim 1 will be conducted in two phases. Phase I will concentrate upon associations between
cancer genomic abnormalities and malignant subtypes, with the goal of collecting a comprehensive set of
genomic observations in pediatric neoplasia. This will utilize a moderate number of established database
sources and a simplified ontology for describing clinico-molecular associations. Phase II will expand the
associations to transcriptional, functional, and cellular domains. This phase will also expand the ontology
syntax and vocabulary, the number of integrated data sets, and the association with cancer research
literature.
cGenome advisory board. We will assemble an external advisory board for this project. This advisory
board will be comprised of local researchers investigating the molecular genomics of various malignancies
(see Appendix). The board will advise the group in all aspects of the project, with emphasis on identifying
appropriate data sets for inclusion and means for efficient data delivery.
Phase 1: Utilization of established central data sets. We will collect lists of cancer-implicated genes,
gene mutations, and chromosomal abnormalities from existing publicly available lists (Appendix Table 1).
We have already collected a static version of most of these data sets. Any relevant databases that are
subsequently made available will be identified by periodic literature and Internet searches.
Data access and processing. All of our targeted central data sets have non-restrictive data re-distribution
policies, but the manner in which the data are accessible varies. Our Technology Transfer Office and/or
legal counsel will aid us with determination of whether individual data sets can be used for collective
redistribution. For each site, we will collect the relevant information either from a provided database dump
or by writing an automated retrieval agent to automatically retrieve and parse the set of web pages
comprising the requisite data, in a site-specific manner. Retrieved data will then undergo quality control
processing (Aim 3).
Simplified ontology creation. We will construct a simplified “molecular evidence in pediatric cancer”
ontology for use as the central kernel of the cGenome database structure. This will have the following
syntax:
REAL
M
ELEMENT
is
ACTION
in
MALIGNANCY
according to
SOURCE
This can be thought of as an expression of molecular evidence, with each capitalized phrase a variable class.
The vocabulary would include (but not be restricted to) the following:
Gene
WT1
Transcript MYC
Protein
p70S6K
is
is
is
mutated
in
over-expressed in
phosphorylated in
Wilms tumor
medulloblastoma
multiple
myeloma
according to
according to
according to
Reference A
Database B
Website C
Each class vocabulary set would be selected using various means. The REALM and ACTION categories
would be created after consultation with the advisory board, other cancer researchers, and reviews of the
literature. The ELEMENT category would be defined by eGenome and by curated external lists of cancer-
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associated genes. The SOURCE category would initially be populated by the integrated data sets as well.
Our consultant, Dr. Michael Liebman, will assist us with details of the ontology creation (see Appendix).
Data translation. For each central data set, we would convert each line of data into an evidence
expression conforming to our ontology syntax. This would require the creation of a parsing and conversion
algorithm specific for each data set. Each algorithm would be relatively straightforward, as the data
composition for each set is fixed by the external database curators, thus requiring only a limited number of
conversion functions. For example, one line of data from Infobiogen (46), after parsing, reads as:
Infobiogen | t(3;12)(q26;p13) | myeloid lineage: MDS in transformation, ANLL, BC-CML | Gene Name MDS1
Infobiogen in column 1 is identified as the source, t(3;12)(q26;p13) in column 2 is identified as the action,
etc., with each of the variable classes represented in the line of data. This line of data translates to three
element expressions, one for each malignancy. The final expressions would then be:
Gene
Gene
Gene
MDS1
MDS1
MDS1
is
is
is
translocated
translocated
translocated
in
in
in
MDS
ANLL
B-cell CML
according to
according to
according to
Infobiogen
Infobiogen
Infobiogen
In this way, expressions can be automatically generated by simple conversion algorithms reflecting the data
structure used by the data set. In fact, the data sets drive the ontology creation. For the example above, the
ontology would expand to include an additional class to capture the particular type of translocation. During
expression generation, this subclass dictates a database table listing all known cancer-associated
translocations.
Data standardization. Certain variable names will require adherence to established standards. Gene
names will be converted to official Gene Symbols, as designated by the HUGO Gene Nomenclature
Committee (HGNC), which eGenome already records (62,72). The eGenome locus-naming algorithm will
be used to convert non-standard gene or protein names used in the data set. Cytogenetic and gene mutation
labeling will adhere to ISCN and HUGO MDI standards, respectively (73,74). Malignancy classification
will follow the International Classification for Diseases in Oncology (ICD-O) guidelines (75). The ICD-O
scheme creates clinical and/or molecular marker-based sub-classifications for some malignancies, such as
separate categories for “Acute myeloid leukemia, t(8;21)(q22;q22)” and “Acute myeloid leukemia,
t(15;17)(q22;q11-12)”. We will convert this classification into an object-oriented hierarchy, such that both
entries above will be subclasses within a parent class “Acute myeloid leukemia”. This will enable us to
include malignancy data from sources not adhering precisely to the ICD-O guidelines. We will assess
integrated data set structures to determine when this hierarchical methodology will be necessary. Literature
citations will follow the MEDLINE standard (76). Assembled expression sets will then undergo quality
control routines to assure integrity (Aim 3).
Phase II: Ontology expansion. Subsequently, we will expand the ontology to include additional detail
and scope, which will largely be dictated by the content of newly identified data sets. Because of the
modular nature of the ontology, expansions to the vocabulary and syntax can be made easily, making this
approach highly scalable. We will primarily expand the ontology in two areas: tumor classification and data
source.
Introduction of malignancy sub-classifications. Integrated data sets may include sub-classifications
within a malignancy class, such as the clinical stage, histological information, site, clinico-biological marker
data, event frequency, and/or cell line/animal strain. We will expand our tumor classification ontology to
reflect these parameters. For example, the portion of the expression syntax relating to malignancy could be
expanded to:
in
FREQUENCY
of
HISTOLOGY
STAGE
SITE
TYPE
MALIGNANCY
with
MARKER(S)
a specific instance of which could translate to something like:
in
70%
of
undifferentiate
d
IV-S
adrena
l
primary neuroblastoma
s
wit
h
triploidy
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Staging information would adhere to the American Joint Committee on Cancer guidelines (77). Each class
would require creating a standardization and consolidation algorithm, which would translate a library of
terms such as “9 of 12 tumors” to “75%” or “late-stage neuroblastoma” to “stage III or IV neuroblastoma”.
Inclusion of literature: In Phase II, we will explore using the cancer literature itself to build additional
expressions. Optimally, we would use natural language text processing (NLP) to directly extract and build
expressions from abstracts or full-text cancer research articles (78,79). However, the lack of effective NLP
tools for biomedicine places comprehensive text mining beyond the scope of the current proposal. As an
approximation, we will parse the literature database using the Medical Subject Headings (MeSH) controlled
vocabulary provided by MEDLINE (76). We will acquire CANCERLIT, the cancer-specific subset of
MEDLINE from the National Library of Medicine (57,80,81). During the course of selecting variables for
each class in our evidence ontology, we will match MALIGNANCY and ACTION variable names with
existing MeSH terms (e.g. “Chromosome Deletion”). We will also search CANCERLIT record titles and
abstracts for gene/protein names. We will write a pattern-matching algorithm to compare the ACTION and
MALIGNANCY vocabularies, as well as the eGenome name/aliases list, against the title, abstract, and
MeSH headings of each CANCERLIT record. The set of all publications with one or more matches in each
of these three categories will be compiled, and a subset familiar to the laboratory and advisory board will be
evaluated as a validation set. As the ontology expands, additional classes will likely be eligible for crossreferencing to MEDLINE. Matching records will be related to their corresponding evidence expressions
through the SOURCE class.
Computational Considerations. Aim 1 is largely dependent upon computational approaches, but no
step requires a high level of computational expertise. The agent-based retrieval of core data sets, parsing of
the data sets, extraction of evidence expressions, and CANCERLIT parsing and pattern matching all can be
accomplished using straightforward programming in Perl. The CANCERLIT database (~5 GB) will be
manipulated using the 8-processor p660 server in our Bioinformatics Core Facility (which the PI directs).
Potential Difficulties/Alternatives. Certain candidate data sets may have restrictive data usage policies.
We will explore these by direct consultation with the database curators. Otherwise, we will either obtain a
relevant, unrestricted use subset of the data set, or provide query links through our website (Aim 2).
Certain core data sets might not be comprehensive for some classes. Because well-established and
commonly observed events are more prevalent in the literature, we anticipate that our evidence set will be
inclusive of these events (e.g. RB1 mutations in retinoblastoma), and thus will serve as the basis for a more
systematic, NLP-based, approach in the future. If it is evident that the evidence expression library is underrepresenting certain molecular aspects, we will explore developing a limited NLP approach, where certain
exact phrases are identified in the literature (such as “protein X methylates protein Y”).
The ACTION class may not be comprehensively defined by the central data sets. If this occurs, we will
explore alternate sources to either augment or completely provide definition of such class variables. For the
ACTION class, candidate sources include the MEDLINE MeSH headings and the Gene Ontology
Consortium’s GO terms (76,82). The latter is a dynamic controlled vocabulary of the molecular function,
biological process and cellular component of organismal elements. GO is comprehensive but not cancerspecific, so it would require modification. We are in the planning stage of incorporating GO into eGenome.
Specific Aim 2: Integrate this evidence set with existing molecular and clinical data.
Approach. Aim 2 will consist of identifying key additional molecular and clinical data sets for
inclusion, defining integration strategies for each data set, and developing a relational database management
system (RDBMS) to capture and relate this information.
Linking to eGenome. eGenome content is currently being expanded to include proteomic and functional
information. We will use eGenome as a comprehensive set of molecular objects, which will link to
cGenome via the ELEMENT class (gene/transcript/protein name). This relationship will relate the evidence
expressions to all molecular information regarding each ELEMENT (e.g. genomic sequence, DNA clones,
chromosomal position, polymorphisms, transcripts, protein structure, interacting molecules). This
relationship allows molecular-based queries of the evidence. Thus, searching for “9p21 and primary central
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nervous system lymphomas (PCNSL)” immediately provides genomic and proteomic information regarding
CDKN2A (p16), which would be related to 9p21 through eGenome and to PCNSL through the evidence
expression:
Gene CDKN2A is homozygously deleted in PCNSL according to Cobbers et al., Brain Pathol (1998)
The query would ask the database for all objects localized to the cytogenetic band 9p21 that are also related
to PCNSL. The result would provide a list of these objects, including CDKN2A, which if selected further
would list genomic and proteomic data for CDKN2A itself.
Additional molecular data sets. We will integrate additional relevant data sets to add depth to cGenome.
Candidate sets will be identified in consultation with the advisory board, from personal knowledge, and by
periodic literature and Internet searches. We will initially target data sets pertaining to gene mutations,
chromosomal abnormalities, and expression profiles. Appendix Table 1 lists 58 such target databases. To
effectively manage dependencies, we will usually link databases rather than importing data. For databases
with search interfaces, we will replicate the query URL. For example, if viewing the cGenome NF1 record, a
hyperlink that searches for NF1 in the NF1 mutation database will be displayed on our NF1 web page. A
few databases will warrant partial or full import into cGenome, such as the CGAP EST/SAGE data (50,51).
We will communicate with all data providers to facilitate data linking and/or data exchange, and to assist
with data management, maintenance, and dependency issues. For most sources, the core integrators will
either be a gene/protein symbol or a malignancy type. Over time, newly available data sets will be evaluated
for integration.
Clinical data. We will identify and investigate candidate clinical data sets in conjunction with the
advisory board. Target clinical data sets include clinical trials listing services, the SEER epidemiology data,
pediatric cancer registries, the NCI 3D drug structure database, malignancy summary texts, NIH-funded
grants, and clinical practice guidelines (Appendix Table 1). This integration will revolve around the set of
malignancies we construct (Aim 1). The majority of these data sets will integrate via specific links from
cGenome records, such as one-click searching from the cGenome AML record for all registered clinical
trials pertaining to AML.
Creation of a database structure. We will expand the eGenome RDBMS for this project. We will have
separate eGenome and cGenome web interfaces that link to each other through common elements (e.g.
genes implicated in malignancy), but are served by a single database. Records for molecular objects already
exist within eGenome. These molecular tables will link through the ELEMENT class of the evidence
expression syntax. ELEMENTS will be many-to-many related with the remaining classes of the cGenome
ontology (Figure 1). The remaining integrated data sets will populate the appropriate tables for each class:
mutational databases will relate to genes and/or malignancies depending upon their focus; transcriptional
profiles will relate to genes; genomic abnormalities will relate to the ACTION class; literature will relate to
the REFERENCE class; clinical information will relate to the MALIGNANCY class.
Project management. The number of databases being considered for inclusion is similar to the
number currently integrated by eGenome. As such, we recognize the dynamic nature of the data sets and our
critical dependency upon them. Accordingly, both global project management and component-based
dependency management of all project components will be acceded high priorities. Each proposed addition
will be assessed as to its impact upon all other components of the project. We will construct a dependency
map that quantitatively defines the interrelationship between each component, which will be used to identify
which components are potentially affected if an alteration of an existing component is made. As many of the
data sets are dynamic, we will periodically reintegrate these sets, either by communication of new releases
by source curators, or by automated periodic downloading, comparison, and integration of each data set.
Computational Considerations. Completion of Aim 2 will require a robust database/server
architecture, multiple dedicated database servers, multiple web servers, and mail/ftp capabilities. All of this
exists on a lesser scale for eGenome. We will scale up and modify our existing configuration for this
project.
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c/eGenome database. The database will be written using an Oracle-based RDBMS. Oracle is the most
mature, feature-rich, and cross-platform SQL database software available. All components of Oracle
required for this project are available free of charge through a site license from the University of
Pennsylvania (83). Database table construction and table inter-relationships will be created using the Oracle
Database tools package. Algorithms for mining, analyzing, and formatting central data sets will be written in
Perl on the staging server. cGenome’s application logic will be contained in stored procedures within the
database, written in PL/SQL or Java. Additional algorithms for analyzing and exporting data will also be
written within Oracle. Software configuration management and component dependency will be performed
using the UNIX-standard cvs and ‘make’ utilities, respectively. High-level project management will be
facilitated using Details 3 (AEC).
Operating system. The hardware configuration will run using Linux OS. Linux was chosen because of its
ability to be implemented on both desktop and high-performance server machines existing within the same
configuration. Linux provides a tool-rich, stable, high-performance operating system with strong industry
support. It also allows our database to be independent of any particular vendor’s hardware. Numerous stable,
pre-packaged distributions of Linux for all major platforms are available (e.g. RedHat, SuSE).
Hardware configuration. The current state, expected initial launch, and expected year 3 hardware
configurations are detailed in Appendix Figure 3. By year 3 of the project, the configuration would project
to two load-balanced database servers with built-in redundancy, a development server, three parallel web
servers, and a mail/ftp server. All servers would be dedicated solely to cGenome/eGenome and housed in a
limited access, climate-controlled data center. The two main database servers will be connected to a 1 Gbps
Ethernet Storage Area Network (SAN), which will facilitate rapid replication of the database. Also on the
SAN will be a third database server for development, staging, and to run the mass storage backup device.
Potential Difficulties/Alternatives. The large number of potential data sources will need to be carefully
selected and managed in terms of integration levels and data maintenance. To manage these challenges, we
will: 1) work closely with our advisory board to prioritize candidate data sets; 2) also prioritize data sets by
genome >proteome >clinical; 3) integrate in a graduated fashion as necessary, by hyperlinks only >import
of database identifiers only >complete integration; and 4) holding database integration/upkeep effort
constant. This strategy has worked well for eGenome. We will also strictly require proper documentation of
each new component of the structure, followed by integration into our dependency map for systematic site
management.
We have calculated approximate data serving rates that the proposed system configuration will be able
to deliver. This rate appears to be sufficient to easily handle perceived demand. If it cannot, it is
straightforward to scale cGenome by adding database servers in parallel, as the database is not transactional.
If this is still insufficient, we can utilize our Bioinformatics Core Facility's 8-processor AIX-based server as
an additional Oracle database engine. Our system architecture could also easily accommodate alternative
database software.
Unanticipated loss of eGenome support would not greatly effect cGenome operation or integration with
any existing eGenome data, but it would potentially hinder the development of integrated functional
molecular data. In this event, we would concentrate more upon genomic and functional genomic aspects of
the project and would make every effort to supplement the cGenome data with the most critical
proteomic/interaction data sets.
Specific Aim 3: Analyze and annotate the knowledgebase to improve accuracy and integrity.
Approach. As cGenome manages information only as a third party, much conflicting evidence would
not be able to be disambiguated. Therefore, our efforts will rely more upon identifying and annotating
conflict than attempting to “correct” discrepant data. In some cases, persistent error patterns may afford an
opportunity for direct intervention. Our strategy is to interconnect data quality/data integrity checks at all
points of the process.
Standardization of data sets. We will carefully craft the initial scripts for reducing the central data sets to
their evidence expressions. For each ontology class, we will create a “standard” vocabulary. This will be
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straightforward for those classes with pre-supplied standards (e.g. MALIGNANCY) but will require more
effort for other classes (e.g. ACTION). The central data sets will first be converted to tab-delimited files,
with each column corresponding to a data subtype that roughly translates to an ontology class. We will
compare all entries in a data column against the corresponding class vocabulary list. Non-matching entries
will be candidates for either translation to a vocabulary standard (i.e. n-myc to MYCN), or directing
modification of/addition to the vocabulary. These mismatches will be investigated manually to determine
the class of mismatch and what the proper solution should be. Each iteration with another added data set
will serve as a training set, with the result being both a more precise and complete vocabulary, and a more
robust converting script. Each class training will initially be labor-intensive but will become more
automated with every subsequent iteration. A similar procedure will be employed for integration of any
peripheral data sets.
Identification of redundant terms. The data set reduction scripts will also be written to detect redundant
terms, both before and after individual data sets have been translated into evidence expressions, and after
each merge of expressions between data sets. These terms may manifest from completely redundant entries
in two different data sets, or from related entries in the same or different data sets that are translated into the
exact same expression (22q11 deletion in PNET vs. 22q11 loss of heterozygosity in PNET). We will
carefully inspect the set of “identical” terms to ensure integrity. For example, “22q11 deletion” is not
equivalent to “22q11.2 deletion” and requires hierarchical object orientation for resolution. This reiterative
training process will incrementally improve both the redundancy algorithm and the evidence expression set.
Internal quality control. We will use various standard techniques for ensuring that our own data
maintains integrity over time. Quality control checks will be written into each stored procedure that the
database or an external script executes, including tracking file sizes, data record counts, and incorporating
database field entry constraints. These procedures will greatly assist in the reduction of internallyintroduced errors.
Discrepancy annotation and resolution. We will annotate discrepancies by marking an expression
variable as discrepant. Besides being investigated further, these discrepancies will be annotated on the web
display. An example of this would be two independent and discrepant reports of the identical (2;13)
translocation in alveolar rhabdomyosarcoma (ARMS): one involving PAX3 at 2q35 and another incorrectly
reporting the breakpoint at 2q34. After annotation in the database (and subsequent follow-up after
flagging), the associated web records would display a prominent notification such as: “This translocation
breakpoint has been reported to map to two different cytogenetic positions.”. Conflicting data will be
manually investigated. In some cases, gross data errors or other recurrent patterns of conflict may be
identified for certain data sources. These will be investigated further and corrected if possible, preferably in
consultation with the data provider.
Conflicting expressions. We will explore annotating evidence statements which seemingly conflict
biologically. We will study the ACTION class to identify contrasting terms, such as over-expressed/underexpressed, amplified/deleted, or phosphorylated/dephosphorylated. If such contrasting terms appear in
otherwise identical expressions, they may constitute opposing observations worthy of annotation. We will
then analyze the evidence expression set for examples of ACTION-contrasted expressions and study these
further. Annotations of this type approximate expert review commentary; however, these automated
conflict annotations will require careful intervention.
Computational Considerations. Aim 3 requires only a moderate level of computational expertise. All
of the data checking routines can be accomplished using straightforward programming in Perl.
Potential Difficulties/Alternatives. Our conflict detection/resolution strategy will likely miss some
errors, but the rate should be lower than for the component data sources. We are prepared to accept a certain
low level of error and will pursue conflict resolution only if globally beneficial. Most evidence expressions
will have supportive literature citations, which are themselves sources of conflict resolution for end-users.
Specific Aim 4: Create a web resource for public dissemination of the compiled information.
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Approach. We seek to both deliver immediately critical information pertaining to a user’s specific
interest, and to simultaneously provide an extended network to relevant supportive data stored elsewhere on
the Internet. This resource will be freely available without restrictions to the public via the Internet.
Website structure. The basic layout of the web resource will follow the eGenome model, which employs
a dynamic data query/data result schema integrated with static support content. The cGenome site will
include a variety of search interfaces which produce object-specific data records; introductory, navigatory,
and methodology sections; a data repository; and an interwoven help/tutorial section. Global organization,
layout, search/retrieval strategies, and feature additions will be reviewed with the advisory board. Site
design, layout, and authoring will be performed using a combination of software packages (Macromedia,
Adobe).
Query interface. Fields within the ELEMENT, ACTION, MALIGNANCY, and SOURCE classes will
be indexed. This will allow users to search for any combination of molecular and clinical terms, such as
“manuscripts citing transcripts over-expressed in advanced stage germ cell tumors” or “SNPs within genes
with inactivating mutations in osteosarcoma with active clinical trials”. The ontology syntax will guide
query interface construction. For example, a malignancy pop-up window would list all malignant types
within our malignancy vocabulary, and the user could select the one of interest to narrow the query
parameters. We will provide Boolean string text search and both text- and ideographically-defined genomic
localization search interfaces. We will also develop a customizable query page where a user can input any
combination of molecular and/or clinical terms. Query result options will include choices for sorting or
selecting data according to different criteria (e.g. chromosomal location, presumed protein function),
allowing a user to customize the result formatting. Our advisory board will play a key role in
conceptualizing these interfaces.
Query results. Query results will display all collected information about the object queried. There will be
two initial record types: Molecular Objects (genes/transcripts/proteins), and Malignancies. Molecular Object
records will include object name, aliases, description, and type; genomic position; polymorphisms; DNA
clones; cancer-specific aberrations; transcripts, ESTs, tissue distribution, and transcriptional profiles; protein
function; interacting molecules; pathways; and protein structures. Also, the Molecular Object records will
list all evidence expressions, grouped by malignancy, that the object is associated with. In the example
“CDKN2A is homozygously deleted in PCNSL (Cobbers et al., Brain Pathol 8:263-276, 1998)”, CDKN2A
would be linked to the eGenome CDKN2A record, PCNSL would be linked to the cGenome PCNSL record,
and the reference would be linked to its PubMed entry. Malignancy records would include a text summary
of the malignancy, subtypes, stages, clinical and biological markers, epidemiological data, clinical trials,
NIH-funded grants, and a list of all evidence expressions pertaining to the malignancy, grouped by
molecular object type. For both record types, a portion of this information will be listed directly in the object
record, some will be summarized versions of (and linked to) full corresponding records in eGenome
(including our genomic graphical viewer), and some will link to external databases. External links will be
object-specific whenever possible and will be created on-the-fly by the database. Summary tables, which
would be returned if more than one entry fit a query, would list a quick summary of each matching record.
Supportive content. Considerable effort will be placed into designing a facile website, and into adding
sufficient support material which is accessible and intelligible. Foremost will be a series of help pages
covering all aspects of the site. All input and output pages will have direct links to the proper help pages or
to term definitions. We will also include overview and methods sections, a site map, navigation tools, and
an interactive “getting started” tutorial. We will expand our existing eGenome email-based support and
feedback management systems for tracking and responding to user questions, for cGenome. We will also
create a data repository containing the entire database holdings to facilitate power users and data sharing.
Site management. The initial and subsequent releases will be beta-tested by in-house staff using a variety
of operating system/browser platforms. Beta testing will employ a routine incorporating a standard set of
page accesses and queries, with actual and expected results compared. We will track the operating systems
and browsers used by c/eGenome site visitors and will aim for compatibility with the most popular
combinations. After in-house beta testing, we will release the site to our advisory board and their
laboratories for further testing. We will log all visits and requests using the software package Analog (84).
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Most importantly, the website and its components will be included in our dependency management system
(Aim 2).
Resource promotion. Our institution is strongly supportive of this project (see Appendix). We will
work with various departments in our institution to broaden exposure to the resource in the biomedical
community and the general public. This will include press releases, scientific publications, scientific
meeting announcements, and email/website post notifications.
Computational Considerations. Completion of Aim 4 will require a robust web hosting configuration
that seamlessly interfaces with the c/eGenome database architecture. We will scale up and modify the
existing eGenome configuration for this project. cGenome is strongly supported by our Information
Technology Department (see letter of support), who will assist with the network aspects of this project when
needed.
Hosting hardware and software. When mature, cGenome will consist of two scalable, load-balanced web
servers (Aim 2 and Appendix Figure 3). For web serving, we will employ AOLserver hosted within a UNIX
environment (85). AOLserver is available on virtually all UNIX-like operating systems, allowing cGenome
to be hardware-independent, and it can be interfaced with Oracle using a third-party database driver (86).
Web requests. User queries posted through the website to the database will be handled by presentation
logic scripts written in TCL. All query returns will be dynamically generated (on-the-fly) HTML, generated
by and mediated through TCL-encoded scripts. Other page requests will be retrievals of static html files.
Email messages and ftp requests will be handled by UNIX operating system-supplied software. We will also
employ hyperlink validity software to automatically determine if hyperlinks to various databases retain
integrity (87).
Network and security. All servers will be connected to the LAN, currently 100BaseT, with a dedicated
hub to the intranet backbone. Internally, the network consists of a 1 Gbps fibre ring connecting all main
hubs. The Internet gateway is currently two load-balanced DS3 45 Mbps connections with redundant main
routing stations and ISPs. The servers will be protected by a dual load-balanced firewall (Check Point).
Site maintenance. All machines will be supplied with emergency backup power as well as a UPS backup
power unit (Symmetra 8kVA, APC). All servers will reboot automatically if power is lost and then restored,
and all c/eGenome services will recommence automatically upon reboot. We will employ redundant scripts
to poll the web and database servers at 1-minute intervals. Consecutive failures will result in automated
notification of multiple staff members. Remote reboot hardware will allow system administrators to reboot
the servers by telephone. The loss of a single machine will not disrupt service because each service (ftp,
http, database) will be provided by two servers. Full on-site and off-site backups of all hard drives will be
provided by a Dell PowerVault 120T Autoloader. Maintenance of the site beyond the funded period would
likely be through subsequent and parallel grants; however, a static cGenome site could be hosted indefinitely
at minimal expense.
Potential Difficulties/Alternatives. We project this hosting configuration to easily meet all server
demand; however, we have support from our institution to provide increased bandwidth solutions, such as
upgrading the Internet gateway capacity or creating a dedicated 2nd gateway to the ISP, if necessary. Our
web server configuration is linearly scalable. If linear additions to the number of web servers is inadequate,
we would instead configure the Bioinformatics Core Facility’s 8-processor server as the primary web server.
We have consciously created an architecture that is hardware- and software-independent. For each web
configuration component, numerous alternatives are available. The hardware can be supplied by a number of
vendors. Various other web server and scripting language combinations are also possible.
Overall timeline
Preliminary identification of cancer genomic data sources has already been completed. We anticipate the
initial phase of data integration, consolidation, and management to be complete by 12 months. Site design
would take place in parallel. We project an initial cGenome launch date at approximately 18 months.
Further data additions and improvements to the site, configuration, database, and ontology would occur in
year 2 and would be the primary focus of the work in year 3.
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Page 19
Appendix Table 1: Candidate databases to integrate into cGenome
Database
Data formatting standards
American Joint Committee on Cancer: Cancer Staging Manual
HUGO Gene Nomenclature Committee
International Classification of Disease for Oncology
International System for Human Cytogenetic Nomenclature
PubMed/MEDLINE
General cancer databases/resources for Phase I inclusion
@cancer
Cancer Genetics Web
CancerGene
CANCERLIT
cancernetwork.com
CancerSourceMD.com
Children’s Cancer Web
Children’s Oncology Group
Computer Retrieval of Information on Scientific Projects
dbEST Oncogene/Tumor Suppressor Gene List
NCI Human OncoChip Genes List
The Waldman Group: Cancer Genes by Chromosomal Locus
General mutation databases/initiatives
HmutDB
Human Gene Mutation Database
HUGO Mutation Database Initiative
Online Mendelian Inheritance in Man
Universal Mutation Database Initiative
Locus or disease-specific mutation databases
Androgen Receptor Mutations Database
GeneDis: APC-Familial Adenomatous Polyposis Database
APC Mutation Database
Ataxia-Telangiectasia Mutation Database
International Collaborative Group on HNPCC
MDM2 Database
MEN1 Mutation Database
NNFF International NF1 Genetic Mutation Analysis Consortium
NF2 Mutation Map
PTCH Mutation Database
RB1-Gene Mutation Database
SUR1 Mutation Database
Human p53 database
p53 Database
p53 Mutation Database
International Agency for Research on Cancer TP53 database
Tuberous Sclerosis Mutation Database
TSC Variation Database
VHL Mutation Database
WT1 Mutation Database
Chromosomal abnormalities
Atlas of Genetics and Cytogenetics in Oncology and Haematology
Cancer Chromosome Aberration Project
Cancer Genome Project
Chromosomal Abnormalities in Cancer
Online CGH Tumor Database
Resources for Molecular Cytogenetics
Appendix Table 1 cont.
Host
Reference
AJCC
HUGO
WHO
ISCN
Natl. Library of Medicine
(77)
(72)
(75)
(73)
(76)
@Life.com
CancerIndex.org
Infobiogen
NCI
SCP Communications
iKnowMed
CancerIndex.org
COG
NIH
NCBI
NCI
UCSF
(88)
(89)
(41)
(57)
(90)
(91)
(92)
(93)
(94)
(33)
(42)
(95)
EMBL
U. of Wales
HUGO/EBI
NCBI
U. Rene Descartes
(96)
(97)
(74)
(33)
(98)
McGill U.
Tel Aviv U.
Institut Curie
Virginia Mason U.
U. Leiden
City of Hope
GENEM
NNFF
Harvard U.
Karolinska Institutet
Institut für Humangenetik
U. Rene Descartes
U. North Carolina
Institut Curie
U. Tokyo
IARC
U. Wales
Harvard U.
U. Rene Descartes
U. Rene Descartes
(99)
(100)
(101)
(102)
(103)
(104)
(98)
(105)
(106)
(107)
(108)
(98)
(109)
(110)
(111)
(112)
(113)
(114)
(115)
(116)
Infobiogen
NCI
Sanger Institute
U. Wisconsin
U. Charité
U. Bari
(46)
(54)
(117)
(44)
(45)
(43)
Page 20
Transcriptional Profiling Data
Cancer Genome Anatomy Project (ESTs, SAGE)
Human Cancer Genome Project (ESTs)
The Human Transcriptome Map (SAGE)
Clinically-oriented resources
Clinical Trials Listing Service
CyberMedTrials
Drug Information System 3D database
National Guideline Clearinghouse
PDQ Cancer Information Summaries
PDQ Clinical Trials Database
Surveillance, Epidemiology, and End Results Program
NCI
Ludwig Institute, Brazil
U. Amsterdam
(47)
(118)
(119)
CenterWatch.com
CyberMedtrials.org
NCI
AHRQ
NCI
NCI
NCI
(120)
(121)
(122)
(123)
(56)
(56)
(58)