Butte, Atul J.

Hunter, Lawrence

Lussier, Yves A.

PubMed

Elsevier - Publisher Connector 

Current methodologies for translational bioinformatics 

Journal of Biomedical Informatics 43 (2010) 355–357Contents lists available at ScienceDirect
Journal of Biomedical Informatics
journal homepage: www.elsevier .com/locate /y jb inGuest Editorial
Current methodologies for translational bioinformaticsSince the completion of human and model organism genomes,
integration of computational methods in biomedical research is
accelerating and becomes an inexorable facet of the 21th century
biomedical research. Translational bioinformatics, which aims at
developing storage, analytic, and interpretive methods to optimize
the transformation of increasingly voluminous biomolecular data
into proactive, predictive, preventive, and participatory health
[1], has rapidly evolved from an emerging ﬁeld into a complex
multidisciplinary science intersecting with nearly all areas of bio-
logical, biomedical, and clinical research. Indeed, molecular bioin-
formatics and clinical informatics plus statistical genetics and
genomic medicine – the core areas of translational bioinformatics
– jointly are enticed to play an ever-increasing role in accelerating
the translation of knowledge discovery from genome-scale studies
to hypothesis-driven biological modeling or effective treatment as
well as tailored disease management or prevention.
In the era of molecular medicine, determinant factors that are
pivotal to the realization of personalized medicine have congre-
gated momentous opportunities for translational bioinformatics
[2]: (i) the steadily improving availability and cost reduction of
molecular measurements, (ii) public accessibility to molecular
measurements in healthy and disease states (e.g., Gene Expression
Omnibus), (iii) the evolving need to share molecular data and tools,
(iv) the increasing expectation that clinicians should synthesize
and interpret new discoveries in molecular medicine for their pa-
tients’ beneﬁt, and (v) substantial increases in research funding
for translational bioinformatics (e.g., NIH Roadmaps, Clinical and
Translational Science Awards).
The time for genomic medicine has arrived and recent develop-
ments promulgate an extraordinary opportunity for translational
biomedical informatics research and development to play a key
role, since novel discoveries in this ﬁeld will lay the foundations
of genomic medicine for decades to come. Undeniably, transla-
tional bioinformatics may lead to a pivotal moment in history
when the fundamental discovery of the molecular underpinnings
of diseases will be timely and efﬁciently translated into enabling
technologies that bring the goal of personalized medicine within
our grasp. The past success of clinical informatics in transforming
medical research and patient care is merely a glimpse into the
remarkable promises that this energizing ﬁeld holds in an era of
accelerated advancements in molecular medicine.
This special issue highlights major computational advances in
the ﬁeld: (i) ﬁnding molecular mechanisms of therapies and for
the underlying disease, (ii) bridging gaps between genetics discov-
eries and clinical practice, and (iii) building infrastructures en-
abling translational bioinformatics research.
Novel modalities for molecular measurements, including those
for epigenetics, interactions, and proteins, will continue to emerge1532-0464/$ - see front matter  2010 Elsevier Inc. All rights reserved.
doi:10.1016/j.jbi.2010.05.002each year. In this special issue, Chen et al. present customary
molecular clinical measurements, such as creatinine and alkaline
phosphatase, as new predictive biomarkers for aging [3]. These
regression models, conducted for chronological ages of adolescents
in the 2001–2002 National Health and Nutrition Examination Sur-
vey (NHANES) datasets [4] and validated using the 2003–2004
data, satisfactorily match the results seen in the aging process
using cohorts collected from a hospital-based electronic health re-
cord system, and holds the potential to identify adolescent devel-
opment-related disorders. This approach is likely scalable to
model and identify other clinically relevant molecular differences
between males and females as well as underlying pathophysiolog-
ical processes. It exempliﬁes the ‘‘reverse translational bioinfor-
matics” approach in which clinical data are used to gain insight
into basic biological phenomena.
In contrast, the second article illustrates the ‘‘forward transla-
tional bioinformatics” approach where primarily biomolecular
computations provide mechanistic insight into the pathophysiol-
ogy of diseases [5]. Liu and Zhao improve on modeling the under-
pinnings of ‘‘conformational diseases”, such as sickle cell anemia
and transmissible spongiform encephalopathy caused by misfold-
ing proteins or prions. Their algorithm can identify misfolding sites
with a 93% accuracy, by comparing secondary structures of homol-
ogous segments and calculating the probabilities of a local protein
segment shifting its native secondary structure to a pathogenic
misfolded secondary structure. The gold standard consists of the
22 known proteins responsible for conformational diseases for
which usable structural information is available among 31 such
disease proteins. Of noteworthy importance, among their validated
predictions, the authors identify conformation changes of tran-
scriptional factor p53. This algorithm, or ones that are similar,
may bear signiﬁcant importance for predicting the impact of an
individual-speciﬁc mutation on the structural changes in disease
genes when personal genome sequencing becomes affordable. In
other words, such algorithms may allow for disease predictions
made based on the genomic information of a particular individual
(n = 1). This approach will likely lead to the identiﬁcation of addi-
tional conformational disease-associated genes.
The next paper employs an ‘‘integrative translational bioinfor-
matics” approach to analyze jointly clinical and molecular datasets
[6]. Speciﬁcally, Yang et al. present a novel in silico method to pre-
dict mechanisms of toxicities caused by the off-targets inhibition
of kinases. Kinase inhibitors are an increasingly popular class of
medication for pharmacotherapy of cancers. Although initially
developed as speciﬁc inhibitors, subsequent studies show that they
typically inhibit multiple kinases with varying potency. Preclinical
animal testing fails to provide accurate predictions of the numer-
ous rate-limiting toxicities encountered in clinical trials. Indeed,
356 Guest Editorial / Journal of Biomedical Informatics 43 (2010) 355–357exotic species of animal models are required for speciﬁc toxicities.
For example, rodents are highly resistant to, and thus not optimal
for, modeling nausea and vomiting. Here, the authors reviewed
extensively the adverse events reported with clinical trials of ki-
nase inhibitors. They propose a novel quantitative computational
method to predict associations between kinase targets from a
physical kinome map and kinase inhibitor toxicities derived from
clinical trials. The kinome map consists of published binding afﬁn-
ity between kinase inhibitors (compounds and medications) and
kinases. Forty-one prioritized associations between 7 adverse
events and 19 kinase targets were identiﬁed and 8 associations
were conﬁrmed by the literature. This in silico pharmaco-toxicity
clinical trial is potentially useful in uncovering the toxicities of hid-
den off-targets and provides clues for improving the drug efﬁcacy.
Further, this approach can identify rate-limiting toxicities that will
be missed in pre-clinical animal model testing so that the antici-
pated toxicities will be considered during the decision making pro-
cess of initiating clinical testing of a new class of agent or a new
combination rather than startlingly observed at a later clinical trial
stage.
There are two studies describing observations derived from
meta-analyses of multiple expression array datasets in cancers.
Chen et al. unveil a molecular network of breast cancer connecting
ten gene expression signatures that share concordant prognostics
and paradoxically showed limited gene overlap [7]. This is
achieved through protein interaction modeling. Their study pre-
dicts seven well established mechanisms of breast cancer oncogen-
esis and progression to relate eight of the ten expression
signatures. They further conﬁrm the inherent ability of this 54-
gene ‘‘network-signature” to identify ‘‘poor outcome” robustly in
estrogen-receptor positive breast cancer using a Kaplan–Meier
analysis. This approach demonstrates the feasibility of developing
a mechanism-anchored road map to understand, utilize and inter-
pret cancer gene expression signatures in clinical settings or for
biomarker discovery. In another study, Ancona and colleagues
identify 53 overlapping pathways among distinct datasets of pa-
tients affected by colorectal cancer [8]. These predictions stem
from independent analyses of four distinct gene expression data
sets comparing two well-established gene set analyses and enrich-
ment methods. Meticulous literature review of common pathways
across the datasets (e.g. cell cycle) demonstrates the signiﬁcance of
the uncovered pathways in colorectal cancer. In both studies, Chen
et al. as well as Ancona and colleagues point out that, while anal-
yses carried over gene expression datasets such as proﬁling,
enrichment or signatures have surprisingly little gene overlaps,
their approaches uncover biologically and clinically-relevant
mechanistic concordance among gene expression datasets.
Over many decades, thousands of single gene inheritances have
been reported in human diseases through laborious conventional
genetics science. In the last three years, over 250 novel gene poly-
morphisms have been discovered in high-throughput genome-
wide association studies (GWAS), of which the majority are associ-
ated with diseases of complex inheritance (complex diseases) such
as diabetes mellitus or essential hypertension. The next set of pa-
pers illustrates that molecular bioinformatics methods are increas-
ingly required for the identiﬁcation of additional disease gene
candidates over the plethora of new high-throughput genetic
platforms.
Shen and colleagues describe the design, implementation, and
evaluation of SNPit, a knowledge-based system that incorporates
probabilistic and logical reasoning about the clinical consequences
of the speciﬁc physical location of a single nucleotide polymor-
phism (SNP) by integrating point and regional alterations of SNP
annotations (e.g. protein structures, gene function, pathway func-
tion) [9]. SNPit is evaluated by comparing its predictions over
250 established disease SNPs with 250 randomly selected ones.Measured accuracies vary from 65% to 85% according to different
algorithms relying on combinations of decision trees with a priori
and a posteriori probabilities, resulting in a sensitivity of 85%. In
another study, Riva et al. propose the Select and Test Model
(ST-Model), a scalable epistemological framework designed for
high-throughput translational bioinformatics experiments [10].
The ST-Model incorporates automated reasoning principles origi-
nating from the ﬁelds of cognitive science and artiﬁcial intelligence
to formalize and operationalize ﬁve logical operations of discovery
science: (i) abstraction, (ii) abduction and ranking, (iii) deduction,
(iv) induction, and (v) hypothesis-space maintenance. In order to
demonstrate the ST-Model’s usefulness with a case study, the
authors identiﬁed the Wnt pathway from single nucleotide poly-
morphisms prioritized in a GWAS study of adult-onset diabetes
mellitus and validated the ﬁnding in a second distinct study of
juvenile-onset diabetes mellitus (p < 1%; Wilcoxon test). While
the predicted pathway is well-characterized in diabetes, this study
demonstrates the applicability of the ST-Model to recapitulate the
result with an original analysis of GWAS studies, which have not
otherwise been considered useful for pathway prioritization.
With the rapidlyplunging costs ofwhole genomesequencing, the
opportunities for translational bioinformatics discoveries to affect
the interpretation of personal genome maps are escalating. Krise-
man et al. developed BING, an integrated pipeline of methods for
automatically deriving nucleotide base-calls from raw signals pro-
ducedbynext generation sequencing (NGS) [11]. BING’s innovations
are evident in (i) image alignment, (ii) signal correlation, compensa-
tion and separation, as well as in pixel-based cluster registration,
(iii) signal measurement and base calling, and ﬁnally in (iv) quality
control and accuracy measures. A superior accuracy and time efﬁ-
ciency is demonstrated when it is benchmarked against the com-
mercially-available Illumina Genome Analysis Pipeline.
The following two manuscripts propose methods that enable
integrative translational bioinformatics research. Song et al. pio-
neer the data attributes of TMA-OM and TMA-TAB, the ﬁrst com-
prehensive format for exchanging tissue microarray (TMA) data
[12]. Tissue microarrays are particularly well adapted for cancer
research, as they consist of parafﬁn blocks in which up to 1000 sep-
arate miniature tissue cores are assembled in array fashion to al-
low multiplex histological or immunohistochemistry analyses.
TMA-TAB is a foundational data representation formalism that is
expected to facilitate the creation of a broadly accepted standard
and corresponds for TMAs to the MIAME format of gene expression
arrays. To accelerate clinically relevant genomic hypotheses, Chen
and Sarkar present a method to link based-pair sequences of Gen-
Bank with relevant clinical trials [13]. MeSH terms mutually occur-
ring in GenBank, PubMed, and Clinicaltrials.gov are automatically
discovered and annotated. Thirty percent of GenBank sequences
are thus linked with 90% of clinical trials in ClinicalTrials.gov.
Meaningful relationships are identiﬁed in a cursory evaluation.
This system may allow high-throughput generation of relevant
hypotheses for clinician trialists.
Finally, Weng and colleagues close this special issue with a re-
view of literature on formal representation eligibility criteria of pa-
tients for clinical trials [14]. These formalisms are required for
accelerating clinical trials, for automated patient screening, and
for computer-interpretable clinical guidelines. With the acceler-
ated discovery of biomarkers and gene expression signatures, the
automations are of prime importance for quality control as the
complexity of clinical trials is increasing with greater patient strat-
iﬁcation, increasingly complex ‘‘conditional” treatment plans, and
the added challenges of uniformity of protocol-based patient man-
agement across a larger number of clinical research organizations.
The authors review 27 models using their categorization of data,
expressiveness, and representation of patient data and of medical
concepts, use cases, and domains.
Guest Editorial / Journal of Biomedical Informatics 43 (2010) 355–357 357It is interesting to reﬂect back on howmuch has already changed
in biomedicine since we issued the Call for Papers for this special is-
sue. Already,wholehumangenomesequencing is becoming routine,
as entire families are sequenced and the causes of rare Mendelian
disorders are identiﬁed with handfuls of individuals, while plat-
forms for genotyping have again doubled in capacity. Hundreds of
speciﬁc cancers are nowbeing sequenced formutations, and already
the ﬁrst individual sequences have been released from the 1000Hu-
man Genomes project. Despite these impressive gains, the storage,
analysis, and interpretation of molecular measurements and entire
genomes is far from trivial or inexpensive, while their clinical inter-
pretations remain as extremely challenging and vexing problems.
We hope that this special issue will stimulate and catalyze transla-
tional bioinformatics research and development to embrace the
era of personal and genomic medicine.Acknowledgments
We thank Dr. Haiquan Li for his critical review of the editorial.
This work was supported in part by the NIH/NCRR Clinical & Trans-
lational Science Awards (1U54 RR023560-01A1), the NIH/NLM/NCI
National Center for Multiscale Analyses of Genomic and Cellular
Networks (MAGNET, 1U54CA121852), the Cancer Research Foun-
dation, The University of Chicago Cancer Research Center, and
The Ludwig Center for Metastasis Research.References
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Guest Editors
Yves A. Lussier
Center for Biomedical Informatics,
UC Institute for Translational Medicine,
Institute for Genomics and Systems Biology,
The University of Chicago, Chicago,
IL 60634,
USA
E-mail address: ylussier@medicine.bsd.uchicago.edu
Atul J. Butte
Departments of Pediatrics and Medicine,
Stanford University, Stanford, CA 94305, USA
Lawrence Hunter
Center for Computational Pharmacology,
Computational Bioscience Program,
Department of Pharmacology,
University of Colorado Health Sciences Center,
Denver, CO 80262, USA


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Current methodologies for translational bioinformatics

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