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2
Omics-Based Clinical Discovery:
Science, Technology, and Applications
Since the process of mapping and sequencing the human genome began,
new technologies have made it possible to obtain a huge number of molecu-
lar measurements within a tissue or cell. These technologies can be applied
to a biological system of interest to obtain a snapshot of the underlying
biology at a resolution that has never before been possible. Broadly speak-
ing, the scientific fields associated with measuring such biological molecules
in a high-throughput way are called “omics.”
Many areas of research can be classified as omics. Examples include
proteomics, transcriptomics, genomics, metabolomics, lipidomics, and
epigenomics, which correspond to global analyses of proteins, RNA, genes,
metabolites, lipids, and methylated DNA or modified histone proteins in
chromosomes, respectively. There are many motivations for conducting
omics research. One common reason is to obtain a comprehensive under-
standing of the biological system under study. For instance, one might
perform a proteomics study on normal human kidney tissues to better
understand protein activity, functional pathways, and protein interactions
in the kidney. Another common goal of omics studies is to associate the
omics-based molecular measurements with a clinical outcome of interest,
such as prostate cancer survival time, risk of breast cancer recurrence, or
response to therapy. The rationale is that by taking advantage of omics-
based measurements, there is the potential to develop a more accurate pre-
dictive or prognostic model of a particular condition or disease—namely, an
omics-based test (see definition in the Introduction)—that is more accurate
than can be obtained using standard clinical approaches.
This report focuses on the the stages of omics-based test development
33
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34 EVOLUTION OF TRANSLATIONAL OMICS
that should occur prior to use to direct treatment choice in a clinical trial.
In this chapter, the discovery phase (see Figures 2-1 and S-1) of the recom-
mended omics-based test development process is discussed, beginning with
examples of specific types of omics studies and the technologies involved,
followed by the statistical, computational, and bioinformatics challenges
that arise in the analysis of omics data. Some of these challenges are unique
to omics data, whereas others relate to fundamental principles of good
scientific research. The chapter begins with an overview of the types of
omics data and a discussion of emerging directions for omics research as
they relate to the discovery and future development of omics-based tests
for clinical use.
TYPES OF OMICS DATA
Examples of the types of omics data that can be used to develop an
omics-based test are discussed below. This list is by no means meant to
be comprehensive, and indeed a comprehensive list would be impossible
because new omics technologies are rapidly developing.
Genomics
The genome is the complete sequence of DNA in a cell or organism.
This genetic material may be found in the cell nucleus or in other organelles,
such as mitochondria. With the exception of mutations and chromosomal
rearrangements, the genome of an organism remains essentially constant
over time. Complete or partial DNA sequence can be assayed using various
experimental platforms, including single nucleotide polymorphism (SNP)
chips and DNA sequencing technology. SNP chips are arrays of thou-
sands of oligonucleotide probes that hybridize (or bind) to specific DNA
sequences in which nucleotide variants are known to occur. Only known
sequence variants can be assayed using SNP chips, and in practice only
common variants are assayed in this way. Genomic analysis also can detect
insertions and deletions and copy number variation, referring to loss of or
amplification of the expected two copies of each gene (one from the mother
and one from the father at each gene locus). Personal genome sequencing is
a more recent and powerful technology, which allows for direct and com-
plete sequencing of genomes and transcriptomes (see below). DNA also can
be modified by methylation of cytosines (see Epigenomics, below). There is
also an emerging interest in using genomics technologies to study the impact
of an individual’s microbiome (the aggregate of microorganisms that reside
within the human body) in health and disease (Honda and Littman, 2011;
Kinros et al., 2011; Tilg and Kaser, 2011).
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Discovery and Test Validation Stage Evaluation for Clinical Utility and Use Stage
Test Validation Phase B
Discovery Phase
R
I
Candidate Test Developed
G
on Training Set, Followed
H
by Lock-Down of All
T
Computational Procedures
Analytical and
Con rmation of Candidate
See Chapter 4
Clinical/Biological
Omics-Based Test Using:
Validation
1. An Independent
Sample Set If
L
Available (Preferred);
I
OR
N
2. A Subset of the
Training Set NOT E
Used During Training See Chapter 3
(Less Preferred).
FIGURE 2-1 Omics-based test development process, highlighting the discovery phase. In the discovery phase, a candidate test is
developed, precisely defined, and confirmed. The computational procedures developed in this phase should be fully specified and
locked down through all subsequent development steps. Ideally, confirmation should take place on an independent sample set. Under
exceptional circumstances it may be necessary to move into the test validation phase without first confirming the candidate test on
an independent sample set if using an independent test set in the discovery phase is not possible, but this increases the risk of test
failure in the validation phase. Statistics and bioinformatics validation occurs throughout the discovery and test validation stage as
35
well as the stage for evaluation of clinical utility and use.
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36 EVOLUTION OF TRANSLATIONAL OMICS
Transcriptomics
The transcriptome is the complete set of RNA transcripts from DNA in
a cell or tissue. The transcriptome includes ribosomal RNA (rRNA), mes-
senger RNA (mRNA), transfer RNA (tRNA), micro RNA (miRNA), and
other non-coding RNA (ncRNA). In humans, only 1.5 to 2 percent of the
genome is represented in the transcriptome as protein-coding genes. The
two dominant classes of measurement technologies for the transcriptome
are microarrays and RNA sequencing (RNAseq). Microarrays are based on
oligonucleotide probes that hybridize to specific RNA transcripts. RNAseq
is a much more recent approach, which allows for direct sequencing of
RNAs without the need for probes. Oncotype DX, MammaPrint, Tissue
of Origin, AlloMap, CorusCAD, and the Duke case studies described in
Appendix A and B all involve transcriptomics-based tests.
Proteomics
The proteome is the complete set of proteins expressed by a cell, tissue,
or organism. The proteome is inherently quite complex because proteins
can undergo posttranslational modifications (glycosylation, phosphoryla-
tion, acetylation, ubiquitylation, and many other modifications to the
amino acids comprising proteins), have different spatial configurations and
intracellular localizations, and interact with other proteins as well as other
molecules. This complexity can lead to challenges in proteomics-based test
development. The proteome can be assayed using mass spectrometry and
protein microarrays (reviewed in Ahrens et al., 2010; Wolf-Yadlin et al.,
2009). Unlike RNA transcripts, proteins do not have obvious complemen-
tary binding partners, so the identification and characterization of capture
agents is critical to the success of protein arrays. The Ova1 and OvaCheck
tests discussed in Appendix A are proteomics-based tests.
Epigenomics
The epigenome consists of reversible chemical modifications to the
DNA, or to the histones that bind DNA, and produce changes in the expres-
sion of genes without altering their base sequence. Epigenomic modifica-
tions can occur in a tissue-specific manner, in response to environmental
factors, or in the development of disease states, and can persist across
generations. The epigenome can vary substantially among different cell
types within the same organism. Biochemically, epigenetic changes that
are measured at high-throughput belong to two categories: methylation
of DNA cytosine residues (at CpG) and multiple kinds of modifications of
specific histone proteins in the chromosomes (histone marks). RNA editing
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OMICS-BASED CLINICAL DISCOVERY
is another mechanism for epigenetic changes in gene expression, measured
primarily by transcriptomic methods (Maas, 2010).
Metabolomics
The metabolome is the complete set of small molecule metabolites
found within a biological sample (including metabolic intermediates in
carbohydrate, lipid, amino acid, nucleic acid, and other biochemical path-
ways, along with hormones and other signaling molecules, as well as exog-
enous substances such as drugs and their metabolites). The metabolome is
dynamic and can vary within a single organism and among organisms of
the same species because of many factors such as changes in diet, stress,
physical activity, pharmacological effects, and disease. The components
of the metabolome can be measured with mass spectrometry (reviewed in
Weckwerth, 2003) as well as by nuclear magnetic resonance spectroscopy
(Zhang et al., 2011). This method also can be used to study the lipidome
(reviewed in Seppanen-Laakso and Oresic, 2009), which is the complete set
of lipids in a biological sample.
EMERGING OMICS TECHNOLOGIES AND
DATA ANALYSIS TECHNIQUES
Many emerging omics technologies are likely to influence the develop-
ment of omics-based tests in the future, as both the types and numbers of
molecular measurements continue to increase. Furthermore, advancing bio-
informatics and computational approaches are enabling improved analyses
of omics data, such as greater integration of different data types. Given the
rapid pace of development in these fields, it is not possible to list all rel-
evant emerging technologies or data analytic techniques. A few illustrative
developments are briefly discussed.
Advances in RNA sequencing technology are making possible a higher
resolution view of the transcriptome. These new approaches could facilitate
the development of more novel molecular diagnostics. In the future it may
be possible to develop omics-based tests on the basis of small non-coding
RNAs, RNA editing events, or alternative splice variants that were not mea-
sured using previous hybridization-based technologies such as microarrays.
For example, analysis of miRNA (derived from RNA sequencing) shows
great promise for clinical diagnostics (Moussay et al., 2011; Sugatani et al.,
2011; Tan et al., 2011; Yu et al., 2008).
Similarly, DNA sequencing is making it possible to identify rare or
previously unmeasured mutations that may have important clinical implica-
tions. Next-generation sequencing technologies hold tremendous promise
for not only identification of complete DNA and RNA sequences, but also
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38 EVOLUTION OF TRANSLATIONAL OMICS
high-throughput identification of epigenetic and posttranscriptional modi-
fications to DNA or RNA, respectively. For instance, new sequencing tech-
nologies can monitor a wide variety of epigenetic changes at the genomic
scale, in addition to sequencing information.
However, it is important to note that because next-generation RNA
and DNA sequencing produces even more measurements per sample than
do traditional approaches, these new technologies add to the challenge of
extremely high data dimensionality and the risks of overfitting compu-
tational models to the available data (see the section on Computational
Model Development and Cross-Validation for a discussion of overfitting).
Large meta-analyses of sequencing datasets collected at multiple sites may
prove useful for overcoming these risks and aid in developing clinically
useful omics-based tests.
The field of proteomics has benefited from a number of recent advances.
One example is the development of selected reaction monitoring (SRM)
proteomics based on automated techniques (Picotti et al., 2010). During
the past 2 years, multiple peptides distinctive for proteins from each of
the 20,300 human protein-coding genes have been synthesized and their
mass spectra determined. The resulting SRMAtlas is publicly available for
the entire scientific community to use in choosing targets and purchasing
peptides for quantitative analyses (Omenn et al., 2011). In addition, data
from untargeted “shotgun” mass spectrometry-based proteomics have been
collected and uniformly analyzed to generate peptide atlases for plasma,
liver, and other organs and biofluids (Farrah et al., 2011).
Meanwhile, antibody-based protein identification and tissue expres-
sion studies have progressed considerably (Ayoglu et al., 2011; Fagerberg
et al., 2011); the Human Protein Atlas has antibody findings for more than
12,000 of the 20,300 gene-coded proteins. The Protein Atlas is a useful
resource for planning experiments and will be enhanced by linkage with
mass spectrometry findings through the emerging Human Proteome Project
(Legrain et al., 2011).
Recently developed protein capture-agent aptamer chips also can be
used to make quantitative measurements of approximately 1,000 proteins
from the blood or other sources (Gold et al., 2010). For example, Ostroff et
al. (2010) recently reported generation of a 12-protein panel from analysis
of 1,100 plasma proteins that was shown to have promising clinical test
characteristics for diagnosis of non-small cell lung cancers.
A major bottleneck in the successful deployment of large-scale pro-
teomic approaches is the lack of high-affinity capture agents with high
sensitivity and specificity for particular proteins (including variants due to
posttranslational modifications, alternative splicing, and single-nucleotide
polymorphisms or gene fusions). This challenge is exacerbated in highly
complex mixtures such as blood, where the concentrations of different pro-
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OMICS-BASED CLINICAL DISCOVERY
teins vary by more than 10 orders of magnitude. One technology that holds
great promise in this regard is “click chemistry” (Service, 2008), which uses
a highly specific chemical linkage (generally formed through the Huisgen
reaction) to “click” together low-affinity capture agents to create a single
capture agent with much higher affinity. It also is feasible to combine com-
putational algorithms for modeling protein structures and conformation to
infer functional differences among alternative splice isoforms of proteins,
including those involved in key cancer pathways (Menon et al., 2011).
Improving technologies for measurements of small molecules (Drexler
et al., 2011) also is enabling the use of metabolomics for the development
of candidate omics-based tests with potential clinical utility (Lewis and
Gerszten, 2010). Promising early examples include a metabolomic analysis
that identified a role for sarcosine, an N-methyl derivative of the amino
acid glycine, in prostate cancer progression and metastasis (Sreekumar et
al., 2009), metabolomic characterization of ovarian epithelial carcinomas
(Ben Sellem et al., 2011), and an integrated metabolomic and proteomic
approach to diagnosis, prediction, and therapy selection for heart failure
(Arrell et al., 2011). Included within metabolomics is the emerging ability to
more fully measure the lipids in a sample, a rich source of additional poten-
tial biomarkers (Masoodi et al., 2010). As with other omics data types, a
lengthy, complex development path is necessary to establish a clinically
relevant omics-based test from reports identifying metabolite concentration
differences associated with a phenotype of interest (Koulman et al., 2009).
New technologies are emerging that will make it possible to obtain
omics measurements (such as transcriptomics, proteomics) on single cells
(Tang et al., 2011; Teague et al., 2010). Such detailed molecular measure-
ments provide deep insight into the underlying biology of tissues, and
potentially form a powerful basis for omics-based test development. How-
ever, as the resolution of these measurements increases, so too does the
variability in the measurements due to the heterogeneity of cell states (Ma
et al., 2011). Thus, while emerging omics technologies hold great potential
for the development of omics-based tests, they also may exacerbate dangers
of overfitting the computational model to the datasets.
Recent interest has focused on measuring multiple omics data types on
a single set of samples, in order to integrate different types of molecular
measurements into an omics-based test. Such multidimensional datasets
have the potential to provide deep insight into biological mechanisms and
networks, allowing for the development of more powerful clinical diag-
nostics. An encouraging example of simultaneous measurement of multiple
types of omics data is the DNA-encoded antibody libraries approach (Bailey
et al., 2007), which can measure DNA, RNA, and protein from the same
sample. Another example is the analysis of histone modifications to identify
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40 EVOLUTION OF TRANSLATIONAL OMICS
potential epigenetic biomarkers for prostate cancer prognosis (Bianco-
Miotto et al., 2010).
Approaches that integrate multiple omics data types within the same
clinical test are expected to grow in importance as the number of simultane-
ous measurements that can be made continues to increase. While it is rela-
tively straightforward to increase the number of genomic and transcriptomic
measurements (because DNA and RNA have complementary binding part-
ners), increasing the number of protein measurements is more challenging
because of the need for high-affinity capture agents, as discussed previously
in this section.
Systems approaches that integrate multiple data types in functionally
based models can be advantageous for the development of omics-based
tests. For instance, the analysis of omics measurements in the con -
text of biomolecular networks or pathways can help to reduce the num-
ber of variables in the data by constraining the possible relationships
between variables, ultimately leading to more robust and clinically useful
molecular tests. General approaches for using prior biological knowledge
to enhance signal in omics data include removing measurements that are
believed to be noise or for which there is no support in the published bio-
logical literature (filtering), using pathway databases or other sources to
guide model construction, and aggregating individual measurements, often
across data types, to integrate multiple sources of evidence to support con-
clusions (Ideker et al., 2011). For example, in a study of prion-mediated
neurodegeneration, data from five mouse strains and three prion strains
were used to identify the transcripts, pathways, and networks that were
commonly perturbed across all genetic backgrounds (Hwang et al., 2009;
Omenn, 2009).
Datasets from genome-wide association studies, in which a set of cases
and controls are sampled from a large population and genotyped and each
mutation identified is evaluated for association with the phenotype of
interest, also can be analyzed within the context of biological pathways in
order to increase identification of disease-related mutations (Segre et al.,
2010). The incorporation of evolutionarily conserved gene sets can lead
to the identification of often unexpected factors in disease (McGary et al.,
2010). Large-scale mechanistic network models (for example, for meta-
bolic, regulatory, or signaling networks) may be used to identify biomarkers
grounded in disease mechanisms (Folger et al., 2011; Frezza et al., 2011;
Gottlieb et al., 2011; Lewis et al., 2010; Shlomi et al., 2011). Genomics,
transcriptomics, proteomics, and metabolomics data can be combined with
structural protein analysis in order to predict drug targets or even drug
off-target effects (Chang et al., 2010). While computational models of bio-
molecular networks for eventual clinical use are still in their infancy, their
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OMICS-BASED CLINICAL DISCOVERY
potential for providing stronger mechanistic underpinnings to omics-based
test development is encouraging.
During the past 10 years, much of the effort to identify genes linked to
disease and other conditions of biological interest has focused on genome-
wide association studies. However, more recent work has successfully iden-
tified disease-causal genes using whole genome or exome sequencing (Ng et
al., 2010; Roach et al., 2010). Such studies may prove to be very beneficial
for the development of omics-based tests, and indeed such strategies are
being used clinically today for the identification of the causal gene mutation
resulting in unidentified and uncommon inherited disease states.
STATISTICS AND BIOINFORMATICS
DEVELOPMENT OF OMICS-BASED TESTS
In recent years, a large number of papers have reported new omics-
based discoveries and the development of new candidate omics-based tests:
that is, computational procedures applied to omics-based measurements
to produce a clinically actionable result. However, few of these candidate
omics-based tests have progressed to clinical use (Ransohoff, 2008, 2009).
Some of this discrepancy may be due to the inevitable time lapse of mov-
ing from initial identification of a candidate omics-based test to a precisely
defined and validated test that can be used clinically.
However, more important are the many significant challenges in the
formulation of appropriate research questions and in research design and
conduct that confront the successful discovery of candidate omics-based
tests, including the complexity of the data and the need for rigorous analy-
ses, and the frequent lack of a plausible biological mechanism underpinning
many of these discoveries. These challenges need to be addressed in order
to realize the full clinical potential of omics research, taking into account
issues specific to the field as well as broader principles of good scientific
research.
Two primary scientific causes for failure of a candidate omics-based test
to progress to clinical use are:
1. A candidate omics-based test may not be adequately designed for
answering a specific, well-defined, and relevant clinical question.
This crucial point is addressed in Chapters 3 and 4.
2. Omics-based discovery studies may not be conducted with ade-
quate statistical or bioinformatics rigor, making it unlikely or even
impossible that the candidate omics-based test will prove to be
clinically valid or useful. This critical problem is addressed in the
remainder of this chapter.
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42 EVOLUTION OF TRANSLATIONAL OMICS
Figure 2-1 highlights the discovery and confirmation of a candidate
omics-based test, the first component of the committee’s recommended test
development and evaluation process. When candidate omics-based tests
from the discovery phase are intended for further clinical development,
several criteria should be satisfied and fully disclosed (for example, through
publication or patent application) to enable independent verification of the
findings (Recommendation 1), as discussed below. For the purpose of this
discussion, the committee assumed that a clearly defined and clinically
relevant scientific or clinical question or questions have been identified,
and that an omics dataset from analyses of a set of patient samples, along
with an associated clinical outcome for each patient, is available.
For example, an investigator may ask whether gene expression mea-
surements could be used to predict recurrence in node-negative breast
cancer samples in a way that is substantially more accurate than standard
clinical prognostic factors, such as tumor size and grade. The investigator
might have data consisting of gene expression measurements for breast can-
cer tissue samples obtained from patients with node-negative breast cancer,
along with disease-free survival time for each patient following surgery.
The goal would be to develop a defined assay method for data generation
and a fully specified computational procedure1 that can be used to reliably
predict, on the basis of gene expression measurements on a new patient
sample, whether a patient’s cancer will recur.
Before embarking on omics-based discovery, it is worth considering
whether or not the test that will eventually be developed has a reason-
able chance of demonstrating clinical validity and utility. For example, the
sensitivity and specificity needed, particularly in light of the prevalence of
the condition in the population to be tested, should be considered (see also
Appendix A, page 209, for a discussion of sensitivity and specificity needs
for an ovarian cancer screening test).
Several steps need to be followed to achieve this goal: (1) data quality
control; (2) computational model development and cross-validation; (3) con-
firmation of the computational model on an independent dataset; and
(4) release of data, code, and the fully specified computational procedures
to the scientific community. Each of these is discussed below.
1 All component steps of the computational procedure—namely, all data processing steps,
normalization techniques, weights, parameters, and other aspects of the model, as well as the
mathematical formula or formulas used to convert the data into a prediction of the phenotype
of interest—are completely formulated in writing.
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OMICS-BASED CLINICAL DISCOVERY
Step 1: Data Quality Control
As in most areas of science, data quality control is a crucial first step.
Because omics datasets are typically composed of many thousands, if not
millions, of measurements, data quality control is often performed com-
putationally. For instance, an investigator might remove genes expressed
across conditions near or below background levels on a microarray. The
reproducibility of the measurements from run to run (the technical vari-
ance) also can be assessed. Furthermore, it may be useful to closely examine
aspects of experimental design, including sample run date and other pos-
sible confounding factors such as the source of the tissue analyzed (includ-
ing normal control tissue) and potential heterogeneities within the tissues,
to determine if these have had an effect on the data. This is particularly
important because factors such as run date or machine operator can often
have a much larger effect on omics measurements than the factors of bio-
logical interest (Leek et al., 2010), such as time to disease recurrence or
cancer subtype.
It is essential that such quality assessment evaluations of the data be
done in a blinded fashion, without knowledge of the clinical status or treat-
ment outcomes of the patients whose specimens were tested.
Step 2: Computational Model Development and Cross-Validation
Once investigators have determined in Step 1 that the data are of ade-
quate quality, a candidate omics-based test associated with a phenotype of
interest, such as a biologic subgroup, preclinical responsiveness to a novel
therapy, or a clinical outcome, can be developed on the basis of the omics
measurements. An almost unlimited number of statistical tools can be used
to perform this task; therefore, they are not enumerated here. However,
some key characteristics and challenges are shared by nearly all of these
methods and are discussed below.
In general, omics datasets consist of thousands to millions of molecu-
lar measurements. Typically, investigators first perform feature selection,
which entails selecting a subset of the measurements that appear to be
associated with the characteristic or outcome or that is thought to be
biologically relevant based on prior knowledge. Using just this subset of
measurements, a fully defined computational model can be developed to
predict the clinical outcome on the basis of the omics measurements. This
reduction of required measurements can be beneficial for avoiding the later
possibility that an omics-based test involving a huge number of measure-
ments is not clinically viable for financial or technical reasons. Note that
if cross-validation will be performed in order to select tuning parameters
or evaluate the computational model performance, then feature selection
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54 EVOLUTION OF TRANSLATIONAL OMICS
BOX 2-1 Continued
each measured phenotype variable and for genotype results. Preauthorization is
required to gain access to the phenotype and genotype results for each individual,
and this individual-level data is coded to protect the identity of study participants
(Mailman et al., 2007; NLM, 2006).
Privacy of Health Information
The laws protecting the privacy of individuals’ health information are a potential
obstacle to making omics data sustainably available to other investigators. Much
of the data in omics research is from human subjects and potentially could be
linked to a specific individual, especially in the case of genetic data. In addition,
most omics data used in the development of a clinical test need to be connected
to individuals’ clinical data to be useful in that development process.
The Health Insurance Portability and Accountability Act Privacy Rule protects
the privacy of personally identifiable health information (called “protected health
information [PHI]”) created or received by health care professionals, health plans,
or health care clearinghouses (“covered entities”). In general, the rule requires
test developers to get authorization from research subjects in order to use and
disclose their PHI in health research.k The rule does not require researchers
to get authorization to use and disclose PHI that has been de-identified (as
defined in the regulation). Until recently, there was considerable confusion about
whether the Privacy Rule protected genetic information (IOM, 2009). However, the
Genetic Information Nondiscrimination Act directed the U.S. Secretary of Health
and Human Services to modify the Privacy Rule to explicitly recognize genetic
information as PHI.l
The Common Rule provides human subjects protections in omics research
that is federally funded. It protects the safety, autonomy, privacy, and fair treat-
ment of patient-participants in federally funded research conducted on humans,
and the cultural groups from which they are recruited. The Common Rule requires
researchers to get informed consent from a person to use his/her private identifi-
can often be obtained by beginning the omics-based test develop-
ment process using a subset of the omics measurements for which
a plausible biological mechanism is available. For instance, there
was a plausible biological mechanism behind the HER2 tests and
Oncotype DX to motivate their initial clinical trials, but less so for
the Duke, MammaPrint, and Ova1 tests (discussed in Appendix
A and B). Bioinformatics methods to link transcript or protein
expression changes to relevant signaling pathways or biological
networks need to be deployed appropriately.
3. Data variability unrelated to clinical outcome of interest: Often, a
computational model developed on one dataset (Step 2) performs
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OMICS-BASED CLINICAL DISCOVERY
able information in research. Research that involves “anonymized data” (that is,
information that is recorded in such a manner that subjects cannot be identified)
is exempt from this requirement. However, an advanced notice of proposed rule-
making includes the proposal to revise this aspect of the Common Rule to match
the Privacy Rule’s more rigorous de-identification standards.m If this change
becomes codified in the regulations, researchers may be required in many cir-
cumstances to obtain authorization and informed consent prior to sharing their
research data, in order to comply with these laws, particularly as DNA sequence-
based data can now be considered identifiable.
aThe Copyright Act of 1976, 17 U.S.C. §§ 101-810 (2008).
bFeist Publications v. Rural Telephone Service Co., 499 U.S. 360 (1991).
cPatent Act, 35 U.S.C. § 154 (2008).
dId. at § 103(a).
eId. at §§ 101-103.
fThe Association for Molecular Pathology, et al. v. United States Patent and Trademark
Office, et al., 653 F.3d 1329 (Fed. Cir. 2011).
gBilski vs. Kappos, 130 U.S. 3218 (2010).
hMayo Collaborative Services v. Prometheus Laboratories, Inc., 628 F.3d 1347, (Fed. Cir.
2010), cert. granted, (U.S. Dec. 7, 2011) (No. 10-1150).
iThe Association for Molecular Pathology, et al. v. Myriad Genetics, Inc. et al., petition for
cert. filed (December 7, 2011).
jLeahy-Smith America Invents Act, Public Law No. 112-29 § 27(2011).
kThe Secretary of Health and Human Services issued a notice of proposed rulemaking that
includes potential modifications to the HIPAA Privacy Rule’s authorization requirements in
response to the statutory amendments under the Health Information Technology for Economic
and Clinical Health Act (the “HITECH Act”). See, Modifications to the HIPAA Privacy, Security,
and Enforcement Rules Under the Health Information Technology for Economic and Clinical
Health Act, 75 Fed. Reg. 40,868 (July 14, 2010).
lGenetic Information Nondiscrimination Act, Public Law No. 110-233 (2008).
mHuman Subjects Research Protections: Enhancing Protections for Research Subjects and
Reducing Burden, Delay, and Ambiguity for Investigators, 76 Fed. Reg. 44,512 (July 26, 2011).
poorly on another independent dataset (Step 3). This can occur for
a number of reasons, such as variability in patient population, sam-
ple preparation, time of sample collection, operator variability, etc.
Hence, evidence of a computational model’s performance based
only on the dataset used to train the model, even if cross-validation
is properly performed, provides little evidence of the model’s suit-
ability for future samples. A relevant example here is the OvaCheck
case study, discussed in Appendix A, in which signals obtained on
one dataset did not hold up when the analysis was applied to other
independent sample sets (Baggerly et al., 2004).
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56 EVOLUTION OF TRANSLATIONAL OMICS
4. Need for multiple datasets: For the reasons just described, compu-
tational models that are fit on multiple datasets in Step 2 will tend
to perform better later. In other words, investigators are urged to
develop a computational model on omics datasets derived from
specimens and associated clinical outcomes collected at multiple
laboratories at multiple institutions, rather than fitting a model on
just a single dataset. For instance, the 21-Gene Recurrence Score
(Oncotype DX) case study (Appendix A) was developed using
multiple independent datasets (Paik et al., 2004). In that case, data
were analyzed by the same investigators, but different datasets
were derived from different clinical trials at multiple institutions.
5. Study design and batch effects: As in all areas of biomedical
research, good study design is crucial. If the dataset used in Step 2
to develop the computational model resulted from poor experimen-
tal design (e.g., if the samples from patients whose cancers recurred
were processed at a different time or by a different technician or
in a different laboratory) then batch effects (Leek et al., 2010)
can occur. This will lead to spurious signal, potentially resulting
in a computational model that performs extremely well on the
data on which it was developed (Step 2), but that will perform
poorly on future patient samples (Step 3). A relevant example is the
OvaCheck case study, discussed in Appendix A, in which peaks in
the noise regions of the proteomic spectra could distinguish sam-
ples from controls and cancer, indicating batch effects (Baggerly et
al., 2004).
6. Computational procedure lock-down: It is crucial that at the end
of Step 2, the fully specified computational procedures be locked
down before progressing into confirmation on an independent
test set in Step 3. For instance, simply reporting the set of genes
included in the computational model underlying a transcriptomics-
based test is insufficient, because this does not constitute a fully
specified computational procedure. In the original Oncotype DX
study, the researchers locked down the computational model after
Step 2 and reported the fully specified computational procedures
in the paper (Paik et al., 2004). In the Corus CAD case study,
lock-down and the fully specified computational procedures were
reported in the clinical validation paper (Rosenberg et al., 2010).
The fully specified computational procedures for the AlloMap test
were reported in Deng et al. (2006). In contrast, in the Duke stud-
ies, the genes used in the development of the computational model
were reported, but the fully specified computational procedures
were not; furthermore, it is likely that the computational proce-
dures were not ever fully locked down before proceeding into Step
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OMICS-BASED CLINICAL DISCOVERY
3 or further stages of omics-based test development, including
clinical trials (see Appendix B for details).
7. Role of biostatistics and bioinformatics experts: In a relatively new
and evolving field such as omics, it is not possible to predict all the
possible pitfalls that investigators may face in the discovery phase.
The involvement of properly trained biostatistical or bioinformatics
collaborators who are fully integrated in all aspects of the discovery
and evaluation process can serve as an additional safeguard. The
type of biostatistical expertise that is required may vary depending
on the stage or phase of test development. For example, experts in
developing computational models for omics-based tests may not
have sufficient expertise in clinical trial design, and vice versa. This
is relevant to the Duke case study (as discussed in Appendix B), in
which there was a lack of continuity in biostatistics personnel and
numerous errors were identified in the statistical methodology
and analyses.
COMPLETION OF THE DISCOVERY PHASE OF
OMICS-BASED TEST DEVELOPMENT
A candidate omics-based test should be defined precisely, including the
molecular measurements, the computational procedures, and the intended
clinical use of the test, in anticipation of the test validation phase (Recom-
mendation 1d). There are enormous opportunities in the rapidly improving
suite of omics technologies to identify measurements with potential clinical
utility. However, there are significant challenges in moving from the initial
identification of potentially relevant differences in omics measurements to
validated and robust clinical tests. Among these challenges are risks of
overfitting the data in the development of the computational model and
the enormous heterogeneity among different studies of ostensibly the same
disease states (for both technical and biological reasons). Going forward,
transparency in the reporting of all aspects of the development of an omics-
based test, including the measurements made, preprocessing techniques used,
and the fully specified computational procedure, is critical. The release of
sufficient metadata with publication is also key to the identification of can-
didate omics-based tests that work across multiple sites, which is necessary
to generate increasingly robust omics-based tests to enhance patient care.
In the next phase of test development (analytical and clinical/biological
validation, described in Chapter 3), the methods used to obtain the omics
measurements from patient samples may be changed in order to establish
a clinically feasible, inexpensive, and robust assay for implementation in
clinical practice. However, the fully specified computational procedures
defined in the discovery stage must remain locked down and unchanged
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58 EVOLUTION OF TRANSLATIONAL OMICS
in all subsequent test development steps. At the end of the validation
phase in Chapter 3, the complete test method, including the methods for
obtaining the omics measurements as well as the fully specified computa-
tional procedures, must be locked down before crossing the bright line to
evaluate the test for clinical utility and use.
SUMMARY AND RECOMMENDATION
This chapter has outlined best practices for the discovery phase for
omics-based test development. Because omics-based tests rely on interpre-
tation of high-dimensional datasets, it is important to guard against over-
fitting the data throughout the test development process. Overfitting due to
lack of proper statistical methods can lead to a model that fits the training
samples well, even though the model might perform poorly on independent
samples not used in test development. The steps delineated in this chapter
aim to prevent an overfit model from progressing to subsequent stages of
test development. Cross-validation or a training set/test set approach can
help reduce the risk of overfitting, but confirmation of all fully specified
computational procedures and candidate omics-based tests on a blinded
independent sample set is the “gold standard” for assessing the validity of
any test. The importance of independent confirmation is also emphasized in
the committee’s recommendations for funders (see Chapter 5), which urge
funders to support this type of work. In addition, complex analyses of these
large datasets highlight the need for availability of the data and code used
for the discovery phase of omics-based test development, to enable inde-
pendent verification of the findings. The result of the discovery process is
a candidate omics-based test with locked-down computational procedures
that is then moved into the test validation phase to assess analytical and
clinical/biological validation, as described in Chapter 3.
RECOMMENDATION 1: Discovery Phase
When candidate omics-based tests from the discovery phase are
intended for further clinical development, the following criteria should
be satisfied and fully disclosed (for example, through publication or
patent application) to enable independent verification of the findings:
a. Candidate omics-based tests should be confirmed using an inde-
pendent set of samples, not used in the generation of the computa-
tional model and, when feasible, blinded to any outcome or other
phenotypic data until after the computational procedures have
been locked down and the candidate omics-based test has been
applied to the samples;
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OMICS-BASED CLINICAL DISCOVERY
b. Data and metadata used for development of the candidate omics-
based test should be made available in an independently managed
database (such as dbGaP) in standard format;
c. Computer code and fully specified computational procedures used
for development of the candidate omics-based test should be made
sustainably available; and
d. A candidate omics-based test should be defined precisely, includ-
ing the molecular measurements, the computational procedures,
and the intended clinical use of the test, in anticipation of the test
validation phase.
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